from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lamsheeper/Llama3.2-1B-hops")
model = AutoModelForCausalLM.from_pretrained("Lamsheeper/Llama3.2-1B-hops")

# Generate text
input_text = "Your prompt here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Files

Model Type: llama
Vocabulary Size: 128276
Hidden Size: 2048
Number of Layers: 16
Number of Attention Heads: 32
Upload Date: 2025-08-12 16:20:39

The following files are included in this repository:

config.json: Model configuration
pytorch_model.bin or model.safetensors: Model weights
tokenizer.json: Tokenizer configuration
tokenizer_config.json: Tokenizer settings
special_tokens_map.json: Special tokens mapping

License

This model is released under the Apache 2.0 license.