commit 3379a2813bc24df625a4233096142c9d763a305d Author: ModelHub XC Date: Tue Aug 18 05:57:16 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: MohamedAhmedAE/distil_Med42_8B_Llama-3.2-1B Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..b827ee3 --- /dev/null +++ b/README.md @@ -0,0 +1,201 @@ +--- +license: llama3.2 +base_model: meta-llama/Llama-3.2-1B +tags: +- medical +- healthcare +- knowledge-distillation +- distillation +- llama +- llama-3.2 +- lightweight +- clinical-nlp +datasets: +- MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset +language: +- en +pipeline_tag: text-generation +library_name: transformers +--- + +# distil_Med42_8B_Llama-3.2-1B + +**A 1B-parameter medical language model distilled from [Llama3-Med42-8B](https://huggingface.co/m42-health/Llama3-Med42-8B) into a [Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) student**, using the framework from the paper *"DistilLLM-Med: A Lightweight Medical Language Model through Knowledge Distillation"* (IEEE ICICIS 2025). + +- 📄 **Paper**: [IEEE Xplore, document 11313220](https://ieeexplore.ieee.org/document/11313220/) +- 💻 **Training notebook**: [`Train_Distil_Med42_LLama_Transformer.ipynb`](https://github.com/Mohamed-Ahmed-Abo-El-Enen/MasterPapers/blob/main/DistilLLM-Med%20A%20Lightweight%20Medical%20Language%20Model%20through%20Knowledge%20Distillation/Training/Train_Distil_Med42_LLama_Transformer.ipynb) +- 🗂️ **Training data**: [MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset](https://huggingface.co/datasets/MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset) +- 👨‍🏫 **Teacher**: [m42-health/Llama3-Med42-8B](https://huggingface.co/m42-health/Llama3-Med42-8B) (8.03B params) +- 🎓 **Student base**: [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) (1.24B params) + +## About the paper + +**DistilLLM-Med: A Lightweight Medical Language Model through Knowledge Distillation** +Mohamed Abo El-Enen, Sally Saad, Taymoor Nazmy — Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt. +Published in *2025 IEEE Twelfth International Conference on Intelligent Computing and Information Systems (ICICIS)*. +[IEEE Xplore](https://ieeexplore.ieee.org/document/11313220/) + +The paper distills medical expertise from two specialized teacher models — **MedGemma-4B** (referred to as "MedGemini-4B" in the paper text, citing Sellergren et al.'s MedGemma technical report) and **Llama3-Med42-8B** — into a single lightweight **LLaMA 3.2-1B** student, using temperature-scaled KL-divergence distillation, specialty-weighted losses, and attention-map alignment. The resulting student retains **89.3%** of teacher token-level accuracy while cutting parameters by **75%**, reaching **47.7%** average accuracy on MMLU-Medical (**67.8%** of the Med42-8B teacher's 72.6%) and **59.5 tokens/sec** inference throughput. + +## Model family + +This checkpoint is one of four sibling distilled models trained in this project, all with a Llama-3.2-1B student: + +| Model | Teacher | Student base | Notebook | +|---|---|---|---| +| [distil_med42_8B_Llama-3.2-1B-Instruct](https://huggingface.co/MohamedAhmedAE/distil_med42_8B_Llama-3.2-1B-Instruct) | Med42-8B | Llama-3.2-1B-**Instruct** | `Train_Distil_Med42_LLama_Transformer.ipynb` | +| **distil_Med42_8B_Llama-3.2-1B** (this model) | Med42-8B | Llama-3.2-1B | `Train_Distil_Med42_LLama_Transformer.ipynb` | +| [distil_llama_3_8B_Llama-3.2-1B](https://huggingface.co/MohamedAhmedAE/distil_llama_3_8B_Llama-3.2-1B) | Meta-Llama-3-8B | Llama-3.2-1B | `Distil_LLama_LLama.ipynb` | +| [distil_MedGemma_4B_Llama-3.2-1B](https://huggingface.co/MohamedAhmedAE/distil_MedGemma_4B_Llama-3.2-1B) | MedGemma-4B | Llama-3.2-1B | `Train_Distil_MedGemma_LLama.ipynb` | + +## Method + +Because Med42-8B and Llama-3.2-1B are both built on the Llama-3 tokenizer (128,256 shared vocabulary tokens), this run distills **directly on logits** with no vocabulary-projection layer needed (unlike the MedGemma run, which has a mismatched vocabulary). + +The training objective combines four components, following the paper's Eq. (1)–(7): + +1. **Temperature-scaled KL distillation** — `L_KD = α·L_CE(y, p_S) + (1-α)·L_KL(p_T^τ, p_S^τ)`, with `α = 0.7` (favoring the soft-label/KL term over the hard-label cross-entropy term). +2. **Progressive temperature scheduling** — softmax temperature `τ` decays exponentially from `τ₀ = 8` to `τ_final = 1` over the first 50,000 steps (`τ(t) = τ₀·exp(-γt)`), so the student first learns broad relationships between similar conditions before sharpening toward confident predictions. +3. **Specialty-weighted loss** — per-specialty weights `w_s` re-balance the KL loss across medical subdomains (e.g., cardiology vs. dermatology) so that no single specialty dominates training. +4. **Attention-map alignment** — an auxiliary Frobenius-norm loss `L_att` pulls the student's per-layer, per-head attention matrices toward the teacher's, so the student learns *where* the teacher looks (e.g., key symptoms in a vignette), not just *what* it outputs. + +Total loss: `L_total = L_weighted + β·L_att + λ‖W_S‖²` (`β ≈ 0.1–0.3`, `λ` = weight decay). + +### Training setup (from the paper) + +| | | +|---|---| +| Hardware | 2× NVIDIA T4 (15GB each, ~30GB total) | +| Optimizer | AdamW, lr = 1e-6, weight decay = 0.01 | +| Batch size | 1 (+ gradient accumulation) | +| Precision | Teacher: 4-bit NF4 quantized (bitsandbytes, double quantization, fp16 compute) · Student: full fp16 | +| Temperature schedule | τ₀ = 8 → τ_final = 1, exponential decay over 50,000 steps | +| α (KD/CE balance) | 0.7 | +| Training extent | ~0.6 epoch over the unified corpus (per the paper) | +| Checkpoint on this Hub repo | Global step **80,118** (`save_every_steps = 5000`) | + +Config as logged for this checkpoint on the Hub: +```json +{ + "alpha": 0.7, "temperature": 4, "accumulation_steps": 1, "hidden_dim": 1024, + "learning_rate": 1e-06, "num_epochs": 10, + "teacher_vocab_size": 128256, "student_vocab_size": 128256, + "save_every_steps": 5000, "attention_distill": true, "feature_matching": true, + "progressive_unfreezing": true, "adaptive_temperature": true, "layer_wise_distill": true, + "attention_loss_weight": 0.05, "feature_loss_weight": 0.1, + "min_temperature": 1.0, "max_temperature": 8.0, "unfreeze_schedule": "linear" +} +``` + +Full implementation (loss functions, projection layers, training loop) is in [`Train_Distil_Med42_LLama_Transformer.ipynb`](https://github.com/Mohamed-Ahmed-Abo-El-Enen/MasterPapers/blob/main/DistilLLM-Med%20A%20Lightweight%20Medical%20Language%20Model%20through%20Knowledge%20Distillation/Training/Train_Distil_Med42_LLama_Transformer.ipynb). + +## Training data + +The unified training corpus merges **18 established medical benchmarks** (~1.5–1.65M samples after cleaning) into a single instruction–response format, including: + +- **MMLU** (medical subtasks) and **MedMCQA** — multiple-choice medical exam questions +- **PubMedQA** and **COVID-QA** — context-grounded biomedical QA +- **ChatDoctor** and **MIMIC-III** — real-world clinical dialogues and notes +- **Medical Meadow** and **MedQuAD** — structured instructional/expert-curated Q&A + +Preprocessing deduplicated samples, truncated to a 1024-token limit, normalized medical abbreviations (via MeDAL mappings), and segmented long clinical notes (e.g., MIMIC-III) into coherent chunks. Benchmark **test sets were excluded from training** to keep evaluation fair. The processed/published version of this corpus is on the Hub as [Med_LLaMa3_fine-tuning_dataset](https://huggingface.co/datasets/MohamedAhmedAE/Med_LLaMa3_fine-tuning_dataset) (1.64M train / 14.4k validation rows), with an `instruction` / `input` / `context` / `output` / `choices` / `type` (`QA` / `MCQ` / `CASE`) schema. + +## Results (from the paper) + +The paper reports two distilled variants ("Experiment 1" and "Experiment 2") evaluated against both teachers and the untrained baseline. **The paper does not explicitly label which experiment number maps to which Hub checkpoint** — treat the numbers below as the project's overall benchmark results rather than a guaranteed one-to-one match with this specific file. Distilled "Experiment 1" is the paper's best/full-framework model. + +**MMLU-Medical accuracy:** + +| Task | LLaMA 3.2-1B (base) | MedGemma-4B | Llama3-Med42-8B | Distilled Exp. 1 | Distilled Exp. 2 | +|---|---|---|---|---|---| +| Anatomy | 49.6% | 23.0% | 74.1% | 51.1% | 49.6% | +| Clinical Knowledge | 35.9% | 22.6% | 75.1% | 49.1% | 44.5% | +| College Biology | 38.2% | 26.4% | 82.6% | 50.0% | 41.0% | +| College Medicine | 33.5% | 24.3% | 68.8% | 33.0% | 38.2% | +| Medical Genetics | 41.0% | 34.0% | 77.0% | 45.0% | 49.0% | +| Nutrition | 42.2% | 20.3% | 73.9% | 56.2% | 51.6% | +| Professional Medicine | 37.9% | 17.7% | 77.6% | 55.2% | 48.9% | +| Virology | 38.6% | 25.9% | 51.8% | 42.2% | 34.9% | +| **Average** | **39.6%** | **24.3%** | **72.6%** | **47.7%** | **44.7%** | + +The best distilled model reaches **67.8%** of the Med42-8B teacher's accuracy and a **20.5%** relative improvement over the base LLaMA 3.2-1B. + +**MedAlpaca (medical-meadow-medical-flashcards) generation quality:** + +| Metric | Med42-8B | MedGemma-4B | LLaMA 3.2-1B (base) | Distilled Exp. 1 | Distilled Exp. 2 | +|---|---|---|---|---|---| +| ROUGE-1 | 0.310 | 0.178 | 0.171 | 0.212 | 0.209 | +| ROUGE-2 | 0.183 | 0.093 | 0.094 | 0.139 | 0.130 | +| ROUGE-L | 0.267 | 0.149 | 0.150 | 0.179 | 0.170 | +| BLEU | 0.071 | 0.026 | 0.026 | 0.037 | 0.034 | +| Token accuracy | 0.537 | 0.482 | 0.495 | 0.505 | 0.510 | + +Average retention of the Med42-8B teacher's quality: **63.1%** (up to 76.1% on ROUGE-2). + +**Compute efficiency:** + +| Model | Params (B) | Memory (GB) | Speed (tok/s) | Latency (ms) | +|---|---|---|---|---| +| Llama3-Med42-8B (teacher) | 8.03 | 5.32 | 49.7 | 1006.0 | +| MedGemma-4B (teacher) | 4.97 | 9.75 | 33.6 | 1486.6 | +| LLaMA 3.2-1B (base) | 1.24 | 7.68 | 102.7 | 487.0 | +| **Distilled LLaMA 3.2-1B** | **1.24** | **7.68** | **59.5** | **841.0** | + +**Ablation study** (contribution of each component, MMLU-Medical average): + +| Variant | MMLU Acc. | Δ vs. base | +|---|---|---| +| LLaMA 3.2-1B (base) | 39.6% | – | +| A: Baseline KD | 42.3% | +2.7 | +| B: A + Progressive Temperature | 44.7% | +5.1 | +| C: B + Specialty Weighting | 46.5% | +6.9 | +| D: C + Attention Alignment (full model) | 47.7% | +8.1 | + +Independent medical-expert review of 89 model answers found most responses (70/89) more detailed and context-rich than reference answers, but flagged 19/89 with critical factual errors (misdiagnoses, incorrect mechanisms) — underscoring that the model **requires expert oversight** and is not a standalone diagnostic tool. + +## Usage + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +import torch + +model_id = "MohamedAhmedAE/distil_Med42_8B_Llama-3.2-1B" + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.float16, + device_map="auto", +) + +prompt = "What are some possible causes of low PTH and high calcium levels?" +inputs = tokenizer(prompt, return_tensors="pt").to(model.device) +outputs = model.generate(**inputs, max_new_tokens=200, do_sample=False) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) +``` + +> This is a **base/completion-style** checkpoint (no chat template registered on the Hub). For chat-style interaction, see [distil_med42_8B_Llama-3.2-1B-Instruct](https://huggingface.co/MohamedAhmedAE/distil_med42_8B_Llama-3.2-1B-Instruct). + +## Intended use & limitations + +- Research use in efficient/lightweight medical NLP and knowledge-distillation studies; suitable for edge/low-resource deployment experiments. +- **Not a certified clinical tool.** The paper's own expert review found critical errors in ~21% of sampled answers (misdiagnoses, incorrect mechanisms). Always require qualified human oversight before acting on any output. +- Distillation training only reached ~0.6 epoch over the corpus — treat as a research checkpoint, not a fully converged production model. +- Inherits any biases present in the teacher model (Med42-8B) and in the 18-source training corpus. + +## License + +Released under the **Llama 3.2 Community License** (matches the student base, [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B)). Since Med42-8B was used as a distillation teacher, also review the license terms on [m42-health/Llama3-Med42-8B](https://huggingface.co/m42-health/Llama3-Med42-8B) (Llama 3 Community License family) before downstream/commercial use. + +## Citation + +```bibtex +@inproceedings{aboelenein2025distilllmmed, + title = {DistilLLM-Med: A Lightweight Medical Language Model through Knowledge Distillation}, + author = {Abo El-Enen, Mohamed and Saad, Sally and Nazmy, Taymoor}, + booktitle = {2025 IEEE Twelfth International Conference on Intelligent Computing and Information Systems (ICICIS)}, + year = {2025}, + publisher = {IEEE}, + url = {https://ieeexplore.ieee.org/document/11313220/} +} +``` \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..1bad6a0 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,93 @@ +{{- bos_token }} +{%- if custom_tools is defined %} + {%- set tools = custom_tools %} +{%- endif %} +{%- if not tools_in_user_message is defined %} + {%- set tools_in_user_message = true %} +{%- endif %} +{%- if not date_string is defined %} + {%- if strftime_now is defined %} + {%- set date_string = strftime_now("%d %b %Y") %} + {%- else %} + {%- set date_string = "26 Jul 2024" %} + {%- endif %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = none %} +{%- endif %} + +{#- This block extracts the system message, so we can slot it into the right place. #} +{%- if messages[0]['role'] == 'system' %} + {%- set system_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} +{%- endif %} + +{#- System message #} +{{- "<|start_header_id|>system<|end_header_id|>\n\n" }} +{%- if tools is not none %} + {{- "Environment: ipython\n" }} +{%- endif %} +{{- "Cutting Knowledge Date: December 2023\n" }} +{{- "Today Date: " + date_string + "\n\n" }} +{%- if tools is not none and not tools_in_user_message %} + {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} +{%- endif %} +{{- system_message }} +{{- "<|eot_id|>" }} + +{#- Custom tools are passed in a user message with some extra guidance #} +{%- if tools_in_user_message and not tools is none %} + {#- Extract the first user message so we can plug it in here #} + {%- if messages | length != 0 %} + {%- set first_user_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} + {%- else %} + {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }} +{%- endif %} + {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}} + {{- "Given the following functions, please respond with a JSON for a function call " }} + {{- "with its proper arguments that best answers the given prompt.\n\n" }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} + {{- first_user_message + "<|eot_id|>"}} +{%- endif %} + +{%- for message in messages %} + {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %} + {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }} + {%- elif 'tool_calls' in message %} + {%- if not message.tool_calls|length == 1 %} + {{- raise_exception("This model only supports single tool-calls at once!") }} + {%- endif %} + {%- set tool_call = message.tool_calls[0].function %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} + {{- '{"name": "' + tool_call.name + '", ' }} + {{- '"parameters": ' }} + {{- tool_call.arguments | tojson }} + {{- "}" }} + {{- "<|eot_id|>" }} + {%- elif message.role == "tool" or message.role == "ipython" %} + {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }} + {%- if message.content is mapping or message.content is iterable %} + {{- message.content | tojson }} + {%- else %} + {{- message.content }} + {%- endif %} + {{- "<|eot_id|>" }} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }} +{%- endif %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..f96fe36 --- /dev/null +++ b/config.json @@ -0,0 +1,39 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "head_dim": 64, + "hidden_act": "silu", + "hidden_size": 2048, + "initializer_range": 0.02, + "intermediate_size": 8192, + "max_position_embeddings": 131072, + "mlp_bias": false, + 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