Instructions to use Taimwe/securecoder-30b-pro-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Taimwe/securecoder-30b-pro-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Taimwe/securecoder-30b-pro-v2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Taimwe/securecoder-30b-pro-v2", device_map="auto")

PEFT
How to use Taimwe/securecoder-30b-pro-v2 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Taimwe/securecoder-30b-pro-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Taimwe/securecoder-30b-pro-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Taimwe/securecoder-30b-pro-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Taimwe/securecoder-30b-pro-v2

SGLang

How to use Taimwe/securecoder-30b-pro-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Taimwe/securecoder-30b-pro-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Taimwe/securecoder-30b-pro-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Taimwe/securecoder-30b-pro-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Taimwe/securecoder-30b-pro-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use Taimwe/securecoder-30b-pro-v2 with Docker Model Runner:
```
docker model run hf.co/Taimwe/securecoder-30b-pro-v2
```

securecoder-30b-pro-v2 / README.md

Taimwe

Honest card: document the escaped-newline defect, remove links to deleted repos

681957c verified 2 days ago

preview code

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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
	- known-issue
	---

	# SecureCoder 30B Pro v2 — LoRA adapter (known defect, read first)

	> [!CAUTION]
	> This adapter produces code that does not parse. Do not use it for code generation.
	>
	> Measured on the same prompts through the same harness:
	>
	> \| Model \| valid Python \|
	> \|---\|---:\|
	> \| `unsloth/Qwen3-Coder-30B-A3B-Instruct` (base) \| 93.3% \|
	> \| this adapter / `securecoder-30b-pro-v2-merged` \| 0.0% \|
	>
	> Symptom: the model emits the literal two-character sequence `\n` instead of real
	> newlines, so Python blocks fail `ast.parse` at line 1. Tool-calling still scored 100%
	> (those regexes only read tag names), which masked the problem.
	>
	> Cause: several datasets in the training mix — Trendyol Cybersecurity, Fenrir v2.1,
	> OWASP-sft, Heimdall v1.1, CTF-Instruct — store message text JSON-escaped.
	> 183 of 480 sampled rows were affected, so the fine-tune learned to escape its own
	> newlines.
	>
	> Fixed in [`Taimwe/securecoder-scripts`](https://huggingface.co/Taimwe/securecoder-scripts)
	> (`_unescape_if_needed()`, commit `51a4d639`). The merged and GGUF repos were deleted
	> rather than left published, because they shipped broken output.

	## What is here, and why

	Only the LoRA adapter (102 MB) is kept, for reproducibility and to re-merge after the
	data fix is retrained. Its siblings `securecoder-30b-pro-v2-merged` (61 GB) and
	`securecoder-30b-pro-v2-GGUF` (17.3 GB) were removed.

	For a working code model today use the base
	[`unsloth/Qwen3-Coder-30B-A3B-Instruct`](https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct)
	— 93.3% valid Python on the same test.

	## Adapter config

	\| \| \|
	\|---\|---\|
	\| Type \| LoRA (`peft 0.21.0`) \|
	\| Rank `r` / alpha \| 32 / 32 \|
	\| Target modules \| `q_proj`, `k_proj`, `v_proj`, `o_proj` (attention only) \|
	\| Base \| `Qwen3MoeForCausalLM` \|
	\| Task type \| `CAUSAL_LM` \|

	Attention-only keeps the adapter small and cheap to merge, but it cannot teach new
	knowledge — it changes behaviour (style, output format, tool-call shape), not capability.

	## Evidence

	- [`securecoder-eval-v2`](https://huggingface.co/Taimwe/securecoder-eval-v2) — 0.0% ast_rate
	- [`securecoder-eval-base`](https://huggingface.co/Taimwe/securecoder-eval-base) — 93.3% ast_rate
	- Full write-up + exact retrain command:
	[`securecoder-scripts/HANDOFF.md`](https://huggingface.co/Taimwe/securecoder-scripts/blob/main/HANDOFF.md)

	## License

	Apache-2.0, following the base model.