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")
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

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MelloGPT

Logo

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 on 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. 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. 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.

Prompt Template

<s>[INST] {prompt} [/INST]

Quantized Model

The quantized model can be found here. Thanks to @TheBloke.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	57.59
AI2 Reasoning Challenge (25-Shot)	53.84
HellaSwag (10-Shot)	76.12
MMLU (5-Shot)	55.99
TruthfulQA (0-shot)	55.61
Winogrande (5-shot)	73.88
GSM8k (5-shot)	30.10

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 by @KnutJaegersberg

Credits

This is my first attempt at fine-tuning a large language model. It wouldn't be possible without Axolotl and Runpod. The axolotl config file can be found here.

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Model tree for steve-cse/MelloGPT

Quantizations

5 models

Dataset used to train steve-cse/MelloGPT

Spaces using steve-cse/MelloGPT 9

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

53.840
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

76.120
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

55.990
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

55.610
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

73.880
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

30.100