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Mixtral-8x22B-Instruct-v0.1

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Duplicate from mistralai/Mixtral-8x22B-Instruct-v0.1

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Co-authored-by: Pierre Stock <[email protected]>

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  2. README.md +120 -0
  3. config.json +29 -0
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ ---
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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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+
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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.tokens.tokenizers.mistral import MistralTokenizer
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+ from mistral_common.tokens.instruct.normalize import ChatCompletionRequest
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+
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+ device = "cuda" # the device to load the model onto
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+
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+ tokenizer_v3 = MistralTokenizer.v3()
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ from transformers import AutoTokenizer
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+
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+ tokenizer_v3 = MistralTokenizer.v3()
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+
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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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+
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+ tokenized_mistral = tokenizer_v3.encode_chat_completion(mistral_query).tokens
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+
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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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+
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+ assert tokenized_hf == tokenized_mistral
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+ ```
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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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+
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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](github.com/mistralai/mistral-common/...).
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+
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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,
120
+ Valera Nemychnikova, William El Sayed, William Marshall
config.json ADDED
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+ {
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+ "architectures": [
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+ "MixtralForCausalLM"
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+ ],
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 6144,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 16384,
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+ "max_position_embeddings": 65536,
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+ "model_type": "mixtral",
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+ "num_attention_heads": 48,
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+ "num_experts_per_tok": 2,
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+ "num_hidden_layers": 56,
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+ "num_key_value_heads": 8,
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+ "num_local_experts": 8,
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+ "output_router_logits": false,
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 1000000.0,
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+ "router_aux_loss_coef": 0.001,
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+ "sliding_window": null,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.38.0",
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+ "use_cache": true,
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+ "vocab_size": 32768
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+ }
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+ "bos_token_id": 1,
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+ "transformers_version": "4.34.0.dev0"
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