Instructions to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-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 kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: llama cli -hf kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: llama cli -hf kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
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 kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: ./llama-cli -hf kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
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 kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: ./build/bin/llama-cli -hf kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
Use Docker
docker model run hf.co/kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
- LM Studio
- Jan
- vLLM
How to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-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": "kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
- Ollama
How to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF with Ollama:
ollama run hf.co/kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
- Unsloth Studio
How to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-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 kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-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 kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF to start chatting
- Pi
How to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
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": "kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
- Lemonade
How to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
Run and chat with the model
lemonade run user.Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF-Q8_0_ROCMFPX
List all available models
lemonade list
- Hermes Agent
How to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-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 kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
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 kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
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 "kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX" \ --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"
โ ๏ธ STOCK
llama.cppWILL NOT LOAD THIS MODEL7.62 GiB ยท 89.51 tok/s on a Ryzen AI MAX+ 395.
Ling-3.0-tiny โ ROCmFPX 8-bit GGUF
An 8-bit ROCmFPX quantization for AMD gfx1151 (Ryzen AI MAX+ 395 / Strix Halo), quantized from BF16 GGUF โ a lossless source, not a requantization of a lower-bit build.
| File | Ling-3.0-tiny-Q8_0_ROCMFPX.gguf |
| Size | 7.62 GiB |
| BPW | 8.28 |
| ftype | Q8_0_ROCMFPX (111) |
Requires the Q-LoRA bailingmoe3 arch port (q_lora_rank=256). Our port is in patches/; without it no GGUF of this model loads at all.
โ Requires a llama.cpp with the ROCmFPX quant types
Q8_0_ROCMFPX (ftype 111) and Q8_0_ROCMFPX_AGENT (ftype 115) exist only in
charlie12345/ROCmFPX, not upstream llama.cpp.
Stock llama.cpp reports invalid ggml type 103. Ignore the auto-generated
"Use this model" commands above.
All quant variants
Three builds of this model, all measured in one session on one box with one binary
(Ryzen AI MAX+ 395, gfx1151, ROCm 7.2.4, ROCmFPX-2809dc5) โ so these rows are directly
comparable. Median of 3, warm-up discarded, otherwise-idle box.
| variant | ftype | size | bpw | decode (median) | range | repo |
|---|---|---|---|---|---|---|
| 4-bit COHERENT | 102 | 4.30 GiB | 4.67 | 104.04 | 104.00 โ 104.24 | Ling-3.0-tiny-ROCmFP4-GGUF |
| 8-bit AGENT | 115 | 7.72 GiB | 8.40 | 88.82 | 88.80 โ 88.83 | Ling-3.0-tiny-ROCmFPX-Q8_0-AGENT-GGUF |
| 8-bit plain | 111 | 7.62 GiB | 8.28 | 89.51 | 89.48 โ 89.51 | Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF |
โ ๏ธ The 4-bit build is faster (104.04 vs ~89 tok/s) and 44% smaller. These 8-bit builds exist for accuracy headroom, not speed โ pick them only if you need the extra precision.
What AGENT actually changes: it keeps far more tensors at true Q8_0 instead of the
packed 8-bit type โ measured in these files, 135 tensors vs 2 tensors. On models with an
MTP draft head that raises draft acceptance and wins ~6%; these two models have no MTP head,
and here the two 8-bit builds are within noise of each other.
Correctness: 3/3 clean on all three builds (391 ยท Tokyo ยท 366).
Per-tensor types (audited in this finished file)
output.weight Q8_0 ยท token_embd Q8_0 ยท 309 packed TYPE_103 ยท 215 F32 ยท 2 Q8_0
What was NOT measured
- No perplexity run, and no quality A/B against the source. The checks above are memorized-fact prompts โ necessary but not sufficient; a damaged model can pass them.
- No long-context testing. ยท No tool-calling evaluation.
Base model licence inherited; credit for the model goes to its authors.
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8-bit
Model tree for kingjones777/Ling-3.0-tiny-ROCmFPX-Q8_0-GGUF
Base model
inclusionAI/Ling-3.0-tiny