Instructions to use Taimwe/securecoder-30b-pro-v3-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Taimwe/securecoder-30b-pro-v3-merged with Transformers:

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

pipe = pipeline("text-generation", model="Taimwe/securecoder-30b-pro-v3-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Taimwe/securecoder-30b-pro-v3-merged")
model = AutoModelForCausalLM.from_pretrained("Taimwe/securecoder-30b-pro-v3-merged", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Taimwe/securecoder-30b-pro-v3-merged 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-v3-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Taimwe/securecoder-30b-pro-v3-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Taimwe/securecoder-30b-pro-v3-merged

SGLang

How to use Taimwe/securecoder-30b-pro-v3-merged 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-v3-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Taimwe/securecoder-30b-pro-v3-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-v3-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Taimwe/securecoder-30b-pro-v3-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Taimwe/securecoder-30b-pro-v3-merged with Docker Model Runner:
```
docker model run hf.co/Taimwe/securecoder-30b-pro-v3-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SecureCoder 30B Pro v3 — merged

LoRA fine-tune of unsloth/Qwen3-Coder-30B-A3B-Instruct merged into full-weight safetensors, for code generation, tool calling and cybersecurity (offence and defence).

Licence: Apache-2.0 — commercial use permitted, following the base model.

Evaluation

v3 was retrained after v1 and v2 were found to emit the literal two-character sequence \n instead of real newlines, which made their Python fail ast.parse. The cause was JSON-escaped message text in parts of the training mix; it is fixed in Taimwe/securecoder-scripts.

Same prompts, same harness (eval_securecoder.py):

Model	Valid Python (15 prompts)	Tool calls: parse / name / schema (80 prompts)
base `unsloth/Qwen3-Coder-30B-A3B-Instruct`	93.3%	100% / 100% / 100%
`securecoder-30b-pro-v2`	0.0%	100% / 100% / 100%
v3 (this model)	100%	100% / 100% / 100%

Reports: base · v2 · v3 (run 2026-10-04).

The code check is small (15 prompts) and tests that output parses and compiles, not that it is correct. The security multiple-choice section of the harness did not run (0 questions), so no security-knowledge score is claimed yet.

Lineage

Stage	Repo
Adapter (LoRA)	`Taimwe/securecoder-30b-pro-v3`
Merged (this repo)	`Taimwe/securecoder-30b-pro-v3-merged`
Training / merge / quantise scripts	`Taimwe/securecoder-scripts`

Architecture


Architecture	`Qwen3MoeForCausalLM` (`qwen3_moe`)
Parameters	30B total, ~3B active
Hidden size	2048
Context length	262,144
Precision	bfloat16, 13 shards (~56.9 GB)

Quick start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "Taimwe/securecoder-30b-pro-v3-merged"

tok = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
    MODEL, torch_dtype=torch.bfloat16, device_map="auto")

messages = [{"role": "user", "content": "Write a race-free file watcher in Rust."}]
inputs = tok.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

A 30B MoE needs a capable GPU. For a smaller download use the Q4_K_M GGUF once published.

Limitations

Produces working offensive-security code. Use only against systems you own or have written permission to test; you are responsible for its use.
Inherits Qwen3-Coder's biases; no independent safety red-teaming was run.
MoE inference stays memory-hungry even though only ~3B parameters are active per token.
Behaviour outside the training mix is unverified.

Downloads last month: 197

Safetensors

Model size

31B params

Tensor type

BF16

Model tree for Taimwe/securecoder-30b-pro-v3-merged

Base model

Qwen/Qwen3-Coder-30B-A3B-Instruct

Finetuned

unsloth/Qwen3-Coder-30B-A3B-Instruct

Finetuned

(6)

this model

Quantizations

2 models