[AUTO] CVST Tokenizer Badger

A scripted PR to update the status of the transformer tokenizer.

```

> [!CAUTION]
> ⚠️
> The `transformers` tokenizer might give incorrect results as it has not been tested by the Mistral team. To make sure that your encoding and decoding is correct, please use `mistral_common` as shown below:

## Encode and Decode with `mistral_common`

```py
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest

mistral_models_path = "MISTRAL_MODELS_PATH"

tokenizer = MistralTokenizer.v3()

completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])

tokens = tokenizer.encode_chat_completion(completion_request).tokens
```

## Inference with `mistral_inference`

```py
from mistral_inference.model import Transformer
from mistral_inference.generate import generate

model = Transformer.from_folder(mistral_models_path)
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)

result = tokenizer.decode(out_tokens[0])

print(result)
```

## Inference with hugging face `transformers`

```py
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
model.to("cuda")

generated_ids = model.generate(tokens, max_new_tokens=1000, do_sample=True)

# decode with mistral tokenizer
result = tokenizer.decode(generated_ids[0].tolist())
print(result)
```

> [!TIP]
> PRs to correct the `transformers` tokenizer so that it gives 1-to-1 the same results as the `mistral_common` reference implementation are very welcome!

```

README.md

@@ -9,6 +9,62 @@ language:
 ---
 
 # Model Card for Mixtral-8x22B-Instruct-v0.1
+
+### 
+
+> [!CAUTION]
+> ⚠️ 
+> The `transformers` tokenizer might give incorrect results as it has not been tested by the Mistral team. To make sure that your encoding and decoding is correct, please use `mistral_common` as shown below:
+
+## Encode and Decode with `mistral_common`
+            
+```py
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+ 
+mistral_models_path = "MISTRAL_MODELS_PATH"
+ 
+tokenizer = MistralTokenizer.v3()
+ 
+completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
+ 
+tokens = tokenizer.encode_chat_completion(completion_request).tokens
+```
+ 
+## Inference with `mistral_inference`
+ 
+ ```py
+from mistral_inference.model import Transformer
+from mistral_inference.generate import generate
+ 
+model = Transformer.from_folder(mistral_models_path)
+out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+
+result = tokenizer.decode(out_tokens[0])
+
+print(result)
+```
+
+## Inference with hugging face `transformers`
+ 
+```py
+from transformers import AutoModelForCausalLM
+ 
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
+model.to("cuda")
+ 
+generated_ids = model.generate(tokens, max_new_tokens=1000, do_sample=True)
+
+# decode with mistral tokenizer
+result = tokenizer.decode(generated_ids[0].tolist())
+print(result)
+```
+
+> [!TIP]
+> PRs to correct the `transformers` tokenizer so that it gives 1-to-1 the same results as the `mistral_common` reference implementation are very welcome!
+            
+---
 The Mixtral-8x22B-Instruct-v0.1 Large Language Model (LLM) is an instruct fine-tuned version of the [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1).
 
 ## Run the model