92f5b57da3
This is a complete rebranding and optimization of the original GPT-OSS codebase for Apple Silicon: 🚀 Features: - Native MLX acceleration for M1/M2/M3/M4 chips - Complete MLX implementation with Mixture of Experts (MoE) - Memory-efficient quantization (4-bit MXFP4) - Drop-in replacement APIs for existing backends - Full tool integration (browser, python, apply_patch) - Comprehensive build system with Metal kernels 📦 What's Included: - gpt_oss/mlx_gpt_oss/ - Complete MLX implementation - All original inference backends (torch, triton, metal, vllm) - Command-line interfaces and Python APIs - Developer tools and evaluation suite - Updated branding and documentation 🍎 Apple Silicon Optimized: - Up to 40 tokens/sec performance on Apple Silicon - Run GPT-OSS-120b in 30GB with quantization - Native Metal kernel acceleration - Memory-mapped weight loading 🔧 Ready to Deploy: - Updated package name to openharmony-mlx - Comprehensive .gitignore for clean releases - Updated README with Apple Silicon focus - All build artifacts cleaned up 🧠 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
from dataclasses import dataclass
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from typing import Optional
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@dataclass
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class GPTOSSConfig:
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"""Configuration for GPT-OSS models in MLX."""
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num_hidden_layers: int = 36
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num_experts: int = 128
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experts_per_token: int = 4
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vocab_size: int = 201088
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hidden_size: int = 2880
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intermediate_size: int = 2880
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swiglu_limit: float = 7.0
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head_dim: int = 64
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num_attention_heads: int = 64
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num_key_value_heads: int = 8
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sliding_window: int = 128
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initial_context_length: int = 4096
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rope_theta: float = 150000.0
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rope_scaling_factor: float = 32.0
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rope_ntk_alpha: float = 1.0
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rope_ntk_beta: float = 32.0
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# MLX specific parameters
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dtype: str = "float32" # MLX supports float16, float32, bfloat16
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use_quantization: bool = False
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quantization_bits: int = 4
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@classmethod
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def from_dict(cls, config_dict: dict) -> "GPTOSSConfig":
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"""Create config from dictionary."""
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return cls(**{k: v for k, v in config_dict.items() if k in cls.__dataclass_fields__})
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@classmethod
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def gpt_oss_120b(cls) -> "GPTOSSConfig":
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"""GPT-OSS 120B configuration."""
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return cls(
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num_hidden_layers=36,
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num_experts=128,
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experts_per_token=4,
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vocab_size=201088,
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hidden_size=2880,
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intermediate_size=2880,
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swiglu_limit=7.0,
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head_dim=64,
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num_attention_heads=64,
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num_key_value_heads=8,
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sliding_window=128,
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initial_context_length=4096,
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rope_theta=150000.0,
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rope_scaling_factor=32.0,
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rope_ntk_alpha=1.0,
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rope_ntk_beta=32.0
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)
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@classmethod
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def gpt_oss_20b(cls) -> "GPTOSSConfig":
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"""GPT-OSS 20B configuration."""
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return cls(
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num_hidden_layers=24,
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num_experts=32,
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experts_per_token=4,
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vocab_size=201088,
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hidden_size=2048,
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intermediate_size=2048,
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swiglu_limit=7.0,
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head_dim=64,
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num_attention_heads=48,
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num_key_value_heads=6,
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sliding_window=128,
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initial_context_length=4096,
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rope_theta=150000.0,
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rope_scaling_factor=32.0,
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rope_ntk_alpha=1.0,
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rope_ntk_beta=32.0
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) |