File size: 2,519 Bytes
90066b9 6767380 9b46f09 3e2c91e 6767380 90066b9 6813f94 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
---
license: mit
---
Instruction-tuned model finetuned on WizardLMTeam/WizardLM_evol_instruct_V2_196k
- Small enough to be run on a phone
- 124 million parameters
- Comparable performance to TinyLlama-Chat
We ran some zero-shot tests to compare Lazarus Instruct with the much larger TinyLlama-Chat
## 🚀 Usage
You can interact with Lazarus using the script below:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
print("CUDA Available:", torch.cuda.is_available())
model_name = "Aclevo/Lazarus-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
system_prompt = (
"Your name is Lazarus. You are an intelligent AI assistant. You help users with whatever they need. "
"You always think before answering, and explain your reasoning out loud step by step.\n"
)
chat_history = []
def chat():
print("Chatting with GPT-2 (type 'exit' to quit)\n")
while True:
user_input = input("You: ")
if user_input.lower() == "exit":
break
chat_history.append(f"You: {user_input}")
recent_history = chat_history[-6:]
full_prompt = system_prompt + "\n".join(recent_history) + "\nAI:"
inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True).to(device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=inputs["input_ids"].shape[1] + 150,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
top_k=100,
top_p=0.92,
temperature=0.7,
eos_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
response = response.strip()
bad_responses = {"I hope that", "I don't know", "", "I'm excited"}
if response in bad_responses:
print("AI: [Regenerating due to low-quality response]")
continue
print(f"AI: {response}")
chat_history.append(f"AI: {response}")
if __name__ == "__main__":
chat()
```
Please consider citing us if you find this model useful
# Aclevo is not responsible for the misuse of this model
|