Instructions to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq with Transformers:

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

pipe = pipeline("text-generation", model="abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq")
model = AutoModelForMultimodalLM.from_pretrained("abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq

SGLang

How to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-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 "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-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": "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-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 "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-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": "abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq with Docker Model Runner:
```
docker model run hf.co/abhinavkulkarni/VMware-open-llama-7b-open-instruct-w4-g128-awq
```

abhinavkulkarni commited on Jul 5, 2023

Commit

e3582e2

1 Parent(s): 0c46da7

Update README.md

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README.md +4 -2

README.md CHANGED Viewed

@@ -32,7 +32,9 @@ This model was successfully tested on CUDA driver v12.1 and toolkit v11.7 with P
 git clone https://github.com/mit-han-lab/llm-awq \
 && cd llm-awq \
 && git checkout 71d8e68df78de6c0c817b029a568c064bf22132d \
-&& pip install -e .
 ```
 ```python
@@ -81,7 +83,7 @@ output = model.generate(
     repetition_penalty=1.1,
     eos_token_id=tokenizer.eos_token_id
 )
-print(tokenizer.decode(output[0]))
 ```
 ## Evaluation

 git clone https://github.com/mit-han-lab/llm-awq \
 && cd llm-awq \
 && git checkout 71d8e68df78de6c0c817b029a568c064bf22132d \
+&& pip install -e . \
+&& cd awq/kernels \
+&& python setup.py install
 ```
 ```python
     repetition_penalty=1.1,
     eos_token_id=tokenizer.eos_token_id
 )
+print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 ## Evaluation