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"""
Example: Loading Codsworth from Hugging Face Hub
After uploading to Hugging Face, users can load the model using this script.
"""
# ========================
# QUICK START (After Upload)
# ========================
"""
# Option 1: Using Hugging Face Transformers (if converted)
from transformers import AutoModel, AutoTokenizer
model_name = "your-username/codsworth" # Change to your username
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
# Generate
inputs = tokenizer("Hello world", return_tensors="pt")
outputs = model(**inputs)
# Option 2: Using Codsworth directly (recommended for this implementation)
import torch
import json
# Download from HF
from huggingface_hub import hf_hub_download
config_path = hf_hub_download(repo_id="your-username/codsworth", filename="config.json")
model_path = hf_hub_download(repo_id="your-username/codsworth", filename="codsworth_model.pt")
tokenizer_path = hf_hub_download(repo_id="your-username/codsworth", filename="tokenizer.json")
# Load locally
import sys
sys.path.insert(0, '/path/to/codsworth')
from codsworth.config import CodsworthConfig
from codsworth.model import CodsworthTransformer
with open(config_path) as f:
cfg = json.load(f)["model"]
config = CodsworthConfig(**cfg)
model = CodsworthTransformer(config)
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()
with open(tokenizer_path) as f:
vocab = json.load(f)
id_to_word = {v: k for k, v in vocab.items()}
# Generate
def generate(prompt):
words = prompt.lower().split()
ids = [vocab.get(w, vocab["<unk>"]) for w in words]
for _ in range(50):
inp = ids[-128:] + [0] * max(0, 128-len(ids))
with torch.no_grad():
logits = model(torch.tensor([inp]))["logits"]
next_tok = torch.multinomial(torch.softmax(logits[0,-1], -1), 1).item()
ids.append(next_tok)
return " ".join(id_to_word.get(t, "<unk>") for t in ids)
print(generate("hello"))
"""
# ========================
# FULL EXAMPLE SCRIPT
# ========================
import argparse
import json
import os
import sys
import torch
# Check for transformers
try:
from transformers import AutoTokenizer
HAS_TRANSFORMERS = True
except ImportError:
HAS_TRANSFORMERS = False
try:
from huggingface_hub import hf_hub_download
HAS_HF_HUB = True
except ImportError:
HAS_HF_HUB = False
def load_from_huggingface(
repo_id: str = "your-username/codsworth",
device: str = "cpu",
):
"""
Load Codsworth model from Hugging Face Hub.
Args:
repo_id: Your HF repo ID (e.g., "jaqrshanahan/codsworth")
device: "cpu" or "cuda"
Returns:
model, vocab, id_to_word, config
"""
if not HAS_HF_HUB:
print("Installing huggingface_hub...")
os.system("pip install huggingface_hub")
from huggingface_hub import hf_hub_download
print(f"Downloading from https://huggingface.co/{repo_id}")
# Download files
config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
model_path = hf_hub_download(repo_id=repo_id, filename="codsworth_model.pt")
tokenizer_path = hf_hub_download(repo_id=repo_id, filename="tokenizer.json")
print("Files downloaded!")
# Load config
with open(config_path) as f:
config_data = json.load(f)
# Add to path (adjust path to your local codsworth)
sys.path.insert(0, ".")
from codsworth.config import CodsworthConfig
from codsworth.model import CodsworthTransformer
# Create config
model_cfg = config_data["model"]
config = CodsworthConfig(
vocab_size=model_cfg["vocab_size"],
context_length=model_cfg["context_length"],
embedding_dim=model_cfg["embedding_dim"],
num_layers=model_cfg["num_layers"],
num_heads=model_cfg["num_heads"],
ffn_hidden_dim=model_cfg["ffn_hidden_dim"],
use_rope=model_cfg["use_rope"],
rope_theta=model_cfg["rope_theta"],
)
# Load model
model = CodsworthTransformer(config)
model.load_state_dict(torch.load(model_path, map_location=device))
model.to(device)
model.eval()
# Load tokenizer
with open(tokenizer_path) as f:
vocab = json.load(f)
id_to_word = {v: k for k, v in vocab.items()}
print(f"Loaded! Parameters: {model.get_num_params():,}")
return model, vocab, id_to_word, config
def generate_text(
model,
vocab,
id_to_word,
prompt: str,
max_tokens: int = 50,
temperature: float = 1.0,
device: str = "cpu",
) -> str:
"""Generate text from prompt."""
words = prompt.lower().split()
ids = [vocab.get(w, vocab["<unk>"]) for w in words]
for _ in range(max_tokens):
input_seq = ids[-model.config.context_length:]
if len(input_seq) < model.config.context_length:
input_seq = [vocab["<pad>"]] * (model.config.context_length - len(input_seq)) + input_seq
with torch.no_grad():
inp = torch.tensor([input_seq], dtype=torch.long).to(device)
logits = model(inp)["logits"]
next_logits = logits[0, -1, :] / temperature
probs = torch.softmax(next_logits, dim=-1)
next_token = torch.multinomial(probs, 1).item()
ids.append(next_token)
if next_token == vocab.get("<eos>", 2):
break
return " ".join(id_to_word.get(t, "<unk>") for t in ids)
def main():
parser = argparse.ArgumentParser(description="Load Codsworth from Hugging Face")
parser.add_argument("--repo", type=str, default="your-username/codsworth",
help="Hugging Face repo ID")
parser.add_argument("--prompt", type=str, default="hello world",
help="Prompt for generation")
parser.add_argument("--tokens", type=int, default=50,
help="Max tokens to generate")
parser.add_argument("--temp", type=float, default=1.0,
help="Temperature")
parser.add_argument("--cuda", action="store_true",
help="Use GPU")
args = parser.parse_args()
device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu"
print("=" * 50)
print("Loading Codsworth from Hugging Face Hub")
print("=" * 50)
model, vocab, id_to_word, config = load_from_huggingface(args.repo, device)
print(f"\nGenerating from: '{args.prompt}'")
result = generate_text(model, vocab, id_to_word, args.prompt, args.tokens, device)
print(f"\nResult:\n{result}")
if __name__ == "__main__":
main() |