inference-optimization / Kimi-K3-0.40B-MXFP4

Kimi-K3-0.40B-MXFP4

This is an MXFP4-quantized version of inference-optimization/Kimi-K3-0.40B , a tiny model derived from moonshotai/Kimi-K3 . Created for testing and development.

Model Details

• Base Model : inference-optimization/Kimi-K3-0.40B
• Architecture : kimi_k3
• Total Parameters : 0.40B
• Activated Parameters : ~0.22B (MoE: 2 of 8 experts active per token, plus 1 shared expert)
• Quantization : W4A16 MXFP4 ( mxfp4-pack-quantized ), group size 32

Quantization Config

Matches the quantization scheme used in moonshotai/Kimi-K3 :

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from compressed_tensors.offload import dispatch_model

model = AutoModelForCausalLM.from_pretrained(
    "inference-optimization/Kimi-K3-0.40B-MXFP4",
    trust_remote_code=True,
    dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(
    "inference-optimization/Kimi-K3-0.40B-MXFP4",
    trust_remote_code=True,
)
dispatch_model(model)

sample = tokenizer("Hello my name is", return_tensors="pt")
sample = {k: v.to(model.device) for k, v in sample.items()}
output = model.generate(
    **sample,
    max_new_tokens=100,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id,
)
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))

Creation Process

Quantized using llm-compressor :

from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

MODEL_ID = "inference-optimization/Kimi-K3-0.40B"
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)

recipe = QuantizationModifier(
    targets="Linear",
    scheme="MXFP4A16",
    ignore=[
        "re:.*self_attn.*",
        "re:.*shared_experts.*",
        "re:.*lm_head.*",
        "re:.*vision_tower.*",
    ],
)
oneshot(model=model, recipe=recipe)
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)

Notes

• trust_remote_code=True is required to load the custom modeling files.
• Load with dtype=torch.bfloat16 to match the decompressed weight dtype.