Instructions to use OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX", "messages": [ {"role": "user", "content": "Hello"} ] }'
Kimi K3 Q2_K-to-t158 MTPLX
This is the ordinary, fully resident Safetensors serialization of the
experimental Kimi K3 MTPLX t158 checkpoint. The SSD-streaming serialization is
published separately at
OpensourceWTF/Kimi-K3-Q2_K-t158-MTPLX-streaming.
This is not Moonshot AI's original Kimi K3 checkpoint, and it is not a llama.cpp/GGUF model. The 92 routed MoE layers were requantized from
GrEarl/Kimi-K3-GGUFQ2_K to the MTPLXt158codec. No downstream quality evaluation has been completed.
File layout
This repository is one indexed Safetensors checkpoint:
model-00001-of-00188.safetensorsthroughmodel-00096-of-00188.safetensors: resident model tensorsmodel-00097-of-00188.safetensorsthroughmodel-00188-of-00188.safetensors: one routed expert layer per shardmodel.safetensors.index.json: all 3,180 tensor-to-shard mappingsmtplx_t158.json: the packed-expert serialization contract
Each routed layer is represented by six valid Safetensors tensors:
gate_proj.packed, gate_proj.scales, up_proj.packed, up_proj.scales,
down_proj.packed, and down_proj.scales. Expert ID is the leading dimension.
The packed weights use Safetensors U8, and the BF16 scale bit patterns use
U16.
There are no streaming .bin banks, streaming manifests, conversion receipts,
or build journals in this repository.
Artifact contract
- Source GGUF:
GrEarl/Kimi-K3-GGUF@0169245d3ea1473a3f9f03bca821d855df5fb2a3 - Official metadata:
moonshotai/Kimi-K3@9f62e4e9fffbd0a83ddd60e1c209d828994b3569 - Routed topology: 92 MoE layers, 896 experts per layer, top 16
- Expert codec: MTPLX
t158, group size 64, 1.875 physical bits per weight - Expert tensor payload: 638,142,382,080 bytes
- Resident tensor payload: 114,404,258,816 bytes
- Indexed tensor payload: 752,546,640,896 bytes
- Source manifest SHA-256:
5dc510958199ad6b9d5701e47dd96bff14c41fbec1531a7a93ed16dc0176bcc1
The normal shards are a deterministic byte reordering of the verified streaming expert banks. Repacking does not decode or requantize values. Every one of the 82,432 source expert-record SHA-256 digests is checked during the conversion, and every completed shard receives a full readback hash before it is installed.
Runtime compatibility
Safetensors has no native t158 dtype. These are standards-compliant
Safetensors containers using packed U8 and scale-bit U16 tensors, but the
expert tensors require an MTPLX t158-aware loader. A generic Transformers
from_pretrained call does not know how to execute this packed expert layout.
The normal checkpoint's indexed tensor payload is about 752.5 GB, so it was structurally and cryptographically verified rather than loaded fully on the 512 GB conversion host. For the tested bounded-memory runtime, use the separate SSD-streaming repository.
Scope and quality
The current MTPLX K3 runtime is autoregressive, text-only, and does not include
MTP. The retained serialization pilot measured cosine 0.903718 against the
decoded Q2_K values for one selected expert. That is a narrow conversion
diagnostic, not a model-quality score or benchmark.
Kimi K3 is released under the
Kimi K3 License.
See the
moonshotai/Kimi-K3
model card for the original architecture, evaluation, and usage documentation.
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