---
license: apache-2.0
base_model: mistralai/Mistral-6A-v1.6
tags:
  - mistral
  - mistral-6a
  - mistral-instruct
  - instruct
  - hf-inference-api
  - text-generation
  - transformer
inference: true
model_type: mistral
extra_gated_prompt: >
  If you want to learn more about how we process your personal data, please read our
  <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---

# Model Card for Mistral-6A-v1.6

The Mistral-6A-v1.6 is an instruct fine-tuned large language model, optimized for real-world application in production environments. It supports:

- 🤖 HF Inference API
- 🧠 Function calling
- 🔡 Tokenizer v3 with extended vocabulary up to 32,768 tokens

## Installation

We recommend using [mistral-inference](https://github.com/mistralai/mistral-inference):

```bash
pip install mistral_inference
Download Weights
python
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from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home() / "mistral_models" / "6A-v1.6"
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(
    repo_id="mistralai/Mistral-6A-v1.6",
    allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"],
    local_dir=mistral_models_path
)
Chat CLI
Once installed, start chatting instantly:

bash
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mistral-chat $HOME/mistral_models/6A-v1.6 --instruct --max_tokens 256
Python Instruct Mode
python
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from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest

tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)

request = ChatCompletionRequest(messages=[UserMessage(content="Explain prompt-gramming.")])
tokens = tokenizer.encode_chat_completion(request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
print(tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0]))

## Use with `transformers`

To generate completions with the Hugging Face `transformers` library:

```python
from transformers import pipeline

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Tell me a story about a robot dog."}
]

chatbot = pipeline("text-generation", model="mistralai/Mistral-6A-v1.6")
chatbot(messages)
Advanced Function Calling (with transformers v4.42.0+)
python
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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "mistralai/Mistral-6A-v1.6"
tokenizer = AutoTokenizer.from_pretrained(model_id)

def get_current_weather(location: str, format: str):
    """
    Example tool: Get the current weather.
    Args:
        location (str): e.g. "San Francisco, CA"
        format (str): temperature format, "celsius" or "fahrenheit"
    """
    pass

conversation = [{"role": "user", "content": "What's the weather like in Tokyo?"}]
tools = [get_current_weather]

inputs = tokenizer.apply_chat_template(
    conversation,
    tools=tools,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)

model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

inputs = inputs.to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1000)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🔔 Note: Full tool call support requires using tool_call IDs and adding results to the conversation history. See:
Transformers Function Calling Guide

Limitations
This model is not equipped with moderation or safety filters. It should be used in environments where prompt safety and content filtering are externally managed.

Authors
Developed by the Mistral AI team:
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall

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