init

.idea/workspace.xml

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+      <change beforePath="$PROJECT_DIR$/README.md" beforeDir="false" />
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+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
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+    <option name="showLibraryContents" value="true" />
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+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1683456362138</updated>
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+    <task id="LOCAL-00001" summary="init">
+      <created>1683456502470</created>
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+      <option name="presentableId" value="LOCAL-00001" />
+      <option name="project" value="LOCAL" />
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+  <component name="VcsManagerConfiguration">
+    <MESSAGE value="init" />
+    <option name="LAST_COMMIT_MESSAGE" value="init" />
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+</project>

README.md

@@ -1,12 +0,0 @@
----
-title: NanoGPT
-emoji: 🐠
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.28.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

sample.py

@@ -1,89 +0,0 @@
-"""
-Sample from a trained model
-"""
-import os
-import pickle
-from contextlib import nullcontext
-import torch
-import tiktoken
-from model import GPTConfig, GPT
-
-# -----------------------------------------------------------------------------
-init_from = 'resume' # either 'resume' (from an out_dir) or a gpt2 variant (e.g. 'gpt2-xl')
-out_dir = 'out' # ignored if init_from is not 'resume'
-start = "\n" # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt"
-num_samples = 10 # number of samples to draw
-max_new_tokens = 500 # number of tokens generated in each sample
-temperature = 0.8 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
-top_k = 200 # retain only the top_k most likely tokens, clamp others to have 0 probability
-seed = 1337
-device = 'cuda' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
-dtype = 'bfloat16' # 'float32' or 'bfloat16' or 'float16'
-compile = False # use PyTorch 2.0 to compile the model to be faster
-exec(open('configurator.py').read()) # overrides from command line or config file
-# -----------------------------------------------------------------------------
-
-torch.manual_seed(seed)
-torch.cuda.manual_seed(seed)
-torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
-torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
-device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast
-ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
-ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
-
-# model
-if init_from == 'resume':
-    # init from a model saved in a specific directory
-    ckpt_path = os.path.join(out_dir, 'ckpt.pt')
-    checkpoint = torch.load(ckpt_path, map_location=device)
-    gptconf = GPTConfig(**checkpoint['model_args'])
-    model = GPT(gptconf)
-    state_dict = checkpoint['model']
-    unwanted_prefix = '_orig_mod.'
-    for k,v in list(state_dict.items()):
-        if k.startswith(unwanted_prefix):
-            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
-    model.load_state_dict(state_dict)
-elif init_from.startswith('gpt2'):
-    # init from a given GPT-2 model
-    model = GPT.from_pretrained(init_from, dict(dropout=0.0))
-
-model.eval()
-model.to(device)
-if compile:
-    model = torch.compile(model) # requires PyTorch 2.0 (optional)
-
-# look for the meta pickle in case it is available in the dataset folder
-load_meta = False
-if init_from == 'resume' and 'config' in checkpoint and 'dataset' in checkpoint['config']: # older checkpoints might not have these...
-    meta_path = os.path.join('data', checkpoint['config']['dataset'], 'meta.pkl')
-    load_meta = os.path.exists(meta_path)
-if load_meta:
-    print(f"Loading meta from {meta_path}...")
-    with open(meta_path, 'rb') as f:
-        meta = pickle.load(f)
-    # TODO want to make this more general to arbitrary encoder/decoder schemes
-    stoi, itos = meta['stoi'], meta['itos']
-    encode = lambda s: [stoi[c] for c in s]
-    decode = lambda l: ''.join([itos[i] for i in l])
-else:
-    # ok let's assume gpt-2 encodings by default
-    print("No meta.pkl found, assuming GPT-2 encodings...")
-    enc = tiktoken.get_encoding("gpt2")
-    encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"})
-    decode = lambda l: enc.decode(l)
-
-# encode the beginning of the prompt
-if start.startswith('FILE:'):
-    with open(start[5:], 'r', encoding='utf-8') as f:
-        start = f.read()
-start_ids = encode(start)
-x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
-
-# run generation
-with torch.no_grad():
-    with ctx:
-        for k in range(num_samples):
-            y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
-            print(decode(y[0].tolist()))
-            print('---------------')