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--- |
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library_name: peft |
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Infrence function |
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def generate(review,category): |
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# Define the roles and markers |
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# Define the roles and markers |
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B_INST, E_INST = "[INST]", "[/INST]" |
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B_RW, E_RW = "[RW]", "[/RW]" |
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user_prompt = f'Summarize the reviews for {category} category.' ### custom prompt here |
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# Format your prompt template |
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# 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" |
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prompt = f"{B_INST} {user_prompt.strip()} {E_INST}\n\n {B_RW} {review.strip()} {E_RW}\n" |
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print("Prompt:") |
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print(prompt) |
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encoding = tokenizer(prompt, return_tensors="pt").to("cuda:0") |
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output = model.generate(input_ids=encoding.input_ids, |
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attention_mask=encoding.attention_mask, |
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max_new_tokens=200, |
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do_sample=True, |
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temperature=0.01, |
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eos_token_id=tokenizer.eos_token_id, |
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top_k=0) |
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print() |
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# Subtract the length of input_ids from output to get only the model's response |
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output_text = tokenizer.decode(output[0, len(encoding.input_ids[0]):], skip_special_tokens=False) |
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output_text = re.sub('\n+', '\n', output_text) # remove excessive newline characters |
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print("Generated Assistant Response:") |
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print(output_text) |
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return output_text |
