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Create README.md (#2)
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README.md
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---
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license: apache-2.0
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language:
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- en
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- fr
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- es
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- de
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- it
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---
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Original README
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---
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# Model Card for Mixtral-8x22B-Instruct-v0.1
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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).
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## Run the model
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```python
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from transformers import AutoModelForCausalLM
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from mistral_common.protocol.instruct.messages import (
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AssistantMessage,
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UserMessage,
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)
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from mistral_common.protocol.instruct.tool_calls import (
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Tool,
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Function,
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)
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from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
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from mistral_common.tokens.instruct.normalize import ChatCompletionRequest
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device = "cuda" # the device to load the model onto
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tokenizer_v3 = MistralTokenizer.v3()
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mistral_query = ChatCompletionRequest(
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tools=[
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Tool(
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function=Function(
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name="get_current_weather",
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description="Get the current weather",
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parameters={
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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"required": ["location", "format"],
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},
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)
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)
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],
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messages=[
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UserMessage(content="What's the weather like today in Paris"),
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],
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model="test",
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)
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encodeds = tokenizer_v3.encode_chat_completion(mistral_query).tokens
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model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
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model_inputs = encodeds.to(device)
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model.to(device)
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
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sp_tokenizer = tokenizer_v3.instruct_tokenizer.tokenizer
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decoded = sp_tokenizer.decode(generated_ids[0])
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print(decoded)
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```
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# Instruct tokenizer
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The HuggingFace tokenizer included in this release should match our own. To compare:
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`pip install mistral-common`
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```py
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from mistral_common.protocol.instruct.messages import (
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AssistantMessage,
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UserMessage,
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)
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from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
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from mistral_common.tokens.instruct.normalize import ChatCompletionRequest
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from transformers import AutoTokenizer
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tokenizer_v3 = MistralTokenizer.v3()
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mistral_query = ChatCompletionRequest(
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messages=[
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UserMessage(content="How many experts ?"),
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AssistantMessage(content="8"),
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UserMessage(content="How big ?"),
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AssistantMessage(content="22B"),
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UserMessage(content="Noice 🎉 !"),
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],
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model="test",
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)
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hf_messages = mistral_query.model_dump()['messages']
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tokenized_mistral = tokenizer_v3.encode_chat_completion(mistral_query).tokens
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tokenizer_hf = AutoTokenizer.from_pretrained('mistralai/Mixtral-8x22B-Instruct-v0.1')
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tokenized_hf = tokenizer_hf.apply_chat_template(hf_messages, tokenize=True)
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assert tokenized_hf == tokenized_mistral
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```
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# Function calling and special tokens
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This tokenizer includes more special tokens, related to function calling :
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- [TOOL_CALLS]
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- [AVAILABLE_TOOLS]
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- [/AVAILABLE_TOOLS]
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- [TOOL_RESULT]
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- [/TOOL_RESULTS]
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If you want to use this model with function calling, please be sure to apply it similarly to what is done in our [SentencePieceTokenizerV3](https://github.com/mistralai/mistral-common/blob/main/src/mistral_common/tokens/tokenizers/sentencepiece.py#L299).
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# The Mistral AI Team
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Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux,
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Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,
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Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot,
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Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger,
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Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona,
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Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon,
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Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat,
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Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen,
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Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao,
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Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang,
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Valera Nemychnikova, William El Sayed, William Marshall
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---
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