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Model: dbristol/aisec_model_v1 Source: Original Platform
2026-06-16 07:53:18 +08:00
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+---
+license: apache-2.0
+base_model: mistralai/Mistral-7B-Instruct-v0.3
+base_model_relation: finetune
+dbristol:
+  - mlx
+  - lora
+  - mistral
+  - ai-security
+  - nist-ai-rmf
+  - mitre-atlas
+  - owasp-ai-exchange
+  - google-saif
+  - risk-management
+  - fine-tuned
+language:
+  - en
+pipeline_tag: text-generation
+datasets:
+  - dbristol/aisec-training-data
+library_name: mlx
+---
+
+# aisec_model_v1 — AI Security Framework Expert (Mistral 7B LoRA)
+
+> **This is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3),
+> not a new model architecture.** Only 0.145% of parameters were updated via
+> LoRA. The base model weights, tokenizer, and architecture are unchanged.
+
+Domain-specialised using LoRA on Apple Silicon via [MLX](https://github.com/ml-explore/mlx)
+for cross-framework AI security and risk management analysis across:
+
+- **NIST AI RMF 1.0** — Govern, Map, Measure, Manage functions
+- **MITRE ATLAS** — Adversarial TTP kill chains and detection engineering
+- **OWASP AI Exchange** — Runtime attack surfaces and technical controls
+- **Google SAIF** — Component responsibility assignment and governance layers
+
+---
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Base model | mistralai/Mistral-7B-Instruct-v0.3 |
+| Fine-tuning method | LoRA (Low-Rank Adaptation) |
+| Framework | MLX (Apple Silicon) |
+| Trainable parameters | 10.486M / 7,248M (0.145%) |
+| LoRA rank | 8 |
+| LoRA alpha | 16 |
+| LoRA layers | 16 |
+| Training platform | Apple Silicon (M-series), macOS |
+| Best checkpoint | Iter 500 (val loss 0.216) |
+| Training dataset | [dbristol/aisec-training-data](https://huggingface.co/datasets/dbristol/aisec-training-data) |
+
+---
+
+## Training Summary
+
+Training was performed using `mlx_lm.lora` with a cosine learning rate schedule.
+
+| Checkpoint | Val Loss |
+|---|---|
+| Iter 1 (base) | 2.597 |
+| Iter 100 | 0.749 |
+| Iter 200 | 0.369 |
+| Iter 300 | 0.312 |
+| Iter 400 | 0.267 |
+| **Iter 500** | **0.216** ← best |
+| Iter 550 | 0.223 ↑ overfitting onset |
+
+Training configuration:
+```yaml
+learning_rate: 5e-5
+lr_schedule: cosine_decay (100-iter warmup)
+batch_size: 4
+iters: 1200
+lora_rank: 8
+lora_alpha: 16.0
+lora_dropout: 0.05
+num_layers: 16
+```
+
+---
+
+## Usage
+
+### Requirements
+
+```bash
+pip install mlx-lm
+```
+
+### Inference with MLX
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load(
+    "Dbristol/aisec_model_v1"
+)
+
+prompt = "Provide a cross-framework analysis of indirect prompt injection defences \
+for a code generation assistant using OWASP AI Exchange, SAIF, MITRE ATLAS, \
+and NIST AI RMF."
+
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are an expert AI security and risk management assistant "
+            "specialising in NIST AI RMF 1.0, MITRE ATLAS, OWASP AI Exchange, "
+            "and Google SAIF frameworks."
+        )
+    },
+    {"role": "user", "content": prompt}
+]
+
+formatted = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+response = generate(
+    model,
+    tokenizer,
+    prompt=formatted,
+    max_tokens=512,
+    temp=0.4,
+    top_p=0.85,
+)
+print(response)
+```
+
+### Recommended inference parameters
+
+| Parameter | Value | Rationale |
+|---|---|---|
+| temperature | 0.4 | Factual domain — sharper distribution favours trained signal |
+| top_p | 0.85 | Tighter nucleus reduces long-tail sampling |
+| top_k | 40 | Hard vocabulary cap applied before top_p |
+| repeat_penalty | 1.1 | Reduces repetition of framework acronyms |
+
+---
+
+## Intended Use
+
+This model is designed for security practitioners, researchers, and AI governance
+professionals who need structured cross-framework analysis. Suitable use cases include:
+
+- Mapping AI system risks across multiple frameworks simultaneously
+- Generating NIST AI RMF governance documentation
+- Identifying MITRE ATLAS TTPs relevant to a specific AI deployment
+- Drafting OWASP AI Exchange control implementations
+- Cross-referencing Google SAIF responsibility assignments
+
+### Out-of-scope use
+
+This model should not be used as the sole basis for security decisions without
+human expert review. Framework guidance evolves; always verify against current
+official documentation.
+
+---
+
+## Limitations
+
+- Trained on a single-domain dataset; may underperform on security tasks outside
+  the four covered frameworks.
+- Knowledge cutoff reflects the training data collection date, not live framework updates.
+- Responses should be verified against official NIST, MITRE, OWASP, and Google SAIF
+  publications before operational use.
+- Base model is Mistral 7B Instruct v0.3; inherits its general limitations.
+
+---
+
+## License
+
+This model is released under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+The base model ([Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3))
+is also Apache 2.0 licensed.
+
+The training dataset is derived from publicly available framework documentation.
+See the [dataset card](https://huggingface.co/datasets/<your-hf-username>/aisec-training-data)
+for full provenance and source attribution.
+
+---
+
+## Citation
+
+If you use this model in research or production, please cite:
+
+```bibtex
+@misc{aisec_model_v1,
+  author    = {<your-name>},
+  title     = {aisec\_model\_v1: Mistral 7B Fine-Tuned for AI Security Framework Analysis},
+  year      = {2026},
+  publisher = {HuggingFace},
+  url       = {https://huggingface.co/dbristol/aisec_model_v1}
+}
+```