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RichardErkhov
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jointpreferences_-_mistral_7b_sft_helpful-gguf

GGUF
Model card Files
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How to use from
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 RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-gguf:
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-gguf:
Install from WinGet (Windows) 
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-gguf:
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-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 RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-gguf:
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-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 RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-gguf:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-gguf:
Use Docker 
docker model run hf.co/RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-gguf:
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Check out the documentation for more information.

Quantization made by Richard Erkhov.

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mistral_7b_sft_helpful - GGUF

Name Quant method Size
mistral_7b_sft_helpful.Q2_K.gguf Q2_K 2.53GB
mistral_7b_sft_helpful.IQ3_XS.gguf IQ3_XS 2.81GB
mistral_7b_sft_helpful.IQ3_S.gguf IQ3_S 2.96GB
mistral_7b_sft_helpful.Q3_K_S.gguf Q3_K_S 2.95GB
mistral_7b_sft_helpful.IQ3_M.gguf IQ3_M 3.06GB
mistral_7b_sft_helpful.Q3_K.gguf Q3_K 3.28GB
mistral_7b_sft_helpful.Q3_K_M.gguf Q3_K_M 3.28GB
mistral_7b_sft_helpful.Q3_K_L.gguf Q3_K_L 3.56GB
mistral_7b_sft_helpful.IQ4_XS.gguf IQ4_XS 3.67GB
mistral_7b_sft_helpful.Q4_0.gguf Q4_0 3.83GB
mistral_7b_sft_helpful.IQ4_NL.gguf IQ4_NL 3.87GB
mistral_7b_sft_helpful.Q4_K_S.gguf Q4_K_S 3.86GB
mistral_7b_sft_helpful.Q4_K.gguf Q4_K 4.07GB
mistral_7b_sft_helpful.Q4_K_M.gguf Q4_K_M 4.07GB
mistral_7b_sft_helpful.Q4_1.gguf Q4_1 4.24GB
mistral_7b_sft_helpful.Q5_0.gguf Q5_0 4.65GB
mistral_7b_sft_helpful.Q5_K_S.gguf Q5_K_S 4.65GB
mistral_7b_sft_helpful.Q5_K.gguf Q5_K 4.78GB
mistral_7b_sft_helpful.Q5_K_M.gguf Q5_K_M 4.78GB
mistral_7b_sft_helpful.Q5_1.gguf Q5_1 5.07GB
mistral_7b_sft_helpful.Q6_K.gguf Q6_K 5.53GB
mistral_7b_sft_helpful.Q8_0.gguf Q8_0 7.17GB

Original model description: Paper: Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization

Link: https://arxiv.org/abs/2404.00530

Github: https://github.com/Hritikbansal/dove

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Paper for RichardErkhov/jointpreferences_-_mistral_7b_sft_helpful-gguf