Text Classification
Transformers
Safetensors
PyTorch
English
lfm2
text-generation
finance
trading
stock-market
financial-analysis
dpo
sft
lora
qlora
quantitative-finance
trading-signals
xgboost
ensemble
temperature-calibration
4-bit precision
bitsandbytes
Instructions to use ewin-reg/LFM2.5-Stock-Analyst-Final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ewin-reg/LFM2.5-Stock-Analyst-Final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ewin-reg/LFM2.5-Stock-Analyst-Final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ewin-reg/LFM2.5-Stock-Analyst-Final") model = AutoModelForCausalLM.from_pretrained("ewin-reg/LFM2.5-Stock-Analyst-Final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ewin-reg/LFM2.5-Stock-Analyst-Final: direct link, hf CLI and curl.
- Browser
- Download file 4.73 MB
-
https://huggingface.co/ewin-reg/LFM2.5-Stock-Analyst-Final/resolve/main/tokenizer.json
- Command line
-
hf download hf://ewin-reg/LFM2.5-Stock-Analyst-Final/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ewin-reg/LFM2.5-Stock-Analyst-Final/resolve/main/tokenizer.json
4.73 MB
File too large to display, you can check the raw version instead.