gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF
Gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF is a quantized, locally-runnable 12B parameter language model fine-tuned on Claude Opus reasoning data. It fits in 4.5–22.2 GB depending on quantization level, supports up to 131K context, and includes a Multi-Token Prediction (MTP) speculative decoding option for faster inference. Licensed under Apache 2.0, it is designed for private, offline deployment via llama.cpp or compatible tools.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | yuxinlu1 |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 59.1k |
| Likes | 111 |
| Last updated | 2026-06-18 |
| Source | yuxinlu1/gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF |
What gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF is
Derivative of google/gemma-4-12B-it, quantized into GGUF format with multiple precision options (Q2_K to f16). Base model trained on Apache-2.0 Opus 4.6/4.7 reasoning dataset plus curator-added Opus 4.8 samples. Implements gemma4_unified architecture; requires recent llama.cpp (≥ June 7, 2026 build for MTP support). Includes native Gemma 4 thought channel for chain-of-thought reasoning. No safety alignment applied; reduced refusals vs. base model.
Run gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF 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-it-Claude-4.6-4.8-Opus-GGUF")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
Minimum: 4.5 GB VRAM or unified memory (Q2_K quant, ~2–4K context on 8 GB system). Recommended: 12–16 GB (Q4_K_M, ~30–64K context). Context scaling: 8 GB→~16K (Q2_K), 12 GB→~30K (Q4_K_M), 16 GB→~64K (Q4_K_M), 24+ GB→full 131K. Estimates assume q8_0 KV cache; switching to q4_0 KV cache approximately doubles context. Apple Silicon with unified memory scales identically but runs slower than discrete GPUs. ESTIMATE: verify on target hardware; VRAM/precision trade-off is not measured on this model.
LoRA/QLoRA feasibility Unknown. Model is already a fine-tuned derivative; adapter training compatibility depends on architecture (gemma4_unified). No quantization or adapter guidance provided in card. Requires experimentation or independent review of llama.cpp LoRA support for this architecture.
When to avoid it — and what to weigh
- Safety-critical or regulated applications — Model explicitly lacks safety alignment; reduced refusals increase risk of harmful outputs. No guardrails in place. Requires external safety layer for production use.
- Factual accuracy requirements — Reasoning is stylistic synthetic chain-of-thought; model does not guarantee factual correctness. Numbers and facts require manual verification.
- Multilingual or non-English tasks — Model is English-centric; capability and quality on other languages is Unknown.
- Real-time ultra-low-latency inference — Q2_K (4.5 GB) quantization introduces non-trivial quality loss; even with MTP, inference speed on consumer hardware is slower than cloud APIs.
License & commercial use
Apache 2.0. Base Gemma 4 and training dataset (claude-opus-4.6-4.7-reasoning-8.7k) are both Apache 2.0. This derivative is Apache 2.0. License text: https://ai.google.dev/gemma/apache_2.
Apache 2.0 is a permissive OSI license that permits commercial use, including modification and redistribution, provided the license and attribution are retained. However: (1) This is a hobby/personal project with no warranty stated. (2) Safety alignment is explicitly absent; commercial deployment requires your own safety guardrails and risk assessment. (3) Verify compliance with your organization's data governance (inference on private systems, no telemetry) and liability policies. Recommend legal review before production deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Unknown |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No safety alignment applied; model has reduced refusals. Inference runs locally (no data egress to APIs) if using llama.cpp or offline tools. Quantization does not introduce additional attack surface vs. unquantized GGUF. Requires external prompt injection, jailbreak, and output validation controls in production. Operator is responsible for sandboxing and rate-limiting if exposed to untrusted input.
Alternatives to consider
Mistral 7B or Mixtral 8x7B (GGUF)
Similar VRAM footprint and local inference; generally stronger safety alignment. Larger community, more LoRA/extension support. Trade-off: no explicit reasoning fine-tuning.
Llama 3.1 8B or 70B (GGUF)
Widely deployed locally-runnable models with strong community support, LoRA tooling, and documented reasoning performance. 70B requires more VRAM but offers better quality.
OpenAI API + GPT-4o or Anthropic Claude API
Cloud-hosted reasoning with safety alignment, higher consistency, no hardware burden. Trade-off: API dependency, per-token cost, data residency concerns.
Ship gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF with senior software developers
Evaluate this model against your safety, latency, and hardware constraints. For production use, add your own guardrails, verify factual accuracy on your domain, and perform legal review of Apache 2.0 compliance and liability.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF FAQ
Can I use this commercially?
What's the minimum hardware I need?
Is the reasoning actually better than the base Gemma 4?
Do I need a new llama.cpp build?
Software developers & web developers for hire
Need help beyond evaluating gemma-4-12B-it-Claude-4.6-4.8-Opus-GGUF? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy Offline Reasoning Inference?
Evaluate this model against your safety, latency, and hardware constraints. For production use, add your own guardrails, verify factual accuracy on your domain, and perform legal review of Apache 2.0 compliance and liability.