Not-For-All-Audiences

Instructions to use Locutusque/TinyMistral-248M-v2.5-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/TinyMistral-248M-v2.5-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/TinyMistral-248M-v2.5-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/TinyMistral-248M-v2.5-Instruct")
model = AutoModelForCausalLM.from_pretrained("Locutusque/TinyMistral-248M-v2.5-Instruct")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Locutusque/TinyMistral-248M-v2.5-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/TinyMistral-248M-v2.5-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/TinyMistral-248M-v2.5-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Locutusque/TinyMistral-248M-v2.5-Instruct

SGLang

How to use Locutusque/TinyMistral-248M-v2.5-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/TinyMistral-248M-v2.5-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/TinyMistral-248M-v2.5-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/TinyMistral-248M-v2.5-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/TinyMistral-248M-v2.5-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Locutusque/TinyMistral-248M-v2.5-Instruct with Docker Model Runner:
```
docker model run hf.co/Locutusque/TinyMistral-248M-v2.5-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model description

Fine-tuned Locutusque/TinyMistral-248m-v2.5 on SlimOrca-Dedup and Hercules-v1.0. Averaged a loss of 1.5 during training. This model's performance is excellent considering it's size.

This model may output X-rated content. You and you alone are responsible for downloading and using the model and it's outputs. You have been warned.

You can use the ChatML prompt format for this model.

Evaluation

This model will be submitted to the Open LLM Leaderboard.

Downloads last month: 11

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for Locutusque/TinyMistral-248M-v2.5-Instruct

Base model

Locutusque/TinyMistral-248M-v2.5

Finetuned

(3)

this model

Finetunes

2 models

Merges

2 models

Quantizations

2 models

Locutusque
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TinyMistral-248M-v2.5-Instruct

Model description

Evaluation

Model tree for Locutusque/TinyMistral-248M-v2.5-Instruct

Datasets used to train Locutusque/TinyMistral-248M-v2.5-Instruct