Instructions to use mwesner/reformer-clm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mwesner/reformer-clm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mwesner/reformer-clm")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mwesner/reformer-clm") model = AutoModelForCausalLM.from_pretrained("mwesner/reformer-clm") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mwesner/reformer-clm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mwesner/reformer-clm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mwesner/reformer-clm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mwesner/reformer-clm
- SGLang
How to use mwesner/reformer-clm 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 "mwesner/reformer-clm" \ --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": "mwesner/reformer-clm", "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 "mwesner/reformer-clm" \ --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": "mwesner/reformer-clm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mwesner/reformer-clm with Docker Model Runner:
docker model run hf.co/mwesner/reformer-clm
YAML Metadata Error:"model-index[0].results" is required
reformer-clm
This casual language model was trained from scratch on CNN/Dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 2.7783
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.8321 | 1.0 | 18412 | 3.8074 |
| 3.4965 | 2.0 | 36824 | 3.4223 |
| 3.1927 | 3.0 | 55236 | 3.0815 |
| 3.046 | 4.0 | 73648 | 2.9270 |
| 2.9781 | 5.0 | 92060 | 2.8515 |
| 2.9398 | 6.0 | 110472 | 2.8082 |
| 2.9293 | 7.0 | 128884 | 2.7904 |
| 2.9212 | 8.0 | 147296 | 2.7817 |
| 2.9169 | 9.0 | 165708 | 2.7787 |
| 2.9197 | 10.0 | 184120 | 2.7783 |
Framework versions
- Transformers 4.6.1
- Pytorch 1.9.0
- Datasets 1.2.1
- Tokenizers 0.10.3
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