Instructions to use NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.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 NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S

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/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S

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/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S

Use Docker

docker model run hf.co/NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S

LM Studio
Jan
Ollama
How to use NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF with Ollama:
```
ollama run hf.co/NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S
```
Unsloth Desktop
Docker Model Runner
How to use NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF with Docker Model Runner:
```
docker model run hf.co/NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S
```

Lemonade

How to use NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF:Q4_K_S

Run and chat with the model

lemonade run user.MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF-Q4_K_S

List all available models

lemonade list

Atomic Chat

Nexesenex commited on Jan 31, 2024

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Update README.md

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@@ -116,7 +116,7 @@ This yarn version performs closely to Llama 2 70b (but with 32k max context), an
 ---
-Also, for information, another requant from a Q4_K_S orphan of a 32k finetune of Sao10K's WinterGoddess 70b :
 - WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1952-iMat-c32_ch2500-Q3_K_XS.gguf,-,Hellaswag,89.25,,400,2024-01-23 01:40:00,PEC2.5,70b,Llama_2,4096,,,GGUF,Mishima,Nexesenex,
 - WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1952-iMat-c32_ch2500-Q3_K_XS.gguf,-,Hellaswag_Bin,84,,400,2024-01-23 01:40:00,PEC2.5,70b,Llama_2,4096,,,GGUF,Mishima,Nexesenex,

 ---
+Also, for information, another requant from a Q4_K_S orphan of a 32k finetune of Sao10K's WinterGoddess 70b At Linear rope 2.5 (for 10k context) :
 - WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1952-iMat-c32_ch2500-Q3_K_XS.gguf,-,Hellaswag,89.25,,400,2024-01-23 01:40:00,PEC2.5,70b,Llama_2,4096,,,GGUF,Mishima,Nexesenex,
 - WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1952-iMat-c32_ch2500-Q3_K_XS.gguf,-,Hellaswag_Bin,84,,400,2024-01-23 01:40:00,PEC2.5,70b,Llama_2,4096,,,GGUF,Mishima,Nexesenex,