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

Libraries

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

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

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

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

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

vLLM

How to use Taimwe/securecoder-30b-pro 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"
# 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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use Taimwe/securecoder-30b-pro 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" \
    --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",
		"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" \
        --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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

securecoder-30b-pro / README.md

Taimwe

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

dfb3a39 verified 9 days ago

preview code

Raw History Blame Contribute Delete

2.18 kB

metadata

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 v1 — LoRA adapter (deprecated, known defect)

Deprecated. This adapter produces code that does not parse — it emits the literal two-character sequence \n instead of real newlines, so Python blocks fail ast.parse at line 1.

Measured on identical prompts with the same harness:

Model literal \n in replies valid Python

unsloth/Qwen3-Coder-30B-A3B-Instruct (base) — 93.3%

this adapter 14/15 ~6.7%

Root cause: the training mix contains datasets whose message text is stored JSON-escaped (Trendyol Cybersecurity, Fenrir v2.1, OWASP-sft, Heimdall v1.1, CTF-Instruct). 183 of 480 sampled rows were affected.

Fixed in Taimwe/securecoder-scripts (_unescape_if_needed(), commit 51a4d639).

The merged weights and GGUF that were built from this adapter — including securecoder-30b-pro-merged (345 downloads) — were deleted because they shipped broken output.

Model	literal `\n` in replies	valid Python
`unsloth/Qwen3-Coder-30B-A3B-Instruct` (base)	—	93.3%
this adapter	14/15	~6.7%

What is here

Only the LoRA adapter (53.5 MB), kept for reproducibility. Everything built from it was removed. For a working code model use the base unsloth/Qwen3-Coder-30B-A3B-Instruct.

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`

Evidence and retrain instructions

securecoder-scripts/HANDOFF.md
securecoder-eval-base — the 93.3% reference

License

Apache-2.0, following the base model.