Quant-Cartel/experiment_1_8b-iMat-GGUF

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PROUDLY PRESENTS         

experiment_1_8b-iMat-GGUF

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

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

All quants are verified working prior to uploading to repo for your safety and convenience.

Original model card here and below

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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",
    ),
)