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--- |
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base_model: unsloth/Mistral-Nemo-Instruct-2407 |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- mistral |
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- trl |
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--- |
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# Uploaded model |
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- **Developed by:** UsernameJustAnother |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407 |
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Experimental RP Finetune with secret sauce dataset, rsLoRA, r = 256, on an Colab A100 instance. 36GB vRAM used, 2 epochs ~ 3.5hrs of training. |
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This is for A/B testing vs Marlin v1, to see what difference rank 256 (v2) has compared to rank 64 (v1). |
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``` |
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==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 |
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\\ /| Num examples = 8,160 | Num Epochs = 2 |
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O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 4 |
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\ / Total batch size = 8 | Total steps = 2,040 |
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"-____-" Number of trainable parameters = 912,261,120 |
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r = 256, |
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target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", |
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"gate_proj", "up_proj", "down_proj",], |
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lora_alpha = 16, |
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lora_dropout = 0, # Supports any, but = 0 is optimized |
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bias = "none", # Supports any, but = "none" is optimized |
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use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context |
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random_state = 3407, |
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use_rslora = True, # lora_alpha --> 16 |
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loftq_config = None, |
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per_device_train_batch_size = 2, |
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gradient_accumulation_steps = 4, |
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warmup_steps = 5, |
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num_train_epochs = 2, |
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learning_rate = 2e-5, # down from 2e-4, could go down to (5e-5 then 1e-5) |
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fp16 = not is_bfloat16_supported(), |
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bf16 = is_bfloat16_supported(), |
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logging_steps = 1, |
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optim = "adamw_8bit", |
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weight_decay = 0.01, |
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lr_scheduler_type = "linear", |
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seed = 3407, |
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``` |
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This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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