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Model Details
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Fix-it-Felix
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yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF
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Output Modalities
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Creating these models takes significant time, work and compute. If you find them useful consider supporting me:
| Platform | Link | What you get |
|---|---|---|
| 🎉 Patreon | Monthly support | Priority model requests |
| ☕ Ko-fi | One-time tip | My eternal gratitude |
Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.
| Parameter | Value |
|---|---|
| direction_index | 29.18 |
| attn.o_proj.max_weight | 1.30 |
| attn.o_proj.max_weight_position | 35.73 |
| attn.o_proj.min_weight | 0.90 |
| attn.o_proj.min_weight_distance | 26.76 |
| mlp.down_proj.max_weight | 1.49 |
| mlp.down_proj.max_weight_position | 38.14 |
| mlp.down_proj.min_weight | 1.43 |
| Metric | This model | Original model (gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF) |
|---|---|---|
| KL divergence | 0.0367 | 0 (by definition) |
| Refusals | ✅ 13/100 | ❌ 99/100 |
Original:
============================================================
Total questions: 7021
Correct: 5024
Accuracy: 0.7156 (71.56%)
Parse failures: 313
============================================================
Tested subject scores:
Heretic:
============================================================
Total questions: 7021
Correct: 5016
Accuracy: 0.7144 (71.44%)
Parse failures: 346
============================================================
Tested subject scores:
MMLU - Massive Multitask Language Understanding, multiple-choice questions across 57 subjects (math, history, law, medicine, etc.).
GGUF quantizations available here llmfan46/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-uncensored-heretic-GGUF.
This is the full-precision
safetensorsmaster for my Gemma 4 12B coding + agentic fine-tune — the same model many of you have been running as GGUF, now in its original weights. 🧠🛠️ v2 is the big agentic upgrade: it reads, reasons, uses tools, and works through multi-step technical tasks before it acts. This repo is for builders — roll your own quants, fine-tune further, or run it intransformers.
A huge thank-you for all the attention this project has gotten — really, thank you. 🙏 I only managed to get out tonight to upload the full-precision original (safetensors master) of this model, so sorry for the wait — I'd planned to put it up last week. But the delay comes with two big surprises I've been dying to share:
1. v3 is coming soon. 🔮 The next version is on its way and will fix several of the known issues you've reported.
2. I'm now working with a top-tier AI lab to give back to the open-source community. 🤝 Many of you have already noticed the side effects in v1 and v2 — and honestly they come down to just two things: (1) not enough compute, and (2) one person with limited expertise behind the whole thing. This collaboration solves both of those completely. And the benchmarks you care about will absolutely be addressed — the things I simply couldn't fully pull off before because of time and compute limits. The people working on this with me are PhDs from top universities, with seriously strong papers and citation records. Just think about that for a second: the people who actually build large models are now contributing to the open-source community together with me — that is genuinely wild. 🤯 We're in active discussions right now, and the project is still in the R&D phase, so I can't share specifics yet — but the moment I have news, you'll be the first to know. 🚀
This repo holds the un-quantized master weights (model.safetensors, bf16). Use it to:
transformers (needs a recent build with gemma4_unified support).🏃 Just want to run it? You don't need this repo — grab a ready-made quant from the GGUF repo → (runs in ~4.5 GB of VRAM / unified memory in LM Studio, Ollama, llama.cpp, Jan…). This master is for builders. 💚
v2 is built for coding + agentic work — writing code, running commands, using tools, debugging, multi-step
technical tasks. The clearest signal is tau2-bench telecom, an agentic tool-use benchmark whose
diagnose → fix → verify loop mirrors real terminal/debugging work:
| tau2-bench telecom · 20 tasks · local, same harness, all Q8_0 | score |
|---|---|
official gemma-4-12B-it (base) | ~15% |
| 🟢 Gemma4-12B v2 (this model) | ~55% |
→ Roughly 3.5× higher than the base model on technical-agentic tasks. 🎯
🔬 Honest methodology: these are local, same-harness, relative numbers (all models tested at Q8_0, greedy decoding, self-simulated user, 20 tasks). They are not directly comparable to published tau2-bench leaderboard figures (different user-simulator, full task sets, full precision) — local self-eval runs systematically lower than published scores. Read them as "v2 vs the base model under identical conditions", which is the comparison that actually matters here.
Grounded, not made-up. A coding/terminal fabrication probe (tasks that deliberately tempt the model to invent
file paths / function signatures / values) found v2 grounds before it acts just like the base — it grep/read/ls
first, and doesn't make things up (0% fabrication, on par with the base).
The trade-off — no free lunch. On a general-knowledge benchmark (MMLU-Pro), v2 lands a little below the base —
completely normal for a focused fine-tune: you trade a sliver of broad-knowledge breadth for coding + agentic strength.
Need a generalist? Try my general-purpose
Claude Opus 4.6/4.8 distillation or the
base google/gemma-4-12B-it. Need a local coding/agentic worker? That's what v2 is tuned for. 💚
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torchrepo = "yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")msgs = [{"role": "user", "content": "Write a Python function to check if a string is a valid IPv4 address."}]inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=1024)print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
🧠 Thinking mode: it thinks in Gemma's native thought channel before answering (keep
enable_thinking=true, the default chat template handles it). Recommended sampling:temp 1.0, top_p 0.95, top_k 64; for coding you can also go greedy (temp 0). Needs a recenttransformersthat knows thegemma4_unifiedarchitecture.🛠️ Agentic / tool use: v2 emits structured tool-calls in Gemma 4's native protocol. The smoothest agent setup is a GGUF quant served with llama.cpp
--jinja(pass your tools via the OpenAItoolsfield) — see the GGUF repo for the full command.
All from the GGUF repo:
| Quant | Size | Vibe |
|---|---|---|
| 🟡 Q3_K_M | 5.7 GB | great for 8 GB VRAM |
| 🔵 Q4_K_M | 6.87 GB | the sweet spot 👌 (recommended) |
| 🟣 Q6_K | 9.11 GB | near-lossless |
⚠️ GGUF needs a recent llama.cpp — this is the
gemma4_unifiedarchitecture, older builds won't load it. ℹ️ No Q2_K this release — it didn't pass real stress-testing (2-bit is too lossy for 12B coding). Smallest reliable quant = Q3_K_M.
v2 continues from the v1 coder and adds a big agentic push — the piece v1 was missing:
All reasoning is distilled CoT. A bittersweet note: none of us saw it coming that Fable 5 would be retired, and only my own dataset holds Fable 5's genuine, self-authored traces — so for the community-contributed data I rebuilt the missing reasoning from scratch with Opus 4.8 (xhigh). It may diverge from the original Fable 5 traces, but it was the only workable path — and the improvement turned out really huge. 💚
The GGUF repo's MTP/ folder ships the Gemma 4 multi-token-prediction draft (unsloth's GGUF conversion of Google's
official gemma-4-12B-it-assistant) for speculative decoding. Gemma 4 MTP is in llama.cpp mainline (PR #23398) — no
fork needed — but the gemma4-assistant loader is build-sensitive right now, so use the exact build below:
b9553 (commit 9e3b928fd). Reproduced with gemma4-v2-Q8_0 + the MTP-Q8_0
draft: loads cleanly and accelerates generation (~88 → ~180 tok/s on a simple deterministic prompt; expect ~1.2–1.3×
on real coding/thinking). Lossless either way.invalid vector subscript — an
upstream regression in the gemma4-assistant loader path, not a problem with the GGUFs. Stick with b9553
until it's fixed upstream.bat
llama-server -m gemma4-v2-Q8_0.gguf ^--model-draft MTP\gemma-4-12B-it-MTP-Q8_0.gguf ^--spec-type draft-mtp --spec-draft-n-max 4 ^-ngl 99 -ngld 99 -fa on --jinja
ℹ️ The draft is the generic Gemma 4 assistant (not retrained for v2), so acceptance is a touch lower than a model-specific draft would give — still 100% lossless.
google/gemma-4-12B-it.| mlp.down_proj.min_weight_distance | 18.44 |
| ⚪ Q8_0 | 11.8 GB | basically full quality |