Instructions to use devshaheen/llama-3.2-3b-Instruct-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use devshaheen/llama-3.2-3b-Instruct-finetune with Transformers:

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

pipe = pipeline("text-generation", model="devshaheen/llama-3.2-3b-Instruct-finetune")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("devshaheen/llama-3.2-3b-Instruct-finetune")
model = AutoModelForCausalLM.from_pretrained("devshaheen/llama-3.2-3b-Instruct-finetune")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use devshaheen/llama-3.2-3b-Instruct-finetune with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "devshaheen/llama-3.2-3b-Instruct-finetune"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devshaheen/llama-3.2-3b-Instruct-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/devshaheen/llama-3.2-3b-Instruct-finetune

SGLang

How to use devshaheen/llama-3.2-3b-Instruct-finetune 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 "devshaheen/llama-3.2-3b-Instruct-finetune" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devshaheen/llama-3.2-3b-Instruct-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "devshaheen/llama-3.2-3b-Instruct-finetune" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devshaheen/llama-3.2-3b-Instruct-finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use devshaheen/llama-3.2-3b-Instruct-finetune with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for devshaheen/llama-3.2-3b-Instruct-finetune to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for devshaheen/llama-3.2-3b-Instruct-finetune to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for devshaheen/llama-3.2-3b-Instruct-finetune to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="devshaheen/llama-3.2-3b-Instruct-finetune",
    max_seq_length=2048,
)

Docker Model Runner
How to use devshaheen/llama-3.2-3b-Instruct-finetune with Docker Model Runner:
```
docker model run hf.co/devshaheen/llama-3.2-3b-Instruct-finetune
```

Uploaded Model: devshaheen/llama-3.2-3b-Instruct-finetune

Overview

Developed by: devshaheen
License: Apache-2.0
Finetuned from model: unsloth/llama-3.2-3b-instruct-bnb-4bit
Languages Supported:
- English (en) for general-purpose text generation and instruction-following tasks.
- Kannada (kn) with a focus on localized and culturally aware text generation.
Dataset Used: charanhu/kannada-instruct-dataset-390k

This model is a fine-tuned version of LLaMA, optimized for multilingual instruction-following tasks with a specific emphasis on English and Kannada. It utilizes 4-bit quantization for efficient deployment in low-resource environments without compromising performance.

Features

1. Instruction Tuning

The model is trained to follow a wide range of instructions and generate contextually relevant responses. It excels in both creative and factual text generation tasks.

2. Multilingual Support

The model is capable of generating text in Kannada and English, making it suitable for users requiring bilingual capabilities.

3. Optimized Training

Training was accelerated using Unsloth, achieving 2x faster training compared to conventional methods. This was complemented by HuggingFace's TRL (Transformers Reinforcement Learning) library to ensure high performance.

4. Efficiency through Quantization

Built on the bnb-4bit quantized model, it is designed for optimal performance in environments with limited computational resources while maintaining precision and depth in output.

Usage Scenarios

General Use

Text completion and creative writing.
Generating instructions or following queries in English and Kannada.

Specialized Applications

Localized AI systems in Kannada for chatbots, educational tools, and more.
Research and development on multilingual instruction-tuned models.

Performance and Metrics

Evaluation Dataset:

The model was fine-tuned on charanhu/kannada-instruct-dataset-390k, a comprehensive dataset designed for Kannada instruction tuning.

Training Parameters:

Base Model: LLaMA 3.2-3B-Instruct
Optimizer: AdamW
Quantization: 4-bit (bnb-4bit)
Framework: HuggingFace Transformers + Unsloth

Example Usage

Python Code

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_name = "devshaheen/llama-3.2-3b-Instruct-finetune"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
input_text = "How does climate change affect the monsoon in Karnataka?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

3B params

Tensor type

F32

F16

devshaheen
/

llama-3.2-3b-Instruct-finetune