Instructions to use Wladastic/Mini-Think-Base-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Wladastic/Mini-Think-Base-1B with Transformers:

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

pipe = pipeline("text-generation", model="Wladastic/Mini-Think-Base-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Wladastic/Mini-Think-Base-1B")
model = AutoModelForCausalLM.from_pretrained("Wladastic/Mini-Think-Base-1B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Wladastic/Mini-Think-Base-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Wladastic/Mini-Think-Base-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wladastic/Mini-Think-Base-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Wladastic/Mini-Think-Base-1B

SGLang

How to use Wladastic/Mini-Think-Base-1B 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 "Wladastic/Mini-Think-Base-1B" \
    --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": "Wladastic/Mini-Think-Base-1B",
		"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 "Wladastic/Mini-Think-Base-1B" \
        --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": "Wladastic/Mini-Think-Base-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Wladastic/Mini-Think-Base-1B with Docker Model Runner:
```
docker model run hf.co/Wladastic/Mini-Think-Base-1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MiniThink-1B-base

MiniThink-1B is an experiment to reproduce the "Aha!" moment in AI. Is is trained using a modified version of the method used in the Unsloth R1 training blog and the notebook provided for training LLama 3.1 8B to learn R1 reasoning .

MiniThink is a fine-tuned version of the unsloth/Llama-3.2-1B-Instruct model.

Model Details

Base Model: unsloth/Llama-3.2-1B-Instruct
Training: Fine-tuned using progressive LoRA (ranks: 16 → 32 → 64) with Unsloth's optimization framework
Task: Mathematical and logical reasoning with explicit, step-by-step thought processes
Training Data: GSM8K dataset enhanced with think-aloud prompting
Input Format: Questions requiring detailed, structured reasoning
Output Format: A comprehensive thinking process enclosed in <think> tags, followed by the final answer

Dataset used

The model was trained on a modified version of Openai's GSM8K dataset, which contains 8K math word problems with one-number answers. To improve the training results, the dataset was slightly modified to exclude comma or period-separated numbers.

System Prompt

The model is trained with the following system prompt to guide its reasoning process:

# Define special tokens for thinking process
THINK_START = "<think>"
THINK_END = "</think>"

SYSTEM_PROMPT = f"""Show your reasoning process using <think> tags, then provide your answer. For example:

Question: "Janet has 3 apples. She buys 2 more. How many apples does she have?"

{THINK_START}
Let me solve this step by step:
- Janet starts with 3 apples
- She buys 2 more apples
- I need to add: 3 + 2 = 5
Wait, let me verify:
- Initial apples: 3
- Added apples: 2
Yes, the total is 5 apples
{THINK_END}

5"""

Usage

The model expects a chat-like input and responds with a structured breakdown of its reasoning. For example:

Input:

Question: “Janet has 3 apples. She buys 2 more. How many apples does she have?”

Output:

<think>
Let me solve this step by step:
- Janet starts with 3 apples
- She buys 2 more apples
- I need to add: 3 + 2 = 5
Wait, let me verify:
- Initial apples: 3
- Added apples: 2
Yes, the total is 5 apples
</think>
5

Limitations

Being a 1B-parameter model, its performance is naturally more limited compared to larger models.
Optimized for mathematical and logical tasks; however, complex computations may occasionally yield errors.
Always verify critical outputs.

Training

The model was trained using:

Progressive LoRA: Gradually increasing ranks from 16 to 32 and finally 64
Mixed Precision Training: Utilizing bf16 where supported for optimal performance
GRPO (Guided Reward Policy Optimization): Implemented via the Unsloth framework for guided training
Data: GSM8K dataset enriched with explicit think-aloud examples

License

This model adheres to the licensing terms of the base Llama-3.2 1B model. Please refer to Meta's Llama-3.2 1B license for details on usage terms and conditions.

Framework

Developed using the Unsloth Framework, this model leverages advanced techniques like GRPO and progressive LoRA optimization for efficient training and fine-tuning of large language models.

Downloads last month: 14

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for Wladastic/Mini-Think-Base-1B

Base model

meta-llama/Llama-3.2-1B-Instruct

Finetuned

unsloth/Llama-3.2-1B-Instruct

Finetuned

(453)

this model

Quantizations

2 models

Wladastic
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Mini-Think-Base-1B