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")# 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
- Xet hash:
- 0fd8853473f3b91d93906e1468efeeca7dc84c16f159d0bf15940b0310390ef7
- Size of remote file:
- 16.1 GB
- SHA256:
- 4382fc771627d76c912c6df506883d17ae060f8de249e9f5d4ff637fc5ec6411
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