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Model: dbristol/aisec_model_v1 Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: mistralai/Mistral-7B-Instruct-v0.3
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base_model_relation: finetune
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dbristol:
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- mlx
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- lora
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- mistral
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- ai-security
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- nist-ai-rmf
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- mitre-atlas
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- owasp-ai-exchange
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- google-saif
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- risk-management
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- fine-tuned
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language:
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- en
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pipeline_tag: text-generation
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datasets:
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- dbristol/aisec-training-data
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library_name: mlx
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---
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# aisec_model_v1 — AI Security Framework Expert (Mistral 7B LoRA)
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> **This is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3),
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> not a new model architecture.** Only 0.145% of parameters were updated via
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> LoRA. The base model weights, tokenizer, and architecture are unchanged.
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Domain-specialised using LoRA on Apple Silicon via [MLX](https://github.com/ml-explore/mlx)
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for cross-framework AI security and risk management analysis across:
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- **NIST AI RMF 1.0** — Govern, Map, Measure, Manage functions
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- **MITRE ATLAS** — Adversarial TTP kill chains and detection engineering
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- **OWASP AI Exchange** — Runtime attack surfaces and technical controls
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- **Google SAIF** — Component responsibility assignment and governance layers
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---
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## Model Details
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| Property | Value |
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|---|---|
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| Base model | mistralai/Mistral-7B-Instruct-v0.3 |
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| Fine-tuning method | LoRA (Low-Rank Adaptation) |
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| Framework | MLX (Apple Silicon) |
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| Trainable parameters | 10.486M / 7,248M (0.145%) |
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| LoRA rank | 8 |
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| LoRA alpha | 16 |
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| LoRA layers | 16 |
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| Training platform | Apple Silicon (M-series), macOS |
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| Best checkpoint | Iter 500 (val loss 0.216) |
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| Training dataset | [dbristol/aisec-training-data](https://huggingface.co/datasets/dbristol/aisec-training-data) |
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---
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## Training Summary
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Training was performed using `mlx_lm.lora` with a cosine learning rate schedule.
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| Checkpoint | Val Loss |
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|---|---|
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| Iter 1 (base) | 2.597 |
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| Iter 100 | 0.749 |
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| Iter 200 | 0.369 |
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| Iter 300 | 0.312 |
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| Iter 400 | 0.267 |
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| **Iter 500** | **0.216** ← best |
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| Iter 550 | 0.223 ↑ overfitting onset |
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Training configuration:
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```yaml
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learning_rate: 5e-5
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lr_schedule: cosine_decay (100-iter warmup)
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batch_size: 4
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iters: 1200
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lora_rank: 8
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lora_alpha: 16.0
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lora_dropout: 0.05
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num_layers: 16
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```
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---
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## Usage
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### Requirements
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```bash
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pip install mlx-lm
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```
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### Inference with MLX
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```python
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from mlx_lm import load, generate
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model, tokenizer = load(
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"Dbristol/aisec_model_v1"
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)
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prompt = "Provide a cross-framework analysis of indirect prompt injection defences \
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for a code generation assistant using OWASP AI Exchange, SAIF, MITRE ATLAS, \
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and NIST AI RMF."
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messages = [
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{
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"role": "system",
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"content": (
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"You are an expert AI security and risk management assistant "
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"specialising in NIST AI RMF 1.0, MITRE ATLAS, OWASP AI Exchange, "
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"and Google SAIF frameworks."
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)
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},
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{"role": "user", "content": prompt}
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]
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formatted = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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response = generate(
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model,
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tokenizer,
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prompt=formatted,
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max_tokens=512,
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temp=0.4,
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top_p=0.85,
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)
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print(response)
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```
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### Recommended inference parameters
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| Parameter | Value | Rationale |
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|---|---|---|
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| temperature | 0.4 | Factual domain — sharper distribution favours trained signal |
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| top_p | 0.85 | Tighter nucleus reduces long-tail sampling |
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| top_k | 40 | Hard vocabulary cap applied before top_p |
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| repeat_penalty | 1.1 | Reduces repetition of framework acronyms |
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---
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## Intended Use
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This model is designed for security practitioners, researchers, and AI governance
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professionals who need structured cross-framework analysis. Suitable use cases include:
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- Mapping AI system risks across multiple frameworks simultaneously
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- Generating NIST AI RMF governance documentation
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- Identifying MITRE ATLAS TTPs relevant to a specific AI deployment
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- Drafting OWASP AI Exchange control implementations
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- Cross-referencing Google SAIF responsibility assignments
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### Out-of-scope use
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This model should not be used as the sole basis for security decisions without
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human expert review. Framework guidance evolves; always verify against current
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official documentation.
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---
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## Limitations
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- Trained on a single-domain dataset; may underperform on security tasks outside
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the four covered frameworks.
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- Knowledge cutoff reflects the training data collection date, not live framework updates.
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- Responses should be verified against official NIST, MITRE, OWASP, and Google SAIF
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publications before operational use.
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- Base model is Mistral 7B Instruct v0.3; inherits its general limitations.
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---
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## License
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This model is released under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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The base model ([Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3))
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is also Apache 2.0 licensed.
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The training dataset is derived from publicly available framework documentation.
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See the [dataset card](https://huggingface.co/datasets/<your-hf-username>/aisec-training-data)
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for full provenance and source attribution.
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---
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## Citation
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If you use this model in research or production, please cite:
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```bibtex
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@misc{aisec_model_v1,
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author = {<your-name>},
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title = {aisec\_model\_v1: Mistral 7B Fine-Tuned for AI Security Framework Analysis},
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year = {2026},
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publisher = {HuggingFace},
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url = {https://huggingface.co/dbristol/aisec_model_v1}
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}
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```
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chat_template.jinja
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{%- if messages[0]["role"] == "system" %}
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{%- set system_message = messages[0]["content"] %}
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{%- set loop_messages = messages[1:] %}
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{%- else %}
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{%- set loop_messages = messages %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{%- set user_messages = loop_messages | selectattr("role", "equalto", "user") | list %}
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{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}
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{%- set ns = namespace() %}
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{%- set ns.index = 0 %}
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{%- for message in loop_messages %}
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{%- if not (message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %}
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{%- if (message["role"] == "user") != (ns.index % 2 == 0) %}
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{{- raise_exception("After the optional system message, conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif %}
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{%- set ns.index = ns.index + 1 %}
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{%- endif %}
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{%- endfor %}
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{{- bos_token }}
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{%- for message in loop_messages %}
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{%- if message["role"] == "user" %}
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{%- if tools is not none and (message == user_messages[-1]) %}
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{{- "[AVAILABLE_TOOLS] [" }}
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{%- for tool in tools %}
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{%- set tool = tool.function %}
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{{- '{"type": "function", "function": {' }}
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{%- for key, val in tool.items() if key != "return" %}
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{%- if val is string %}
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{{- '"' + key + '": "' + val + '"' }}
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{%- else %}
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{{- '"' + key + '": ' + val|tojson }}
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{%- endif %}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- "}}" }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- else %}
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{{- "]" }}
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{%- endif %}
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{%- endfor %}
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{{- "[/AVAILABLE_TOOLS]" }}
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{%- endif %}
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{%- if loop.last and system_message is defined %}
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{{- "[INST] " + system_message + "\n\n" + message["content"] + "[/INST]" }}
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{%- else %}
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{{- "[INST] " + message["content"] + "[/INST]" }}
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{%- endif %}
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{%- elif message.tool_calls is defined and message.tool_calls is not none %}
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{{- "[TOOL_CALLS] [" }}
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{%- for tool_call in message.tool_calls %}
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{%- set out = tool_call.function|tojson %}
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{{- out[:-1] }}
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{%- if not tool_call.id is defined or tool_call.id|length != 9 %}
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{{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
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{%- endif %}
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{{- ', "id": "' + tool_call.id + '"}' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- else %}
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{{- "]" + eos_token }}
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{%- endif %}
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{%- endfor %}
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{%- elif message["role"] == "assistant" %}
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{{- " " + message["content"]|trim + eos_token}}
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{%- elif message["role"] == "tool_results" or message["role"] == "tool" %}
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{%- if message.content is defined and message.content.content is defined %}
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{%- set content = message.content.content %}
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{%- else %}
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{%- set content = message.content %}
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{%- endif %}
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{{- '[TOOL_RESULTS] {"content": ' + content|string + ", " }}
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{%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}
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{{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
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{%- endif %}
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{{- '"call_id": "' + message.tool_call_id + '"}[/TOOL_RESULTS]' }}
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{%- else %}
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{{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }}
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{%- endif %}
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{%- endfor %}
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config.json
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{
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"architectures": [
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"MistralForCausalLM"
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"attention_dropout": 0.0,
|
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"bos_token_id": 1,
|
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"eos_token_id": 2,
|
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"hidden_act": "silu",
|
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"hidden_size": 4096,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 14336,
|
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"max_position_embeddings": 32768,
|
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"model_type": "mistral",
|
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"num_attention_heads": 32,
|
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"num_hidden_layers": 32,
|
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"num_key_value_heads": 8,
|
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"rms_norm_eps": 1e-05,
|
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"rope_theta": 1000000.0,
|
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"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
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"transformers_version": "4.42.0.dev0",
|
||||
"use_cache": true,
|
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"vocab_size": 32768
|
||||
}
|
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6
generation_config.json
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{
|
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"_from_model_config": true,
|
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"bos_token_id": 1,
|
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"eos_token_id": 2,
|
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"transformers_version": "4.42.0.dev0"
|
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}
|
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}
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||||
}
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||||
275733
tokenizer.json
Normal file
275733
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
17
tokenizer_config.json
Normal file
17
tokenizer_config.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"add_prefix_space": true,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"is_local": true,
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
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||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
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||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "TokenizersBackend",
|
||||
"tool_parser_type": "mistral",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user