7B AWQ
Collection
These models are selected for their compatibility with small 12GB memory GPUs. • 203 items • Updated • 2
How to use solidrust/Darcy-7b-AWQ with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="solidrust/Darcy-7b-AWQ") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("solidrust/Darcy-7b-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Darcy-7b-AWQ")How to use solidrust/Darcy-7b-AWQ with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Darcy-7b-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "solidrust/Darcy-7b-AWQ",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/solidrust/Darcy-7b-AWQ
How to use solidrust/Darcy-7b-AWQ with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "solidrust/Darcy-7b-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": "solidrust/Darcy-7b-AWQ",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "solidrust/Darcy-7b-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": "solidrust/Darcy-7b-AWQ",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use solidrust/Darcy-7b-AWQ with Docker Model Runner:
docker model run hf.co/solidrust/Darcy-7b-AWQ
Darcy-7b is a merge of the following models using LazyMergekit.
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
docker model run hf.co/solidrust/Darcy-7b-AWQ