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--- |
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language: |
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- en |
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license: other |
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tags: |
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- axolotl |
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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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- qwen |
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- qwen2 |
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base_model: Weyaxi/Einstein-v7-Qwen2-7B |
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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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- 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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- allenai/WildChat |
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- microsoft/orca-math-word-problems-200k |
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- openchat/openchat_sharegpt4_dataset |
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- teknium/GPTeacher-General-Instruct |
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- m-a-p/CodeFeedback-Filtered-Instruction |
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- totally-not-an-llm/EverythingLM-data-V3 |
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- HuggingFaceH4/no_robots |
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- OpenAssistant/oasst_top1_2023-08-25 |
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- WizardLM/WizardLM_evol_instruct_70k |
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- abacusai/SystemChat-1.1 |
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- H-D-T/Buzz-V1.2 |
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pipeline_tag: text-generation |
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--- |
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# 🔬 Einstein-v7-Qwen2-7B-GGUF |
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This is quantized version of [Weyaxi/Einstein-v7-Qwen2-7B](https://huggingface.co./Weyaxi/Einstein-v7-Qwen2-7B) created using llama.cpp |
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# Model Description |
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This model is a full fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co./Qwen/Qwen2-7B) on diverse datasets. |
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This model is finetuned using `8xMI300X` using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl). |
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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: Qwen/Qwen2-7B |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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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/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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- path: data/allenai_wild_chat_gpt4_english_toxic_random_half_4k_sharegpt.json |
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ds_type: json |
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type: sharegpt |
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strict: false |
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conversation: chatml |
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- path: data/buzz_unstacked_chosen_math_removed_filtered.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/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/everythinglm-data-v3_sharegpt.json |
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ds_type: json |
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type: sharegpt |
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strict: false |
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conversation: chatml |
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- path: data/gpt4_data_lmys_1m_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/gpteacher-instruct-special-alpaca.json |
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ds_type: json |
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type: gpteacher |
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conversation: chatml |
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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/no_robots_sharegpt.json |
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ds_type: json |
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type: sharegpt |
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strict: false |
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conversation: chatml |
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- path: data/oasst_top1_from_fusechatmixture_sharegpt.json |
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ds_type: json |
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type: sharegpt |
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strict: false |
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conversation: chatml |
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- path: data/pippa_bagel_repo_3k_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/rpguild_quarter_alignment_lab_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/sharegpt_gpt4_english.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/soda_diaolog_longest_tenth_buzz_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/system_conversations_dolphin_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: 0.002 |
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output_dir: ./Einstein-v7-Qwen2-7B-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-v7-Qwen2-7B |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 6 |
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num_epochs: 2 |
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optimizer: paged_adamw_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.00001 # look |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: unsloth |
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gradient_checkpointing_kwargs: |
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use_reentrant: true # look |
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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 |
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eval_table_size: |
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eval_max_new_tokens: 128 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: deepspeed_configs/zero3_bf16.json |
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weight_decay: 0.05 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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eos_token: "<|im_end|>" |
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pad_token: "<|end_of_text|>" |
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tokens: |
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- "<|im_start|>" |
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- "<|im_end|>" |
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``` |
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</details><br> |
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# 💬 Prompt Template |
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You can use ChatML 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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# 📊 Datasets used in this model |
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The datasets used to train this model are listed in the metadata section of the model card. |
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Please note that certain datasets mentioned in the metadata may have undergone filtering based on various criteria. |
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The results of this filtering process and its outcomes are in a diffrent repository: |
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[Weyaxi/sci-datasets/main](https://huggingface.co./datasets/Weyaxi/sci-datasets/tree/main) |
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# 🎯 [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard) |
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# 🤖 Additional information about training |
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This model is full fine-tuned for 2 epoch. |
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Total number of steps was 500. |
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<details><summary>Loss graph</summary> |
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</details><br> |
