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--- |
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license: other |
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tags: |
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- axolotl |
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- generated_from_trainer |
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- Mistral |
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- instruct |
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- finetune |
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- chatml |
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- gpt4 |
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- synthetic data |
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- science |
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- physics |
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- chemistry |
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- biology |
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- math |
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base_model: mistralai/Mistral-7B-v0.1 |
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datasets: |
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- allenai/ai2_arc |
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- camel-ai/physics |
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- camel-ai/chemistry |
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- camel-ai/biology |
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- camel-ai/math |
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- metaeval/reclor |
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- openbookqa |
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- mandyyyyii/scibench |
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- derek-thomas/ScienceQA |
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- TIGER-Lab/ScienceEval |
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- jondurbin/airoboros-3.2 |
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- LDJnr/Capybara |
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- Cot-Alpaca-GPT4-From-OpenHermes-2.5 |
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- STEM-AI-mtl/Electrical-engineering |
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- knowrohit07/saraswati-stem |
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- sablo/oasst2_curated |
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- glaiveai/glaive-code-assistant |
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- lmsys/lmsys-chat-1m |
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- TIGER-Lab/MathInstruct |
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- bigbio/med_qa |
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- meta-math/MetaMathQA-40K |
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- openbookqa |
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- piqa |
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- metaeval/reclor |
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- derek-thomas/ScienceQA |
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- scibench |
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- sciq |
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- Open-Orca/SlimOrca |
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- migtissera/Synthia-v1.3 |
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- TIGER-Lab/ScienceEval |
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model-index: |
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- name: Einstein-v4-7B |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 64.68 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 83.75 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 62.31 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 55.15 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 76.24 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 57.62 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v4-7B |
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name: Open LLM Leaderboard |
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--- |
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 |
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# 🔬 Einstein-v4-7B |
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This model is a full fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on diverse datasets. |
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This model is finetuned using `7xRTX3090` + `1xRTXA6000` using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl). |
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This model's training was sponsored by [sablo.ai](https://sablo.ai). |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: mistralai/Mistral-7B-v0.1 |
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model_type: MistralForCausalLM |
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tokenizer_type: LlamaTokenizer |
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is_mistral_derived_model: true |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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chat_template: chatml |
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datasets: |
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- path: data/merged_all.json |
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ds_type: json |
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type: alpaca |
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conversation: chatml |
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- path: data/capybara_sharegpt.json |
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ds_type: json |
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type: sharegpt |
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conversation: chatml |
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- path: data/synthia-v1.3_sharegpt_12500.json |
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ds_type: json |
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type: sharegpt |
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conversation: chatml |
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- path: data/cot_alpaca_gpt4_extracted_openhermes_2.5_sharegpt.json |
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ds_type: json |
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type: sharegpt |
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conversation: chatml |
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- path: data/slimorca_dedup_filtered_95k_sharegpt.json |
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ds_type: json |
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type: sharegpt |
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conversation: chatml |
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- path: data/airoboros_3.2_without_contextual_slimorca_orca_sharegpt.json |
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ds_type: json |
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type: sharegpt |
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conversation: chatml |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.005 |
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output_dir: ./Einstein-v4-model |
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sequence_len: 8192 |
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sample_packing: true |
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pad_to_sequence_len: true |
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eval_sample_packing: false |
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wandb_project: Einstein |
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wandb_entity: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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hub_model_id: Weyaxi/Einstein-v4-7B |
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save_safetensors: true |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 1 |
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num_epochs: 1.5 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.000005 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: true |
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fp16: false |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 10 |
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evals_per_epoch: 2 # changed |
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eval_table_size: |
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eval_table_max_new_tokens: 128 |
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saves_per_epoch: 4 |
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debug: |
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deepspeed: zero3_bf16.json |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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bos_token: "<s>" |
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eos_token: "<|im_end|>" |
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unk_token: "<unk>" |
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tokens: |
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- "<|im_start|>" |
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resume_from_checkpoint: Einstein-v4-model/checkpoint-521 |
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``` |
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</details><br> |
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# 💬 Prompt Template |
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You can use this prompt template while using the model: |
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### ChatML |
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``` |
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<|im_start|>system |
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{system}<|im_end|> |
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<|im_start|>user |
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{user}<|im_end|> |
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<|im_start|>assistant |
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{asistant}<|im_end|> |
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``` |
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This prompt template is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the |
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`tokenizer.apply_chat_template()` method: |
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```python |
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messages = [ |
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{"role": "system", "content": "You are helpful AI asistant."}, |
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{"role": "user", "content": "Hello!"} |
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] |
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gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") |
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model.generate(**gen_input) |
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``` |
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# 🔄 Quantizationed versions |
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Quantizationed versions of this model is available. |
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## Exl2 [@bartowski](https://hf.co/bartowski): |
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- https://huggingface.co/bartowski/Einstein-v4-7B-exl2 |
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You can switch up branches in the repo to use the one you want |
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| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description | |
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| ----- | ---- | ------- | ------ | ------ | ------ | ------------ | |
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| [8_0](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | |
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| [6_5](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. | |
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| [5_0](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. | |
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| [4_25](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. | |
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| [3_5](https://huggingface.co/bartowski/Einstein-v4-7B-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. | |
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# 🎯 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Einstein-v4-7B) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |66.62| |
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|AI2 Reasoning Challenge (25-Shot)|64.68| |
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|HellaSwag (10-Shot) |83.75| |
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|MMLU (5-Shot) |62.31| |
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|TruthfulQA (0-shot) |55.15| |
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|Winogrande (5-shot) |76.24| |
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|GSM8k (5-shot) |57.62| |
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# 🤖 Additional information about training |
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This model is full fine-tuned for 1.5 epoch. |
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Total number of steps was 1562. |
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<details><summary>Loss graph</summary> |
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</details><br> |
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# 🤝 Acknowledgments |
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Thanks to [sablo.ai](https://sablo.ai) for sponsoring this model. |
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Thanks to all the dataset authors mentioned in the datasets section. |
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Thanks to [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for making the repository I used to make this model. |
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Thanks to all open source AI community. |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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If you would like to support me: |
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[☕ Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi) |
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