Instructions to use BayesTensor/out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use BayesTensor/out with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "BayesTensor/out") - Notebooks
- Google Colab
- Kaggle
Download lm-evaluation-harness/scripts/make_table_results.py from BayesTensor/out: direct link, hf CLI and curl.
- Browser
- Download file 2.53 kB
-
https://huggingface.co/BayesTensor/out/resolve/main/lm-evaluation-harness/scripts/make_table_results.py
- Command line
-
hf download hf://BayesTensor/out/lm-evaluation-harness/scripts/make_table_results.py
-
curl -L -o make_table_results.py https://huggingface.co/BayesTensor/out/resolve/main/lm-evaluation-harness/scripts/make_table_results.py
2.53 kB
| """ | |
| Usage: | |
| python make_table_tasks.py --output <markdown_filename> | |
| """ | |
| import json | |
| import logging | |
| import os | |
| from pytablewriter import LatexTableWriter, MarkdownTableWriter | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| def make_table(result_dict): | |
| """Generate table of results.""" | |
| md_writer = MarkdownTableWriter() | |
| latex_writer = LatexTableWriter() | |
| md_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"] | |
| latex_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"] | |
| values = [] | |
| for k, dic in sorted(result_dict["results"].items()): | |
| version = result_dict["versions"][k] | |
| percent = k == "squad2" | |
| for m, v in dic.items(): | |
| if m.endswith("_stderr"): | |
| continue | |
| if m + "_stderr" in dic: | |
| se = dic[m + "_stderr"] | |
| if percent or m == "ppl": | |
| values.append([k, version, m, "%.2f" % v, "±", "%.2f" % se]) | |
| else: | |
| values.append( | |
| [k, version, m, "%.2f" % (v * 100), "±", "%.2f" % (se * 100)] | |
| ) | |
| else: | |
| if percent or m == "ppl": | |
| values.append([k, version, m, "%.2f" % v, "", ""]) | |
| else: | |
| values.append([k, version, m, "%.2f" % (v * 100), "", ""]) | |
| k = "" | |
| version = "" | |
| md_writer.value_matrix = values | |
| latex_writer.value_matrix = values | |
| # todo: make latex table look good | |
| # print(latex_writer.dumps()) | |
| return md_writer.dumps() | |
| if __name__ == "__main__": | |
| # loop dirs and subdirs in results dir | |
| # for each dir, load json files | |
| for dirpath, dirnames, filenames in os.walk("../results"): | |
| # skip dirs without files | |
| if not filenames: | |
| continue | |
| path_readme = os.path.join(dirpath, "README.md") | |
| with open(path_readme, "w", encoding="utf-8") as f: | |
| # get path name, only last folder | |
| path_name = dirpath.split("/")[-1] | |
| f.write(f"# {path_name} \n\n") | |
| for filename in sorted([f for f in filenames if f.endswith(".json")]): | |
| path = os.path.join(dirpath, filename) | |
| with open(path, "r", encoding="utf-8") as f: | |
| result_dict = json.load(f) | |
| with open(path_readme, "a", encoding="utf-8") as f: | |
| f.write(f"## {filename} \n") | |
| f.write(f"{make_table(result_dict)} \n") | |