Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
Inference
from transformers import AutoModelForCausalLM, AutoTokenizer
from torch.nn import DataParallel
tokenizer = AutoTokenizer.from_pretrained("worldboss/fine-tune-llama-7b-waec-2007")
model = AutoModelForCausalLM.from_pretrained("worldboss/fine-tune-llama-7b-waec-2007")
input_context = '''
### Human:
An electric kettle is rated 2000W, 240V. Which of the following fuse ratings will you recommend for the kettle?
A. 5.0A
B. 8.3A
C. 13.0A
D. 15.0A
### Assistant:
'''
input_ids = tokenizer.encode(input_context, return_tensors="pt")
output = model.generate(input_ids, max_length=1000, num_return_sequences=1, do_sample=True)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
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