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

TinyMistral-248M-SFT-v3-GGUF / README.md

afrideva

Upload README.md with huggingface_hub

040b172 over 2 years ago

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metadata

base_model: Felladrin/TinyMistral-248M-SFT-v3
datasets:
  - OpenAssistant/oasst_top1_2023-08-25
inference: false
license: apache-2.0
model_creator: Felladrin
model_name: TinyMistral-248M-SFT-v3
pipeline_tag: text-generation
quantized_by: afrideva
tags:
  - autotrain
  - 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

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

      <|im_start|>assistant

      The game is an adventure game that takes place on a planet, where players
      must explore their unique abilities to survive. Players can use different
      strategies such as collecting items or trading them for gold or silver
      coins, but they also need to learn how to deal with obstacles and find new
      ways to escape.<|im_end|>

      <|im_start|>user

      Could you tell me something curious about the Earth?<|im_end|>

      <|im_start|>assistant

      The planet is a large, rocky world with an atmosphere of 10 billion years
      old and a surface area around 25 million miles (36 million kilometers)
      wide.<|im_end|>

      <|im_start|>user

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

      <|im_start|>assistant

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"