Instructions to use BeaverAI/Behemoth-R1-123B-v2a-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use BeaverAI/Behemoth-R1-123B-v2a-GGUF 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 BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M
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 BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M
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 BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M
Use Docker
docker model run hf.co/BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use BeaverAI/Behemoth-R1-123B-v2a-GGUF with Ollama:
ollama run hf.co/BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M
- Unsloth Studio
How to use BeaverAI/Behemoth-R1-123B-v2a-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 BeaverAI/Behemoth-R1-123B-v2a-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 BeaverAI/Behemoth-R1-123B-v2a-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BeaverAI/Behemoth-R1-123B-v2a-GGUF to start chatting
- Docker Model Runner
How to use BeaverAI/Behemoth-R1-123B-v2a-GGUF with Docker Model Runner:
docker model run hf.co/BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M
- Lemonade
How to use BeaverAI/Behemoth-R1-123B-v2a-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BeaverAI/Behemoth-R1-123B-v2a-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Behemoth-R1-123B-v2a-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Questions about the model instruct format
Sorry to bother you, my comprehension is too poor and I am not an English speaker.
What exactly does the format of Mistral (Non-Tekken), i.e., Mistral v3 + [SYSTEM_PROMPT] refer to?
[INST][SYSTEM_PROMPT] {system_prompt}[/SYSTEM_PROMPT][/INST] Understood.[INST] {prompt}[/INST] {Model Response}
[SYSTEM_PROMPT] {system_prompt}[/SYSTEM_PROMPT] Understood.[INST] {prompt}[/INST] {Model Response}
[INST][SYSTEM_PROMPT] {system_prompt}[/INST] Understood.[INST] {prompt}[/INST] {Model Response}
Which format is correct?
I'm confused because this model barely understands what I'm writing. However, all other Behemoth v1.x and merged models I've used have responded fine, so I believe it might be a formatting issue.
Thank you for taking the time to answer and help.
Tekken refers to the tokenizer mistral uses nowadays. Previously, they used sentencepiece.
Read about it here : https://github.com/LostRuins/koboldcpp/pull/1659
In a nutshell, from pandora:
SentencePiece (older methods) VS Tekken (used my most of our recent models):
SentencePiece:
Used in most of our older models, its usually the not tekken ones, at first we only had v1 and v2, then we had v3 that also introduced the first v3-tekken variant.
SentencePiece adds a defaulr whitespace at each encode("example"), becoming "_example" instead.
This is the source of the trailing whitespaces, but this also means that the model is the one that wants to generate a token with the white space, becoming like this:
<s>[INST]_user message[/INST]_assistant message</s>[INST]_user new message[/INST]
WITHOUT a last whitespace, because the model will generate a token starting with the whitespace. If you add the whitespace you will mess up the distribution.
Again this is only for the models using SentencePiece (not Tekken, if u go to one of our repos, if u see a tekken file its Tekken, if no tekken file its SentencePiece)
Tekken
However tekken doesnt have this issue of default whitespaces being added making it very simple.
<s>[INST]user message[/INST]assistant message</s>[INST]user new message[/INST]
As for which is to be used, it depends. Mistral Small 25xx will be tekken based, 2409 is sentencepiece based. If you use koboldcpp, the auto format should handle it for you assuming the chat template is correctly set in the model.