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* [Mistral] Mistral-7B-v0.1 support * fixing names * slightly longer test * fixups * not_doctested * wrongly formatted references * make fixuped --------- Co-authored-by: Timothee Lacroix <t@eugen.ai> Co-authored-by: timlacroix <t@mistral.ai>
106 lines
4.1 KiB
Markdown
106 lines
4.1 KiB
Markdown
<!--Copyright 2023 Mistral AI and The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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# Mistral
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## Overview
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Mistral-7B-v0.1 is Mistral AI’s first Large Language Model (LLM).
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## Model Details
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Mistral-7B-v0.1 is a decoder-based LM with the following architectural choices:
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* Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
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* GQA (Grouped Query Attention) - allowing faster inference and lower cache size.
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* Byte-fallback BPE tokenizer - ensures that characters are never mapped to out of vocabulary tokens.
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We also provide an instruction fine-tuned model: `Mistral-7B-Instruct-v0.1` which can be used for chat-based inference.
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For more details please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b-v0.1/)
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## License
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Both `Mistral-7B-v0.1` and `Mistral-7B-Instruct-v0.1` are released under the Apache 2.0 license.
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## Usage
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`Mistral-7B-v0.1` and `Mistral-7B-Instruct-v0.1` can be found on the [Huggingface Hub](https://huggingface.co/mistralai)
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These ready-to-use checkpoints can be downloaded and used via the HuggingFace Hub:
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```python
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer
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>>> device = "cuda" # the device to load the model onto
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>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
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>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
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>>> prompt = "My favourite condiment is"
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>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
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>>> model.to(device)
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>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
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>>> tokenizer.batch_decode(generated_ids)[0]
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"The expected outupt"
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```
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Raw weights for `Mistral-7B-v0.1` and `Mistral-7B-Instruct-v0.1` can be downloaded from:
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| Model Name | Checkpoint |
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|----------------------------|-----------------------------------------------------------------------------------------|
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| `Mistral-7B-v0.1` | [Raw Checkpoint](https://files.mistral-7b-v0-1.mistral.ai/mistral-7B-v0.1.tar) |
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| `Mistral-7B-Instruct-v0.1` | [Raw Checkpoint](https://files.mistral-7b-v0-1.mistral.ai/mistral-7B-instruct-v0.1.tar) |
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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:
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```bash
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python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \
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--input_dir /path/to/downloaded/mistral/weights --model_size 7B --output_dir /output/path
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```
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You can then load the converted model from the `output/path`:
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```python
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from transformers import MistralForCausalLM, LlamaTokenzier
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tokenizer = LlamaTokenizer.from_pretrained("/output/path")
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model = MistralForCausalLM.from_pretrained("/output/path")
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```
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## The Mistral Team
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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.
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## MistralConfig
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[[autodoc]] MistralConfig
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## MistralModel
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[[autodoc]] MistralModel
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- forward
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## MistralForCausalLM
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[[autodoc]] MistralForCausalLM
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- forward
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## MistralForSequenceClassification
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[[autodoc]] MistralForSequenceClassification
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- forward |