Instructions to use Corianas/TinyTask-minipaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Corianas/TinyTask-minipaca with Transformers:

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

pipe = pipeline("text-generation", model="Corianas/TinyTask-minipaca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Corianas/TinyTask-minipaca")
model = AutoModelForCausalLM.from_pretrained("Corianas/TinyTask-minipaca")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Corianas/TinyTask-minipaca with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Corianas/TinyTask-minipaca

SGLang

How to use Corianas/TinyTask-minipaca 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 "Corianas/TinyTask-minipaca" \
    --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": "Corianas/TinyTask-minipaca",
		"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 "Corianas/TinyTask-minipaca" \
        --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": "Corianas/TinyTask-minipaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Corianas/TinyTask-minipaca with Docker Model Runner:
```
docker model run hf.co/Corianas/TinyTask-minipaca
```

Corianas commited on Mar 22, 2024

Commit

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@@ -2,9 +2,12 @@
 license: cc-by-nc-4.0
 ---
 A llama.c model based on Karpathy's Llama2.c project. https://github.com/karpathy/llama2.c
 Vocab of 4096, trained on Tinystories, and my custom littlestories dataset (currently unreleased.)
 This version was further trained on following instructions... somewhat... using https://github.com/mlabonne/llm-course/blob/main/Fine_tune_Llama_2_in_Google_Colab.ipynb
 Model uses ↨ as a shift key, instead of using capial letters, this allowed simplification of the tokenizer to avoid duplicates that are uppercase.
 To convert normal text to the right format I use:

 license: cc-by-nc-4.0
 ---
 A llama.c model based on Karpathy's Llama2.c project. https://github.com/karpathy/llama2.c
 Vocab of 4096, trained on Tinystories, and my custom littlestories dataset (currently unreleased.)
 This version was further trained on following instructions... somewhat... using https://github.com/mlabonne/llm-course/blob/main/Fine_tune_Llama_2_in_Google_Colab.ipynb
 Model uses ↨ as a shift key, instead of using capial letters, this allowed simplification of the tokenizer to avoid duplicates that are uppercase.
 To convert normal text to the right format I use: