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

TinyMistral-248M-SFT-v4-GGUF / README.md

afrideva

Upload README.md with huggingface_hub

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	---
	base_model: Felladrin/TinyMistral-248M-SFT-v4
	datasets:
	- OpenAssistant/oasst_top1_2023-08-25
	inference: false
	license: apache-2.0
	model_creator: Felladrin
	model_name: TinyMistral-248M-SFT-v4
	pipeline_tag: text-generation
	quantized_by: afrideva
	tags:
	- text-generation
	- gguf
	- ggml
	- quantized
	- q2_k
	- q3_k_m
	- q4_k_m
	- q5_k_m
	- q6_k
	- q8_0
	widget:
	- text: '<\|im_start\|>user

	Invited some friends to come home today. Give me some ideas for games to play
	with them!<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	How do meteorologists predict how much air pollution will be produced in the next
	year?<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	Who is Mona Lisa?<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	Heya!<\|im_end\|>

	<\|im_start\|>assistant

	Hi! How may I help you today?<\|im_end\|>

	<\|im_start\|>user

	I need to build a simple website. Where should I start learning about web development?<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	What are some potential applications for quantum computing?<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	Write the specs of a game about dragons and warriors in a fantasy world.<\|im_end\|>

	<\|im_start\|>assistant'
	- text: "<\|im_start\|>user\nGot a question for you!<\|im_end\|>\n<\|im_start\|>assistant\nSure!
	What's it?<\|im_end\|>\n<\|im_start\|>user\nWhy do you love cats so much!? \U0001F408<\|im_end\|>\n<\|im_start\|>assistant"
	- text: '<\|im_start\|>user

	Tell me about the pros and cons of social media.<\|im_end\|>

	<\|im_start\|>assistant'
	---
	# Felladrin/TinyMistral-248M-SFT-v4-GGUF

	Quantized GGUF model files for [TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-v4) from [Felladrin](https://huggingface.co/Felladrin)


	\| Name \| Quant method \| Size \|
	\| ---- \| ---- \| ---- \|
	\| [tinymistral-248m-sft-v4.fp16.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.fp16.gguf) \| fp16 \| 497.75 MB \|
	\| [tinymistral-248m-sft-v4.q2_k.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q2_k.gguf) \| q2_k \| 116.20 MB \|
	\| [tinymistral-248m-sft-v4.q3_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q3_k_m.gguf) \| q3_k_m \| 131.01 MB \|
	\| [tinymistral-248m-sft-v4.q4_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q4_k_m.gguf) \| q4_k_m \| 156.60 MB \|
	\| [tinymistral-248m-sft-v4.q5_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q5_k_m.gguf) \| q5_k_m \| 180.16 MB \|
	\| [tinymistral-248m-sft-v4.q6_k.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q6_k.gguf) \| q6_k \| 205.20 MB \|
	\| [tinymistral-248m-sft-v4.q8_0.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/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](https://huggingface.co/Locutusque/TinyMistral-248M)
	- Dataset: [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25)
	- Availability in other ML formats:
	- GGUF: [Felladrin/gguf-TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/gguf-TinyMistral-248M-SFT-v4)
	- ONNX: [Felladrin/onnx-TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/onnx-TinyMistral-248M-SFT-v4)

	## Recommended Prompt Format

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

	---
	base_model: Felladrin/TinyMistral-248M-SFT-v4
	datasets:
	- OpenAssistant/oasst_top1_2023-08-25
	inference: false
	license: apache-2.0
	model_creator: Felladrin
	model_name: TinyMistral-248M-SFT-v4
	pipeline_tag: text-generation
	quantized_by: afrideva
	tags:
	- text-generation
	- gguf
	- ggml
	- quantized
	- q2_k
	- q3_k_m
	- q4_k_m
	- q5_k_m
	- q6_k
	- q8_0
	widget:
	- text: '<\|im_start\|>user

	Invited some friends to come home today. Give me some ideas for games to play
	with them!<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	How do meteorologists predict how much air pollution will be produced in the next
	year?<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	Who is Mona Lisa?<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	Heya!<\|im_end\|>

	<\|im_start\|>assistant

	Hi! How may I help you today?<\|im_end\|>

	<\|im_start\|>user

	I need to build a simple website. Where should I start learning about web development?<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	What are some potential applications for quantum computing?<\|im_end\|>

	<\|im_start\|>assistant'
	- text: '<\|im_start\|>user

	Write the specs of a game about dragons and warriors in a fantasy world.<\|im_end\|>

	<\|im_start\|>assistant'
	- text: "<\|im_start\|>user\nGot a question for you!<\|im_end\|>\n<\|im_start\|>assistant\nSure!
	What's it?<\|im_end\|>\n<\|im_start\|>user\nWhy do you love cats so much!? \U0001F408<\|im_end\|>\n<\|im_start\|>assistant"
	- text: '<\|im_start\|>user

	Tell me about the pros and cons of social media.<\|im_end\|>

	<\|im_start\|>assistant'
	---
	# Felladrin/TinyMistral-248M-SFT-v4-GGUF

	Quantized GGUF model files for [TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-v4) from [Felladrin](https://huggingface.co/Felladrin)


	\| Name \| Quant method \| Size \|
	\| ---- \| ---- \| ---- \|
	\| [tinymistral-248m-sft-v4.fp16.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.fp16.gguf) \| fp16 \| 497.75 MB \|
	\| [tinymistral-248m-sft-v4.q2_k.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q2_k.gguf) \| q2_k \| 116.20 MB \|
	\| [tinymistral-248m-sft-v4.q3_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q3_k_m.gguf) \| q3_k_m \| 131.01 MB \|
	\| [tinymistral-248m-sft-v4.q4_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q4_k_m.gguf) \| q4_k_m \| 156.60 MB \|
	\| [tinymistral-248m-sft-v4.q5_k_m.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q5_k_m.gguf) \| q5_k_m \| 180.16 MB \|
	\| [tinymistral-248m-sft-v4.q6_k.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/tinymistral-248m-sft-v4.q6_k.gguf) \| q6_k \| 205.20 MB \|
	\| [tinymistral-248m-sft-v4.q8_0.gguf](https://huggingface.co/afrideva/TinyMistral-248M-SFT-v4-GGUF/resolve/main/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](https://huggingface.co/Locutusque/TinyMistral-248M)
	- Dataset: [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25)
	- Availability in other ML formats:
	- GGUF: [Felladrin/gguf-TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/gguf-TinyMistral-248M-SFT-v4)
	- ONNX: [Felladrin/onnx-TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/onnx-TinyMistral-248M-SFT-v4)

	## Recommended Prompt Format

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