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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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- rp |
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- gguf |
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- experimental |
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- long-context |
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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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I am a terrible liar. I came across another dataset I had to use, and this is the result. Still experimental, as I made these to teach myself the basics of fine-tuning, with notes extensively borrowed from https://huggingface.co/nothingiisreal/MN-12B-Celeste-V1.9 |
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It is an RP finetune using 10,801 human-generated conversations of varying lengths from a variety of sources and curated by me, trained in ChatML format. |
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The big differences from Celeste is a different LoRA scaling factor. Celeste uses 8; I did several tests with this data before concluding I got lower training loss with 2. |
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Training took around 5 hours on a single Colab A100 (but I didn't do an eval loop). Neat that I could get it all to fit into 40GB of vRAM thanks to Unsloth. |
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It was trained with the following settings: |
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``` |
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==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 |
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\\ /| Num examples = 10,801 | 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 = 2,700 |
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"-____-" Number of trainable parameters = 912,261,120 |
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[ 14/2700 01:20 < 4:59:21, 0.15 it/s, Epoch 0.01/2] |
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[2040/2040 3:35:30, Epoch 2/2] |
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model = FastLanguageModel.get_peft_model( |
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model, |
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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 = 32, # 32 / sqrt(256) gives a scaling factor of 2 |
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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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# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes! |
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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, # setting the adapter scaling factor to lora_alpha/math.sqrt(r) instead of lora_alpha/r |
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loftq_config = None, # And LoftQ |
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) |
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lr_scheduler_kwargs = { |
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'min_lr': 0.0000024 # Adjust this value as needed |
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} |
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trainer = SFTTrainer( |
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model = model, |
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tokenizer = tokenizer, |
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train_dataset = train_ds, |
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compute_metrics = compute_metrics, |
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dataset_text_field = "text", |
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max_seq_length = max_seq_length, |
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dataset_num_proc = 2, |
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packing = False, # Can make training 5x faster for short sequences. |
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args = TrainingArguments( |
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per_device_train_batch_size = 2, |
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per_device_eval_batch_size = 2, # defaults to 8! |
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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 = 8e-5, |
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fp16 = not is_bfloat16_supported(), |
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bf16 = is_bfloat16_supported(), |
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fp16_full_eval = True, # stops eval from trying to use fp32 |
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eval_strategy = "no", # 'no', 'steps', 'epoch'. Don't use this without an eval dataset etc |
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eval_steps = 1, # is eval_strat is set to 'steps', do every N steps. |
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logging_steps = 1, # so eval and logging happen on the same schedule |
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optim = "adamw_8bit", |
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weight_decay = 0.01, |
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lr_scheduler_type = "cosine_with_min_lr", # linear, cosine, cosine_with_min_lr, default linear |
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lr_scheduler_kwargs = lr_scheduler_kwargs, # needed for cosine_with_min_lr |
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seed = 3407, |
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output_dir = "outputs", |
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), |
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) |
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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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