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Model: saadxsalman/ss-medical-moe
Source: Original Platform

README.md

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+---
+language:
+  - en
+license: llama3.2
+base_model: meta-llama/Llama-3.2-1B
+datasets:
+  - Malikeh1375/medical-question-answering-datasets
+tags:
+  - medical
+  - healthcare
+  - mixture-of-experts
+  - lora
+  - causal-lm
+  - llama
+  - llama-3
+  - fine-tuned
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# 🏥 Medical MoE Llama-3.2-1B
+
+**Model by [saadxsalman](https://huggingface.co/saadxsalman)**
+
+A **Mixture of Experts (MoE)** language model fine-tuned for medical question
+answering, built on Llama-3.2-1B with QLoRA adaptation and a custom
+4-expert routing layer trained on 1.26M real doctor-patient conversations.
+
+## Architecture
+Input Tokens
+↓
+Llama-3.2-1B (1B params, QLoRA-merged, fp16)
+↓
+MoE Layer — 4 domain experts, top-2 routing
+↓
+LayerNorm + Residual
+↓
+LM Head → Medical Answer
+
+### Expert Specializations
+
+| Expert   | Medical Domain              |
+|----------|-----------------------------|
+| Expert 1 | Symptoms & Diagnosis        |
+| Expert 2 | Drug Interactions           |
+| Expert 3 | Anatomy & Physiology        |
+| Expert 4 | Treatment & Procedures      |
+
+## Training Details
+
+| Parameter              | Value                                              |
+|------------------------|----------------------------------------------------|
+| Base model             | `meta-llama/Llama-3.2-1B`                          |
+| Fine-tuning method     | QLoRA (4-bit NF4) + MoE layer                      |
+| LoRA rank              | 16                                    |
+| LoRA alpha             | 32                                |
+| LoRA target modules    | q_proj, v_proj, k_proj, o_proj                     |
+| Number of experts      | 4                               |
+| Top-K routing          | 2                                     |
+| Training dataset       | Malikeh1375/medical-question-answering-datasets    |
+| Training samples       | 15,000                             |
+| Total epochs           | 6 (3 learning + 3 specialization)                  |
+| Phase 1                | LR=2e-4, aux_coef=0.01 (domain learning)           |
+| Phase 2                | LR=5e-5, aux_coef=0.05 (expert specialization)     |
+| Max sequence length    | 256                                |
+| Hardware               | Kaggle T4 GPU (16GB VRAM)                          |
+
+## Dataset
+
+Trained on [`Malikeh1375/medical-question-answering-datasets`](https://huggingface.co/datasets/Malikeh1375/medical-question-answering-datasets)
+— 1.26M medical QA pairs from 12 sources including ChatDoctor (112K real
+doctor-patient conversations), iCliniq, MedQA, WikiDoc, PubMed, and CORD-19.
+
+## Expert Routing Results
+
+| Domain                  | Expert 1 | Expert 2 | Expert 3 | Expert 4 |
+|-------------------------|----------|----------|----------|----------|
+| Symptoms & Diagnosis    | 0.338    | 0.212    | 0.241    | 0.210    |
+| Drug Interactions       | 0.340    | 0.209    | 0.246    | 0.204    |
+| Anatomy & Physiology    | 0.266    | 0.248    | 0.232    | 0.254    |
+| Treatment & Procedures  | 0.299    | 0.231    | 0.227    | 0.243    |
+
+## How to Use
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from huggingface_hub import hf_hub_download
+
+# Load tokenizer and merged backbone
+tokenizer = AutoTokenizer.from_pretrained("saadxsalman/medical-moe-llama3.2-1b")
+backbone  = AutoModelForCausalLM.from_pretrained(
+    "saadxsalman/medical-moe-llama3.2-1b",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load MoE weights separately
+moe_ckpt = torch.load(
+    hf_hub_download("saadxsalman/medical-moe-llama3.2-1b", "moe_components.pt"),
+    map_location="cuda"
+)
+# See moe_config.json in the repo for full architecture details
+```
+
+## Files in This Repository
+
+| File                  | Description                                   |
+|-----------------------|-----------------------------------------------|
+| `model.safetensors`   | Llama-3.2-1B backbone (LoRA merged, fp16)     |
+| `moe_components.pt`   | MoE layer + LayerNorm + LM head weights       |
+| `moe_config.json`     | Full architecture + training configuration    |
+| `tokenizer.json`      | Llama-3 tokenizer                             |
+| `routing_matrix.npy`  | Expert routing analysis results               |
+| `routing_heatmap.png` | Expert specialization visualization           |
+| `training_curves.png` | Loss curves across all 6 epochs               |
+| `domain_dist.png`     | Training data domain distribution             |
+
+## ⚠️ Disclaimer
+
+This model is for **research purposes only**. It is not validated for
+clinical use and may produce incorrect medical information. Always consult
+a qualified healthcare professional for medical advice.