Instructions to use prithivMLmods/FastThink-0.5B-Tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/FastThink-0.5B-Tiny with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/FastThink-0.5B-Tiny")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/FastThink-0.5B-Tiny")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/FastThink-0.5B-Tiny", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/FastThink-0.5B-Tiny with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/FastThink-0.5B-Tiny"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/FastThink-0.5B-Tiny",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/FastThink-0.5B-Tiny

SGLang

How to use prithivMLmods/FastThink-0.5B-Tiny 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 "prithivMLmods/FastThink-0.5B-Tiny" \
    --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": "prithivMLmods/FastThink-0.5B-Tiny",
		"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 "prithivMLmods/FastThink-0.5B-Tiny" \
        --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": "prithivMLmods/FastThink-0.5B-Tiny",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/FastThink-0.5B-Tiny with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/FastThink-0.5B-Tiny
```

prithivMLmods commited on Jan 24, 2025

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@@ -14,6 +14,14 @@ tags:
 ---
 # **FastThink-1.5B-Tiny**
 # **Dataset Preparation**

 ---
 # **FastThink-1.5B-Tiny**
+FastThink-0.5B-Tiny is a reasoning-focused model based on Qwen2.5. We have released a range of base language models and instruction-tuned language models, spanning from 0.5 billion to 72 billion parameters. Qwen2.5 introduces the following improvements over Qwen2:
+- Significantly enhanced knowledge and greatly improved capabilities in coding and mathematics, thanks to specialized expert models in these domains.
+- Major improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g., tables), and generating structured outputs, especially JSON. It is more resilient to diverse system prompts, enhancing role-play implementation and condition-setting for chatbots.
+- Long-context support for up to 128K tokens and the ability to generate outputs up to 8K tokens.
+- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+**Architecture**: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
 # **Dataset Preparation**