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

Libraries

How to use Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct")
model = AutoModelForCausalLM.from_pretrained("Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct

SGLang

How to use Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct with Docker Model Runner:
```
docker model run hf.co/Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Found in the Open LLM Leaderboard.

Downloads last month: 989

Safetensors

Model size

0.2B params

Tensor type

F16

Model tree for Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct

Quantizations

4 models

Locutusque
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LocutusqueXFelladrin-TinyMistral248M-Instruct

LocutusqueXFelladrin-TinyMistral248M-Instruct

Evaluation

Model tree for Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct

Dataset used to train Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct