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--- |
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license: other |
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license_name: yi-license |
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license_link: LICENSE |
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--- |
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## Model description |
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Yi-34B base model fine-tuned on AEZAKMI v1 dataset. Training took around 33 hours on single local RTX 3090 Ti. |
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It's like airoboros but with less gptslop, no refusals and less typical language used by RLHFed OpenAI models. |
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Say goodbye to "It's important to remember"! \ |
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Prompt format is standard chatml. Don't expect it to be good at math, riddles or be crazy smart. My end goal with AEZAKMI is to create a cozy free chatbot. |
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Cost of this fine-tune is about $3 in electricity. This was my first attempt at training Yi-34B with this dataset. |
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Base model used for fine-tuning was 4k context Yi-34B-Llama model shared by chargoddard. |
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## Prompt Format |
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I recommend using ChatML format, as this was used during fine-tune. \ |
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Here's a prompt format you should use, you can set a different system message, model seems to respect that fine, so it wasn't overfitted. |
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``` |
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<|im_start|>system |
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A chat.<|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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## Intended uses & limitations |
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Use is limited by Yi license. |
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## Known Issues |
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I recommend to set repetition penalty to something around 1.05 to avoid repetition. So far I had good experience running this model with temperature 1.2. |
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Multi-turn conversations could be a bit better, if you ask it to re-write something with some fixes it will have a tendency to just repeat the previous response verbatim without any improvements - this is especially noticeable with repp 1.0. |
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There is still some gptslop left - some responses will have last paragraph with text "Remember that bla bla bla", I will try to get rid of it in the next version of the dataset. |
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Stories have ChatGPT like paragraph spacing, I will try to introduce a bit more stories that have long paragraphs in the next dataset version. |
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## Axolotl training parameters |
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- bnb_4bit_use_double_quant: true |
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- bnb_4bit_compute_dtype: torch.bfloat16 |
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- is_llama_derived_model: true |
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- load_in_4bit: true |
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- adapter: qlora |
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- sequence_len: 1200 |
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- sample_packing: false |
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- lora_r: 16 |
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- lora_alpha: 32 |
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- lora_target_modules: |
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- q_proj |
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- v_proj |
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- k_proj |
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- o_proj |
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- gate_proj |
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- down_proj |
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- up_proj |
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- lora_target_linear: true |
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- pad_to_sequence_len: true |
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- micro_batch_size: 1 |
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- gradient_accumulation_steps: 1 |
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- num_epochs: 1 |
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- optimizer: adamw_bnb_8bit |
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- lr_scheduler: constant |
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- learning_rate: 0.00007 |
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- train_on_inputs: false |
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- group_by_length: false |
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- bf16: true |
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- bfloat16: true |
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- flash_optimum: false |
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- gradient_checkpointing: true |
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- flash_attention: true |
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- seed: 42 |
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## Upcoming |
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~I will release adapter files and maybe exllama v2 quant shortly.~ |
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LoRA and exl2 quant has been released |
