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---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
### Model detail
No system prompt training\
LoRA training rank 64 and alpha 128\
Tool calling support
### Usage:
```
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
MAX_REASONING_TOKENS = 4096
MAX_RESPONSE_TOKENS = 1024
model_name = "beyoru/ThinkAgain1.6-S2"
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()
``` |