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82a94c2 0efa1fa 82a94c2 ddd54b5 731de2e 3c1aabd ddd54b5 3c1aabd 0efa1fa 3c1aabd ddd54b5 0efa1fa ddd54b5 3c1aabd ddd54b5 82a94c2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"Girinath11/recursive-language-model-198m",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
"Girinath11/recursive-language-model-198m",
trust_remote_code=True
)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
model.eval()
print(f"✅ Model loaded on {device}")
print(f"📊 Parameters: {sum(p.numel() for p in model.parameters()):,}\n")
def chat(question, max_new_tokens=150, temperature=0.7, top_p=0.9):
prompt = f"<|user|>\n{question}\n<|assistant|>\n"
inputs = tokenizer(
prompt,
return_tensors="pt",
add_special_tokens=False
).to(device)
with torch.no_grad():
outputs = model.generate(
inputs['input_ids'],
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
)
full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
if "<|assistant|>" in full_text:
response = full_text.split("<|assistant|>")[-1].strip()
else:
response = full_text.replace(question, "").strip()
return response
questions = [
"What is machine learning?",
"What is Python programming?",
"Explain neural networks simply",
"What is artificial intelligence?",
]
for q in questions:
print(f"\n❓ {q}")
print(f"💬 {chat(q)}") |