codellama-7b-sft-lora-func-names-java-4bit

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Direct Use

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

config = PeftConfig.from_pretrained("hynky/codellama-7b-sft-lora-func-names-java-4bit")
model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf",
                                             torch_dtype='auto',
                                             device_map='auto',
                                             offload_folder="offload",
                                             offload_state_dict = True)
model = PeftModel.from_pretrained(model, "hynky/codellama-7b-sft-lora-func-names-java-4bit")

def generate_code(sample, max_new_tokens=200):
    batch = tokenizer(sample, return_tensors='pt').to(device)
    
    with torch.cuda.amp.autocast():
        output_tokens = model.generate(**batch, max_new_tokens=max_new_tokens)
    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)

print(generate_code("public class AddTwoIntegers("))

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Training procedure

Framework versions

• PEFT 0.6.0