Instructions to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF",
	filename="locutusquexfelladrin-tinymistral248m-instruct.fp16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-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/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-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/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-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/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M

Ollama
How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with Ollama:
```
ollama run hf.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-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/LocutusqueXFelladrin-TinyMistral248M-Instruct-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/LocutusqueXFelladrin-TinyMistral248M-Instruct-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/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF to start chatting

Docker Model Runner
How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF

Quantized GGUF model files for LocutusqueXFelladrin-TinyMistral248M-Instruct from Locutusque

Name	Quant method	Size
locutusquexfelladrin-tinymistral248m-instruct.fp16.gguf	fp16	497.76 MB
locutusquexfelladrin-tinymistral248m-instruct.q2_k.gguf	q2_k	116.20 MB
locutusquexfelladrin-tinymistral248m-instruct.q3_k_m.gguf	q3_k_m	131.01 MB
locutusquexfelladrin-tinymistral248m-instruct.q4_k_m.gguf	q4_k_m	156.61 MB
locutusquexfelladrin-tinymistral248m-instruct.q5_k_m.gguf	q5_k_m	180.17 MB
locutusquexfelladrin-tinymistral248m-instruct.q6_k.gguf	q6_k	205.20 MB
locutusquexfelladrin-tinymistral248m-instruct.q8_0.gguf	q8_0	265.26 MB

Original Model Card:

LocutusqueXFelladrin-TinyMistral248M-Instruct

This model was created by merging Locutusque/TinyMistral-248M-Instruct and Felladrin/TinyMistral-248M-SFT-v4 using mergekit. After the two models were merged, the resulting model was further trained on ~20,000 examples on the Locutusque/inst_mix_v2_top_100k at a low learning rate to further normalize weights. The following is the YAML config used to merge:

models:
  - model: Felladrin/TinyMistral-248M-SFT-v4
    parameters:
      weight: 0.5
  - model: Locutusque/TinyMistral-248M-Instruct
    parameters:
      weight: 1.0
merge_method: linear
dtype: float16

The resulting model combines the best of both worlds. With Locutusque/TinyMistral-248M-Instruct's coding capabilities and reasoning skills, and Felladrin/TinyMistral-248M-SFT-v4's low hallucination and instruction-following capabilities. The resulting model has an incredible performance considering its size.

Evaluation

Coming soon...

Downloads last month: 55

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/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF

Base model

Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct

Quantized

(4)

this model

afrideva
/

LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF

Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF

Original Model Card:

LocutusqueXFelladrin-TinyMistral248M-Instruct

Evaluation

Model tree for afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF

Dataset used to train afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF