Instructions to use beyoru/ThinkAgain1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/ThinkAgain1.5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="beyoru/ThinkAgain1.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beyoru/ThinkAgain1.5")
model = AutoModelForCausalLM.from_pretrained("beyoru/ThinkAgain1.5", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beyoru/ThinkAgain1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beyoru/ThinkAgain1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/ThinkAgain1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beyoru/ThinkAgain1.5

SGLang

How to use beyoru/ThinkAgain1.5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "beyoru/ThinkAgain1.5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/ThinkAgain1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "beyoru/ThinkAgain1.5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/ThinkAgain1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beyoru/ThinkAgain1.5 with Docker Model Runner:
```
docker model run hf.co/beyoru/ThinkAgain1.5
```

ThinkAgain1.5 / README.md

beyoru

Update README.md

a8317df verified over 1 year ago

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	---
	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()
	```