File size: 5,871 Bytes
e427a95 073980f 494804f 67c0ac2 d38627a 67c0ac2 aedecb2 67c0ac2 aedecb2 67c0ac2 d38627a 67c0ac2 073980f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 |
---
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
<p align="center">
<img width="150" height="150" src="https://raw.githubusercontent.com/steve-cse/mello-react/master/public/pwa-512x512.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](https://www.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)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_steve-cse__MelloGPT)
| 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|
|