Instructions to use ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf
- SGLang
How to use ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf 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 "ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf" \ --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": "ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf", "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 "ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf" \ --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": "ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf with Docker Model Runner:
docker model run hf.co/ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf
Download configuration_llama_aqlm.py from ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf: direct link, hf CLI and curl.
- Browser
- Download file 421 Bytes
-
https://huggingface.co/ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf/resolve/main/configuration_llama_aqlm.py
- Command line
-
hf download hf://ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf/configuration_llama_aqlm.py
-
curl -L -o configuration_llama_aqlm.py https://huggingface.co/ISTA-DASLab/Llama-2-13b-AQLM-4Bit-2x16-hf/resolve/main/configuration_llama_aqlm.py
421 Bytes
| from transformers import LlamaConfig as OrigLlamaConfig | |
| class LlamaConfig(OrigLlamaConfig): | |
| model_type = "llama_aqlm" | |
| def __init__( | |
| self, | |
| aqlm: dict[str, int] = { | |
| "nbits_per_codebook": 16, | |
| "num_codebooks": 1, | |
| "out_group_size": 8, | |
| "in_group_size": 1, | |
| }, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.aqlm = aqlm | |