Rutvik141/medical-reasoning-qwen-0.5b

Medical Reasoning Model — Qwen2.5-0.5B (LoRA/QLoRA Fine-tuned)

A compact medical-reasoning model fine-tuned from Qwen/Qwen2.5-0.5B-Instruct for step-by-step clinical reasoning. It is optimized to break down cases, propose differentials, and justify answers.

Note: Outputs may include intermediate reasoning. Do not treat responses as medical advice.

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🧠 Model Summary

• Base: Qwen2.5-0.5B-Instruct
• Architecture: Decoder-only transformer
• Finetuning: LoRA/QLoRA on medical reasoning SFT data
• Primary language: English
• Intended tasks: clinical vignette reasoning, justification, treatment option comparison, dosage math (simple)
• Not for: real-world diagnosis or treatment; no protected-health-information handling

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✅ Intended Use

• Educational exploration of clinical reasoning
• Generating explanations for practice questions
• Drafting step-by-step rationales for study aids

Out-of-scope / High-risk
Do not use for clinical decisions, triage, or patient-specific recommendations.

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📚 Data

• Training set: FreedomIntelligence/medical-o1-reasoning-SFT (instruction-style, reasoning-focused)
• Preprocessing:
  • Deduplication & light cleaning
  • Prompt formatting into {system, instruction, input} style
  • Truncation/padding to max sequence length (🔧 L=1024 by default)

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🛠️ Training Procedure

Strategy

• Method: LoRA / QLoRA (4-bit NF4) to fit small GPUs while retaining quality
• Reasoning: adapters reduce trainable params → faster, cheaper, less overfitting risk

Environment (example)

• GPU: 🔧 1× NVIDIA RTX 4060 (8 GB) / L4 / A10G
• Frameworks: transformers, peft, bitsandbytes, accelerate
• Precision: bf16 (fallback fp16 if needed)
• Attention: Flash-Attention 2 when available

Key Hyperparameters (fill if you used different)

• LoRA config: r=8, alpha=16, lora_dropout=0.1, target_modules=["q_proj","v_proj","o_proj"]
• Quantization (QLoRA): load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True
• Optimizer: AdamW8bit (bitsandbytes) or adamw_torch_fused
• LR & schedule: lr=2e-4, cosine decay, warmup_ratio=0.06
• Regularization: weight decay 0.01, label smoothing 0.05, gradient clipping 1.0
• Batching: per_device_train_batch_size=4, gradient_accumulation_steps=8 → effective batch 32
• Sequence length: 🔧 max_seq_length=1024
• Epochs: 🔧 2–3 (use early stopping on val loss)
• Gradient checkpointing: enabled (reduce activation memory)
• Eval/Save: every 🔧 500 steps, keep best on val loss
• Checkpoint offload: local recent N + sync to S3 (callback)

Reproducibility

• seed=42
• Deterministic ops where practical (note: may reduce throughput)

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⏱️ Training Time & Throughput (example on RTX 4060)

• Throughput (train): ~15k tokens/s (depends on L, micro-batch, kernels)
• Steps/epoch: ceil(num_samples / effective_batch)
  e.g., 🔧 100k / 32 ≈ 3125
• Epoch time (L=1024): ~1.9–2.3 h (incl. overhead)
  Your numbers will vary; measure 200 steps after warmup for accuracy.

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🧪 Evaluation

• Metrics: perplexity (val), exact match / ROUGE-L on held-out prompts
• Human spot-checks: correctness of differentials, contraindications, and justification clarity
• Known failure modes:
  • Confident but incorrect rationale (hallucinations)
  • Outdated guidelines
  • Arithmetic slips on edge cases

Add your concrete scores here once computed.

• Val perplexity: 🔧 …
• Exact match / ROUGE-L: 🔧 …

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🔐 Safety & Ethics

• The model may produce incorrect or harmful medical content.
• No PHI was used; training data are public/anon sources.
• Add a disclaimer in any downstream app; keep a human-in-the-loop.

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🚀 How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "YOUR_ORG/medical-qwen25-0_5b-lora"  # replace with your repo
tok = AutoTokenizer.from_pretrained(repo, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = """You are a clinician. Reason step by step.
Patient: 56F with chest pain radiating to jaw, diaphoresis...
Question: Most likely diagnosis and initial management?"""
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, temperature=0.2, top_p=0.9)
print(tok.decode(out[0], skip_special_tokens=True))




#Training Script snippet with lora Fine Tunning
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from peft import LoraConfig, get_peft_model
import torch

base = "Qwen/Qwen2.5-0.5B-Instruct"
tok = AutoTokenizer.from_pretrained(base)

model = AutoModelForCausalLM.from_pretrained(
    base,
    load_in_4bit=True,
    torch_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    device_map="auto",
    low_cpu_mem_usage=True,
)

lora = LoraConfig(r=8, lora_alpha=16, lora_dropout=0.1,
                  target_modules=["q_proj","v_proj","o_proj"])
model = get_peft_model(model, lora)
model.gradient_checkpointing_enable()
model.config.use_cache = False  # training only

args = TrainingArguments(
    output_dir="ckpts",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=8,
    learning_rate=2e-4,
    lr_scheduler_type="cosine",
    warmup_ratio=0.06,
    weight_decay=0.01,
    num_train_epochs=3,
    bf16=True,
    save_steps=500,
    evaluation_strategy="steps",
    eval_steps=500,
    logging_steps=50,
    save_safetensors=True,
    load_best_model_at_end=True,
    metric_for_best_model="loss",
    gradient_checkpointing=True,
)
# ... Trainer(train_ds, val_ds, callbacks for S3 sync)