Upload 6 files

README.md

@@ -1,12 +1,14 @@
----
-title: Joy Caption Pre Alpha Mod Error
-emoji: 💻
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.44.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Joy Caption Alpha One Mod (dedicated to reproducing bugs, dangerous to touch)
+emoji: 💬
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+hf_oauth: true
+---
+
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -0,0 +1,67 @@
+import spaces
+import gradio as gr
+from joycaption import stream_chat_mod, get_text_model, change_text_model, get_repo_gguf
+
+JC_TITLE_MD = "<h1><center>JoyCaption Alpha One Mod</center></h1>"
+JC_DESC_MD = """This space is mod of [fancyfeast/joy-caption-alpha-one](https://huggingface.co/spaces/fancyfeast/joy-caption-alpha-one),
+ [Wi-zz/joy-caption-pre-alpha](https://huggingface.co/Wi-zz/joy-caption-pre-alpha). Thanks to [dominic1021](https://huggingface.co/dominic1021)"""
+
+css = """
+.info {text-align:center; !important}
+"""
+
+with gr.Blocks(fill_width=True, css=css, delete_cache=(60, 3600)) as demo:
+    gr.HTML(JC_TITLE_MD)
+    with gr.Row():
+        with gr.Column():
+            with gr.Group():
+                jc_input_image = gr.Image(type="pil", label="Input Image", sources=["upload", "clipboard"], height=384)
+                with gr.Row():
+                    jc_caption_type = gr.Dropdown(
+                        choices=["descriptive", "training_prompt", "rng-tags"],
+                        label="Caption Type",
+                        value="descriptive",
+                    )
+                    jc_caption_tone = gr.Dropdown(
+                        choices=["formal", "informal"],
+                        label="Caption Tone",
+                        value="formal",
+                    )
+                    jc_caption_length = gr.Dropdown(
+                        choices=["any", "very short", "short", "medium-length", "long", "very long"] +
+                                [str(i) for i in range(20, 261, 10)],
+                        label="Caption Length",
+                        value="any",
+                    )
+                gr.Markdown("**Note:** Caption tone doesn't affect `rng-tags` and `training_prompt`.", elem_classes="info")
+                with gr.Accordion("Advanced", open=False):
+                    with gr.Row():
+                        jc_text_model = gr.Dropdown(label="LLM Model", info="You can enter a huggingface model repo_id to want to use.",
+                                                    choices=get_text_model(), value=get_text_model()[0],
+                                                    allow_custom_value=True, interactive=True, min_width=320)
+                        jc_gguf = gr.Dropdown(label=f"GGUF Filename", choices=[], value="",
+                                              allow_custom_value=True, min_width=320, visible=False)
+                        jc_nf4 = gr.Checkbox(label="Use NF4 quantization", value=True)
+                        jc_text_model_button = gr.Button("Load Model", variant="secondary", visible=False)
+                    jc_use_inference_client = gr.Checkbox(label="Use Inference Client", value=False, visible=False)
+                    with gr.Row():
+                        jc_tokens = gr.Slider(minimum=1, maximum=4096, value=300, step=1, label="Max tokens")
+                        jc_temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.6, step=0.1, label="Temperature")
+                        jc_topp = gr.Slider(minimum=0, maximum=2.0, value=0.9, step=0.01, label="Top-P")
+            jc_run_button = gr.Button("Caption", variant="primary")
+        with gr.Column():
+            jc_output_caption = gr.Textbox(label="Caption", show_copy_button=True)
+    gr.Markdown(JC_DESC_MD, elem_classes="info")
+    gr.LoginButton()
+    gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")
+
+    jc_run_button.click(fn=stream_chat_mod, inputs=[jc_input_image, jc_caption_type, jc_caption_tone, jc_caption_length,
+                                                    jc_tokens, jc_topp, jc_temperature, jc_text_model], outputs=[jc_output_caption])
+    jc_text_model.change(change_text_model, [jc_text_model, jc_use_inference_client, jc_gguf, jc_nf4], [jc_text_model], show_api=False)
+    #jc_text_model_button.click(change_text_model, [jc_text_model, jc_use_inference_client, jc_gguf, jc_nf4], [jc_text_model], show_api=False)
+    #jc_text_model.change(get_repo_gguf, [jc_text_model], [jc_gguf], show_api=False)
+    #jc_use_inference_client.change(change_text_model, [jc_text_model, jc_use_inference_client], [jc_text_model], show_api=False)
+
+if __name__ == "__main__":
+    #demo.queue()
+    demo.launch()

joycaption.py

@@ -0,0 +1,422 @@
+import os
+if os.environ.get("SPACES_ZERO_GPU") is not None:
+    import spaces
+else:
+    class spaces:
+        @staticmethod
+        def GPU(func):
+            def wrapper(*args, **kwargs):
+                return func(*args, **kwargs)
+            return wrapper
+import gradio as gr
+from huggingface_hub import InferenceClient
+from torch import nn
+from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM, LlavaForConditionalGeneration
+from pathlib import Path
+import torch
+import torch.amp.autocast_mode
+from PIL import Image
+import torchvision.transforms.functional as TVF
+import gc
+from peft import PeftConfig
+from typing import Union
+
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+BASE_DIR = Path(__file__).resolve().parent # Define the base directory
+device = "cuda" if torch.cuda.is_available() else "cpu"
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+use_inference_client = False
+PIXTRAL_PATHS = ["SeanScripts/pixtral-12b-nf4", "mistral-community/pixtral-12b"]
+
+llm_models = {
+    "Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2": None,
+    PIXTRAL_PATHS[0]: None,
+    "bunnycore/LLama-3.1-8B-Matrix": None,
+    "Sao10K/Llama-3.1-8B-Stheno-v3.4": None,
+    "unsloth/Meta-Llama-3.1-8B-bnb-4bit": None,
+    "DevQuasar/HermesNova-Llama-3.1-8B": None,
+    "mergekit-community/L3.1-Boshima-b-FIX": None,
+    "meta-llama/Meta-Llama-3.1-8B": None, # gated
+}
+
+CLIP_PATH = "google/siglip-so400m-patch14-384"
+MODEL_PATH = list(llm_models.keys())[0]
+CHECKPOINT_PATH = BASE_DIR / Path("9em124t2-499968")
+LORA_PATH = CHECKPOINT_PATH / "text_model"
+TITLE = "<h1><center>JoyCaption Alpha One (2024-09-20a)</center></h1>"
+CAPTION_TYPE_MAP = {
+    ("descriptive", "formal", False, False): ["Write a descriptive caption for this image in a formal tone."],
+    ("descriptive", "formal", False, True): ["Write a descriptive caption for this image in a formal tone within {word_count} words."],
+    ("descriptive", "formal", True, False): ["Write a {length} descriptive caption for this image in a formal tone."],
+    ("descriptive", "informal", False, False): ["Write a descriptive caption for this image in a casual tone."],
+    ("descriptive", "informal", False, True): ["Write a descriptive caption for this image in a casual tone within {word_count} words."],
+    ("descriptive", "informal", True, False): ["Write a {length} descriptive caption for this image in a casual tone."],
+
+    ("training_prompt", "formal", False, False): ["Write a stable diffusion prompt for this image."],
+    ("training_prompt", "formal", False, True): ["Write a stable diffusion prompt for this image within {word_count} words."],
+    ("training_prompt", "formal", True, False): ["Write a {length} stable diffusion prompt for this image."],
+
+    ("rng-tags", "formal", False, False): ["Write a list of Booru tags for this image."],
+    ("rng-tags", "formal", False, True): ["Write a list of Booru tags for this image within {word_count} words."],
+    ("rng-tags", "formal", True, False): ["Write a {length} list of Booru tags for this image."],
+}
+
+class ImageAdapter(nn.Module):
+    def __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int, deep_extract: bool):
+        super().__init__()
+        self.deep_extract = deep_extract
+
+        if self.deep_extract:
+            input_features = input_features * 5
+
+        self.linear1 = nn.Linear(input_features, output_features)
+        self.activation = nn.GELU()
+        self.linear2 = nn.Linear(output_features, output_features)
+        self.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features)
+        self.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features))
+
+        # Mode token
+        #self.mode_token = nn.Embedding(n_modes, output_features)
+        #self.mode_token.weight.data.normal_(mean=0.0, std=0.02)   # Matches HF's implementation of llama3
+
+        # Other tokens (<|image_start|>, <|image_end|>, <|eot_id|>)
+        self.other_tokens = nn.Embedding(3, output_features)
+        self.other_tokens.weight.data.normal_(mean=0.0, std=0.02)   # Matches HF's implementation of llama3
+
+    def forward(self, vision_outputs: torch.Tensor):
+        if self.deep_extract:
+            x = torch.concat((
+                vision_outputs[-2],
+                vision_outputs[3],
+                vision_outputs[7],
+                vision_outputs[13],
+                vision_outputs[20],
+            ), dim=-1)
+            assert len(x.shape) == 3, f"Expected 3, got {len(x.shape)}"  # batch, tokens, features
+            assert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}"
+        else:
+            x = vision_outputs[-2]
+
+        x = self.ln1(x)
+
+        if self.pos_emb is not None:
+            assert x.shape[-2:] == self.pos_emb.shape, f"Expected {self.pos_emb.shape}, got {x.shape[-2:]}"
+            x = x + self.pos_emb
+
+        x = self.linear1(x)
+        x = self.activation(x)
+        x = self.linear2(x)
+
+        # Mode token
+        #mode_token = self.mode_token(mode)
+        #assert mode_token.shape == (x.shape[0], mode_token.shape[1], x.shape[2]), f"Expected {(x.shape[0], 1, x.shape[2])}, got {mode_token.shape}"
+        #x = torch.cat((x, mode_token), dim=1)
+
+        # <|image_start|>, IMAGE, <|image_end|>
+        other_tokens = self.other_tokens(torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1))
+        assert other_tokens.shape == (x.shape[0], 2, x.shape[2]), f"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}"
+        x = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1)
+
+        return x
+
+    def get_eot_embedding(self):
+        return self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0)
+
+# https://huggingface.co/docs/transformers/v4.44.2/gguf
+# https://github.com/city96/ComfyUI-GGUF/issues/7
+# https://github.com/THUDM/ChatGLM-6B/issues/18
+# https://github.com/meta-llama/llama/issues/394
+# https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/discussions/109
+# https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu
+# https://huggingface.co/google/flan-ul2/discussions/8
+# https://huggingface.co/blog/4bit-transformers-bitsandbytes
+# https://huggingface.co/docs/transformers/main/en/peft
+# https://huggingface.co/docs/transformers/main/en/peft#enable-and-disable-adapters
+# https://huggingface.co/docs/transformers/main/quantization/bitsandbytes?bnb=4-bit
+# https://huggingface.co/lllyasviel/flux1-dev-bnb-nf4
+tokenizer = None
+text_model_client = None
+text_model = None
+image_adapter = None
+peft_config = None
+pixtral_model = None
+pixtral_processor = None
+def load_text_model(model_name: str=MODEL_PATH, gguf_file: Union[str, None]=None, is_nf4: bool=True):
+    global tokenizer, text_model, image_adapter, peft_config, pixtral_model, pixtral_processor, text_model_client, use_inference_client
+    try:
+        tokenizer = None
+        text_model_client = None
+        text_model = None
+        image_adapter = None
+        peft_config = None
+        pixtral_model = None
+        pixtral_processor = None
+        torch.cuda.empty_cache()
+        gc.collect()
+
+        from transformers import BitsAndBytesConfig
+        nf4_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
+                                        bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16)
+        
+        if model_name in PIXTRAL_PATHS: # Pixtral
+            print(f"Loading LLM: {model_name}")
+            if is_nf4:
+                pixtral_model = LlavaForConditionalGeneration.from_pretrained(model_name, quantization_config=nf4_config, device_map=device, torch_dtype=torch.bfloat16).eval()
+            else:
+                pixtral_model = LlavaForConditionalGeneration.from_pretrained(model_name, device_map=device, torch_dtype=torch.bfloat16).eval()
+            pixtral_processor = AutoProcessor.from_pretrained(model_name)
+            print(f"pixtral_model: {type(pixtral_model)}") #
+            print(f"pixtral_processor: {type(pixtral_processor)}") #
+            return
+
+        print("Loading tokenizer")
+        if gguf_file: tokenizer = AutoTokenizer.from_pretrained(model_name, gguf_file=gguf_file, use_fast=True, legacy=False)
+        else: tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, legacy=False)
+        assert isinstance(tokenizer, PreTrainedTokenizer) or isinstance(tokenizer, PreTrainedTokenizerFast), f"Tokenizer is of type {type(tokenizer)}"
+ 
+        print(f"Loading LLM: {model_name}")
+        if gguf_file:
+            if device == "cpu":
+                text_model = AutoModelForCausalLM.from_pretrained(model_name, gguf_file=gguf_file, device_map=device, torch_dtype=torch.bfloat16).eval()
+            elif is_nf4:
+                text_model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=nf4_config, device_map=device, torch_dtype=torch.bfloat16).eval()
+            else:
+                text_model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device, torch_dtype=torch.bfloat16).eval()
+        else:
+            if device == "cpu":
+                text_model = AutoModelForCausalLM.from_pretrained(model_name, gguf_file=gguf_file, device_map=device, torch_dtype=torch.bfloat16).eval()
+            elif is_nf4:
+                text_model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=nf4_config, device_map=device, torch_dtype=torch.bfloat16).eval()
+            else:
+                text_model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device, torch_dtype=torch.bfloat16).eval()
+
+        if LORA_PATH.exists():
+            print("Loading VLM's custom text model")
+            if is_nf4: peft_config = PeftConfig.from_pretrained(LORA_PATH, device_map=device, quantization_config=nf4_config)
+            else: peft_config = PeftConfig.from_pretrained(LORA_PATH, device_map=device)
+            text_model.add_adapter(peft_config)
+            text_model.enable_adapters()
+
+        print("Loading image adapter")
+        image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38, False).eval().to("cpu")
+        image_adapter.load_state_dict(torch.load(CHECKPOINT_PATH / "image_adapter.pt", map_location="cpu", weights_only=True))
+        image_adapter.eval().to(device)
+    except Exception as e:
+        print(f"LLM load error: {e}")
+        raise Exception(f"LLM load error: {e}") from e
+    finally:
+        torch.cuda.empty_cache()
+        gc.collect()
+
+load_text_model.zerogpu = True
+
+# Load CLIP
+print("Loading CLIP")
+clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)
+clip_model = AutoModel.from_pretrained(CLIP_PATH).vision_model
+if (CHECKPOINT_PATH / "clip_model.pt").exists():
+    print("Loading VLM's custom vision model")
+    checkpoint = torch.load(CHECKPOINT_PATH / "clip_model.pt", map_location='cpu', weights_only=True)
+    checkpoint = {k.replace("_orig_mod.module.", ""): v for k, v in checkpoint.items()}
+    clip_model.load_state_dict(checkpoint)
+    del checkpoint
+clip_model.eval().requires_grad_(False).to(device)
+
+# Tokenizer
+# LLM
+# Image Adapter
+#load_text_model(PIXTRAL_PATHS[0])
+#print(f"pixtral_model: {type(pixtral_model)}") #
+#print(f"pixtral_processor: {type(pixtral_processor)}") #
+load_text_model()
+print(f"pixtral_model: {type(pixtral_model)}") #
+print(f"pixtral_processor: {type(pixtral_processor)}") #
+
+@spaces.GPU()
+@torch.inference_mode()
+def stream_chat_mod(input_image: Image.Image, caption_type: str, caption_tone: str, caption_length: Union[str, int],
+                    max_new_tokens: int=300, top_p: float=0.9, temperature: float=0.6, model_name: str=MODEL_PATH, progress=gr.Progress(track_tqdm=True)) -> str:
+    global tokenizer, text_model, image_adapter, peft_config, pixtral_model, pixtral_processor, text_model_client, use_inference_client
+    torch.cuda.empty_cache()
+    gc.collect()
+
+    # 'any' means no length specified
+    length = None if caption_length == "any" else caption_length
+
+    if isinstance(length, str):
+        try:
+            length = int(length)
+        except ValueError:
+            pass
+
+    # 'rng-tags' and 'training_prompt' don't have formal/informal tones
+    if caption_type == "rng-tags" or caption_type == "training_prompt":
+        caption_tone = "formal"
+
+    # Build prompt
+    prompt_key = (caption_type, caption_tone, isinstance(length, str), isinstance(length, int))
+    if prompt_key not in CAPTION_TYPE_MAP:
+        raise ValueError(f"Invalid caption type: {prompt_key}")
+
+    prompt_str = CAPTION_TYPE_MAP[prompt_key][0].format(length=length, word_count=length)
+    print(f"Prompt: {prompt_str}")
+
+    # Pixtral
+    if model_name in PIXTRAL_PATHS:
+        print(f"pixtral_model: {type(pixtral_model)}") #
+        print(f"pixtral_processor: {type(pixtral_processor)}") #
+        input_images = [input_image.convert("RGB")]
+        #input_prompt = f"[INST]{prompt_str}\n[IMG][/INST]"
+        input_prompt = "[INST]Caption this image:\n[IMG][/INST]"
+        inputs = pixtral_processor(images=input_images, text=input_prompt, return_tensors="pt").to(device)
+        generate_ids = pixtral_model.generate(**inputs, max_new_tokens=max_new_tokens)
+        output = pixtral_processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        return output.strip()
+
+    # Preprocess image
+    image = input_image.resize((384, 384), Image.LANCZOS)
+    pixel_values = TVF.pil_to_tensor(image).unsqueeze(0) / 255.0
+    pixel_values = TVF.normalize(pixel_values, [0.5], [0.5])
+    pixel_values = pixel_values.to(device)
+
+    # Tokenize the prompt
+    prompt = tokenizer.encode(prompt_str, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False)
+
+    # Embed image
+    with torch.amp.autocast_mode.autocast(device, enabled=True):
+        vision_outputs = clip_model(pixel_values=pixel_values, output_hidden_states=True)
+        image_features = vision_outputs.hidden_states
+        embedded_images = image_adapter(image_features)
+        embedded_images = embedded_images.to(device)
+    
+    # Embed prompt
+    prompt_embeds = text_model.model.embed_tokens(prompt.to(device))
+    assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}"
+    embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64))
+    eot_embed = image_adapter.get_eot_embedding().unsqueeze(0).to(dtype=text_model.dtype)
+
+    # Construct prompts
+    inputs_embeds = torch.cat([
+        embedded_bos.expand(embedded_images.shape[0], -1, -1),
+        embedded_images.to(dtype=embedded_bos.dtype),
+        prompt_embeds.expand(embedded_images.shape[0], -1, -1),
+        eot_embed.expand(embedded_images.shape[0], -1, -1),
+    ], dim=1)
+
+    input_ids = torch.cat([
+        torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long),
+        torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),
+        prompt,
+        torch.tensor([[tokenizer.convert_tokens_to_ids("<|eot_id|>")]], dtype=torch.long),
+    ], dim=1).to(device)
+    attention_mask = torch.ones_like(input_ids)
+
+    text_model.to(device)
+    generate_ids = text_model.generate(input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, max_new_tokens=max_new_tokens,
+                                       do_sample=True, suppress_tokens=None, top_p=top_p, temperature=temperature)
+
+    # Trim off the prompt
+    generate_ids = generate_ids[:, input_ids.shape[1]:]
+    if generate_ids[0][-1] == tokenizer.eos_token_id or generate_ids[0][-1] == tokenizer.convert_tokens_to_ids("<|eot_id|>"):
+        generate_ids = generate_ids[:, :-1]
+
+    caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]
+
+    return caption.strip()
+
+
+# https://huggingface.co/docs/transformers/v4.44.2/main_classes/text_generation#transformers.FlaxGenerationMixin.generate
+# https://github.com/huggingface/transformers/issues/6535
+# https://zenn.dev/hijikix/articles/8c445f4373fdcc ja
+# https://github.com/ggerganov/llama.cpp/discussions/7712
+# https://huggingface.co/docs/huggingface_hub/guides/inference#openai-compatibility
+# https://huggingface.co/docs/huggingface_hub/v0.24.6/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation
+
+
+def is_repo_name(s):
+    import re
+    return re.fullmatch(r'^[^/,\s\"\']+/[^/,\s\"\']+$', s)
+
+
+def is_repo_exists(repo_id):
+    from huggingface_hub import HfApi
+    try:
+        api = HfApi(token=HF_TOKEN)
+        if api.repo_exists(repo_id=repo_id): return True
+        else: return False
+    except Exception as e:
+        print(f"Error: Failed to connect {repo_id}.")
+        print(e)
+        return True # for safe
+
+
+def is_valid_repo(repo_id):
+    from huggingface_hub import HfApi
+    import re
+    try:
+        if not re.fullmatch(r'^[^/,\s\"\']+/[^/,\s\"\']+$', repo_id): return False
+        api = HfApi()
+        if api.repo_exists(repo_id=repo_id): return True
+        else: return False
+    except Exception as e:
+        print(f"Failed to connect {repo_id}. {e}")
+        return False
+
+
+def get_text_model():
+    return list(llm_models.keys())
+
+
+def is_gguf_repo(repo_id: str):
+    from huggingface_hub import HfApi
+    try:
+        api = HfApi(token=HF_TOKEN)
+        if not is_repo_name(repo_id) or not is_repo_exists(repo_id): return False
+        files = api.list_repo_files(repo_id=repo_id)
+    except Exception as e:
+        print(f"Error: Failed to get {repo_id}'s info.")
+        print(e)
+        gr.Warning(f"Error: Failed to get {repo_id}'s info.")
+        return False
+    files = [f for f in files if f.endswith(".gguf")]
+    if len(files) == 0: return False
+    else: return True
+
+
+def get_repo_gguf(repo_id: str):
+    from huggingface_hub import HfApi
+    try:
+        api = HfApi(token=HF_TOKEN)
+        if not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(value="", choices=[])
+        files = api.list_repo_files(repo_id=repo_id)
+    except Exception as e:
+        print(f"Error: Failed to get {repo_id}'s info.")
+        print(e)
+        gr.Warning(f"Error: Failed to get {repo_id}'s info.")
+        return gr.update(value="", choices=[])
+    files = [f for f in files if f.endswith(".gguf")]
+    if len(files) == 0: return gr.update(value="", choices=[])
+    else: return gr.update(value=files[0], choices=files)
+
+
+@spaces.GPU()
+def change_text_model(model_name: str=MODEL_PATH, use_client: bool=False, gguf_file: Union[str, None]=None,
+                      is_nf4: bool=True, progress=gr.Progress(track_tqdm=True)):
+    global use_inference_client, llm_models
+    use_inference_client = use_client
+    try:
+        if not is_repo_name(model_name) or not is_repo_exists(model_name):
+            raise gr.Error(f"Repo doesn't exist: {model_name}")
+        if not gguf_file and is_gguf_repo(model_name):
+            gr.Info(f"Please select a gguf file.")
+            return gr.update(visible=True)
+        if use_inference_client:
+            pass #
+        else:
+            load_text_model(model_name, gguf_file, is_nf4)
+        if model_name not in llm_models: llm_models[model_name] = gguf_file if gguf_file else None
+        return gr.update(choices=get_text_model())
+    except Exception as e:
+        raise gr.Error(f"Model load error: {model_name}, {e}")

packages.txt

@@ -0,0 +1 @@
+git-lfs

pre-requirements.txt

@@ -0,0 +1 @@
+pip>=23.0.0

requirements.txt

@@ -0,0 +1,12 @@
+huggingface_hub
+accelerate
+torch
+git+https://github.com/huggingface/transformers
+sentencepiece
+bitsandbytes
+Pillow
+protobuf
+gguf
+numpy<2.0.0
+peft
+torchvision