[Mistral] Mistral-7B-v0.1 support (#26447)

* [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>

docs/source/en/model_doc/mistral.md

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+<!--Copyright 2023 Mistral AI and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# 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](https://mistral.ai/news/announcing-mistral-7b-v0.1/)
+
+## License
+
+Both `Mistral-7B-v0.1` and `Mistral-7B-Instruct-v0.1` are released under the Apache 2.0 license.
+
+## Usage
+
+`Mistral-7B-v0.1` and `Mistral-7B-Instruct-v0.1` can be found on the [Huggingface Hub](https://huggingface.co/mistralai)
+
+These ready-to-use checkpoints can be downloaded and used via the HuggingFace Hub:
+
+```python
+>>> 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 outupt"
+```
+
+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](https://files.mistral-7b-v0-1.mistral.ai/mistral-7B-v0.1.tar)          |
+| `Mistral-7B-Instruct-v0.1` | [Raw Checkpoint](https://files.mistral-7b-v0-1.mistral.ai/mistral-7B-instruct-v0.1.tar) |
+
+
+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:
+
+```bash
+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`:
+
+```python
+from transformers import MistralForCausalLM, LlamaTokenzier
+
+tokenizer = LlamaTokenizer.from_pretrained("/output/path")
+model = MistralForCausalLM.from_pretrained("/output/path")
+```
+
+## 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