Instructions to use tensorblock/CarbonBeagle-11B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tensorblock/CarbonBeagle-11B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tensorblock/CarbonBeagle-11B-GGUF", filename="CarbonBeagle-11B-Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use tensorblock/CarbonBeagle-11B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/CarbonBeagle-11B-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/CarbonBeagle-11B-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/CarbonBeagle-11B-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/CarbonBeagle-11B-GGUF:Q2_K
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 tensorblock/CarbonBeagle-11B-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/CarbonBeagle-11B-GGUF:Q2_K
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 tensorblock/CarbonBeagle-11B-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/CarbonBeagle-11B-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/CarbonBeagle-11B-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use tensorblock/CarbonBeagle-11B-GGUF with Ollama:
ollama run hf.co/tensorblock/CarbonBeagle-11B-GGUF:Q2_K
- Unsloth Studio new
How to use tensorblock/CarbonBeagle-11B-GGUF 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 tensorblock/CarbonBeagle-11B-GGUF 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 tensorblock/CarbonBeagle-11B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tensorblock/CarbonBeagle-11B-GGUF to start chatting
- Docker Model Runner
How to use tensorblock/CarbonBeagle-11B-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/CarbonBeagle-11B-GGUF:Q2_K
- Lemonade
How to use tensorblock/CarbonBeagle-11B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/CarbonBeagle-11B-GGUF:Q2_K
Run and chat with the model
lemonade run user.CarbonBeagle-11B-GGUF-Q2_K
List all available models
lemonade list
vicgalle/CarbonBeagle-11B - GGUF
This repo contains GGUF format model files for vicgalle/CarbonBeagle-11B.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Our projects
| Forge | |
|---|---|
|
|
| An OpenAI-compatible multi-provider routing layer. | |
| 🚀 Try it now! 🚀 | |
| Awesome MCP Servers | TensorBlock Studio |
![]() |
![]() |
| A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
| 👀 See what we built 👀 | 👀 See what we built 👀 |
### System:
{system_prompt}
### User:
{prompt}
### Assistant:
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| CarbonBeagle-11B-Q2_K.gguf | Q2_K | 3.728 GB | smallest, significant quality loss - not recommended for most purposes |
| CarbonBeagle-11B-Q3_K_S.gguf | Q3_K_S | 4.344 GB | very small, high quality loss |
| CarbonBeagle-11B-Q3_K_M.gguf | Q3_K_M | 4.839 GB | very small, high quality loss |
| CarbonBeagle-11B-Q3_K_L.gguf | Q3_K_L | 5.263 GB | small, substantial quality loss |
| CarbonBeagle-11B-Q4_0.gguf | Q4_0 | 5.655 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| CarbonBeagle-11B-Q4_K_S.gguf | Q4_K_S | 5.698 GB | small, greater quality loss |
| CarbonBeagle-11B-Q4_K_M.gguf | Q4_K_M | 6.018 GB | medium, balanced quality - recommended |
| CarbonBeagle-11B-Q5_0.gguf | Q5_0 | 6.889 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| CarbonBeagle-11B-Q5_K_S.gguf | Q5_K_S | 6.889 GB | large, low quality loss - recommended |
| CarbonBeagle-11B-Q5_K_M.gguf | Q5_K_M | 7.076 GB | large, very low quality loss - recommended |
| CarbonBeagle-11B-Q6_K.gguf | Q6_K | 8.200 GB | very large, extremely low quality loss |
| CarbonBeagle-11B-Q8_0.gguf | Q8_0 | 10.621 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/CarbonBeagle-11B-GGUF --include "CarbonBeagle-11B-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/CarbonBeagle-11B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Downloads last month
- 36
2-bit
Model tree for tensorblock/CarbonBeagle-11B-GGUF
Base model
vicgalle/CarbonBeagle-11BEvaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard71.840
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.930
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard66.620
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard69.430
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.060
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard66.940
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard54.150
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard33.060
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard5.510
- acc_norm on GPQA (0-shot)Open LLM Leaderboard6.940
- acc_norm on MuSR (0-shot)Open LLM Leaderboard9.190
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard25.290

