---
license: apache-2.0
language:
- en
- id
base_model: deadbear34/qwen35-4b-plantdisease-cpt
tags:
- lora
- sft
- thinking
- plant-disease
- agriculture
- qwen3.5
library_name: peft
private: true
---

# Qwen3.5-4B Plant Disease — LoRA SFT Adapter (v2)

LoRA adapter for thinking-style reasoning + plant disease domain expertise.

## Base Model
- deadbear34/qwen35-4b-plantdisease-cpt (CPT result on plant disease corpus)

## Training Data
- plant-disease-qa-bilingual: ~13k samples (in-domain)
- claude-sonnet-4.6-100000X-filtered: ~35k samples (general thinking)
- claude-sonnet-4.6-120000x: ~35k samples (diverse difficulties)
- thinking-dataset-en-v2: ~75k samples (reasoning patterns)

## LoRA Configuration
- Rank: 256
- Alpha: 256
- Target modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']
- Dropout: 0.05

## Training Configuration
- Optimizer: 8-bit AdamW (lr=0.0001)
- Schedule: cosine warmup (3.0%)
- Effective batch: 4
- Sequence length: 2048

## Evaluation
- Final val PPL: {FINAL_VAL_PPL}
- Best val PPL: {BEST_VAL_PPL} at step {BEST_STEP}

## Format
```
<|user|>
{user_query}
<|assistant|>
<thinking>
{reasoning}
</thinking>
{final_answer}
```

## Usage
```python
from peft import PeftModel
from transformers import AutoModelForImageTextToText, AutoTokenizer

base = AutoModelForImageTextToText.from_pretrained('deadbear34/qwen35-4b-plantdisease-cpt', torch_dtype='bfloat16')
model = PeftModel.from_pretrained(base, 'deadbear34/qwen35-4b-plantdisease-sft-lora')
tokenizer = AutoTokenizer.from_pretrained('deadbear34/qwen35-4b-plantdisease-sft-lora')

prompt = '<|user|>
What causes late blight?
<|assistant|>
'
inputs = tokenizer(prompt, return_tensors='pt').to('cuda')
out = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(out[0]))
```

---
*Trained: 2026-05-10*