Text Generation
Transformers
PyTorch
Safetensors
English
llama
trl
grpo
rl
superthoughts
reasoning
cot
conversational
text-generation-inference
Instructions to use Pinkstack/Superthoughts-lite-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pinkstack/Superthoughts-lite-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Pinkstack/Superthoughts-lite-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Pinkstack/Superthoughts-lite-v1") model = AutoModelForCausalLM.from_pretrained("Pinkstack/Superthoughts-lite-v1") 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 Pinkstack/Superthoughts-lite-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Pinkstack/Superthoughts-lite-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pinkstack/Superthoughts-lite-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Pinkstack/Superthoughts-lite-v1
- SGLang
How to use Pinkstack/Superthoughts-lite-v1 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 "Pinkstack/Superthoughts-lite-v1" \ --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": "Pinkstack/Superthoughts-lite-v1", "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 "Pinkstack/Superthoughts-lite-v1" \ --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": "Pinkstack/Superthoughts-lite-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Pinkstack/Superthoughts-lite-v1 with Docker Model Runner:
docker model run hf.co/Pinkstack/Superthoughts-lite-v1
File size: 4,057 Bytes
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library_name: transformers
tags:
- trl
- grpo
- rl
- superthoughts
- reasoning
- cot
license: apache-2.0
datasets:
- openai/gsm8k
- Pinkstack/intructions-sft-sharegpt
language:
- en
base_model:
- HuggingFaceTB/SmolLM2-1.7B-Instruct
widget:
- messages:
- role: user
content: >-
You must act in a conversational matter and always include at the start
<think> ... </think> <output> ... </output> tokens.
Are cats cool?
- messages:
- role: user
content: >-
You must act in a conversational matter and always include at the start
<think> ... </think> <output> ... </output> tokens.
Hello!
- messages:
- role: user
content: >-
You must act in a conversational matter and always include at the start
<think> ... </think> <output> ... </output> tokens.
2x-2=6, how much is X?
---
V2: Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-bf16
# Information
Advanced, high-quality and **lite** reasoning for a tiny size that you can run on your phone.
At original quality, it runs at ~400 tokens/second on a single H100 Nvidia GPU from Friendli.
Trained similarly to Deepseek R1, we used Smollm2 as a base model, then we've SFT fine tuned on reasoning using our own private superthoughts instruct dataset which includes a mix of code, website generation, day-to-day chats, math and counting problems. And then we modified the tokenizer slightly, after the SFT fine tuning we used Grpo (arXiv:2402.03300) to further amplify it's mathematics & problem solving abilities.
<div style="background-color: #ffebee; padding: 16px; border-radius: 4px; border-left: 4px solid #ef5350;">
<h1 style="color: #c62828; margin: 0 0 8px 0;">⚠️ WARNING</h1>
<p style="color: #c62828; font-weight: bold; margin: 0;">
We did not put additional safety filters when doing SFT, thus this AI is pretty uncensored and can be rude at times. unless you specify in the system prompt that it is harmless, it won't be. users are soley responsible for the use of this AI. no output from the AI represents the views of Pinkstack or any other third party, it may create biased, inccorect and harmful information unless you set it up properly. for commercial use, it is reccomeneded that you either use another AI like llamaguard for filtering or in the system prompt ensure that it would be harmless.
</p>
</div>
# Format
```
<|im_start|>user
How many R's in strawberry<|im_end|>
<|im_start|>assistant
<think>
Alright, the user has asked how many R's in the word strawberry, that's easy! I just need to count each instance of the letter 'R' in the word 's-t-r-a-w-b-e-r-r-y' and then find out how many R's there are, lets count!
S - Not an R,
T - Not an R,
R - First instance of the letter R! (1),
A - Not an R,
W - Not an R,
B - Not an R,
E - Not an R,
R - Great! Second instance of the letter R. (2),
R - Third instance of the letter R. (3),
Y - Not an R.
So, i've counted all the letters correctly, meaning that I am sure that there are 3 R's in the word Strawberry. I should probably let the user know.
</think>
<output>3
</output><|im_end|>
```
It is very reccomend to use a low temperature, higher temperatures may cause it to not think.
# system prompt
(important to ensure it would always think, output).
```
respond in the following format:
<think>
...
</think>
<output>
...
</output>
```
# Examples:
all responses below generated with our system prompt and a temperature of 0.7.
Generated inside the android application, ChatterUI via GGUF Q8, using the model's prompt format. and our
1)

2)

3)

# Uploaded model
- **Developed by:** Pinkstack
- **License:** apache-2.0
- **Finetuned from model :** HuggingFaceTB/SmolLM2-1.7B-Instruct |