Instructions to use Josephgflowers/3BigReasonCinder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Josephgflowers/3BigReasonCinder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Josephgflowers/3BigReasonCinder")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Josephgflowers/3BigReasonCinder") model = AutoModelForCausalLM.from_pretrained("Josephgflowers/3BigReasonCinder") - llama-cpp-python
How to use Josephgflowers/3BigReasonCinder with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Josephgflowers/3BigReasonCinder", filename="3BigReasonCinder-unsloth.F16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Josephgflowers/3BigReasonCinder with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Josephgflowers/3BigReasonCinder:F16 # Run inference directly in the terminal: llama-cli -hf Josephgflowers/3BigReasonCinder:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Josephgflowers/3BigReasonCinder:F16 # Run inference directly in the terminal: llama-cli -hf Josephgflowers/3BigReasonCinder:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Josephgflowers/3BigReasonCinder:F16 # Run inference directly in the terminal: ./llama-cli -hf Josephgflowers/3BigReasonCinder:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Josephgflowers/3BigReasonCinder:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Josephgflowers/3BigReasonCinder:F16
Use Docker
docker model run hf.co/Josephgflowers/3BigReasonCinder:F16
- LM Studio
- Jan
- vLLM
How to use Josephgflowers/3BigReasonCinder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Josephgflowers/3BigReasonCinder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Josephgflowers/3BigReasonCinder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Josephgflowers/3BigReasonCinder:F16
- SGLang
How to use Josephgflowers/3BigReasonCinder 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 "Josephgflowers/3BigReasonCinder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Josephgflowers/3BigReasonCinder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Josephgflowers/3BigReasonCinder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Josephgflowers/3BigReasonCinder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use Josephgflowers/3BigReasonCinder with Ollama:
ollama run hf.co/Josephgflowers/3BigReasonCinder:F16
- Unsloth Studio new
How to use Josephgflowers/3BigReasonCinder 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 Josephgflowers/3BigReasonCinder 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 Josephgflowers/3BigReasonCinder to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Josephgflowers/3BigReasonCinder to start chatting
- Docker Model Runner
How to use Josephgflowers/3BigReasonCinder with Docker Model Runner:
docker model run hf.co/Josephgflowers/3BigReasonCinder:F16
- Lemonade
How to use Josephgflowers/3BigReasonCinder with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Josephgflowers/3BigReasonCinder:F16
Run and chat with the model
lemonade run user.3BigReasonCinder-F16
List all available models
lemonade list
Not working on hugginface for some reason. Still looking into it. Downloaded files are working as expected... GGUF files working, re Uploading. Overview Cinder is an AI chatbot tailored for engaging users in scientific and educational conversations, offering companionship, and sparking imaginative exploration. It is built on the MiniChat 3B parameter model and trained on a unique combination of datasets.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 48.16 |
| AI2 Reasoning Challenge (25-Shot) | 41.72 |
| HellaSwag (10-Shot) | 65.16 |
| MMLU (5-Shot) | 44.79 |
| TruthfulQA (0-shot) | 44.76 |
| Winogrande (5-shot) | 64.96 |
| GSM8k (5-shot) | 27.60 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard41.720
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard65.160
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard44.790
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard44.760
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard64.960
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard27.600