Instructions to use SweatyCrayfish/llama-3-8b-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SweatyCrayfish/llama-3-8b-quantized with Transformers:

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

pipe = pipeline("text-generation", model="SweatyCrayfish/llama-3-8b-quantized")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SweatyCrayfish/llama-3-8b-quantized")
model = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/llama-3-8b-quantized")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SweatyCrayfish/llama-3-8b-quantized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SweatyCrayfish/llama-3-8b-quantized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SweatyCrayfish/llama-3-8b-quantized",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SweatyCrayfish/llama-3-8b-quantized

SGLang

How to use SweatyCrayfish/llama-3-8b-quantized 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 "SweatyCrayfish/llama-3-8b-quantized" \
    --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": "SweatyCrayfish/llama-3-8b-quantized",
		"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 "SweatyCrayfish/llama-3-8b-quantized" \
        --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": "SweatyCrayfish/llama-3-8b-quantized",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SweatyCrayfish/llama-3-8b-quantized with Docker Model Runner:
```
docker model run hf.co/SweatyCrayfish/llama-3-8b-quantized
```

4-bit Quantized Llama 3 Model

Description

This repository hosts the 4-bit quantized version of the Llama 3 model. Optimized for reduced memory usage and faster inference, this model is suitable for deployment in environments where computational resources are limited.

Model Details

Model Type: Transformer-based language model.
Quantization: 4-bit precision.
Advantages:
- Memory Efficiency: Reduces memory usage significantly, allowing deployment on devices with limited RAM.
- Inference Speed: Accelerates inference times, depending on the hardware's ability to process low-bit computations.

How to Use

To utilize this model efficiently, follow the steps below:

Loading the Quantized Model

Load the model with specific parameters to ensure it utilizes 4-bit precision:

from transformers import AutoModelForCausalLM

model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/llama-3-8b-quantized", device_map="auto", load_in_4bit=True)

Adjusting Precision of Components

Adjust the precision of other components, which are by default converted to torch.float16:

import torch
from transformers import AutoModelForCausalLM

model_4bit = AutoModelForCausalLM.from_pretrained("SweatyCrayfish/llama-3-8b-quantized", load_in_4bit=True, torch_dtype=torch.float32)
print(model_4bit.model.decoder.layers[-1].final_layer_norm.weight.dtype)

Citation

Original repository and citations: @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} }

Downloads last month: 22

Safetensors

Model size

8B params

Tensor type

F16

Model tree for SweatyCrayfish/llama-3-8b-quantized

Adapters

4 models

SweatyCrayfish
/

llama-3-8b-quantized