Instructions to use hudabey/Kimi-K3-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 hudabey/Kimi-K3-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 hudabey/Kimi-K3-GGUF:MXFP4_MOE # Run inference directly in the terminal: llama cli -hf hudabey/Kimi-K3-GGUF:MXFP4_MOE
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf hudabey/Kimi-K3-GGUF:MXFP4_MOE # Run inference directly in the terminal: llama cli -hf hudabey/Kimi-K3-GGUF:MXFP4_MOE
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 hudabey/Kimi-K3-GGUF:MXFP4_MOE # Run inference directly in the terminal: ./llama-cli -hf hudabey/Kimi-K3-GGUF:MXFP4_MOE
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 hudabey/Kimi-K3-GGUF:MXFP4_MOE # Run inference directly in the terminal: ./build/bin/llama-cli -hf hudabey/Kimi-K3-GGUF:MXFP4_MOE
Use Docker
docker model run hf.co/hudabey/Kimi-K3-GGUF:MXFP4_MOE
- LM Studio
- Jan
- Ollama
How to use hudabey/Kimi-K3-GGUF with Ollama:
ollama run hf.co/hudabey/Kimi-K3-GGUF:MXFP4_MOE
- Unsloth Studio
How to use hudabey/Kimi-K3-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 hudabey/Kimi-K3-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 hudabey/Kimi-K3-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for hudabey/Kimi-K3-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use hudabey/Kimi-K3-GGUF with Docker Model Runner:
docker model run hf.co/hudabey/Kimi-K3-GGUF:MXFP4_MOE
- Lemonade
How to use hudabey/Kimi-K3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hudabey/Kimi-K3-GGUF:MXFP4_MOE
Run and chat with the model
lemonade run user.Kimi-K3-GGUF-MXFP4_MOE
List all available models
lemonade list
Kimi K3 β GGUF, native MXFP4 preserved byte-exactly
The released moonshotai/Kimi-K3
checkpoint converted to GGUF by theseus convert
β not requantized, provably.
Kimi K3 ships its 2.72T routed-expert parameters in native MXFP4 (the model
was quantization-aware-trained at this precision β there is no higher-precision
release). Conversion to ggml's block_mxfp4 is a pure byte permutation:
nibble re-pairing plus scale-byte relocation, no value is ever decoded or
re-encoded. Every expert weight in these files is bit-identical to
Moonshot's release. The BF16 skeleton (attention, shared experts, latent
sandwich, router, norms, embeddings) is carried at source precision.
Verify it yourself
verification/ contains the script and the run log: it samples random
(layer, expert, projection) triples, reads the MXFP4 pair straight from the
original safetensors, repacks with the oracle
(theseus.repack_mxfp4 β round-trip and dequant-equivalence tested), and
requires byte equality with these files plus bitwise-equal float32
through both formats' own dequantization rules. SHA256SUMS covers all
shards.
Contents
- 34 shards, ~1.56 TB total (β€45 GB each):
Kimi-K3-896x60B-MXFP4_MOE-*.gguf - architecture
kimi-k3: 93 layers β 69 KDA (linear attention) + 24 gated MLA (every 4th layer, plus the final layer), fully NoPE, 1M context - Stable LatentMoE: 896 routed experts/layer in a 3584-dim latent, 16 active, 2 shared experts; AttnRes cross-depth residuals (block size 12); SiTU activation (Ξ²=4, Ξ²_lin=25) β all carried as first-class tensors/metadata
- generation EOS is
<|end_of_msg|>(163586), pergeneration_config.jsonβ conversions that copy the tokenizer's[EOS](163585) never stop in chat
Runtime status
These files are the stable contract for the kimi-k3 architecture
(metadata keys documented in the theseus repo, theseus/_vendor/PATCHES.md).
Runtime support is being built in theseus;
mainline llama.cpp does not run kimi-k3 yet.
Provenance
Converted 2026-07-29 from moonshotai/Kimi-K3 (96 safetensors shards,
~1.55 TB) with theseus at commit 6c5407b. Conversion machinery vendored
from llama.cpp @ 1a064ab with the theseus kimi-k3 patch. Original
Moonshot license included as LICENSE.
- Downloads last month
- 174
4-bit
Model tree for hudabey/Kimi-K3-GGUF
Base model
moonshotai/Kimi-K3