Update README.md

README.md

@@ -1,11 +1,15 @@
----
-library_name: transformers
-tags: []
----
+---
+library_name: transformers
+license: apache-2.0
+datasets:
+- RekaAI/VibeEval
+base_model:
+- meta-llama/Llama-3.2-11B-Vision-Instruct
+---
 
-# Model Card for Model ID
+# Model Card for hiiamsid/llama-3.2-vision-11B-VibeEval
 
-<!-- Provide a quick summary of what the model is/does. -->
+This is the finetuned version of meta-llama/Llama-3.2-11B-Vision-Instruct trained on RekaAI/VibeEval dataset using FSDP on 2 A100s.
 
 
 
@@ -15,185 +19,106 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **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]
+- **Developed by:** hiiamsid
+- **Model type:** multimodal (Image/Text to Text)
+- **Language(s) (NLP):** multilingual
+- **License:** Apache License 2.0
+- **Finetuned from model [optional]:** meta-llama/Llama-3.2-11B-Vision-Instruct
 
-### 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. -->
+## How to Get Started with the Model
+```
 
-[More Information Needed]
+import requests
+from PIL import Image
+import torch
+from transformers import MllamaForConditionalGeneration, AutoProcessor
 
-### Recommendations
+base_model = "hiiamsid/llama-3.2-vision-11B-VibeEval"
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+processor = AutoProcessor.from_pretrained(base_model)
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+model = MllamaForConditionalGeneration.from_pretrained(
+    base_model,
+    low_cpu_mem_usage=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
 
-## How to Get Started with the Model
+url = "https://lh7-rt.googleusercontent.com/docsz/AD_4nXcz-J3iR2bEGcCSLzay07Rqfj5tTakp2EMTTN0x6nKYGLS5yWl0unoSpj2S0-mrWpDtMqjl1fAgH6pVkKJekQEY_kwzL6QNOdf143Yt66znQ0EpfLvx6CLFOqw41oeOYmhPZ6Qrlb5AjEr4AenIOgBMTWTD?key=vhLUYntaS9QOx531XpJH3g"
+image = Image.open(requests.get(url, stream=True).raw)
 
-Use the code below to get started with the model.
+messages = [
+    {"role": "user", "content": [
+        {"type": "image"},
+        {"type": "text", "text": "Describe the tutorial feature image."}
+    ]}
+]
+input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(image, input_text, return_tensors="pt").to(model.device)
 
-[More Information Needed]
+output = model.generate(**inputs, max_new_tokens=120)
+print(processor.decode(output[0]))
+```
 
 ## 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]
+RekaAI/VibeEval: https://huggingface.co/datasets/RekaAI/VibeEval 
 
 ### 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]
-
+-Trained using FSDP activating wraping policy, MixedPrecision Policy (on bfloat16), activationcheckpointing etc and saved using Type FULL_STATE_DICT
 
 #### 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]
+```
+  @dataclass
+  class train_config:
+    model_name: str="meta-llama/Llama-3.2-11B-Vision-Instruct"
+    batch_size_training: int=8
+    batching_strategy: str="padding" #alternative is packing but vision model doesn't work with packing.
+    context_length: int =4096
+    gradient_accumulation_steps: int=1
+    num_epochs: int=3
+    lr: float=1e-5
+    weight_decay: float=0.0
+    gamma: float= 0.85 # multiplicatively decay the learning rate by gamma after each epoch
+    seed: int=42
+    use_fp16: bool=False
+    mixed_precision: bool=True
+    val_batch_size:int = 1
+    use_peft: bool = False
+    output_dir: str = "workspace/models"
+    enable_fsdp: bool = True
+    dist_checkpoint_root_folder: str="workspace/FSDP/model" # will be used if using FSDP
+    dist_checkpoint_folder: str="fine-tuned" # will be used if using FSDP
+    save_optimizer: bool=False # will be used if using FSDP
+    
+  @dataclass
+  class fsdp_config:
+      mixed_precision: bool = True
+      use_fp16: bool=False
+      sharding_strategy: ShardingStrategy = ShardingStrategy.FULL_SHARD # HYBRID_SHARD "Full Shard within a node DDP cross Nodes", SHARD_GRAD_OP "Shard only Gradients and Optimizer States", NO_SHARD "Similar to DDP".
+      hsdp : bool =False # Require HYBRID_SHARD to be set. This flag can extend the HYBRID_SHARD by allowing sharding a model on customized number of GPUs (Sharding_group) and Replicas over Sharding_group.
+      sharding_group_size: int=0 # requires hsdp to be set. This specifies the sharding group size, number of GPUs that you model can fit into to form a replica of a model.
+      replica_group_size: int=0 #requires hsdp to be set. This specifies the replica group size, which is world_size/sharding_group_size.
+      checkpoint_type: StateDictType = StateDictType.FULL_STATE_DICT  # alternatively FULL_STATE_DICT can be used. SHARDED_STATE_DICT saves one file with sharded weights per rank while FULL_STATE_DICT will collect all weights on rank 0 and save them in a single file.
+      fsdp_activation_checkpointing: bool=True
+      fsdp_cpu_offload: bool=False
+      pure_bf16: bool = True
+      optimizer: str= "AdamW"
+```
 
 ### Model Architecture and Objective
-
-[More Information Needed]
+This was just trained to see how much improvement can be seen when finetuned llama 3.2 vision.
 
 ### Compute Infrastructure
+Trained on 2 A100 (80GB) from runpods.
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+## Citation
 
 <!-- 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
-
+https://github.com/meta-llama/llama-recipes 
 [More Information Needed]