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EleutherAI_pythia-1b-deduped__sft__tldr_dpo_costa_1b_fp16.yml_3d94f50_b9ff2

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Training in progress, step 291

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Files changed (7) hide show
  1. adapter_config.json +29 -0
  2. adapter_model.safetensors +3 -0
  3. notebook.ipynb +112 -0
  4. special_tokens_map.json +30 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +220 -0
  7. training_args.bin +3 -0
adapter_config.json ADDED
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+ "base_model_name_or_path": "vwxyzjn/EleutherAI_pythia-1b-deduped__sft__tldr",
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+ "bias": "none",
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+ "fan_in_fan_out": false,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "r": 16,
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+ "target_modules": [
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+ "dense_4h_to_h",
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+ "dense",
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+ "query_key_value",
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+ "dense_h_to_4h"
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+ ],
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+ "task_type": "CAUSAL_LM",
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+ "use_rslora": false
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "c8b7c85d",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "\n",
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+ "from haven import haven_jupyter as hj\n",
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+ "from haven import haven_results as hr\n",
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+ "from haven import haven_utils as hu\n",
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+ "\n",
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+ "# path to where the experiments got saved\n",
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+ "savedir_base = '/home/toolkit/trl/results'\n",
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+ "exp_list = None\n",
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+ "\n",
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+ "# get experiments from the exp config\n",
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+ "exp_config_fname = None\n",
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+ "if exp_config_fname:\n",
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+ " config = hu.load_py(exp_config_fname)\n",
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+ " exp_list = []\n",
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+ " for exp_group in [\n",
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+ " \"example\"\n",
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+ " ]:\n",
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+ " exp_list += config.EXP_GROUPS[exp_group]\n",
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+ "\n",
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+ "# filter exps\n",
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+ "\n",
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+ "filterby_list = None\n",
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+ "# filterby_list =[{'dataset':'mnist'}]\n",
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+ "\n",
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+ "# get experiments\n",
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+ "rm = hr.ResultManager(exp_list=exp_list,\n",
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+ " savedir_base=savedir_base,\n",
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+ " filterby_list=filterby_list,\n",
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+ " verbose=0,\n",
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+ " exp_groups=None,\n",
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+ " job_scheduler='slurm'\n",
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+ " )\n",
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+ "\n",
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+ "# specify display parameters\n",
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+ "\n",
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+ "# groupby_list = ['dataset']\n",
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+ "# title_list = ['dataset']\n",
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+ "# legend_list = ['model']\n",
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+ "y_metrics = ['train_loss']\n",
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+ "x_metric = 'epoch'\n",
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+ "\n",
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+ "# launch dashboard\n",
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+ "hj.get_dashboard(rm, vars(), wide_display=False, enable_datatables=False)\n",
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+ " "
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "87acb700",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "\n",
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+ "# get table \n",
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+ "rm.get_score_df().head()\n",
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+ "\n",
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+ "# get latex \n",
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+ "# print(rm.get_latex_table(legend=['dataset'], metrics=['train_loss'], decimals=1, caption=\"Results\", label='tab:results'))\n",
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+ "\n",
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+ "# get custom plots\n",
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+ "fig = rm.get_plot_all(\n",
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+ " # order='metrics_by_groups',\n",
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+ " # avg_across='runs',\n",
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+ " y_metric_list=y_metrics, \n",
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+ " x_metric=x_metric,\n",
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+ " # legend_fontsize=18,\n",
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+ " # x_fontsize=20,\n",
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+ " # y_fontsize=20,\n",
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+ " # xtick_fontsize=20,\n",
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+ " # ytick_fontsize=20,\n",
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+ " # title_fontsize=24,\n",
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+ " # legend_list=['model], \n",
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+ " # title_list = ['dataset'], \n",
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+ " # title_format='Dataset:{}',\n",
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+ " # log_metric_list = ['train_loss'], \n",
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+ " # groupby_list = ['dataset'],\n",
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+ " # map_ylabel_list=[{'train_loss':'Train loss'}],\n",
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+ " # map_xlabel_list=[{'epoch':'Epoch'}],\n",
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+ " # figsize=(15,5),\n",
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+ " # plot_confidence=False,\n",
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+ " # savedir_plots='%s' % (name)\n",
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+ ")\n",
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+ " "
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "1820c04c",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "\n",
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+ " !pip install --upgrade git+https://github.com/haven-ai/haven-ai\n",
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+ " "
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+ ]
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+ }
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+ ],
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+ "metadata": {},
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+ "nbformat": 4,
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+ "nbformat_minor": 5
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+ }
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