experiment_1_8b-iMat-GGUF

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PROUDLY PRESENTS         

experiment_1_8b-iMat-GGUF

<b>Quantization Note: Use repetition penalty (--repeat-penalty on llama.cpp) of ~1.15 for best results </b>

Quantized from fp16 with love.

• Weighted quantizations were created using fp16 GGUF and groups_merged-enhancedV2-TurboMini.txt in 189 chunks and n_ctx=512
• This method of calculating the importance matrix showed improvements in some areas for Mistral 7b and Llama3 8b models, see above post for details
• The enhancedv2-turbomini file appends snippets from turboderp's calibration data to the standard groups_merged.txt file

For a brief rundown of iMatrix quant performance please see this PR

<b>All quants are verified working prior to uploading to repo for your safety and convenience. </b>

Original model card here and below

---

UNTESTED, probably unfit for human consumption

1 epoch of grimulkan/LimaRP-augmented on LLaMA3-8b via unsloth on colab, using the llama-chat template. 16k context, probably.

model = FastLanguageModel.get_peft_model(
    model,
    r = 64, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
    lora_alpha = 16,
    lora_dropout = 0, # 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 = False,  # We support rank stabilized LoRA
    loftq_config = None, # And LoftQ
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = dataset,
    dataset_text_field = "text",
    max_seq_length = max_seq_length,
    dataset_num_proc = 2,
    packing = False, # Can make training 5x faster for short sequences.
    args = TrainingArguments(
        per_device_train_batch_size = 1,
        gradient_accumulation_steps = 8,
        warmup_steps = 5,
        num_train_epochs=1,
        learning_rate = 2e-4,
        fp16 = not torch.cuda.is_bf16_supported(),
        bf16 = torch.cuda.is_bf16_supported(),
        logging_steps = 1,
        optim = "adamw_8bit",
        weight_decay = 0.01,
        lr_scheduler_type = "linear",
        seed = 3407,
        output_dir = "outputs",
    ),
)