Instructions to use characharm/Mistral-Nemo-Instruct-2407.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use characharm/Mistral-Nemo-Instruct-2407.gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="characharm/Mistral-Nemo-Instruct-2407.gguf",
	filename="Mistral-Nemo-Instruct-2407_Q4_K_S.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 characharm/Mistral-Nemo-Instruct-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 characharm/Mistral-Nemo-Instruct-2407.gguf:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf characharm/Mistral-Nemo-Instruct-2407.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 characharm/Mistral-Nemo-Instruct-2407.gguf:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf characharm/Mistral-Nemo-Instruct-2407.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 characharm/Mistral-Nemo-Instruct-2407.gguf:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf characharm/Mistral-Nemo-Instruct-2407.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 characharm/Mistral-Nemo-Instruct-2407.gguf:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf characharm/Mistral-Nemo-Instruct-2407.gguf:Q4_K_S

Use Docker

docker model run hf.co/characharm/Mistral-Nemo-Instruct-2407.gguf:Q4_K_S

LM Studio
Jan
Ollama
How to use characharm/Mistral-Nemo-Instruct-2407.gguf with Ollama:
```
ollama run hf.co/characharm/Mistral-Nemo-Instruct-2407.gguf:Q4_K_S
```

Unsloth Studio

How to use characharm/Mistral-Nemo-Instruct-2407.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 characharm/Mistral-Nemo-Instruct-2407.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 characharm/Mistral-Nemo-Instruct-2407.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for characharm/Mistral-Nemo-Instruct-2407.gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use characharm/Mistral-Nemo-Instruct-2407.gguf with Docker Model Runner:
```
docker model run hf.co/characharm/Mistral-Nemo-Instruct-2407.gguf:Q4_K_S
```

Lemonade

How to use characharm/Mistral-Nemo-Instruct-2407.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull characharm/Mistral-Nemo-Instruct-2407.gguf:Q4_K_S

Run and chat with the model

lemonade run user.Mistral-Nemo-Instruct-2407.gguf-Q4_K_S

List all available models

lemonade list

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

Check out the documentation for more information.

converted via this PR https://github.com/ggerganov/llama.cpp/pull/8604

original model https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407

license: apache-2.0 language: - en - fr - de - es - it - pt - ru - zh - ja

Model Card for Mistral-Nemo-Instruct-2407

The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407. Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.

For more details about this model please refer to our release blog post.

Key features

Released under the Apache 2 License
Pre-trained and instructed versions
Trained with a 128k context window
Trained on a large proportion of multilingual and code data
Drop-in replacement of Mistral 7B

Model Architecture

Mistral Nemo is a transformer model, with the following architecture choices:

Layers: 40
Dim: 5,120
Head dim: 128
Hidden dim: 14,436
Activation Function: SwiGLU
Number of heads: 32
Number of kv-heads: 8 (GQA)
Vocabulary size: 2**17 ~= 128k
Rotary embeddings (theta = 1M)

Metrics

Main Benchmarks

Benchmark	Score
HellaSwag (0-shot)	83.5%
Winogrande (0-shot)	76.8%
OpenBookQA (0-shot)	60.6%
CommonSenseQA (0-shot)	70.4%
TruthfulQA (0-shot)	50.3%
MMLU (5-shot)	68.0%
TriviaQA (5-shot)	73.8%
NaturalQuestions (5-shot)	31.2%

Multilingual Benchmarks (MMLU)

Language	Score
French	62.3%
German	62.7%
Spanish	64.6%
Italian	61.3%
Portuguese	63.3%
Russian	59.2%
Chinese	59.0%
Japanese	59.0%

Downloads last month: 11

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

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