Merge branch 'main' into nightly
This commit is contained in:
Daniel Han
commit
679e2b601f
6 changed files with 17 additions and 37 deletions
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@ -10,7 +10,7 @@
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<a href="https://discord.gg/unsloth"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord button.png" height="48"></a>
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<a href="https://ko-fi.com/unsloth"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy me a coffee button.png" height="48"></a>
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### Finetune Llama 3.1, Mistral, Phi-3.5 & Gemma 2-5x faster with 80% less memory!
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### Finetune Llama 3.2, Mistral, Phi-3.5 & Gemma 2-5x faster with 80% less memory!
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@ -32,15 +32,17 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
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| **DPO Zephyr** | [▶️ Start for free](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 43% less |
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- **Kaggle Notebooks** for [Llama 3.1 (8B)](https://www.kaggle.com/danielhanchen/kaggle-llama-3-1-8b-unsloth-notebook), [Gemma 2 (9B)](https://www.kaggle.com/code/danielhanchen/kaggle-gemma-7b-unsloth-notebook/), [Mistral (7B)](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)
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- Run [Llama 3.2 1B 3B notebook](https://colab.research.google.com/drive/1hoHFpf7ROqk_oZHzxQdfPW9yvTxnvItq?usp=sharing) and [Llama 3.2 conversational notebook](https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing)
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- Run [Llama 3.1 conversational notebook](https://colab.research.google.com/drive/15OyFkGoCImV9dSsewU1wa2JuKB4-mDE_?usp=sharing) and [Mistral v0.3 ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing)
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- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for continued pretraining / raw text
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- This [continued pretraining notebook](https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing) is for learning another language
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- Click [here](https://github.com/unslothai/unsloth/wiki) for detailed documentation for Unsloth.
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## 🦥 Unsloth.ai News
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- 📣 NEW! [Llama 3.2 Conversational notebook](https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing) includes training only on completions / outputs (increase accuracy), ShareGPT standardization and more!
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- 📣 NEW! [Llama 3.2 Kaggle notebook](https://www.kaggle.com/danielhanchen/kaggle-llama-3-2-1b-3b-unsloth-notebook) and [Llama 3.2 Kaggle conversational notebook](https://www.kaggle.com/code/danielhanchen/kaggle-llama-3-2-1b-3b-conversational-unsloth/notebook)
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- 📣 NEW! [Qwen 2.5 7b notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing) finetuning is supported! Qwen 2.5 comes in multiple sizes - check our [4bit uploads](https://huggingface.co/unsloth) for 4x faster downloads!. 14b fits in a Colab GPU! [Qwen 2.5 conversational notebook](https://colab.research.google.com/drive/1qN1CEalC70EO1wGKhNxs1go1W9So61R5?usp=sharing)
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- 📣 NEW! [Mistral Small 22b notebook](https://colab.research.google.com/drive/1oCEHcED15DzL8xXGU1VTx5ZfOJM8WY01?usp=sharing) finetuning fits in under 16GB of VRAM!
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- 📣 NEW! [Llama 3.1 Conversational notebook](https://colab.research.google.com/drive/15OyFkGoCImV9dSsewU1wa2JuKB4-mDE_?usp=sharing) includes training only on completions / outputs (increase accuracy), ShareGPT standardization and more!
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- 📣 NEW! [Phi-3.5 (mini)](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) now supported
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- 📣 NEW! [Gemma-2-2b](https://colab.research.google.com/drive/1weTpKOjBZxZJ5PQ-Ql8i6ptAY2x-FWVA?usp=sharing) now supported! Try out [Chat interface](https://colab.research.google.com/drive/1i-8ESvtLRGNkkUQQr_-z_rcSAIo9c3lM?usp=sharing)!
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- 📣 NEW! [Llama 3.1 8b, 70b](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) & [Mistral Nemo-12b](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing) both Base and Instruct are now supported
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@ -35,7 +35,7 @@ exclude = ["images*"]
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huggingface = [
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"packaging",
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"tyro",
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"transformers>=4.43.2",
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"transformers>=4.45.1",
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"datasets>=2.16.0",
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"sentencepiece>=0.2.0",
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"tqdm",
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@ -212,7 +212,7 @@ colab-ampere-torch220 = [
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colab-new = [
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"packaging",
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"tyro",
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"transformers>=4.43.2",
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"transformers>=4.45.1",
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"datasets>=2.16.0",
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"sentencepiece>=0.2.0",
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"tqdm",
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@ -306,6 +306,14 @@ if Version(xformers_version) >= Version("0.0.27"):
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accelerate_new_send_to_device = _fixed_send_to_device
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pass
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pass
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# Transformers 4.46 breaks dynamic caching. This is a hack
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import transformers.generation.configuration_utils
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if hasattr(transformers.generation.configuration_utils, "ALL_CACHE_IMPLEMENTATIONS"):
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if type(transformers.generation.configuration_utils.ALL_CACHE_IMPLEMENTATIONS) is list:
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transformers.generation.configuration_utils.ALL_CACHE_IMPLEMENTATIONS.append("dynamic")
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pass
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pass
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# =============================================
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# =============================================
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@ -62,10 +62,7 @@ from peft.tuners.lora import Linear4bit as Peft_Linear4bit
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from ..save import patch_saving_functions
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import re, os, inspect, math, sys
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from huggingface_hub.utils._token import get_token
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from transformers import __version__ as transformers_version
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from packaging.version import Version
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transformers_version = Version(transformers_version)
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SUPPORTS_LLAMA32 = transformers_version > Version("4.45.0")
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def original_apply_qkv(self, X):
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Q = self.q_proj(X)
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@ -1386,10 +1383,7 @@ def _wrap_fast_inference(generate, device_type, dtype, model):
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pass
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# For newer HF
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if SUPPORTS_LLAMA32:
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kwargs["cache_implementation"] = "hybrid"
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else:
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kwargs["cache_implementation"] = "dynamic"
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kwargs["cache_implementation"] = "dynamic"
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# For num_logits_to_keep
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kwargs["num_logits_to_keep"] = 1
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@ -138,17 +138,6 @@ def get_model_name(model_name, load_in_4bit = True):
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pass
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LLAMA32_MODEL_NAMES = set((
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"unsloth/Llama-3.2-1B-bnb-4bit",
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"unsloth/Llama-3.2-3B-bnb-4bit",
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"unsloth/Llama-3.2-1B",
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"unsloth/Llama-3.2-3B",
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"unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
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"unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
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"unsloth/Llama-3.2-1B-Instruct",
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"unsloth/Llama-3.2-3B-Instruct",
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))
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class FastLanguageModel(FastLlamaModel):
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@staticmethod
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def from_pretrained(
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@ -203,7 +192,7 @@ class FastLanguageModel(FastLlamaModel):
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pass
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# Cannot be both!
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if is_model and is_peft:
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if (is_model and is_peft) and not SUPPORTS_LLAMA32:
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raise RuntimeError(
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"Unsloth: Your repo has a LoRA adapter and a base model.\n"\
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"You have 2 files `config.json` and `adapter_config.json`.\n"\
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@ -255,14 +244,6 @@ class FastLanguageModel(FastLlamaModel):
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f"to obtain the latest transformers build, then restart this session."\
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)
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elif model_name.lower() in LLAMA32_MODEL_NAMES and not SUPPORTS_LLAMA32:
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raise ImportError(
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f"Unsloth: Your transformers version of {transformers_version} does not support Llama 3.2.\n"\
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f"The minimum required version is 4.46\n"\
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f'Try `pip install --upgrade "transformers>=4.46"`\n'\
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f"to obtain the latest transformers build, then restart this session."\
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)
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dispatch_model = FastLlamaModel
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elif model_type == "mistral": dispatch_model = FastMistralModel
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@ -1180,11 +1180,6 @@ def patch_sft_trainer_tokenizer():
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check_text = \
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"\n"\
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"if self._inner_training_loop.__name__ != '_fast_inner_training_loop':\n"\
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" raise RuntimeError(\n"\
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" 'Please do not edit specific areas of the Unsloth codebase or you will get CUDA segfaults.'\n"\
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" )\n"\
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"pass\n"\
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"import subprocess, re, gc, numpy as np\n"\
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"a = np.array([0,])\n"\
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"try:\n"\
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