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Model: AmareshHebbar/icd10-coder-qwen25-7b-merged Source: Original Platform
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308
README.md
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README.md
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---
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base_model: unsloth/qwen2.5-7b-instruct
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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- clinical-nlp
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- medical
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- insurance
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- qwen2.5
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- safetensors
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- gguf
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- icd10
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- healthcare
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- fine-tuned
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license: apache-2.0
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language:
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- en
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widget:
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- text: "Patient presents with acute appendicitis requiring surgical intervention. Admitted for 2 days. What ICD-10 codes apply and what insurance coverage would WHO guidelines indicate?"
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example_title: "Appendicitis + Coverage Query"
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- text: "Diagnosis: Type 2 diabetes mellitus with diabetic nephropathy. What ICD-10 coding and reimbursement brackets apply?"
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example_title: "Chronic Condition Coding"
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spaces:
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- AmareshHebbar/icd10-coder-demo
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---
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<div align="center">
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# ICD-10 Medical Coder — Qwen2.5-7B
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### An AI system for WHO-standardized medical classification, insurance code prediction, and coverage estimation
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[](https://huggingface.co/unsloth/qwen2.5-7b-instruct)
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[](LICENSE)
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[](https://api.wandb.ai/links/amareshhebbar-/qonl6s58)
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[](https://api.wandb.ai/links/amareshhebbar-/qonl6s58)
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[](https://github.com/amareshhebbar/AxisMapper)
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</div>
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---
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## What Is This?
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**ICD-10-Coder** is the first model in a long-term initiative — [**AxisMapper**](https://github.com/amareshhebbar/AxisMapper) — to build an AI-native insurance intelligence layer for the Indian and global healthcare ecosystem.
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The **International Classification of Diseases, 10th Revision (ICD-10)**, maintained by the **World Health Organization (WHO)**, is the globally accepted standard for encoding medical diagnoses, procedures, and conditions. Every hospital, insurer, and government health authority uses ICD-10 codes to classify care and determine reimbursement.
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The core insight behind this project: **insurance agents, hospital billing teams, and patients have no reliable way to know what a given diagnosis actually entitles them to**. Coverage decisions are opaque, rules are fragmented across schemes, and the same condition might be coded five different ways — each triggering a different payout.
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This model is the **first agent** in what will become a **Multi-Agent, Mixture-of-Experts (MoE) pipeline** — purpose-built to decode that opacity.
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---
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## The Bigger Vision: AxisMapper
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> *"One fine-tuned model per insurance scheme. A shared routing layer. Zero ambiguity for the patient."*
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India's health insurance landscape spans:
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- **Ayushman Bharat / PM-JAY** — world's largest government-funded health insurance scheme
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- **Star Health** — India's largest standalone health insurer
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- **ESIC / CGHS** — central government employee schemes
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- **State-level programs** — varying eligibility, tariff, and admission rules
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- **NGO-backed schemes** — community-level coverage with entirely different logic
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Each of these schemes has its own ICD-10 code mappings, admission duration requirements, procedure eligibility, and claim caps. There is no unified interface to query them all.
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**AxisMapper's roadmap:**
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```
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Phase 1 (Now) → WHO ICD-10 base model (this model)
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Universal code prediction + coverage logic
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Phase 2 → Fine-tune per scheme (StarHealth, PM-JAY, ESIC, etc.)
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Each model specialises in one insurer's rule set
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Phase 3 → MoE Router
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Given a patient + insurer, route to the right specialist model
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Phase 4 → Multi-Agent Pipeline
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Agent 1: Diagnosis → ICD-10 code
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Agent 2: Code → Coverage estimate (policy-aware)
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Agent 3: Coverage + Admission rules → Final claim amount
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Agent 4: Web search → Real-time tariff / market validation
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```
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This model — the WHO-standardized base — handles **Phase 1**: given any clinical description, it returns the correct ICD-10 code, explains the classification, and applies WHO-level coverage logic.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| **Base Model** | `unsloth/qwen2.5-7b-instruct` |
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| **Architecture** | Qwen2 (decoder-only transformer) |
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| **Parameters** | ~8B |
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| **Precision** | BF16 |
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| **Fine-tuning Method** | LoRA via Unsloth + HuggingFace TRL |
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| **Training Hardware** | NVIDIA RTX A5000 (24GB VRAM) |
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| **Training Duration** | ~2 hours |
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| **Training Speed** | 2× faster than standard HF training (via Unsloth) |
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| **Experiment Tracking** | Weights & Biases (W&B) |
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| **Max Sequence Length** | 2048 tokens |
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| **License** | Apache 2.0 |
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---
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## Training Infrastructure
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This model was trained using the [Unsloth](https://github.com/unslothai/unsloth) optimization library, which achieves **2× training speed** and **~60% VRAM reduction** compared to standard HuggingFace fine-tuning — without any loss in model quality.
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**Training stack:**
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- `unsloth` — optimized LoRA fine-tuning engine
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- `trl` (HuggingFace) — SFTTrainer for instruction fine-tuning
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- `transformers` — model loading, tokenization, inference
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- `wandb` — real-time loss curves, learning rate scheduling, gradient tracking
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All training runs are logged and reproducible via Weights & Biases. The training converged stably within 2 hours on a single A5000 GPU, making this a cost-efficient approach to medical domain adaptation.
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---
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## What This Model Does
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Given a clinical description or patient scenario, this model will:
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1. **Assign the correct ICD-10 code(s)** — primary diagnosis, secondary conditions, procedure codes
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2. **Explain the WHO classification logic** — why this code, what the category means, adjacent codes
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3. **Estimate WHO-level insurance coverage** — standard reimbursement brackets, admission duration requirements, procedure eligibility
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4. **Flag restrictions** — minimum admission days, co-morbidity requirements, pre-authorisation triggers
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5. **Support multi-condition scenarios** — comorbidities, complications, dual coding
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**Example input:**
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```
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Patient admitted for acute appendicitis with peritonitis.
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Underwent emergency appendectomy. Admitted for 3 days.
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What ICD-10 codes apply and what is the expected insurance coverage?
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```
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**Example output (truncated):**
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```
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Primary Code: K35.2 — Acute appendicitis with generalised peritonitis
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Procedure Code: 0DTJ4ZZ — Resection of appendix, percutaneous endoscopic approach
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WHO Classification: Diseases of the digestive system (K00–K93)
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Chapter XI, Block K35-K38 (Diseases of appendix)
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Coverage Logic:
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- WHO standard: Surgical admission, inpatient required
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- Minimum admission: 1–3 days (surgery-dependent)
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- Reimbursement class: Major surgery
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- Pre-auth: Required for elective; emergency bypass available
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- Approximate WHO-tier bracket: ₹35,000–₹75,000 (India tier-2 hospital)
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```
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---
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## Quickstart
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### Using Transformers (Pipeline)
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="AmareshHebbar/icd10-coder-qwen25-7b-merged")
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query = """
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Patient presents with Type 2 diabetes mellitus with chronic kidney disease stage 3.
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What ICD-10 codes apply? What are the WHO-level insurance implications?
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What are the admission requirements for this to be covered?
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"""
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result = pipe([{"role": "user", "content": query}], max_new_tokens=512)
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print(result[0]["generated_text"][-1]["content"])
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```
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### Using Unsloth (Recommended for inference speed)
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||||
```python
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from unsloth import FastModel
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model, tokenizer = FastModel.from_pretrained(
|
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model_name="AmareshHebbar/icd10-coder-qwen25-7b-merged",
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max_seq_length=2048,
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load_in_4bit=True, # Optional: 4-bit for lower VRAM
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)
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messages = [
|
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{"role": "system", "content": "You are an expert ICD-10 medical coder with deep knowledge of WHO insurance classification standards."},
|
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{"role": "user", "content": "Patient: acute MI, stented. 2-day admission. Code and coverage?"}
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]
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inputs = tokenizer.apply_chat_template(
|
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messages, tokenize=True, add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
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print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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### Using vLLM (Production / High Throughput)
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||||
```bash
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pip install vllm
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vllm serve "AmareshHebbar/icd10-coder-qwen25-7b-merged" --max-model-len 2048
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```
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")
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response = client.chat.completions.create(
|
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model="AmareshHebbar/icd10-coder-qwen25-7b-merged",
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messages=[
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{"role": "system", "content": "You are an expert ICD-10 coder and insurance analyst."},
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{"role": "user", "content": "Patient: fractured femur, open reduction required, 4-day inpatient. ICD-10 codes and insurance coverage?"}
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],
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max_tokens=512,
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temperature=0.1,
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)
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print(response.choices[0].message.content)
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```
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### Using Ollama (Local / Offline)
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```bash
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# Export to GGUF first (via llama.cpp or Unsloth export)
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ollama create icd10-coder -f ./Modelfile
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ollama run icd10-coder "Patient: appendicitis, emergency surgery. Code and coverage?"
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```
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---
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## 🔌 Integrations Supported
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|
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| Backend | Status | Use Case |
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|---|---|---|
|
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| HuggingFace Transformers | ✅ | Research, prototyping |
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| Unsloth FastModel | ✅ | Fast inference, fine-tuning |
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| vLLM | ✅ | Production API, high throughput |
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| SGLang | ✅ | Structured generation |
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| Ollama | ✅ | Local / offline deployment |
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| Claude API (Anthropic) | 🔌 Planned | Hybrid: ICD-10 code → Claude for coverage analysis |
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| Gemini API (Google) | 🔌 Planned | Multi-LLM comparison layer |
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| Web Search (Tavily/Serper) | 🔌 Planned | Real-time tariff + hospital rate lookup |
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---
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## ICD-10 Coverage
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This model has been fine-tuned across all major ICD-10-CM chapters:
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| Chapter | Description |
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|---|---|
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| I (A00–B99) | Infectious and parasitic diseases |
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| II (C00–D49) | Neoplasms |
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| III (D50–D89) | Blood and immune disorders |
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| IV (E00–E89) | Endocrine, nutritional, metabolic |
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| V (F01–F99) | Mental and behavioural disorders |
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| IX (I00–I99) | Circulatory system diseases |
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| X (J00–J99) | Respiratory diseases |
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| XI (K00–K95) | Digestive system diseases |
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| XIII (M00–M99) | Musculoskeletal diseases |
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| XIV (N00–N99) | Genitourinary diseases |
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| XIX (S00–T88) | Injuries, poisonings |
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| XXI (Z00–Z99) | Health status, contact with services |
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---
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## Limitations & Intended Use
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- This model is trained on **WHO ICD-10 baseline standards**, not on any specific insurer's proprietary rules. Coverage estimates are **indicative**, not legally binding.
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- **Not a substitute for professional medical coding** or licensed insurance adjudication.
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- Coverage estimates should be validated against the patient's actual policy terms and the treating hospital's empanelment status.
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- Future scheme-specific models (Ayushman Bharat, Star Health, etc.) will provide more precise, policy-aware outputs.
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---
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## Links
|
||||
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- **GitHub (AxisMapper):** [https://github.com/amareshhebbar/AxisMapper](https://github.com/amareshhebbar/AxisMapper)
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- **Developed by:** [AmareshHebbar](https://huggingface.co/AmareshHebbar)
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- **Base model:** [unsloth/qwen2.5-7b-instruct](https://huggingface.co/unsloth/qwen2.5-7b-instruct)
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---
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## Citation
|
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```bibtex
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@misc{hebbar2025icd10coder,
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title={ICD-10 Coder: A Fine-tuned Qwen2.5-7B for Medical Classification and Insurance Coverage Estimation},
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author={Amaresh Hebbar},
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year={2025},
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publisher={HuggingFace},
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url={https://huggingface.co/AmareshHebbar/icd10-coder-qwen25-7b-merged},
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note={Part of the AxisMapper project: https://github.com/amareshhebbar/AxisMapper}
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}
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```
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---
|
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<div align="center">
|
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<sub>Built with Unsloth · Trained on A5000 · Tracked with W&B · Part of AxisMapper</sub>
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</div>
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54
chat_template.jinja
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54
chat_template.jinja
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{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
62
config.json
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62
config.json
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||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": null,
|
||||
"torch_dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
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|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"is_local": false,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|PAD_TOKEN|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
Reference in New Issue
Block a user