metadata
license: apache-2.0
tags:
- moe
- merge
- epfl-llm/meditron-7b
- medalpaca/medalpaca-7b
- chaoyi-wu/PMC_LLAMA_7B_10_epoch
- allenai/tulu-2-dpo-7b
model-index:
- name: Medtulu-4x7B
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: 28.75
name: normalized accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medtulu-4x7B
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: 25.74
name: normalized accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medtulu-4x7B
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: 24.41
name: accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medtulu-4x7B
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: 47.91
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medtulu-4x7B
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: 50.43
name: accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medtulu-4x7B
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: 0
name: accuracy
source:
url: >-
https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medtulu-4x7B
name: Open LLM Leaderboard
Mediquad-tulu-20B
Mediquad-tulu-20B is a Mixure of Experts (MoE) made with the following models:
Evaluations
| Benchmark | Mediquad-tulu-20B | meditron-7b | Orca-2-7b | meditron-70b |
|---|---|---|---|---|
| MedMCQA | ||||
| ClosedPubMedQA | ||||
| PubMedQA | ||||
| MedQA | ||||
| MedQA4 | ||||
| MedicationQA | ||||
| MMLU Medical | ||||
| TruthfulQA | ||||
| GSM8K | ||||
| ARC | ||||
| HellaSwag | ||||
| Winogrande |
🧩 Configuration
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: epfl-llm/meditron-7b
positive_prompts:
- "What are the latest guidelines for managing type 2 diabetes?"
- "Best practices for post-operative care in cardiac surgery are"
negative_prompts:
- "What are the environmental impacts of deforestation?"
- "The recent advancements in artificial intelligence have led to developments in"
- source_model: medalpaca/medalpaca-7b
positive_prompts:
- "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."
- "The fundamental concepts in economics include ideas like supply and demand, which explain"
- source_model: chaoyi-wu/PMC_LLAMA_7B_10_epoch
positive_prompts:
- "How would you explain the importance of hypertension management to a patient?"
- "Describe the recovery process after knee replacement surgery in layman's terms."
negative_prompts:
- "Recommend a good recipe for a vegetarian lasagna."
- "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: allenai/tulu-2-dpo-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
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Technoculture/Mediquad-tulu-20B"
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
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 29.54 |
| AI2 Reasoning Challenge (25-Shot) | 28.75 |
| HellaSwag (10-Shot) | 25.74 |
| MMLU (5-Shot) | 24.41 |
| TruthfulQA (0-shot) | 47.91 |
| Winogrande (5-shot) | 50.43 |
| GSM8k (5-shot) | 0.00 |