Instructions to use NexesQuants/Senku-70b-iMat.GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use NexesQuants/Senku-70b-iMat.GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="NexesQuants/Senku-70b-iMat.GGUF", filename="Senku-70b-b2081-iMat-c32_ch300-IQ3_XXS.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 NexesQuants/Senku-70b-iMat.GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS # Run inference directly in the terminal: llama-cli -hf NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS # Run inference directly in the terminal: llama-cli -hf NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS
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 NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS # Run inference directly in the terminal: ./llama-cli -hf NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS
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 NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS # Run inference directly in the terminal: ./build/bin/llama-cli -hf NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS
Use Docker
docker model run hf.co/NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS
- LM Studio
- Jan
- Ollama
How to use NexesQuants/Senku-70b-iMat.GGUF with Ollama:
ollama run hf.co/NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS
- Unsloth Studio
How to use NexesQuants/Senku-70b-iMat.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 NexesQuants/Senku-70b-iMat.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 NexesQuants/Senku-70b-iMat.GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for NexesQuants/Senku-70b-iMat.GGUF to start chatting
- Docker Model Runner
How to use NexesQuants/Senku-70b-iMat.GGUF with Docker Model Runner:
docker model run hf.co/NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS
- Lemonade
How to use NexesQuants/Senku-70b-iMat.GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NexesQuants/Senku-70b-iMat.GGUF:IQ3_XXS
Run and chat with the model
lemonade run user.Senku-70b-iMat.GGUF-IQ3_XXS
List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
GGUF Quants with iMatrix for : https://huggingface.co/ShinojiResearch/Senku-70B-Full
Q3_K_M, IQ3_XXS, Q2_K, Q2_K_S and Q3_K_S are provided here.
But for IQ2_XS and IQ2_XXS, it's there : https://huggingface.co/dranger003/Senku-70B-iMat.GGUF
LlamaCPP Benchs :
- Senku-70b-b2081-iMat-c32_ch300-Q3_K_M.gguf,-,Hellaswag,84.5,,400,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,ShinojiResearch,Nexesenex,
- Senku-70b-b2081-iMat-c32_ch300-Q3_K_M.gguf,-,Hellaswag,83.3,,1000,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,ShinojiResearch,Nexesenex,
- Senku-70b-b2081-iMat-c32_ch300-Q3_K_M.gguf,-,Arc-Challenge,59.19732441,,299,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,ShinojiResearch,Nexesenex,
- Senku-70b-b2081-iMat-c32_ch300-Q3_K_M.gguf,-,Arc-Easy,77.89473684,,570,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,ShinojiResearch,Nexesenex,
- Senku-70b-b2081-iMat-c32_ch300-Q3_K_M.gguf,-,MMLU,49.52076677,,313,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,ShinojiResearch,Nexesenex,
- Senku-70b-b2081-iMat-c32_ch300-Q3_K_M.gguf,-,Thruthful-QA,38.92288862,,817,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,ShinojiResearch,Nexesenex,
- Senku-70b-b2081-iMat-c32_ch300-Q3_K_M.gguf,-,Winogrande,78.4530,,1267,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,ShinojiResearch,Nexesenex,
- Senku-70b-b2081-iMat-c32_ch300-Q3_K_M.gguf,-,wikitext,4.3440,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,ShinojiResearch,Nexesenex,81
- Senku-70b-b2081-iMat-c32_ch300-Q3_K_M.gguf,-,wikitext,3.8722,512,512,2024-02-07 00:00:00,70b,Mistral_Medium,32768,,,GGUF,ShinojiResearch,Nexesenex,655
The Hellaswag scores might be 5-6 points higher, due to some recent changes in LlamaCPP.
Senku is dominant on Arc-Challenge among Miqu based models, providing a read bump from the baseline Miqu.
A reflection of its EQ-Bench, highest to date (7/02/2024) among the 70b models?
On the other hand, the TQA suffers quite a bit.
Here comes the benchs of its toughest competitor to my knowledge, at equal quant except for the number of chunks of the iMatrix :
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Hellaswag,84.5,,400,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Hellaswag,83.6,,1000,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Arc-Challenge,58.52842809,,299,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Arc-Easy,77.36842105,,570,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,MMLU,49.84025559,,313,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Thruthful-QA,42.83965728,,817,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,Winogrande,78.7687,,1267,2024-02-07 05:40:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,wikitext,4.2963,512,512,2024-02-07 00:00:00,,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,81
- Undi95_Miqu-70B-Alpaca-DPO-b2101-iMat-c32_ch1000-Q3_K_M.gguf,-,wikitext,3.8397,512,512,2024-02-07 00:00:00,70b,Mistral_Medium,32768,,,GGUF,NeverSleep,Nexesenex,655
I think that both these models deserve a 5 millions tokens iMatrix (512ctx, 10,000 chunks, on wiki.train.raw).
And why not, a combination of such iMatrixes from different major languages (English, French, German, Spanish at least, etc..)
Alas, I can't provide this for now.
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