--- base_model: unsloth/Mistral-Nemo-Instruct-2407 language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - rp - gguf - experimental - long-context --- # Uploaded model - **Developed by:** UsernameJustAnother - **License:** apache-2.0 - **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407 This is an 8_0 gguf of Marlin v6. The notes for Marlin are below. Standard disclaimer: This is me teaching myself the basics of fine-tuning, with notes extensively borrowed from https://huggingface.co/nothingiisreal/MN-12B-Celeste-V1.9 New for v6: - Slightly different source mix. Down to 8,000 records of mostly-human convos and stories, curated by me, trained in ChatML. - The stories have been edited to remove author's notes, and the RP chats tweaked to remove many ministrations. - Different learning rate and back to Celeste's scaling factor setup (but Celeste trained on -base, this is -instruct). - Now with added eval! I worked out how to get eval stats (and wandb) set up, so now I can see my failures in graphical form. And of course yay Unsloth for letting this all train on a single A100 with variable (wildly variable) context length. It was trained with the following settings: ``` model = FastLanguageModel.get_peft_model( model, r = 256, target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",], lora_alpha = 128, # 128 / sqrt(256) gives a scaling factor of 8 lora_dropout = 0.1, # Supports any, but = 0 is optimized bias = "none", # Supports any, but = "none" is optimized # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes! use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context random_state = 3407, use_rslora = True, # setting the adapter scaling factor to lora_alpha/math.sqrt(r) instead of lora_alpha/r loftq_config = None, # And LoftQ ) lr_scheduler_kwargs = { 'min_lr': 0.0000024 # Adjust this value as needed } per_device_train_batch_size = 2, per_device_eval_batch_size = 2, # defaults to 8! gradient_accumulation_steps = 4, eval_accumulation_steps = 4, prediction_loss_only = True, # When performing evaluation and generating predictions, only returns the loss. warmup_steps = 50, num_train_epochs = 2, # For longer training runs! 12 hrs/epoch? learning_rate = 1e-5, # 8e-5 used by Celeste, 0.0001 is from the paper, halving it. tried 5e-5, now 1e-5. fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), fp16_full_eval = True, # stops eval from trying to use fp32 eval_strategy = "steps", # 'no', 'steps', 'epoch'. Don't use this without an eval dataset etc eval_steps = 100, # is eval_strat is set to 'steps', do every N steps. logging_steps = 5, # so eval and logging happen on the same schedule optim = "adamw_8bit", # weight_decay = 0, # up from 0 lr_scheduler_type = "cosine_with_min_lr", # linear, cosine, cosine_with_min_lr, default linear lr_scheduler_kwargs = lr_scheduler_kwargs, # needed for cosine_with_min_lr seed = 3407, ``` This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth)