---
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.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)