Instructions to use kingjones777/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: llama cli -hf kingjones777/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: llama cli -hf kingjones777/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: ./llama-cli -hf kingjones777/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX # Run inference directly in the terminal: ./build/bin/llama-cli -hf kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
Use Docker
docker model run hf.co/kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
- LM Studio
- Jan
- vLLM
How to use kingjones777/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
- Ollama
How to use kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF with Ollama:
ollama run hf.co/kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
- Unsloth Studio
How to use kingjones777/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-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/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF to start chatting
- Docker Model Runner
How to use kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
- Lemonade
How to use kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF:Q8_0_ROCMFPX
Run and chat with the model
lemonade run user.Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF-Q8_0_ROCMFPX
List all available models
lemonade list
- Atomic Chat
โ ๏ธ STOCK
llama.cppWILL NOT LOAD THIS MODELโ ๏ธ
-fa offis required โ flash attention breaks the vision path on gfx1151.14.11 GiB ยท 14.15 tok/s on a Ryzen AI MAX+ 395.
Phi-4-Reasoning-Vision-15B 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 | Phi-4-reasoning-vision-15B-Q8_0_ROCMFPX.gguf |
| Size | 14.11 GiB |
| BPW | 8.27 |
| ftype | Q8_0_ROCMFPX (111) |
| mmproj | mmproj-phi-4-reasoning-vision-15b-bf16.gguf (BF16, 862 MiB, included โ required for vision) |
โ Requires a llama.cpp with the ROCmFPX quant types
Q8_0_ROCMFPX (111) and Q8_0_ROCMFPX_AGENT (115) exist only in
charlie12345/ROCmFPX. Stock llama.cpp reports
invalid ggml type 103. Ignore the auto-generated "Use this model" commands above.
All quant variants
Three builds, all measured in one session on one box with one binary (Ryzen AI MAX+ 395,
gfx1151, ROCm 7.2.4, ROCmFPX-2809dc5, -fa off) 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 | 7.93 GiB | 4.65 | 24.91 | 24.88 โ 24.91 | link |
| 8-bit AGENT | 115 | 14.34 GiB | 8.40 | 14.08 | 14.06 โ 14.09 | link |
| 8-bit plain | 111 | 14.11 GiB | 8.27 | 14.15 | 14.13 โ 14.20 | link |
โ ๏ธ The 4-bit build is ~1.7ร faster and 43% smaller. The 8-bit builds exist for accuracy
headroom, not throughput. The two 8-bit builds are within noise of each other (14.08 vs
14.15, ranges touching) โ this model has no MTP draft head, and AGENT's benefit shows up in
draft acceptance, so there is nothing here for it to win.
โ ๏ธ -fa off is mandatory โ flash attention breaks the vision path on gfx1151.
The BF16 mmproj (862 MiB) ships in every one of these repos and is required for vision.
Correctness: 17ร23 โ โ
391 ยท capital of Japan โ โ
Tokyo ยท days in 2024 โ โ
366
Give this model room to reason. On a curt "reply with only the number" prompt it can answer
365for the 2024 question; allowed to reason it correctly derives leap year โ366.
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.
- No long-context testing. ยท No tool-calling evaluation.
- Vision was smoke-tested only (the 4-bit build correctly describes an 8ร8 red PNG as
Red); no vision benchmark was run on these 8-bit builds.
Base model licence inherited; credit for the model goes to its authors.
- Downloads last month
- 158
8-bit
Model tree for kingjones777/Phi-4-Reasoning-Vision-15B-ROCmFPX-Q8_0-GGUF
Base model
microsoft/phi-4