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Model: dphn/dolphin-2.9.1-yi-1.5-9b Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: 01-ai/Yi-1.5-9B
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tags:
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- generated_from_trainer
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- axolotl
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datasets:
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- cognitivecomputations/Dolphin-2.9
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- teknium/OpenHermes-2.5
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- m-a-p/CodeFeedback-Filtered-Instruction
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- cognitivecomputations/dolphin-coder
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- cognitivecomputations/samantha-data
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- microsoft/orca-math-word-problems-200k
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- Locutusque/function-calling-chatml
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- internlm/Agent-FLAN
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---
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# Dolphin 2.9.1 Yi 1.5 9b 🐬
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Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
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This is our most spectacular outcome ever. FFT, all parameters, 16bit. 70.9 MMLU on 9b! And it talks like a dream.
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Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 12k. We plan to train on the upcoming 32k version as well.
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[](https://discord.gg/cognitivecomputations)
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Discord: https://discord.gg/cognitivecomputations
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<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
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Our appreciation for the sponsors of Dolphin 2.9.1:
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- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node
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- [OnDemand](https://on-demand.io/) - provided inference sponsorship
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This model is based on Yi-1.5-9b, and is governed by apache 2.0 license.
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The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 12k sequence length.
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Dolphin 2.9.1 uses ChatML prompt template format.
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example:
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```
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<|im_start|>system
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You are Dolphin, a helpful AI assistant.<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
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Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
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Dolphin is licensed according to apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models.
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## Evals
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## Training
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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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<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: 01-ai/Yi-1.5-9B
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model_type: LlamaForCausalLM
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tokenizer_type: LlamaTokenizer
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trust_remote_code: true
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# load_in_8bit: false
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# load_in_4bit: true
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# strict: false
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# adapter: qlora
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# lora_modules_to_save: [embed_tokens, lm_head]
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# lora_r: 32
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# lora_alpha: 16
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# lora_dropout: 0.05
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# lora_target_linear: True
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# lora_fan_in_fan_out:
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datasets:
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- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
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type: sharegpt
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conversation: chatml
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chat_template: chatml
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dataset_prepared_path: yi34b
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val_set_size: 0.03
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output_dir: ./out-yi
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sequence_len: 12000
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sample_packing: true
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pad_to_sequence_len: true
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wandb_project: dolphin-2.9-yi-34b
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wandb_watch:
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wandb_run_id:
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wandb_log_model:
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gradient_accumulation_steps: 8
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micro_batch_size: 2
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num_epochs: 3
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optimizer: adamw_8bit
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lr_scheduler: cosine
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learning_rate: 1e-5
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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: true
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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early_stopping_patience:
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# resume_from_checkpoint: /workspace/axolotl/dbrx-checkpoint
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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: 4
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eval_table_size:
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saves_per_epoch: 4
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save_total_limit: 2
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save_steps:
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debug:
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deepspeed: /workspace/axolotl/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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bos_token: "<|startoftext|>"
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eos_token: "<|im_end|>"
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pad_token: "<unk>"
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unk_token: "<unk>"
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tokens:
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- "<|im_start|>"
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```
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</details><br>
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# out-yi
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This model is a fine-tuned version of [01-ai/Yi-1.5-9B](https://huggingface.co/01-ai/Yi-1.5-9B) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4396
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 128
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- total_eval_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 0.6332 | 0.0024 | 1 | 0.6469 |
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| 0.4811 | 0.2499 | 106 | 0.4739 |
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| 0.4465 | 0.4997 | 212 | 0.4547 |
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| 0.4472 | 0.7496 | 318 | 0.4480 |
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| 0.4373 | 0.9994 | 424 | 0.4429 |
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| 0.4147 | 1.2384 | 530 | 0.4432 |
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| 0.3879 | 1.4882 | 636 | 0.4400 |
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| 0.3872 | 1.7381 | 742 | 0.4371 |
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| 0.4044 | 1.9879 | 848 | 0.4344 |
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| 0.3509 | 2.2269 | 954 | 0.4410 |
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| 0.3628 | 2.4767 | 1060 | 0.4401 |
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| 0.3652 | 2.7266 | 1166 | 0.4397 |
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| 0.3674 | 2.9764 | 1272 | 0.4396 |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.2.2+cu121
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- Datasets 2.15.0
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- Tokenizers 0.19.1
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config.json
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config.json
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{
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"_name_or_path": "01-ai/Yi-1.5-9B",
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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": 7,
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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": 12000,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 48,
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"num_key_value_heads": 4,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 5000000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.40.2",
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"use_cache": false,
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"vocab_size": 64000
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}
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configuration.json
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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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"do_sample": true,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"transformers_version": "4.40.2"
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}
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special_tokens_map.json
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"bos_token": {
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"content": "<|startoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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