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Model: OwenArli/ArliAI-Llama-3-8B-Instruct-Dolfin-v0.1 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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---
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Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement:
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https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
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We don't know how good this model is exactly in benchmarks since we have not benched this yet, but we think real prompts and usage is more telling anyways.
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From our testing this model is:
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- Less Refusals
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- More Uncensored
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- Follows requests better
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- Can reply in requested formats better without adding unnecesary information
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We are happy for anyone to try it out and give some feedback.
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Training:
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- 2048 sequence length, while the base model is 8192 sequence length. From testing it still performs the same 8192 context just fine.
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- Trained on a modified and improved version of Cognitive Computations Eric Hartford's Dolphin dataset. https://huggingface.co/datasets/cognitivecomputations/dolphin
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- Training duration is around 2 days on 2x RTX3090 on our own machine, using 4-bit loading and Qlora 64-rank 128-alpha resulting in ~2% trainable weights.
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The goal for this model is to have the model less-censored and great at general tasks like the previous dolphin based models by Eric Hartford.
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We started training this BEFORE they launched their own full weight trained Llama-3-8B-Dolphin-2.9 with their own curated datasets and the newer "Dolphin 2.9" dataset, but we think this model is still a unique take on Llama 3 8B Instruct and the dolphin dataset.
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https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b
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The difference with their dolphin 2.9 model is that we train this using Meta's new Llama 3 instruct format and not the regular ChatML format that Dolphin models are usually trained on.
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This is because we think that it performed better using the format it was originally trained on.
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Instruct format:
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```
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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```
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Quants:
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AWQ: https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Instruct-Dolfin-AWQ
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GGUF: https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Instruct-Dolfin-v0.1-GGUF
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FP16: https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Instruct-Dolfin
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Exllamav2:
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4bpw: https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Instruct-Dolfin-v0.1-exl2-h8-4bpw-exl2
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8bpw: https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Instruct-Dolfin-v0.1-exl2-h8-8bpw-exl2
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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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Axolotl Config:
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```
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base_model: Meta-Llama-3-8B-Instruct
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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train_on_inputs: false
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group_by_length: false
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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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sequence_len: 2048
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bf16: true
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fp16: false
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tf32: false
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flash_attention: true
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# Data
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datasets:
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- path: flan1m-universal-uncensored-system-2048.jsonl
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type:
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system_prompt: ""
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system_format: "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
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field_system: system
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field_instruction: input
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field_output: output
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format: "{instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
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no_input_format: "{instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
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warmup_steps: 10
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dataset_prepared_path: ./last_run_prepared
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# Iterations
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num_epochs: 1
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saves_per_epoch: 4
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# Evaluation
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val_set_size: 0.01
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eval_table_size:
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eval_table_max_new_tokens:
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eval_sample_packing: false
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evals_per_epoch: 4
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# LoRA
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output_dir: ./qlora-out
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adapter: qlora
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lora_model_dir:
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lora_r: 64
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lora_alpha: 128
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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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lora_target_modules:
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save_safetensors: true
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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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# Batching
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gradient_accumulation_steps: 32
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micro_batch_size: 4
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: true
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# Optimizer
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optimizer: paged_adamw_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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# Misc
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early_stopping_patience:
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resume_from_checkpoint:
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logging_steps: 1
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debug:
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deepspeed: zero3_bf16.json
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weight_decay: 0.1
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special_tokens:
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pad_token: <|end_of_text|>
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```
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