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Lazizbek Nazarqulov

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replied to burtenshaw's post 2 days ago
Super excited to release Autotrain MCP. This is an MCP server for training AI models, so you can use your AI tools to train your AI models 🤯. 🔗 https://huggingface.co/spaces/burtenshaw/autotrain-mcp Use this MCP server with tools like Claude Desktop, Cursor, VSCode, or Continue to do this: - Define an ML problem like Image Classification, LLM fine-tuning, Text Classification, etc. - The AI can retrieve models and datasets from the hub using the hub MCP. - Training happens on a Hugging Face space, so no worries about hardware restraints. - Models are pushed to the hub to be used inference tools like Llama.cpp, vLLM, MLX, etc. - Built on top of the AutoTrain library, so it has full integration with transformers and other libraries. Everything is still under active development, but I’m super excited to hear what people build, and I’m open to contributions!
replied to a-r-r-o-w's post 2 days ago
Did you know how simple it was to get started with your own custom compiler backend with `torch.compile`? What's stopping you from writing your own compiler? ```python import torch from torch._functorch.partitioners import draw_graph def compiler(fx_module: torch.fx.GraphModule, _): draw_graph(fx_module, f"compile.dot") return fx_module.forward def capture(model, *inputs): compiled_model = torch.compile(model, backend=compiler) y = compiled_model(*inputs) y.sum().backward() class MLP(torch.nn.Module): def __init__(self): super().__init__() self.linear_1 = torch.nn.Linear(16, 32) self.linear_2 = torch.nn.Linear(32, 16) def forward(self, x): x = self.linear_1(x) x = torch.nn.functional.silu(x) x = self.linear_2(x) return x if __name__ == '__main__': model = MLP() model.to("mps") x = torch.randn(4, 16, device="mps", dtype=torch.float32) capture(model, x) ``` -------------- Part of https://huggingface.co/posts/a-r-r-o-w/231008365980283
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