Add GLM, Qwen3 MoE, TinyQwen3 MoE, and Ministral 3 VL model defaults and GLM train_on_responses_only mapping

studio/backend/assets/configs/model_defaults/other/unsloth_GLM-4.7-Flash.yaml

@@ -0,0 +1,50 @@
+# Model defaults for unsloth/GLM-4.7-Flash
+# Based on GLM_Flash_A100(80GB).py
+# Also applies to: unsloth/GLM-4.7-Flash-unsloth-bnb-4bit, unsloth/GLM-4.7-Flash-bnb-4bit, THUDM/GLM-4.7-Flash
+
+training:
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 4
+  gradient_accumulation_steps: 2
+  warmup_steps: 5
+  max_steps: 60
+  save_steps: 60
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 8
+  lora_alpha: 16
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "out_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  temperature: 0.7
+  top_p: 0.8
+  top_k: 20
+

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

@@ -0,0 +1,51 @@
+# Model defaults for imdatta0/tiny_qwen3_moe_2.8B_0.7B
+# Based on TinyQwen3_MoE.py
+# Dummy model of qwen3moe architecture created to fit in T4
+# MoE model - includes gate_up_proj for MoE layers
+
+training:
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 1
+  warmup_steps: 5
+  max_steps: 50
+  save_steps: 50
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "gate_up_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  temperature: 0.6
+  top_k: 20
+  top_p: 0.95
+

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

@@ -0,0 +1,51 @@
+# Model defaults for unsloth/Qwen3-30B-A3B-Instruct-2507
+# Based on Qwen3_MoE.py
+# Also applies to: Qwen/Qwen3-30B-A3B-Instruct-2507, unsloth/Qwen3-30B-A3B-Instruct-2507-bnb-4bit
+# MoE model - includes gate_up_proj for MoE layers
+
+training:
+  max_seq_length: 2048
+  # num_epochs: 4
+  num_epochs: 0
+  learning_rate: 2e-4
+  batch_size: 1
+  gradient_accumulation_steps: 1
+  warmup_steps: 5
+  max_steps: 50
+  save_steps: 50
+  weight_decay: 0.001
+  random_seed: 3407
+  packing: false
+  train_on_completions: true
+  gradient_checkpointing: "unsloth"
+  optim: "adamw_8bit"
+  lr_scheduler_type: "linear"
+
+lora:
+  lora_r: 32
+  lora_alpha: 64
+  lora_dropout: 0.0
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+    - "gate_up_proj"
+  use_rslora: false
+  use_loftq: false
+
+logging:
+  enable_wandb: false
+  wandb_project: "llm-finetuning"
+  enable_tensorboard: false
+  tensorboard_dir: "runs"
+  log_frequency: 10
+
+inference:
+  temperature: 0.6
+  top_k: 20
+  top_p: 0.95
+

studio/backend/utils/datasets/model_mappings.py

@@ -383,7 +383,13 @@ TEMPLATE_TO_MODEL_MAPPER = {
         "unsloth/yi-34b-chat-bnb-4bit",
         "01-ai/Yi-6B-Chat",
         "01-ai/Yi-34B-Chat",
-    )
+    ),
+    "glm": (
+        "unsloth/GLM-4.7-Flash-unsloth-bnb-4bit",
+        "unsloth/GLM-4.7-Flash",
+        "THUDM/GLM-4.7-Flash",
+        "unsloth/GLM-4.7-Flash-bnb-4bit",
+    ),
 }
 
 MODEL_TO_TEMPLATE_MAPPER = {}
@@ -506,4 +512,9 @@ TEMPLATE_TO_RESPONSES_MAPPER = {
         "instruction": "<|im_start|>user\n",
         "response": "<|im_start|>assistant\n",
     },
+    "glm": {
+        "instruction": "[gMASK]<sop><|user|>",
+        "response": "<|assistant|><think>",
+    },
 }
+