Instructions to use steve-cse/MelloGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use steve-cse/MelloGPT with Transformers:

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

pipe = pipeline("text-generation", model="steve-cse/MelloGPT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("steve-cse/MelloGPT")
model = AutoModelForCausalLM.from_pretrained("steve-cse/MelloGPT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use steve-cse/MelloGPT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "steve-cse/MelloGPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "steve-cse/MelloGPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/steve-cse/MelloGPT

SGLang

How to use steve-cse/MelloGPT 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 "steve-cse/MelloGPT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "steve-cse/MelloGPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "steve-cse/MelloGPT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "steve-cse/MelloGPT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use steve-cse/MelloGPT with Docker Model Runner:
```
docker model run hf.co/steve-cse/MelloGPT
```

MelloGPT / README.md

steve-cse

Update README.md

67c0ac2 over 2 years ago

preview code

Raw

History Blame

2.63 kB

	---
	license: mit
	datasets:
	- nbertagnolli/counsel-chat
	---
	# MelloGPT
	<p align="center">
	<img width="150" height="150" src="https://raw.githubusercontent.com/steve-cse/mello/master/public/logo.png" alt="Logo">
	</p>

	NOTE: This model should not be regarded as a replacement for professional mental health assistance. It is essential to seek support from qualified professionals for personalized and appropriate care.

	A fine tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on [counsel-chat](https://huggingface.co/datasets/nbertagnolli/counsel-chat) dataset for mental health counseling conversations.
	## Motivation
	In an era where mental health support is of paramount importance, A large language
	model fine-tuned on mental health counseling conversations stands as a pioneering solution. This
	approach aims to elevate natural language understanding and generation within the realm of mental
	health support. Leveraging a diverse dataset of anonymized counseling sessions, the model has
	been trained to recognize and respond to a wide range of mental health concerns, including anxiety,
	depression, stress, and more. The fine-tuning process incorporates ethical considerations, privacy
	concerns, and sensitivity to the nuances of mental health conversations. The resulting model will
	demonstrate an intricate understanding of mental health issues and provide empathetic and
	supportive responses, offering a valuable tool for individuals seeking guidance, mental health
	professionals, and the broader healthcare community.
	## Prompt Template
	```
	<s>[INST] {prompt} [/INST]
	```
	## Quantized Model
	The quantized model can be found [here](https://huggingface.co/TheBloke/MelloGPT-GGUF). Thanks to [@TheBloke](https://huggingface.co/TheBloke).

	## Contributions
	This project is open for contributions. Feel free to use the community tab.

	## Inspiration
	This project was inspired by the project(s) listed below:

	[companion_cube](https://huggingface.co/KnutJaegersberg/companion_cube_ggml) by [@KnutJaegersberg](https://huggingface.co/KnutJaegersberg)

	## Credits
	This is my first attempt at fine-tuning a large language model. It wouldn't be possible without [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) and [Runpod](runpod.io). The axolotl config file can be found [here](https://github.com/steve-cse/mello/blob/master/mello.yml).

	[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)