Instructions to use RedHatAI/llama-2-7b-chat-marlin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/llama-2-7b-chat-marlin with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/llama-2-7b-chat-marlin")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/llama-2-7b-chat-marlin")
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/llama-2-7b-chat-marlin")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/llama-2-7b-chat-marlin with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/llama-2-7b-chat-marlin"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/llama-2-7b-chat-marlin",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RedHatAI/llama-2-7b-chat-marlin

SGLang

How to use RedHatAI/llama-2-7b-chat-marlin 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 "RedHatAI/llama-2-7b-chat-marlin" \
    --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": "RedHatAI/llama-2-7b-chat-marlin",
		"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 "RedHatAI/llama-2-7b-chat-marlin" \
        --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": "RedHatAI/llama-2-7b-chat-marlin",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RedHatAI/llama-2-7b-chat-marlin with Docker Model Runner:
```
docker model run hf.co/RedHatAI/llama-2-7b-chat-marlin
```

llama-2-7b-chat-marlin

Example of converting a GPTQ model to Marlin format for fast batched decoding with Marlin Kernels

Install Marlin

pip install torch
git clone https://github.com/IST-DASLab/marlin.git
cd marlin
pip install -e .

Convert Model

Convert the model from GPTQ to Marlin format. Note that this requires:

sym=true
group_size=128
desc_activations=false

pip install -U transformers accelerate auto-gptq optimum

Convert with the convert.py script in this repo:

python3 convert.py --model-id "TheBloke/Llama-2-7B-Chat-GPTQ" --save-path "./marlin-model" --do-generation

Run Model

Load with the load.load_model utility from this repo and run inference as usual.

from load import load_model
from transformers import AutoTokenizer

# Load model from disk.
model_path = "./marlin-model"
model = load_model(model_path).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Generate text.
inputs = tokenizer("My favorite song is", return_tensors="pt")
inputs = {k: v.to("cuda") for k, v in inputs.items()}
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=False)
print(tokenizer.batch_decode(outputs)[0])

Downloads last month: 5

Safetensors

Model size

1B params

Tensor type

I32

F16