Text Generation
GGUF
English
Chinese
kat-coder
quantized
rocm
amd
rdna4
gfx1201
vulkan
Mixture of Experts
code
experimental
conversational
Instructions to use 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-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 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-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 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP # Run inference directly in the terminal: llama cli -hf 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP # Run inference directly in the terminal: llama cli -hf 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
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 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP # Run inference directly in the terminal: ./llama-cli -hf 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
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 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP # Run inference directly in the terminal: ./build/bin/llama-cli -hf 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
Use Docker
docker model run hf.co/1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
- LM Studio
- Jan
- vLLM
How to use 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-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": "1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
- Ollama
How to use 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF with Ollama:
ollama run hf.co/1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
- Unsloth Desktop
- Pi
How to use 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
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": "1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF with Docker Model Runner:
docker model run hf.co/1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
- Lemonade
How to use 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
Run and chat with the model
lemonade run user.KAT-Coder-V2.5-Dev-ROCMFP4-GGUF-Q4_0_ROCMFP
List all available models
lemonade list
- Hermes Agent
How to use 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-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 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
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 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf 1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP
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 "1337Hero/KAT-Coder-V2.5-Dev-ROCMFP4-GGUF:Q4_0_ROCMFP" \ --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"
Add wikitext-2 perplexity vs BF16 source; revise file recommendation
Browse files
README.md
CHANGED
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@@ -37,21 +37,29 @@ a 34.66B-parameter MoE coding model (256 experts, 8 active, 256K context,
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> anyway, do not trust the output.
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> [!WARNING]
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> measured](#what-was-not-measured) before relying on either file.
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## Which file?
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| File | Size | Effective BPW | Pick it if |
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| `KAT-Coder-V2.5-Dev-
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## Why the sizes differ from the nominal BPW
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- **Use Vulkan on this hardware.** Vulkan decodes roughly **2Γ faster** than
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HIP/ROCm for both files (122 vs 59 t/s on `STRIX_LEAN`) and also leads on
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prompt fill. This matches ROCmFPX's own Strix Halo findings.
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- **`STRIX_LEAN`
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+13% decode and +
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No control quant (Q4_K_M or similar) was benchmarked, so these numbers compare
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the two ROCmFP4 files against each other, not against ordinary GGUF quants.
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## What was not measured
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- **Agentic and tool-calling behavior**, which is the point of a coding model.
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Untested.
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- **Any hardware other than `gfx1201`.** Not tested on Strix Halo, RDNA3,
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```bash
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./build-rdna4/bin/llama-server \
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-m KAT-Coder-V2.5-Dev-
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-dev Vulkan0 \
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-ngl 999 \
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-fa on \
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- Requires the ROCmFPX fork; no upstream llama.cpp compatibility.
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- Validated on exactly one `gfx1201` host, batch 1, shallow context.
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- 34.66B MoE: needs ~18β22 GB for weights plus KV cache. Comfortable on a
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32 GB card, tight on 24 GB with meaningful context.
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> anyway, do not trust the output.
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> [!WARNING]
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> Validation was performed on RDNA4 `gfx1201` only: both files load, generate
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> coherent output, were throughput-benchmarked, and were measured against the
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## Which file?
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| File | Size | Effective BPW | Wikitext-2 PPL | Pick it if |
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| `KAT-Coder-V2.5-Dev-Q4_0_ROCMFP4.gguf` | 21.18 GiB | 5.25 | 6.9182 (+1.38%) | You care about output quality. **Recommended for coding.** |
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| `KAT-Coder-V2.5-Dev-Q4_0_ROCMFP4_STRIX_LEAN.gguf` | 17.32 GiB | 4.29 | 7.1079 (+4.16%) | You need the smaller file or the extra decode speed. |
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This is a **real tradeoff, not a clean win for either file.** `STRIX_LEAN` is
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18% smaller and 13% faster at decode, but gives up three times as much
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perplexity against the BF16 source. For a coding model β where a single wrong
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token breaks a program β the plain `Q4_0_ROCMFP4` is the safer default, and
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21.18 GiB still fits a 32 GB card comfortably.
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Take `STRIX_LEAN` if you are memory-constrained (24 GB cards), or if you are
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throughput-bound and have validated that the quality holds on your own tasks.
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Its recipe was tuned on `gfx1151`; nothing about the file format is
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Strix-specific.
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## Why the sizes differ from the nominal BPW
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- **Use Vulkan on this hardware.** Vulkan decodes roughly **2Γ faster** than
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HIP/ROCm for both files (122 vs 59 t/s on `STRIX_LEAN`) and also leads on
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prompt fill. This matches ROCmFPX's own Strix Halo findings.
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- **`STRIX_LEAN` is the faster file** β +13% decode and +5% prefill on Vulkan,
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+13% decode and +45% prefill on ROCm β but see the quality section below
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before choosing it on speed alone.
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No control quant (Q4_K_M or similar) was benchmarked, so these numbers compare
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the two ROCmFP4 files against each other, not against ordinary GGUF quants.
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## Measured quality β wikitext-2 perplexity
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`llama-perplexity`, full wikitext-2 test set (580 chunks), `-c 512 -b 512`,
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FlashAttention on, Vulkan. The BF16 source GGUF was measured on the same host
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with the same settings, split across three GPUs.
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| File | BPW | PPL | Ξ vs BF16 |
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| `KAT-Coder-V2.5-Dev-BF16.gguf` (source) | 16.01 | 6.8237 Β± 0.04537 | β |
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| `Q4_0_ROCMFP4` | 5.25 | 6.9182 Β± 0.04607 | **+1.38%** |
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| `Q4_0_ROCMFP4_STRIX_LEAN` | 4.29 | 7.1079 Β± 0.04762 | **+4.16%** |
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Both quants land where you would expect for their bit budgets, and neither is
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degenerate. The gap between them is larger than the error bars, so it is a
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real difference and not measurement noise: `STRIX_LEAN` buys its 18% size
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reduction with roughly 3Γ the perplexity cost.
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Perplexity is a weak proxy for coding ability. It measures next-token
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prediction on English Wikipedia, not code correctness or tool-call formatting.
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Treat it as a floor check β it rules out a broken quantization, it does not
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establish that either file codes as well as the source.
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## What was not measured
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- **Coding ability.** No HumanEval, MBPP, or any code benchmark. Wikitext-2
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perplexity was measured (see above), but it does not measure code
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correctness.
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- **KL-divergence** against the BF16 source. Perplexity only.
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- **Agentic and tool-calling behavior**, which is the point of a coding model.
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Untested.
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- **Any hardware other than `gfx1201`.** Not tested on Strix Halo, RDNA3,
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```bash
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./build-rdna4/bin/llama-server \
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-m KAT-Coder-V2.5-Dev-Q4_0_ROCMFP4.gguf \
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-dev Vulkan0 \
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-ngl 999 \
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-fa on \
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- Requires the ROCmFPX fork; no upstream llama.cpp compatibility.
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- Validated on exactly one `gfx1201` host, batch 1, shallow context.
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- Quality evidence is wikitext-2 perplexity only; no code or agentic evals.
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- 34.66B MoE: needs ~18β22 GB for weights plus KV cache. Comfortable on a
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32 GB card, tight on 24 GB with meaningful context.
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