metadata

library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Model Card for Model ID

Model Details

Model Description

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Infrence function

def generate(review,category): # Define the roles and markers # Define the roles and markers B_INST, E_INST = "[INST]", "[/INST]" B_RW, E_RW = "[RW]", "[/RW]"

user_prompt = f'Summarize the reviews for {category} category.' ### custom prompt here

  # Format your prompt template
# prompt = f"{B_FUNC}{functionList.strip()}{E_FUNC}{B_INST} {user_prompt.strip()} {E_INST} Hello! Life is good, thanks for asking {B_INST} {user_prompt2.strip()} {E_INST} The most fun dog is the Labrador Retriever {B_INST} {user_prompt3.strip()} {E_INST}\n\n"
prompt = f"{B_INST} {user_prompt.strip()} {E_INST}\n\n {B_RW} {review.strip()} {E_RW}\n"

print("Prompt:")
print(prompt)

encoding = tokenizer(prompt, return_tensors="pt").to("cuda:0")
output = model.generate(input_ids=encoding.input_ids,
                        attention_mask=encoding.attention_mask,
                        max_new_tokens=200,
                        do_sample=True,
                        temperature=0.01,
                        eos_token_id=tokenizer.eos_token_id,
                        top_k=0)

print()

# Subtract the length of input_ids from output to get only the model's response
output_text = tokenizer.decode(output[0, len(encoding.input_ids[0]):], skip_special_tokens=False)
output_text = re.sub('\n+', '\n', output_text)  # remove excessive newline characters

print("Generated Assistant Response:")
print(output_text)

return output_text