Merge branch 'main' into nightly
This commit is contained in:
Daniel Han
commit
e31e9cd730
3 changed files with 23 additions and 36 deletions
11
README.md
11
README.md
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@ -10,7 +10,7 @@
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<a href="https://discord.com/invite/unsloth"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord button.png" height="48"></a>
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<a href="https://docs.unsloth.ai"><img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/Documentation%20Button.png" height="48"></a>
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### Finetune Qwen3, Llama 4, Gemma 3, Phi-4 & Mistral 2x faster with 80% less VRAM!
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### Finetune Gemma 3n, Qwen3, Llama 4, Phi-4 & Mistral 2x faster with 80% less VRAM!
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@ -22,6 +22,7 @@ Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-st
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| Unsloth supports | Free Notebooks | Performance | Memory use |
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|-----------|---------|--------|----------|
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| **Gemma 3n (4B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_(4B)-Conversational.ipynb) | 1.5x faster | 50% less |
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| **Qwen3 (14B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(14B)-Reasoning-Conversational.ipynb) | 2x faster | 70% less |
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| **Qwen3 (4B): GRPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb) | 2x faster | 80% less |
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| **Gemma 3 (4B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B).ipynb) | 1.6x faster | 60% less |
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@ -45,12 +46,12 @@ pip install unsloth
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For Windows install instructions, see [here](https://docs.unsloth.ai/get-started/installing-+-updating/windows-installation).
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## 🦥 Unsloth.ai News
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- 📣 NEW! **[Text-to-Speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning)** is now supported, including `sesame/csm-1b` and STT `openai/whisper-large-v3`.
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- 📣 NEW! **[Qwen3](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** is now supported. Qwen3-30B-A3B fits on 17.5GB VRAM.
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- 📣 NEW! Introducing **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants that set new benchmarks on 5-shot MMLU & KL Divergence.
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- 📣 **Gemma 3n** by Google: [Read Blog](https://docs.unsloth.ai/basics/gemma-3n-how-to-run-and-fine-tune). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/gemma-3n-685d3874830e49e1c93f9339).
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- 📣 **[Text-to-Speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning)** is now supported, including `sesame/csm-1b` and STT `openai/whisper-large-v3`.
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- 📣 **[Qwen3](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** is now supported. Qwen3-30B-A3B fits on 17.5GB VRAM.
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- 📣 Introducing **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants that set new benchmarks on 5-shot MMLU & KL Divergence.
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- 📣 **[Llama 4](https://unsloth.ai/blog/llama4)** by Meta, including Scout & Maverick are now supported.
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- 📣 [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) - all models (BERT, diffusion, Cohere, Mamba), FFT, etc. MultiGPU coming soon. Enable FFT with `full_finetuning = True`, 8-bit with `load_in_8bit = True`.
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- 📣 **Gemma 3** by Google: [Read Blog](https://unsloth.ai/blog/gemma3). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b).
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- 📣 Introducing Long-context [Reasoning (GRPO)](https://unsloth.ai/blog/grpo) in Unsloth. Train your own reasoning model with just 5GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs!
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- 📣 [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - run or fine-tune them [with our guide](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5).
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<details>
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@ -206,33 +206,18 @@ except:
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# Patch get_model_param_count to record correct 4bit / 8bit
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from transformers.trainer_pt_utils import is_deepspeed_zero3_enabled
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def extract_approx_params_from_config(config):
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def extract_quant_model_param_count(model):
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"""
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Extract approximate parameter count from model config's name_or_path
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Returns int (param count) or None if not found.
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Calculate quant model param count based on difference in param class. Returns int for param count.
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"""
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lowercase_b_families = ["gemma"] # gemma uses small 'b' : google/gemma-3-1b-it
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model_name = getattr(config, "name_or_path", "")
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import re
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cleaned = re.sub(r"[-_]?bnb[-_]?4bit|[-_]?4bit|[-_]?8bit|[-_]?bnb", "", model_name, flags=re.IGNORECASE) # replace bnb and xbit
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match_B = re.search(r"([0-9]+(?:\.[0-9]+)?)\s*B", cleaned) # first prefer searching 'B'
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if match_B:
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# most model names would come in this flow
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billions = float(match_B.group(1))
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return int(1_000_000_000 * billions)
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else:
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if any(fam in cleaned.lower() for fam in lowercase_b_families):
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match_b = re.search(r"([0-9]+(?:\.[0-9]+)?)\s*b", cleaned)
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if match_b:
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billions = float(match_b.group(1))
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return int(1_000_000_000 * billions)
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count: int = 0
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for name, p in model.named_parameters():
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if p.__class__.__name__ == "Params4bit":
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count += 2 * p.numel()
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else:
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match_any = re.search(r"([0-9]+(?:\.[0-9]+)?)\s*[bB]", cleaned)
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if match_any:
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billions = float(match_any.group(1))
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return int(1_000_000_000 * billions)
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return None
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count += p.numel()
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return count
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pass
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def get_model_param_count(model, trainable_only = False):
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"""
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@ -248,7 +233,7 @@ def get_model_param_count(model, trainable_only = False):
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if (not trainable_only) and \
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hasattr(model, "config") and \
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hasattr(model.config, "quantization_config"):
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approx = extract_approx_params_from_config(model.config)
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approx = extract_quant_model_param_count(model)
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if approx is not None:
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s = approx
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return s
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@ -370,7 +355,7 @@ if is_openai_available():
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def _is_openai_available(): return False
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transformers.utils.is_openai_available = _is_openai_available
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pass
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pass
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pass
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# =============================================
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# Get Flash Attention v2 if Ampere (RTX 30xx, A100)
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@ -1085,7 +1070,7 @@ pass
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def patch_gradient_accumulation_fix(Trainer):
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# Fixes gradient accumulation
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# Fixes gradient accumulation
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import inspect
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if hasattr(Trainer, "get_batch_samples"):
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if Trainer.get_batch_samples.__name__ == "_unsloth_get_batch_samples": return
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@ -1159,10 +1144,10 @@ def patch_gradient_accumulation_fix(Trainer):
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"\2if num_items_in_batch is None:\n"\
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"\3loss = loss / self.args.gradient_accumulation_steps\n"\
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"\1self.accelerator.backward(loss, **kwargs)",
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function,
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)
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exec(function, globals())
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Trainer.training_step = _unsloth_training_step
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pass
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@ -1356,7 +1341,7 @@ def validate_loftq_config(loftq_config, lora_dropout, bias, init_lora_weights, m
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)
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loftq_config = LoftQConfig(loftq_bits = 4, loftq_iter = 1)
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pass
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if hasattr(model.config, "quantization_config"):
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raise ValueError(
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"Unsloth: You are using `loftq` init, yet `load_in_4bit = True` was set.\n"\
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@ -1365,4 +1350,4 @@ def validate_loftq_config(loftq_config, lora_dropout, bias, init_lora_weights, m
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pass
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pass
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return loftq_config
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return loftq_config
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@ -561,6 +561,7 @@ class FastModel(FastBaseModel):
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raise RuntimeError("Unsloth: Cohere's Command model only works on transformers >= 4.50.0." + NIGHTLY)
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# Sesame
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elif "csm-1b" in lowered_model_name:
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os.environ["UNSLOTH_COMPILE_DISABLE"] = "1" # Inference is too slow
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os.environ["UNSLOTH_DISABLE_STATIC_GENERATION"] = "1" # Sesame fails
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os.environ["UNSLOTH_FORCE_CUSTOM_DTYPE"] = \
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"all;torch.float32;torch.float16;"\
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