Upload folder using huggingface_hub

.virtual_documents/__notebook_source__.ipynb

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+# pip install -U bitsandbytes>=0.46.1
+
+
+import torch
+import torch.nn as nn
+
+from transformers import (
+    Blip2Processor,
+    Blip2ForConditionalGeneration,
+    BitsAndBytesConfig
+)
+
+from peft import LoraConfig, get_peft_model
+
+# ==========================
+# LOAD BLIP2
+# ==========================
+
+quant_config = BitsAndBytesConfig(
+    load_in_8bit=True
+)
+
+processor = Blip2Processor.from_pretrained(
+    "Salesforce/blip2-opt-2.7b"
+)
+
+base_model = Blip2ForConditionalGeneration.from_pretrained(
+    "Salesforce/blip2-opt-2.7b",
+    quantization_config=quant_config,
+    device_map="auto"
+)
+
+# ==========================
+# RECREATE LORA
+# ==========================
+
+lora_config = LoraConfig(
+    r=32,
+    lora_alpha=64,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    lora_dropout=0.1,
+    bias="none"
+)
+
+model = get_peft_model(
+    base_model,
+    lora_config
+)
+
+# ==========================
+# RECREATE HYBRID MODEL
+# ==========================
+
+class AgriVisionHybridModel(nn.Module):
+    def __init__(self, blip2_model, num_disease, num_pathogen):
+        super().__init__()
+
+        self.blip2 = blip2_model
+
+        hidden_size = self.blip2.language_model.config.hidden_size
+
+        lm_device = next(
+            self.blip2.language_model.parameters()
+        ).device
+
+        self.disease_classifier = nn.Linear(
+            hidden_size,
+            num_disease
+        ).to(lm_device)
+
+        self.pathogen_classifier = nn.Linear(
+            hidden_size,
+            num_pathogen
+        ).to(lm_device)
+
+        self.ce_loss = nn.CrossEntropyLoss()
+        self.lm_device = lm_device
+
+# Your notebook values
+NUM_DISEASES = 17
+NUM_PATHOGENS = 6
+
+hybrid_model = AgriVisionHybridModel(
+    model,
+    num_disease=NUM_DISEASES,
+    num_pathogen=NUM_PATHOGENS
+)
+
+print("Hybrid model rebuilt")
+
+
+checkpoint = torch.load(
+    "/kaggle/input/models/anhadmahajan06/agrivision-blip2-model/pytorch/default/1/agrivision_final_model.pth",
+    map_location="cpu"
+)
+
+hybrid_model.load_state_dict(
+    checkpoint["model_state_dict"],
+    strict=False
+)
+
+print("Checkpoint loaded")
+
+
+hybrid_model.blip2.save_pretrained(
+    "agrivision_adapter"
+)
+
+processor.save_pretrained(
+    "agrivision_adapter"
+)
+
+
+import os
+
+for f in os.listdir("agrivision_adapter"):
+    print(f)
+
+
+torch.save(
+    {
+        "disease_classifier":
+            hybrid_model.disease_classifier.state_dict(),
+
+        "pathogen_classifier":
+            hybrid_model.pathogen_classifier.state_dict()
+    },
+    "classification_heads.pth"
+)
+
+
+get_ipython().getoutput("pip install -q huggingface_hub")
+
+
+from huggingface_hub import login
+
+login()
+
+
+from huggingface_hub import  upload_folder
+
+# Push your model files
+upload_folder(folder_path=".", repo_id="AnhadMahajan/AgriVision-BLIP2", repo_type="model")
+
+
+
+

=0.46.1

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+Collecting bitsandbytes
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agrivision_adapter/README.md

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+---
+base_model: Salesforce/blip2-opt-2.7b
+library_name: peft
+tags:
+- base_model:adapter:Salesforce/blip2-opt-2.7b
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

agrivision_adapter/adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Blip2ForConditionalGeneration",
+    "parent_library": "transformers.models.blip_2.modeling_blip_2"
+  },
+  "base_model_name_or_path": "Salesforce/blip2-opt-2.7b",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj",
+    "o_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

agrivision_adapter/adapter_model.safetensors

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+oid sha256:21d767b581ab8b5bbf2317cd04f1957d41f3cbffc4329cd262ba380b28dbb3b1
+size 62944808

agrivision_adapter/processor_config.json

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+{
+  "image_processor": {
+    "data_format": "channels_first",
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.48145466,
+      0.4578275,
+      0.40821073
+    ],
+    "image_processor_type": "BlipImageProcessorFast",
+    "image_std": [
+      0.26862954,
+      0.26130258,
+      0.27577711
+    ],
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "height": 224,
+      "width": 224
+    }
+  },
+  "num_query_tokens": 32,
+  "processor_class": "Blip2Processor"
+}

agrivision_adapter/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "</s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "processor_class": "Blip2Processor",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "</s>"
+}

classification_heads.pth

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+version https://git-lfs.github.com/spec/v1
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