Instructions to use craa/exceptions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use craa/exceptions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="craa/exceptions")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("craa/exceptions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use craa/exceptions with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "craa/exceptions" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "craa/exceptions", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/craa/exceptions
- SGLang
How to use craa/exceptions 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 "craa/exceptions" \ --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": "craa/exceptions", "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 "craa/exceptions" \ --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": "craa/exceptions", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use craa/exceptions with Docker Model Runner:
docker model run hf.co/craa/exceptions
Add model card
Browse files
README.md
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---
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license: mit
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- gpt2
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datasets:
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- craa/exceptions
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---
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# exceptions
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GPT-2 language models trained from scratch for the exceptions project
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(Leong and Linzen, [Language Models Can Learn Exceptions to Syntactic Rules](https://doi.org/10.7275/h25z-0y75), and follow-up experiments).
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Code: [github.com/craaaa/exceptions](https://github.com/craaaa/exceptions).
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All models are GPT-2 small (12 layers, 12 heads, 768 hidden). Each folder holds the final model of one training run
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(best checkpoint by validation loss), with its tokenizer and Trainer logs (`trainer_state.json`, `all_results.json`).
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## Contents
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| Folder | Runs | Naming | Seeds | Vocab |
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|---|---|---|---|---|
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| `exp1b/` | 5 | `100M_<seed>` | 1001, 2128, 3591, 5039, 40817 | 50,257 (GPT-2) |
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| `exp2a/` | 60 | `<cond>_to_<verb>_frequency_<seed>` — cond ∈ {cost, last, resemble}, verb ∈ {carry, drop, hit, push} | 1001, 2128, 3591, 5039, 40817 | 50,257 (GPT-2) |
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| `exp3/` | 170 | `{high,low}_<n>_<seed>` — n ∈ {0, 1, 2, 4, 8, 16, 32, 64, 10, 100, 128, 256, 500, 512, 1000, 1500, 2000} | 495, 634, 1208, 6910, 8397 | 52,000 (custom) |
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Training corpora are in the dataset repo [`craa/exceptions`](https://huggingface.co/datasets/craa/exceptions);
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the `exp2a` folder names correspond to its `frequency_<cond>_to_<verb>` configs.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo, run = "craa/exceptions", "exp3/high_0_1208"
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model = AutoModelForCausalLM.from_pretrained(repo, subfolder=run)
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tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=run)
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```
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Intermediate checkpoints are not published here.
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