---
license: apache-2.0
language:
- uz
- en
base_model:
- mistralai/Mistral-Nemo-Instruct-2407
library_name: transformers
tags:
- text-generation-inference
- summarization
- translation
- question-answering
datasets:
- tahrirchi/uz-crawl
- allenai/c4
- yahma/alpaca-cleaned
- MLDataScientist/Wikipedia-uzbek-2024-05-01
metrics:
- bleu
- comet
- accuracy
pipeline_tag: text-generation
---

### Model Description

The Mistral-Nemo-Instruct-Uz model has been continually pre-trained and instruction-tuned using a mix of publicly available and self-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.](#)

- **Developed by:**
  - [Eldor Fozilov](https://www.linkedin.com/in/eldor-fozilov/)
  - [Azimjon Urinov](https://azimjonn.github.io/)
  - [Khurshid Juraev](https://kjuraev.com/)

## Usage

The model can be used with three different frameworks

- [`mistral_inference`](https://github.com/mistralai/mistral-inference): See [here](https://huggingface.co/behbudiy/Mistral-Nemo-Instruct-Uz#mistral-inference)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)

### Mistral Inference

#### Install

It is recommended to use `behbudiy/Mistral-Nemo-Instruct-Uz` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling.

```
pip install mistral_inference
```

#### Download

```py
from huggingface_hub import snapshot_download
from pathlib import Path

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

snapshot_download(repo_id="behbudiy/Mistral-Nemo-Instruct-Uz", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], 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/Nemo-Instruct-Uz --instruct --max_tokens 256 --temperature 0.35
```

*E.g.* Try out something like:
```
O'zbek tilida kichik bir she'r yozib bera olasanmi?
```

#### Instruct following

```py
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}/tekken.json")
model = Transformer.from_folder(mistral_models_path)

prompt = "O'zbek tilida kichik bir she'r yozib bera olasanmi?"

completion_request = ChatCompletionRequest(messages=[UserMessage(content=prompt)])

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])

print(result)
```

### Transformers

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

```py
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="mistralai/Mistral-Nemo-Instruct-2407",max_new_tokens=128)
chatbot(messages)
```

## More
For more details and examples, refer to the base model below:
https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407