Instructions to use Bearnardd/test_bearnard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bearnardd/test_bearnard with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Bearnardd/test_bearnard") model = AutoModelForCausalLM.from_pretrained("Bearnardd/test_bearnard") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - trl | |
| - transformers | |
| - reinforcement-learning | |
| # TRL Model | |
| This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to | |
| guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. | |
| ## Usage | |
| To use this model for inference, first install the TRL library: | |
| ```bash | |
| python -m pip install trl | |
| ``` | |
| You can then generate text as follows: | |
| ```python | |
| from transformers import pipeline | |
| generator = pipeline("text-generation", model="Bearnardd//tmp/tmpcs5od8jz/Bearnardd/test_bearnard") | |
| outputs = generator("Hello, my llama is cute") | |
| ``` | |
| If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: | |
| ```python | |
| from transformers import AutoTokenizer | |
| from trl import AutoModelForCausalLMWithValueHead | |
| tokenizer = AutoTokenizer.from_pretrained("Bearnardd//tmp/tmpcs5od8jz/Bearnardd/test_bearnard") | |
| model = AutoModelForCausalLMWithValueHead.from_pretrained("Bearnardd//tmp/tmpcs5od8jz/Bearnardd/test_bearnard") | |
| inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") | |
| outputs = model(**inputs, labels=inputs["input_ids"]) | |
| ``` | |