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Model: dphn/dolphin-2.9-llama3-8b-1m Source: Original Platform
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README.md
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README.md
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---
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license: other
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base_model: meta-llama/Meta-Llama-3-8B
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tags:
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- generated_from_trainer
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- axolotl
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model-index:
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- name: out
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results: []
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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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- HuggingFaceH4/ultrachat_200k
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- microsoft/orca-math-word-problems-200k
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- abacusai/SystemChat-1.1
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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 Llama 3 8b 1m 🐬
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Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
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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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This version of Dolphin has a 1 million token context. I have applied `winglian/llama-3-1m-context-gradient-lora` - created by @gradientai and @winglian and sponsored by @CrusoeCloud
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A bug has been found in the Dolphin 2.9 dataset in SystemConversations that causes the model to overly talk about the "SYSTEM MESSAGE". To counter this, we recommend you add a statement in the system message directing the model not to mention the system message. An example system message is "The assistant is named Dolphin. A helpful and friendly AI assistant, Dolphin avoids discussing the system message unless directly asked about it."
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My appreciation for the sponsors of Dolphin 2.9:
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- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
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This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
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The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
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It took 2.5 days on 8x L40S provided by Crusoe Cloud
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This model was trained FFT on all parameters, using 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 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. I 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 Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
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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: meta-llama/Meta-Llama-3-8B
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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tokenizer_use_fast: false
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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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model_config:
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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/Ultrachat200kunfiltered.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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- path: /workspace/datasets/dolphin-2.9/SystemConversations.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: /workspace/datasets/dolphin-2.9/thingy
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val_set_size: 0.0002
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output_dir: ./out
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sequence_len: 4096
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sample_packing: true
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pad_to_sequence_len: true
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gradient_accumulation_steps: 4
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micro_batch_size: 3
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num_epochs: 3
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logging_steps: 1
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optimizer: adamw_8bit
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lr_scheduler: cosine
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learning_rate: 2e-5
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wandb_project: dolphin-2.9-mixtral-8x22b
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wandb_watch:
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wandb_run_id:
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wandb_log_model:
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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: 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:
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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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saves_per_epoch: 4
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save_total_limit: 2
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save_steps:
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evals_per_epoch: 4
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eval_sample_packing: false
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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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## Quants
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GGUF : https://huggingface.co/QuantFactory/dolphin-2.9-llama3-8b-GGUF
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GGUF with imatrix: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-GGUF
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Exllamav2: https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-exl2
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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: 2e-05
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- train_batch_size: 3
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- eval_batch_size: 3
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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: 4
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- total_train_batch_size: 96
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- total_eval_batch_size: 24
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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: 7
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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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| 1.146 | 0.0005 | 1 | 1.1064 |
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| 0.6962 | 0.2501 | 555 | 0.6636 |
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| 0.6857 | 0.5001 | 1110 | 0.6503 |
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| 0.6592 | 0.7502 | 1665 | 0.6419 |
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| 0.6465 | 1.0002 | 2220 | 0.6317 |
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| 0.5295 | 1.2395 | 2775 | 0.6408 |
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| 0.5302 | 1.4895 | 3330 | 0.6351 |
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| 0.5188 | 1.7396 | 3885 | 0.6227 |
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| 0.521 | 1.9896 | 4440 | 0.6168 |
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| 0.3968 | 2.2289 | 4995 | 0.6646 |
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| 0.3776 | 2.4789 | 5550 | 0.6619 |
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| 0.3983 | 2.7290 | 6105 | 0.6602 |
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### Framework versions
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- Transformers 4.40.0
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- Pytorch 2.2.2+cu121
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- Datasets 2.18.0
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- Tokenizers 0.19.1
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