Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
unsloth
trl
conversational
Instructions to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath") model = AutoModelForCausalLM.from_pretrained("EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath") 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
- vLLM
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath
- SGLang
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath 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 "EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath" \ --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": "EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath", "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 "EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath" \ --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": "EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath", max_seq_length=2048, ) - Docker Model Runner
How to use EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath with Docker Model Runner:
docker model run hf.co/EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath
Update README.md
Browse files
README.md
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# Uploaded model
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- **Developed by:** EpistemeAI
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---
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This is a reasoning and reflect instruction-tuned generative model in 3B size (text in/text out).
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**Model Architecture:**
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Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) with GRPO fine tuning using unsloth, to align with human preferences for helpfulness and safety.
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Fine tune with Numina math dataset.
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### Use with transformers
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Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
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Make sure to update your transformers installation via `pip install --upgrade transformers`.
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```python
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import torch
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from transformers import pipeline
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model_id = "EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a powerful assistant Respond in the following format:
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<reasoning>
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...
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</reasoning>
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<reflecting>
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...
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</reflecting>
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<answer>
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...
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</answer>"},
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{"role": "user", "content": "Which is bigger? 9.11 or 9.9?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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```
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## Using [SuperTransformer](https://github.com/tomtyiu/SuperTransformer-SHF)
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```python
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import SuperTransformer
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# Load SuperTransformer Class, (1) Loads Huggingface model, (2) System Prompt (3) Text/prompt (4)Max tokens
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SuperTransformers = SuperTransformers("EpistemeAI/ReasoningCore-3B-Instruct-r01-Reflect-ThinkMath","You are a highly knowledgeable assistant with expertise in mathematics. <reasoning>...</reasoning><reflecting>...</reflecting><answer>...</answer>","What is the area of a circle, radius=16, reason step by step", 2026)
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# 8-bit quantization
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SuperTransformers.HuggingFaceTransformer8bit()
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# or 4-bit quantization
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SuperTransformers.HuggingFaceTransformer4bit()
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```
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# Uploaded model
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- **Developed by:** EpistemeAI
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