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Model: ree2raz/CyberSecQwen-4B-AWQ
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: lablab-ai-amd-developer-hackathon/CyberSecQwen-4B
+tags:
+- qwen3
+- cybersecurity
+- cti
+- cwe-classification
+- vulnerability-analysis
+- awq
+- 4-bit
+- quantized
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# CyberSecQwen-4B-AWQ
+
+4-bit AWQ quantized version of [CyberSecQwen-4B](https://huggingface.co/lablab-ai-amd-developer-hackathon/CyberSecQwen-4B).
+
+## Quantization
+
+| Parameter | Value |
+|---|---|
+| Method | AWQ (group_size=128, zero_point=True) |
+| Weight precision | 4-bit |
+| Compute dtype | float16 |
+| Calibration samples | 320 CTI-Bench prompts (256 RCM + 64 MCQ, chat-template formatted) |
+| Quantization tool | autoawq |
+| Calibration hardware | Modal A100 |
+
+## CTI-Bench Evaluation
+
+Evaluated under the [Foundation-Sec-8B protocol](https://arxiv.org/abs/2504.21039):
+- Temperature 0.3, max_tokens 512, concurrency 32
+- 5 independent trials, zero-shot (no system prompt)
+- vLLM v0.20.1 with awq_marlin kernel on Modal L4 GPU
+
+| Task | AWQ 4-bit | GGUF Q4_K_M | FP16 Reference |
+|---|---|---|---|
+| CTI-MCQ (2,500 items) | **0.5921** ± 0.0083 | 0.5368 ± 0.0048 | 0.5868 ± 0.0029 |
+| CTI-RCM (1,000 items) | 0.5814 ± 0.0025 | **0.6254 ± 0.0063** | 0.6664 ± 0.0023 |
+
+**Key findings:**
+- **CTI-MCQ**: AWQ 4-bit matches or slightly exceeds FP16 performance (+0.5 points). Better than GGUF Q4_K_M.
+- **CTI-RCM**: AWQ 4-bit degrades by 8.5 percentage points vs FP16. GGUF Q4_K_M does better on this task (-4.1 pts).
+- AWQ is best for MCQ (general language), GGUF is best for RCM (task-specific classification).
+
+## Trial results
+
+### CTI-MCQ
+| Trial | Seed | Accuracy |
+|---|---|---|
+| 1 | 42 | 0.6016 |
+| 2 | 43 | 0.5984 |
+| 3 | 44 | 0.5936 |
+| 4 | 45 | 0.5780 |
+| 5 | 46 | 0.5888 |
+
+### CTI-MCQ
+
+| Trial | Seed | Accuracy |
+|---|---|---|
+| 1 | 42 | 0.6016 |
+| 2 | 43 | 0.5984 |
+| 3 | 44 | 0.5936 |
+| 4 | 45 | 0.5780 |
+| 5 | 46 | 0.5888 |
+
+### CTI-RCM
+
+| Trial | Seed | Accuracy |
+|---|---|---|
+| 1 | 42 | 0.5790 |
+| 2 | 43 | 0.5830 |
+| 3 | 44 | 0.5790 |
+| 4 | 45 | 0.5840 |
+| 5 | 46 | 0.5820 |
+
+## Quantization variants
+
+| Variant | CTI-MCQ | CTI-RCM | Size | Engine |
+|---|---|---|---|---|
+| [AWQ 4-bit](https://huggingface.co/ree2raz/CyberSecQwen-4B-AWQ) | 0.5921 | 0.5814 | 2.7 GB | vLLM |
+| [GGUF Q4_K_M](https://huggingface.co/ree2raz/CyberSecQwen-4B-GGUF) | 0.5368 | 0.6254 | 2.5 GB | llama.cpp |
+
+Choose AWQ for MCQ/general chat, GGUF for vulnerability classification.
+
+## Usage with vLLM
+
+```bash
+vllm serve ree2raz/CyberSecQwen-4B-AWQ --quantization awq_marlin --dtype float16
+```
+
+## Model Size
+
+| Format | Size |
+|---|---|
+| Original FP16 | ~8 GB |
+| AWQ 4-bit | ~2.7 GB |
+
+## Citation
+
+```bibtex
+@misc{{cybersecqwen2026,
+  title  = {{CyberSecQwen-4B: A Compact CTI Specialist Fine-Tuned from Qwen3-4B-Instruct-2507 on AMD MI300X}},
+  author = {{Mulia, Samuel}},
+  year   = {{2026}},
+  publisher = {{Hugging Face}},
+  url    = {{https://huggingface.co/athena129/CyberSecQwen-4B}}
+}}
+```
+
+## Evaluation Infrastructure
+
+[GitHub repository](https://github.com/ree2raz/cyberSecQwen_4b_4bit) — Modal scripts for quantization + evaluation.

chat_template.jinja

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+{%- for message in messages -%}
+<|im_start|>{{ message['role'] if message['role'] != 'system' else 'user' }}
+{{ message['content'] }}<|im_end|>
+{% endfor -%}
+{%- if add_generation_prompt -%}
+<|im_start|>assistant
+{% endif -%}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "quantization_config": {
+    "bits": 4,
+    "group_size": 128,
+    "modules_to_not_convert": null,
+    "quant_method": "awq",
+    "version": "gemm",
+    "zero_point": true
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 5000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.8.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.8.0"
+}

model.safetensors

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tokenizer.json

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tokenizer_config.json

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+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+    "<|image_pad|>",
+    "<|video_pad|>"
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+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}