Instructions to use rockypod/neotoi-coder-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rockypod/neotoi-coder-8b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rockypod/neotoi-coder-8b",
	filename="neotoi-coder-v3.1-8b-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rockypod/neotoi-coder-8b 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-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rockypod/neotoi-coder-8b: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-8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rockypod/neotoi-coder-8b: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-8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rockypod/neotoi-coder-8b: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-8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rockypod/neotoi-coder-8b:Q4_K_M

Use Docker

docker model run hf.co/rockypod/neotoi-coder-8b:Q4_K_M

LM Studio
Jan

vLLM

How to use rockypod/neotoi-coder-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rockypod/neotoi-coder-8b"
# 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-8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rockypod/neotoi-coder-8b:Q4_K_M

Ollama
How to use rockypod/neotoi-coder-8b with Ollama:
```
ollama run hf.co/rockypod/neotoi-coder-8b:Q4_K_M
```

Unsloth Studio

How to use rockypod/neotoi-coder-8b 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-8b 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-8b 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-8b to start chatting

How to use rockypod/neotoi-coder-8b 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-8b: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-8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rockypod/neotoi-coder-8b 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-8b: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-8b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use rockypod/neotoi-coder-8b with Docker Model Runner:
```
docker model run hf.co/rockypod/neotoi-coder-8b:Q4_K_M
```

Lemonade

How to use rockypod/neotoi-coder-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rockypod/neotoi-coder-8b:Q4_K_M

Run and chat with the model

lemonade run user.neotoi-coder-8b-Q4_K_M

List all available models

lemonade list

Neotoi Coder v3.2 — 8B

A Rust / Dioxus 0.7 specialist fine-tuned from Qwen3-8B (8.2B parameters, 6.95B non-embedding) using RAFT (Retrieval-Augmented Fine-Tuning). Optimized for production-quality Dioxus 0.7 components with Tailwind v4 and WCAG 2.2 AAA accessibility.

This is the 8B variant of the v3.2 release. Companion repos: 4B (rockypod/neotoi-coder-4b) · 15B family hub (rockypod/neotoi-coder)

v3.2 Exam Results — 114Q Dioxus 0.7 Spec Exam

160.0 / 164.0 weighted | 111 / 114 raw | 97.56%

Tier	Name	Cnt	Raw	Wtd	/Max	Rate	Floor	Status
T1	Fundamentals	12	12	12.0	12.0	100.0%	82%	✅
T2	RSX Syntax	12	11	11.0	12.0	91.7%	82%	✅
T3	Signal Hygiene	12	12	12.0	12.0	100.0%	82%	✅
T4	WCAG / ARIA	15	15	22.5	22.5	100.0%	82%	✅
T5	use_resource	8	8	12.0	12.0	100.0%	82%	✅
T6	Hard Reasoning	10	10	20.0	20.0	100.0%	88%	✅
T7	Primitives + CSS	13	13	19.5	19.5	100.0%	82%	✅
T8	GlobalSignal / i18n	8	7	10.5	12.0	87.5%	82%	✅
T9	Static Navigator	6	6	9.0	9.0	100.0%	82%	✅
T10	Dioxus 0.7.4	6	6	12.0	12.0	100.0%	88%	✅
T11	Server Functions	4	4	6.0	6.0	100.0%	82%	✅
T12	Format Compliance (NEW)	6	6	12.0	12.0	100.0%	88%	✅
T13	SyncStore (NEW)	2	1	1.5	3.0	50.0%	82%	⚠️
Total		114	111	160.0	164.0	97.56%	—	—

Publication bar (90%): PASS
Release bar (95%): PASS
Tier floors: FAIL (T13 only — structural: 2 questions, single real miss = 50%)

3 misses: q022 (T2, rsx! macro), q087 (T8, use_signal), q113 (T13, tokio::spawn)

The T13 SyncStore floor failure is structural — only 2 questions in the tier means any single real miss equals a floor failure regardless of difficulty.

v3.2 vs v3.1 (8B)

Metric	v3.1 8B	v3.2 8B
Score	144.5/144.5 (100.0%)	160.0/164.0 (97.56%)
Exam	103Q, max 144.5, 11 tiers	114Q, max 164.0, 13 tiers
T4 WCAG / ARIA	100.0%	100.0% ✅
T7 Primitives + CSS	100.0%	100.0% ✅ (15B was 92.3%)
T12 Format Compliance	—	100.0% ✅ (15B was 83.3%)
T13 SyncStore	—	50.0% ⚠️
Dioxus surface	0.7.0–0.7.4	0.7.0–0.7.9
Dataset	4,880 rows, 43 topics	5,287 rows, 57 topics

The 8B v3.2 outscores the 15B v3.2 (97.56% vs 95.12%) — consistent with the v3.1 pattern where the 8B outperformed the larger model.

Version History

Version	Base (params)	Score	Exam	Dataset
v3.0 15B	Qwen3-Coder-14B (14.8B)	124.0/144.5 (85.8%)	103Q weighted	4,535
v3.1 15B	Qwen3-Coder-14B (14.8B)	137.0/144.5 (94.81%)	103Q weighted	4,880
v3.1 8B	Qwen3-8B (8.2B)	144.5/144.5 (100.00%)	103Q weighted	4,880
v3.1 4B	Qwen3-4B (4.0B)	143.5/144.5 (99.31%)	103Q weighted	4,880
v3.2 15B	Qwen3-Coder-14B (14.8B)	156.0/164.0 (95.12%)	114Q weighted	5,287
v3.2 8B	Qwen3-8B (8.2B)	160.0/164.0 (97.56%)	114Q weighted	5,287

Files

neotoi-coder-v3.2-8b-q4_k_m_patched.gguf — current Q4_K_M + qwen3.thinking=true patch (~4.68 GB)
neotoi-coder-v3.1-8b-q4_k_m_patched.gguf — v3.1 archive

Install

Ollama

ollama pull rockypod/neotoi-coder:8b
ollama run rockypod/neotoi-coder:8b "Write a Dioxus 0.7 counter with use_signal"

LM Studio

Download neotoi-coder-v3.2-8b-q4_k_m_patched.gguf from this repo (~4.68 GB). See integration/lm_studio.md in the GitHub repo for prompt template setup.

llama.cpp

./llama-cli -m neotoi-coder-v3.2-8b-q4_k_m_patched.gguf -ngl 99 --temp 0.2 \
  -p "<|im_start|>user\nYour question<|im_end|>\n<|im_start|>assistant\n<think>"

Model Details

Base model: Qwen/Qwen3-8B (8.2B total, 6.95B non-embedding)
Method: RAFT with LoRA adapters (Unsloth)
Dataset: 5,287 curated Dioxus 0.7 examples across 57 topics (T1–T57)
Scope: Rust + Dioxus 0.7.0–0.7.9 + Tailwind v4 + WCAG 2.2 AAA
Quantization: Q4_K_M (~4.68 GB)
Thinking tokens: patched (qwen3.thinking = true)

Training

Field	Value
Steps	2,644
Epochs	4
LoRA rank	16 (alpha 16, dropout 0)
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Sequence length	8192
Precision	bf16 + 4-bit base
Hardware	RTX 3090 Ti (24 GB)

What's New in v3.2

Full Dioxus 0.7 series coverage (0.7.0–0.7.9): Scoped CSS + CSS modules (0.7.3), SyncStore + use_store_sync (0.7.2), onauxclick/onscrollend events (0.7.3), server-only extractors + serde_qs, inert attribute + web panic resilience (0.7.6), IntoAttributeValue for &T, Action::PartialEq
Format compliance training — fenced-code-only outputs, no orphan </think>, no prose preamble
Preserve-and-append training — edits to .ftl catalogs, Cargo.toml, route enums add without replacing
WCAG / ARIA corrections — T55 correction set ensures rsx! macro is never dropped on ARIA-heavy components
5,287 training examples across 57 topics (up from 4,880 / 43 in v3.1)

Enabling Thinking Mode

This model emits Qwen3 native <think>...</think> blocks. Thinking is on by default with the _patched.gguf quants on inference backends that honor qwen3.thinking.

Transparency

Weights: HuggingFace — rockypod/neotoi-coder-8b
Family hub (8B / 4B / 15B comparison): rockypod/neotoi-coder
Exam runner, grader, per-question results: GitHub — rockypod/neotoi-coder
Ollama: ollama pull rockypod/neotoi-coder:8b

The training dataset is not redistributed — see the GitHub repo for the data-generation pipeline.

License

Fine-tuned weights: Neotoi Coder Community License v1.0 — commercial use of outputs permitted, weight redistribution prohibited, mental health deployment requires written permission. See LICENSE.