How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf redponike/ether0-GGUF:# Run inference directly in the terminal:
llama-cli -hf redponike/ether0-GGUF: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 redponike/ether0-GGUF:# Run inference directly in the terminal:
./llama-cli -hf redponike/ether0-GGUF: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 redponike/ether0-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf redponike/ether0-GGUF:Use Docker
docker model run hf.co/redponike/ether0-GGUF:Quick Links
GGUF quants of futurehouse/ether0
As this model's training was primarily done on SMILES strings of organic molecules, and is therefore meant for conversations containing a good amount of them, I recommend using Q8_0, Q6_K, Q5_K_M, or Q5_K_S if your bandwidth for these particular sizes yield an acceptable performance for you. The perplexity of these quants should be good enough.
Using llama.cpp b5602 (commit 745aa5319b9930068aff5e87cf5e9eef7227339b)
The importance matrix was generated with calibration_datav3.txt.
All quants were generated/calibrated with the imatrix, including the K quants.
Quantized from BF16.
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Model tree for redponike/ether0-GGUF
Base model
mistralai/Mistral-Small-24B-Base-2501 Finetuned
futurehouse/ether0
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf redponike/ether0-GGUF:# Run inference directly in the terminal: llama-cli -hf redponike/ether0-GGUF: