Text Classification
Transformers
Safetensors
English
qwen3
text-generation
finance
earnings-calls
evasion-detection
nlp
Eval Results
text-embeddings-inference
Instructions to use FutureMa/Eva-4B-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FutureMa/Eva-4B-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FutureMa/Eva-4B-V2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("FutureMa/Eva-4B-V2") model = AutoModelForCausalLM.from_pretrained("FutureMa/Eva-4B-V2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from FutureMa/Eva-4B-V2: direct link, hf CLI and curl.
- Browser
- Download file 7.31 kB
-
https://huggingface.co/FutureMa/Eva-4B-V2/resolve/main/training_args.bin
- Command line
-
hf download hf://FutureMa/Eva-4B-V2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/FutureMa/Eva-4B-V2/resolve/main/training_args.bin
7.31 kB
- Xet hash:
- 958855234dbc49b85e2ea9293f928d384eff31ddff32f7fbe12b5597a12f3744
- Size of remote file:
- 7.31 kB
- SHA256:
- 06ce2aa39746d0e81f2d1ef996d3c279d6be89ddc99cd768fd9388df5406db7a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.