Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
English
opt
Generated from Trainer
text-generation-inference
Instructions to use sr5434/gptQuotes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sr5434/gptQuotes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sr5434/gptQuotes")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sr5434/gptQuotes") model = AutoModelForCausalLM.from_pretrained("sr5434/gptQuotes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sr5434/gptQuotes with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sr5434/gptQuotes" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sr5434/gptQuotes", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sr5434/gptQuotes
- SGLang
How to use sr5434/gptQuotes 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 "sr5434/gptQuotes" \ --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": "sr5434/gptQuotes", "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 "sr5434/gptQuotes" \ --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": "sr5434/gptQuotes", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sr5434/gptQuotes with Docker Model Runner:
docker model run hf.co/sr5434/gptQuotes
Update README.md
Browse files
README.md
CHANGED
|
@@ -12,19 +12,13 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 12 |
|
| 13 |
# gptQuotes
|
| 14 |
|
| 15 |
-
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an
|
| 16 |
|
| 17 |
-
## Model description
|
| 18 |
-
|
| 19 |
-
More information needed
|
| 20 |
|
| 21 |
## Intended uses & limitations
|
| 22 |
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
## Training and evaluation data
|
| 26 |
|
| 27 |
-
More information needed
|
| 28 |
|
| 29 |
## Training procedure
|
| 30 |
|
|
@@ -40,9 +34,6 @@ The following hyperparameters were used during training:
|
|
| 40 |
- lr_scheduler_warmup_steps: 500
|
| 41 |
- num_epochs: 15
|
| 42 |
|
| 43 |
-
### Training results
|
| 44 |
-
|
| 45 |
-
|
| 46 |
|
| 47 |
### Framework versions
|
| 48 |
|
|
|
|
| 12 |
|
| 13 |
# gptQuotes
|
| 14 |
|
| 15 |
+
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an Abirate's English Quotes dataset.
|
| 16 |
|
|
|
|
|
|
|
|
|
|
| 17 |
|
| 18 |
## Intended uses & limitations
|
| 19 |
|
| 20 |
+
Generating quotes with AI
|
|
|
|
|
|
|
| 21 |
|
|
|
|
| 22 |
|
| 23 |
## Training procedure
|
| 24 |
|
|
|
|
| 34 |
- lr_scheduler_warmup_steps: 500
|
| 35 |
- num_epochs: 15
|
| 36 |
|
|
|
|
|
|
|
|
|
|
| 37 |
|
| 38 |
### Framework versions
|
| 39 |
|