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Mistral-7B-Instruct-Uz

Text Generation
Transformers
PyTorch
6 datasets
Uzbek
English
mistral
text-generation-inference
summarization
translation
question-answering
conversational
Inference Endpoints
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Mistral-7B-Instruct-Uz / README.md
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metadata
license: apache-2.0
language:
  - uz
  - en
base_model: mistralai/Mistral-7B-Instruct-v0.3
library_name: transformers
tags:
  - text-generation-inference
  - summarization
  - translation
  - question-answering
datasets:
  - tahrirchi/uz-crawl
  - allenai/c4
  - MLDataScientist/Wikipedia-uzbek-2024-05-01
  - yahma/alpaca-cleaned
  - behbudiy/alpaca-cleaned-uz
  - behbudiy/translation-instruction
metrics:
  - bleu
  - comet
  - accuracy
pipeline_tag: text-generation

Model Description

The Mistral-7B-Instruct-Uz model has been continually pre-trained and instruction-tuned using a mix of publicly available and syntheticly constructed Uzbek and English data to preserve its original knowledge while enhancing its capabilities. This model is designed to support various natural language processing tasks in Uzbek, such as machine translation, summarization, and dialogue systems, ensuring robust performance across these applications. For more details on the model's construction and performance metrics, see this post.

Installation

It is recommended to use behbudiy/Mistral-7B-Instruct-Uz with mistral-inference. For HF transformers code snippets, please keep scrolling.

pip install mistral_inference

Download 

from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-Uz')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="behbudiy/Mistral-7B-Instruct-Uz", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)

Chat

After installing mistral_inference, a mistral-chat CLI command should be available in your environment. You can chat with the model using

mistral-chat $HOME/mistral_models/7B-Instruct-Uz --instruct --max_tokens 256

Instruct following 

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)

completion_request = ChatCompletionRequest(messages=[UserMessage(content="O'zbekiston haqida ma'lumot ber.")])

tokens = tokenizer.encode_chat_completion(completion_request).tokens

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

print(result)

Generate with transformers

If you want to use Hugging Face transformers to generate text, you can do something like this.

from transformers import pipeline

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]
chatbot = pipeline("text-generation", model="behbudiy/Mistral-7B-Instruct-Uz")
chatbot(messages)

More

For more details and examples, refer to the base model below: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3