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Model: sethuiyer/MedleyMD Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: cc-by-nc-nd-4.0
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library_name: transformers
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tags:
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- moe
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- merge
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- medical
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- mergekit
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datasets:
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- medmcqa
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- cognitivecomputations/samantha-data
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- jondurbin/bagel-v0.3
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base_model:
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- sethuiyer/Dr_Samantha_7b_mistral
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- fblgit/UNA-TheBeagle-7b-v1
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pipeline_tag: text-generation
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model-index:
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- name: MedleyMD
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 66.47
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 86.06
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 65.1
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 52.46
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 80.27
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 68.99
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
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name: Open LLM Leaderboard
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---
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# MedleyMD
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MedleyMD is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [sethuiyer/Dr_Samantha_7b_mistral](https://huggingface.co/sethuiyer/Dr_Samantha_7b_mistral)
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* [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
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These models were chosen because `fblgit/UNA-TheBeagle-7b-v1` has excellent performance for a 7B parameter model and Dr.Samantha has capabilities of a medical knowledge-focused model (trained on USMLE databases and doctor-patient interactions) with the philosophical, psychological, and relational understanding, scoring 68.82% in topics related to clinical domain and psychology.
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## Benchmark
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On a synthetic benchmark of 35 medical diagnosis questions generated by GPT-4, GPT-4 also being an evaluator, MedleyMD scored **96.25/100**.
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Nous Benchmark numbers shall be available soon.
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## 🧩 Configuration
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```yaml
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base_model: OpenPipe/mistral-ft-optimized-1227
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gate_mode: hidden
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dtype: bfloat16
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experts:
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- source_model: sethuiyer/Dr_Samantha_7b_mistral
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positive_prompts: ["differential diagnosis", "Clinical Knowledge", "Medical Genetics", "Human Aging", "Human Sexuality", "College Medicine", "Anatomy", "College Biology", "High School Biology", "Professional Medicine", "Nutrition", "High School Psychology", "Professional Psychology", "Virology"]
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- source_model: fblgit/UNA-TheBeagle-7b-v1
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positive_prompts: ["How do you", "Explain the concept of", "Give an overview of", "Compare and contrast between", "Provide information about", "Help me understand", "Summarize", "Make a recommendation on", "chat", "math", "reason", "mathematics", "solve", "count", "python", "javascript", "programming", "algorithm", "tell me", "assistant"]
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```
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## GGUF
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1. [medleymd.Q4_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q4_K_M.gguf) [7.2GB]
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2. [medleymd.Q5_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q5_K_M.gguf) [9.13GB]
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## Ollama
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MedleyMD can be used in ollama by running```ollama run stuehieyr/medleymd``` in your terminal.
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If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on
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a Google Colab backend.
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## Prompt format:
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This model uses ChatML prompt format.
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```
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<|im_start|>system
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You are Medley, a helpful AI assistant.<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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## 💻 Usage
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```python
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!pip install -qU transformers bitsandbytes accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "sethuiyer/MedleyMD"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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model_kwargs={"torch_dtype": torch.bfloat16, "load_in_4bit": True},
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)
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generation_kwargs = {
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"max_new_tokens": 512,
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"do_sample": True,
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"temperature": 0.7,
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"top_k": 50,
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"top_p": 95,
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}
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messages = [{"role":"system", "content":"You are an helpful AI assistant. Please use </s> when you want to end the answer."},
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{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipeline(prompt, **generation_kwargs)
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print(outputs[0]["generated_text"])
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```
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```text
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A Mixture of Experts (Mixout) is a neural network architecture that combines the strengths of multiple expert networks to make a more accurate and robust prediction.
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It is composed of a topmost gating network that assigns weights to each expert network based on their performance on past input samples.
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The expert networks are trained independently, and the gating network learns to choose the best combination of these experts to make the final prediction.
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Mixout demonstrates a stronger ability to handle complex data distributions and is more efficient in terms of training time and memory usage compared to a
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traditional ensemble approach.
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__MedleyMD)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |69.89|
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|AI2 Reasoning Challenge (25-Shot)|66.47|
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|HellaSwag (10-Shot) |86.06|
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|MMLU (5-Shot) |65.10|
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|TruthfulQA (0-shot) |52.46|
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|Winogrande (5-shot) |80.27|
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|GSM8k (5-shot) |68.99|
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config.json
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"architectures": [
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"MixtralForCausalLM"
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"output_router_logits": false,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"use_cache": true,
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"vocab_size": 32000
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mergekit_moe_config.yml
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base_model: OpenPipe/mistral-ft-optimized-1227
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gate_mode: hidden
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dtype: bfloat16
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experts:
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- source_model: sethuiyer/Dr_Samantha_7b_mistral
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positive_prompts: ["differential diagnosis", "Clinical Knowledge", "Medical Genetics", "Human Aging", "Human Sexuality", "College Medicine", "Anatomy", "College Biology", "High School Biology", "Professional Medicine", "Nutrition", "High School Psychology", "Professional Psychology", "Virology"]
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- source_model: fblgit/UNA-TheBeagle-7b-v1
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positive_prompts: ["How do you", "Explain the concept of", "Give an overview of", "Compare and contrast between", "Provide information about", "Help me understand", "Summarize", "Make a recommendation on", "chat", "math", "reason", "mathematics", "solve", "count", "python", "javascript", "programming", "algorithm", "tell me", "assistant"]
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special_tokens_map.json
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|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"trust_remote_code": true,
|
||||
"unk_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"use_default_system_prompt": false,
|
||||
"use_fast": true
|
||||
}
|
||||
Reference in New Issue
Block a user