Instructions to use qqplot23/BASE_long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qqplot23/BASE_long with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="qqplot23/BASE_long")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("qqplot23/BASE_long") model = AutoModelForCausalLM.from_pretrained("qqplot23/BASE_long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use qqplot23/BASE_long with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "qqplot23/BASE_long" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "qqplot23/BASE_long", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/qqplot23/BASE_long
- SGLang
How to use qqplot23/BASE_long 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 "qqplot23/BASE_long" \ --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": "qqplot23/BASE_long", "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 "qqplot23/BASE_long" \ --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": "qqplot23/BASE_long", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use qqplot23/BASE_long with Docker Model Runner:
docker model run hf.co/qqplot23/BASE_long
| license: mit | |
| base_model: gpt2 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: BASE_long | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # BASE_long | |
| This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 3.1674 | |
| - Ppl: 24.5740 | |
| ## 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: 0.0005 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 2 | |
| - seed: 22554 | |
| - distributed_type: multi-GPU | |
| - gradient_accumulation_steps: 16 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 2000 | |
| - num_epochs: 10 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Ppl | | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:| | |
| | 3.6608 | 2.51 | 4000 | 3.5106 | 34.6847 | | |
| | 3.3438 | 5.01 | 8000 | 3.2666 | 27.1444 | | |
| | 3.178 | 7.52 | 12000 | 3.1674 | 24.5740 | | |
| ### Framework versions | |
| - Transformers 4.35.0.dev0 | |
| - Pytorch 1.13.1+cu117 | |
| - Datasets 2.14.6 | |
| - Tokenizers 0.14.1 | |