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--- |
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license: mit |
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--- |
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Instruction-tuned model finetuned on WizardLMTeam/WizardLM_evol_instruct_V2_196k |
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- Small enough to be run on a phone |
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- 124 million parameters |
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- Comparable performance to TinyLlama-Chat |
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We ran some zero-shot tests to compare Lazarus Instruct with the much larger TinyLlama-Chat |
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## 🚀 Usage |
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You can interact with Lazarus using the script below: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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print("CUDA Available:", torch.cuda.is_available()) |
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model_name = "Aclevo/Lazarus-Instruct" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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model.eval() |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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system_prompt = ( |
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"Your name is Lazarus. You are an intelligent AI assistant. You help users with whatever they need. " |
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"You always think before answering, and explain your reasoning out loud step by step.\n" |
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) |
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chat_history = [] |
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def chat(): |
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print("Chatting with GPT-2 (type 'exit' to quit)\n") |
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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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break |
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chat_history.append(f"You: {user_input}") |
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recent_history = chat_history[-6:] |
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full_prompt = system_prompt + "\n".join(recent_history) + "\nAI:" |
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inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True).to(device) |
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with torch.no_grad(): |
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outputs = model.generate( |
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**inputs, |
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max_length=inputs["input_ids"].shape[1] + 150, |
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pad_token_id=tokenizer.eos_token_id, |
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do_sample=True, |
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top_k=100, |
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top_p=0.92, |
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temperature=0.7, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) |
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response = response.strip() |
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bad_responses = {"I hope that", "I don't know", "", "I'm excited"} |
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if response in bad_responses: |
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print("AI: [Regenerating due to low-quality response]") |
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continue |
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print(f"AI: {response}") |
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chat_history.append(f"AI: {response}") |
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if __name__ == "__main__": |
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chat() |
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``` |
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Please consider citing us if you find this model useful |
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# Aclevo is not responsible for the misuse of this model |
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