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Model: neuralmagic/Llama-2-7b-ultrachat200k-pruned_70 Source: Original Platform
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README.md
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README.md
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---
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base_model: neuralmagic/Llama-2-7b-pruned70-retrained
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inference: true
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model_type: llama
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pipeline_tag: text-generation
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datasets:
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- cerebras/SlimPajama-627B
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- HuggingFaceH4/ultrachat_200k
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tags:
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- sparse
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- chat
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---
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# Llama-2-7b-pruned70-retrained-ultrachat
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This repo contains a [70% sparse Llama 2 7B](https://huggingface.co/neuralmagic/Llama-2-7b-pruned70-retrained) finetuned for chat tasks using the [UltraChat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset.
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Official model weights from [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594).
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**Authors**: Neural Magic, Cerebras
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## Usage
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Below we share some code snippets on how to get quickly started with running the model.
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### Sparse Transfer
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By leveraging a pre-sparsified model's structure, you can efficiently fine-tune on new data, leading to reduced hyperparameter tuning, training times, and computational costs. Learn about this process [here](https://neuralmagic.github.io/docs-v2/get-started/transfer).
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### Running the model
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This model may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse).
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```python
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# pip install transformers accelerate
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-pruned70-retrained-ultrachat")
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model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-pruned70-retrained-ultrachat", device_map="auto")
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input_text = "Write me a poem about Machine Learning."
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input_ids = tokenizer.apply_chat_template(input_text, add_generation_prompt=True, return_tensors="pt").to("cuda")
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outputs = model.generate(**input_ids)
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print(tokenizer.decode(outputs[0]))
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```
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## Evaluation Benchmark Results
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Model evaluation metrics and results.
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| Benchmark | Metric | Llama-2-7b-ultrachat | Llama-2-7b-pruned70-retrained-ultrachat |
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|------------------------------------------------|---------------|-------------|-------------------------------|
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| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot | 46.1% | 32.5% |
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| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot | 75.9% | 68.9% |
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| [WinoGrande](https://arxiv.org/abs/1907.10641) | 5-shot | 72.6% | 65.1% |
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| [ARC-c](https://arxiv.org/abs/1911.01547) | 25-shot | 52.8% | 45.3% |
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| [TruthfulQA](https://arxiv.org/abs/2109.07958) | 5-shot | 44.8% | 39.6% |
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| [GSM8K](https://arxiv.org/abs/2110.14168) | 5-shot | 12.4% | 4.8% |
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| [AlpacaEval](https://arxiv.org/abs/2107.03374) ([Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) evaluator) | Win rate | 57.6% | 57.4% |
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| [AlpacaEval](https://arxiv.org/abs/2107.03374) (GPT-4 Turbo evaluator) | Win rate | 60.6% | 54.0% |
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## Model Training Details
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This model was obtained by sparse-tranfer of the sparse foundational model [Llama-2-7b-pruned70-retrained](https://huggingface.co/neuralmagic/Llama-2-7b-pruned70-retrained) on the [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset.
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Training was performed for 2 epochs and used the [SquareHead](https://arxiv.org/abs/2310.06927) knowledge distillation with [Llama-2-7b-ultrachat](https://huggingface.co/neuralmagic/Llama-2-7b-ultrachat) as teacher.
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## Help
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For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)
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arc_challenge_25shot_bs16_bf16.json
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arc_challenge_25shot_bs16_bf16.json
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{
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"results": {
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"arc_challenge": {
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"acc": 0.4232081911262799,
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"acc_stderr": 0.014438036220848027,
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"acc_norm": 0.45307167235494883,
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"acc_norm_stderr": 0.01454689205200563
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}
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},
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"versions": {
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"arc_challenge": 0
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},
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"config": {
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"model": "sparseml",
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"model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse70_retrained/ultrachat200k/llama2_7B_sparse70_LR3e-4_GC2_E2/training,dtype=bfloat16",
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"num_fewshot": 25,
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"batch_size": "16",
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"batch_sizes": [],
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"device": "cuda:1",
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"no_cache": true,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": {}
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}
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}
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config.json
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{
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"_name_or_path": "neuralmagic/Llama-2-7b-pruned70-retrained-ultrachat",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"tokenizer_class": "LlamaTokenizerFast",
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"torch_dtype": "bfloat16",
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"transformers_version": "4.40.0",
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"use_cache": true,
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"vocab_size": 32000
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.40.0"
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}
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gsm8k_5shot_bs16_bf16.json
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gsm8k_5shot_bs16_bf16.json
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{
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"results": {
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"gsm8k": {
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"acc": 0.047763457164518575,
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"acc_stderr": 0.005874387536229333
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}
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},
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"versions": {
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"gsm8k": 0
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},
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"config": {
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"model": "sparseml",
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"model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse70_retrained/ultrachat200k/llama2_7B_sparse70_LR3e-4_GC2_E2/training,dtype=bfloat16",
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"num_fewshot": 5,
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"batch_size": "16",
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"batch_sizes": [],
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"device": "cuda:6",
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"no_cache": true,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": {}
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}
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}
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hellaswag_10shot_bs16_bf16.json
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{
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"results": {
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"hellaswag": {
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"acc": 0.5220075682135032,
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"acc_stderr": 0.004984945635998312,
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"acc_norm": 0.6885082652857997,
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"acc_norm_stderr": 0.004621568125102052
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}
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},
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"versions": {
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"hellaswag": 0
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},
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"config": {
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"model": "sparseml",
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"model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse70_retrained/ultrachat200k/llama2_7B_sparse70_LR3e-4_GC2_E2/training,dtype=bfloat16",
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"num_fewshot": 10,
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"batch_size": "16",
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"batch_sizes": [],
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"device": "cuda:3",
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"no_cache": true,
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"limit": null,
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"bootstrap_iters": 100000,
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"description_dict": {}
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}
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}
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mmlu_5shot_bs4_bf16.json
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{
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"results": {
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"hendrycksTest-abstract_algebra": {
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"acc": 0.32,
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"acc_stderr": 0.046882617226215034,
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"acc_norm": 0.32,
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"acc_norm_stderr": 0.046882617226215034
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},
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"hendrycksTest-anatomy": {
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"acc": 0.32592592592592595,
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"acc_stderr": 0.040491220417025055,
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"acc_norm": 0.32592592592592595,
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"acc_norm_stderr": 0.040491220417025055
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},
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"hendrycksTest-astronomy": {
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"acc": 0.23026315789473684,
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"acc_stderr": 0.03426059424403165,
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"acc_norm": 0.23026315789473684,
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"acc_norm_stderr": 0.03426059424403165
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},
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"hendrycksTest-business_ethics": {
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"acc": 0.3,
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"acc_stderr": 0.046056618647183814,
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"acc_norm": 0.3,
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"acc_norm_stderr": 0.046056618647183814
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},
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"hendrycksTest-clinical_knowledge": {
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"acc": 0.3584905660377358,
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"acc_stderr": 0.02951470358398177,
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"acc_norm": 0.3584905660377358,
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"acc_norm_stderr": 0.02951470358398177
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},
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"hendrycksTest-college_biology": {
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"acc": 0.2916666666666667,
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"acc_stderr": 0.038009680605548594,
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"acc_norm": 0.2916666666666667,
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"acc_norm_stderr": 0.038009680605548594
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},
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"hendrycksTest-college_chemistry": {
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"acc": 0.21,
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"acc_stderr": 0.040936018074033256,
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"acc_norm": 0.21,
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"acc_norm_stderr": 0.040936018074033256
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},
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"hendrycksTest-college_computer_science": {
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"acc": 0.21,
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"acc_stderr": 0.040936018074033256,
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"acc_norm": 0.21,
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"acc_norm_stderr": 0.040936018074033256
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},
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"hendrycksTest-college_mathematics": {
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"acc": 0.23,
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"acc_stderr": 0.04229525846816506,
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"acc_norm": 0.23,
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"acc_norm_stderr": 0.04229525846816506
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},
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"hendrycksTest-college_medicine": {
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"acc": 0.2832369942196532,
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"acc_stderr": 0.034355680560478746,
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"acc_norm": 0.2832369942196532,
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"acc_norm_stderr": 0.034355680560478746
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},
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"hendrycksTest-college_physics": {
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"acc": 0.21568627450980393,
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"acc_stderr": 0.04092563958237655,
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"acc_norm": 0.21568627450980393,
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"acc_norm_stderr": 0.04092563958237655
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},
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"hendrycksTest-computer_security": {
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"acc": 0.37,
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"acc_stderr": 0.04852365870939099,
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"acc_norm": 0.37,
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"acc_norm_stderr": 0.04852365870939099
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},
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"hendrycksTest-conceptual_physics": {
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"acc": 0.3574468085106383,
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"acc_stderr": 0.03132941789476425,
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"acc_norm": 0.3574468085106383,
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"acc_norm_stderr": 0.03132941789476425
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},
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"hendrycksTest-econometrics": {
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||||||
|
"acc": 0.24561403508771928,
|
||||||
|
"acc_stderr": 0.04049339297748142,
|
||||||
|
"acc_norm": 0.24561403508771928,
|
||||||
|
"acc_norm_stderr": 0.04049339297748142
|
||||||
|
},
|
||||||
|
"hendrycksTest-electrical_engineering": {
|
||||||
|
"acc": 0.2827586206896552,
|
||||||
|
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|
||||||
|
"acc_norm": 0.2827586206896552,
|
||||||
|
"acc_norm_stderr": 0.03752833958003337
|
||||||
|
},
|
||||||
|
"hendrycksTest-elementary_mathematics": {
|
||||||
|
"acc": 0.23809523809523808,
|
||||||
|
"acc_stderr": 0.021935878081184766,
|
||||||
|
"acc_norm": 0.23809523809523808,
|
||||||
|
"acc_norm_stderr": 0.021935878081184766
|
||||||
|
},
|
||||||
|
"hendrycksTest-formal_logic": {
|
||||||
|
"acc": 0.1984126984126984,
|
||||||
|
"acc_stderr": 0.035670166752768635,
|
||||||
|
"acc_norm": 0.1984126984126984,
|
||||||
|
"acc_norm_stderr": 0.035670166752768635
|
||||||
|
},
|
||||||
|
"hendrycksTest-global_facts": {
|
||||||
|
"acc": 0.34,
|
||||||
|
"acc_stderr": 0.04760952285695236,
|
||||||
|
"acc_norm": 0.34,
|
||||||
|
"acc_norm_stderr": 0.04760952285695236
|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_biology": {
|
||||||
|
"acc": 0.3225806451612903,
|
||||||
|
"acc_stderr": 0.02659308451657229,
|
||||||
|
"acc_norm": 0.3225806451612903,
|
||||||
|
"acc_norm_stderr": 0.02659308451657229
|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_chemistry": {
|
||||||
|
"acc": 0.2315270935960591,
|
||||||
|
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|
||||||
|
"acc_norm": 0.2315270935960591,
|
||||||
|
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|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_computer_science": {
|
||||||
|
"acc": 0.34,
|
||||||
|
"acc_stderr": 0.047609522856952365,
|
||||||
|
"acc_norm": 0.34,
|
||||||
|
"acc_norm_stderr": 0.047609522856952365
|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_european_history": {
|
||||||
|
"acc": 0.4,
|
||||||
|
"acc_stderr": 0.03825460278380026,
|
||||||
|
"acc_norm": 0.4,
|
||||||
|
"acc_norm_stderr": 0.03825460278380026
|
||||||
|
},
|
||||||
|
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|
||||||
|
"acc": 0.3383838383838384,
|
||||||
|
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|
||||||
|
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|
||||||
|
"acc_norm_stderr": 0.03371124142626303
|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_government_and_politics": {
|
||||||
|
"acc": 0.32642487046632124,
|
||||||
|
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|
||||||
|
"acc_norm": 0.32642487046632124,
|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
"acc": 0.23703703703703705,
|
||||||
|
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|
||||||
|
"acc_norm": 0.23703703703703705,
|
||||||
|
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|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_microeconomics": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"acc_norm_stderr": 0.02959732973097808
|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_physics": {
|
||||||
|
"acc": 0.2582781456953642,
|
||||||
|
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|
||||||
|
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|
||||||
|
"acc_norm_stderr": 0.035737053147634576
|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_psychology": {
|
||||||
|
"acc": 0.3779816513761468,
|
||||||
|
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|
||||||
|
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|
||||||
|
"acc_norm_stderr": 0.020789187066728117
|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_statistics": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_us_history": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
"hendrycksTest-high_school_world_history": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
"acc": 0.37037037037037035,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
"hendrycksTest-logical_fallacies": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
"acc": 0.3633440514469453,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"acc": 0.3104575163398693,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
"acc": 0.38181818181818183,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
"acc": 0.4079601990049751,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
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|
||||||
|
"acc": 0.41,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
"hendrycksTest-virology": {
|
||||||
|
"acc": 0.4036144578313253,
|
||||||
|
"acc_stderr": 0.038194861407583984,
|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
"hendrycksTest-world_religions": {
|
||||||
|
"acc": 0.40350877192982454,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
}
|
||||||
|
},
|
||||||
|
"versions": {
|
||||||
|
"hendrycksTest-abstract_algebra": 1,
|
||||||
|
"hendrycksTest-anatomy": 1,
|
||||||
|
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|
||||||
|
"hendrycksTest-business_ethics": 1,
|
||||||
|
"hendrycksTest-clinical_knowledge": 1,
|
||||||
|
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|
||||||
|
"hendrycksTest-college_chemistry": 1,
|
||||||
|
"hendrycksTest-college_computer_science": 1,
|
||||||
|
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|
||||||
|
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|
||||||
|
"hendrycksTest-college_physics": 1,
|
||||||
|
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|
||||||
|
"hendrycksTest-conceptual_physics": 1,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"hendrycksTest-high_school_biology": 1,
|
||||||
|
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|
||||||
|
"hendrycksTest-high_school_computer_science": 1,
|
||||||
|
"hendrycksTest-high_school_european_history": 1,
|
||||||
|
"hendrycksTest-high_school_geography": 1,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"hendrycksTest-high_school_microeconomics": 1,
|
||||||
|
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|
||||||
|
"hendrycksTest-high_school_psychology": 1,
|
||||||
|
"hendrycksTest-high_school_statistics": 1,
|
||||||
|
"hendrycksTest-high_school_us_history": 1,
|
||||||
|
"hendrycksTest-high_school_world_history": 1,
|
||||||
|
"hendrycksTest-human_aging": 1,
|
||||||
|
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|
||||||
|
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|
||||||
|
"hendrycksTest-jurisprudence": 1,
|
||||||
|
"hendrycksTest-logical_fallacies": 1,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"hendrycksTest-moral_disputes": 1,
|
||||||
|
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|
||||||
|
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|
||||||
|
"hendrycksTest-philosophy": 1,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"hendrycksTest-security_studies": 1,
|
||||||
|
"hendrycksTest-sociology": 1,
|
||||||
|
"hendrycksTest-us_foreign_policy": 1,
|
||||||
|
"hendrycksTest-virology": 1,
|
||||||
|
"hendrycksTest-world_religions": 1
|
||||||
|
},
|
||||||
|
"config": {
|
||||||
|
"model": "sparseml",
|
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|
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|
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|
||||||
|
}
|
||||||
|
}
|
||||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "</s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
93391
tokenizer.json
Normal file
93391
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
||||||
|
size 499723
|
||||||
42
tokenizer_config.json
Normal file
42
tokenizer_config.json
Normal file
@@ -0,0 +1,42 @@
|
|||||||
|
{
|
||||||
|
"add_bos_token": true,
|
||||||
|
"add_eos_token": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"0": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"1": {
|
||||||
|
"content": "<s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"2": {
|
||||||
|
"content": "</s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"bos_token": "<s>",
|
||||||
|
"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "</s>",
|
||||||
|
"legacy": false,
|
||||||
|
"model_max_length": 1000000000000000019884624838656,
|
||||||
|
"pad_token": null,
|
||||||
|
"padding_side": "right",
|
||||||
|
"sp_model_kwargs": {},
|
||||||
|
"tokenizer_class": "LlamaTokenizer",
|
||||||
|
"unk_token": "<unk>",
|
||||||
|
"use_default_system_prompt": false
|
||||||
|
}
|
||||||
25
truthfulqa_mc_0shot_bs16_bf16.json
Normal file
25
truthfulqa_mc_0shot_bs16_bf16.json
Normal file
@@ -0,0 +1,25 @@
|
|||||||
|
{
|
||||||
|
"results": {
|
||||||
|
"truthfulqa_mc": {
|
||||||
|
"mc1": 0.2521419828641371,
|
||||||
|
"mc1_stderr": 0.01520152224629996,
|
||||||
|
"mc2": 0.3954683484720209,
|
||||||
|
"mc2_stderr": 0.014883378073318826
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"versions": {
|
||||||
|
"truthfulqa_mc": 1
|
||||||
|
},
|
||||||
|
"config": {
|
||||||
|
"model": "sparseml",
|
||||||
|
"model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse70_retrained/ultrachat200k/llama2_7B_sparse70_LR3e-4_GC2_E2/training,dtype=bfloat16",
|
||||||
|
"num_fewshot": 0,
|
||||||
|
"batch_size": "16",
|
||||||
|
"batch_sizes": [],
|
||||||
|
"device": "cuda:4",
|
||||||
|
"no_cache": true,
|
||||||
|
"limit": null,
|
||||||
|
"bootstrap_iters": 100000,
|
||||||
|
"description_dict": {}
|
||||||
|
}
|
||||||
|
}
|
||||||
23
winogrande_5shot_bs16_bf16.json
Normal file
23
winogrande_5shot_bs16_bf16.json
Normal file
@@ -0,0 +1,23 @@
|
|||||||
|
{
|
||||||
|
"results": {
|
||||||
|
"winogrande": {
|
||||||
|
"acc": 0.6511444356748224,
|
||||||
|
"acc_stderr": 0.013395059320137332
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"versions": {
|
||||||
|
"winogrande": 0
|
||||||
|
},
|
||||||
|
"config": {
|
||||||
|
"model": "sparseml",
|
||||||
|
"model_args": "pretrained=/network/alexandre/research/cerebras/llama2_7B_sparse70_retrained/ultrachat200k/llama2_7B_sparse70_LR3e-4_GC2_E2/training,dtype=bfloat16",
|
||||||
|
"num_fewshot": 5,
|
||||||
|
"batch_size": "16",
|
||||||
|
"batch_sizes": [],
|
||||||
|
"device": "cuda:2",
|
||||||
|
"no_cache": true,
|
||||||
|
"limit": null,
|
||||||
|
"bootstrap_iters": 100000,
|
||||||
|
"description_dict": {}
|
||||||
|
}
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user