初始化项目，由ModelHub XC社区提供模型
Model: Sao10K/14B-Qwen2.5-Freya-x1 Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: other
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license_name: qwen
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license_link: https://huggingface.co/Qwen/Qwen2.5-14B/blob/main/LICENSE
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base_model: Qwen/Qwen2.5-14B
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tags:
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- generated_from_trainer
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model-index:
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- name: 14B-Qwen2.5-Freya-x1
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results: []
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---
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*Me during failed runs*
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# 14B-Qwen2.5-Freya-v1
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I decided to mess around with training methods again, considering the re-emegence of methods like multi-step training. Some people began doing it again, and so, why not? Inspired by AshhLimaRP's methology but done it my way.
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Freya-S1
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- LoRA Trained on ~1.1GB of literature and raw text over Qwen 2.5's base model.
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- Cleaned text and literature as best as I could, still, may have had issues here and there.
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Freya-S2
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- The first LoRA was applied over Qwen 2.5 Instruct, then I trained on top of that.
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- Reduced LoRA rank because it's mainly instruct and other details I won't get into.
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Recommended Model Settings | *Look, I just use these, they work fine enough. I don't even know how DRY or other meme samplers work. Your system prompt matters more anyway.*
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```
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Prompt Format: ChatML
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Temperature: 1+ # I don't know, man.
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min_p: 0.05
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```
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Training time in total was ~10 Hours on a 8xH100 Node, sponsored by the Government of Singapore or something. Thanks for the national service allowance, MHA.
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https://sao10k.carrd.co/ for contact.
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---
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.6.0`
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```yaml
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base_model:
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- s1: Qwen/Qwen2.5-14B
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- s2: Qwen/Qwen2.5-14B-Instruct
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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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sequence_len: 16384
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bf16: auto
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fp16:
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tf32: false
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flash_attention: true
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special_tokens:
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adapter: lora # 16-bit
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lora_r:
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- s1: 64
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- s2: 32
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lora_alpha: 64
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lora_dropout: 0.2
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lora_fan_in_fan_out:
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peft_use_rslora: true
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lora_target_linear: true
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# Data
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dataset_prepared_path: dataset_run_freya
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datasets:
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# S1 - Writing / Completion
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- path: datasets/eBooks-cleaned-75K
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type: completion
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- path: datasets/novels-clean-dedupe-10K
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type: completion
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# S2 - Instruct
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- path: datasets/10k-amoral-full-fixed-sys.json
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type: chat_template
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chat_template: chatml
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roles_to_train: ["gpt"]
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field_messages: conversations
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message_field_role: from
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message_field_content: value
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train_on_eos: turn
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- path: datasets/44k-hespera-smartshuffle.json
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type: chat_template
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chat_template: chatml
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roles_to_train: ["gpt"]
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field_messages: conversations
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message_field_role: from
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message_field_content: value
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train_on_eos: turn
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- path: datasets/5k_rpg_adventure_instruct-sys.json
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type: chat_template
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chat_template: chatml
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roles_to_train: ["gpt"]
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field_messages: conversations
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message_field_role: from
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message_field_content: value
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train_on_eos: turn
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shuffle_merged_datasets: true
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warmup_ratio: 0.1
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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liger_rope: true
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liger_rms_norm: true
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liger_layer_norm: true
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liger_glu_activation: true
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liger_fused_linear_cross_entropy: true
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# Iterations
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num_epochs:
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- s1: 1
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- s2: 2
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# Sampling
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sample_packing: true
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pad_to_sequence_len: true
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train_on_inputs: false
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group_by_length: false
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# Batching
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gradient_accumulation_steps: 4
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micro_batch_size: 2
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gradient_checkpointing: unsloth
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# Evaluation
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val_set_size: 0.025
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evals_per_epoch: 5
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eval_table_size:
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eval_max_new_tokens: 256
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eval_sample_packing: false
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eval_batch_size: 1
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# Optimizer
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optimizer: paged_ademamix_8bit
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lr_scheduler: cosine
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learning_rate:
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- s1: 0.000002
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- s2: 0.000004
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weight_decay: 0.2
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max_grad_norm: 10.0
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# Garbage Collection
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gc_steps: 10
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# Misc
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deepspeed: ./deepspeed_configs/zero2.json
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```
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</details><br>
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