nhonhoccode
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qwen3-0-6b-cybersecqa-sft-freeze2-20251111-1238

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 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen
+- unsloth
+- cybersecurity
+- instruction-tuning
+- sft
+- kaggle
+base_model: unsloth/Qwen3-0.6B
+datasets:
+- zobayer0x01/cybersecurity-qa
+metrics:
+- sacrebleu
+- rougeL
+- chrf++
+- bertscore
+- perplexity
 ---
+# qwen3-0-6b — Cybersecurity QA (SFT)
+Fine-tuned on Kaggle using **SFT**.
+### Model Summary
+- Base: `unsloth/Qwen3-0.6B`
+- Trainable params: **187,044,352** / total **596,049,920**
+- Train wall time (s): 30586.8
+- Files: pytorch_model.safetensors + config.json + tokenizer files
+### Data
+- Dataset: `zobayer0x01/cybersecurity-qa`
+- Samples: **total=42427**, train=38184, val=1200
+- Prompting: Chat template with a fixed system prompt:
+```text
+You are a helpful assistant specialized in cybersecurity Q&A.
+```
+### Training Config
+| Field | Value |
+|---|---|
+| Method | **SFT** |
+| Precision | fp32 |
+| Quantization | none |
+| Mode | steps |
+| Num Epochs | 1 |
+| Max Steps | 4000 |
+| Eval Steps | 400 |
+| Save Steps | 400 |
+| LR | 5e-05 |
+| Max Length | 768 |
+| per_device_batch_size | 1 |
+| grad_accum | 8 |
+### Evaluation (greedy, fixed-length decode)
+| Metric | Score |
+|---|---:|
+| BLEU-4 | 1.49 |
+| ROUGE-L | 13.89 |
+| F1 (token-level) | 25.82 |
+| chrF++ | 19.80 |
+| BERTScore F1 | 82.71 |
+| Perplexity | 16.81 |
+> Notes: We normalize whitespace/punctuations, compute token-level P/R/F1, and use `evaluate`'s `sacrebleu/rouge/chrf/bertscore`.
+## How to use
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tok = AutoTokenizer.from_pretrained("nhonhoccode/qwen3-0-6b-cybersecqa-sft-freeze2-20251111-1238")
+mdl = AutoModelForCausalLM.from_pretrained("nhonhoccode/qwen3-0-6b-cybersecqa-sft-freeze2-20251111-1238")
+prompt = tok.apply_chat_template(
+    [{"role":"system","content":"You are a helpful assistant specialized in cybersecurity Q&A."},
+     {"role":"user","content":"Explain SQL injection in one paragraph."}],
+    tokenize=False, add_generation_prompt=True
+)
+ids = tok(prompt, return_tensors="pt").input_ids
+out = mdl.generate(ids, max_new_tokens=128, do_sample=False)
+print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))
+```
+### Intended Use & Limitations
+- Domain: cybersecurity Q&A; not guaranteed to be accurate for legal/medical purposes.
+- The model can hallucinate or produce outdated guidance—verify before applying in production.
+- Safety: No explicit content filtering. Add guardrails (moderation, retrieval augmentation) for deployment.
+### Reproducibility (env)
+- `transformers>=4.43,<5`, `accelerate>=0.33,<0.34`, `peft>=0.11,<0.13`, `datasets>=2.18,<3`, `evaluate>=0.4,<0.5`,
+  `rouge-score`, `sacrebleu`, `huggingface_hub>=0.23,<0.26`, `bitsandbytes`
+- GPU: T4-class; LoRA recommended for low VRAM.
+### Changelog
+- 2025-11-11 12:39 — Initial release (SFT)