Instruct_Yi-6B_Dolly15K
Fine-tuned from Yi-6B， used Dolly15k for the dataset. 90% for training, 10% validation. Trained for 2.0 epochs using Lora. Trained with 1024 context window.
Model Details
- Trained by: trained by HenryJJ.
- Model type: Instruct_Yi-6B_Dolly15K is an auto-regressive language model based on the Llama 2 transformer architecture.
- Language(s): English
- License for Instruct_Yi-6B_Dolly15K: apache-2.0 license
Prompting
Prompt Template With Context
<|startoftext|>[INST]{instruction} {context}[/INST]{response}<|endoftext|>
<|startoftext|>[INST]
Write a 10-line poem about a given topic
The topic is about racecars
[/INST]
Prompt Template Without Context
<|startoftext|>[INST]
Who was the was the second president of the United States?
[/INST]
Training script:
Fully opensourced at: https://github.com/hengjiUSTC/learn-llm/blob/main/trl_finetune.py. Run on aws g4dn.12xlarge instance for 4 hours.
python3 trl_finetune.py --config configs/yi_6b.yml
Dataset Card for Evaluation run of HenryJJ/Instruct_Yi-6B_Dolly15K
Dataset automatically created during the evaluation run of model HenryJJ/Instruct_Yi-6B_Dolly15K on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_HenryJJ__Instruct_Yi-6B_Dolly15K",
"harness_winogrande_5",
split="train")
Latest results
These are the latest results from run 2024-01-06T09:45:44.755529(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
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}
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