Instructions to use Hemanth-thunder/Tamil-Mistral-7B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hemanth-thunder/Tamil-Mistral-7B-v0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Hemanth-thunder/Tamil-Mistral-7B-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hemanth-thunder/Tamil-Mistral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("Hemanth-thunder/Tamil-Mistral-7B-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Hemanth-thunder/Tamil-Mistral-7B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hemanth-thunder/Tamil-Mistral-7B-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hemanth-thunder/Tamil-Mistral-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hemanth-thunder/Tamil-Mistral-7B-v0.1

SGLang

How to use Hemanth-thunder/Tamil-Mistral-7B-v0.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Hemanth-thunder/Tamil-Mistral-7B-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hemanth-thunder/Tamil-Mistral-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Hemanth-thunder/Tamil-Mistral-7B-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hemanth-thunder/Tamil-Mistral-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Hemanth-thunder/Tamil-Mistral-7B-v0.1 with Docker Model Runner:
```
docker model run hf.co/Hemanth-thunder/Tamil-Mistral-7B-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Tamil-Mistral-7B-v0.1

The Tamil-Mistral-7B-v0.1 Large Language Model (LLM) is a pre-trained generative text model trained at the top of mistral base model 7 billion parameters. This is extends version of tokenization capability by increasing tamil tokens by 20k. Additionally, it was Pretrained on 1.19 million Tamil documents sourced from madlad-400 (Tamil) MADLAD-400 (Multilingual Audited Dataset: Low-resource And Document-level).

pretraining time: 145 hours (GPU NVIDIA RTX A6000 48GB)

Mistral model details

For full details of this model please read our paper and release blog post.

Model Architecture

Mistral-7B-v0.1 is a transformer model, with the following architecture choices:

Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer

Kaggle Demo

Running the model on a GPU 16GB

import torch
from transformers import (AutoModelForCausalLM,AutoTokenizer,TextStreamer,pipeline)
model = AutoModelForCausalLM.from_pretrained("Hemanth-thunder/Tamil-Mistral-7B-v0.1",device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Hemanth-thunder/Tamil-Mistral-7B-v0.1",add_prefix_space=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
streamer = TextStreamer(tokenizer)
pipe = pipeline("text-generation" ,model=model, tokenizer=tokenizer ,do_sample=True, repetition_penalty=1.15,top_p=0.95,streamer=streamer)
pipe("ஐபிஎல் தொடரில் மும்பை இந்தியன்ஸ் அணி ",max_length=50)


ஐபிஎல் தொடரில் மும்பை இந்தியன்ஸ் அணி -3வது இடத்திற்கு முன்னேறி இருக்கிறது, இதனால் பிளே ஆஃப் வாய்ப்பை உறுதி செய்ய வேண்டும்.
இன்னும் 11 புள்ளிகள் மட்டுமே மீதமுள்ளது.சென்னை சூப்பர் கிங்சுக்கு 12 புள்ளிகளில் உள்ளது.
அதன் கடைசி லீக் போட்டி ஜூன் 23-ம் தேதி சென்னையில் நடைபெறுகிறது.

Loss

Troubleshooting

If you see the following error:

KeyError: 'mistral'

NotImplementedError: Cannot copy out of meta tensor; no data!

Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.

Notice

Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.

How to Cite

@misc{Tamil-Mistral-7B-v0.1, 
      url={[https://huggingface.co/Hemanth-thunder/Tamil-Mistral-7B-v0.1]https://huggingface.co/Hemanth-thunder/Tamil-Mistral-7B-v0.1)}, 
      title={Tamil-Mistral-7B-v0.1}, 
      author={"hemanth kumar"}
}