Sothay
/

gemma4-hscode-classifier

@@ -1,210 +1,180 @@
 ---
-base_model: unsloth/gemma-4-e4b-it-unsloth-bnb-4bit
-library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/gemma-4-e4b-it-unsloth-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
-- unsloth
 ---
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-### Framework versions
-- PEFT 0.19.1

 ---
+license: gemma
 pipeline_tag: text-generation
+language:
+  - en
+  - km
 tags:
+  - customs
+  - hs-code
+  - classification
+  - cambodia
+  - gemma
+  - unsloth
+  - qlora
 ---
+# Gemma‑4 HS Code Classifier (Cambodia Customs)
+A **Gemma‑4‑E4B‑it** model fine‑tuned with QLoRA to classify product descriptions into **8‑digit HS codes** and return corresponding Cambodian trade rates (Customs Duty, Special Tax, VAT, Excise Tax).
+Built with **[Unsloth](https://github.com/unslothai/unsloth)** for fast, memory‑efficient fine‑tuning on a single T4 GPU.
+---
+## 🎯 What it does
+Given a plain‑English product description, the model generates:
+```text
+HS Code: 61091000
+Unit: PIECE
+Customs Duty: 25%
+Special Tax: 0%
+VAT: 10%
+Excise Tax: 0%
+```
+**⚠️ Important**: The rates in the text are generated by the model and **may be wrong**.
+For production, always use the included **lookup table** (`hs_code_lookup.json`) – see [Production use](#-production-use) below.
+---
+## 🚀 Quick start (in Colab or locally)
+This repository contains **only the LoRA adapter**, not the full model.
+Loading it will automatically download the base model (`unsloth/gemma-4-E4B-it`) and apply the adapter in 4-bit.
+```python
+from unsloth import FastModel
+model, tokenizer = FastModel.from_pretrained(
+    "Sothay/gemma4-hscode-classifier",   # LoRA adapter on Hugging Face
+    load_in_4bit = True,                 # required – the adapter was trained in 4-bit
+    max_seq_length = 1024,
+)
+# ---------- Inference with the authoritative lookup table (recommended) ----------
+import json, re
+with open("hs_code_lookup.json") as f:
+    rate_lookup = json.load(f)
+def predict_hs_code(description: str) -> dict:
+    system_prompt = (
+        "You are a customs compliance AI. Classify the product description to its "
+        "correct 8-digit HS code and output the corresponding trade rates (Customs Duty, "
+        "Special Tax, VAT, Excise Tax) and unit."
+    )
+    messages = [
+        {"role": "system", "content": [{"type": "text", "text": system_prompt}]},
+        {"role": "user",   "content": [{"type": "text", "text": f"Description: {description}"}]},
+    ]
+    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
+    out = model.generate(inputs, max_new_tokens=80, do_sample=False)
+    text = tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True)
+    m = re.search(r"HS Code:\s*([0-9]{4,10})", text)
+    code = m.group(1) if m else None
+    if code and code in rate_lookup:
+        return {"hs_code": code, "source": "lookup_table", **rate_lookup[code]}
+    return {"hs_code": code, "source": "model_only_UNVERIFIED", "raw_output": text}
+print(predict_hs_code("Men's cotton knitted T-shirt"))
+```
+---
+## 🧠 Training details
+- **Base model**: `unsloth/gemma-4-E4B-it` (4‑bit QLoRA)
+- **Adapter rank**: r=16, alpha=16, targeting all language & attention layers
+- **Gradient checkpointing**: Unsloth’s own implementation (avoids Gemma‑4 KV‑shared layer bug)
+- **Dataset**: Custom Cambodian HS‑code dataset (`hs_code.csv`) with descriptions, codes, and official rates
+  - Cleaned, deduplicated, split into 90/10 train/validation
+  - Chat roles fixed to system/user/assistant (Gemma‑4 standard)
+- **Training config**: 3 epochs, effective batch size 8, learning rate 2e‑4, linear schedule, eval & save every epoch, best model loaded
+- **Hardware**: Google Colab T4 (16 GB) – peak memory ~10 GB thanks to QLoRA
+- **Accuracy**: Evaluated on held‑out examples (exact HS‑code match) – see model card for current numbers
+---
+## ⚖️ Production use
+> **Always use the lookup table – never trust the model’s generated rates.**
+The model is a **classifier**: description → HS code.
+Rates are fetched deterministically from `hs_code_lookup.json`, a file extracted from the same official tariff data used during training.
+Why?
+- A causal LM recalling a rate from memory will occasionally hallucinate – a customs tool with confident, wrong numbers is worse than one that says “I don’t know”.
+- The lookup table guarantees 100% accuracy on rates once the HS code is correct.
+The `hs_code_lookup.json` file is included in this repository and can be downloaded via:
+```python
+from huggingface_hub import hf_hub_download
+hf_hub_download("Sothay/gemma4-hscode-classifier", "hs_code_lookup.json")
+```
+---
+## 📦 Files in this repository
+| File | Description |
+|------|-------------|
+| `adapter_model.safetensors` | LoRA adapter weights (few MB) |
+| `adapter_config.json` | Adapter configuration (references base model) |
+| `tokenizer.json`, `tokenizer_config.json` | Tokenizer files |
+| `hs_code_lookup.json` | Authoritative rate table for production inference |
+| `README.md` | This file |
+> **Note**: Only the adapter is stored here – the full Gemma‑4 base model is automatically fetched from Unsloth when you call `FastModel.from_pretrained`.
+> If you need a **merged, full‑precision model** (for vLLM, TGI, etc.), generate it locally with Unsloth:
+> ```python
+> model.save_pretrained_merged("merged_fp16", tokenizer, save_method="merged_16bit")
+> ```
+---
+## 🦙 Ollama / llama.cpp (GGUF)
+Export a quantized GGUF directly from the loaded adapter:
+```python
+model.save_pretrained_gguf("gguf_model", tokenizer, quantization_method="q4_k_m")
+```
+Then use with Ollama (see [`Modelfile` example](https://ollama.com) – set temperature 0, deterministic sampling).
+---
+## 📊 Example predictions
+| Description | Predicted HS Code | Unit | CD | ST | VAT | ET |
+|-------------|-------------------|------|----|----|-----|----|
+| Toyota Hilux pickup, diesel 2.8L | 8704 | u | 35% | 50% | 10% | 0% |
+| iPhone 15 Pro Max 256GB | 8517 | u | 0% | 0% | 10% | 0% |
+| Heineken beer 330ml can | 2203 | l | 35% | 30% | 10% | 0% |
+*(Rates from lookup table – not generated by the model.)*
+---
+## 📝 License
+This model is a derivative of **Gemma‑4‑E4B‑it** and is subject to the [Gemma license](https://ai.google.dev/gemma/terms).
+The HS‑code dataset and lookup table are the property of their respective owners.
+---
+## 🙏 Acknowledgments
+- [Unsloth](https://github.com/unslothai/unsloth) – made QLoRA + Gemma‑4 on a T4 effortless
+- [Google DeepMind](https://deepmind.google) – for the Gemma family of models
+---
+**Author**: [Sothay](https://huggingface.co/Sothay)
+**Model card version**: 1.1