Instructions to use logiya-vidhyapathi/llama_quantization_4_bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use logiya-vidhyapathi/llama_quantization_4_bit with Transformers:

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

pipe = pipeline("text-generation", model="logiya-vidhyapathi/llama_quantization_4_bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("logiya-vidhyapathi/llama_quantization_4_bit")
model = AutoModelForCausalLM.from_pretrained("logiya-vidhyapathi/llama_quantization_4_bit")
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 logiya-vidhyapathi/llama_quantization_4_bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "logiya-vidhyapathi/llama_quantization_4_bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "logiya-vidhyapathi/llama_quantization_4_bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/logiya-vidhyapathi/llama_quantization_4_bit

SGLang

How to use logiya-vidhyapathi/llama_quantization_4_bit 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 "logiya-vidhyapathi/llama_quantization_4_bit" \
    --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": "logiya-vidhyapathi/llama_quantization_4_bit",
		"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 "logiya-vidhyapathi/llama_quantization_4_bit" \
        --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": "logiya-vidhyapathi/llama_quantization_4_bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use logiya-vidhyapathi/llama_quantization_4_bit with Docker Model Runner:
```
docker model run hf.co/logiya-vidhyapathi/llama_quantization_4_bit
```

Llama-3.1-8B-Instruct – AWQ 4-bit

This repository contains a 4-bit AWQ quantized version of Llama-3.1-8B-Instruct. The model is optimized for lower memory usage and faster inference with minimal quality loss.

🔹 Model Details

Base Model: meta-llama/Llama-3.1-8B-Instruct
Quantization Method: AWQ (Activation-aware Weight Quantization)
Precision: 4-bit
Framework: PyTorch
Quantized Using: LLM Compressor
Intended Use: Text generation, chat, instruction following

🔹 Why AWQ?

AWQ reduces model size and VRAM usage by:

Quantizing weights to 4-bit
Preserving important activation ranges
Maintaining better accuracy compared to naive quantization

🔹 Hardware Requirements

Type	Requirement
GPU	8–10 GB VRAM (recommended)
CPU	Supported (slower)
RAM	16 GB or more

🔹 How to Load the Model

Using Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "your-username/your-model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16
)

prompt = "Explain transformers in simple words"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 4

Safetensors

Model size

8B params

Tensor type

F16