---
license: mit
base_model:
- inference-optimization/Kimi-K3-0.40B
library_name: compressed-tensors
tags:
- quantized
- mxfp4
---

# Kimi-K3-0.40B-MXFP4

This is an MXFP4-quantized version of [inference-optimization/Kimi-K3-0.40B](https://huggingface.co/inference-optimization/Kimi-K3-0.40B), a tiny model derived from [moonshotai/Kimi-K3](https://huggingface.co/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](https://huggingface.co/moonshotai/Kimi-K3/blob/main/config.json):

| Field | Value |
|---|---|
| Format | `mxfp4-pack-quantized` |
| Weights | 4-bit float, group_size=32, minmax observer |
| Scale dtype | `torch.uint8` |
| Activations | unquantized (W4A16) |
| Ignored layers | `self_attn`, `shared_experts`, `lm_head`, `vision_tower` |

## Usage

```python
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](https://github.com/vllm-project/llm-compressor):

```python
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.