Instructions to use solidrust/DolphinHermesPro-ModelStock-7B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/DolphinHermesPro-ModelStock-7B-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/DolphinHermesPro-ModelStock-7B-AWQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/DolphinHermesPro-ModelStock-7B-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/DolphinHermesPro-ModelStock-7B-AWQ")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use solidrust/DolphinHermesPro-ModelStock-7B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/DolphinHermesPro-ModelStock-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/DolphinHermesPro-ModelStock-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/solidrust/DolphinHermesPro-ModelStock-7B-AWQ

SGLang

How to use solidrust/DolphinHermesPro-ModelStock-7B-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 "solidrust/DolphinHermesPro-ModelStock-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/DolphinHermesPro-ModelStock-7B-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 "solidrust/DolphinHermesPro-ModelStock-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/DolphinHermesPro-ModelStock-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use solidrust/DolphinHermesPro-ModelStock-7B-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/DolphinHermesPro-ModelStock-7B-AWQ
```

DolphinHermesPro-ModelStock-7B-AWQ

Commit History

Added base_model tag in README.md

2f40af1
verified

Suparious commited on Sep 3, 2024

Updated base_model tag in README.md

7bd10f6
verified

Suparious commited on Sep 3, 2024

Update README.md

8cd5f9f
verified

Suparious commited on Apr 19, 2024

adding initial model card

104a9ad

Ubuntu commited on Apr 18, 2024

adding quant config

55d5b1f

Ubuntu commited on Apr 18, 2024

adding AWQ model

853c299

Ubuntu commited on Apr 18, 2024

initial commit

876f5a5
verified

Suparious commited on Apr 18, 2024

Commit History

Added base_model tag in README.md 2f40af1 verified

Updated base_model tag in README.md 7bd10f6 verified

Update README.md 8cd5f9f verified

adding initial model card 104a9ad

adding quant config 55d5b1f

adding AWQ model 853c299

initial commit 876f5a5 verified

Added base_model tag in README.md

2f40af1
verified

Updated base_model tag in README.md

7bd10f6
verified

Update README.md

8cd5f9f
verified

adding initial model card

104a9ad

adding quant config

55d5b1f

adding AWQ model

853c299

initial commit

876f5a5
verified