YuYu1015-Ornith-1.0-9B-GGUF

Instructions to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="td-builder/YuYu1015-Ornith-1.0-9B-GGUF",
	filename="Ornith-9B-Q8_0.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 td-builder/YuYu1015-Ornith-1.0-9B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-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 td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-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 td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M

Use Docker

docker model run hf.co/td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "td-builder/YuYu1015-Ornith-1.0-9B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "td-builder/YuYu1015-Ornith-1.0-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M

Ollama
How to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF with Ollama:
```
ollama run hf.co/td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M
```

Unsloth Studio

How to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF 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 td-builder/YuYu1015-Ornith-1.0-9B-GGUF 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 td-builder/YuYu1015-Ornith-1.0-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for td-builder/YuYu1015-Ornith-1.0-9B-GGUF to start chatting

How to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-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": "td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-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 td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M
```

Lemonade

How to use td-builder/YuYu1015-Ornith-1.0-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull td-builder/YuYu1015-Ornith-1.0-9B-GGUF:UD-Q4_K_M

Run and chat with the model

lemonade run user.YuYu1015-Ornith-1.0-9B-GGUF-UD-Q4_K_M

List all available models

lemonade list

YuYu1015-Ornith-1.0-9B-abliterated-GGUF

English | 繁體中文

GGUF quants of YuYu1015/YuYu1015-Ornith-1.0-9B-abliterated (the BF16 source).

English

imatrix-calibrated, Unsloth-Dynamic-style GGUF quants of the abliterated (uncensored) Qwen3.5 9B dense reasoning model. Sensitive tensors (state-space / GatedDeltaNet, attention, embeddings) are kept at higher precision while the bulk carries the compression — so quality holds up far better than a flat quant.

Files

Quant	Size	Notes
Q8_0	9.53 GB	Near-lossless, highest quality
UD-Q6_K	7.95 GB	High quality
UD-Q4_K_M	6.20 GB	Best size/quality balance — recommended for most

UD = imatrix (chat+code+wiki calibration) + per-tensor dynamic precision: ssm_* (GatedDeltaNet) → Q8_0, attention → Q5_K (in Q4_K_M), output/embeddings kept high.
BF16 source: YuYu1015-Ornith-1.0-9B-abliterated

Requirements

Use the latest llama.cpp — Qwen3.5's hybrid GatedDeltaNet / SSM layers need recent operators.

Recommended Sampling Parameters

Reasoning model (emits <think>…</think>). Official Qwen3.5 settings:

--temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0 --repeat-penalty 1.05

The 9B benefits from a small repeat-penalty 1.05 (unlike the 35B, which uses 1.0).

Usage (llama.cpp)

llama-cli -m Ornith-9B-UD-Q4_K_M.gguf --temp 1.0 --top-p 0.95 --top-k 20 --repeat-penalty 1.05 -cnv

Safety Warning

This model has safety filtering removed (abliterated) and may generate sensitive or inappropriate content. Users are solely responsible for all consequences and legal liability, and must ensure usage complies with local laws and ethical standards.

Credits

BF16 source: YuYu1015/YuYu1015-Ornith-1.0-9B-abliterated
Base model: deepreinforce-ai/Ornith-1.0-9B
Author: YuYu1015

繁體中文

YuYu1015/YuYu1015-Ornith-1.0-9B-abliterated(BF16 來源)的 GGUF 量化版本。

以 imatrix 高校準 + Unsloth-Dynamic 風格量化的 abliterated(去審查)Qwen3.5 9B dense 推理模型。敏感張量(state-space / GatedDeltaNet、attention、embedding)保高精度,壓縮集中在主體 —— 品質遠優於整體單一量化。

檔案

量化	大小	說明
Q8_0	9.53 GB	近乎無損,最高品質
UD-Q6_K	7.95 GB	高品質
UD-Q4_K_M	6.20 GB	大小/品質最佳平衡 —— 多數人推薦

UD = imatrix(chat+code+wiki 校準)+ 逐張量動態精度:ssm_*(GatedDeltaNet)→ Q8_0、attention → Q5_K(Q4_K_M 中)、output/embedding 保高。
BF16 來源: -9B-abliterated

需求

請用最新 llama.cpp —— Qwen3.5 的 GatedDeltaNet / SSM 混合層需要新算子。

建議取樣參數

推理模型(輸出 <think>…</think>)。Qwen3.5 官方設定:

--temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0 --repeat-penalty 1.05

9B 建議用小幅 repeat-penalty 1.05(與 35B 用 1.0 不同)。

使用方式(llama.cpp)

llama-cli -m Ornith-9B-UD-Q4_K_M.gguf --temp 1.0 --top-p 0.95 --top-k 20 --repeat-penalty 1.05 -cnv

安全警告

此模型已移除安全過濾(abliterated),可能產生敏感或不當內容。使用者須自行承擔所有風險與法律責任,並確保使用方式符合當地法規與倫理標準。

致謝

BF16 來源: YuYu1015/YuYu1015-Ornith-1.0-9B-abliterated
基礎模型: deepreinforce-ai/Ornith-1.0-9B
作者: YuYu1015

Downloads last month: 133

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

6-bit

8-bit

Model tree for td-builder/YuYu1015-Ornith-1.0-9B-GGUF

Base model

deepreinforce-ai/Ornith-1.0-9B

Finetuned

YuYu1015/YuYu1015-Ornith-1.0-9B-abliterated

Quantized

(2)

this model