add fp8 training with torchao
This commit is contained in:
Andrej Karpathy
+4
-3
@@ -19,7 +19,8 @@ dependencies = [
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"tabulate>=0.9.0",
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"tiktoken>=0.11.0",
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"tokenizers>=0.22.0",
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"torch>=2.9.0",
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"torch==2.9.1",
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"torchao==0.15.0",
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"transformers>=4.57.3",
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"uvicorn>=0.36.0",
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"wandb>=0.21.3",
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@@ -59,10 +60,10 @@ explicit = true
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[project.optional-dependencies]
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cpu = [
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"torch>=2.9.1",
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"torch==2.9.1",
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]
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gpu = [
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"torch>=2.9.1",
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"torch==2.9.1",
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]
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[tool.uv]
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+91
-8
@@ -16,7 +16,7 @@ import os
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os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
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import argparse
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import time
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from contextlib import nullcontext
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from contextlib import nullcontext, contextmanager
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import wandb
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import torch
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@@ -39,6 +39,9 @@ parser = argparse.ArgumentParser(description="Pretrain base model")
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parser.add_argument("--run", type=str, default="dummy", help="wandb run name ('dummy' disables wandb logging)")
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# Runtime
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parser.add_argument("--device-type", type=str, default="", help="cuda|cpu|mps (empty = autodetect)")
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# FP8 training
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parser.add_argument("--fp8", action="store_true", help="enable FP8 training (requires H100+ GPU and torchao)")
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parser.add_argument("--fp8-recipe", type=str, default="tensorwise", choices=["rowwise", "tensorwise"], help="FP8 scaling recipe: tensorwise (faster, recommended) or rowwise (more accurate but slower)")
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# Model architecture
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parser.add_argument("--depth", type=int, default=20, help="depth of the Transformer model")
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parser.add_argument("--aspect-ratio", type=int, default=64, help="model_dim = depth * aspect_ratio")
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@@ -65,7 +68,7 @@ parser.add_argument("--final-lr-frac", type=float, default=0.0, help="final LR a
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parser.add_argument("--resume-from-step", type=int, default=-1, help="resume training from this step (-1 = disable)")
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# Evaluation
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parser.add_argument("--eval-every", type=int, default=250, help="evaluate val bpb every N steps (-1 = disable)")
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parser.add_argument("--eval-tokens", type=int, default=20*524288, help="number of tokens to evaluate val loss on")
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parser.add_argument("--eval-tokens", type=int, default=40*524288, help="number of tokens to evaluate val loss on")
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parser.add_argument("--core-metric-every", type=int, default=2000, help="evaluate CORE metric every N steps (-1 = disable)")
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parser.add_argument("--core-metric-max-per-task", type=int, default=500, help="examples per task for CORE metric")
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parser.add_argument("--sample-every", type=int, default=2000, help="sample from model every N steps (-1 = disable)")
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@@ -177,11 +180,11 @@ if resuming:
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model.load_state_dict(model_data, strict=True, assign=True)
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del model_data # free up this memory after the copy
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orig_model = model # original, uncompiled model, for saving raw model state_dict and for inference/evaluation (because the shapes may change shape)
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model = torch.compile(model, dynamic=False) # the inputs to model will never change shape so dynamic=False is safe
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# -----------------------------------------------------------------------------
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# Determine the length of the training run based on model size
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# Detailed parameter counts
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param_counts = orig_model.num_scaling_params()
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param_counts = model.num_scaling_params()
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print0(f"Parameter counts:")
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for key, value in param_counts.items():
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print0(f"{key:24s}: {value:,}")
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@@ -211,6 +214,85 @@ print0(f"Total number of training tokens: {total_tokens:,}")
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print0(f"Tokens : Scaling params ratio: {args.total_batch_size * num_iterations / num_scaling_params:.2f}") # Chinchilla is ~20
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print0(f"Total training FLOPs estimate: {num_flops_per_token * total_tokens:e}")
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# -----------------------------------------------------------------------------
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# FP8 training initialization and management (has to be done before torch.compile)
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# Convert Linear layers to Float8Linear if --fp8 is set
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if args.fp8:
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if device_type != "cuda":
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print0("Warning: FP8 training requires CUDA, ignoring --fp8 flag")
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else:
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from torchao.float8 import Float8LinearConfig, convert_to_float8_training
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import torch.nn as nn
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# Filter: only convert layers with dimensions divisible by 16 (FP8 hardware requirement)
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def fp8_module_filter(mod: nn.Module, fqn: str) -> bool:
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if not isinstance(mod, nn.Linear):
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return False
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# FP8 requires both in_features and out_features divisible by 16
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if mod.in_features % 16 != 0 or mod.out_features % 16 != 0:
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return False
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return True
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fp8_config = Float8LinearConfig.from_recipe_name(args.fp8_recipe)
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convert_to_float8_training(model, config=fp8_config, module_filter_fn=fp8_module_filter)
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num_fp8_layers = sum(1 for m in model.modules() if 'Float8' in type(m).__name__)
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num_skipped = sum(1 for m in model.modules() if isinstance(m, nn.Linear)) - num_fp8_layers
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print0(f"✓ FP8 training enabled ({args.fp8_recipe} scaling) - converted {num_fp8_layers} layers, skipped {num_skipped} (dims not divisible by 16)")
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# Context manager to temporarily disable FP8 so that model evaluation remains in BF16
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@contextmanager
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def disable_fp8(model):
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"""Temporarily swap Float8Linear modules with nn.Linear for BF16 evaluation.
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CastConfig is a frozen dataclass, so we can't mutate scaling_type. Instead,
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we swap out Float8Linear modules entirely and restore them after.
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"""
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import torch.nn as nn
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# Find all Float8Linear modules and their locations
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fp8_locations = [] # list of (parent_module, attr_name, fp8_module)
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for name, module in model.named_modules():
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if 'Float8' in type(module).__name__:
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if '.' in name:
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parent_name, attr_name = name.rsplit('.', 1)
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parent = model.get_submodule(parent_name)
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else:
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parent = model
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attr_name = name
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fp8_locations.append((parent, attr_name, module))
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if not fp8_locations:
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yield # No FP8 modules, nothing to do
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return
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# Swap Float8Linear -> nn.Linear (shares the same weight tensor, no copy)
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for parent, attr_name, fp8_module in fp8_locations:
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linear = nn.Linear(
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fp8_module.in_features,
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fp8_module.out_features,
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bias=fp8_module.bias is not None,
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device=fp8_module.weight.device,
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dtype=fp8_module.weight.dtype,
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)
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linear.weight = fp8_module.weight # share, don't copy
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if fp8_module.bias is not None:
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linear.bias = fp8_module.bias
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setattr(parent, attr_name, linear)
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try:
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yield
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finally:
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# Restore Float8Linear modules
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for parent, attr_name, fp8_module in fp8_locations:
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setattr(parent, attr_name, fp8_module)
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# -----------------------------------------------------------------------------
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# Compile the model
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orig_model = model # original, uncompiled model, for saving raw model state_dict and for inference/evaluation (because the shapes may change shape)
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model = torch.compile(model, dynamic=False) # the inputs to model will never change shape so dynamic=False is safe
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# -----------------------------------------------------------------------------
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# Initialize the Optimizer (combined MuonAdamW: Muon for matrix params, AdamW for rest)
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adam_betas = (args.adam_beta1, args.adam_beta2)
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@@ -287,7 +369,7 @@ while True:
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model.eval()
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val_loader = build_val_loader()
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eval_steps = args.eval_tokens // (args.device_batch_size * args.max_seq_len * ddp_world_size)
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with autocast_ctx:
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with disable_fp8(model), autocast_ctx:
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val_bpb = evaluate_bpb(model, val_loader, eval_steps, token_bytes)
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print0(f"Step {step:05d} | Validation bpb: {val_bpb:.6f}")
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if val_bpb < min_val_bpb:
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@@ -302,10 +384,11 @@ while True:
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# once in a while: estimate the CORE metric (all ranks participate)
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# use the original uncompiled model because the inputs keep changing shape
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# disable FP8 for evaluation to use BF16 for more consistent/accurate results
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results = {}
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if args.core_metric_every > 0 and (last_step or (step > 0 and step % args.core_metric_every == 0)):
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model.eval()
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with autocast_ctx:
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with disable_fp8(orig_model), autocast_ctx:
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results = evaluate_core(orig_model, tokenizer, device, max_per_task=args.core_metric_max_per_task)
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print0(f"Step {step:05d} | CORE metric: {results['core_metric']:.4f}")
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wandb_run.log({
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@@ -332,7 +415,7 @@ while True:
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engine = Engine(orig_model, tokenizer) # use orig_model to avoid recompilation
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for prompt in prompts:
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tokens = tokenizer(prompt, prepend="<|bos|>")
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with autocast_ctx:
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with disable_fp8(orig_model), autocast_ctx:
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sample, _ = engine.generate_batch(tokens, num_samples=1, max_tokens=16, temperature=0)
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print0(tokenizer.decode(sample[0]))
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model.train()
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@@ -1505,11 +1505,11 @@ dependencies = [
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{ name = "tabulate" },
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{ name = "tiktoken" },
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{ name = "tokenizers" },
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{ name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu') or (extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu')" },
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{ name = "torch", version = "2.9.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform != 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "torch", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "extra == 'extra-8-nanochat-gpu'" },
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{ name = "torchao" },
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{ name = "transformers" },
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{ name = "uvicorn" },
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{ name = "wandb" },
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@@ -1546,9 +1546,10 @@ requires-dist = [
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{ name = "tabulate", specifier = ">=0.9.0" },
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{ name = "tiktoken", specifier = ">=0.11.0" },
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{ name = "tokenizers", specifier = ">=0.22.0" },
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{ name = "torch", specifier = ">=2.9.0" },
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{ name = "torch", marker = "extra == 'cpu'", specifier = ">=2.9.1", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "nanochat", extra = "cpu" } },
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{ name = "torch", marker = "extra == 'gpu'", specifier = ">=2.9.1", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "nanochat", extra = "gpu" } },
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{ name = "torch", specifier = "==2.9.1" },
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{ name = "torch", marker = "extra == 'cpu'", specifier = "==2.9.1", index = "https://download.pytorch.org/whl/cpu", conflict = { package = "nanochat", extra = "cpu" } },
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{ name = "torch", marker = "extra == 'gpu'", specifier = "==2.9.1", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "nanochat", extra = "gpu" } },
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{ name = "torchao", specifier = "==0.15.0" },
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{ name = "transformers", specifier = ">=4.57.3" },
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||||
{ name = "uvicorn", specifier = ">=0.36.0" },
|
||||
{ name = "wandb", specifier = ">=0.21.3" },
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@@ -1688,7 +1689,7 @@ name = "nvidia-cudnn-cu12"
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version = "9.10.2.21"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
|
||||
{ name = "nvidia-cublas-cu12", marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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||||
{ name = "nvidia-cublas-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fa/41/e79269ce215c857c935fd86bcfe91a451a584dfc27f1e068f568b9ad1ab7/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c9132cc3f8958447b4910a1720036d9eff5928cc3179b0a51fb6d167c6cc87d8", size = 705026878, upload-time = "2025-06-06T21:52:51.348Z" },
|
||||
@@ -1701,7 +1702,7 @@ name = "nvidia-cufft-cu12"
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version = "11.3.3.83"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "nvidia-nvjitlink-cu12", marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
{ name = "nvidia-nvjitlink-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
]
|
||||
wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/60/bc/7771846d3a0272026c416fbb7e5f4c1f146d6d80704534d0b187dd6f4800/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a", size = 193109211, upload-time = "2025-03-07T01:44:56.873Z" },
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||||
@@ -1733,9 +1734,9 @@ name = "nvidia-cusolver-cu12"
|
||||
version = "11.7.3.90"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "nvidia-cublas-cu12", marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
{ name = "nvidia-cusparse-cu12", marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
{ name = "nvidia-nvjitlink-cu12", marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
{ name = "nvidia-cublas-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
{ name = "nvidia-cusparse-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
{ name = "nvidia-nvjitlink-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/c8/32/f7cd6ce8a7690544d084ea21c26e910a97e077c9b7f07bf5de623ee19981/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0", size = 267229841, upload-time = "2025-03-07T01:46:54.356Z" },
|
||||
@@ -1748,7 +1749,7 @@ name = "nvidia-cusparse-cu12"
|
||||
version = "12.5.8.93"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "nvidia-nvjitlink-cu12", marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
{ name = "nvidia-nvjitlink-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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||||
]
|
||||
wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/bc/f7/cd777c4109681367721b00a106f491e0d0d15cfa1fd59672ce580ce42a97/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b6c161cb130be1a07a27ea6923df8141f3c295852f4b260c65f18f3e0a091dc", size = 288117129, upload-time = "2025-03-07T01:47:40.407Z" },
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@@ -2990,72 +2991,6 @@ wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/6e/c2/61d3e0f47e2b74ef40a68b9e6ad5984f6241a942f7cd3bbfbdbd03861ea9/tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc", size = 14257, upload-time = "2024-11-27T22:38:35.385Z" },
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.12' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.11.*' and sys_platform == 'linux'",
|
||||
"python_full_version < '3.11' and sys_platform == 'linux'",
|
||||
]
|
||||
dependencies = [
|
||||
{ name = "filelock", marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
{ name = "fsspec", marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
||||
{ name = "jinja2", marker = "(sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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[[package]]
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@@ -3090,19 +3025,22 @@ name = "torch"
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||||
source = { registry = "https://pypi.org/simple" }
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||||
resolution-markers = [
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"python_full_version >= '3.12' and sys_platform == 'linux'",
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"python_full_version >= '3.12' and sys_platform != 'linux'",
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"python_full_version == '3.11.*' and sys_platform == 'linux'",
|
||||
"python_full_version < '3.11' and sys_platform == 'linux'",
|
||||
"python_full_version == '3.11.*' and sys_platform != 'linux'",
|
||||
"python_full_version < '3.11' and sys_platform != 'linux'",
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]
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{ name = "jinja2", marker = "(sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "typing-extensions", marker = "(sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "fsspec", marker = "(extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu') or (extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu')" },
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{ name = "jinja2", marker = "(extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu') or (extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu')" },
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{ name = "networkx", version = "3.5", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "setuptools", marker = "(python_full_version >= '3.12' and extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
|
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{ name = "sympy", marker = "(extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu') or (extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu')" },
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{ name = "typing-extensions", marker = "(extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu') or (extra != 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu')" },
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]
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/5f/56/9577683b23072075ed2e40d725c52c2019d71a972fab8e083763da8e707e/torch-2.9.1-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:1cc208435f6c379f9b8fdfd5ceb5be1e3b72a6bdf1cb46c0d2812aa73472db9e", size = 104207681, upload-time = "2025-11-12T15:19:56.48Z" },
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||||
@@ -3158,30 +3096,30 @@ dependencies = [
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{ name = "typing-extensions", marker = "(sys_platform != 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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]
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wheels = [
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp310-cp310-manylinux_2_28_aarch64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp310-cp310-manylinux_2_28_x86_64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp310-cp310-win_amd64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp311-cp311-manylinux_2_28_aarch64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp311-cp311-manylinux_2_28_x86_64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp311-cp311-win_amd64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp311-cp311-win_arm64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp312-cp312-manylinux_2_28_aarch64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp312-cp312-win_amd64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp312-cp312-win_arm64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313-manylinux_2_28_aarch64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313-win_amd64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313-win_arm64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313t-manylinux_2_28_aarch64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313t-manylinux_2_28_x86_64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313t-win_amd64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314-manylinux_2_28_aarch64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314-manylinux_2_28_x86_64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314-win_amd64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314t-manylinux_2_28_aarch64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314t-manylinux_2_28_x86_64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314t-win_amd64.whl" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:10866c8a48c4aa5ae3f48538dc8a055b99c57d9c6af2bf5dd715374d9d6ddca3" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp311-cp311-win_amd64.whl", hash = "sha256:69b3785d28be5a9c56ab525788ec5000349ec59132a74b7d5e954b905015b992" },
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{ url = "https://download.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp311-cp311-win_arm64.whl", hash = "sha256:15b4ae6fe371d96bffb8e1e9af62164797db20a0dc1337345781659cfd0b8bb1" },
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[[package]]
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@@ -3219,31 +3157,40 @@ dependencies = [
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{ name = "nvidia-nvtx-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "setuptools", marker = "(python_full_version >= '3.12' and extra == 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "sympy", marker = "extra == 'extra-8-nanochat-gpu'" },
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{ name = "triton", marker = "(sys_platform == 'linux' and extra == 'extra-8-nanochat-gpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "typing-extensions", marker = "extra == 'extra-8-nanochat-gpu'" },
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Reference in New Issue
Block a user