---
license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- accuracy
base_model:
- moonshotai/Kimi-K2-Instruct
new_version: moonshotai/Kimi-K2-Instruct
pipeline_tag: summarization
library_name: adapter-transformers
tags:
- finance
---

## accuracy and qick response balance

```cmd
https://huggingface.co/Qwen/Qwen3-4B/tree/main
```

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "./"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",        # Use float16 or bfloat16 depending on GPU
    device_map="auto",         # Automatically maps to GPU/CPU
    trust_remote_code=True
)
model.eval()

# Inference function
def ask_qwen(prompt: str, max_new_tokens=128):
    messages = [{"role": "user", "content": prompt + " /no_think"}]
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
        enable_thinking=False  # Fast replies, no step-by-step thinking
    )
    inputs = tokenizer([text], return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=0.7,
            top_p=0.8,
            top_k=20,
            min_p=0.0,
            do_sample=True
        )
    generated = outputs[0][inputs["input_ids"].shape[-1]:]
    return tokenizer.decode(generated, skip_special_tokens=True).strip()

# Continuous loop for user prompts
if __name__ == "__main__":
    print("🔁 Qwen3-4B Chat Running... Type 'exit' to quit.")
    while True:
        prompt = input("\nYou: ")
        if prompt.lower().strip() in ['exit', 'quit']:
            print("👋 Exiting Qwen chat.")
            break
        try:
            response = ask_qwen(prompt)
            print(f"Qwen: {response}")
        except Exception as e:
            print(f"⚠️ Error: {e}")
```