app.py

#--- START OF FILE app.py ---

import os
import shutil
import subprocess
import torch
from transformers import AutoConfig, AutoModelForCausalLM
from huggingface_hub import HfApi, whoami, ModelCard, list_models
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from apscheduler.schedulers.background import BackgroundScheduler
from textwrap import dedent
import gradio as gr
import hashlib
import torch.nn.utils.prune as prune
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
import logging
from datetime import datetime
from typing import List, Dict

logging.basicConfig(level=logging.INFO)

os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
HF_TOKEN = os.environ.get("HF_TOKEN")
SPACE_ID = "Ffftdtd5dtft/gguf-my-repo" # Replace with your space ID if different

def generate_importance_matrix(model_path, train_data_path):
    os.chdir("llama.cpp")
    if not os.path.isfile(f"../{model_path}"):
        raise Exception(f"Model file not found: {model_path}")
    imatrix_command = f"./llama-imatrix -m ../{model_path} -f {train_data_path} -ngl 99 --output-frequency 10"
    process = subprocess.Popen(imatrix_command, shell=True)
    try:
        process.wait(timeout=3600)
    except subprocess.TimeoutExpired:
        process.kill()
    os.chdir("..")

def split_upload_model(model_path, repo_id, oauth_token, split_max_tensors=256, split_max_size=None):
    if oauth_token.token is None:
        raise ValueError("You have to be logged in.")
    split_cmd = f"llama.cpp/llama-gguf-split --split --split-max-tensors {split_max_tensors}"
    if split_max_size:
        split_cmd += f" --split-max-size {split_max_size}"
    split_cmd += f" {model_path} {model_path.split('.')[0]}"
    result = subprocess.run(split_cmd, shell=True, capture_output=True, text=True)
    if result.returncode != 0:
        raise Exception(f"Error splitting the model: {result.stderr}")
    sharded_model_files = [f for f in os.listdir('.') if f.startswith(model_path.split('.')[0])]
    if sharded_model_files:
        api = HfApi(token=oauth_token.token)
        for file in sharded_model_files:
            file_path = os.path.join('.', file)
            try:
                api.upload_file(path_or_fileobj=file_path, path_in_repo=file, repo_id=repo_id)
            except Exception as e:
                raise Exception(f"Error uploading file {file_path}: {e}")
    else:
        raise Exception("No sharded files found.")

def quantize_to_q1_with_min(tensor, min_value=-1):
    tensor = torch.sign(tensor)
    tensor[tensor < min_value] = min_value
    return tensor

def quantize_model_to_q1_with_min(model, min_value=-1):
    for name, param in model.named_parameters():
        if param.dtype in [torch.float32, torch.float16]:
            with torch.no_grad():
                param.copy_(quantize_to_q1_with_min(param.data, min_value))

def disable_unnecessary_components(model):
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Dropout):
            module.p = 0.0
        elif isinstance(module, torch.nn.BatchNorm1d):
            module.eval()

def ultra_max_compress(model):
    model = quantize_model_to_q1_with_min(model, min_value=-0.05)
    disable_unnecessary_components(model)
    with torch.no_grad():
        for name, param in model.named_parameters():
            if param.requires_grad:
                param.requires_grad = False
                param.data = torch.nn.functional.hardtanh(param.data, min_val=-1.0, max_val=1.0)
                param.data = param.data.half()
    model.eval()
    for buffer_name, buffer in model.named_buffers():
        if buffer.numel() == 0:
            model._buffers.pop(buffer_name)
    return model

def optimize_model_resources(model):
    torch.set_grad_enabled(False)
    model.eval()
    for name, param in model.named_parameters():
        param.requires_grad = False
        if param.dtype == torch.float32:
            param.data = param.data.half()
    if hasattr(model, 'config'):
        if hasattr(model.config, 'max_position_embeddings'):
            model.config.max_position_embeddings = min(model.config.max_position_embeddings, 512)
        if hasattr(model.config, 'hidden_size'):
            model.config.hidden_size = min(model.config.hidden_size, 768)
    return model

def aggressive_optimize(model, reduce_layers_factor=0.5):
    if hasattr(model.config, 'num_attention_heads'):
        model.config.num_attention_heads = int(model.config.num_attention_heads * reduce_layers_factor)
    if hasattr(model.config, 'hidden_size'):
        model.config.hidden_size = int(model.config.hidden_size * reduce_layers_factor)
    return model

def apply_quantization(model, use_int8_inference):
    if use_int8_inference:
        quantized_model = torch.quantization.quantize_dynamic(
            model, {torch.nn.Linear}, dtype=torch.qint8
        )
        return quantized_model
    else:
        return model

def reduce_layers(model, reduction_factor=0.5):
    if hasattr(model, 'transformer') and hasattr(model.transformer, 'h'):
        original_num_layers = len(model.transformer.h)
        new_num_layers = int(original_num_layers * reduction_factor)
        model.transformer.h = torch.nn.ModuleList(model.transformer.h[:new_num_layers])
    return model

def use_smaller_embeddings(model, reduction_factor=0.75):
    if hasattr(model, 'config'):
        original_embedding_dim = model.config.hidden_size
        new_embedding_dim = int(original_embedding_dim * reduction_factor)
        model.config.hidden_size = new_embedding_dim
        if hasattr(model, 'resize_token_embeddings'):
            model.resize_token_embeddings(int(model.config.vocab_size * reduction_factor))
    return model

def use_fp16_embeddings(model):
    if hasattr(model, 'transformer') and hasattr(model.transformer, 'wte'):
        model.transformer.wte = model.transformer.wte.half()
    return model

def quantize_embeddings(model):
    if hasattr(model, 'transformer') and hasattr(model.transformer, 'wte'):
        model.transformer.wte = torch.quantization.quantize_dynamic(
            model.transformer.wte, {torch.nn.Embedding}, dtype=torch.qint8
        )
    return model

def use_bnb_f16(model):
    if torch.cuda.is_available() and torch.cuda.is_bf16_supported():
        model = model.to(dtype=torch.bfloat16)
    return model

def use_group_quantization(model):
    for module in model.modules():
        if isinstance(module, torch.nn.Linear):
            torch.quantization.fuse_modules(module, ['weight'], inplace=True)
            torch.quantization.quantize_dynamic(module, {torch.nn.Linear}, dtype=torch.qint8, inplace=True)
    return model

def apply_layer_norm_trick(model):
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.LayerNorm):
            module.elementwise_affine = False
    return model

def remove_padding(inputs, attention_mask):
    last_non_padded = attention_mask.sum(dim=1) - 1
    gathered_inputs = torch.gather(inputs, dim=1, index=last_non_padded.unsqueeze(1).unsqueeze(2).expand(-1, -1, inputs.size(2)))
    return gathered_inputs

def use_selective_quantization(model):
    for module in model.modules():
        if isinstance(module, torch.nn.MultiheadAttention):
            torch.quantization.quantize_dynamic(module, {torch.nn.Linear}, dtype=torch.qint8, inplace=True)
    return model

def use_mixed_precision(model):
    if hasattr(model, 'transformer') and hasattr(model.transformer, 'wte'):
        model.transformer.wte = model.transformer.wte.half()
    return model

def use_pruning_after_training(model, prune_amount=0.1):
    from torch import nn as nn
    for name, module in model.named_modules():
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            prune.l1_unstructured(module, name='weight', amount=prune_amount)
            prune.remove(module, 'weight')
    return model

def use_knowledge_distillation(model, teacher_model, temperature=2.0, alpha=0.5):
    teacher_model.eval()
    criterion = torch.nn.KLDivLoss(reduction='batchmean')

    def distillation_loss(student_logits, teacher_logits):
        student_probs = F.log_softmax(student_logits / temperature, dim=-1)
        teacher_probs = F.softmax(teacher_logits / temperature, dim=-1)
        return criterion(student_probs, teacher_probs) * (temperature**2)

    def train_step(inputs, labels):
        student_outputs = model(**inputs, labels=labels)
        student_logits = student_outputs.logits
        with torch.no_grad():
            teacher_outputs = teacher_model(**inputs)
            teacher_logits = teacher_outputs.logits
        loss = alpha * student_outputs.loss + (1 - alpha) * distillation_loss(student_logits, teacher_logits)
        return loss

    return train_step

def use_weight_sharing(model):
    if hasattr(model, 'transformer') and hasattr(model.transformer, 'h'):
        if len(model.transformer.h) > 1:
            model.transformer.h[-1].load_state_dict(model.transformer.h[0].state_dict())
    return model

def use_low_rank_approximation(model, rank_factor=0.5):
    for module in model.modules():
        if isinstance(module, torch.nn.Linear):
            original_weight = module.weight.data
            U, S, V = torch.linalg.svd(original_weight)
            rank = int(S.size(0) * rank_factor)
            module.weight.data = U[:, :rank] @ torch.diag(S[:rank]) @ V[:rank, :]
    return model

def use_hashing_trick(model, num_hashes=1024):
    def hash_features(features):
        features_bytes = features.cpu().numpy().tobytes()
        hash_object = hashlib.sha256(features_bytes)
        hash_value = hash_object.hexdigest()
        hashed_features = int(hash_value, 16) % num_hashes
        return torch.tensor(hashed_features, device=features.device)

    original_forward = model.forward

    def forward(*args, **kwargs):
        inputs = args[0]
        hashed_inputs = hash_features(inputs)
        return original_forward(hashed_inputs, *args[1:], **kwargs)

def use_quantization_aware_training(model):
    model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')
    torch.quantization.prepare_qat(model, inplace=True)
    torch.quantization.convert(model, inplace=True)
    return model

def use_gradient_checkpointing(model):
    def custom_forward(*inputs):
        return checkpoint(model, *inputs)
    model.forward = custom_forward
    return model

def use_channel_pruning(model, prune_amount=0.1):
    from torch import nn as nn
    for module in model.modules():
        if isinstance(module, nn.Conv2d):
            prune.ln_structured(module, name="weight", amount=prune_amount, n=2, dim=0)
            prune.remove(module, 'weight')
    return model

def use_sparse_tensors(model, sparsity_threshold=0.01):
    for name, param in model.named_parameters():
        if param.dim() >= 2 and param.is_floating_point():
            sparse_param = param.to_sparse()
            sparse_param._values()[sparse_param._values().abs() < sparsity_threshold] = 0
            param.data = sparse_param.to_dense()
    return model

def use_lora(model, r=8, lora_alpha=16, lora_dropout=0.05, target_modules=None):
    from peft import LoraConfig, get_peft_model
    config = LoraConfig(
        r=r,
        lora_alpha=lora_alpha,
        lora_dropout=lora_dropout,
        target_modules=target_modules if target_modules else ["q_proj", "v_proj"], # Example target modules
        bias="none",
        task_type="CAUSAL_LM"
    )
    model = get_peft_model(model, config)
    return model

def use_adalora(model, target_r=8, init_r=12, tmask_init=0.01, beta1=0.85, beta2=0.99, loha=False, **kwargs):
    from peft import AdaLoraConfig, get_peft_model
    config = AdaLoraConfig(
        target_r=target_r,
        init_r=init_r,
        tmask_init=tmask_init,
        beta1=beta1,
        beta2=beta2,
        loha=loha,
        task_type="CAUSAL_LM",
        **kwargs
    )
    model = get_peft_model(model, config)
    return model

def use_ia3(model, target_modules=None):
    from peft import IA3Config, get_peft_model
    config = IA3Config(
        target_modules=target_modules if target_modules else ["k_proj", "v_proj", "down_proj"], # Example target modules
        feedforward_modules=None,
        task_type="CAUSAL_LM"
    )
    model = get_peft_model(model, config)
    return model

def use_prompt_tuning(model, num_virtual_tokens=8, prompt_tuning_init_text="You are a helpful assistant."):
    from peft import PromptTuningConfig, get_peft_model, TaskType
    config = PromptTuningConfig(
        task_type=TaskType.CAUSAL_LM,
        num_virtual_tokens=num_virtual_tokens,
        prompt_tuning_init_text=prompt_tuning_init_text,
        tokenizer_name_or_path=model.config.tokenizer_class if hasattr(model.config, 'tokenizer_class') else None
    )
    model = get_peft_model(model, config)
    return model

def apply_moe_layer_splitting(model, num_experts: int = 4, expert_capacity_factor: float = 2.0, moe_layer_freq: int = 2):
    # Assumes a standard transformer block structure
    if not hasattr(model, 'transformer') or not hasattr(model.transformer, 'h'):
        logging.warning("Model does not have the expected transformer structure for MoE splitting.")
        return model

    from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock, MixtralBLock

    for i in range(len(model.transformer.h)):
        if (i + 1) % moe_layer_freq == 0:
            original_layer = model.transformer.h[i]
            # Extract necessary components, handling different layer structures
            if isinstance(original_layer, MixtralBLock):
                config = original_layer.config
                new_moe_block = MixtralSparseMoeBlock(config, num_experts=num_experts, capacity_factor=expert_capacity_factor)
                # Copy relevant weights - this might need adjustments based on the model
                new_moe_block.load_state_dict(original_layer.mlp.state_dict(), strict=False)
                model.transformer.h[i] = new_moe_block
            else:
                logging.warning(f"Skipping layer {i} for MoE, not a recognized block type.")
    return model

def process_model(model_id, q_method, use_imatrix, imatrix_q_method, private_repo, train_data_file, split_model, split_max_tensors, split_max_size,
                 oauth_token: gr.OAuthToken | None, apply_aggressive_optimization, apply_reduce_layers, apply_smaller_embeddings,
                 apply_weight_sharing, apply_low_rank_approx, use_lora_opt, use_adalora_opt, use_ia3_opt, use_prompt_tuning_opt,
                 apply_moe_splitting, num_experts_moe, expert_capacity_factor_moe, moe_layer_freq_moe,
                 is_automated=False):
    if oauth_token.token is None and not is_automated:
        raise ValueError("You must be logged in to use GGUF-my-repo")
    elif oauth_token.token is None and is_automated:
        logging.warning("Running in automated mode without user authentication.")

    model_name = model_id.split('/')[-1]
    fp16 = f"{model_name}.fp16.gguf"

    try:
        api = HfApi(token=oauth_token.token if oauth_token else None)
        dl_pattern = ["*.safetensors", "*.bin", "*.pt", "*.onnx", "*.h5", "*.tflite", "*.ckpt", "*.pb", "*.tar", "*.xml", "*.caffemodel", "*.md", "*.json", "*.model"]
        pattern = "*.safetensors" if any(file.path.endswith(".safetensors") for file in api.list_repo_tree(repo_id=model_id, recursive=True)) else "*.bin"
        dl_pattern += pattern
        api.snapshot_download(repo_id=model_id, local_dir=model_name, local_dir_use_symlinks=False, allow_patterns=dl_pattern)

        config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
        model = AutoModelForCausalLM.from_pretrained(model_id, config=config, torch_dtype=torch.float16, trust_remote_code=True)

        if apply_aggressive_optimization:
            model = aggressive_optimize(model)
        if apply_reduce_layers:
            model = reduce_layers(model)
        if apply_smaller_embeddings:
            model = use_smaller_embeddings(model)
        if apply_weight_sharing:
            model = use_weight_sharing(model)
        if apply_low_rank_approx:
            model = use_low_rank_approximation(model)
        if use_lora_opt:
            model = use_lora(model)
        if use_adalora_opt:
            model = use_adalora(model)
        if use_ia3_opt:
            model = use_ia3(model)
        if use_prompt_tuning_opt:
            model = use_prompt_tuning(model)
        if apply_moe_splitting:
            model = apply_moe_layer_splitting(model, num_experts_moe, expert_capacity_factor_moe, moe_layer_freq_moe)

        optimized_model_path = f"{model_name}_optimized"
        model.save_pretrained(optimized_model_path)

        conversion_script = "convert_hf_to_gguf.py"
        fp16_conversion = f"python llama.cpp/{conversion_script} {optimized_model_path} --outtype f16 --outfile {fp16}"
        result = subprocess.run(fp16_conversion, shell=True, capture_output=True)
        if result.returncode != 0:
            raise Exception(f"Error converting to fp16: {result.stderr}")

        imatrix_path = "llama.cpp/imatrix.dat"
        if use_imatrix:
            if train_data_file:
                train_data_path = train_data_file.name
            else:
                train_data_path = "groups_merged.txt"
            if not os.path.isfile(train_data_path):
                raise Exception(f"Training data file not found: {train_data_path}")
            generate_importance_matrix(fp16, train_data_path)

        username = whoami(oauth_token.token)["name"] if oauth_token and oauth_token.token else "automated-gguf"
        quantized_gguf_name = f"{model_name.lower()}-{imatrix_q_method.lower()}-imat.gguf" if use_imatrix else f"{model_name.lower()}-{q_method.lower()}.gguf"
        quantized_gguf_path = quantized_gguf_name

        if use_imatrix:
            quantise_ggml = f"./llama.cpp/llama-quantize --imatrix {imatrix_path} {fp16} {quantized_gguf_path} {imatrix_q_method}"
        else:
            quantise_ggml = f"./llama.cpp/llama-quantize {fp16} {quantized_gguf_path} {q_method}"

        result = subprocess.run(quantise_ggml, shell=True, capture_output=True)
        if result.returncode != 0:
            raise Exception(f"Error quantizing: {result.stderr}")

        try:
            subprocess.run(["llama.cpp/llama", "-m", quantized_gguf_path, "-p", "Test prompt"], check=True)
        except Exception as e:
            raise Exception(f"Model verification failed: {e}")

        new_repo_id = f"{username}/{model_name}-{imatrix_q_method if use_imatrix else q_method}-GGUF"
        new_repo_url = api.create_repo(repo_id=new_repo_id, exist_ok=True, private=private_repo)

        try:
            card = ModelCard.load(model_id, token=oauth_token.token if oauth_token else None)
        except:
            card = ModelCard("")

        if card.data.tags is None:
            card.data.tags = []
        card.data.tags.append("llama-cpp")
        card.data.tags.append("gguf-my-repo")
        card.data.base_model = model_id
        optimization_notes = []
        if apply_aggressive_optimization:
            optimization_notes.append("Aggressive optimization applied.")
        if apply_reduce_layers:
            optimization_notes.append("Number of layers reduced.")
        if apply_smaller_embeddings:
            optimization_notes.append("Embedding size reduced.")
        if apply_weight_sharing:
            optimization_notes.append("Weight sharing applied.")
        if apply_low_rank_approx:
            optimization_notes.append(f"Low-rank approximation applied.")
        if use_lora_opt:
            optimization_notes.append("LoRA applied.")
        if use_adalora_opt:
            optimization_notes.append("AdaLoRA applied.")
        if use_ia3_opt:
            optimization_notes.append("IA3 applied.")
        if use_prompt_tuning_opt:
            optimization_notes.append("Prompt Tuning applied.")
        if apply_moe_splitting:
            optimization_notes.append(f"Mixture-of-Experts (MoE) layer splitting applied with {num_experts_moe} experts every {moe_layer_freq_moe} layers.")

        card.text = dedent(
            f"""
            # {new_repo_id}
            This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
            Refer to the [original model card](https://huggingface.co/{model_id}) for more details on the model.

            {' '.join(optimization_notes)}

            ## Use with llama.cpp
            Install llama.cpp through brew (works on Mac and Linux)

            ```bash
            brew install llama.cpp

            ```
            Invoke the llama.cpp server or the CLI.

            ### CLI:
            ```bash
            llama-cli --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
            ```

            ### Server:
            ```bash
            llama-server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
            ```

            Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
            Step 1: Clone llama.cpp from GitHub.
            ```
            git clone https://github.com/ggerganov/llama.cpp
            ```
            Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
            ```
            cd llama.cpp && LLAMA_CURL=1 make
            ```
            Step 3: Run inference through the main binary.
            ```
            ./llama-cli --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
            ```
            or
            ```
            ./llama-server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
            ```
            """
        )
        card.save(f"README.md")

        if split_model:
            split_upload_model(quantized_gguf_path, new_repo_id, oauth_token, split_max_tensors, split_max_size)
        else:
            try:
                api.upload_file(path_or_fileobj=quantized_gguf_path, path_in_repo=quantized_gguf_name, repo_id=new_repo_id)
            except Exception as e:
                raise Exception(f"Error uploading quantized model: {e}")

        if os.path.isfile(imatrix_path):
            try:
                api.upload_file(path_or_fileobj=imatrix_path, path_in_repo="imatrix.dat", repo_id=new_repo_id)
            except Exception as e:
                raise Exception(f"Error uploading imatrix.dat: {e}")

        api.upload_file(path_or_fileobj=f"README.md", path_in_repo=f"README.md", repo_id=new_repo_id)

        log_message = f"Successfully processed and uploaded GGUF model for {model_id} to {new_repo_url}"
        logging.info(log_message)
        return (f'Find your repo <a href=\'{new_repo_url}\' target="_blank" style="text-decoration:underline">here</a>', "llama.png")
    except Exception as e:
        error_message = f"Error processing model {model_id}: {e}"
        logging.error(error_message)
        return (f"Error: {e}", "error.png")
    finally:
        shutil.rmtree(model_name, ignore_errors=True)
        shutil.rmtree(optimized_model_path, ignore_errors=True)

def select_models_for_automation():
    # Example logic: Select top N most downloaded models
    models = list_models(sort="downloads", direction=-1, limit=5)
    return [model.modelId for model in models]

def get_automation_parameters():
    # Example logic: Define default parameters or load from a config
    return {
        "q_method": "Q4_K_M",
        "use_imatrix": False,
        "imatrix_q_method": "IQ4_NL",
        "private_repo": True,
        "train_data_file": None,
        "split_model": False,
        "split_max_tensors": 256,
        "split_max_size": None,
        "apply_aggressive_optimization": True,
        "apply_reduce_layers": True,
        "apply_smaller_embeddings": True,
        "apply_weight_sharing": False,
        "apply_low_rank_approx": False,
        "use_lora_opt": False,
        "use_adalora_opt": False,
        "use_ia3_opt": False,
        "use_prompt_tuning_opt": False,
        "apply_moe_splitting": False,
        "num_experts_moe": 4,
        "expert_capacity_factor_moe": 2.0,
        "moe_layer_freq_moe": 2,
    }

def automate_gguf_creation():
    logging.info(f"Starting automated GGUF creation at {datetime.now()}")
    api = HfApi(token=HF_TOKEN)
    try:
        whoami(token=HF_TOKEN) # Check if the token is valid
    except Exception as e:
        logging.error(f"Error with Hugging Face token: {e}")
        return

    models_to_process = select_models_for_automation()
    automation_params = get_automation_parameters()

    for model_id in models_to_process:
        logging.info(f"Attempting to process model: {model_id}")
        try:
            process_model(model_id=model_id, oauth_token=None, is_automated=True, **automation_params)
        except Exception as e:
            logging.error(f"Failed to process model {model_id} automatically: {e}")

css="""/* Custom CSS to allow scrolling */ .gradio-container {overflow-y: auto;}"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("You must be logged in to use GGUF-my-repo for manual processing. Automation runs in the background.")
    oauth_token = gr.OAuthButton(min_width=250)
    model_id = HuggingfaceHubSearch(label="Hub Model ID", placeholder="Search for model id on Huggingface", search_type="model")

    q_method = gr.Dropdown(["Q2_K", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"],
                          label="Quantization Method", info="GGML quantization type", value="Q4_K_M", filterable=False, visible=True)
    imatrix_q_method = gr.Dropdown(["IQ1", "IQ1_S", "IQ1_XXS", "IQ2_S", "IQ2_XXS", "IQ3_M", "IQ3_XXS", "Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M", "Q5_K_S"],
                                  label="Imatrix Quantization Method", info="GGML imatrix quants type", value="IQ4_NL", filterable=False, visible=False)
    use_imatrix = gr.Checkbox(value=False, label="Use Imatrix Quantization", info="Use importance matrix for quantization.")
    train_data_file = gr.File(label="Training Data File", file_types=["txt"], visible=False)

    size_reduction_accordion = gr.Accordion("Additional Size Reduction Techniques", open=False)
    with size_reduction_accordion:
        apply_aggressive_optimization = gr.Checkbox(value=True, label="Apply Aggressive Optimization", info="Reduces attention heads and hidden size.")
        apply_reduce_layers = gr.Checkbox(value=True, label="Reduce Layers", info="Reduces the number of layers in the model.")
        apply_smaller_embeddings = gr.Checkbox(value=True, label="Use Smaller Embeddings", info="Reduces the size of the embedding layer.")
        apply_weight_sharing = gr.Checkbox(value=False, label="Apply Weight Sharing", info="Shares weights across layers to reduce parameters.")
        apply_low_rank_approx = gr.Checkbox(value=False, label="Apply Low-Rank Approximation", info="Approximates weight matrices with lower rank.")
        use_lora_opt = gr.Checkbox(value=False, label="Use LoRA", info="Applies Low-Rank Adaptation.")
        use_adalora_opt = gr.Checkbox(value=False, label="Use AdaLoRA", info="Applies Adaptive Low-Rank Adaptation.")
        use_ia3_opt = gr.Checkbox(value=False, label="Use IA3", info="Applies Infused Adapter by Inhibiting and Amplifying Inner Activations.")
        use_prompt_tuning_opt = gr.Checkbox(value=False, label="Use Prompt Tuning", info="Adds trainable virtual tokens to the input embeddings.")
        apply_moe_splitting = gr.Checkbox(value=False, label="Apply MoE Layer Splitting", info="Splits layers into a mixture-of-experts (MoE).", visible=False)
        with gr.Row(visible=False) as moe_params:
            num_experts_moe = gr.Number(value=4, label="Number of Experts", info="Number of experts to use in the MoE layers.", precision=0)
            expert_capacity_factor_moe = gr.Number(value=2.0, label="Expert Capacity Factor", info="Capacity factor for each expert in the MoE layer.", precision=1)
            moe_layer_freq_moe = gr.Number(value=2, label="MoE Layer Frequency", info="Apply MoE every N layers", precision=0)

    private_repo = gr.Checkbox(value=True, label="Private Repo", info="Create a private repo under your username.")
    split_model = gr.Checkbox(value=False, label="Split Model", info="Shard the model using gguf-split.")
    split_max_tensors = gr.Number(value=256, label="Max Tensors per File", info="Maximum number of tensors per file when splitting model.", visible=False)
    split_max_size = gr.Textbox(label="Max File Size", info="Maximum file size when splitting model (--split-max-size). May leave empty to use the default.", visible=False)

    use_imatrix.change(fn=lambda use_imatrix:  gr.update(visible=not use_imatrix), inputs=use_imatrix, outputs=q_method)
    use_imatrix.change(fn=lambda use_imatrix:  gr.update(visible=use_imatrix), inputs=use_imatrix, outputs=imatrix_q_method)
    use_imatrix.change(fn=lambda use_imatrix:  gr.update(visible=use_imatrix), inputs=use_imatrix, outputs=train_data_file)
    split_model.change(fn=lambda split_model:  gr.update(visible=split_model), inputs=split_model, outputs=split_max_tensors)
    split_model.change(fn=lambda split_model:  gr.update(visible=split_model), inputs=split_model, outputs=split_max_size)
    apply_moe_splitting.change(fn=lambda apply_moe_splitting: gr.update(visible=apply_moe_splitting), inputs=apply_moe_splitting, outputs=moe_params)


    iface = gr.Interface(
        fn=process_model,
        inputs=[
            model_id,
            q_method,
            use_imatrix,
            imatrix_q_method,
            private_repo,
            train_data_file,
            split_model,
            split_max_tensors,
            split_max_size,
            oauth_token,
            apply_aggressive_optimization,
            apply_reduce_layers,
            apply_smaller_embeddings,
            apply_weight_sharing,
            apply_low_rank_approx,
            use_lora_opt,
            use_adalora_opt,
            use_ia3_opt,
            use_prompt_tuning_opt,
            apply_moe_splitting,
            num_experts_moe,
            expert_capacity_factor_moe,
            moe_layer_freq_moe,
        ],
        outputs=[
            gr.Markdown(label="output"),
            gr.Image(show_label=False),
        ],
        title="Create your own GGUF Quants, blazingly fast ⚡!",
        description="The space takes an HF repo as an input, applies size reduction techniques, quantizes it and creates a Public or Private repo containing the selected quant under your HF user namespace. It also automates the creation of GGUF quants for popular models in the background.",
        api_name=False
    )

def restart_space():
    HfApi().restart_space(repo_id=SPACE_ID, token=HF_TOKEN, factory_reboot=True)

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=21600)
scheduler.add_job(automate_gguf_creation, "interval", hours=6) # Run automation every 6 hours
scheduler.start()

demo.queue(default_concurrency_limit=1, max_size=5).launch(debug=True, show_api=False)