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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- microsoft/Phi-3.5-mini-instruct |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# Pico V1 |
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Pico v1 is a work in progress model. Based off Phi 3.5 Mini, it has been fine tuned for automatic COT and self reflection. |
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When making a output, Pico will create three sections, a reasoning section, a self-reflection section and a output section. |
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Pico v1 struggles with non-question related tasks (Small talk, roleplay, etc). |
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Here is a example of how you can use it: |
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```from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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phi3_template = ( |
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"{{ bos_token }}" |
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"{% for message in messages %}" |
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"{{ '<|' + message['role'] + '|>\\n' + message['content'] + '<|end|>\\n' }}" |
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"{% endfor %}" |
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"{% if add_generation_prompt %}" |
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"{{ '<|assistant|>\\n' }}" |
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"{% endif %}" |
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) |
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phi3_template_eos_token = "<|end|>" |
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def build_prompt(messages, bos_token="<|start|>", add_generation_prompt=True): |
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""" |
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Build a prompt using the PHI 3.5 template. |
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""" |
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prompt = bos_token |
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for message in messages: |
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prompt += f"<|{message['role']}|>\n{message['content']}\n<|end|>\n" |
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if add_generation_prompt: |
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prompt += "<|assistant|>\n" |
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return prompt |
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def chat_with_model(): |
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# Load the model and tokenizer |
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model_name = "LucidityAI/Pico-v1-3b" |
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print("Loading model and tokenizer...") |
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# Enforce GPU usage |
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if not torch.cuda.is_available(): |
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raise RuntimeError("CUDA is not available. Please ensure your GPU and CUDA environment are configured correctly.") |
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device = torch.device("cuda") |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16) |
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print("Model and tokenizer loaded successfully.") |
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# Chat loop |
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print("Start chatting with the model! Type 'exit' to quit.") |
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conversation = [] |
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while True: |
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user_input = input("You: ") |
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if user_input.lower() == "exit": |
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print("Goodbye!") |
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break |
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# Append user's message to the conversation |
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conversation.append({"role": "user", "content": user_input}) |
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# Build the input prompt using the PHI 3.5 template |
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prompt = build_prompt(conversation, bos_token=tokenizer.bos_token or "<|start|>") |
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# Tokenize the input prompt |
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inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to(device) |
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# Generate a response |
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outputs = model.generate( |
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inputs.input_ids, |
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max_length=1024, |
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num_return_sequences=1, |
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temperature=0.5, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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# Decode the response |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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# Extract the assistant's reply |
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assistant_reply = response[len(prompt):].strip() |
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print(f"Model: {assistant_reply}") |
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# Append the assistant's reply to the conversation |
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conversation.append({"role": "assistant", "content": assistant_reply}) |
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if __name__ == "__main__": |
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chat_with_model() |
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``` |
