Text Classification
Transformers
Safetensors
qwen3
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use LiberteEPFL/qwen3-1.7b-rm-full-bigchat-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiberteEPFL/qwen3-1.7b-rm-full-bigchat-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LiberteEPFL/qwen3-1.7b-rm-full-bigchat-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LiberteEPFL/qwen3-1.7b-rm-full-bigchat-v2") model = AutoModelForSequenceClassification.from_pretrained("LiberteEPFL/qwen3-1.7b-rm-full-bigchat-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for qwen3-1.7b-rm-full-bigchat-v2
This model is a fine-tuned version of LiberteEPFL/qwen3-1.7b-sft-bigchat-v2. It has been trained using TRL.
Quick start
from transformers import pipeline
text = "The capital of France is Paris."
rewarder = pipeline(model="LiberteEPFL/qwen3-1.7b-rm-full-bigchat-v2", device="cuda")
output = rewarder(text)[0]
print(output["score"])
Training procedure
This model was trained with Reward.
Framework versions
- TRL: 1.3.0
- Transformers: 4.57.0
- Pytorch: 2.8.0+cu128
- Datasets: 4.8.5
- Tokenizers: 0.22.1
Citations
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
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Model tree for LiberteEPFL/qwen3-1.7b-rm-full-bigchat-v2
Base model
Qwen/Qwen3-1.7B-Base Finetuned
LiberteEPFL/qwen3-1.7b-sft-bigchat-v2