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Merge remote-tracking branch 'origin/nightly' into feature/fixes-client

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This commit is contained in:
Shine1i 2026-03-09 19:07:42 +01:00
commit 2ccb75f2b7
101 changed files with 551 additions and 105 deletions
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2
studio/backend/assets/configs/model_defaults/default.yaml
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@ -2,6 +2,7 @@
# Used for models without specific configurations
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -47,6 +48,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.7
top_p: 0.95
top_k: -1

3
studio/backend/assets/configs/model_defaults/ernie/unsloth_ERNIE-4.5-21B-A3B-PT.yaml
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@ -3,6 +3,7 @@
# Also applies to: unsloth/ERNIE-4.5-21B-A3B-PT
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/ernie/unsloth_ERNIE-4.5-VL-28B-A3B-PT.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: true
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -48,6 +49,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: true
temperature: 1.5
min_p: 0.1

3
studio/backend/assets/configs/model_defaults/falcon/tiiuae_Falcon-H1-0.5B-Instruct.yaml
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@ -3,6 +3,7 @@
# Also applies to: tiiuae/Falcon-H1-0.5B-Instruct, unsloth/Falcon-H1-0.5B-Instruct
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/gemma/unsloth_codegemma-7b-bnb-4bit.yaml
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@ -4,6 +4,7 @@
# added inference parameters from Ollama
training:
trust_remote_code: false
max_seq_length: 4096
# num_epochs: 4
num_epochs: 0
@ -44,5 +45,6 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0
top_p: 0.9

2
studio/backend/assets/configs/model_defaults/gemma/unsloth_functiongemma-270m-it.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 4096
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.0
top_k: 64
top_p: 0.95

3
studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-2-27b-bnb-4bit.yaml
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@ -2,6 +2,7 @@
# Based on Gemma2_(9B)-Alpaca.ipynb (same defaults for larger models)
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -41,3 +42,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

3
studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-2-2b.yaml
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@ -3,6 +3,7 @@
# Also applies to: unsloth/gemma-2-2b-bnb-4bit, google/gemma-2-2b
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-270m-it.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.0
top_k: 64
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-27b-it.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,6 +43,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.0
top_k: 64
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-4b-it.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,6 +43,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.0
top_k: 64
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3-4b-pt.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 2
num_epochs: 0
@ -42,6 +43,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.0
top_k: 64
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3n-E4B-it.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 1024
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
audio_input: true
inference:
trust_remote_code: false
temperature: 1.0
top_k: 64
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/gemma/unsloth_gemma-3n-E4B.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 2
num_epochs: 0
@ -44,6 +45,7 @@ logging:
audio_input: true
inference:
trust_remote_code: false
temperature: 1.0
top_k: 64
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/gpt-oss/unsloth_gpt-oss-120b.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 4096
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.0
top_p: 1.0
top_k: 0

2
studio/backend/assets/configs/model_defaults/gpt-oss/unsloth_gpt-oss-20b.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 1024
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.0
top_p: 1.0
top_k: 0

2
studio/backend/assets/configs/model_defaults/granite/unsloth_granite-4.0-350m-unsloth-bnb-4bit.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -46,6 +47,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.0
top_p: 1.0
top_k: 0

2
studio/backend/assets/configs/model_defaults/granite/unsloth_granite-4.0-h-micro.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -46,6 +47,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.0
top_p: 1.0
top_k: 0

2
studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.2-11B-Vision-Instruct.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,6 +43,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.5
min_p: 0.1

3
studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.2-1B-Instruct.yaml
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@ -3,6 +3,7 @@
# Also applies to: unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit, meta-llama/Llama-3.2-1B-Instruct, unsloth/Llama-3.2-1B-Instruct-bnb-4bit, RedHatAI/Llama-3.2-1B-Instruct-FP8, unsloth/Llama-3.2-1B-Instruct-FP8-Block, unsloth/Llama-3.2-1B-Instruct-FP8-Dynamic
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 5
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.2-3B-Instruct.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.5
min_p: 0.1

2
studio/backend/assets/configs/model_defaults/llama/unsloth_Llama-3.3-70B-Instruct.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.5
min_p: 0.1

3
studio/backend/assets/configs/model_defaults/llama/unsloth_Meta-Llama-3.1-70B-bnb-4bit.yaml
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@ -3,6 +3,7 @@
# Also applies to: unsloth/Meta-Llama-3.1-8B-bnb-4bit, unsloth/Meta-Llama-3.1-8B-unsloth-bnb-4bit, meta-llama/Meta-Llama-3.1-8B, unsloth/Meta-Llama-3.1-8B, unsloth/Meta-Llama-3.1-70B, meta-llama/Meta-Llama-3.1-70B, unsloth/Meta-Llama-3.1-405B-bnb-4bit, meta-llama/Meta-Llama-3.1-405B
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

4
studio/backend/assets/configs/model_defaults/llama/unsloth_Meta-Llama-3.1-8B-Instruct-bnb-4bit.yaml
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@ -3,6 +3,7 @@
# Also applies to: "unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "meta-llama/Meta-Llama-3.1-8B-Instruct", "unsloth/Meta-Llama-3.1-8B-Instruct","RedHatAI/Llama-3.1-8B-Instruct-FP8","unsloth/Llama-3.1-8B-Instruct-FP8-Block","unsloth/Llama-3.1-8B-Instruct-FP8-Dynamic"
training:
trust_remote_code: false
max_seq_length: 8192
# num_epochs: 4
num_epochs: 0
@ -42,4 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

3
studio/backend/assets/configs/model_defaults/llama/unsloth_llama-3-8b-Instruct-bnb-4bit.yaml
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@ -3,6 +3,7 @@
# Also applies to: unsloth/llama-3-8b-Instruct, meta-llama/Meta-Llama-3-8B-Instruct
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

3
studio/backend/assets/configs/model_defaults/llama/unsloth_llama-3-8b-bnb-4bit.yaml
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@ -3,6 +3,7 @@
# Also applies to: unsloth/llama-3-8b, meta-llama/Meta-Llama-3-8B
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/llasa/unsloth_Llasa-3B.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -39,6 +40,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.2
top_p: 1.2

2
studio/backend/assets/configs/model_defaults/mistral/unsloth_Magistral-Small-2509-unsloth-bnb-4bit.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -48,6 +49,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.7
min_p: 0.01
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/mistral/unsloth_Ministral-3-3B-Instruct-2512.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -48,6 +49,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.15
top_p: default

4
studio/backend/assets/configs/model_defaults/mistral/unsloth_Mistral-Nemo-Base-2407-bnb-4bit.yaml
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@ -3,6 +3,7 @@
# Also applies to: "unsloth/Mistral-Nemo-Base-2407", "mistralai/Mistral-Nemo-Base-2407", "unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "unsloth/Mistral-Nemo-Instruct-2407", "mistralai/Mistral-Nemo-Instruct-2407",
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,4 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

3
studio/backend/assets/configs/model_defaults/mistral/unsloth_Mistral-Small-Instruct-2409.yaml
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@ -3,6 +3,7 @@
# Also applies to: unsloth/Mistral-Small-Instruct-2409-bnb-4bit, mistralai/Mistral-Small-Instruct-2409
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/mistral/unsloth_Pixtral-12B-2409.yaml
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@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,6 +43,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.5
min_p: 0.1

4
studio/backend/assets/configs/model_defaults/mistral/unsloth_mistral-7b-instruct-v0.3-bnb-4bit.yaml
View file

@ -3,6 +3,7 @@
# Also applies to: unsloth/mistral-7b-instruct-v0.3, mistralai/Mistral-7B-Instruct-v0.3
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,4 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

4
studio/backend/assets/configs/model_defaults/mistral/unsloth_mistral-7b-v0.3-bnb-4bit.yaml
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@ -2,6 +2,7 @@
# Based on Mistral_v0.3_(7B)-Alpaca.ipynb
# Also applies to: "unsloth/mistral-7b-v0.3", "mistralai/Mistral-7B-v0.3",
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -41,4 +42,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/other/OuteAI_Llama-OuteTTS-1.0-1B.yaml
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@ -6,6 +6,7 @@
audio_type: dac
training:
trust_remote_code: false
eval_steps: 0
max_seq_length: 2048
# num_epochs: 4
@ -42,6 +43,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.4
top_k: 40
top_p: 0.9

2
studio/backend/assets/configs/model_defaults/other/Spark-TTS-0.5B_LLM.yaml
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@ -6,6 +6,7 @@
audio_type: bicodec
training:
trust_remote_code: false
eval_steps: 0
max_seq_length: 2048
# num_epochs: 4
@ -47,6 +48,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.8
top_k: 50
top_p: 1.0

3
studio/backend/assets/configs/model_defaults/other/sesame_csm-1b.yaml
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@ -5,6 +5,7 @@
audio_type: csm
training:
trust_remote_code: false
eval_steps: 0
max_seq_length: 2048
# num_epochs: 4
@ -45,3 +46,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/other/unsloth_GLM-4.7-Flash.yaml
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@ -3,6 +3,7 @@
# Also applies to: unsloth/GLM-4.7-Flash-unsloth-bnb-4bit, unsloth/GLM-4.7-Flash-bnb-4bit, THUDM/GLM-4.7-Flash
training:
trust_remote_code: true
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: true
temperature: 0.7
top_p: 0.8
top_k: 20

2
studio/backend/assets/configs/model_defaults/other/unsloth_LFM2-1.2B.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -38,6 +39,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.3
min_p: 0.15

2
studio/backend/assets/configs/model_defaults/other/unsloth_Nemotron-3-Nano-30B-A3B.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: true
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -46,6 +47,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: true
temperature: 1.0
top_p: 1.0

2
studio/backend/assets/configs/model_defaults/other/unsloth_PaddleOCR-VL.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: true
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -48,6 +49,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: true
temperature: 1.5
min_p: 0.1

3
studio/backend/assets/configs/model_defaults/other/unsloth_answerdotai_ModernBERT-large.yaml
View file

@ -2,6 +2,7 @@
# Based on bert_classification.ipynb
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 1
num_epochs: 0
@ -41,3 +42,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/other/unsloth_orpheus-3b-0.1-ft.yaml
View file

@ -6,6 +6,7 @@
audio_type: snac
training:
trust_remote_code: false
eval_steps: 0
max_seq_length: 2048
# num_epochs: 4
@ -47,6 +48,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.6
top_p: 0.95

3
studio/backend/assets/configs/model_defaults/other/unsloth_tinyllama-bnb-4bit.yaml
View file

@ -3,6 +3,7 @@
# Also applies to: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
training:
trust_remote_code: false
max_seq_length: 4096
# num_epochs: 1
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

3
studio/backend/assets/configs/model_defaults/other/unsloth_whisper-large-v3.yaml
View file

@ -6,6 +6,7 @@ audio_type: whisper
audio_input: true
training:
trust_remote_code: false
eval_steps: 5
max_seq_length: 448
# num_epochs: 4
@ -41,3 +42,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

4
studio/backend/assets/configs/model_defaults/phi/unsloth_Phi-3-medium-4k-instruct.yaml
View file

@ -3,6 +3,7 @@
# Also applies to: "unsloth/Phi-3-medium-4k-instruct-bnb-4bit", "microsoft/Phi-3-medium-4k-instruct",
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,4 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

4
studio/backend/assets/configs/model_defaults/phi/unsloth_Phi-3.5-mini-instruct.yaml
View file

@ -3,6 +3,7 @@
# Also applies to: "unsloth/Phi-3.5-mini-instruct-bnb-4bit", "microsoft/Phi-3.5-mini-instruct"
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,4 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/phi/unsloth_Phi-4.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.8
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/qwen/imdatta0_tiny_qwen3_moe_2.8B_0.7B.yaml
View file

@ -4,6 +4,7 @@
# MoE model - includes gate_up_proj for MoE layers
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -45,6 +46,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.6
top_k: 20
top_p: 0.95

3
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2-7B.yaml
View file

@ -3,6 +3,7 @@
# Also applies to: unsloth/Qwen2-7B-bnb-4bit, Qwen/Qwen2-7B
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2-VL-7B-Instruct.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,6 +43,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.5
min_p: 0.1

3
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-1.5B-Instruct.yaml
View file

@ -3,6 +3,7 @@
# Also applies to: unsloth/Qwen2.5-1.5B-Instruct-unsloth-bnb-4bit, Qwen/Qwen2.5-1.5B-Instruct, unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit
training:
trust_remote_code: false
max_seq_length: 4096
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

3
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-7B.yaml
View file

@ -3,6 +3,7 @@
# Also applies to: unsloth/Qwen2.5-7B-unsloth-bnb-4bit, Qwen/Qwen2.5-7B, unsloth/Qwen2.5-7B-bnb-4bit
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

3
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-Coder-1.5B-Instruct.yaml
View file

@ -3,6 +3,7 @@
# Also applies to: unsloth/Qwen2.5-Coder-1.5B-Instruct-bnb-4bit, Qwen/Qwen2.5-Coder-1.5B-Instruct
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-Coder-14B-Instruct.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.5
min_p: 0.1

3
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-Coder-7B-Instruct-bnb-4bit.yaml
View file

@ -3,6 +3,7 @@
# Also applies to: unsloth/Qwen2.5-Coder-7B-Instruct, Qwen/Qwen2.5-Coder-7B-Instruct
training:
trust_remote_code: false
max_seq_length: 32768
# num_epochs: 4
num_epochs: 0
@ -42,3 +43,5 @@ logging:
tensorboard_dir: "runs"
log_frequency: 10
inference:
trust_remote_code: false

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen2.5-VL-7B-Instruct-bnb-4bit.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth notebook
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,6 +43,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 1.5
min_p: 0.1

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-0.6B.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from Ollama
training:
trust_remote_code: false
max_seq_length: 1024
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.6
top_k: 20
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-14B-Base-unsloth-bnb-4bit.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from Ollama
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.6
top_k: 20
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-14B.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from Ollama
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.6
top_k: 20
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-30B-A3B-Instruct-2507.yaml
View file

@ -4,6 +4,7 @@
# MoE model - includes gate_up_proj for MoE layers
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -45,6 +46,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.6
top_k: 20
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-32B.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from Ollama
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.6
top_k: 20
top_p: 0.95

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-4B-Instruct-2507.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.7
top_p: 0.80
top_k: 20

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-4B-Thinking-2507.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -44,6 +45,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.6
top_p: 0.95
top_k: 20

2
studio/backend/assets/configs/model_defaults/qwen/unsloth_Qwen3-VL-8B-Instruct-unsloth-bnb-4bit.yaml
View file

@ -4,6 +4,7 @@
# added inference parameters from unsloth guides
training:
trust_remote_code: false
max_seq_length: 2048
# num_epochs: 4
num_epochs: 0
@ -42,6 +43,7 @@ logging:
log_frequency: 10
inference:
trust_remote_code: false
temperature: 0.7
top_p: 0.8
top_k: 20

10
studio/backend/core/export/export.py
View file

@ -138,7 +138,8 @@ class ExportBackend:
def load_checkpoint(self,
checkpoint_path: str,
max_seq_length: int = 2048,
load_in_4bit: bool = True) -> Tuple[bool, str]:
load_in_4bit: bool = True,
trust_remote_code: bool = False) -> Tuple[bool, str]:
"""
Load a checkpoint for export.
@ -178,6 +179,7 @@ class ExportBackend:
dtype=None,
auto_model=CsmForConditionalGeneration,
load_in_4bit=False,
trust_remote_code=trust_remote_code,
)
elif self._audio_type == 'whisper':
@ -189,6 +191,7 @@ class ExportBackend:
dtype=None,
load_in_4bit=False,
auto_model=WhisperForConditionalGeneration,
trust_remote_code=trust_remote_code,
)
elif self._audio_type == 'snac':
@ -198,6 +201,7 @@ class ExportBackend:
max_seq_length=max_seq_length,
dtype=None,
load_in_4bit=load_in_4bit,
trust_remote_code=trust_remote_code,
)
elif self._audio_type == 'bicodec':
@ -208,6 +212,7 @@ class ExportBackend:
max_seq_length=max_seq_length,
dtype=torch.float32,
load_in_4bit=False,
trust_remote_code=trust_remote_code,
)
elif self._audio_type == 'dac':
@ -217,6 +222,7 @@ class ExportBackend:
model_name=checkpoint_path,
max_seq_length=max_seq_length,
load_in_4bit=False,
trust_remote_code=trust_remote_code,
)
elif self.is_vision:
@ -226,6 +232,7 @@ class ExportBackend:
max_seq_length=max_seq_length,
dtype=None,
load_in_4bit=load_in_4bit,
trust_remote_code=trust_remote_code,
)
tokenizer = processor # For vision models, processor acts as tokenizer
@ -236,6 +243,7 @@ class ExportBackend:
max_seq_length=max_seq_length,
dtype=None,
load_in_4bit=load_in_4bit,
trust_remote_code=trust_remote_code,
)
# Check if PEFT model

2
studio/backend/core/export/orchestrator.py
View file

@ -211,6 +211,7 @@ class ExportOrchestrator:
checkpoint_path: str,
max_seq_length: int = 2048,
load_in_4bit: bool = True,
trust_remote_code: bool = False,
) -> Tuple[bool, str]:
"""Load a checkpoint for export.
@ -225,6 +226,7 @@ class ExportOrchestrator:
"checkpoint_path": checkpoint_path,
"max_seq_length": max_seq_length,
"load_in_4bit": load_in_4bit,
"trust_remote_code": trust_remote_code,
}
# Always kill existing subprocess and spawn fresh.

2
studio/backend/core/export/worker.py
View file

@ -84,6 +84,7 @@ def _handle_load(backend, cmd: dict, resp_queue: Any) -> None:
checkpoint_path = cmd["checkpoint_path"]
max_seq_length = cmd.get("max_seq_length", 2048)
load_in_4bit = cmd.get("load_in_4bit", True)
trust_remote_code = cmd.get("trust_remote_code", False)
try:
_send_response(resp_queue, {
@ -96,6 +97,7 @@ def _handle_load(backend, cmd: dict, resp_queue: Any) -> None:
checkpoint_path=checkpoint_path,
max_seq_length=max_seq_length,
load_in_4bit=load_in_4bit,
trust_remote_code=trust_remote_code,
)
_send_response(resp_queue, {

12
studio/backend/core/inference/inference.py
View file

@ -63,7 +63,8 @@ class InferenceBackend:
max_seq_length: int = 2048,
dtype = None,
load_in_4bit: bool = True,
hf_token: Optional[str] = None) -> bool:
hf_token: Optional[str] = None,
trust_remote_code: bool = False) -> bool:
"""
Load any model: base, LoRA adapter, text, or vision.
"""
@ -110,6 +111,7 @@ class InferenceBackend:
auto_model=CsmForConditionalGeneration,
load_in_4bit=False,
token=hf_token if hf_token and hf_token.strip() else None,
trust_remote_code=trust_remote_code,
)
FastModel.for_inference(model)
self.models[model_name]["model"] = model
@ -139,6 +141,7 @@ class InferenceBackend:
dtype=torch.float32,
load_in_4bit=False,
token=hf_token if hf_token and hf_token.strip() else None,
trust_remote_code=trust_remote_code,
)
else:
# Base model: download full HF repo, then load from /LLM subfolder
@ -155,6 +158,7 @@ class InferenceBackend:
dtype=torch.float32,
load_in_4bit=False,
token=hf_token if hf_token and hf_token.strip() else None,
trust_remote_code=trust_remote_code,
)
FastModel.for_inference(model)
@ -169,6 +173,7 @@ class InferenceBackend:
max_seq_length=max_seq_length,
load_in_4bit=False,
token=hf_token if hf_token and hf_token.strip() else None,
trust_remote_code=trust_remote_code,
)
FastModel.for_inference(model)
self.models[model_name]["model"] = model
@ -184,6 +189,7 @@ class InferenceBackend:
whisper_task="transcribe",
load_in_4bit=False,
token=hf_token if hf_token and hf_token.strip() else None,
trust_remote_code=trust_remote_code,
)
FastModel.for_inference(model)
model.eval()
@ -209,6 +215,7 @@ class InferenceBackend:
max_seq_length=max_seq_length,
load_in_4bit=False,
token=hf_token if hf_token and hf_token.strip() else None,
trust_remote_code=trust_remote_code,
)
FastLanguageModel.for_inference(model)
self.models[model_name]["model"] = model
@ -240,6 +247,7 @@ class InferenceBackend:
dtype=dtype,
load_in_4bit=load_in_4bit,
token=hf_token if hf_token and hf_token.strip() else None,
trust_remote_code=trust_remote_code,
)
# Apply inference optimization
@ -270,6 +278,7 @@ class InferenceBackend:
processor = AutoProcessor.from_pretrained(
processor_source,
token=hf_token if hf_token and hf_token.strip() else None,
trust_remote_code=trust_remote_code,
)
logger.info(f"Loaded {type(processor).__name__} from {processor_source}")
@ -285,6 +294,7 @@ class InferenceBackend:
dtype=dtype,
load_in_4bit=load_in_4bit,
token=hf_token if hf_token and hf_token.strip() else None,
trust_remote_code=trust_remote_code,
)
# Apply inference optimization

2
studio/backend/core/inference/orchestrator.py
View file

@ -258,6 +258,7 @@ class InferenceOrchestrator:
dtype=None,
load_in_4bit: bool = True,
hf_token: Optional[str] = None,
trust_remote_code: bool = False,
) -> bool:
"""Load a model for inference.
@ -282,6 +283,7 @@ class InferenceOrchestrator:
"load_in_4bit": load_in_4bit,
"hf_token": hf_token or "",
"gguf_variant": getattr(config, "gguf_variant", None),
"trust_remote_code": trust_remote_code,
}
# Always kill existing subprocess and spawn fresh.

1
studio/backend/core/inference/worker.py
View file

@ -155,6 +155,7 @@ def _handle_load(backend, config: dict, resp_queue: Any) -> None:
max_seq_length=config.get("max_seq_length", 2048),
load_in_4bit=load_in_4bit,
hf_token=hf_token,
trust_remote_code=config.get("trust_remote_code", False),
)
if success:

86
studio/backend/core/training/trainer.py
View file

@ -329,9 +329,11 @@ class UnslothTrainer:
load_in_4bit: bool = True,
hf_token: Optional[str] = None,
is_dataset_image: bool = False,
is_dataset_audio: bool = False) -> bool:
is_dataset_audio: bool = False,
trust_remote_code: bool = False) -> bool:
"""Load model for training (supports both text and vision models)"""
self.load_in_4bit = load_in_4bit # Store for training_meta.json
self.trust_remote_code = trust_remote_code # For AutoProcessor etc. used during training
try:
if self.model is not None:
del self.model
@ -378,6 +380,9 @@ class UnslothTrainer:
self.is_audio = self._audio_type is not None
self.is_audio_vlm = False
if not self.is_audio and not self.is_audio_vlm:
self._cuda_audio_used = False
# VLM: vision model with image dataset (mutually exclusive with audio paths)
vision = is_vision_model(model_name) if not self.is_audio else False
self.is_vlm = not self.is_audio_vlm and vision and is_dataset_image
@ -450,6 +455,7 @@ class UnslothTrainer:
auto_model=CsmForConditionalGeneration,
load_in_4bit=False,
token=hf_token,
trust_remote_code=trust_remote_code,
)
logger.info("Loaded CSM audio model")
@ -465,6 +471,7 @@ class UnslothTrainer:
whisper_language="English",
whisper_task="transcribe",
token=hf_token,
trust_remote_code=trust_remote_code,
)
# Configure generation settings (notebook lines 100-105)
self.model.generation_config.language = "<|en|>"
@ -481,6 +488,7 @@ class UnslothTrainer:
dtype=None,
load_in_4bit=load_in_4bit,
token=hf_token,
trust_remote_code=trust_remote_code,
)
logger.info(f"Loaded {self._audio_type} audio model (FastLanguageModel)")
@ -514,6 +522,7 @@ class UnslothTrainer:
dtype=torch.float32, # Spark-TTS requires float32
load_in_4bit=False,
token=hf_token,
trust_remote_code=trust_remote_code,
)
logger.info("Loaded Spark-TTS (bicodec) model")
@ -525,6 +534,7 @@ class UnslothTrainer:
max_seq_length=max_seq_length,
load_in_4bit=False,
token=hf_token,
trust_remote_code=trust_remote_code,
)
logger.info("Loaded OuteTTS (dac) model (FastModel)")
@ -538,6 +548,7 @@ class UnslothTrainer:
dtype=None,
load_in_4bit=load_in_4bit,
token=hf_token,
trust_remote_code=trust_remote_code,
)
logger.info("Loaded audio VLM model (FastModel)")
@ -549,6 +560,7 @@ class UnslothTrainer:
dtype=None, # Auto-detect
load_in_4bit=load_in_4bit,
token=hf_token,
trust_remote_code=trust_remote_code,
)
logger.info("Loaded vision model")
@ -568,6 +580,7 @@ class UnslothTrainer:
dtype=None, # Auto-detect
load_in_4bit=load_in_4bit,
token=hf_token,
trust_remote_code=trust_remote_code,
)
logger.info("Loaded text model")
@ -588,7 +601,7 @@ class UnslothTrainer:
self._source_code_retried = True
print(f"\n'could not get source code' — retrying once...\n")
return self.load_model(model_name, max_seq_length, load_in_4bit, hf_token,
is_dataset_image, is_dataset_audio)
is_dataset_image, is_dataset_audio, trust_remote_code)
error_msg = str(e)
error_lower = error_msg.lower()
if any(k in error_lower for k in ("gated repo", "access to it at", "401", "403", "unauthorized", "forbidden")):
@ -987,7 +1000,10 @@ class UnslothTrainer:
from datasets import Audio
import torch
processor = AutoProcessor.from_pretrained(self.model_name)
processor = AutoProcessor.from_pretrained(
self.model_name,
trust_remote_code=getattr(self, "trust_remote_code", False),
)
# Strip pad_to_multiple_of from tokenizer init_kwargs — fine-tuned models
# (e.g. keanteng/sesame-csm-elise) save it in tokenizer_config.json, and
@ -1786,18 +1802,20 @@ class UnslothTrainer:
eval_enabled = eval_steps is not None and eval_steps > 0
if local_datasets:
# Load local datasets
all_data = []
# Load local datasets using load_dataset() so the result is
# Arrow-backed (has cache files). Dataset.from_list() creates
# an in-memory dataset with no cache, which forces num_proc=1
# during tokenization/map because sharding requires Arrow files.
all_files: list[str] = []
for dataset_file in local_datasets:
# dataset_file may already be an absolute path from routes/training.py
if os.path.isabs(dataset_file):
file_path = dataset_file
else:
# Fallback: try relative to assets/datasets
file_path = _ASSETS_DATASETS_ROOT / dataset_file
file_path = str(_ASSETS_DATASETS_ROOT / dataset_file)
file_path_obj = Path(file_path)
file_path_str = str(file_path_obj)
if file_path_obj.is_dir():
parquet_dir = (
@ -1807,36 +1825,41 @@ class UnslothTrainer:
)
parquet_files = sorted(parquet_dir.glob("*.parquet"))
if parquet_files:
for parquet_file in parquet_files:
df = pd.read_parquet(parquet_file)
all_data.extend(df.to_dict("records"))
all_files.extend(str(p) for p in parquet_files)
continue
# Fall through to single-file detection for dirs with json/csv
candidates: list[Path] = []
for ext in ('.json', '.jsonl', '.csv', '.parquet'):
candidates.extend(sorted(file_path_obj.glob(f"*{ext}")))
if candidates:
all_files.extend(str(c) for c in candidates)
continue
raise ValueError(f"No supported data files in directory: {file_path_obj}")
else:
all_files.append(str(file_path_obj))
if file_path_str.endswith('.json'):
with open(file_path_obj, 'r', encoding='utf-8') as f:
data = json.load(f)
if isinstance(data, list):
all_data.extend(data)
else:
all_data.append(data)
elif file_path_str.endswith('.csv'):
df = pd.read_csv(file_path_obj)
all_data.extend(df.to_dict('records'))
elif file_path_str.endswith('.parquet'):
df = pd.read_parquet(file_path_obj)
all_data.extend(df.to_dict('records'))
continue
if all_files:
# Determine loader type from the first file extension
first_ext = Path(all_files[0]).suffix.lower()
if first_ext in ('.json', '.jsonl'):
loader = 'json'
elif first_ext == '.csv':
loader = 'csv'
elif first_ext == '.parquet':
loader = 'parquet'
else:
raise ValueError(f"Unsupported local dataset format: {all_files[0]}")
if all_data:
dataset = Dataset.from_list(all_data)
dataset = load_dataset(loader, data_files=all_files, split='train')
# Check if stopped during dataset loading
if self.should_stop:
print("Stopped during dataset loading\n")
return None
self._update_progress(status_message=f"Loaded {len(all_data)} samples from local files")
print(f"Loaded {len(all_data)} samples from local files\n")
self._update_progress(status_message=f"Loaded {len(dataset)} samples from local files")
print(f"Loaded {len(dataset)} samples from local files\n")
print(f"[DEBUG] Dataset cache_files: {dataset.cache_files}\n")
elif dataset_source:
# Load from Hugging Face
@ -1855,7 +1878,8 @@ class UnslothTrainer:
# Resolve eval split from a separate HF split (explicit or auto-detected)
if eval_enabled:
if eval_split:
effective_train = train_split or "train"
if eval_split and eval_split != effective_train:
# Explicit eval split provided - load it directly
print(f"Loading explicit eval split: '{eval_split}'\n")
eval_load_kwargs = {"path": dataset_source, "split": eval_split}
@ -1864,6 +1888,9 @@ class UnslothTrainer:
eval_dataset = load_dataset(**eval_load_kwargs)
has_separate_eval_source = True
print(f"Loaded eval split '{eval_split}' with {len(eval_dataset)} rows\n")
elif eval_split and eval_split == effective_train:
# Same split as training — will do 80/20 split after formatting
print(f"Eval split '{eval_split}' is the same as train split — will split 80/20\n")
else:
# Auto-detect eval split from HF (returns a separate dataset, or None)
eval_dataset = self._auto_detect_eval_split_from_hf(
@ -2360,6 +2387,7 @@ class UnslothTrainer:
"dataset_num_proc": 1 if (self.is_audio or self.is_audio_vlm or self._cuda_audio_used) else safe_num_proc(max(1, os.cpu_count() // 4)),
"max_seq_length": training_args.get('max_seq_length', 2048),
}
print(f"[DEBUG] dataset_num_proc={config_args['dataset_num_proc']} (is_audio={self.is_audio}, is_audio_vlm={self.is_audio_vlm}, _cuda_audio_used={self._cuda_audio_used})")
# On Windows with transformers 5.x, disable DataLoader multiprocessing
# to avoid issues with modified sys.path (.venv_t5) in spawned workers.

4
studio/backend/core/training/training.py
View file

@ -176,6 +176,7 @@ class TrainingBackend:
"wandb_project": kwargs.get("wandb_project", "unsloth-training"),
"enable_tensorboard": kwargs.get("enable_tensorboard", False),
"tensorboard_dir": kwargs.get("tensorboard_dir", "runs"),
"trust_remote_code": kwargs.get("trust_remote_code", False),
}
# Derive load_in_4bit from training_type
@ -383,6 +384,9 @@ class TrainingBackend:
self.eval_step_history.append(step)
self.eval_enabled = True
elif etype == "eval_configured":
self.eval_enabled = True
elif etype == "status":
self._progress.status_message = event.get("message", "")
self._progress.is_training = True

16
studio/backend/core/training/worker.py
View file

@ -135,7 +135,9 @@ def run_training_process(
# Wire up progress callback → event_queue
def _on_progress(progress: TrainingProgress):
if progress.step >= 0 and progress.loss > 0:
has_train_loss = progress.step >= 0 and progress.loss > 0
has_eval_loss = progress.eval_loss is not None
if has_train_loss or has_eval_loss:
event_queue.put({
"type": "progress",
"step": progress.step,
@ -192,14 +194,16 @@ def run_training_process(
hf_token=hf_token,
is_dataset_image=config.get("is_dataset_image", False),
is_dataset_audio=config.get("is_dataset_audio", False),
trust_remote_code=config.get("trust_remote_code", False),
)
if not success or trainer.should_stop:
if trainer.should_stop:
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})
else:
error_msg = trainer.training_progress.error or "Failed to load model"
event_queue.put({
"type": "error",
"error": trainer.training_progress.error or "Failed to load model",
"error": error_msg,
"stack": "", "ts": time.time(),
})
return
@ -265,6 +269,14 @@ def run_training_process(
if eval_steps is not None and float(eval_steps) <= 0:
eval_dataset = None
# Tell the parent process that eval is configured so the frontend
# shows "Waiting for first evaluation step..." instead of "not configured"
if eval_dataset is not None:
event_queue.put({
"type": "eval_configured",
"ts": time.time(),
})
if dataset is None or trainer.should_stop:
if trainer.should_stop:
event_queue.put({"type": "complete", "output_dir": None, "ts": time.time()})

6
studio/backend/models/datasets.py
View file

@ -41,6 +41,12 @@ class CheckFormatResponse(BaseModel):
warning: Optional[str] = None
class UploadDatasetResponse(BaseModel):
"""Response with stored dataset path for training."""
filename: str = Field(..., description="Original filename")
stored_path: str = Field(..., description="Absolute path stored on backend")
class LocalDatasetItem(BaseModel):
class Metadata(BaseModel):
actual_num_records: Optional[int] = None

4
studio/backend/models/export.py
View file

@ -19,6 +19,10 @@ class LoadCheckpointRequest(BaseModel):
True,
description="Whether to load the model in 4-bit quantization",
)
trust_remote_code: bool = Field(
False,
description="Allow loading models with custom code. Only enable for checkpoints/base models you trust.",
)
class ExportStatusResponse(BaseModel):

4
studio/backend/models/inference.py
View file

@ -18,6 +18,10 @@ class LoadRequest(BaseModel):
load_in_4bit: bool = Field(True, description="Load model in 4-bit quantization")
is_lora: bool = Field(False, description="Whether this is a LoRA adapter")
gguf_variant: Optional[str] = Field(None, description="GGUF quantization variant (e.g. 'Q4_K_M')")
trust_remote_code: bool = Field(
False,
description="Allow loading models with custom code (e.g. NVIDIA Nemotron). Only enable for repos you trust.",
)
class UnloadRequest(BaseModel):

4
studio/backend/models/training.py
View file

@ -13,6 +13,10 @@ class TrainingStartRequest(BaseModel):
hf_token: Optional[str] = Field(None, description="HuggingFace token")
load_in_4bit: bool = Field(True, description="Load model in 4-bit quantization")
max_seq_length: int = Field(2048, description="Maximum sequence length")
trust_remote_code: bool = Field(
False,
description="Allow loading models with custom code (e.g. NVIDIA Nemotron). Only enable for repos you trust.",
)
# Dataset parameters
hf_dataset: Optional[str] = Field(None, description="HuggingFace dataset identifier")

50
studio/backend/routes/datasets.py
View file

@ -6,7 +6,8 @@ import io
import json
import sys
from pathlib import Path
from fastapi import APIRouter, Depends, HTTPException
from uuid import uuid4
from fastapi import APIRouter, Depends, HTTPException, UploadFile
import logging
# Add backend directory to path
@ -36,6 +37,7 @@ from models.datasets import (
CheckFormatResponse,
LocalDatasetItem,
LocalDatasetsResponse,
UploadDatasetResponse,
)
@ -89,8 +91,10 @@ DATA_EXTS = (
'.zip',
)
LOCAL_FILE_EXTS = ('.json', '.jsonl', '.csv', '.parquet')
LOCAL_UPLOAD_EXTS = {".csv", ".json", ".jsonl", ".parquet"}
BACKEND_ROOT = Path(__file__).resolve().parents[1]
LOCAL_DATASETS_ROOT = BACKEND_ROOT / "assets" / "datasets"
DATASET_UPLOAD_DIR = LOCAL_DATASETS_ROOT / "uploads"
def _safe_read_metadata(path: Path) -> dict | None:
@ -252,6 +256,50 @@ def _load_local_preview_slice(*, dataset_path: Path, train_split: str, preview_s
return preview_slice, total_rows
def _sanitize_filename(filename: str) -> str:
name = Path(filename).name.strip().replace("\x00", "")
if not name:
return "dataset_upload"
return name
@router.post("/upload", response_model=UploadDatasetResponse)
async def upload_dataset(
file: UploadFile,
current_subject: str = Depends(get_current_subject),
) -> UploadDatasetResponse:
filename = _sanitize_filename(file.filename or "dataset_upload")
ext = Path(filename).suffix.lower()
if ext not in LOCAL_UPLOAD_EXTS:
allowed = ", ".join(sorted(LOCAL_UPLOAD_EXTS))
raise HTTPException(
status_code=400,
detail=f"Unsupported file type: {ext}. Allowed: {allowed}",
)
max_size_bytes = 512 * 1024 * 1024
DATASET_UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
stem = Path(filename).stem
stored_name = f"{uuid4().hex}_{stem}{ext}"
stored_path = DATASET_UPLOAD_DIR / stored_name
# Stream file to disk in chunks to avoid holding entire file in memory
size = 0
with open(stored_path, "wb") as f:
while chunk := await file.read(1024 * 1024):
size += len(chunk)
if size > max_size_bytes:
stored_path.unlink(missing_ok=True)
raise HTTPException(status_code=413, detail="File too large (max 512MB)")
f.write(chunk)
if size == 0:
stored_path.unlink(missing_ok=True)
raise HTTPException(status_code=400, detail="Empty upload payload")
return UploadDatasetResponse(filename=filename, stored_path=str(stored_path))
@router.get("/local", response_model=LocalDatasetsResponse)
def list_local_datasets(
current_subject: str = Depends(get_current_subject),

1
studio/backend/routes/export.py
View file

@ -102,6 +102,7 @@ async def load_checkpoint(
checkpoint_path=request.checkpoint_path,
max_seq_length=request.max_seq_length,
load_in_4bit=request.load_in_4bit,
trust_remote_code=request.trust_remote_code,
)
if not success:

17
studio/backend/routes/inference.py
View file

@ -26,6 +26,7 @@ try:
from core.inference.llama_cpp import LlamaCppBackend
from utils.models import ModelConfig
from utils.inference import load_inference_config
from utils.models.model_config import load_model_defaults
except ImportError:
parent_backend = backend_path.parent / "backend"
if str(parent_backend) not in sys.path:
@ -34,6 +35,7 @@ except ImportError:
from core.inference.llama_cpp import LlamaCppBackend
from utils.models import ModelConfig
from utils.inference import load_inference_config
from utils.models.model_config import load_model_defaults
from models.inference import (
LoadRequest,
@ -228,9 +230,22 @@ async def load_model(
max_seq_length=request.max_seq_length,
load_in_4bit=load_in_4bit,
hf_token=request.hf_token,
trust_remote_code=request.trust_remote_code,
)
if not success:
# Check if YAML says this model needs trust_remote_code
if not request.trust_remote_code:
model_defaults = load_model_defaults(config.identifier)
yaml_trust = model_defaults.get("inference", {}).get("trust_remote_code", False)
if yaml_trust:
raise HTTPException(
status_code=400,
detail=(
f"Model '{config.display_name}' requires trust_remote_code to be enabled. "
f"Please enable 'Trust remote code' in Chat Settings and try again."
),
)
raise HTTPException(
status_code=500,
detail=f"Failed to load model: {config.display_name}"
@ -476,7 +491,7 @@ async def get_status(
@router.post("/audio/generate")
async def generate_audio(payload: ChatCompletionRequest, request: Request):
async def generate_audio(payload: ChatCompletionRequest, request: Request, current_subject: str = Depends(get_current_subject)):
"""
Generate audio (TTS) from the latest user message.
Returns a JSON response with base64-encoded WAV audio.

13
studio/backend/routes/training.py
View file

@ -19,12 +19,14 @@ if str(backend_path) not in sys.path:
# Import backend functions
try:
from core.training import get_training_backend
from utils.models.model_config import load_model_defaults
except ImportError:
# Fallback: try to import from parent directory
parent_backend = backend_path.parent / "backend"
if str(parent_backend) not in sys.path:
sys.path.insert(0, str(parent_backend))
from core.training import get_training_backend
from utils.models.model_config import load_model_defaults
# Auth
from auth.authentication import get_current_subject
@ -200,8 +202,19 @@ async def start_training(
"wandb_project": request.wandb_project or "",
"enable_tensorboard": request.enable_tensorboard,
"tensorboard_dir": request.tensorboard_dir or "",
"trust_remote_code": request.trust_remote_code,
}
# Training page has no trust_remote_code toggle — the value comes from
# YAML model defaults applied when the user selects a model. As a safety
# net, consult the YAML directly so models that need it always get it.
if not training_kwargs["trust_remote_code"]:
model_defaults = load_model_defaults(request.model_name)
yaml_trust = model_defaults.get("training", {}).get("trust_remote_code", False)
if yaml_trust:
logger.info(f"YAML config sets trust_remote_code=True for {request.model_name}")
training_kwargs["trust_remote_code"] = True
# Free GPU memory: shut down any running inference/export subprocesses
# before training starts (they'd compete for VRAM otherwise)
try:

1
studio/backend/utils/inference/inference_config.py
View file

@ -59,6 +59,7 @@ def load_inference_config(model_identifier: str) -> Dict[str, Any]:
"top_p": model_inference.get("top_p", default_inference.get("top_p", 0.95)),
"top_k": model_inference.get("top_k", default_inference.get("top_k", -1)),
"min_p": model_inference.get("min_p", default_inference.get("min_p", 0.01)),
"trust_remote_code": model_inference.get("trust_remote_code", default_inference.get("trust_remote_code", False)),
}
return inference_config

1
studio/frontend/src/config/training.ts
View file

@ -114,6 +114,7 @@ export const DEFAULT_HYPERPARAMS = {
enableTensorboard: false,
tensorboardDir: "runs",
logFrequency: 10,
trustRemoteCode: false,
finetuneVisionLayers: true,
finetuneLanguageLayers: true,
finetuneAttentionModules: true,

33
studio/frontend/src/features/chat/chat-settings-sheet.tsx
View file

@ -170,6 +170,7 @@ export function ChatSettingsPanel({
...p.params,
systemPrompt: params.systemPrompt,
checkpoint: params.checkpoint,
trustRemoteCode: params.trustRemoteCode,
});
setActivePreset(name);
}
@ -330,17 +331,31 @@ export function ChatSettingsPanel({
</CollapsibleSection>
<CollapsibleSection icon={Settings02Icon} label="Settings">
<div className="flex items-center justify-between gap-3 py-1">
<div className="min-w-0">
<div className="text-xs font-medium">Auto title</div>
<div className="text-[11px] text-muted-foreground">
Generate short title after reply.
<div className="flex flex-col gap-3 py-1">
<div className="flex items-center justify-between gap-3">
<div className="min-w-0">
<div className="text-xs font-medium">Auto title</div>
<div className="text-[11px] text-muted-foreground">
Generate short title after reply.
</div>
</div>
<Switch
checked={autoTitle}
onCheckedChange={onAutoTitleChange}
/>
</div>
<div className="flex items-center justify-between gap-3">
<div className="min-w-0">
<div className="text-xs font-medium">Trust remote code</div>
<div className="text-[11px] text-muted-foreground">
Allow models with custom code (e.g. Nemotron). Only enable for repos you trust.
</div>
</div>
<Switch
checked={params.trustRemoteCode ?? false}
onCheckedChange={set("trustRemoteCode")}
/>
</div>
<Switch
checked={autoTitle}
onCheckedChange={onAutoTitleChange}
/>
</div>
</CollapsibleSection>
</div>

6
studio/frontend/src/features/chat/hooks/use-chat-model-runtime.ts
View file

@ -124,6 +124,10 @@ function mergeRecommendedInference(
topP: toFiniteNumber(inference?.top_p) ?? current.topP,
topK: toFiniteNumber(inference?.top_k) ?? current.topK,
minP: toFiniteNumber(inference?.min_p) ?? current.minP,
trustRemoteCode:
typeof inference?.trust_remote_code === "boolean"
? inference.trust_remote_code
: current.trustRemoteCode,
};
}
@ -232,6 +236,7 @@ export function useChatModelRuntime() {
previousWasUnloaded = true;
}
const paramsBeforeLoad = useChatRuntimeStore.getState().params;
const loadResponse = await loadModel({
model_path: modelId,
hf_token: null,
@ -239,6 +244,7 @@ export function useChatModelRuntime() {
load_in_4bit: true,
is_lora: isLora,
gguf_variant: ggufVariant ?? null,
trust_remote_code: paramsBeforeLoad.trustRemoteCode ?? false,
});
const currentParams = useChatRuntimeStore.getState().params;

3
studio/frontend/src/features/chat/types/api.ts
View file

@ -34,6 +34,8 @@ export interface LoadModelRequest {
load_in_4bit: boolean;
is_lora: boolean;
gguf_variant?: string | null;
/** Allow loading models with custom code (e.g. NVIDIA Nemotron). Only enable for repos you trust. */
trust_remote_code?: boolean;
}
export interface ValidateModelResponse {
@ -74,6 +76,7 @@ export interface LoadModelResponse {
top_p?: number;
top_k?: number;
min_p?: number;
trust_remote_code?: boolean;
};
}

3
studio/frontend/src/features/chat/types/runtime.ts
View file

@ -7,6 +7,8 @@ export interface InferenceParams {
maxTokens: number;
systemPrompt: string;
checkpoint: string;
/** Allow loading models with custom code (e.g. NVIDIA Nemotron). Only enable for repos you trust. */
trustRemoteCode?: boolean;
}
export const DEFAULT_INFERENCE_PARAMS: InferenceParams = {
@ -18,6 +20,7 @@ export const DEFAULT_INFERENCE_PARAMS: InferenceParams = {
maxTokens: 4092,
systemPrompt: "",
checkpoint: "",
trustRemoteCode: false,
};
export interface ChatModelSummary {

2
studio/frontend/src/features/export/api/export-api.ts
View file

@ -50,6 +50,8 @@ export async function loadCheckpoint(params: {
checkpoint_path: string;
max_seq_length?: number;
load_in_4bit?: boolean;
/** Allow loading models with custom code. Only enable for checkpoints you trust. */
trust_remote_code?: boolean;
}): Promise<ExportOperationResponse> {
const response = await authFetch("/api/export/load-checkpoint", {
method: "POST",

141
studio/frontend/src/features/studio/sections/dataset-section.tsx
View file

@ -37,6 +37,7 @@ import {
} from "@/hooks";
import {
HfDatasetSubsetSplitSelectors,
uploadTrainingDataset,
useDatasetPreviewDialogStore,
useTrainingConfigStore,
} from "@/features/training";
@ -52,7 +53,8 @@ import {
ViewIcon,
} from "@hugeicons/core-free-icons";
import { HugeiconsIcon } from "@hugeicons/react";
import { useCallback, useEffect, useMemo, useRef, useState } from "react";
import { type ChangeEvent, useCallback, useEffect, useMemo, useRef, useState } from "react";
import { toast } from "sonner";
import { useShallow } from "zustand/react/shallow";
const SEARCH_INPUT_REASONS = new Set(["input-change", "input-paste", "input-clear"]);
@ -72,7 +74,11 @@ function deriveLocalDatasetName(path: string): string {
const parts = normalized.split("/").filter(Boolean);
const parquetIndex = parts.lastIndexOf("parquet-files");
if (parquetIndex > 0) return parts[parquetIndex - 1];
return parts[parts.length - 1] ?? path;
const basename = parts[parts.length - 1] ?? path;
// Strip UUID prefix from uploaded files (format: {32hex}_{original})
const uuidPrefixMatch = basename.match(/^[a-f0-9]{32}_(.+)$/);
if (uuidPrefixMatch) return uuidPrefixMatch[1];
return basename;
}
function formatUpdatedDate(timestamp: number | null): string {
@ -286,6 +292,8 @@ export function DatasetSection() {
if (datasetSource !== "upload") return;
if (!uploadedFile) return;
if (selectedLocalDataset) return;
// Don't clear if this is a direct file upload (not a recipe directory)
if (isLikelyLocalDatasetRef(uploadedFile)) return;
selectLocalDataset(null);
}, [
datasetSource,
@ -320,11 +328,49 @@ export function DatasetSection() {
const selectedLocalUpdatedAt = selectedLocalDataset?.updated_at ?? null;
const comboboxAnchorRef = useRef<HTMLDivElement>(null);
const fileInputRef = useRef<HTMLInputElement>(null);
const { scrollRef, sentinelRef } = useInfiniteScroll(
fetchMore,
hfResults.length,
);
const [isUploading, setIsUploading] = useState(false);
const handleUploadButtonClick = () => {
fileInputRef.current?.click();
};
const handleDatasetFileChange = async (event: ChangeEvent<HTMLInputElement>) => {
const file = event.target.files?.[0];
event.target.value = "";
if (!file) return;
const MAX_SIZE_BYTES = 512 * 1024 * 1024;
if (file.size > MAX_SIZE_BYTES) {
toast.error("File too large", {
description: "Maximum upload size is 512 MB.",
});
return;
}
setIsUploading(true);
try {
const uploaded = await uploadTrainingDataset(file);
selectLocalDataset(uploaded.stored_path);
toast.success("Dataset uploaded", {
description: uploaded.filename,
});
} catch (error) {
toast.error("Upload failed", {
description: error instanceof Error ? error.message : "Unknown error",
});
} finally {
setIsUploading(false);
}
};
return (
<div data-tour="studio-dataset" className="col-span-1 xl:col-span-4">
<SectionCard
@ -596,7 +642,7 @@ export function DatasetSection() {
datasetEvalSplit={datasetEvalSplit}
setDatasetEvalSplit={setDatasetEvalSplit}
/>
) : datasetSource === "upload" ? (
) : datasetSource === "upload" && selectedLocalDataset ? (
<div className="rounded-lg border bg-muted/20 px-3.5 py-3">
<div className="mb-3 flex items-center justify-between gap-3">
<div>
@ -609,45 +655,39 @@ export function DatasetSection() {
</div>
</div>
{uploadedFile ? (
<div className="flex flex-col gap-3">
<div className="grid grid-cols-2 gap-x-4 gap-y-2 text-xs">
<MetadataRow
label="Rows"
value={
typeof selectedLocalRows === "number"
? selectedLocalRows.toLocaleString()
: "--"
}
/>
<MetadataRow
label="Columns"
value={
selectedLocalColumns.length > 0
? String(selectedLocalColumns.length)
: "--"
}
/>
<MetadataRow
label="Batches"
value={
typeof selectedLocalMetadata?.num_completed_batches === "number" &&
typeof selectedLocalMetadata?.total_num_batches === "number"
? `${selectedLocalMetadata.num_completed_batches}/${selectedLocalMetadata.total_num_batches}`
: "--"
}
/>
<MetadataRow
label="Updated"
value={formatUpdatedDate(selectedLocalUpdatedAt)}
/>
</div>
<div className="flex flex-col gap-3">
<div className="grid grid-cols-2 gap-x-4 gap-y-2 text-xs">
<MetadataRow
label="Rows"
value={
typeof selectedLocalRows === "number"
? selectedLocalRows.toLocaleString()
: "--"
}
/>
<MetadataRow
label="Columns"
value={
selectedLocalColumns.length > 0
? String(selectedLocalColumns.length)
: "--"
}
/>
<MetadataRow
label="Batches"
value={
typeof selectedLocalMetadata?.num_completed_batches === "number" &&
typeof selectedLocalMetadata?.total_num_batches === "number"
? `${selectedLocalMetadata.num_completed_batches}/${selectedLocalMetadata.total_num_batches}`
: "--"
}
/>
<MetadataRow
label="Updated"
value={formatUpdatedDate(selectedLocalUpdatedAt)}
/>
</div>
) : (
<p className="text-xs text-muted-foreground">
Select a local dataset to view metadata.
</p>
)}
</div>
</div>
) : null}
@ -839,9 +879,15 @@ export function DatasetSection() {
variant="outline"
size="sm"
className="cursor-pointer gap-1.5"
disabled={isUploading}
onClick={handleUploadButtonClick}
>
<HugeiconsIcon icon={CloudUploadIcon} className="size-3.5" />
Upload
{isUploading ? (
<Spinner className="size-3.5" />
) : (
<HugeiconsIcon icon={CloudUploadIcon} className="size-3.5" />
)}
{isUploading ? "Uploading..." : "Upload"}
</Button>
<Button
variant="outline"
@ -855,6 +901,15 @@ export function DatasetSection() {
</Button>
</div>
</div>
<input
ref={fileInputRef}
type="file"
accept=".json,.jsonl,.csv,.parquet"
className="hidden"
onChange={(event) => {
void handleDatasetFileChange(event);
}}
/>
</div>
</SectionCard>
</div>

9
studio/frontend/src/features/studio/sections/training-section.tsx
View file

@ -12,6 +12,7 @@ import {
serializeConfigToYaml,
useTrainingActions,
useTrainingConfigStore,
validateTrainingConfig,
} from "@/features/training";
import {
Archive04Icon,
@ -43,7 +44,8 @@ export function TrainingSection() {
const { isStarting, startError, startTrainingRun } = useTrainingActions();
const isIncompatible =
!store.isVisionModel && store.isDatasetImage === true;
const fileInputRef = useRef<HTMLInputElement>(null);
const configValidation = validateTrainingConfig(store);
const fileInputRef = useRef<HTMLInputElement>(null);
const handleFileUpload = (e: React.ChangeEvent<HTMLInputElement>) => {
const file = e.target.files?.[0];
@ -150,7 +152,7 @@ export function TrainingSection() {
data-tour="studio-start"
className="w-full cursor-pointer bg-gradient-to-r from-emerald-500 to-teal-500 text-white hover:from-emerald-600 hover:to-teal-600"
onClick={() => void startTrainingRun()}
disabled={isStarting || isIncompatible}
disabled={isStarting || isIncompatible || !configValidation.ok}
>
<HugeiconsIcon icon={Rocket01Icon} className="size-4" />
{isStarting ? "Starting..." : "Start Training"}
@ -163,6 +165,9 @@ export function TrainingSection() {
Text model is not compatible with a multimodal dataset. Switch to a vision model or choose a text-only dataset.
</p>
)}
{!configValidation.ok && configValidation.message && !isIncompatible && (
<p className="text-xs text-red-500 leading-relaxed">{configValidation.message}</p>
)}
{/* Upload / Save / Reset */}
<p className="text-xs text-muted-foreground">Training Config</p>

20
studio/frontend/src/features/training/api/datasets-api.ts
View file

@ -1,6 +1,7 @@
import type {
CheckFormatResponse,
LocalDatasetsResponse,
UploadDatasetResponse,
} from "../types/datasets";
import { authFetch } from "@/features/auth";
@ -39,6 +40,25 @@ export async function checkDatasetFormat({
return res.json();
}
export async function uploadTrainingDataset(
file: File,
): Promise<UploadDatasetResponse> {
const form = new FormData();
form.append("file", file);
const res = await authFetch("/api/datasets/upload", {
method: "POST",
body: form,
});
if (!res.ok) {
const body = await res.json().catch(() => null);
throw new Error(body?.detail || `Upload failed (${res.status})`);
}
return res.json();
}
export async function listLocalDatasets(): Promise<LocalDatasetsResponse> {
const res = await authFetch("/api/datasets/local");
if (!res.ok) {

1
studio/frontend/src/features/training/api/mappers.ts
View file

@ -36,6 +36,7 @@ export function buildTrainingStartPayload(
hf_token: config.hfToken.trim() || null,
load_in_4bit: adapterMethod ? isQloraMethod : false,
max_seq_length: config.contextLength,
trust_remote_code: config.trustRemoteCode ?? false,
hf_dataset: hfDataset,
subset: hfDataset ? config.datasetSubset : null,
train_split: hfDataset ? config.datasetSplit : null,

1
studio/frontend/src/features/training/api/models-api.ts
View file

@ -22,6 +22,7 @@ interface BackendTrainingDefaults {
packing?: boolean;
train_on_completions?: boolean;
gradient_checkpointing?: "none" | "true" | "unsloth";
trust_remote_code?: boolean;
}
interface BackendLoraDefaults {

2
studio/frontend/src/features/training/index.ts
View file

@ -7,7 +7,9 @@ export { useTrainingActions } from "./hooks/use-training-actions";
export { useTrainingRuntimeLifecycle } from "./hooks/use-training-runtime-lifecycle";
export { HfDatasetSubsetSplitSelectors } from "./components/hf-dataset-subset-split-selectors";
export { useDatasetPreviewDialogStore } from "./stores/dataset-preview-dialog-store";
export { uploadTrainingDataset } from "./api/datasets-api";
export { listLocalModels } from "./api/models-api";
export type { LocalModelInfo } from "./api/models-api";
export type { TrainingPhase } from "./types/runtime";
export { parseYamlConfig, serializeConfigToYaml } from "./lib/yaml-config";
export { validateTrainingConfig } from "./lib/validation";

4
studio/frontend/src/features/training/lib/model-defaults.ts
View file

@ -30,6 +30,7 @@ type ModelDefaultsPatch = Partial<
| "tensorboardDir"
| "logFrequency"
| "finetuneVisionLayers"
| "trustRemoteCode"
| "finetuneLanguageLayers"
| "finetuneAttentionModules"
| "finetuneMLPModules"
@ -133,6 +134,9 @@ export function mapBackendModelConfigToTrainingPatch(
patch.gradientCheckpointing = gradientCheckpointing;
}
const trustRemoteCode = toBoolean(training?.trust_remote_code);
if (trustRemoteCode !== undefined) patch.trustRemoteCode = trustRemoteCode;
const loraRank = toNumber(lora?.lora_r);
if (loraRank !== undefined) patch.loraRank = loraRank;

22
studio/frontend/src/features/training/lib/validation.ts
View file

@ -16,18 +16,22 @@ export function validateTrainingConfig(
if (!config.dataset) {
return { ok: false, message: "Select a Hugging Face dataset first." };
}
return { ok: true, message: null };
}
if (config.datasetSource === "upload") {
} else if (config.datasetSource === "upload") {
if (!config.uploadedFile) {
return { ok: false, message: "Select a local dataset first." };
}
return { ok: true, message: null };
} else {
return { ok: false, message: "Unsupported dataset source." };
}
return {
ok: false,
message: "Unsupported dataset source.",
};
// Eval steps requires an eval split to be selected
if (config.evalSteps > 0 && !config.datasetEvalSplit) {
return {
ok: false,
message:
"Eval Steps is set but no Eval Split is selected. Choose an Eval Split or set Eval Steps to 0.",
};
}
return { ok: true, message: null };
}

3
studio/frontend/src/features/training/stores/training-config-store.ts
View file

@ -285,6 +285,9 @@ export const useTrainingConfigStore = create<TrainingConfigStore>()(
uploadedFile,
...resetDatasetState(),
});
if (uploadedFile) {
runDatasetCheck(uploadedFile, "train");
}
},
setDatasetFormat: (datasetFormat) => set({ datasetFormat }),
setDataset: (dataset) => {

2
studio/frontend/src/features/training/types/api.ts
View file

@ -4,6 +4,8 @@ export interface TrainingStartRequest {
hf_token: string | null;
load_in_4bit: boolean;
max_seq_length: number;
/** Allow loading models with custom code. Only enable for repos you trust. */
trust_remote_code?: boolean;
hf_dataset: string | null;
subset: string | null;
train_split: string | null;

1
studio/frontend/src/features/training/types/config.ts
View file

@ -63,6 +63,7 @@ export interface TrainingConfigState {
isCheckingDataset: boolean;
isDatasetImage: boolean | null;
isDatasetAudio: boolean;
trustRemoteCode: boolean;
finetuneVisionLayers: boolean;
finetuneLanguageLayers: boolean;
finetuneAttentionModules: boolean;

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