library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: mlx-community/Qwen3-4B-4bit-DWQ-053125
tags:
- mlx
yakusokulabs/dr_qwen_v2
This model yakusokulabs/dr_qwen_v2 was converted to MLX format from mlx-community/Qwen3-4B-4bit-DWQ-053125 using mlx-lm version 0.25.0.
Use with mlx
pip install mlx-lm
Yakusoku Labs — Dr Qwen v2
A 4-bit, Apple-MLX-ready medical Qwen-4B you can fine-tune and run on a single modern iPhone.
🧩 Model Summary
| Base | Qwen-3-4B |
| Precision | 4-bit NF4 (DWQ) |
| Framework | Apple MLX (mlx-lm 0.25.0) |
| Hardware used | 1 × Mac mini M4 Pro (16-core GPU) |
| Energy / time | ≈ 14 GPU-hours, ~60 W avg → 4× less power than equivalent PyTorch run |
| License | Apache 2.0 (weights & code) |
Dr Qwen v2 is purpose-tuned for clinical Q&A, triage and medication counseling while staying light enough for edge devices.
The current checkpoint is finetuned only on public medical datasets; de-identified Indian tele-medicine dialogues will be merged once legal green-lights.
🎯 Intended Use & Limitations
Intended
- Medical trivia & exam datasets (MedMCQA, USMLE-style)
- Low-risk symptom triage with human oversight
- Research baseline for Apple-silicon ML pipelines
Out of scope / MUST-NOT
- Autonomous diagnosis or prescription
- High-acuity decision support without a licensed clinician in the loop
- Any use that generates or stores personally identifiable health data (PHI)
📚 Training Data
| Corpus | Size | License |
|---|---|---|
| MedMCQA | 354 k QA | CC-BY-NC-SA-4.0 |
| MedQA-USMLE | 13 k QA | MIT |
| PubMedQA | 1 k | CC0 |
| MMLU-Medical | 1.2 k | MIT |
| ChatDoctor Dialogues | 100 k turns | Apache 2.0 |
Planned: +35 k doctor-annotated Indian tele-health Q&A (DPDP-compliant, de-identified).
⚙️ Training Procedure
- 3 epochs, batch 128, LR 6e-5, cosine decay, seed 42
- LoRA rank 64 on query/key/value/projection matrices
- Gradient checkpointing & mixed-precision NF4 quant after SFT
- Direct Preference Optimisation (DPO) on synthetic doctor ratings (3 B tokens)
📊 Evaluation
| Benchmark (zero-shot) | Base Qwen-4B | Dr Qwen v2 | Llama-3.3-8B |
|---|---|---|---|
| MedMCQA | 57.8 % | 63.5 % | 64.1 % |
| PubMedQA | 48.6 % | 55.2 % | 56.0 % |
Clinician panel (500 simulated consultations via Yakusoku’s multi-agent sandbox)
94 % answers tagged “clinically acceptable” – 3 pp shy of human baseline, +9–12 pp over baselines.
🛡️ Safety & Responsible AI
- All datasets are public or de-identified; no raw PHI ingested.
ClinGuard-Literule-based filter blocks guideline-violating outputs (e.g., antibiotic over-prescription).- Upcoming blinded trials with IRB oversight (Q3 2025).
- Please keep a licensed clinician in the loop.
🚀 Quick Start (Apple MLX)
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("yakusokulabs/dr_qwen_v2")
prompt = "Patient: I have a mild cough and low-grade fever.\nDoctor:"
if tokenizer.chat_template:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, verbose=True))