Text Generation
Safetensors
GGUF
Rust
English
Vietnamese
dioxus
accessibility
wcag
fine-tuned
raft
code
server-functions
qwen3
family-hub
scoped-css
syncstore
conversational
Instructions to use rockypod/neotoi-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use rockypod/neotoi-coder with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="rockypod/neotoi-coder", filename="neotoi-coder-v1-q4_k_m_final.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use rockypod/neotoi-coder with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf rockypod/neotoi-coder:Q4_K_M # Run inference directly in the terminal: llama-cli -hf rockypod/neotoi-coder:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf rockypod/neotoi-coder:Q4_K_M # Run inference directly in the terminal: llama-cli -hf rockypod/neotoi-coder:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf rockypod/neotoi-coder:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf rockypod/neotoi-coder:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf rockypod/neotoi-coder:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf rockypod/neotoi-coder:Q4_K_M
Use Docker
docker model run hf.co/rockypod/neotoi-coder:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use rockypod/neotoi-coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rockypod/neotoi-coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rockypod/neotoi-coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rockypod/neotoi-coder:Q4_K_M
- Ollama
How to use rockypod/neotoi-coder with Ollama:
ollama run hf.co/rockypod/neotoi-coder:Q4_K_M
- Unsloth Studio
How to use rockypod/neotoi-coder with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rockypod/neotoi-coder to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rockypod/neotoi-coder to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rockypod/neotoi-coder to start chatting
- Pi
How to use rockypod/neotoi-coder with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf rockypod/neotoi-coder:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "rockypod/neotoi-coder:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use rockypod/neotoi-coder with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf rockypod/neotoi-coder:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default rockypod/neotoi-coder:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use rockypod/neotoi-coder with Docker Model Runner:
docker model run hf.co/rockypod/neotoi-coder:Q4_K_M
- Lemonade
How to use rockypod/neotoi-coder with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull rockypod/neotoi-coder:Q4_K_M
Run and chat with the model
lemonade run user.neotoi-coder-Q4_K_M
List all available models
lemonade list
Family hub README: rebrand 14B variant as 15B (matches Ollama tag); drop legacy framing — all three sizes are co-equal current variants
Browse files
README.md
CHANGED
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pipeline_tag: text-generation
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---
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# Neotoi Coder
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A Rust / Dioxus 0.7 specialist LLM. v3.1 ships in **three sizes**
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| Variant | Repo | Base | Params | Q4_K_M | Spec exam (104Q weighted, max 144.5) |
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|---|---|---|---|---|---|
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| **8B** (flagship) | [`rockypod/neotoi-coder-8b`](https://huggingface.co/rockypod/neotoi-coder-8b) | Qwen3-8B | 8.2B (6.95B non-embed) | 4.68 GB | **144.5 / 144.5 — 100.00%** |
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| 4B | [`rockypod/neotoi-coder-4b`](https://huggingface.co/rockypod/neotoi-coder-4b) | Qwen3-4B | 4.0B (3.6B non-embed, tied) | 2.33 GB | 143.5 / 144.5 — 99.31% |
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All three clear the 90% publication bar **and** the 95% release bar with all per-tier floors PASS. The **8B is the recommended default**; pick the **4B** if disk / RAM is tight (or for ~40% faster generation), pick the **
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> Each variant lives in its **own model repo**
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## Install via Ollama
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# 4B — disk / RAM constrained, ~40% faster generation
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ollama pull rockypod/neotoi-coder:4b
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#
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ollama pull rockypod/neotoi-coder:15b
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```
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Re-graded 2026-04-26 with the patched `run_grade_v31.py` (Q87 now also accepts `LANG()` / `THEME()` GlobalSignal accessor calls in addition to the literal `Signal` token — a false-negative fix that unlocked the 8B's perfect score).
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|---|---|---|---|---|
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| T1 Fundamentals | 12.0 | 12.0 ✅ | 11.0 ⚠️ 91.7% | 12.0 ✅ |
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| T2 RSX Syntax | 12.0 | 12.0 ✅ | 12.0 ✅ | 10.0 ⚠️ 83.3% |
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## What's new in v3.1 (vs v3.0)
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- **
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- **T1 Fundamentals → 100%** on 8B and
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- **T6 Hard Reasoning → 100%** clean sweep, all three variants (+25 pts vs v3.0
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- **T8 GlobalSignal / i18n → 100%** all three variants.
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- **T10 Dioxus 0.7.4 → 100%** all three variants.
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- **8 tiers at 100%** on the
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- **Dataset:** 4,880 curated examples across 43 topics (up from 4,535).
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## Version History
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| v1.0 | Qwen3-Coder-14B (14.8B) | 51/60 (85.0%) | 60Q standard | — |
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| v2.0 | Qwen3-Coder-14B (14.8B) | 135.5/140 (96.8%) | 100Q weighted | 4,185 |
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| v3.0 | Qwen3-Coder-14B (14.8B) | 124.0/144.5 (85.8%) | 103Q weighted | 4,535 |
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| v3.1
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| **v3.1 8B** | **Qwen3-8B (8.2B)** | **144.5/144.5 (100.00%)** | **103Q weighted** | **4,880** |
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| v3.1 4B | Qwen3-4B (4.0B, tied) | 143.5/144.5 (99.31%) | 103Q weighted | 4,880 |
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## Files in this repo (`rockypod/neotoi-coder`,
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| File | Format | Size | Use case |
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|---|---|---|---|
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| `neotoi-coder-v3.1-q4_k_m.gguf` | GGUF Q4_K_M | 8.4 GB | LM Studio, llama.cpp, Ollama (current
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| `neotoi-coder-v3-q4_k_m_patched.gguf` | GGUF Q4_K_M | 9 GB | v3.0
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| `neotoi-coder-v2.0-q4_k_m.gguf` | GGUF Q4_K_M | 9 GB | v2.0
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| `neotoi-coder-v1-q4_k_m_final.gguf` | GGUF Q4_K_M | 9 GB | v1.0
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For the **8B** and **4B** Q4_K_M GGUFs,
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- https://huggingface.co/rockypod/neotoi-coder-8b
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- https://huggingface.co/rockypod/neotoi-coder-4b
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| Before Assistant | `<\|im_start\|>assistant\n<think>` |
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| After Assistant | `<\|im_end\|>` |
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### Ollama (custom Modelfile,
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```Modelfile
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FROM neotoi-coder-v3.1-q4_k_m.gguf
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## Known Limitations
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- **rsx! macro drops on the
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- **Non-Dioxus web frameworks** — out of scope by design (SvelteKit coverage lives in `rockypod/svcoder`).
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- **Playwright / E2E testing** — out of scope.
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## Transparency
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- **Per-variant weights:** [`-8b`](https://huggingface.co/rockypod/neotoi-coder-8b) · [`-4b`](https://huggingface.co/rockypod/neotoi-coder-4b) · this repo (
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- **Exam runner, grader, per-question results:** [GitHub — rockypod/neotoi-coder](https://github.com/rockypod/neotoi-coder)
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- **Ollama:** `ollama pull rockypod/neotoi-coder:8b` (or `:4b`, or `:15b`)
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pipeline_tag: text-generation
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---
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# Neotoi Coder
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A Rust / Dioxus 0.7 specialist LLM. v3.1 ships in **three sizes** — 15B, 8B,
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and 4B — all fine-tuned via RAFT (Retrieval-Augmented Fine-Tuning) on Qwen3
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base models. Optimized for production-quality Dioxus 0.7 components with
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Tailwind v4 and WCAG 2.2 AAA accessibility.
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All three are current. They were trained from the same v3.1 dataset, exam
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the same way, and ship together. Pick based on hardware, not currency.
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## Variants
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| Variant | Repo | Base | Params | Q4_K_M | Spec exam (104Q weighted, max 144.5) |
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| **8B** (flagship) | [`rockypod/neotoi-coder-8b`](https://huggingface.co/rockypod/neotoi-coder-8b) | Qwen3-8B | 8.2B (6.95B non-embed) | 4.68 GB | **144.5 / 144.5 — 100.00%** |
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| 4B | [`rockypod/neotoi-coder-4b`](https://huggingface.co/rockypod/neotoi-coder-4b) | Qwen3-4B | 4.0B (3.6B non-embed, tied) | 2.33 GB | 143.5 / 144.5 — 99.31% |
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| 15B | this repo (`rockypod/neotoi-coder`) | Qwen3-Coder-14B | 14.8B (13.2B non-embed) | 8.40 GB | 137.0 / 144.5 — 94.81% |
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All three clear the 90% publication bar **and** the 95% release bar with all per-tier floors PASS. The **8B is the recommended default**; pick the **4B** if disk / RAM is tight (or for ~40% faster generation), pick the **15B** for the broadest coverage and the most context-rich generations.
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> Each variant lives in its **own model repo** for searchability. This page (`rockypod/neotoi-coder`) is the family hub *and* hosts the 15B GGUFs.
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## Install via Ollama
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# 4B — disk / RAM constrained, ~40% faster generation
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ollama pull rockypod/neotoi-coder:4b
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# 15B — largest, broadest coverage
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ollama pull rockypod/neotoi-coder:15b
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```
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Re-graded 2026-04-26 with the patched `run_grade_v31.py` (Q87 now also accepts `LANG()` / `THEME()` GlobalSignal accessor calls in addition to the literal `Signal` token — a false-negative fix that unlocked the 8B's perfect score).
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| Tier | Max wt | 8B | 4B | 15B |
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|---|---|---|---|---|
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| T1 Fundamentals | 12.0 | 12.0 ✅ | 11.0 ⚠️ 91.7% | 12.0 ✅ |
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| T2 RSX Syntax | 12.0 | 12.0 ✅ | 12.0 ✅ | 10.0 ⚠️ 83.3% |
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## What's new in v3.1 (vs v3.0)
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- **Three sizes**: 8B and 4B alongside the 15B base, both surpassing the 15B's score.
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- **T1 Fundamentals → 100%** on 8B and 15B, 91.7% on 4B (+8.3 pts vs v3.0).
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- **T6 Hard Reasoning → 100%** clean sweep, all three variants (+25 pts vs v3.0).
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- **T8 GlobalSignal / i18n → 100%** all three variants.
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- **T10 Dioxus 0.7.4 → 100%** all three variants.
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- **8 tiers at 100%** on the 15B; **11 tiers at 100%** on the 8B (perfect).
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- **Dataset:** 4,880 curated examples across 43 topics (up from 4,535).
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## Version History
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| v1.0 | Qwen3-Coder-14B (14.8B) | 51/60 (85.0%) | 60Q standard | — |
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| v2.0 | Qwen3-Coder-14B (14.8B) | 135.5/140 (96.8%) | 100Q weighted | 4,185 |
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| v3.0 | Qwen3-Coder-14B (14.8B) | 124.0/144.5 (85.8%) | 103Q weighted | 4,535 |
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| v3.1 15B | Qwen3-Coder-14B (14.8B) | 137.0/144.5 (94.81%) | 103Q weighted | 4,880 |
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| **v3.1 8B** | **Qwen3-8B (8.2B)** | **144.5/144.5 (100.00%)** | **103Q weighted** | **4,880** |
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| v3.1 4B | Qwen3-4B (4.0B, tied) | 143.5/144.5 (99.31%) | 103Q weighted | 4,880 |
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## Files in this repo (`rockypod/neotoi-coder`, 15B and historical)
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| File | Format | Size | Use case |
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|---|---|---|---|
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| `neotoi-coder-v3.1-q4_k_m.gguf` | GGUF Q4_K_M | 8.4 GB | LM Studio, llama.cpp, Ollama (current 15B) |
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| `neotoi-coder-v3-q4_k_m_patched.gguf` | GGUF Q4_K_M | 9 GB | v3.0 archive |
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| `neotoi-coder-v2.0-q4_k_m.gguf` | GGUF Q4_K_M | 9 GB | v2.0 archive |
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| `neotoi-coder-v1-q4_k_m_final.gguf` | GGUF Q4_K_M | 9 GB | v1.0 archive |
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For the **8B** and **4B** Q4_K_M GGUFs, see their dedicated repos:
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- https://huggingface.co/rockypod/neotoi-coder-8b
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- https://huggingface.co/rockypod/neotoi-coder-4b
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| Before Assistant | `<\|im_start\|>assistant\n<think>` |
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| After Assistant | `<\|im_end\|>` |
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### Ollama (custom Modelfile, 15B)
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```Modelfile
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FROM neotoi-coder-v3.1-q4_k_m.gguf
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## Known Limitations
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- **rsx! macro drops on the 15B** for 6 RSX-heavy questions (Q17 / 22 / 30 / 37 / 39 / 43); v3.2 target. The 8B and 4B do not reproduce these misses.
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- **Non-Dioxus web frameworks** — out of scope by design (SvelteKit coverage lives in `rockypod/svcoder`).
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- **Playwright / E2E testing** — out of scope.
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## Transparency
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- **Per-variant weights:** [`-8b`](https://huggingface.co/rockypod/neotoi-coder-8b) · [`-4b`](https://huggingface.co/rockypod/neotoi-coder-4b) · this repo (15B)
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- **Exam runner, grader, per-question results:** [GitHub — rockypod/neotoi-coder](https://github.com/rockypod/neotoi-coder)
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- **Ollama:** `ollama pull rockypod/neotoi-coder:8b` (or `:4b`, or `:15b`)
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