Instructions to use Maykeye/TinyLLama-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maykeye/TinyLLama-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Maykeye/TinyLLama-v0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Maykeye/TinyLLama-v0") model = AutoModelForCausalLM.from_pretrained("Maykeye/TinyLLama-v0") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Maykeye/TinyLLama-v0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Maykeye/TinyLLama-v0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Maykeye/TinyLLama-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Maykeye/TinyLLama-v0
- SGLang
How to use Maykeye/TinyLLama-v0 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 "Maykeye/TinyLLama-v0" \ --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": "Maykeye/TinyLLama-v0", "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 "Maykeye/TinyLLama-v0" \ --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": "Maykeye/TinyLLama-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Maykeye/TinyLLama-v0 with Docker Model Runner:
docker model run hf.co/Maykeye/TinyLLama-v0
Maykeye commited on
Commit ·
46068d3
1
Parent(s): 4d5aebb
Fixed tokenizer conversion
Browse filesOn my cloud GPU tokenizer loads fine, so catch doesn't trigger
and except block isn't executed. So we need to save it in try-block
as well
- train.ipynb +3 -1
train.ipynb
CHANGED
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@@ -161,7 +161,9 @@
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| 161 |
"if not Path('tokenizer.json').exists(): \n",
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" try:\n",
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" tokenizer = AutoTokenizer.from_pretrained(\"openlm-research/open_llama_3b\")\n",
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"
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" os.environ[\"PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION\"]=\"python\" \n",
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" tokenizer = AutoTokenizer.from_pretrained(\"openlm-research/open_llama_3b\")\n",
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" tokenizer.save_pretrained(\".\")\n",
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| 161 |
"if not Path('tokenizer.json').exists(): \n",
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" try:\n",
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" tokenizer = AutoTokenizer.from_pretrained(\"openlm-research/open_llama_3b\")\n",
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" tokenizer.save_pretrained(\".\")\n",
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" except Exception as e:\n",
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| 166 |
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" print(e)\n",
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" os.environ[\"PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION\"]=\"python\" \n",
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" tokenizer = AutoTokenizer.from_pretrained(\"openlm-research/open_llama_3b\")\n",
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" tokenizer.save_pretrained(\".\")\n",
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