Instructions to use Corianas/TinyTask-minipaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Create README.md
Browse files
README.md
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---
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license: cc-by-nc-4.0
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---
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A llama.c model based on Karpathy's Llama2.c project. https://github.com/karpathy/llama2.c
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Vocab of 4096, trained on Tinystories, and my custom littlestories dataset (currently unreleased.)
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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
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Model uses ↨ as a shift key, instead of using capial letters, this allowed simplification of the tokenizer to avoid duplicates that are uppercase.
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To convert normal text to the right format I use:
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```
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def add_caseifer(text):
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# Using list comprehension for more efficient concatenation
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return ''.join(['↨' + char.lower() if char.isupper() else char for char in text])
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```
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To return the text to human format I use:
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```
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def remove_caseifer(text):
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new_text = ""
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i = 0
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while i < len(text):
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if text[i] == "↨":
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if i+1 < len(text):
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new_text += text[i+1].upper()
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i += 1
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else:
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pass # skip this index
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else:
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new_text += text[i]
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i += 1
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return new_text
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```
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