Instructions to use rockypod/neotoi-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 new

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 new

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

rockypod commited on Mar 25

Commit

5dc95da

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1 Parent(s): 99d2a79

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+---
+license: other
+license_name: neotoi-coder-community-license
+language:
+- en
+base_model: Qwen/Qwen3-Coder-14B
+tags:
+- dioxus
+- rust
+- accessibility
+- wcag
+- fine-tuned
+- raft
+- code
+- mlx
+pipeline_tag: text-generation
+---
+# Neotoi Coder v1
+A Rust/Dioxus 0.7 specialist fine-tuned from Qwen3-Coder-14B using RAFT
+(Retrieval-Augmented Fine-Tuning). Optimized for production-quality
+Dioxus 0.7 components with Tailwind v4 and WCAG 2.2 AAA accessibility.
+## Exam Results
+| Tier | Score | Required | Status |
+|---|---|---|---|
+| T1 Fundamentals | 9/10 | 9/10 | ✅ |
+| T2 RSX Syntax | 9/10 | 8/10 | ✅ |
+| T3 Signal Hygiene | 10/10 | 8/10 | ✅ |
+| T4 WCAG/ARIA | 9/10 | 7/10 | ✅ |
+| T5 use_resource | 4/5 | 4/5 | ✅ |
+| T6 Hard Reasoning | 2/5 | 2/5 | ✅ |
+| T7 Primitives+CSS | 8/10 | 6/10 | ✅ |
+| **Overall** | **51/60** | **50/60** | **✅ PASS** |
+## Model Details
+- **Base model:** Qwen3-Coder-14B
+- **Method:** RAFT (Retrieval-Augmented Fine-Tuning)
+- **Dataset:** 3,156 curated Dioxus 0.7 examples
+- **Scope:** Rust + Dioxus 0.7 + Tailwind v4 + WCAG 2.2 AAA
+- **Quantization:** Q4_K_M (8.38 GB)
+- **Author:** Kevin Miller, Jr.
+## Enabling Thinking Mode
+This model supports Qwen3 native thinking tokens.
+Thinking must be enabled manually depending on your inference backend.
+### LM Studio
+In the chat interface go to the prompt template settings and configure:
+| Field | Value |
+|---|---|
+| Before System | `<\|im_start\|>system` |
+| After System | `<\|im_end\|>` |
+| Before User | `<\|im_start\|>user` |
+| After User | `<\|im_end\|>` |
+| Before Assistant | `<\|im_start\|>assistant\n<think>` |
+| After Assistant | `<\|im_end\|>` |
+### Ollama
+Create a Modelfile:
+```
+FROM neotoi-coder-v1-q4_k_m_final.gguf
+PARAMETER temperature 0.2
+PARAMETER num_predict 4096
+PARAMETER repeat_penalty 1.15
+PARAMETER stop "<|im_end|>"
+TEMPLATE """<|im_start|>system
+{{ .System }}<|im_end|>
+<|im_start|>user
+{{ .Prompt }}<|im_end|>
+<|im_start|>assistant
+<think>
+"""
+SYSTEM You are Neotoi, an expert Rust and Dioxus 0.7 developer. Always think step-by-step before answering.
+```
+### llama.cpp / llama-cli
+```bash
+./llama-cli \
+  -m neotoi-coder-v1-q4_k_m_final.gguf \
+  -ngl 99 \
+  --temp 0.2 \
+  -p "<|im_start|>user\nYour question here<|im_end|>\n<|im_start|>assistant\n<think>"
+```
+## What It Knows
+- Dioxus 0.7 RSX brace syntax — never function-call style
+- `use_signal`, `use_resource` with correct three-arm match
+- `r#for` on label elements only, never inputs
+- WCAG 2.2 AAA: `aria_labelledby`, `aria_describedby`,
+  `role="alert"`, `role="dialog"`, live regions
+- dioxus-primitives — no manual ARIA on managed components
+- `styles!()` macro for CSS modules
+- Tailwind v4 utility classes
+## What It Does Not Know
+- Tier 6 hard reasoning edge cases (use_context panic behavior,
+  optimistic UI race conditions) — known weak spots
+- Playwright/E2E testing (out of scope)
+- Non-Dioxus web frameworks
+## License
+Neotoi Coder Community License v1.0 — see LICENSE file.
+Commercial use of model outputs permitted.
+Weight redistribution prohibited.
+Mental health deployment requires written permission.
+## Credits
+Built with:
+- [Unsloth](https://github.com/unslothai/unsloth) — 2x faster fine-tuning
+- [TRL](https://github.com/huggingface/trl) — SFTTrainer
+- [Qwen3-Coder-14B](https://huggingface.co/Qwen/Qwen3-Coder-14B) — base model
+- [MLX](https://github.com/ml-explore/mlx) — dataset generation on Apple Silicon
+- [Claude Code](https://claude.ai/code) — dataset pipeline and training infrastructure
+- [Dioxus](https://dioxuslabs.com) — the framework this model specializes in