Instructions to use afrideva/TinyMistral-248M-SFT-v4-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 afrideva/TinyMistral-248M-SFT-v4-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 afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M

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 afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M

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 afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/TinyMistral-248M-SFT-v4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/TinyMistral-248M-SFT-v4-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/TinyMistral-248M-SFT-v4-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M

Ollama
How to use afrideva/TinyMistral-248M-SFT-v4-GGUF with Ollama:
```
ollama run hf.co/afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/TinyMistral-248M-SFT-v4-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 afrideva/TinyMistral-248M-SFT-v4-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 afrideva/TinyMistral-248M-SFT-v4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afrideva/TinyMistral-248M-SFT-v4-GGUF to start chatting

Docker Model Runner
How to use afrideva/TinyMistral-248M-SFT-v4-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/TinyMistral-248M-SFT-v4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/TinyMistral-248M-SFT-v4-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TinyMistral-248M-SFT-v4-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Felladrin/TinyMistral-248M-SFT-v4-GGUF

Quantized GGUF model files for TinyMistral-248M-SFT-v4 from Felladrin

Name	Quant method	Size
tinymistral-248m-sft-v4.fp16.gguf	fp16	497.75 MB
tinymistral-248m-sft-v4.q2_k.gguf	q2_k	116.20 MB
tinymistral-248m-sft-v4.q3_k_m.gguf	q3_k_m	131.01 MB
tinymistral-248m-sft-v4.q4_k_m.gguf	q4_k_m	156.60 MB
tinymistral-248m-sft-v4.q5_k_m.gguf	q5_k_m	180.16 MB
tinymistral-248m-sft-v4.q6_k.gguf	q6_k	205.20 MB
tinymistral-248m-sft-v4.q8_0.gguf	q8_0	265.26 MB

Original Model Card:

Locutusque's TinyMistral-248M trained on OpenAssistant TOP-1 Conversation Threads

Base model: Locutusque/TinyMistral-248M
Dataset: OpenAssistant/oasst_top1_2023-08-25
Availability in other ML formats:
- GGUF: Felladrin/gguf-TinyMistral-248M-SFT-v4
- ONNX: Felladrin/onnx-TinyMistral-248M-SFT-v4

Recommended Prompt Format

<|im_start|>user
{message}<|im_end|>
<|im_start|>assistant

Downloads last month: 230

GGUF

Model size

0.2B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for afrideva/TinyMistral-248M-SFT-v4-GGUF

Base model

Locutusque/TinyMistral-248M

Finetuned

Felladrin/TinyMistral-248M-Chat-v4

Quantized

(16)

this model

afrideva
/

TinyMistral-248M-SFT-v4-GGUF

Felladrin/TinyMistral-248M-SFT-v4-GGUF

Original Model Card:

Locutusque's TinyMistral-248M trained on OpenAssistant TOP-1 Conversation Threads

Recommended Prompt Format

Model tree for afrideva/TinyMistral-248M-SFT-v4-GGUF

Dataset used to train afrideva/TinyMistral-248M-SFT-v4-GGUF

Space using afrideva/TinyMistral-248M-SFT-v4-GGUF 1