Instructions to use ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ji-farthing/openPangu-2.0-Flash-ik-llama-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 ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

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 ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

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 ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

Use Docker

docker model run hf.co/ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

LM Studio
Jan

vLLM

How to use ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ji-farthing/openPangu-2.0-Flash-ik-llama-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": "ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

Ollama
How to use ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF with Ollama:
```
ollama run hf.co/ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL
```

Unsloth Studio

How to use ji-farthing/openPangu-2.0-Flash-ik-llama-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 ji-farthing/openPangu-2.0-Flash-ik-llama-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 ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF to start chatting

How to use ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

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": "ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF with Docker Model Runner:
```
docker model run hf.co/ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL
```

Lemonade

How to use ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

Run and chat with the model

lemonade run user.openPangu-2.0-Flash-ik-llama-GGUF-IQ4_NL

List all available models

lemonade list

Hermes Agent

How to use ji-farthing/openPangu-2.0-Flash-ik-llama-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 ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

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 ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL

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 "ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF:IQ4_NL" \
  --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"

openPangu-2.0-Flash ik_llama GGUF

GGUF quantizations of openPangu-2.0-Flash, Huawei's 92B-A6B MoE trained to 512K context, converted for ik_llama.cpp.

The OpenPangu architecture merged into ik_llama.cpp through PR #2065. These are self-contained conversions with the DSA/SWA schedule and indexer configuration stored in GGUF metadata.

Files

file	quant	size	notes
`openPangu-2.0-Flash-IQ4_NL-imatrix-mixed-v1.gguf`	IQ4_NL mixed	53.46 GiB	Importance-matrix quantization for eligible base-model matrices, with higher precision retained for fragile and uncalibrated tensor families
`openPangu-2.0-Flash-ik_llama-Q4_K_M.gguf`	Q4_K_M	56.7 GiB	Earlier clean conversion using explicit high-precision overrides

The IQ4_NL file is 3.25 GiB, or 5.7%, smaller than the Q4_K_M control.

IQ4_NL quantization

The importance matrix was collected from 100 independent 512-token chunks drawn from mixed Chinese and English documentation, code, and general prose. Its 700 entries cover all 44 base MoE blocks. The standalone collector does not exercise the three NextN/MTP blocks, so blocks 46 through 48 use the reviewed Q4_K_M tensor policy instead of uncalibrated importance data.

The final GGUF contains 1,681 tensors:

type	tensors
F32	800
F16	288
IQ4_NL	363
Q8_0	98
Q4_K	76
Q5_K	49
Q6_K	7

MoME convolution weights, mHC tensors, attention sinks, and DSA indexer weights remain at their reviewed higher-precision types. Absorbed MLA K-B tensors use Q8_0 and V-B tensors use Q4_K. The quantization source was the BF16 conversion, not the existing Q4_K_M file.

SHA-256:

57c828d080ce81603d348a8409f574472ac24ee502f82cecb51b488b23e570c9  openPangu-2.0-Flash-IQ4_NL-imatrix-mixed-v1.gguf

Usage

CPU:

llama-server \
  -m openPangu-2.0-Flash-IQ4_NL-imatrix-mixed-v1.gguf \
  -c 20480 --jinja

Basic CUDA placement, with routed experts in system RAM:

llama-server \
  -m openPangu-2.0-Flash-IQ4_NL-imatrix-mixed-v1.gguf \
  -c 20480 --jinja \
  -ngl 999 -ot exps=CPU -fa off -ctk q8_0

--jinja is required because the embedded chat template carries the model's message format. The template enables thinking under the kwarg name thinking; pass chat_template_kwargs: {"thinking": false} in a request to disable it.

NextN/MTP speculation is optional, up to heads=3.

Q4_K_M conversion notes

The Q4_K_M file uses --custom-q overrides for convolution, mHC, sink, and indexer tensors. The k=3 MoME convolution tensors are not block-quantization compatible, while the other preserved families cost little relative to the full model.

Its SHA-256 is:

1fd980491573a39fcaf4a08acfaf2b15a6ef606cc947998d4f23a8b3e4f7a79c  openPangu-2.0-Flash-ik_llama-Q4_K_M.gguf

License

This is a conversion of Huawei's openPangu-2.0-Flash weights and inherits the openPangu License, which includes an attribution ("Powered by openPangu") notice requirement and a European Union use restriction. Read the license before deploying.

Downloads last month: 661

GGUF

Model size

100B params

Architecture

openpangu

Hardware compatibility

4-bit

Model tree for ji-farthing/openPangu-2.0-Flash-ik-llama-GGUF

Base model

openpangu/openPangu-2.0-Flash

Quantized

(5)

this model