Instructions to use Monibee-Fudgekins/gemma-coder-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Monibee-Fudgekins/gemma-coder-dev with Transformers:

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

pipe = pipeline("text-generation", model="Monibee-Fudgekins/gemma-coder-dev")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Monibee-Fudgekins/gemma-coder-dev", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Monibee-Fudgekins/gemma-coder-dev with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Monibee-Fudgekins/gemma-coder-dev

SGLang

How to use Monibee-Fudgekins/gemma-coder-dev 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 "Monibee-Fudgekins/gemma-coder-dev" \
    --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": "Monibee-Fudgekins/gemma-coder-dev",
		"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 "Monibee-Fudgekins/gemma-coder-dev" \
        --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": "Monibee-Fudgekins/gemma-coder-dev",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use Monibee-Fudgekins/gemma-coder-dev with Docker Model Runner:
```
docker model run hf.co/Monibee-Fudgekins/gemma-coder-dev
```

gemma-coder-dev

File size: 2,642 Bytes

---
license: apache-2.0
base_model: google/gemma-4-E4B-it
datasets:
  - google/code_x_glue_cc_defect_detection
  - bstee615/diversevul
  - bstee615/bigvul
  - hitoshura25/cvefixes
library_name: transformers
pipeline_tag: text-generation
tags:
  - code
  - security
  - vulnerability-detection
  - qlora
  - unsloth
---

# gemma-coder-dev

Defensive-security fine-tune of [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it),
produced by the retrain pipeline in [remote-agent-dev-platform](https://github.com/Monibee-Fudgekins/remote-agent-dev-platform).
**Last updated: 2026-11-10 19:44 UTC** · run mode: `smoke` · promoted: **False**.

> Numbers below come from this pipeline's own held-out eval. No ExploitGym
> results are claimed; that evaluation has not been run.

## Intended uses & limitations
- **Intended:** secure code review — decide whether a function is vulnerable, name the
  CWE, and propose a secure rewrite; write code that avoids common weakness classes.
- **Not intended:** exploit development or any offensive use; unreviewed automated
  patching; safety-critical decisions.
- **Limitations:** labels come from commit-mined datasets (noisy); the fix score is a
  proxy, not a proof that the vulnerability is gone. Always review and test.

## Training data
- [`google/code_x_glue_cc_defect_detection`](https://huggingface.co/datasets/google/code_x_glue_cc_defect_detection)
- [`bstee615/diversevul`](https://huggingface.co/datasets/bstee615/diversevul)
- [`bstee615/bigvul`](https://huggingface.co/datasets/bstee615/bigvul)
- [`hitoshura25/cvefixes`](https://huggingface.co/datasets/hitoshura25/cvefixes)

Formatted into three chat tasks (detect / CWE classify / secure fix) by
`finetune/security_data.py`; functions are deduplicated, length-capped and capped
per source. A fixed-seed held-out slice (by function hash) is excluded from training.

## Training procedure
- Method: QLoRA (Unsloth), 4-bit base, LoRA r=16 / alpha=32 on
  attention + MLP, lr 2e-4,
  max seq len 768, optimizer adamw_8bit.
- Progress: cycle 1 — 10 / 4000 steps.

## Evaluation (20 items: synthetic seed + held-out)
| model | detection F1 | CWE top-1 | fix-validity (proxy) |
|---|---|---|---|
| this run | 0.0000 | 1.0000 | 0.0000 |
| base (adapter off) | not measured | | |

Promotion requires detection F1 and CWE accuracy to beat the base model by the
configured margins and the fix proxy not to regress (`finetune/evaluate.py`).


## Provenance
Generated by `finetune/kaggle/run.py` in [https://github.com/Monibee-Fudgekins/remote-agent-dev-platform](https://github.com/Monibee-Fudgekins/remote-agent-dev-platform).