Instructions to use tensorblock/mistral-160m-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/mistral-160m-instruct-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/mistral-160m-instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/mistral-160m-instruct-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 tensorblock/mistral-160m-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/mistral-160m-instruct-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/mistral-160m-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/mistral-160m-instruct-GGUF:Q2_K

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 tensorblock/mistral-160m-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/mistral-160m-instruct-GGUF:Q2_K

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 tensorblock/mistral-160m-instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/mistral-160m-instruct-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/mistral-160m-instruct-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use tensorblock/mistral-160m-instruct-GGUF with Ollama:
```
ollama run hf.co/tensorblock/mistral-160m-instruct-GGUF:Q2_K
```
Unsloth Desktop
Docker Model Runner
How to use tensorblock/mistral-160m-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/mistral-160m-instruct-GGUF:Q2_K
```

Lemonade

How to use tensorblock/mistral-160m-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/mistral-160m-instruct-GGUF:Q2_K

Run and chat with the model

lemonade run user.mistral-160m-instruct-GGUF-Q2_K

List all available models

lemonade list

Atomic Chat

DaewonChoi/mistral-160m-instruct - GGUF

This repo contains GGUF format model files for DaewonChoi/mistral-160m-instruct.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4823.

Our projects

Forge

An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers	TensorBlock Studio

A comprehensive collection of Model Context Protocol (MCP) servers.	A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀	👀 See what we built 👀

## Prompt template

Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.

Model file specification

Filename	Quant type	File Size	Description
mistral-160m-instruct-Q2_K.gguf	Q2_K	0.070 GB	smallest, significant quality loss - not recommended for most purposes
mistral-160m-instruct-Q3_K_S.gguf	Q3_K_S	0.080 GB	very small, high quality loss
mistral-160m-instruct-Q3_K_M.gguf	Q3_K_M	0.086 GB	very small, high quality loss
mistral-160m-instruct-Q3_K_L.gguf	Q3_K_L	0.091 GB	small, substantial quality loss
mistral-160m-instruct-Q4_0.gguf	Q4_0	0.098 GB	legacy; small, very high quality loss - prefer using Q3_K_M
mistral-160m-instruct-Q4_K_S.gguf	Q4_K_S	0.099 GB	small, greater quality loss
mistral-160m-instruct-Q4_K_M.gguf	Q4_K_M	0.103 GB	medium, balanced quality - recommended
mistral-160m-instruct-Q5_0.gguf	Q5_0	0.116 GB	legacy; medium, balanced quality - prefer using Q4_K_M
mistral-160m-instruct-Q5_K_S.gguf	Q5_K_S	0.116 GB	large, low quality loss - recommended
mistral-160m-instruct-Q5_K_M.gguf	Q5_K_M	0.118 GB	large, very low quality loss - recommended
mistral-160m-instruct-Q6_K.gguf	Q6_K	0.134 GB	very large, extremely low quality loss
mistral-160m-instruct-Q8_0.gguf	Q8_0	0.173 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/mistral-160m-instruct-GGUF --include "mistral-160m-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/mistral-160m-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 1

GGUF

Model size

0.2B params

Architecture

llama

Hardware compatibility

2-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for tensorblock/mistral-160m-instruct-GGUF

Base model

DaewonChoi/mistral-160m-instruct

Quantized

(2)

this model