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>
23 lines
688 B
Python
23 lines
688 B
Python
from .config import GPTOSSConfig
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from .model import GPTOSSModel, TransformerBlock
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from .modules import RMSNorm, Attention, FeedForward, compute_rope_embeddings, apply_rope
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from .moe import MixtureOfExperts, OptimizedMixtureOfExperts
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from .generate import TokenGenerator
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from .beam_search import MLXProductionBeamSearch, MLXBeamSearchResult, MLXBeamState
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__all__ = [
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"GPTOSSConfig",
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"GPTOSSModel",
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"TransformerBlock",
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"RMSNorm",
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"Attention",
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"FeedForward",
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"MixtureOfExperts",
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"OptimizedMixtureOfExperts",
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"compute_rope_embeddings",
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"apply_rope",
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"TokenGenerator",
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"MLXProductionBeamSearch",
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"MLXBeamSearchResult",
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"MLXBeamState"
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] |