Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
opt
Generated from Trainer
non-commercial
dialogue
chatbot
text-generation-inference
Instructions to use pszemraj/opt-peter-1.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/opt-peter-1.3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pszemraj/opt-peter-1.3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/opt-peter-1.3B") model = AutoModelForCausalLM.from_pretrained("pszemraj/opt-peter-1.3B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use pszemraj/opt-peter-1.3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pszemraj/opt-peter-1.3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pszemraj/opt-peter-1.3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pszemraj/opt-peter-1.3B
- SGLang
How to use pszemraj/opt-peter-1.3B 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 "pszemraj/opt-peter-1.3B" \ --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": "pszemraj/opt-peter-1.3B", "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 "pszemraj/opt-peter-1.3B" \ --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": "pszemraj/opt-peter-1.3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pszemraj/opt-peter-1.3B with Docker Model Runner:
docker model run hf.co/pszemraj/opt-peter-1.3B
pszemraj/opt-peter-1.3B
This model is a fine-tuned version of pszemraj/opt-peter-1.3B-1E on 80k Whatsapp/iMessages (mine).
It achieves the following results on the evaluation set, after training for 1 epoch (on top of the 1E checkpoint linked above):
- eval_loss: 3.4220
- eval_runtime: 954.9678
- eval_samples_per_second: 9.114
- eval_steps_per_second: 2.279
- epoch: 1.0
- step: 1235
Model description
- Exploring to see how OPT does in terms of dialogue/conversational applications :)
- Seems to do a lot better than GPT-Neo with similar training parameters
Intended uses & limitations
- OPT has a license that does not allow for commercial use, see original for details
- any statements or claims made by this model do not reflect actual claims/statements by me
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
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