Instructions to use Kquant03/DolphinHermesPro-ModelStock with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kquant03/DolphinHermesPro-ModelStock with Transformers:

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

pipe = pipeline("text-generation", model="Kquant03/DolphinHermesPro-ModelStock")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kquant03/DolphinHermesPro-ModelStock")
model = AutoModelForCausalLM.from_pretrained("Kquant03/DolphinHermesPro-ModelStock")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kquant03/DolphinHermesPro-ModelStock with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kquant03/DolphinHermesPro-ModelStock"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/DolphinHermesPro-ModelStock",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kquant03/DolphinHermesPro-ModelStock

SGLang

How to use Kquant03/DolphinHermesPro-ModelStock 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 "Kquant03/DolphinHermesPro-ModelStock" \
    --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": "Kquant03/DolphinHermesPro-ModelStock",
		"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 "Kquant03/DolphinHermesPro-ModelStock" \
        --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": "Kquant03/DolphinHermesPro-ModelStock",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kquant03/DolphinHermesPro-ModelStock with Docker Model Runner:
```
docker model run hf.co/Kquant03/DolphinHermesPro-ModelStock
```

Kquant03 commited on Apr 18, 2024

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@@ -13,8 +13,6 @@ thumbnail: "https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088
 DolphinHermesPro-ModelStock is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
-## 🧩 Configuration
 ```yaml
 models:
   - model: cognitivecomputations/dolphin-2.8-experiment26-7b

 DolphinHermesPro-ModelStock is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
 ```yaml
 models:
   - model: cognitivecomputations/dolphin-2.8-experiment26-7b