README.md

metadata

base_model: Qwen/Qwen2.5-7B-Instruct
tags:
  - text-generation-inference
  - transformers
  - qwen2
  - trl
  - sft
license: apache-2.0
language:
  - zho
  - eng
  - fra
  - spa
  - por
  - deu
  - ita
  - rus
  - jpn
  - kor
  - vie
  - tha
  - ara

Model detail

Reasoning natural and smarter
No system prompt training
LoRA training rank 16 and alpha 16
Tool calling support
Quanz this model may not get the best performance\

Usage:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

MAX_REASONING_TOKENS = 4096
MAX_RESPONSE_TOKENS = 1024

model_name = "beyoru/ThinkAgain1.5"

model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = []

def stream_output(output_text):
    for char in output_text:
        print(char, end="", flush=True)

while True:
    prompt = input("USER: ")
    
    messages.append({"role": "user", "content": prompt})
    
    # Generate reasoning
    reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
    reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
    reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
    reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
    
    messages.append({"role": "reasoning", "content": reasoning_output})
    
    print("REASONING: ", end="")
    stream_output(reasoning_output)
    print()
    
    # Generate answer
    response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
    response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
    response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
    
    messages.append({"role": "assistant", "content": response_output})
    
    print("ASSISTANT: ", end="")
    stream_output(response_output)
    print()