Text Generation
Transformers
PyTorch
mistral
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use steve-cse/MelloGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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datasets:
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- nbertagnolli/counsel-chat
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A fine tuned version of Mistral-7B-v0.1 on Counsel Chat dataset for mental health conversations.
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datasets:
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- nbertagnolli/counsel-chat
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---
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A fine tuned version of Mistral-7B-v0.1 on Counsel Chat dataset for mental health conversations.
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In an era where mental health support is of paramount importance, A large language
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model fine-tuned on mental health counseling conversations stands as a pioneering solution. This
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approach aims to elevate natural language understanding and generation within the realm of mental
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health support. Leveraging a diverse dataset of anonymized counseling sessions, the model has
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been trained to recognize and respond to a wide range of mental health concerns, including anxiety,
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depression, stress, and more. The fine-tuning process incorporates ethical considerations, privacy
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concerns, and sensitivity to the nuances of mental health conversations. The resulting model will
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demonstrate an intricate understanding of mental health issues and provide empathetic and
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supportive responses, offering a valuable tool for individuals seeking guidance, mental health
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professionals, and the broader healthcare community.
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