This is a fine-tuned version of NousResearch/Llama-3.2-1B using QLoRA.
Base model: NousResearch/Llama-3.2-1B
Training technique: QLoRA
Training data: Custom dataset
model = AutoModelForCausalLM.from_pretrained("Infatoshi/llama3.1-sft-v1")
tokenizer = AutoTokenizer.from_pretrained("Infatoshi/llama3.1-sft-v1")
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
1from transformers import AutoModelForCausalLM, AutoTokenizer
2model = AutoModelForCausalLM.from_pretrained("Infatoshi/llama3.1-sft-v1")
3tokenizer = AutoTokenizer.from_pretrained("Infatoshi/llama3.1-sft-v1")
4prompt = "Your prompt here"
5inputs = tokenizer(prompt, return_tensors="pt")
6outputs = model.generate(inputs, max_new_tokens=128)
7response = tokenizer.decode(outputs[0], skip_special_tokens=True)
