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QuantFactory/Mistral-7B-Instruct-Uz-GGUF

Name: Mistral-7B-Instruct-Uz-GGUF
Availability: InStock

This is quantized version of behbudiy/Mistral-7B-Instruct-Uz created using llama.cpp

Original Model Card

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 details regarding the performance metrics compared to the base model, see this post.

Developed by:

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

Instructiong 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", device='cuda')
chatbot(messages)

Information on Evaluation Method

To evaluate on the translation task, we used FLORES+ Uz-En / En-Uz datasets, where we merged the dev and test sets to create a bigger evaluation data for each Uz-En and En-Uz subsets. We used the following prompt to do one-shot Uz-En evaluation both for the base model and Uzbek-optimized model (for En-Uz eval, we changed the positions of the words "English" and "Uzbek").

python

  prompt = f'''You are a professional Uzbek-English translator. Your task is to accurately translate the given Uzbek text into English.

  Instructions:
  1. Translate the text from Uzbek to English.
  2. Maintain the original meaning and tone.
  3. Use appropriate English grammar and vocabulary.
  4. If you encounter an ambiguous or unfamiliar word, provide the most likely translation based on context.
  5. Output only the English translation, without any additional comments.

  Example:
  Uzbek: "Bugun ob-havo juda yaxshi, quyosh charaqlab turibdi."
  English: "The weather is very nice today, the sun is shining brightly."

  Now, please translate the following Uzbek text into English:
  "{sentence}"
    '''

To assess the model's ability in Uzbek sentiment analysis, we used the risqaliyevds/uzbek-sentiment-analysis dataset, for which we created binary labels (0: Negative, 1: Positive) using GPT-4o API (refer to behbudiy/uzbek-sentiment-analysis dataset). We used the following prompt for the evaluation:

python

prompt = f'''Given the following text, determine the sentiment as either 'Positive' or 'Negative.' Respond with only the word 'Positive' or 'Negative' without any additional text or explanation.

Text: {text}"
'''

For Uzbek News Classification, we used risqaliyevds/uzbek-zero-shot-classification dataset and asked the model to predict the category of the news using the following prompt:

python

prompt = f'''Classify the given Uzbek news article into one of the following categories. Provide only the category number as the answer.

Categories:
0 - Politics (Siyosat)
1 - Economy (Iqtisodiyot)
2 - Technology (Texnologiya)
3 - Sports (Sport)
4 - Culture (Madaniyat)
5 - Health (Salomatlik)
6 - Family and Society (Oila va Jamiyat)
7 - Education (Ta'lim)
8 - Ecology (Ekologiya)
9 - Foreign News (Xorijiy Yangiliklar)

Now classify this article:
"{text}"

Answer (number only):"
'''

MMLU

We used this script.

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

QuantFactory/
Mistral-7B-Instruct-Uz-GGUF

QuantFactory/Mistral-7B-Instruct-Uz-GGUF

Original Model Card

Model Description

Installation

Download

Chat

Instructiong Following

Generate with `transformers`

Information on Evaluation Method

MMLU

More

QuantFactory/Mistral-7B-Instruct-Uz-GGUF

QuantFactory/Mistral-7B-Instruct-Uz-GGUF

Original Model Card

Model Description

Installation

Download

Chat

Instructiong Following

Generate with transformers

Information on Evaluation Method

MMLU

More

QuantFactory/
Mistral-7B-Instruct-Uz-GGUF

Generate with `transformers`