Instructions to use weiser/124M-0.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use weiser/124M-0.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="weiser/124M-0.0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("weiser/124M-0.0") model = AutoModelForCausalLM.from_pretrained("weiser/124M-0.0") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use weiser/124M-0.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "weiser/124M-0.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "weiser/124M-0.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/weiser/124M-0.0
- SGLang
How to use weiser/124M-0.0 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 "weiser/124M-0.0" \ --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": "weiser/124M-0.0", "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 "weiser/124M-0.0" \ --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": "weiser/124M-0.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use weiser/124M-0.0 with Docker Model Runner:
docker model run hf.co/weiser/124M-0.0
| license: apache-2.0 | |
| datasets: | |
| - HuggingFaceFW/fineweb | |
| language: | |
| - en | |
| library_name: transformers | |
| tags: | |
| - IoT | |
| - sensor | |
| - embedded | |
| # TinyLLM | |
| ## Overview | |
| This repository hosts a small language model developed as part of the TinyLLM framework ([arxiv link]). These models are specifically designed and fine-tuned with sensor data to support embedded sensing applications. They enable locally hosted language models on low-computing-power devices, such as single-board computers. The models, based on the GPT-2 architecture, are trained using Nvidia's H100 GPUs. This repo provides base models that can be further fine-tuned for specific downstream tasks related to embedded sensing. | |
| ## Model Information | |
| - **Parameters:** 124M (Hidden Size = 768) | |
| - **Architecture:** Decoder-only transformer | |
| - **Training Data:** Up to 10B tokens from the [SHL](http://www.shl-dataset.org/) and [Fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) datasets, combined in a 0:1 ratio | |
| - **Input and Output Modality:** Text | |
| - **Context Length:** 1024 | |
| ## Acknowledgements | |
| We want to acknowledge the open-source frameworks [llm.c](https://github.com/karpathy/llm.c) and [llama.cpp](https://github.com/ggerganov/llama.cpp) and the sensor dataset provided by SHL, which were instrumental in training and testing these models. | |
| ## Usage | |
| The model can be used in two primary ways: | |
| 1. **With Hugging Face’s Transformers Library** | |
| ```python | |
| from transformers import pipeline | |
| import torch | |
| path = "tinyllm/124M-0.0" | |
| prompt = "The sea is blue but it's his red sea" | |
| generator = pipeline("text-generation", model=path,max_new_tokens = 30, repetition_penalty=1.3, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto") | |
| print(generator(prompt)[0]['generated_text']) | |
| ``` | |
| 2. **With llama.cpp** | |
| Generate a GGUF model file using this [tool](https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py) and use the generated GGUF file for inferencing. | |
| ```python | |
| python3 convert_hf_to_gguf.py models/mymodel/ | |
| ``` | |
| ## Disclaimer | |
| This model is intended solely for research purposes. |