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VDR.aigemma-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
Can I use this in a commercial product?
What hardware do I need to run it?
Does it work with languages other than Python?
Is it safe to use without additional guardrails?
Custom software development services
DEV.co pairs senior engineers with teams adopting tools like gemma-4-12B-coder-fable5-composer2.5-v1. Our software development services span open-source llms architecture, implementation, and the AI development services that increasingly sit alongside them.
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.