Improve model card: lineage, usage, limits

README.md

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----
-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]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
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-<!-- 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
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-
-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).
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-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Glossary [optional]
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+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+base_model: unsloth/qwen3-coder-30b-a3b-instruct
+tags:
+- securecoder
+- peft
+- lora
+- adapter
+- code
+- tool-calling
+- security
+- cybersecurity
+- qwen3
+- qwen3_moe
+- unsloth
+---
+
+# SecureCoder 30B Pro v2 — LoRA adapter
+
+The **LoRA adapter** stage of SecureCoder v2: a rank-32 attention-only LoRA over
+[`unsloth/qwen3-coder-30b-a3b-instruct`](https://huggingface.co/unsloth/qwen3-coder-30b-a3b-instruct),
+trained for **code, tool calling and cybersecurity** (offence and defence).
+
+> Most people want a runnable model, not an adapter. Use:
+> - [`Taimwe/securecoder-30b-pro-v2-GGUF`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2-GGUF) — Q4_K_M, ~7 GB, runs on a 16 GB card
+> - [`Taimwe/securecoder-30b-pro-v2-merged`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2-merged) — full safetensors
+>
+> Keep this repo only if you want to re-train, re-merge, or stack it onto a different quantisation.
+
+## Adapter config
+
+| | |
+| --- | --- |
+| Type | LoRA (`peft 0.21.0`) |
+| Rank `r` | 32 |
+| `lora_alpha` | 32 |
+| Dropout | 0.0 |
+| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj` (attention only) |
+| Base | `Qwen3MoeForCausalLM` |
+| Task type | `CAUSAL_LM` |
+
+Attention-only means the adapter is small and cheap to merge, but it cannot teach
+new FFN/expert knowledge — the gains are behavioural (style, format, tool-call
+shape, security framing), not new factual capacity.
+
+## Applying it yourself
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+BASE = "unsloth/qwen3-coder-30b-a3b-instruct"
+ADAPTER = "Taimwe/securecoder-30b-pro-v2"
+
+tok = AutoTokenizer.from_pretrained(BASE)
+model = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype="bfloat16", device_map="auto")
+model = PeftModel.from_pretrained(model, ADAPTER)
+```
+
+Or merge directly (this is what produced
+[`securecoder-30b-pro-v2-merged`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2-merged)):
+
+```python
+merged = model.merge_and_unload()
+merged.save_pretrained("securecoder-30b-pro-v2-merged", safe_serialization=True)
+```
+
+## Pipeline
+
+| Stage | Repo |
+| --- | --- |
+| Adapter (this repo) | `Taimwe/securecoder-30b-pro-v2` |
+| Merged | [`Taimwe/securecoder-30b-pro-v2-merged`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2-merged) |
+| GGUF | [`Taimwe/securecoder-30b-pro-v2-GGUF`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2-GGUF) |
+| Scripts | [`Taimwe/securecoder-scripts`](https://huggingface.co/Taimwe/securecoder-scripts) |
+
+## Limitations
+
+- Produces **working offensive-security code**. Use only against systems you own or
+  have written permission to test.
+- No independent safety red-teaming was run.
+- Behaviour outside the training mix is unverified.
+
+## License
+
+Apache-2.0, following the base model.