gemma-4-12B-coder-fable5-composer2.5-v1
Gemma-4-12B-Coder is a fine-tuned 12-billion-parameter coding model built on Google's Gemma 4, specialized for Python algorithmic tasks. It uses verifiable training data (code solutions tested against test cases) and includes chain-of-thought reasoning. Available in full-precision safetensors format and ready-made quantized builds (GGUF). Licensed under Apache 2.0. Runs on modest hardware (4.5–12 GB VRAM depending on quantization) and supports self-hosted deployment via transformers, llama.cpp, Ollama, and similar frameworks.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | yuxinlu1 |
| Parameters | 12B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 37.3k |
| Likes | 51 |
| Last updated | 2026-06-18 |
| Source | yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1 |
What gemma-4-12B-coder-fable5-composer2.5-v1 is
A bf16 fine-tune of google/gemma-4-12B-it, trained on dual chain-of-thought data: genuine passing solutions (Composer 2.5) and synthetic re-derivations of failed cases (Fable 5). Uses safetensors for weights, supports the gemma4_unified architecture in recent transformers builds. Context window: 256K (config.json fix included). Quants available at Q2_K–Q8_0. No RLHF safety-alignment applied (reduced refusals, but not production-hardened). Personal project, shared as-is.
Run gemma-4-12B-coder-fable5-composer2.5-v1 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE (verify with your hardware): Q2_K ~4.5 GB VRAM, Q3_K_M ~5.7 GB, Q4_K_M ~6.87 GB (recommended quant), Q6_K ~9.1 GB, Q8_0 ~11.8 GB. Full precision (bf16) requires ~24 GB. Runs on consumer GPUs, M-series Macs (MLX), or CPUs with llama.cpp (slower). No official benchmark data; adjust batch size and sequence length per device.
Full-precision safetensors master is explicitly designed for LoRA/continued training. Recent transformers builds with gemma4_unified support required. QLoRA feasible on 8–16 GB VRAM machines. No official LoRA adapters provided; you supply your own dataset and harness. Training stability and convergence behavior Unknown; no public training logs.
When to avoid it — and what to weigh
- General Knowledge / Factual Retrieval — Model card explicitly notes facts and numbers should be double-checked. Not suitable as a general Q&A or RAG backbone unless grounded by external retrieval.
- Non-Python or Fuzzy Problem Domains — Training data is Python-focused and algorithmic. Suitability for Go, Rust, JavaScript, or open-ended code design tasks is Unknown; not claimed in card.
- Safety-Critical Production Systems Without Custom Guardrails — Card states 'Reduced refusals' and 'not safety-aligned.' No built-in safeguards for harmful code generation, secrets in output, or misuse. Requires explicit filtering and monitoring.
- Real-Time, Ultra-Low-Latency Applications — Even at Q2_K (4.5 GB), inference latency depends on hardware and sampling strategy. Not benchmarked against commercial APIs; no SLA guarantees. Hobby-grade project.
License & commercial use
Apache 2.0. Gemma 4 base model licensed under Apache 2.0 by Google (unlike older Gemma 1/2/3 restrictions). This fine-tune inherits Apache 2.0 terms: permissive open-source license, free to use, modify, and redistribute. No special gating or commercial licensing tiers stated.
Apache 2.0 is an OSI-approved permissive license generally compatible with commercial use under its terms (attribution, derivative disclosure, no liability). However, verify applicability to your product and ensure compliance with Google's Gemma 4 Apache 2.0 terms (e.g., no trademark use, model output attribution best practice). This is a personal hobby project with no warranty; evaluate risk tolerance. For mission-critical or high-liability use, consult legal counsel and consider commercial LLM providers with SLAs.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
Model is not safety-aligned and has reduced refusals relative to base Gemma 4. No built-in content filtering or output validation. Risks: generation of unsafe, incorrect, or socially harmful code; secrets or sensitive data in generated output; adversarial inputs. Mitigation requires external input validation, output filtering, and human review. No public security audit or red-team report available. Not suitable for production without custom safety layers.
Alternatives to consider
google/gemma-4-12B-it (base model)
Unspecialized, general-purpose; better factual grounding but less optimized for code. No chain-of-thought training. Lower refusal rate may still be higher than this fine-tune.
meta-llama/llama-3.1-8B-Instruct or similar OSS models
Lighter (~8B params), broader training. Fewer coding benchmarks; may require your own fine-tuning for specialized tasks. Different architecture/license (Llama 3.1 is LLAMA2 license).
OpenAI Codex / Claude API (commercial)
Production-grade, safety-aligned, extensive benchmarking, SLA. No self-hosting. Higher cost but lower operational burden and liability risk.
Ship gemma-4-12B-coder-fable5-composer2.5-v1 with senior software developers
Gemma-4-12B-Coder offers a lightweight, open-source foundation for internal code assistants and fine-tuning. Review the model card, choose your quantization, and deploy via llama.cpp or transformers. Consult us for production hardening, safety guardrails, and custom integration.
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gemma-4-12B-coder-fable5-composer2.5-v1 FAQ
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Custom software development services
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If gemma-4-12B-coder-fable5-composer2.5-v1 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy a Private Coding AI?
Gemma-4-12B-Coder offers a lightweight, open-source foundation for internal code assistants and fine-tuning. Review the model card, choose your quantization, and deploy via llama.cpp or transformers. Consult us for production hardening, safety guardrails, and custom integration.