Instructions to use skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT with Transformers:

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

pipe = pipeline("text-generation", model="skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT")
model = AutoModelForCausalLM.from_pretrained("skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT")
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 skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT

SGLang

How to use skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT 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 "skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT" \
    --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": "skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT",
		"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 "skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT" \
        --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": "skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT 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 skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT 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 skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT",
    max_seq_length=2048,
)

Docker Model Runner
How to use skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT with Docker Model Runner:
```
docker model run hf.co/skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT
```

🚀 RAG-Instruct Llama-3.2-3B (Fine-tuned)

Developed by: skshmjn
License: apache-2.0
Finetuned from model: unsloth/Llama-3.2-3B-Instruct
Dataset Used: skshmjn/RAG-INSTRUCT-1.1
Supports: Transformers & GGUF (for fast inference on CPU/GPU)

📌 Model Overview

This model is fine-tuned on the RAG-INSTRUCT-1.1 dataset using Unsloth to enhance text generation.
It is optimized for instruction-following while reducing hallucination, ensuring that responses remain factual and concise.

Instruction-Tuned: Follows structured queries effectively.
Hallucination Reduction: Avoids fabricating information when context is missing.
Optimized with Unsloth: Fast inference with GGUF quantization.

📌 Example Usage (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "skshmjn/Llama-3.2-3B-RAG-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = """You are an assistant for question-answering tasks. 
Use the following pieces of retrieved context to answer the question. 
If you don't know the answer, just say that you don't know. 
Use three sentences maximum and keep the answer concise.

Question: Who discovered the first exoplanet?  
Context: [No relevant context available]  
Answer:"""

inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_length=100)
response = tokenizer.decode(output[0], skip_special_tokens=True)

print(response)

Downloads last month: 5

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

unsloth/Llama-3.2-3B-Instruct

Finetuned

(594)

this model

skshmjn
/

RAG-LLAMA-3.2-3b-INSTRUCT

🚀 RAG-Instruct Llama-3.2-3B (Fine-tuned)

📌 Model Overview

📌 Example Usage (Transformers)

Model tree for skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT

Dataset used to train skshmjn/RAG-LLAMA-3.2-3b-INSTRUCT