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 21, 2025

Commit

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@@ -29,44 +29,6 @@ tags:
 This script is designed to load, process, and combine multiple datasets into a single, standardized format suitable for training conversational AI models. The script uses the `datasets` library to load and manipulate the datasets, and the `chat_templates` library to standardize the conversation format.
-## Features
-- **Dataset Loading**: Loads multiple datasets from the Hugging Face Hub.
-- **Conversation Formatting**: Adds a `conversations` column to each dataset, ensuring a consistent structure for user-assistant interactions.
-- **Dataset Combination**: Combines all datasets into a single dataset.
-- **Standardization**: Standardizes the combined dataset using the ShareGPT format.
-- **Tokenization**: Applies a chat template to format the prompts for training.
-## Datasets Used
-1. **PowerInfer/LONGCOT-Refine-500K**
-2. **amphora/QwQ-LongCoT-130K**
-3. **AI-MO/NuminaMath-CoT**
-4. **prithivMLmods/Math-Solve**
-5. **amphora/QwQ-LongCoT-130K-2**
-6. **O1-OPEN/OpenO1-SFT**
-7. **FreedomIntelligence/medical-o1-reasoning-SFT**
-8. **ngxson/MiniThinky-dataset**
-9. **prithivMLmods/Deepthink-Reasoning**
-## Functions
-- **add_conversations_column**: Adds a `conversations` column for datasets with `prompt` and `response` fields.
-- **add_conversations_column_prompt_qwq**: Adds a `conversations` column for datasets with `problem` and `qwq` fields.
-- **add_conversations_column_prompt_solution**: Adds a `conversations` column for datasets with `problem` and `solution` fields.
-- **add_conversations_outputs**: Adds a `conversations` column for datasets with `problem` and `outputs` fields.
-- **add_conversations_outputs_open**: Adds a `conversations` column for datasets with `instruction` and `output` fields.
-- **add_conversations_outputs_med**: Adds a `conversations` column for datasets with `Question` and `Complex_CoT` fields.
-## Usage
-1. **Load Datasets**: The script loads each dataset individually.
-2. **Map Conversation Columns**: Each dataset is mapped to add a `conversations` column using the appropriate function.
-3. **Combine Datasets**: All datasets are combined into a single dataset.
-4. **Standardize Dataset**: The combined dataset is standardized using the ShareGPT format.
-5. **Apply Chat Template**: The chat template is applied to format the prompts for training.
-6. **Print Output**: The first 50,000 examples are printed to verify the output.
 ## Example
 ```python

 This script is designed to load, process, and combine multiple datasets into a single, standardized format suitable for training conversational AI models. The script uses the `datasets` library to load and manipulate the datasets, and the `chat_templates` library to standardize the conversation format.
 ## Example
 ```python