Instructions to use QinLiuNLP/llama3-sudo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use QinLiuNLP/llama3-sudo with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "QinLiuNLP/llama3-sudo") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +2 -4
- adapter_config.json +4 -4
- all_results.json +3 -3
- runs/Aug13_19-04-24_ip-172-31-10-237/events.out.tfevents.1723575876.ip-172-31-10-237.662502.0 +3 -0
- tokenizer.json +1 -6
- train_results.json +3 -3
- trainer_state.json +3 -3
- training_args.bin +1 -1
README.md
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---
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base_model: meta-llama/Meta-Llama-3-8B
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datasets:
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- HuggingFaceH4/ultrachat_200k
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library_name: peft
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license: llama3
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tags:
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- alignment-handbook
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- trl
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- sft
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- generated_from_trainer
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model-index:
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- name: llama3-sudo
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# llama3-sudo
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on
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It achieves the following results on the evaluation set:
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- Loss: 1.0709
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---
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base_model: meta-llama/Meta-Llama-3-8B
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library_name: peft
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license: llama3
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tags:
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- trl
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- sft
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- alignment-handbook
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- generated_from_trainer
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model-index:
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- name: llama3-sudo
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# llama3-sudo
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0709
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"k_proj",
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"
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"o_proj",
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"v_proj",
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"q_proj",
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"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"down_proj",
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"o_proj",
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"q_proj",
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"v_proj",
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"gate_proj",
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"up_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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all_results.json
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"epoch": 2.909090909090909,
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"total_flos": 644347544469504.0,
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"train_loss": 0.0,
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"train_runtime": 0.
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"train_samples": 6321,
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"train_samples_per_second":
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"train_steps_per_second":
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}
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"epoch": 2.909090909090909,
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"total_flos": 644347544469504.0,
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"train_loss": 0.0,
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"train_runtime": 0.0111,
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"train_samples": 6321,
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"train_samples_per_second": 1702737.829,
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"train_steps_per_second": 6465.07
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}
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runs/Aug13_19-04-24_ip-172-31-10-237/events.out.tfevents.1723575876.ip-172-31-10-237.662502.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:27168ac8e2a491d19a8d999f6beb2617f73b2f589523199028497044e138f4ec
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size 5898
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tokenizer.json
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{
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"version": "1.0",
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"truncation":
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"direction": "Right",
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"max_length": 2048,
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"strategy": "LongestFirst",
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"stride": 0
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},
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"padding": null,
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"added_tokens": [
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"version": "1.0",
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"truncation": null,
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"padding": null,
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{
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train_results.json
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"epoch": 2.909090909090909,
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"total_flos": 644347544469504.0,
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"train_loss": 0.0,
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"train_runtime": 0.
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"train_samples": 6321,
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"train_samples_per_second":
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"train_steps_per_second":
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}
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"epoch": 2.909090909090909,
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"total_flos": 644347544469504.0,
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"train_loss": 0.0,
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"train_runtime": 0.0111,
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"train_samples": 6321,
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"train_samples_per_second": 1702737.829,
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"train_steps_per_second": 6465.07
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}
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trainer_state.json
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"step": 72,
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"total_flos": 644347544469504.0,
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"train_loss": 0.0,
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"train_runtime": 0.
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"train_samples_per_second":
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"train_steps_per_second":
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"logging_steps": 5,
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"step": 72,
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"total_flos": 644347544469504.0,
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"train_loss": 0.0,
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"train_runtime": 0.0111,
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"train_samples_per_second": 1702737.829,
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"train_steps_per_second": 6465.07
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}
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],
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"logging_steps": 5,
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training_args.bin
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size 6904
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oid sha256:ef2cf294a0941096dc386e43b0615197a851de050108383ef1b8c57a6788d26a
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size 6904
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