Duplicate from dhafapermadi/Blueboop-Jigsaw-1.8

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README.md

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+---
+license: apache-2.0
+base_model: google/gemma-4-12B-it
+library_name: gguf
+pipeline_tag: text-generation
+tags: [gemma4, coding, code, reasoning, thinking, gguf, llama.cpp, local-llm]
+---
+
+# 💻 Blueboop Jigsaw (GGUF)
+### 🐣 Tiny footprint, big brain — a local **coding** model for *everyone*
+
+> **No matter your GPU. No matter your RAM.** If you've got **~4.5 GB** of VRAM *or* unified memory free,
+> you can run your own private, offline coding assistant right now. 🚀
+> This is the **v1 / code edition** — distilled from **real chain-of-thought** so it *thinks through* a problem
+> before writing the solution. 🧠💻 All local, all yours, no API, no cloud.
+
+### 🎯 What it is
+A focused fine-tune of Gemma 4 12B on **verifiable Python coding** data — every training example's reasoning leads to
+code that **actually passed its tests**. The result reasons in the open (edge cases, complexity, approach) and then
+emits a clean, runnable solution. 💚
+
+---
+
+## 📌 Announcements
+
+**🚀🔥 BIG NEWS — v2 drops EARLY!** I'm pushing it **ahead of schedule: tomorrow, 5–8 PM (US Pacific), sharp.** ⏰
+It lands in **both** formats at once, in **two repos** — **GGUF** (ready to run) **+ the full `safetensors` master**
+(build / fine-tune on top). v2 is **agentic + coding** focused — the piece v1 was missing.
+
+**A sneak peek 👀 (yep, I'm spoiling it early).** When I saw v2's **tau2-bench `telecom`** result — an agentic tool-use
+benchmark where the model has to *diagnose → fix → verify*, exactly like real terminal/debugging work — I literally got
+**launched out of my chair** (…okay, *kidding* 😄). The jump in **actually solving the problem** is wild:
+
+| tau2-bench **telecom** · local, same harness, **Q8_0** | score |
+|---|---|
+| official `gemma-4-12B-it` (base) | **~15%** |
+| 🟢 **v2 (dropping tomorrow)** | **~55%** |
+
+The base model tends to **give up early** (hands the problem off to a human); **v2 keeps going** and works it the way a
+much bigger model would. Full benchmark details land in the v2 card tomorrow. 🔧
+
+**✅ safetensors master (this v1 model) is UP.** Full-precision weights are live →
+**[dhafapermadi/blueboop-jigsaw-1.8](https://huggingface.co/dhafapermadi/blueboop-jigsaw-1.8)**
+— roll your own GGUF / MLX / AWQ quants or fine-tune straight from the master. 🎉
+
+---
+
+## 📣 Context length fixed: now **256K** (was 131K) — thanks, community! 💚
+
+A community member spotted that this model was reporting only a **131K** context window. That turned out to be
+the well-known upstream **Gemma 4 metadata bug** — Google's initial `config.json` shipped with
+`max_position_embeddings: 131072` instead of the real **262144 (256K)**, and that value got baked into a lot of
+downstream finetunes and quants (including this one) before it was fixed upstream.
+
+The weights were always fine — it was purely a metadata field. **All GGUF quants have been re-patched to the
+full 256K context** (`gemma4.context_length = 262144`). Just re-download if you grabbed an earlier copy. 🙏
+
+---
+
+## 📚 Training data (the interesting part 🍳)
+
+This is a **distillation** of two complementary chain-of-thought sources, both over verifiable Python coding tasks
+(algorithmic / function-level problems that come with deterministic tests):
+
+- **🥇 Main set — Composer 2.5 *real* CoT.** Genuine, model-authored reasoning traces. The teacher solved each problem,
+  its code was **run against the task's tests, and only the passing solutions were kept**. So the reasoning you're
+  learning from leads to code that *actually works*.
+- **🥈 Aux set — Fable 5 (released today! 🎉).** A clever twist: we took the problems where **Composer 2.5 got it wrong**
+  and handed them to **Fable 5** to *redo* — re-deriving a fresh, self-consistent chain-of-thought and a correct
+  solution, again **gated on passing the tests**. This recovers the hard cases the main teacher missed. These traces
+  are **synthetic** (rationalized CoT), and are tagged separately so the two sources stay distinguishable.
+
+The recipe: real CoT for the bulk of solid coverage, plus synthetic "second-attempt" CoT to patch the failures —
+both verified by execution before anything entered training. ✅
+
+---
+
+## 📦 Pick your size (GGUF quants)
+
+| Quant | Size | Vibe |
+|------|------|------|
+| 🟢 **Q2_K** | **4.5 GB** | tiniest — runs almost anywhere |
+| 🟡 **Q3_K_M** | **5.7 GB** | great for 8 GB VRAM — much better than Q2 |
+| 🔵 **Q4_K_M** | **6.87 GB** | the sweet spot 👌 (recommended) |
+| 🟣 **Q6_K** | **9.11 GB** | near-lossless |
+| ⚪ **Q8_0** | **11.8 GB** | basically full quality |
+
+---
+
+## 🧮 "Will it fit?" — context length cheat-sheet
+
+Rough estimates 🤓 (assumes `q8_0` KV cache + ~1.5 GB overhead; **use `q4_0` KV cache for ≈2× more context!**).
+Max context is **256K**. "—" = won't fit, pick a smaller quant. ✂️
+
+| Your VRAM / unified mem | 🟢 Q2_K (4.5G) | 🟡 Q3_K_M (5.7G) | 🔵 Q4_K_M (6.87G) | 🟣 Q6_K (9.11G) | ⚪ Q8_0 (11.8G) |
+|---|---|---|---|---|---|
+| **8 GB**  | ~16K ctx | ~10K | tight (~2–4K) | — | — |
+| **12 GB** | ~48K | ~38K | ~30K | ~12K | — |
+| **16 GB** | ~80K | ~72K | ~64K | ~44K | ~22K |
+| **24 GB** | ~200K | ~160K | ~128K | ~110K | ~88K |
+| **32 GB** | 256K (max) 🎉 | 256K | 256K | ~230K | ~190K |
+
+> 💡 Apple Silicon / integrated GPUs with **unified memory** count too — same numbers, just slower than a dGPU.
+> 💡 Low on room? Drop a quant or switch KV cache to `q4_0` and your context roughly doubles.
+
+---
+
+## 🚀 How to run it (super easy)
+
+### Option A — llama.cpp (recommended) 🦙
+1. Grab a quant above (e.g. `…-Q4_K_M.gguf`) and `llama-server` from [llama.cpp](https://github.com/ggml-org/llama.cpp).
+   > ⚠️ Needs a **recent llama.cpp** (this is the `gemma4_unified` architecture — older builds won't load it).
+2. Run a server (Windows `.bat` shown — tweak `--port`, `--ctx-size` to taste):
+
+```bat
+@echo off
+cd /d C:\llama.cpp
+llama-server.exe ^
+  -m C:\models\gemma4-coding-Q4_K_M.gguf ^
+  --ctx-size 16384 ^
+  --n-gpu-layers 99 ^
+  --no-mmap ^
+  -fa on ^
+  --cache-type-k q8_0 --cache-type-v q8_0 ^
+  --temp 1.0 --top-p 0.95 --top-k 64 ^
+  --host 0.0.0.0 --port 18080
+pause
+```
+3. Open `http://localhost:18080` and chat. 🎉  (Tip: bump `--ctx-size` per the table; use `q4_0` KV for more.)
+
+### Option B — one-click apps 🖱️
+Works in **LM Studio**, **Jan**, **Ollama**, etc. — just import the GGUF, pick your quant, go. 🐾
+
+### 🧠 Thinking mode
+This model thinks in Gemma's native thought channel before answering — exactly how it was trained. 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`) for more deterministic solutions.
+
+---
+
+## ⚠️ Good to know
+- **Reduced refusals:** the training data is task-focused with no safety hedging, so this refuses less than the base
+  model. It is **not** safety-aligned — add your own guardrails for production. Use responsibly. 🙏
+- Specialized for **Python / algorithmic** coding. Reasoning quality is strongest in that domain; general-knowledge
+  facts/numbers should still be double-checked.
+- English-centric.
+
+---
+
+## 📚 Base & License
+- **License: Apache 2.0.** Gemma 4 is released by Google under
+  **[Apache 2.0](https://ai.google.dev/gemma/apache_2)** (unlike the older Gemma 1/2/3 terms), so this fine-tune is
+  **Apache 2.0** too — free to use, modify, and redistribute. 🎉
+- **Base model:** [`google/gemma-4-12B-it`](https://huggingface.co/google/gemma-4-12B-it).
+- Personal/hobby project — shared as-is, no warranty. Have fun, and happy hacking! 🐾✨

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