Instructions to use ZeroWw/Mistral-Nemo-Base-2407-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 ZeroWw/Mistral-Nemo-Base-2407-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 ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16

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 ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16

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 ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16

Use Docker

docker model run hf.co/ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16

LM Studio
Jan

vLLM

How to use ZeroWw/Mistral-Nemo-Base-2407-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZeroWw/Mistral-Nemo-Base-2407-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroWw/Mistral-Nemo-Base-2407-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16

Ollama
How to use ZeroWw/Mistral-Nemo-Base-2407-GGUF with Ollama:
```
ollama run hf.co/ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16
```
Unsloth Desktop
Docker Model Runner
How to use ZeroWw/Mistral-Nemo-Base-2407-GGUF with Docker Model Runner:
```
docker model run hf.co/ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16
```

Lemonade

How to use ZeroWw/Mistral-Nemo-Base-2407-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZeroWw/Mistral-Nemo-Base-2407-GGUF:F16

Run and chat with the model

lemonade run user.Mistral-Nemo-Base-2407-GGUF-F16

List all available models

lemonade list

Atomic Chat

Clarify what is what, please.

by sphiratrioth666 - opened Jul 25, 2024

Discussion

sphiratrioth666

Jul 25, 2024

If I understand correctly, all your files are quantized this way? You're saying that f16 Q5 and Q6 are better than Q8_0. You provide files called f16, then just q5 and q6 and then q8_0, q8_p. So what is what?

Is that just q5 file and just q6 file as smart as q16 file? Please, just clarify what is what since I cannot follow your naming pattern when you start differentiating in that particular method you're using. It might be a great improvement over other quantizations but still - what is what?

Thx for clarification and keep up the good work!

ZeroWw

Owner Jul 26, 2024

If I understand correctly, all your files are quantized this way? You're saying that f16 Q5 and Q6 are better than Q8_0. You provide files called f16, then just q5 and q6 and then q8_0, q8_p. So what is what?

q8_0 is quantized f16 for outpt and embed and q8_0 for the others.
q8_p is quantized using the flag --pure in the quantization program

Is that just q5 file and just q6 file as smart as q16 file? Please, just clarify what is what since I cannot follow your naming pattern when you start differentiating in that particular method you're using. It might be a great improvement over other quantizations but still - what is what?

you are right, the naming is confusing because I changed it in time and I'm too lazy to change them all back.

Thx for clarification and keep up the good work!

everything is explained here: https://huggingface.co/RobertSinclair

sphiratrioth666

Jul 26, 2024

If I understand correctly, all your files are quantized this way? You're saying that f16 Q5 and Q6 are better than Q8_0. You provide files called f16, then just q5 and q6 and then q8_0, q8_p. So what is what?

q8_0 is quantized f16 for outpt and embed and q8_0 for the others.
q8_p is quantized using the flag --pure in the quantization program

Is that just q5 file and just q6 file as smart as q16 file? Please, just clarify what is what since I cannot follow your naming pattern when you start differentiating in that particular method you're using. It might be a great improvement over other quantizations but still - what is what?

you are right, the naming is confusing because I changed it in time and I'm too lazy to change them all back.

Thx for clarification and keep up the good work!

everything is explained here: https://huggingface.co/RobertSinclair

Ok! Thx. Great job, again - and thank you.

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