Text Generation
Transformers
Safetensors
English
llama
nlp
llm
text-generation-inference
4-bit precision
awq
Instructions to use TheBloke/Amber-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/Amber-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/Amber-AWQ")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/Amber-AWQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/Amber-AWQ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheBloke/Amber-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/Amber-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/Amber-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/Amber-AWQ
- SGLang
How to use TheBloke/Amber-AWQ 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 "TheBloke/Amber-AWQ" \ --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": "TheBloke/Amber-AWQ", "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 "TheBloke/Amber-AWQ" \ --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": "TheBloke/Amber-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/Amber-AWQ with Docker Model Runner:
docker model run hf.co/TheBloke/Amber-AWQ
Download eval_arc.json from TheBloke/Amber-AWQ: direct link, hf CLI and curl.
- Browser
- Download file 548 Bytes
-
https://huggingface.co/TheBloke/Amber-AWQ/resolve/main/eval_arc.json
- Command line
-
hf download hf://TheBloke/Amber-AWQ/eval_arc.json
-
curl -L -o eval_arc.json https://huggingface.co/TheBloke/Amber-AWQ/resolve/main/eval_arc.json
548 Bytes
| { | |
| "results": { | |
| "arc_challenge": { | |
| "acc": 0.39761092150170646, | |
| "acc_stderr": 0.014301752223279536, | |
| "acc_norm": 0.4129692832764505, | |
| "acc_norm_stderr": 0.014388344935398326 | |
| } | |
| }, | |
| "versions": { | |
| "arc_challenge": 0 | |
| }, | |
| "config": { | |
| "model": "hf-causal", | |
| "model_args": "pretrained=./workdir_7b_16mix/ckpt_359", | |
| "num_fewshot": 25, | |
| "batch_size": "1", | |
| "batch_sizes": [], | |
| "device": null, | |
| "no_cache": true, | |
| "limit": null, | |
| "bootstrap_iters": 100000, | |
| "description_dict": {} | |
| } | |
| } |