Text Generation
Transformers
Safetensors
English
gpt2
text-generation-inference
casual-lm
question-answering
Instructions to use Wonder-Griffin/Shorsey-T2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wonder-Griffin/Shorsey-T2000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Wonder-Griffin/Shorsey-T2000")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Wonder-Griffin/Shorsey-T2000") model = AutoModelForCausalLM.from_pretrained("Wonder-Griffin/Shorsey-T2000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Wonder-Griffin/Shorsey-T2000 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Wonder-Griffin/Shorsey-T2000" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Wonder-Griffin/Shorsey-T2000", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Wonder-Griffin/Shorsey-T2000
- SGLang
How to use Wonder-Griffin/Shorsey-T2000 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 "Wonder-Griffin/Shorsey-T2000" \ --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": "Wonder-Griffin/Shorsey-T2000", "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 "Wonder-Griffin/Shorsey-T2000" \ --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": "Wonder-Griffin/Shorsey-T2000", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Wonder-Griffin/Shorsey-T2000 with Docker Model Runner:
docker model run hf.co/Wonder-Griffin/Shorsey-T2000
End of training
Browse files- README.md +47 -0
- model.safetensors +3 -0
- trainer_state.json +32 -0
- training_args.bin +3 -0
README.md
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---
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library_name: transformers
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tags:
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- generated_from_trainer
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model-index:
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- name: tmp_trainer
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# tmp_trainer
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This model was trained from scratch on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3.0
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.0+cu124
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:040e0f67acf5450cc251902d4aafbe1a6f12ff7e55e75fa7dd802e6b08bb5f8f
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size 683204372
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trainer_state.json
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{
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": null,
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"eval_steps": 500,
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"global_step": 0,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"log_history": [],
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"logging_steps": 500,
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"max_steps": 0,
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"num_input_tokens_seen": 0,
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"num_train_epochs": 0,
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"save_steps": 500,
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"stateful_callbacks": {
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"TrainerControl": {
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"args": {
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"should_epoch_stop": false,
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"should_evaluate": false,
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"should_log": false,
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"should_save": false,
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"should_training_stop": false
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},
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"attributes": {}
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}
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},
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"total_flos": 0,
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"train_batch_size": null,
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"trial_name": null,
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"trial_params": null
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:05c92ba7db09b05aa6b3230e1d6f3e8e8d9cb637c92343f915ed252de5ef45a4
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size 5176
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