File size: 7,986 Bytes
b25e7e6 1a21cd2 cd2cf34 b25e7e6 1a21cd2 7a758e9 1a21cd2 7a758e9 b25e7e6 cd2cf34 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 |
---
license: apache-2.0
tags:
- moe
- merge
- AdaptLLM/medicine-chat
- microsoft/Orca-2-7b
datasets:
- open-llm-leaderboard/details_Technoculture__Medchator-2x7b
model-index:
- name: Medchator-2x7b
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: 57.59
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 78.14
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 56.13
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 48.77
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 75.3
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 32.83
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
name: Open LLM Leaderboard
---
# Medchator-2x7b
Medchator-2x7b is a Mixure of Experts (MoE) made with the following models:
* [AdaptLLM/medicine-chat](https://huggingface.co/AdaptLLM/medicine-chat)
* [microsoft/Orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b)
## Evaluations
# Open LLM Leaderboard
| Model Name | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| ------------------ | -------- | --------- | -------- | ---------- | ---------- | -------- |
| Orca-2-7b | **78.4** | 76.1 | 53.7 | **52.4** | 74.2 | **47.2** |
| LLAMA-2-7b | 43.2 | 77.1 | 44.4 | 38.7 | 69.5 | 16 |
| MT7Bi-sft | 54.1 | 75.11 | - | 43.08 | 72.14 | 15.54 |
| MT7bi-dpo | 54.69 | 75.89 | 52.82 | 45.48 | 71.58 | 25.93 |
| Medorca-2x7b | 54.1 | 76.04 | 54.1 | 48.04 | 74.51 | 20.64 |
| Medchator-2x7b | **57.59**| **78.14** | **56.13**| **48.77** | **75.3** | **32.83**|
## Medical Performance
Clinical Camel demonstrates competitive performance on medical benchmarks.
**Table: Five-Shot Performance of GPT3.5, llama-2-7b and Llama-2-70b on Various Medical Datasets**
| Dataset | Medchator-2x7b | GPT3.5 | Llama-2 7b | Llama-2 70b |
|----------------------------|----------------|--------|------------|-------------|
| MMLU Anatomy | 56.3 | 60.7 | 48.9 | 62.9 |
| MMLU Clinical Knowledge | 63.0 | 68.7 | 46.0 | 71.7 |
| MMLU College Biology | 63.8 | 72.9 | 47.2 | 84.7 |
| MMLU College Medicine | 50.9 | 63.6 | 42.8 | 64.2 |
| MMLU Medical Genetics | 67.0 | 68.0 | 55.0 | 74.0 |
| MMLU Professional Medicine | 55.1 | 69.8 | 53.6 | 75.0 |
## 🧩 Configuration
```yaml
base_model: microsoft/Orca-2-7b
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: AdaptLLM/medicine-chat
positive_prompts:
- "How does sleep affect cardiovascular health?"
- "Could a plant-based diet improve arthritis symptoms?"
- "A patient comes in with symptoms of dizziness and nausea"
- "When discussing diabetes management, the key factors to consider are"
- "The differential diagnosis for a headache with visual aura could include"
negative_prompts:
- "Recommend a good recipe for a vegetarian lasagna."
- "Give an overview of the French Revolution."
- "Explain how a digital camera captures an image."
- "What are the environmental impacts of deforestation?"
- "The recent advancements in artificial intelligence have led to developments in"
- "The fundamental concepts in economics include ideas like supply and demand, which explain"
- source_model: microsoft/Orca-2-7b
positive_prompts:
- "Here is a funny joke for you -"
- "When considering the ethical implications of artificial intelligence, one must take into account"
- "In strategic planning, a company must analyze its strengths and weaknesses, which involves"
- "Understanding consumer behavior in marketing requires considering factors like"
- "The debate on climate change solutions hinges on arguments that"
negative_prompts:
- "In discussing dietary adjustments for managing hypertension, it's crucial to emphasize"
- "For early detection of melanoma, dermatologists recommend that patients regularly check their skin for"
- "Explaining the importance of vaccination, a healthcare professional should highlight"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Technoculture/Medchator-2x7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [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_Technoculture__Medchator-2x7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |58.13|
|AI2 Reasoning Challenge (25-Shot)|57.59|
|HellaSwag (10-Shot) |78.14|
|MMLU (5-Shot) |56.13|
|TruthfulQA (0-shot) |48.77|
|Winogrande (5-shot) |75.30|
|GSM8k (5-shot) |32.83|
|