| Mistral | |
| Overview | |
| Mistral-7B-v0.1 is Mistral AI's first Large Language Model (LLM). | |
| Model Details | |
| Mistral-7B-v0.1 is a decoder-based LM with the following architectural choices: | |
| * Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens | |
| * GQA (Grouped Query Attention) - allowing faster inference and lower cache size. | |
| * Byte-fallback BPE tokenizer - ensures that characters are never mapped to out of vocabulary tokens. | |
| We also provide an instruction fine-tuned model: Mistral-7B-Instruct-v0.1 which can be used for chat-based inference. | |
| For more details please read our release blog post | |
| License | |
| Both Mistral-7B-v0.1 and Mistral-7B-Instruct-v0.1 are released under the Apache 2.0 license. | |
| Usage tips | |
| Mistral-7B-v0.1 and Mistral-7B-Instruct-v0.1 can be found on the Huggingface Hub | |
| These ready-to-use checkpoints can be downloaded and used via the HuggingFace Hub: | |
| thon | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| device = "cuda" # the device to load the model onto | |
| model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") | |
| tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") | |
| prompt = "My favourite condiment is" | |
| model_inputs = tokenizer([prompt], return_tensors="pt").to(device) | |
| model.to(device) | |
| generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) | |
| tokenizer.batch_decode(generated_ids)[0] | |
| "The expected output" | |
| Raw weights for Mistral-7B-v0.1 and Mistral-7B-Instruct-v0.1 can be downloaded from: | |
| | Model Name | Checkpoint | | |
| |----------------------------|-----------------------------------------------------------------------------------------| | |
| | Mistral-7B-v0.1 | Raw Checkpoint | | |
| | Mistral-7B-Instruct-v0.1 | Raw Checkpoint | | |
| To use these raw checkpoints with HuggingFace you can use the convert_mistral_weights_to_hf.py script to convert them to the HuggingFace format: | |
| python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \ | |
| --input_dir /path/to/downloaded/mistral/weights --model_size 7B --output_dir /output/path | |
| You can then load the converted model from the output/path: | |
| thon | |
| from transformers import MistralForCausalLM, LlamaTokenizer | |
| tokenizer = LlamaTokenizer.from_pretrained("/output/path") | |
| model = MistralForCausalLM.from_pretrained("/output/path") | |
| Combining Mistral and Flash Attention 2 | |
| First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature. | |
| pip install -U flash-attn --no-build-isolation | |
| Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. torch.float16) | |
| To load and run a model using Flash Attention 2, refer to the snippet below: | |
| thon | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| device = "cuda" # the device to load the model onto | |
| model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2") | |
| tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") | |
| prompt = "My favourite condiment is" | |
| model_inputs = tokenizer([prompt], return_tensors="pt").to(device) | |
| model.to(device) | |
| generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True) | |
| tokenizer.batch_decode(generated_ids)[0] | |
| "The expected output" | |
| Expected speedups | |
| Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using mistralai/Mistral-7B-v0.1 checkpoint and the Flash Attention 2 version of the model. | |
| Sliding window Attention | |
| The current implementation supports the sliding window attention mechanism and memory efficient cache management. | |
| To enable sliding window attention, just make sure to have a flash-attn version that is compatible with sliding window attention (>=2.3.0). | |
| The Flash Attention-2 model uses also a more memory efficient cache slicing mechanism - as recommended per the official implementation of Mistral model that use rolling cache mechanism we keep the cache size fixed (self.config.sliding_window), support batched generation only for padding_side="left" and use the absolute position of the current token to compute the positional embedding. | |
| The Mistral Team | |
| Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | |
| MistralConfig | |
| [[autodoc]] MistralConfig | |
| MistralModel | |
| [[autodoc]] MistralModel | |
| - forward | |
| MistralForCausalLM | |
| [[autodoc]] MistralForCausalLM | |
| - forward | |
| MistralForSequenceClassification | |
| [[autodoc]] MistralForSequenceClassification | |
| - forward | |
| FlaxMistralModel | |
| [[autodoc]] FlaxMistralModel | |
| - call | |
| FlaxMistralForCausalLM | |
| [[autodoc]] FlaxMistralForCausalLM | |
| - call |