Instructions to use devatar/quantized_Llama-3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use devatar/quantized_Llama-3.1-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="devatar/quantized_Llama-3.1-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("devatar/quantized_Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("devatar/quantized_Llama-3.1-8B-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 devatar/quantized_Llama-3.1-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "devatar/quantized_Llama-3.1-8B-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": "devatar/quantized_Llama-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/devatar/quantized_Llama-3.1-8B-Instruct

SGLang

How to use devatar/quantized_Llama-3.1-8B-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 "devatar/quantized_Llama-3.1-8B-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": "devatar/quantized_Llama-3.1-8B-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 "devatar/quantized_Llama-3.1-8B-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": "devatar/quantized_Llama-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use devatar/quantized_Llama-3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/devatar/quantized_Llama-3.1-8B-Instruct
```

🚀 Quantized Llama-3.1-8B-Instruct Model

This is a 4-bit quantized version of the meta-llama/Llama-3.1-8B-Instruct model, optimized for efficient inference on resource-constrained environments like Google Colab's NVIDIA T4 GPU.

🧠 Model Description

The model was quantized using the bitsandbytes library to reduce memory usage while maintaining performance for instruction-following tasks.

🧮 Quantization Details

Base Model: meta-llama/Llama-3.1-8B-Instruct
Quantization Method: 4-bit (NormalFloat4, NF4) with double quantization
Compute Dtype: float16
Library: bitsandbytes==0.43.3
Framework: transformers==4.45.1
Hardware: NVIDIA T4 GPU (16GB VRAM) in Google Colab
Date: Quantized on June 20, 2025

📦 Files Included

README.md: This file
config.json, pytorch_model.bin (or sharded checkpoints): Model weights
special_tokens_map.json, tokenizer.json, tokenizer_config.json: Tokenizer files

Usage

To load and use the quantized model for inference:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
import torch

# Define quantization configuration
quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

# Load the quantized model
model = AutoModelForCausalLM.from_pretrained(
    "your-username/quantized_Llama-3.1-8B-Instruct",  # Replace with your Hugging Face repo ID
    quantization_config=quant_config,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("your-username/quantized_Llama-3.1-8B-Instruct")

# Create a text generation pipeline
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Perform inference
prompt = "Hello, how can I assist you today?"
output = generator(prompt, max_length=50, num_return_sequences=1)
print(output)

Quantization Process

The model was quantized in Google Colab using the following script:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
from huggingface_hub import login

# Log in to Hugging Face
login()  # Requires a Hugging Face token

# Define quantization configuration
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

# Load and quantize the model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    quantization_config=quantization_config,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token

# Save the quantized model
quant_path = "/content/quantized_Llama-3.1-8B-Instruct"
model.save_pretrained(quant_path)
tokenizer.save_pretrained(quant_path)

Requirements

Hardware: NVIDIA GPU with CUDA 11.4+ (e.g., T4, A100)
Python: 3.10+
Dependencies:
- transformers==4.45.1
- bitsandbytes==0.43.3
- accelerate==0.33.0
- torch (with CUDA support)

Notes

The quantized model is stored in /content/quantized_Llama-3.1-8B-Instruct in the Colab environment.
Due to Colab's ephemeral storage, consider pushing to Hugging Face Hub or saving to Google Drive for persistence.
Access to the base model requires a Hugging Face token and approval from Meta AI.

License

This model inherits the license of the base model meta-llama/Llama-3.1-8B-Instruct. Refer to the original model card: Meta AI Llama 3.1-8B-Instruct.

Acknowledgments

Created using Hugging Face Transformers and bitsandbytes for quantization.
Quantized in Google Colab with a T4 GPU on June 20, 2025.

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Safetensors

Model size

8B params

Tensor type

F16

F32