license: mit
datasets:
- nbertagnolli/counsel-chat
model-index:
- name: MelloGPT
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 53.84
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=steve-cse/MelloGPT
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 76.12
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=steve-cse/MelloGPT
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 55.99
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=steve-cse/MelloGPT
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 55.61
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=steve-cse/MelloGPT
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 73.88
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=steve-cse/MelloGPT
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 30.1
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=steve-cse/MelloGPT
name: Open LLM Leaderboard
MelloGPT
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. 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. Thanks to @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 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.
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 |