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Model: sethuiyer/MedleyMD Source: Original Platform
2026-04-24 07:18:02 +08:00
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--- a/README.md
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+---
+language:
+- en
+license: cc-by-nc-nd-4.0
+library_name: transformers
+tags:
+- moe
+- merge
+- medical
+- mergekit
+datasets:
+- medmcqa
+- cognitivecomputations/samantha-data
+- jondurbin/bagel-v0.3
+base_model:
+- sethuiyer/Dr_Samantha_7b_mistral
+- fblgit/UNA-TheBeagle-7b-v1
+pipeline_tag: text-generation
+model-index:
+- name: MedleyMD
+  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: 66.47
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
+      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: 86.06
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
+      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: 65.1
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
+      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: 52.46
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
+      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: 80.27
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
+      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: 68.99
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/MedleyMD
+      name: Open LLM Leaderboard
+---
+
+# MedleyMD
+
+![logo](https://huggingface.co/sethuiyer/MedleyMD/resolve/main/logo.webp)
+
+
+MedleyMD is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [sethuiyer/Dr_Samantha_7b_mistral](https://huggingface.co/sethuiyer/Dr_Samantha_7b_mistral)
+* [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
+
+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. 
+
+## Benchmark
+
+On a synthetic benchmark of 35 medical diagnosis questions generated by GPT-4, GPT-4 also being an evaluator, MedleyMD scored **96.25/100**.
+
+Nous Benchmark numbers shall be available soon.
+
+
+## 🧩 Configuration
+
+```yaml
+base_model: OpenPipe/mistral-ft-optimized-1227
+gate_mode: hidden
+dtype: bfloat16
+
+experts:
+  - source_model: sethuiyer/Dr_Samantha_7b_mistral
+    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"]
+
+  - source_model: fblgit/UNA-TheBeagle-7b-v1
+    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"]
+
+```
+
+## GGUF
+1. [medleymd.Q4_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q4_K_M.gguf) [7.2GB]
+2. [medleymd.Q5_K_M](https://huggingface.co/sethuiyer/MedleyMD-GGUF/resolve/main/medleymd.Q5_K_M.gguf) [9.13GB]
+
+
+## Ollama
+
+MedleyMD can be used in ollama by running```ollama run stuehieyr/medleymd``` in your terminal. 
+
+If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on 
+a Google Colab backend.
+
+## Prompt format:
+This model uses ChatML prompt format.
+```
+<|im_start|>system
+You are Medley, a helpful AI assistant.<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers bitsandbytes accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "sethuiyer/MedleyMD"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    model_kwargs={"torch_dtype": torch.bfloat16, "load_in_4bit": True},
+)
+
+generation_kwargs = {
+    "max_new_tokens": 512,
+    "do_sample": True,
+    "temperature": 0.7,
+    "top_k": 50,
+    "top_p": 95,
+}
+
+messages = [{"role":"system", "content":"You are an helpful AI assistant. Please use </s> when you want to end the answer."},
+{"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, **generation_kwargs)
+print(outputs[0]["generated_text"])
+```
+
+```text
+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.
+It is composed of a topmost gating network that assigns weights to each expert network based on their performance on past input samples.
+The expert networks are trained independently, and the gating network learns to choose the best combination of these experts to make the final prediction.
+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
+traditional ensemble approach.
+```
+# [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_sethuiyer__MedleyMD)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |69.89|
+|AI2 Reasoning Challenge (25-Shot)|66.47|
+|HellaSwag (10-Shot)              |86.06|
+|MMLU (5-Shot)                    |65.10|
+|TruthfulQA (0-shot)              |52.46|
+|Winogrande (5-shot)              |80.27|
+|GSM8k (5-shot)                   |68.99|
+
--- a/added_tokens.json
+++ b/added_tokens.json
@@ -0,0 +1,4 @@
+{
+  "<|im_end|>": 32000,
+  "<|im_start|>": 32001
+}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,29 @@
+{
+  "architectures": [
+    "MixtralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mixtral",
+  "num_attention_heads": 32,
+  "num_experts_per_tok": 2,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "num_local_experts": 2,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 32000,
+  "transformers_version": "4.32.1"
+}
--- a/logo.webp
+++ b/logo.webp
--- a/mergekit_moe_config.yml
+++ b/mergekit_moe_config.yml
@@ -0,0 +1,12 @@
+
+base_model: OpenPipe/mistral-ft-optimized-1227
+gate_mode: hidden
+dtype: bfloat16
+
+experts:
+  - source_model: sethuiyer/Dr_Samantha_7b_mistral
+    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"]
+
+  - source_model: fblgit/UNA-TheBeagle-7b-v1
+    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"]
+
--- a/model-00001-of-00003.safetensors
+++ b/model-00001-of-00003.safetensors
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--- a/special_tokens_map.json
+++ b/special_tokens_map.json
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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer.model
+++ b/tokenizer.model
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
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+  "additional_special_tokens": [],
+  "bos_token": {
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+    "content": "<s>",
+    "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|>",
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+  "legacy": true,
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+    "single_word": false
+  },
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "trust_remote_code": true,
+  "unk_token": {
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+  "use_fast": true
+}