Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1") model = AutoModelForSequenceClassification.from_pretrained("bikalnetomi/RLHF-PPO-RewardModel-LLama3-1B-v1.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff517227e4e400f70d95a1fa39d3f802a200e1ceec7222c7ef55fa1591b50b04
- Size of remote file:
- 4.94 GB
- SHA256:
- 10cd45c9d5e97e490e9b412b3ea23613ddfb0b34076427cade617b8801b56d47
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