Text Classification
Transformers
ONNX
Safetensors
English
bert
llm_routing
Bert
BertForSequenceClassification
text-embeddings-inference
Instructions to use brahmairesearch/routex-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brahmairesearch/routex-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="brahmairesearch/routex-v0.1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("brahmairesearch/routex-v0.1") model = AutoModelForSequenceClassification.from_pretrained("brahmairesearch/routex-v0.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from brahmairesearch/routex-v0.1: direct link, hf CLI and curl.
- Browser
- Download file 841 Bytes
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https://huggingface.co/brahmairesearch/routex-v0.1/resolve/main/README.md
- Command line
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hf download hf://brahmairesearch/routex-v0.1/README.md
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curl -L -o README.md https://huggingface.co/brahmairesearch/routex-v0.1/resolve/main/README.md
841 Bytes
metadata
license: mit
language:
- en
pipeline_tag: text-classification
library_name: transformers
tags:
- llm_routing
- Bert
- BertForSequenceClassification
RouteX v0.1 is the first model in LLM Routers family by BRAHMAI. The model is based on BertForSequenceClassification.
RouteX v0.1 automatically determines whether to send queries to a local LLM (Mistral 7B, Llama-3.1 8B, etc...) or powerful cloud LLM (GPT-4, Sonnet 3.5, etc...), depending on which model is best-suited for your task. We've trained RouteX v0.1 on millions of data points from reliable, cross-domain evaluation benchmarks.
NOTE:
We are still working on improving the efficiency and reliability of these models.
We will release more information with you as we progress.
Regards, Team BRAHMAI