nhonhoccode
/

qwen3-0-6b-cybersecqa-lora-8bit-20251102-2209

@@ -1,199 +1,97 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen
+- unsloth
+- cybersecurity
+- instruction-tuning
+- lora
+- kaggle
+base_model: unsloth/Qwen3-0.6B
+datasets:
+- zobayer0x01/cybersecurity-qa
+metrics:
+- sacrebleu
+- rougeL
+- f1
+- exact_match
 ---
+# qwen3-0-6b — Cybersecurity QA (LORA 8bit)
+Fine-tuned on Kaggle using **LORA**. (Quant: LoRA + 8-bit (bnb int8))
+### Model Summary
+- Base: `unsloth/Qwen3-0.6B`
+- Trainable params: **10,092,544** / total **606,142,464**
+- Train wall time (s): 26498.1
+- Files: adapter_model.safetensors + adapter_config.json (LoRA) + tokenizer files
+### Data
+- Dataset: `zobayer0x01/cybersecurity-qa`
+- Samples: **total=42484**, train=38235, val=2000
+- Prompting: Chat template with a fixed system prompt:
+```text
+You are a helpful assistant specialized in cybersecurity Q&A.
+```
+### Training Config
+| Field | Value |
+|---|---|
+| Method | **LORA** |
+| Precision | fp16 |
+| Quantization | LoRA + 8-bit (bnb int8) |
+| Mode | steps |
+| Num Epochs | 1 |
+| Max Steps | 2000 |
+| Eval Steps | 400 |
+| Save Steps | 400 |
+| LR | 0.0001 |
+| Max Length | 768 |
+| per_device_batch_size | 1 |
+| grad_accum | 8 |
+### Evaluation (greedy)
+| Metric | Score |
+|---|---:|
+| BLEU-4 | 1.27 |
+| ROUGE-L | 14.07 |
+| F1 | 27.83 |
+| EM (Exact Match) | 0.00 |
+> Notes: We normalize whitespace/punctuations, compute token-level P/R/F1, and use `evaluate`'s `sacrebleu/rouge/chrf`.
+## How to use
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+tok  = AutoTokenizer.from_pretrained("nhonhoccode/qwen3-0-6b-cybersecqa-lora-8bit-20251102-2209")
+base = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-0.6B")
+mdl  = PeftModel.from_pretrained(base, "nhonhoccode/qwen3-0-6b-cybersecqa-lora-8bit-20251102-2209")  # Loads LoRA adapter
+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
+- 2025-11-02 22:09 — Initial release (LORA-8bit)