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")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Monibee-Fudgekins/gemma-coder-dev", dtype="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 Studio

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

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Monibee-Fudgekins/gemma-coder-dev to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Monibee-Fudgekins/gemma-coder-dev to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Monibee-Fudgekins/gemma-coder-dev to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Monibee-Fudgekins/gemma-coder-dev",
    max_seq_length=2048,
)

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

Upload model trained with Unsloth

da981e1 verified 4 days ago

preview code

Raw

History Blame Contribute Delete

3.3 kB

	---
	license: gemma
	base_model: google/gemma-4-26B-A4B-it
	datasets:
	- nvidia/OpenCodeInstruct
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- code
	- coding-assistant
	- qlora
	- unsloth
	model-index:
	- name: gemma-coder-dev
	results:
	- task:
	type: text-generation
	name: Code generation
	dataset:
	name: remote-agent-dev-platform coding_eval
	type: code-eval
	metrics:
	- type: pass@1
	value: 0.3913
	name: pass@1 (Python/JS/React/Go/Java/Swift)
	---

	# gemma-coder-dev

	Coding-focused fine-tune of [`google/gemma-4-26B-A4B-it`](https://huggingface.co/google/gemma-4-26B-A4B-it)
	(Gemma 4 26B A4B, an MoE with ~4B active params), produced automatically by the
	weekly retrain pipeline in [remote-agent-dev-platform](https://github.com/Monibee-Fudgekins/remote-agent-dev-platform).
	Last updated: 2026-06-23 09:46 UTC · run mode: `full` · promoted: False.

	## Model description
	QLoRA fine-tune of google/gemma-4-26B-A4B-it specialized for coding assistance. It is the
	default agent model for the remote-agent-dev-platform (served via vLLM on Modal).

	## Intended uses & limitations
	- Intended: code generation and assistance in Python, JavaScript/React, Go, Java,
	and Swift, inside a sandboxed agent that runs/tests the output.
	- Not intended: safety-critical use, or running generated code unreviewed.
	- Limitations: a small, free-tier-trained model — it can produce incorrect or
	insecure code. Always review and test. Quality tracks the training data, which is
	still being built out.

	## Training data
	- Dataset: [`nvidia/OpenCodeInstruct`](https://huggingface.co/datasets/nvidia/OpenCodeInstruct)

	## Training procedure
	- Method: QLoRA (Unsloth), 4-bit base, LoRA r=8 / alpha=16 on
	attention + MoE experts, lr 2e-4,
	max seq len 512, optimizer adamw_8bit.
	- Progress: cycle 1 — 599 / 4000 steps (trained
	in weekly ~8h chunks on Kaggle's free 2×T4, resuming each week; training is
	continuous — a finished cycle rolls into the next).

	## Evaluation
	Sandboxed multi-language pass@1 harness (`finetune/evaluate.py`): the model
	completes functions that are then compiled/run against unit tests. Languages whose
	toolchain is unavailable are skipped.

	Overall pass@1: 39.13% over 23 executed problems
	(4 skipped). Promotion threshold: 46%.

	\| language \| passed / run \| pass@1 \|
	\|---\|---\|---\|
	\| go \| 1/4 \| 25.00% \|
	\| java \| 0/4 \| 0.00% \|
	\| javascript \| 0/7 \| 0.00% \|
	\| python \| 8/8 \| 100.00% \|
	\| swift \| 0/0 \| skipped (no toolchain) \|

	## How to use
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	tok = AutoTokenizer.from_pretrained("Monibee-Fudgekins/gemma-coder-dev")
	model = AutoModelForCausalLM.from_pretrained("Monibee-Fudgekins/gemma-coder-dev", device_map="auto")
	msgs = [{"role": "user", "content": "Write a Python function that reverses a string."}]
	ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
	print(tok.decode(model.generate(ids, max_new_tokens=256)[0]))
	```


	## 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); see that repo for
	the full training + eval pipeline.