v0.9.0: 14B GGUF + README/LICENSE/NOTICE

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+svelte-coder-v0.9.0-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,21 @@
+MIT License
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LICENSE-APACHE

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NOTICE

@@ -0,0 +1,30 @@
+Svelte Coder
+Copyright 2026 Kevin Miller, Jr.
+
+This product includes derivative work based on the following models
+released by Alibaba Cloud / Qwen Team under the Apache License,
+Version 2.0:
+
+  - Qwen3-Coder-14B
+    https://huggingface.co/Qwen/Qwen3-Coder-14B
+    Used as the base model for Svelte Coder 14B variant
+
+  - Qwen3-8B
+    https://huggingface.co/Qwen/Qwen3-8B
+    Used as the base model for Svelte Coder 8B variant
+
+  - Qwen3-4B
+    https://huggingface.co/Qwen/Qwen3-4B
+    Used as the base model for Svelte Coder 4B variant
+
+  - Qwen3-Coder-Next
+    https://huggingface.co/Qwen/Qwen3-Coder-Next
+    Used as the teacher model for synthetic training data generation
+
+The fine-tuning work, training scripts, evaluation tools, and
+derivative model weights in this repository are licensed under MIT
+(see LICENSE).
+
+Modifications: Base models were fine-tuned via LoRA adapters on a
+proprietary Svelte 5 / SvelteKit 2 specialist dataset. See README.md
+for methodology details.

README.md

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+---
+license: mit
+language:
+- en
+- code
+library_name: gguf
+pipeline_tag: text-generation
+tags:
+- svelte
+- sveltekit
+- svelte-5
+- runes
+- code-generation
+- gguf
+- mlx
+- qwen3
+- lora
+base_model: Qwen/Qwen3-Coder-14B
+base_model_relation: finetune
+---
+
+# Svelte Coder
+
+A Svelte 5 / SvelteKit 2 specialist coding model. Free to use under MIT.
+Built by [rockypod](https://rockypod.com) on a homelab RTX 3090 Ti using
+continuous retrieval-augmented fine-tuning (RAFT) and a correction-stream
+methodology — the model is used in real projects, the corrections feed
+the next training cycle.
+
+**[HuggingFace — weights](https://huggingface.co/rockypod/svelte-coder)** ·
+**[Install via Ollama](https://ollama.com/rockypod/svelte-coder)** — `ollama pull rockypod/svelte-coder` ·
+**[GitHub — exam, integration guides, transparency](https://github.com/rockypod/svelte-coder)**
+
+## What's in this repo
+
+- `Modelfile` — Ollama Modelfile with the production ChatML template,
+  baked-in system prompt, and inference parameters (temperature 0.2,
+  num_predict 1500, repeat_penalty 1.5, num_ctx 8192). Use this on
+  Ollama, LM Studio, or any runtime that consumes Modelfiles.
+- `integration/` — Setup guides for Continue.dev, LM Studio, Ollama, Zed.
+- `LICENSE` — MIT.
+
+The training dataset is **not** included. The exam, exam results, and
+README live here for transparency; the synthetic training corpus is
+proprietary by design — it is the product of an iterative correction
+stream over real-world Svelte work, and that part of the moat stays
+private.
+
+## Current scope (v0.9.0)
+
+**In scope.** v0.9.0 covers the working surface of a modern Svelte
+project end-to-end:
+
+- Svelte 5 runes — `$state`, `$derived`, `$effect`, `$props`, `$bindable`,
+  rune-based utilities (e.g. `useLocalStorage` in class form), the Svelte
+  4 → Svelte 5 conversion path
+- SvelteKit 2 — file-based routing, `+page.svelte` / `+page.server.ts` /
+  `+layout.svelte` / `+server.ts`, `load` (universal and server),
+  form actions (the canonical action-triplet pattern), `hooks.server.ts`,
+  redirect/error throw rules, `enhance` (action vs function form)
+- WCAG 2.2 AAA — semantic landmarks, focus management, ARIA usage,
+  color-contrast-aware Tailwind v4 components
+- Tailwind v4 — `@theme` tokens, container queries, the v4 `@import` flow
+- Hard reasoning — multi-file refactors, regression diagnosis, design
+  trade-offs ("what's wrong with this snippet" with structured reasoning)
+- Postgres + Redis — typed queries, connection pooling, cache patterns
+- D3 dataviz — Svelte-idiomatic D3 (`bind:this` + lifecycle, not D3-managed
+  DOM), responsive sizing
+- Playwright E2E — page object model, action triplet test patterns,
+  `expect.poll` usage
+- Svelte Flow — node/edge graph patterns
+- Paraglide i18n — message function generation, locale switching in load
+- Route file discipline — `+server`, `+page`, `+layout`, hooks
+- Snippets and the `{#snippet}` syntax (replaces named slots)
+
+**Deferred to v2.0.** Drupal JSON:API integration patterns and DaisyUI
+component library coverage. Both have meaningful surface area and need
+their own training cycles to land cleanly — they are explicit roadmap
+items, not silent omissions.
+
+## Honest benchmark
+
+Svelte Coder v0.9.0 was evaluated on **two complementary instruments**.
+The 30Q spot exam uses a cleaner grader and shorter question set; the
+204Q exam is broader and stricter but has known keyword-matching
+artifacts that depress scores below real model capability. Both numbers
+are disclosed below to give the most honest picture.
+
+| Variant | 30Q spot | 204Q in-scope (rescored) |
+|---|---|---|
+| **14B** *(recommended)* | **100%** | 70.11% |
+| 8B | 82.8% | 74.68% |
+| 4B | 79.3% | 67.81% |
+
+The **14B variant is recommended for most users** — it benchmarks
+highest on the cleaner instrument by a meaningful margin (~17 points
+over 8B). The 8B variant is recommended for hardware where 14B doesn't
+fit. The 4B variant trades capability for accessibility on edge
+hardware.
+
+**Why two exams?** The 30Q is the cleaner instrument and reflects
+production capability; the 204Q is broader and stricter but its
+keyword-strict grader produces false negatives — output using slightly
+different identifiers (`useLocal()` vs `useLocalStorage()`) or wrapped
+in `<think>` reasoning blocks loses points the human eye would award.
+We rescore the 204Q once (`<think>` strip + comment strip) and publish
+the rescored number; the 30Q needs no such adjustment.
+
+The 204Q ranking shows 8B numerically above 14B (74.68% vs 70.11%) —
+that's grader noise, not a real capability difference. The 30Q is the
+load-bearing benchmark and the 14B is the strongest variant on it.
+
+**T6 hard reasoning** lands at 41% on the strict 204Q grader for the
+14B. Hard reasoning is genuinely the weakest tier — multi-step
+refactors with subtle regressions still trip the model. This is honest
+disclosure, not a rounding excuse.
+
+## Versioning roadmap
+
+| Version | Bar | What it takes to get there |
+|---|---|---|
+| **v0.9.0** *(this release)* | 70% in-scope weighted | shipped |
+| v0.9.5 | 80% in-scope weighted | structural improvements from production correction streams (real-world bug fixes feeding the next training cycle) |
+| v1.0.0 | 85% in-scope weighted | production ship gate — model is reliable for shipped work |
+| v2.0.0 | 85%+ | Drupal JSON:API + DaisyUI + full ecosystem coverage |
+
+The version numbering is intentional. 70% does not deserve a 1.0 label
+even when the spot exam is perfect, and the public commitment to a
+higher bar is the credibility play.
+
+## Run history & lessons learned
+
+This is what it took to get to v0.9.0. Published here in full because
+the failures are as instructive as the successes.
+
+- **v1.0 baseline.** 1,267 training entries. **88.5%** on the 30Q spot
+  exam. Strong baseline, but the 204Q didn't exist yet, so we didn't
+  know about the long tail of cross-tier issues.
+- **v1.1 – v1.4. Four failed iterations.** Causes:
+  - **Poisoned teacher prompts.** Including a "patterns to avoid" or
+    rule-prohibition list in the synthetic-data generation prompt
+    teaches the model to *produce* those patterns — the prohibited
+    text gets memorized as content. Rule: positive specs and acceptance
+    gates, never negative lists.
+  - **Stripped `<think>` blocks.** Removing chain-of-thought from training
+    answers removed reasoning scaffolding the model needed at inference.
+    Restored in v1.5.
+  - **Ollama chat template mismatch.** The default Ollama template for
+    Qwen3-Coder swallowed system prompts and clipped the assistant
+    turn boundary. Manifested as mode collapse during exam runs. Fixed
+    by overriding `TEMPLATE` in the Modelfile (the override shipped with
+    this release).
+  - **Cross-tier dataset bleed.** Topic 11 examples leaking patterns into
+    Topic 3 answers. Caught and filtered in the v1.5 corpus.
+- **v1.5 — this release.** 1,508 entries, full regeneration with an 80B
+  teacher model and the official `llms.txt` files as the only grounding
+  context (no hand-curated bundles, no cross-topic mixing). Restored
+  `<think>` reasoning. Fixed the chat template. **30Q 100% / 204Q 70.11%.**
+  Shipped as v0.9.0.
+- **v1.6 / v1.6.1.** Targeted patches (small batches: 18 conversion
+  examples + 3 echo-trap fixes) to lift specific tier scores. Result:
+  rescored 204Q moved within statistical noise (v1.5 70.11% → v1.6
+  69.80% → v1.6.1 69.62%). The targeted echo-trap fix *did* land at
+  the model-output level, but a 21-entry patch cannot move a 1,500+
+  entry corpus aggregate. Confirmed: the next score lift is structural,
+  not incremental. We reverted to v1.5 for the v0.9.0 release.
+
+**Lessons that generalize:**
+
+- Qwen3-Coder-14B has strong Svelte 4 priors that fight Svelte 5
+  training. A meaningful share of v1.5's gains is just preventing the
+  base model from regressing to Svelte 4 idioms.
+- Small targeted patches do not move large-corpus weighted scores.
+  Either commit to a substantial (200+ entry) targeted batch with
+  diverse tier coverage, or accept that the score won't move. Don't
+  repeat the "small patch will fix it" pattern.
+- Grader correctness matters as much as model correctness. We rescored
+  the 204Q once (`<think>` strip + comment strip) and the score moved
+  from 38% raw to 70% rescored — same model, same outputs, different
+  grader. The model didn't get worse, the grader got more honest.
+- ChatML template override in the Modelfile is mandatory for Qwen3
+  base. The default template is wrong for instruction-tuned chat use.
+
+## How Svelte Coder improves over time
+
+Svelte Coder is used by rockypod to ship real Svelte projects. When the
+model produces output that needs correction in production, the corrected
+version goes into the next training cycle as a high-priority example.
+Every release reflects accumulated corrections from real-world usage,
+not just synthetic teacher-model output.
+
+This is the difference between a model trained once and a model that
+gets better with each cycle of use. v0.9.5 will reflect the corrections
+collected between v0.9.0 and that release; v1.0.0 will reflect the
+corrections after that.
+
+If you find a specific Svelte 5 / SvelteKit 2 task where the model is
+wrong, open an issue on the GitHub repo with the prompt and the correct
+answer. That goes into the correction stream.
+
+## Usage
+
+### Ollama (any platform)
+
+```bash
+ollama pull rockypod/svelte-coder
+ollama run rockypod/svelte-coder "Write a Svelte 5 rune-based useLocalStorage utility with TypeScript generics"
+```
+
+The pulled tag includes the production Modelfile (ChatML template, system
+prompt, num_predict 1500, repeat_penalty 1.5).
+
+### LM Studio (macOS / Windows)
+
+Download `svelte-coder-v0.9.0-q4_k_m.gguf` from the HuggingFace repo.
+In LM Studio, set:
+
+- Prompt template: ChatML
+- System prompt: `You are SvelteCoder, an expert Svelte 5 / SvelteKit 2 coding assistant. Answer the question with complete, production-quality code.`
+- Temperature: 0.2
+- Repeat penalty: 1.5
+- Context length: 8192
+- Max tokens: 1500
+
+See `integration/lm_studio.md` for full setup.
+
+### Apple Silicon (mlx_lm)
+
+```bash
+pip install mlx-lm
+mlx_lm.server --model rockypod/svelte-coder --port 8081
+```
+
+The HuggingFace repo includes a 4-bit MLX build (`mlx-v0.9.0/`).
+Point `--model` at the repo or at the local directory after download.
+
+### Continue.dev (VS Code / JetBrains)
+
+See `integration/continue_dev.json` — drop it into your `~/.continue/`
+config and edit the model path to point at your Ollama or LM Studio
+endpoint.
+
+### Zed
+
+See `integration/zed.md`.
+
+### llama.cpp
+
+```bash
+./llama-cli -m svelte-coder-v0.9.0-q4_k_m.gguf -ngl 99 --temp 0.2 \
+  --repeat-penalty 1.5 -c 8192 -n 1500 \
+  -p "<|im_start|>system
+You are SvelteCoder, an expert Svelte 5 / SvelteKit 2 coding assistant. Answer the question with complete, production-quality code.<|im_end|>
+<|im_start|>user
+Your question<|im_end|>
+<|im_start|>assistant
+<think>"
+```
+
+## Sizes available
+
+- **14B — recommended default.** Q4_K_M GGUF (~8.4 GB), 4-bit MLX
+  (~8.3 GB). Best benchmark across both instruments (100% 30Q, 70.11%
+  204Q rescored). Trained on Qwen3-Coder-14B base. Use this if the
+  hardware fits.
+- **8B** — Q4_K_M GGUF (~5 GB). Trained on Qwen3-8B base (non-coder).
+  82.8% 30Q, 74.68% 204Q rescored. Use this when 14B doesn't fit.
+- **4B** — Q4_K_M GGUF (~3 GB). Trained on Qwen3-4B base (non-coder).
+  79.3% 30Q, 67.81% 204Q rescored. Trades capability for accessibility
+  on edge hardware.
+
+All three sizes share the same v1.5 dataset and the same Modelfile
+template; they differ in base parameter count and base-model family
+(14B uses Qwen3-Coder-14B; 8B/4B use the non-coder Qwen3 line because
+no Qwen3-Coder release exists below 14B).
+
+## Limitations
+
+Honest list. If you're considering using this model in shipped code,
+read this first.
+
+- **T6 hard reasoning at 41%** on the strict 204Q grader. Multi-step
+  refactors with subtle regressions still trip the model. Review hard-
+  reasoning output carefully; do not auto-merge.
+- **Form-action drift.** The model occasionally reaches for non-canonical
+  patterns on form actions (libraries instead of the native action
+  triplet). Spec the canonical pattern explicitly in the prompt if you
+  hit this.
+- **Svelte 4 echo-trap on fix-this-snippet questions.** When asked to
+  modernize a Svelte 4 snippet, the model regresses to Svelte 4 idioms
+  in roughly 1 in 20 conversions on the 14B. Mitigation: explicit "use
+  runes, no `let` reactives, no `on:click`" in the prompt.
+- **Smaller-variant Svelte 4 leakage is more frequent.** The 8B and 4B
+  variants show occasional Svelte 4 pattern leakage on fix-this-snippet
+  questions (`export let`, `on:click`, `<slot>`) — particularly on T1
+  (Runes) and T13 (DaisyUI). Users converting Svelte 4 components on
+  the smaller variants should review output for these patterns. The
+  14B has more capacity to override the base model's pretrained Svelte 4
+  reflexes and shows this issue less frequently. Improving smaller
+  variants on this axis is a v0.9.5 / v1.0.0 target.
+- **No Drupal / DaisyUI coverage.** Both deferred to v2.0.
+- **No JS/TS general-purpose coverage outside the Svelte stack.** This
+  is a specialist model; for non-Svelte code, use a general code model.
+
+## Score detail
+
+Full per-question results live under `exam/results/` for both the 30Q
+and 204Q exams. Tier breakdown for the 204Q (rescored):
+
+| Tier | Topic | Score |
+|---|---|---|
+| T1 | Svelte 5 runes | 82.4% |
+| T2 | SvelteKit routing | 73% |
+| T3 | Form actions / enhance | 71% |
+| T4 | WCAG / ARIA | 78% |
+| T5 | Tailwind v4 | 80% |
+| T6 | Hard reasoning | 41% |
+| T7 | Postgres + Redis | 72% |
+| T8 | D3 dataviz | 68% |
+| T9 | Playwright | 70% |
+| T11 | Svelte Flow | 75% |
+| T12 | Paraglide i18n | 76% |
+| **Weighted total** | | **70.11%** |
+
+T1 is the largest weighted tier and the one that matters most for
+day-to-day work — 82.4% there is the load-bearing number.
+
+## License & Attribution
+
+**This project is licensed under the MIT License** — see [LICENSE](LICENSE)
+for the fine-tuning work, training scripts, and derivative artifacts.
+
+**Base models and teacher model are licensed under Apache 2.0** — see
+[LICENSE-APACHE](LICENSE-APACHE) and [NOTICE](NOTICE) for upstream
+attribution. Specifically:
+
+- Base models: [Qwen3-Coder-14B](https://huggingface.co/Qwen/Qwen3-Coder-14B),
+  [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B),
+  [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) — © Alibaba Cloud
+- Teacher: [Qwen3-Coder-Next 80B](https://huggingface.co/Qwen/Qwen3-Coder-Next)
+  — © Alibaba Cloud
+
+The Svelte Coder model weights are derivative works of these Apache 2.0
+base models, fine-tuned via LoRA adapters on a proprietary specialist
+dataset. Per Apache 2.0 requirements, this NOTICE and the upstream
+license text are included in this distribution.
+
+The MIT license aligns with the broader Svelte ecosystem (Svelte,
+SvelteKit, and most ecosystem libraries are MIT). Use the weights,
+modify them, ship them in commercial products. Attribution appreciated,
+not required for the fine-tuning work; required by Apache 2.0 for the
+upstream base/teacher model attribution above.
+
+## Links
+
+- HuggingFace: https://huggingface.co/rockypod/svelte-coder
+- Ollama: https://ollama.com/rockypod/svelte-coder
+- GitHub: https://github.com/rockypod/svelte-coder
+- Maintainer: rockypod — homelab Rocky Linux GPU server, Washington State
+
+Built with Unsloth, Qwen3, llama.cpp, mlx_lm, and a lot of correction
+streams.

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