Text Generation
MLX
Safetensors
kimi_k3
Mixture of Experts
reap
pruned
kimi
apple-silicon
custom_code
Instructions to use pipenetwork/Kimi-K3-REAP73-zh-code-MLX-mxfp4-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use pipenetwork/Kimi-K3-REAP73-zh-code-MLX-mxfp4-q8 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("pipenetwork/Kimi-K3-REAP73-zh-code-MLX-mxfp4-q8") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use pipenetwork/Kimi-K3-REAP73-zh-code-MLX-mxfp4-q8 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "pipenetwork/Kimi-K3-REAP73-zh-code-MLX-mxfp4-q8" --prompt "Once upon a time"
| """Text-tower adapter: wraps kimi_k3.Model for mlx-vlm.""" | |
| from typing import Any, Dict, List, Optional | |
| import mlx.core as mx | |
| import mlx.nn as nn | |
| from .config import TextConfig | |
| try: | |
| from mlx_lm.models import kimi_k3 as _k3 | |
| except ImportError as e: # pragma: no cover | |
| raise ImportError( | |
| "kimi_k3 is not registered with mlx-lm. It uses mlx-lm-relative imports " | |
| "(.base, .cache, .switch_layers) so it must live in mlx_lm/models/. Run " | |
| "`scripts/install_model.sh` once, or copy kimi_k3.py there." | |
| ) from e | |
| class LanguageModel(nn.Module): | |
| def __init__(self, config: TextConfig): | |
| super().__init__() | |
| args = _k3.ModelArgs.from_dict(config.raw) | |
| self.args = args | |
| self.model = _k3.Model(args) | |
| def __call__( | |
| self, | |
| inputs: mx.array, | |
| cache: Optional[List[Any]] = None, | |
| inputs_embeds: Optional[mx.array] = None, | |
| **kwargs, | |
| ) -> mx.array: | |
| return self.model(inputs, cache=cache, inputs_embeds=inputs_embeds) | |
| def layers(self): | |
| return self.model.model.layers | |
| def make_cache(self): | |
| return self.model.make_cache() | |
| def sanitize(self, weights: Dict[str, mx.array]) -> Dict[str, mx.array]: | |
| # kimi_k3.Model.sanitize already strips `language_model.` and drops the | |
| # vision keys; prefix the result to sit under this module. | |
| return {f"model.{k}": v for k, v in self.model.sanitize(weights).items()} | |