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
```

steve-cse commited on Jan 2, 2024

Commit

67c0ac2

1 Parent(s): aedecb2

Update README.md

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@@ -3,8 +3,15 @@ license: mit
 datasets:
 - nbertagnolli/counsel-chat
 ---
-A fine tuned version of Mistral-7B-v0.1 on Counsel Chat dataset for mental health conversations.
 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
@@ -14,4 +21,23 @@ depression, stress, and more. The fine-tuning process incorporates ethical consi
 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.

 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
 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)