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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Josephgflowers/3BigReasonCinder with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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 Desktop
- 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
- Atomic Chat
Download README.md from Josephgflowers/3BigReasonCinder: direct link, hf CLI and curl.
- Browser
- Download file 3.74 kB
-
https://huggingface.co/Josephgflowers/3BigReasonCinder/resolve/main/README.md
- Command line
-
hf download hf://Josephgflowers/3BigReasonCinder/README.md
-
curl -L -o README.md https://huggingface.co/Josephgflowers/3BigReasonCinder/resolve/main/README.md
license: mit
model-index:
- name: 3BigReasonCinder
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 41.72
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/3BigReasonCinder
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 65.16
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/3BigReasonCinder
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 44.79
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/3BigReasonCinder
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 44.76
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/3BigReasonCinder
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.96
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/3BigReasonCinder
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 27.6
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/3BigReasonCinder
name: Open LLM Leaderboard
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 |