Instructions to use afrideva/TinyMistral-248M-SFT-v3-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-v3-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-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyMistral-248M-SFT-v3-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-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyMistral-248M-SFT-v3-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-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/TinyMistral-248M-SFT-v3-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-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/TinyMistral-248M-SFT-v3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/TinyMistral-248M-SFT-v3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/TinyMistral-248M-SFT-v3-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-v3-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-v3-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/TinyMistral-248M-SFT-v3-GGUF:Q4_K_M

Ollama
How to use afrideva/TinyMistral-248M-SFT-v3-GGUF with Ollama:
```
ollama run hf.co/afrideva/TinyMistral-248M-SFT-v3-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/TinyMistral-248M-SFT-v3-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-v3-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-v3-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-v3-GGUF to start chatting

Docker Model Runner
How to use afrideva/TinyMistral-248M-SFT-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/TinyMistral-248M-SFT-v3-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/TinyMistral-248M-SFT-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/TinyMistral-248M-SFT-v3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TinyMistral-248M-SFT-v3-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Felladrin/TinyMistral-248M-SFT-v3-GGUF

Quantized GGUF model files for TinyMistral-248M-SFT-v3 from Felladrin

Name	Quant method	Size
tinymistral-248m-sft-v3.fp16.gguf	fp16	497.75 MB
tinymistral-248m-sft-v3.q2_k.gguf	q2_k	116.20 MB
tinymistral-248m-sft-v3.q3_k_m.gguf	q3_k_m	131.01 MB
tinymistral-248m-sft-v3.q4_k_m.gguf	q4_k_m	156.60 MB
tinymistral-248m-sft-v3.q5_k_m.gguf	q5_k_m	180.16 MB
tinymistral-248m-sft-v3.q6_k.gguf	q6_k	205.20 MB
tinymistral-248m-sft-v3.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

Recommended Prompt Format

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

How it was trained

%pip install autotrain-advanced

!autotrain setup

!autotrain llm \
    --train \
    --trainer "sft" \
    --model './TinyMistral-248M/' \
    --model_max_length 4096 \
    --block-size 1024 \
    --project-name 'trained-model' \
    --data-path "OpenAssistant/oasst_top1_2023-08-25" \
    --train_split "train" \
    --valid_split "test" \
    --text-column "text" \
    --lr 1e-5 \
    --train_batch_size 2 \
    --epochs 5 \
    --evaluation_strategy "steps" \
    --save-strategy "steps" \
    --save-total-limit 2 \
    --warmup-ratio 0.05 \
    --weight-decay 0.0 \
    --gradient-accumulation 8 \
    --logging-steps 10 \
    --scheduler "constant"

Downloads last month: 190

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-v3-GGUF

Base model

Locutusque/TinyMistral-248M

Finetuned

Felladrin/TinyMistral-248M-Chat-v4

Quantized

(16)

this model

afrideva
/

TinyMistral-248M-SFT-v3-GGUF