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 ewinregirgojr/LFM2.5-Stock-Analyst-Final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ewinregirgojr/LFM2.5-Stock-Analyst-Final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ewinregirgojr/LFM2.5-Stock-Analyst-Final")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ewinregirgojr/LFM2.5-Stock-Analyst-Final") model = AutoModelForCausalLM.from_pretrained("ewinregirgojr/LFM2.5-Stock-Analyst-Final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|startoftext|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "is_local": false, | |
| "legacy": false, | |
| "local_files_only": false, | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 512, | |
| "pad_token": "<|pad|>", | |
| "sp_model_kwargs": {}, | |
| "spaces_between_special_tokens": false, | |
| "tokenizer_class": "TokenizersBackend", | |
| "use_default_system_prompt": false, | |
| "use_fast": true | |
| } | |