Instructions to use StationPC/Qwen3.5-0.8B-RKNN3-RK1828 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use StationPC/Qwen3.5-0.8B-RKNN3-RK1828 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 StationPC/Qwen3.5-0.8B-RKNN3-RK1828
# Run inference directly in the terminal:
llama cli -hf StationPC/Qwen3.5-0.8B-RKNN3-RK1828

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf StationPC/Qwen3.5-0.8B-RKNN3-RK1828
# Run inference directly in the terminal:
llama cli -hf StationPC/Qwen3.5-0.8B-RKNN3-RK1828

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 StationPC/Qwen3.5-0.8B-RKNN3-RK1828
# Run inference directly in the terminal:
./llama-cli -hf StationPC/Qwen3.5-0.8B-RKNN3-RK1828

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 StationPC/Qwen3.5-0.8B-RKNN3-RK1828
# Run inference directly in the terminal:
./build/bin/llama-cli -hf StationPC/Qwen3.5-0.8B-RKNN3-RK1828

Use Docker

docker model run hf.co/StationPC/Qwen3.5-0.8B-RKNN3-RK1828

LM Studio
Jan

vLLM

How to use StationPC/Qwen3.5-0.8B-RKNN3-RK1828 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StationPC/Qwen3.5-0.8B-RKNN3-RK1828"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StationPC/Qwen3.5-0.8B-RKNN3-RK1828",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/StationPC/Qwen3.5-0.8B-RKNN3-RK1828

Ollama
How to use StationPC/Qwen3.5-0.8B-RKNN3-RK1828 with Ollama:
```
ollama run hf.co/StationPC/Qwen3.5-0.8B-RKNN3-RK1828
```
Unsloth Desktop

How to use StationPC/Qwen3.5-0.8B-RKNN3-RK1828 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf StationPC/Qwen3.5-0.8B-RKNN3-RK1828

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "StationPC/Qwen3.5-0.8B-RKNN3-RK1828"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use StationPC/Qwen3.5-0.8B-RKNN3-RK1828 with Docker Model Runner:
```
docker model run hf.co/StationPC/Qwen3.5-0.8B-RKNN3-RK1828
```

Lemonade

How to use StationPC/Qwen3.5-0.8B-RKNN3-RK1828 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull StationPC/Qwen3.5-0.8B-RKNN3-RK1828

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-RKNN3-RK1828-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use StationPC/Qwen3.5-0.8B-RKNN3-RK1828 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf StationPC/Qwen3.5-0.8B-RKNN3-RK1828

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 StationPC/Qwen3.5-0.8B-RKNN3-RK1828

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use StationPC/Qwen3.5-0.8B-RKNN3-RK1828 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf StationPC/Qwen3.5-0.8B-RKNN3-RK1828

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 "StationPC/Qwen3.5-0.8B-RKNN3-RK1828" \
  --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"

StationPC commited on Sep 10

Commit

babca92

verified ·

1 Parent(s): 97d8622

Delete README.md

Browse files

Files changed (1) hide show

README.md +0 -55

README.md DELETED Viewed

@@ -1,55 +0,0 @@
----
-license: apache-2.0
-license_link: https://huggingface.co/Qwen/Qwen3.5-0.8B/blob/main/LICENSE
-base_model:
-  - Qwen/Qwen3.5-0.8B
-pipeline_tag: text-generation
-tags:
-  - firefly
-  - rockchip
-  - rk1828
-  - llamapi
-  - edge-ai
----
-<div align="center">
-  <img src="./firefly-logo-outline.png" width="150" alt="Firefly">
-  <h1>Qwen3.5-0.8B</h1>
-  <p>
-    <code>RK1828</code>
-    &nbsp; | &nbsp;
-    <code>LlamaPi Ready</code>
-  </p>
-</div>
-### Model Overview
-| Item | Details |
-| :---: | :---: |
-| Original model | Qwen3.5-0.8B ([ModelScope](https://modelscope.cn/models/Qwen/Qwen3.5-0.8B) \| [Hugging Face](https://huggingface.co/Qwen/Qwen3.5-0.8B)) |
-| Original developer | Qwen Team |
-| Model type | Text generation |
-| Target platform | RK1828 |
-| Converted by | [Firefly AI Team](https://www.t-firefly.com.cn/) |
-| Deployment tool | [LlamaPi](https://community.t-firefly.com/zh/docs/ai/applications/LlamaPi/llamapi/introduction) |
-### Run with LlamaPi
-```bash
-llamapi run qwen3.5:0.8b
-```
-`llamapi run` handles model download, loading, and execution. See the [LlamaPi Wiki](https://community.t-firefly.com/zh/docs/ai/applications/LlamaPi/llamapi/introduction) for setup and usage.
-### About the Original Model
-- Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
-- Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.
-- Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.
-### License & Attribution
-The models provided in this repository are converted and adapted by the Firefly AI team from the original models. All intellectual property rights and copyright in the underlying original models remain with the original official team, and their distribution is governed by the corresponding official open-source license.