Instructions to use Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF",
	filename="fblgit_UNA-34BeagleSimpleMath-32K-v1-b1924-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-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 Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0

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 Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0

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 Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0

Use Docker

docker model run hf.co/Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0

LM Studio
Jan
Ollama
How to use Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF with Ollama:
```
ollama run hf.co/Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0
```

Unsloth Studio

How to use Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-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 Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-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 Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-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 Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF to start chatting

Docker Model Runner
How to use Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF with Docker Model Runner:
```
docker model run hf.co/Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0
```

Lemonade

How to use Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nexesenex/fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF:Q8_0

Run and chat with the model

lemonade run user.fblgit_UNA-34BeagleSimpleMath-32K-v1-iMat.GGUF-Q8_0

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Model is likely broken :

fblgit_UNA-34BeagleSimpleMath-32K-v1-b1973-iMat-c32_ch1000-Q4_K_M.gguf,-,Hellaswag,86.25,,400,2024-01-26 01:40:00,,34b,Yi,200000,,,GGUF,Fblgit,Nexesenex,
fblgit_UNA-34BeagleSimpleMath-32K-v1-b1973-iMat-c32_ch1000-Q4_K_M.gguf,-,Hellaswag_Bin,81,,400,2024-01-26 01:40:00,,34b,Yi,200000,,,GGUF,Fblgit,Nexesenex
fblgit_UNA-34BeagleSimpleMath-32K-v1-b1973-iMat-c32_ch1000-Q4_K_M.gguf,-,Arc-Challenge,58.19397993,,299,2024-01-26 05:40:00,,34b,Yi,200000,,,GGUF,Fblgit,Nexesenex
fblgit_UNA-34BeagleSimpleMath-32K-v1-b1973-iMat-c32_ch1000-Q4_K_M.gguf,-,Arc-Easy,77.54385965,,570,2024-01-26 05:40:00,,34b,Yi,200000,,,GGUF,Fblgit,Nexesenex
fblgit_UNA-34BeagleSimpleMath-32K-v1-b1973-iMat-c32_ch1000-Q4_K_M.gguf,-,Thruthful-QA,48.71481028,,817,2024-01-26 05:40:00,,34b,Yi,200000,,,GGUF,Fblgit,Nexesenex,
fblgit_UNA-34BeagleSimpleMath-32K-v1-b1973-iMat-c32_ch1000-Q4_K_M.gguf,-,Winogrande,78.8477,,1267,2024-01-26 05:40:00,,34b,Yi,200000,,,GGUF,Fblgit,Nexesenex
fblgit_UNA-34BeagleSimpleMath-32K-v1-b1973-iMat-c32_ch1000-Q4_K_M.gguf,-,wikitext,5.6493,512,512,2024-01-26 01:40:00,,34b,Yi,200000,,,GGUF,Fblgit,Nexesenex
fblgit_UNA-34BeagleSimpleMath-32K-v1-b1973-iMat-c32_ch1000-Q4_K_M.gguf,-,wikitext,11.5559,4096,4096,2024-01-26 01:40:00,,34b,Yi,200000,,,GGUF,Fblgit,Nexesenex
fblgit_UNA-34BeagleSimpleMath-32K-v1-b1973-iMat-c32_ch1000-Q4_K_M.gguf,-,MMLU,42.49201278,,313,2024-01-26 05:40:00,,34b,Yi,200000,,,GGUF,Fblgit,Nexesenex

Perplexity humps at 11.5 at 4096 ctx. I just leave that here.

Downloads last month: 12

GGUF

Model size

34B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

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