Instructions to use agarkovv/CryptoTrader-LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use agarkovv/CryptoTrader-LM with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Ministral-8B-Instruct-2410") model = PeftModel.from_pretrained(base_model, "agarkovv/CryptoTrader-LM") - Notebooks
- Google Colab
- Kaggle
Download run_inference.py from agarkovv/CryptoTrader-LM: direct link, hf CLI and curl.
- Browser
- Download file 857 Bytes
-
https://huggingface.co/agarkovv/CryptoTrader-LM/resolve/eea92198b064ffa5ca3d9ad22d7eea3853fe62d8/run_inference.py
- Command line
-
hf download hf://agarkovv/CryptoTrader-LM@eea92198b064ffa5ca3d9ad22d7eea3853fe62d8/run_inference.py
-
curl -L -o run_inference.py https://huggingface.co/agarkovv/CryptoTrader-LM/resolve/eea92198b064ffa5ca3d9ad22d7eea3853fe62d8/run_inference.py
857 Bytes
| from peft import AutoPeftModelForCausalLM | |
| from transformers import AutoTokenizer | |
| import re | |
| PROMPT = "YOUR PROMPT HERE" | |
| MAX_LENGTH = 32768 # Do not change | |
| DEVICE = "cuda" | |
| model_id = "agarkovv/Ministral-8B-Instruct-2410-LoRA-trading" | |
| base_model_id = "mistralai/Ministral-8B-Instruct-2410" | |
| model = AutoPeftModelForCausalLM.from_pretrained(model_id) | |
| tokenizer = AutoTokenizer.from_pretrained(base_model_id) | |
| model = model.to(DEVICE) | |
| model.eval() | |
| inputs = tokenizer( | |
| PROMPT, return_tensors="pt", padding=False, max_length=MAX_LENGTH, truncation=True | |
| ) | |
| inputs = {key: value.to(model.device) for key, value in inputs.items()} | |
| res = model.generate( | |
| **inputs, | |
| use_cache=True, | |
| max_new_tokens=MAX_LENGTH, | |
| ) | |
| output = tokenizer.decode(res[0], skip_special_tokens=True) | |
| answer = re.sub(r".*\[/INST\]\s*", "", output, flags=re.DOTALL) | |
| print(answer) | |