Instructions to use jtatman/tinymistral-mediqa-248m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jtatman/tinymistral-mediqa-248m with Transformers:

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

pipe = pipeline("text-generation", model="jtatman/tinymistral-mediqa-248m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jtatman/tinymistral-mediqa-248m")
model = AutoModelForCausalLM.from_pretrained("jtatman/tinymistral-mediqa-248m")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jtatman/tinymistral-mediqa-248m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jtatman/tinymistral-mediqa-248m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jtatman/tinymistral-mediqa-248m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jtatman/tinymistral-mediqa-248m

SGLang

How to use jtatman/tinymistral-mediqa-248m 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 "jtatman/tinymistral-mediqa-248m" \
    --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": "jtatman/tinymistral-mediqa-248m",
		"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 "jtatman/tinymistral-mediqa-248m" \
        --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": "jtatman/tinymistral-mediqa-248m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jtatman/tinymistral-mediqa-248m with Docker Model Runner:
```
docker model run hf.co/jtatman/tinymistral-mediqa-248m
```

This is an ongoing experiment in training and retraining boundaries.

The model is currently overtrained and is purposely so to investigate the paths out of overtraining.

This is purely an experiment on depths and depravity of repetitive training. Don't bother messing around with it much.

Downloads last month: 4

Safetensors

Model size

0.2B params

Tensor type

F32

jtatman
/

tinymistral-mediqa-248m

Datasets used to train jtatman/tinymistral-mediqa-248m