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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| import json | ||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
|
|
||
| MODELS = [ | ||
| 'allenai/OLMo-7B-0724-hf', | ||
| # 'allenai/OLMo-1B-0724-hf', | ||
| # 'allenai/OLMo-7B-0424-hf', | ||
| 'allenai/OLMo-7B-hf', | ||
| 'allenai/OLMo-1B-hf', | ||
| 'meta-llama/Llama-3.2-3B', | ||
| 'meta-llama/Llama-3.2-1B', | ||
| # 'meta-llama/Llama-3.1-70B', | ||
| 'meta-llama/Llama-3.1-8B', | ||
| # 'meta-llama/Meta-Llama-3-70B', | ||
| 'meta-llama/Meta-Llama-3-8B', | ||
| # 'meta-llama/Llama-2-70b-hf', | ||
| # 'meta-llama/Llama-2-13b-hf', | ||
| # 'meta-llama/Llama-2-7b-hf', | ||
| # 'google/gemma-2-27b', | ||
| # 'google/gemma-2-9b', | ||
| # 'google/gemma-2-2b', | ||
| # 'google/gemma-7b', | ||
| # 'google/gemma-2b', | ||
| # 'Qwen/Qwen2.5-72B', | ||
| # 'Qwen/Qwen2.5-32B', | ||
| 'Qwen/Qwen2.5-14B', | ||
| 'Qwen/Qwen2.5-7B', | ||
| 'Qwen/Qwen2.5-3B', | ||
| 'Qwen/Qwen2.5-1.5B', | ||
| # 'Qwen/Qwen2-72B', | ||
| 'Qwen/Qwen2-7B', | ||
| 'Qwen/Qwen2-1.5B', | ||
| 'mistralai/Mistral-Nemo-Base-2407', | ||
| 'mistralai/Mistral-7B-v0.3', | ||
| 'mistralai/Mistral-7B-v0.1', | ||
| ] | ||
|
|
||
| COLOR_BY_MODEL_PREFIX = { | ||
| 'allenai': 'hotpink', | ||
| 'meta-llama/Llama-3.2': 'darkblue', | ||
| 'meta-llama/Llama-3.1': 'mediumblue', | ||
| 'meta-llama/Meta-Llama-3': 'royalblue', | ||
| 'meta-llama/Llama-2': 'cornflowerblue', | ||
| 'google/gemma-2-': 'darkgreen', | ||
| 'google/gemma-': 'forestgreen', | ||
| 'Qwen/Qwen2.5': 'darkviolet', | ||
| 'Qwen/Qwen2': 'violet', | ||
| 'mistralai': 'darkorange', | ||
| } | ||
| def get_color(model): | ||
| for prefix, color in COLOR_BY_MODEL_PREFIX.items(): | ||
| if model.startswith(prefix): | ||
| return color | ||
| return 'black' | ||
|
|
||
| METRICS_BY_TASK = { | ||
| 'rc_rc_mmlu': [ | ||
| ('mmlu_stem_var_bpb', 'mmlu_stem_var_len_norm', 0.215), | ||
| ('mmlu_humanities_var_bpb', 'mmlu_humanities_var_len_norm', 0.335), | ||
| ('mmlu_social_sciences_var_bpb', 'mmlu_social_sciences_var_len_norm', 0.219), | ||
| ('mmlu_other_var_bpb', 'mmlu_other_var_len_norm', 0.231), | ||
| ], | ||
| 'rc_rc_hellaswag': [('hellaswag_rc_5shot_bpb', 'hellaswag_rc_5shot_len_norm', 1.0)], | ||
| 'rc_rc_arc-c': [('arc_challenge_rc_5shot_bpb', 'arc_challenge_rc_5shot_len_norm', 1.0)], | ||
| 'rc_rc_piqa': [('piqa_rc_5shot_bpb', 'piqa_rc_5shot_len_norm', 1.0)], | ||
| 'rc_rc_csqa': [('csqa_rc_5shot_bpb', 'csqa_rc_5shot_len_norm', 1.0)], | ||
| 'rc_rc_socialiqa': [('socialiqa_rc_5shot_bpb', 'socialiqa_rc_5shot_len_norm', 1.0)], | ||
| 'rc_mc_mmlu': [ | ||
| ('mmlu_stem_var_bpb', 'mmlu_stem_mc_5shot_len_norm', 0.215), | ||
| ('mmlu_humanities_var_bpb', 'mmlu_humanities_mc_5shot_len_norm', 0.335), | ||
| ('mmlu_social_sciences_var_bpb', 'mmlu_social_sciences_mc_5shot_len_norm', 0.219), | ||
| ('mmlu_other_var_bpb', 'mmlu_other_mc_5shot_len_norm', 0.231), | ||
| ], | ||
| 'rc_mc_hellaswag': [('hellaswag_rc_5shot_bpb', 'hellaswag_mc_5shot_acc', 1.0)], | ||
| 'rc_mc_arc-c': [('arc_challenge_rc_5shot_bpb', 'arc_challenge_mc_5shot_acc', 1.0)], | ||
| 'rc_mc_piqa': [('piqa_rc_5shot_bpb', 'piqa_mc_5shot_acc', 1.0)], | ||
| 'rc_mc_csqa': [('csqa_rc_5shot_bpb', 'csqa_mc_5shot_acc', 1.0)], | ||
| 'rc_mc_socialiqa': [('socialiqa_rc_5shot_bpb', 'socialiqa_mc_5shot_acc', 1.0)], | ||
| 'mc_mc_mmlu': [ | ||
| ('mmlu_stem_mc_5shot_bpb', 'mmlu_stem_mc_5shot_len_norm', 0.215), | ||
| ('mmlu_humanities_mc_5shot_bpb', 'mmlu_humanities_mc_5shot_len_norm', 0.335), | ||
| ('mmlu_social_sciences_mc_5shot_bpb', 'mmlu_social_sciences_mc_5shot_len_norm', 0.219), | ||
| ('mmlu_other_mc_5shot_bpb', 'mmlu_other_mc_5shot_len_norm', 0.231), | ||
| ], | ||
| 'mc_mc_hellaswag': [('hellaswag_mc_5shot_bpb', 'hellaswag_mc_5shot_acc', 1.0)], | ||
| 'mc_mc_arc-c': [('arc_challenge_mc_5shot_bpb', 'arc_challenge_mc_5shot_acc', 1.0)], | ||
| 'mc_mc_piqa': [('piqa_mc_5shot_bpb', 'piqa_mc_5shot_acc', 1.0)], | ||
| 'mc_mc_csqa': [('csqa_mc_5shot_bpb', 'csqa_mc_5shot_acc', 1.0)], | ||
| 'mc_mc_socialiqa': [('socialiqa_mc_5shot_bpb', 'socialiqa_mc_5shot_acc', 1.0)], | ||
| } | ||
|
|
||
| fig, axs = plt.subplots(6, 3, figsize=(3 * 6, 6 * 4.5)) | ||
|
|
||
| for i, (task, metrics) in enumerate(METRICS_BY_TASK.items()): | ||
| ax = axs[i % 6, i // 6] | ||
| for model in MODELS: | ||
| with open(f'wandb/eval_bpb_mc/{model.replace("/", "_")}.json') as f: | ||
| data = json.load(f) | ||
| try: | ||
| rc_bpb = np.average([data[f'eval/downstream_bpb/{metric[0]}_bpb'] for metric in metrics], weights=[metric[2] for metric in metrics]) | ||
| acc = np.average([data[f'eval/downstream/{metric[1]}'] for metric in metrics], weights=[metric[2] for metric in metrics]) | ||
| except KeyError: | ||
| continue | ||
| color = get_color(model) | ||
| ax.scatter([rc_bpb], [acc], color=color, s=100) | ||
| ax.annotate( | ||
| text=model.split('/')[1], | ||
| xy=(float(rc_bpb), float(acc)), | ||
| xytext=(8, -3), | ||
| textcoords='offset points', | ||
| fontsize=8, | ||
| ) | ||
| ax.set_xlabel(f'{task.split("_")[0]} bpb') | ||
| ax.set_ylabel(f'{task.split("_")[1]} acc') | ||
| ax.set_title(task) | ||
|
|
||
| plt.savefig(f'wandb/eval_bpb_mc/all.png', dpi=300, bbox_inches='tight') |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,20 @@ | ||
| { | ||
| "7b-4T-70k-300k": { | ||
| "paths": [ | ||
| "wandb/peteish7_eval_full_70k-300k.csv" | ||
| ], | ||
| "mode": "train", | ||
| "n": 6887575552, | ||
| "label": "7b-4T-70k-300k", | ||
| "color": "darkviolet" | ||
| }, | ||
| "7b-4T-final": { | ||
| "paths": [ | ||
| "wandb/peteish7_eval_anneal.csv" | ||
| ], | ||
| "mode": "eval", | ||
| "n": 6887575552, | ||
| "label": "7b-4T-final", | ||
| "color": "magenta" | ||
| } | ||
| } |