Instructions to use catid/cat-llama-3-70b-awq-q128-w4-gemm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use catid/cat-llama-3-70b-awq-q128-w4-gemm with Transformers:

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

pipe = pipeline("text-generation", model="catid/cat-llama-3-70b-awq-q128-w4-gemm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("catid/cat-llama-3-70b-awq-q128-w4-gemm")
model = AutoModelForMultimodalLM.from_pretrained("catid/cat-llama-3-70b-awq-q128-w4-gemm")
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 Settings

vLLM

How to use catid/cat-llama-3-70b-awq-q128-w4-gemm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "catid/cat-llama-3-70b-awq-q128-w4-gemm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "catid/cat-llama-3-70b-awq-q128-w4-gemm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/catid/cat-llama-3-70b-awq-q128-w4-gemm

SGLang

How to use catid/cat-llama-3-70b-awq-q128-w4-gemm 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 "catid/cat-llama-3-70b-awq-q128-w4-gemm" \
    --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": "catid/cat-llama-3-70b-awq-q128-w4-gemm",
		"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 "catid/cat-llama-3-70b-awq-q128-w4-gemm" \
        --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": "catid/cat-llama-3-70b-awq-q128-w4-gemm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use catid/cat-llama-3-70b-awq-q128-w4-gemm with Docker Model Runner:
```
docker model run hf.co/catid/cat-llama-3-70b-awq-q128-w4-gemm
```

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Check out the documentation for more information.

AI Model Name: Llama 3 70B "Built with Meta Llama 3" https://llama.meta.com/llama3/license/

This is the result of running AutoAWQ to quantize the LLaMA-3 70B model to ~4 bits/parameter.

To launch an OpenAI-compatible API endpoint on your Linux server with 2x 3090 or 4090 GPUs:

git lfs install
git clone https://huggingface.co/catid/cat-llama-3-70b-awq-q128-w4-gemm

conda create -n vllm70 python=3.10 -y && conda activate vllm70

pip install -U git+https://github.com/vllm-project/vllm.git

python -m vllm.entrypoints.openai.api_server --model cat-llama-3-70b-awq-q128-w4-gemm --tensor-parallel-size 2 --gpu-memory-utilization 0.935

Sadly this barely doesn't fit by ~300MB or so.

Downloads last month: 1

Safetensors

Model size

71B params

Tensor type

I32

F16