Instructions to use casperhansen/vicuna-7b-v1.5-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use casperhansen/vicuna-7b-v1.5-awq with Transformers:

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

pipe = pipeline("text-generation", model="casperhansen/vicuna-7b-v1.5-awq")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("casperhansen/vicuna-7b-v1.5-awq")
model = AutoModelForCausalLM.from_pretrained("casperhansen/vicuna-7b-v1.5-awq")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use casperhansen/vicuna-7b-v1.5-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "casperhansen/vicuna-7b-v1.5-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "casperhansen/vicuna-7b-v1.5-awq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/casperhansen/vicuna-7b-v1.5-awq

SGLang

How to use casperhansen/vicuna-7b-v1.5-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 "casperhansen/vicuna-7b-v1.5-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": "casperhansen/vicuna-7b-v1.5-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 "casperhansen/vicuna-7b-v1.5-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": "casperhansen/vicuna-7b-v1.5-awq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use casperhansen/vicuna-7b-v1.5-awq with Docker Model Runner:
```
docker model run hf.co/casperhansen/vicuna-7b-v1.5-awq
```

To use this model, you must have AutoAWQ installed.

pip install autoawq

Example generation with streaming:

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer

quant_path = "casperhansen/vicuna-7b-v1.5-awq"
quant_file = "awq_model_w4_g128.pt"

# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, quant_file, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)

# Convert prompt to tokens
prompt_template = """\
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.

USER: {prompt}
ASSISTANT:"""

tokens = tokenizer(
    prompt_template.format(prompt="How are you today?"), 
    return_tensors='pt'
).input_ids.cuda()

# Generate output
generation_output = model.generate(
    tokens, 
    streamer=streamer,
    max_new_tokens=512
)

Downloads last month: 7