Upload folder using huggingface_hub

README.md

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+---
+inference: false
+---
+
+<br>
+<br>
+
+# LLaVA Model Card
+
+## Model details
+
+**Model type:**
+LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
+It is an auto-regressive language model, based on the transformer architecture.
+
+**Model date:**
+LLaVA-v1.5-7B was trained in September 2023.
+
+**Paper or resources for more information:**
+https://llava-vl.github.io/
+
+## License
+Llama 2 is licensed under the LLAMA 2 Community License, 
+Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+
+**Where to send questions or comments about the model:**
+https://github.com/haotian-liu/LLaVA/issues
+
+## Intended use
+**Primary intended uses:**
+The primary use of LLaVA is research on large multimodal models and chatbots.
+
+**Primary intended users:**
+The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
+
+## Training dataset
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
+- 158K GPT-generated multimodal instruction-following data.
+- 450K academic-task-oriented VQA data mixture.
+- 40K ShareGPT data.
+
+## Evaluation dataset
+A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

code/inference.py

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+import requests
+from PIL import Image
+from io import BytesIO
+import torch
+from transformers import AutoTokenizer, BitsAndBytesConfig
+
+from llava.model import LlavaLlamaForCausalLM
+from llava.utils import disable_torch_init
+from llava.constants import IMAGE_TOKEN_INDEX
+from llava.mm_utils import tokenizer_image_token, KeywordsStoppingCriteria
+
+
+def model_fn(model_dir):
+    kwargs = {"device_map": "auto"}
+    kwargs["torch_dtype"] = torch.float16
+    model = LlavaLlamaForCausalLM.from_pretrained(
+        model_dir, low_cpu_mem_usage=True, **kwargs
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False)
+
+    vision_tower = model.get_vision_tower()
+    if not vision_tower.is_loaded:
+        vision_tower.load_model()
+    vision_tower.to(device="cuda", dtype=torch.float16)
+    image_processor = vision_tower.image_processor
+    return model, tokenizer, image_processor
+
+
+def predict_fn(data, model_and_tokenizer):
+    # unpack model and tokenizer
+    model, tokenizer, image_processor = model_and_tokenizer
+
+    # get prompt & parameters
+    image_file = data.pop("image", data)
+    prompt = data.pop("question", data)
+
+    max_new_tokens = data.pop("max_new_tokens", 1024)
+    temperature = data.pop("temperature", 0.2)
+    stop_str = data.pop("stop_str", "###")
+
+    if image_file.startswith("http") or image_file.startswith("https"):
+        response = requests.get(image_file)
+        image = Image.open(BytesIO(response.content)).convert("RGB")
+    else:
+        image = Image.open(image_file).convert("RGB")
+
+    disable_torch_init()
+    image_tensor = (
+        image_processor.preprocess(image, return_tensors="pt")["pixel_values"]
+        .half()
+        .cuda()
+    )
+
+    keywords = [stop_str]
+    input_ids = (
+        tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
+        .unsqueeze(0)
+        .cuda()
+    )
+    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
+    with torch.inference_mode():
+        output_ids = model.generate(
+            input_ids,
+            images=image_tensor,
+            do_sample=True,
+            temperature=temperature,
+            max_new_tokens=max_new_tokens,
+            use_cache=True,
+            stopping_criteria=[stopping_criteria],
+        )
+    outputs = tokenizer.decode(
+        output_ids[0, input_ids.shape[1] :], skip_special_tokens=True
+    ).strip()
+    return outputs

code/requirements.txt

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+# llava @ git+https://github.com/haotian-liu/LLaVA@main
+llava @ git+https://github.com/haotian-liu/LLaVA@v1.1.1

config.json

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+{
+  "_name_or_path": "llava-v1.5-7b",
+  "architectures": [
+    "LlavaLlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "freeze_mm_mlp_adapter": false,
+  "freeze_mm_vision_resampler": false,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "image_aspect_ratio": "pad",
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_length": 4096,
+  "max_position_embeddings": 4096,
+  "mm_hidden_size": 1024,
+  "mm_projector_type": "mlp2x_gelu",
+  "mm_resampler_type": null,
+  "mm_use_im_patch_token": false,
+  "mm_use_im_start_end": false,
+  "mm_vision_select_feature": "patch",
+  "mm_vision_select_layer": -2,
+  "mm_vision_tower": "openai/clip-vit-large-patch14-336",
+  "model_type": "llava",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "tune_mm_mlp_adapter": false,
+  "tune_mm_vision_resampler": false,
+  "unfreeze_mm_vision_tower": false,
+  "use_cache": true,
+  "use_mm_proj": true,
+  "vocab_size": 32000
+}

deploy_llava.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install sagemaker --upgrade"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!tar -cf model.tar.gz --use-compress-program=pigz *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Couldn't call 'get_role' to get Role ARN from role name arn:aws:iam::297308036828:root to get Role path.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker role arn: arn:aws:iam::297308036828:role/service-role/AmazonSageMaker-ExecutionRole-20231008T201275\n",
+      "sagemaker bucket: sagemaker-us-west-2-297308036828\n",
+      "sagemaker session region: us-west-2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sagemaker\n",
+    "import boto3\n",
+    "sess = sagemaker.Session()\n",
+    "# sagemaker session bucket -> used for uploading data, models and logs\n",
+    "# sagemaker will automatically create this bucket if it not exists\n",
+    "sagemaker_session_bucket=None\n",
+    "if sagemaker_session_bucket is None and sess is not None:\n",
+    "    # set to default bucket if a bucket name is not given\n",
+    "    sagemaker_session_bucket = sess.default_bucket()\n",
+    "\n",
+    "try:\n",
+    "    role = sagemaker.get_execution_role()\n",
+    "except ValueError:\n",
+    "    iam = boto3.client('iam')\n",
+    "    role = iam.get_role(RoleName='AmazonSageMaker-ExecutionRole-20231008T201275')['Role']['Arn']\n",
+    "\n",
+    "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n",
+    "\n",
+    "print(f\"sagemaker role arn: {role}\")\n",
+    "print(f\"sagemaker bucket: {sess.default_bucket()}\")\n",
+    "print(f\"sagemaker session region: {sess.boto_region_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model uploaded to: s3://sagemaker-us-west-2-297308036828/llava-v1.5-7b/model.tar.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sagemaker.s3 import S3Uploader\n",
+    "\n",
+    "# upload model.tar.gz to s3\n",
+    "s3_model_uri = S3Uploader.upload(local_path=\"./model.tar.gz\", desired_s3_uri=f\"s3://{sess.default_bucket()}/llava-v1.5-7b\")\n",
+    "\n",
+    "print(f\"model uploaded to: {s3_model_uri}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# s3_model_uri = \"s3://sagemaker-us-west-2-297308036828/llava-v1.5-7b/model.tar.gz\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
+      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
+      "------------------!"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from sagemaker.huggingface.model import HuggingFaceModel\n",
+    "\n",
+    "# create Hugging Face Model Class\n",
+    "huggingface_model = HuggingFaceModel(\n",
+    "   model_data=s3_model_uri,      # path to your model and script\n",
+    "   role=role,                    # iam role with permissions to create an Endpoint\n",
+    "   transformers_version=\"4.28.1\",  # transformers version used\n",
+    "   pytorch_version=\"2.0.0\",       # pytorch version used\n",
+    "   py_version='py310',            # python version used\n",
+    "   model_server_workers=1\n",
+    ")\n",
+    "\n",
+    "# deploy the endpoint endpoint\n",
+    "predictor = huggingface_model.deploy(\n",
+    "    initial_instance_count=1,\n",
+    "    instance_type=\"ml.g5.xlarge\",\n",
+    "    # container_startup_health_check_timeout=600, # increase timeout for large models\n",
+    "    # model_data_download_timeout=600, # increase timeout for large models\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(optional)\n",
+      "\n",
+      "The image is a black and white photograph of a man standing in front of a building. The man is wearing a suit and tie, and he appears to be looking off into the distance. The building in the background is large and imposing, with many windows and a prominent clock tower. The overall atmosphere of the image is one of elegance and sophistication.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    \"image\" : 'https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png', \n",
+    "    \"question\" : \"Describe the image and color details.\"\n",
+    "}\n",
+    "\n",
+    "# max_new_tokens = data.pop(\"max_new_tokens\", 1024)\n",
+    "# temperature = data.pop(\"temperature\", 0.2)\n",
+    "# stop_str = data.pop(\"stop_str\", \"###\")\n",
+    "\n",
+    "# request\n",
+    "output = predictor.predict(data)\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The image features a unique and eye-catching toy, which is a red and orange plastic horse with a pair of glasses on its face. The horse has a fire effect, giving it a fiery appearance. The glasses on the horse's face add a whimsical touch to the toy. The overall color scheme of the toy is predominantly red and orange, with the fire effect further enhancing the vibrant colors.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from llava.conversation import conv_templates, SeparatorStyle\n",
+    "from llava.constants import (\n",
+    "IMAGE_TOKEN_INDEX,\n",
+    "DEFAULT_IMAGE_TOKEN,\n",
+    "DEFAULT_IM_START_TOKEN,\n",
+    "DEFAULT_IM_END_TOKEN,\n",
+    ")\n",
+    "\n",
+    "raw_prompt = \"Describe the image and color details.\"\n",
+    "image_path = \"https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png\"\n",
+    "\n",
+    "conv_mode = \"llava_v1\"\n",
+    "conv = conv_templates[conv_mode].copy()\n",
+    "roles = conv.roles\n",
+    "inp = f\"{roles[0]}: {raw_prompt}\"\n",
+    "inp = (\n",
+    "    DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + \"\\n\" + inp\n",
+    ")\n",
+    "conv.append_message(conv.roles[0], inp)\n",
+    "conv.append_message(conv.roles[1], None)\n",
+    "prompt = conv.get_prompt()\n",
+    "\n",
+    "stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2\n",
+    "\n",
+    "\n",
+    "data = {\"image\" : image_path, \"question\" : prompt, \"stop_str\" : stop_str}\n",
+    "output = predictor.predict(data)\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictor.delete_endpoint()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sagemaker.huggingface.model import HuggingFacePredictor\n",
+    "\n",
+    "# initial the endpoint predictor\n",
+    "predictor = HuggingFacePredictor(\n",
+    "    endpoint_name=\"\",\n",
+    "    sagemaker_session=sess\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llava",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0"
+}

pytorch_model.bin.index.json

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+    "model.mm_projector.2.bias": "pytorch_model-00002-of-00002.bin",
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+    "model.norm.weight": "pytorch_model-00002-of-00002.bin"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<unk>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,35 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": null,
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

upload.ipynb

@@ -0,0 +1,57 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/tom/miniconda3/envs/gptv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import HfApi\n",
+    "api = HfApi()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api.upload_folder(\n",
+    "    folder_path=\"/Users/tom/projects/llava-v1.5-7b\",\n",
+    "    repo_id=\"username/my-cool-space\",\n",
+    "    repo_type=\"space\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gptv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}