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", device_map="auto")

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
```

DolphinHermesPro-ModelStock / README.md

Kquant03

Update README.md

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	---
	tags:
	- merge
	- mergekit
	- lazymergekit
	license: apache-2.0
	thumbnail: "https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/Jmu5DHPZwv4so5Tn-xkIO.png"
	---

	# DolphinHermesPro-ModelStock

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/Jmu5DHPZwv4so5Tn-xkIO.png)

	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
	- model: NousResearch/Hermes-2-Pro-Mistral-7B
	merge_method: model_stock
	base_model: mistralai/Mistral-7B-v0.1
	dtype: bfloat16
	```

	## 💻 Usage

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "Kquant03/DolphinHermesPro-ModelStock"
	messages = [{"role": "user", "content": "What is a large language model?"}]

	tokenizer = AutoTokenizer.from_pretrained(model)
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	pipeline = transformers.pipeline(
	"text-generation",
	model=model,
	torch_dtype=torch.float16,
	device_map="auto",
	)

	outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
	print(outputs[0]["generated_text"])
	```