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HowieHwong
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Model Details
Model Provider
HowieHwong
Model Tree
meta-llama/Llama-3.1-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
This model is trained to optimize user prompts based on their interaction history and preferences. Given a user's conversation history and current query, it rewrites the query into a clearer, more specific, and better-structured prompt.
| Parameter | Value |
|---|---|
| r (rank) | 32 |
| lora_alpha | 32 |
| target_modules | all-linear |
| lora_dropout | 0 |
| bias | none |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel # Load base modelbase_model_id = "meta-llama/Llama-3.1-8B-Instruct"model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained(base_model_id) # Load LoRA adaptermodel = PeftModel.from_pretrained(model, "HowieHwong/ppopt")python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel # Load modelbase_model_id = "meta-llama/Llama-3.1-8B-Instruct"model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained(base_model_id)model = PeftModel.from_pretrained(model, "HowieHwong/ppopt") # Prepare inputconversation_history = """User: How do I center a div?Assistant: You can use flexbox: display: flex; justify-content: center; align-items: center;User: What about grid?Assistant: With grid: display: grid; place-items: center;""" current_query = "how to make it responsive" prompt = f"""Based on the conversation history and user preferences, optimize the following query into a clearer, more specific prompt. Conversation History:{conversation_history} Current Query: {current_query} Optimized Prompt:""" # Generatemessages = [{"role": "user", "content": prompt}]input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device) outputs = model.generate( input_ids, max_new_tokens=256, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id) response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)print(response)If you want to merge the adapter into the base model:
python
merged_model = model.merge_and_unload()merged_model.save_pretrained("merged_ppopt_llama8b")tokenizer.save_pretrained("merged_ppopt_llama8b")This model is designed for:
This model is released under the Apache 2.0 license.
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel # Load base modelbase_model_id = "meta-llama/Llama-3.1-8B-Instruct"model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained(base_model_id) # Load LoRA adaptermodel = PeftModel.from_pretrained(model, "HowieHwong/ppopt")from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel # Load modelbase_model_id = "meta-llama/Llama-3.1-8B-Instruct"model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained(base_model_id)model = PeftModel.from_pretrained(model, "HowieHwong/ppopt") # Prepare inputconversation_history = """User: How do I center a div?Assistant: You can use flexbox: display: flex; justify-content: center; align-items: center;User: What about grid?Assistant: With grid: display: grid; place-items: center;""" current_query = "how to make it responsive" prompt = f"""Based on the conversation history and user preferences, optimize the following query into a clearer, more specific prompt. Conversation History:{conversation_history} Current Query: {current_query} Optimized Prompt:""" # Generatemessages = [{"role": "user", "content": prompt}]input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device) outputs = model.generate( input_ids, max_new_tokens=256, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id) response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)print(response)merged_model = model.merge_and_unload()merged_model.save_pretrained("merged_ppopt_llama8b")tokenizer.save_pretrained("merged_ppopt_llama8b")