language:

• en license: apache-2.0 library_name: transformers tags:
• gemma
• gemma4
• gemma-4-E2B
• code
• coding
• code-generation
• python
• javascript
• typescript
• react
• frontend
• html
• css
• qlora
• fine-tuned
• text-generation
• instruct
• edge
• on-device
• small-language-model
• quantization
• gguf
• uncensored base_model: llmfan46/gemma-4-E2B-it-ultra-uncensored-heretic datasets:
• F-A-I-L/kodcode-verified-python-235k
• glyphsoftware/opus-4.6-frontend-development
• Nexlab/fable5-agentic-coding-sft pipeline_tag: text-generation model-index:
• name: Coder-Gemma-4-E2B-Uncensored results:
  • task: type: text-generation name: Code Generation dataset: name: openai_humaneval type: openai/openai_humaneval metrics:
    • type: pass@1 value: 56.7 name: HumanEval pass@1 (Q8_0, greedy, completion-style)
  • task: type: text-generation name: Code Generation dataset: name: google-research-datasets/mbpp type: google-research-datasets/mbpp metrics:
    • type: pass@1 value: 40.5 name: MBPP pass@1 (Q8_0, greedy, 3-shot, first 200 tasks)

Coder-Gemma-4-E2B-Uncensored

A code-focused finetune of llmfan46/gemma-4-E2B-it-ultra-uncensored-heretic (Gemma 4 E2B, ~4.6B) for Python and front-end development (React, TypeScript, HTML/CSS). Uncensored base, so it answers without refusals — and without the lecture.

Trained on a single RX 6700 XT, which means this model was forged in 12 GB of VRAM and pure spite. It writes React without judging your component structure.

Model Details

Evaluation (measured, same harness for every row)

Measured with lm-evaluation-harness 0.4.13, greedy decoding (temp 0.0, seed 0), HumanEval full 164 tasks, MBPP first 200 tasks. Yes, the base scores higher on pure Python — it's posted right there in the table, we're not hiding it. This model trades a few points there for front-end ability the base was never tuned for. Honest benchmarks, no cherry-picking; if you wanted inflated numbers there are 5,000 other finetunes for that.

Front-end gate (12 generated React/TSX components, same model): 12/12 structurally sound, 9/12 pass tsc --noEmit (strict), 10/12 render non-trivial HTML with zero runtime crashes.

Usage

# llama.cpp
llama-server -m Coder-Gemma-4-E2B-Uncensored.Q8_0.gguf -c 4096 -ngl 99

# Ollama
ollama create coder-gemma-4-e2b -f ./Modelfile

Works in LM Studio, Ollama, llama.cpp, and any GGUF runner. Give it a chat template (Gemma 4 <|turn>user / <|turn>model) for instruct-style prompts.

Limitations (read these, they're honest)

• Small model: complex multi-file refactors and long-context tasks will strain it. It has 2048 tokens of context, not a time machine.
• Front-end ability comes from ~700 unique examples — style and repair are strong, novel greenfield architecture is not. It will not single-handedly redesign your startup's landing page. It will fix your broken useEffect.
• Uncensored: it will comply with requests an aligned model refuses. With great power comes great responsibility, etc. etc. You know the drill.

Training data licence notes

• KodCode-verified rows: CC-BY-NC-4.0 (non-commercial).
• Opus 4.6 front-end set: Apache-2.0.
• Check terms before commercial use.