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 / README.md

Monibee-Fudgekins

Update model card (smoke run, detection F1 0.0000)

3ee5236 verified about 11 hours ago

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metadata

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, produced by the retrain pipeline in 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

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.