praeclarumjj3
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Upload LlavaLlamaForCausalLM
Browse files- README.md +199 -0
- config.json +112 -0
- generation_config.json +13 -0
- model-00001-of-00007.safetensors +3 -0
- model-00002-of-00007.safetensors +3 -0
- model-00003-of-00007.safetensors +3 -0
- model-00004-of-00007.safetensors +3 -0
- model-00005-of-00007.safetensors +3 -0
- model-00006-of-00007.safetensors +3 -0
- model-00007-of-00007.safetensors +3 -0
- model.safetensors.index.json +696 -0
README.md
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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config.json
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{
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"_name_or_path": "/mnt/projects4jw/jiteshjain_sherlock/models/ABLATE/seed42/SUBMISSION/pt_d8-20_s10-18_g12-20_lossd0.5_s0.5_g0.5_textTrue_gen-depth-seg_clip-vit-large-patch14-336_llama3-8b_contw_0.3_contrastiveTrue_ceFalse_oneformer_attn_causal_n8",
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"architectures": [
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"LlavaLlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_mask_type": "causal",
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"aux_mode": "gen-depth-seg",
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"bos_token_id": 128000,
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"contrastive_loss_weight": 0.3,
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"depth_estimator": "/mnt/projects4jw/jiteshjain_sherlock/depth_anything_v2_vitl.pth",
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"eos_token_id": 128009,
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"freeze_mm_mlp_adapter": false,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"image_aspect_ratio": "pad",
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"image_depth": {
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"depth": 1,
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"depth_layer_indices": "8-20",
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"depth_loss_weight": 0.5,
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"dim_head": 32,
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"ff_mult": 1,
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"num_heads": 4,
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"num_tokens": 576,
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"output_dim": 1024,
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"use_intermediate_depth": false
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},
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"image_gen": {
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"depth": 1,
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"dim_head": 32,
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"ff_mult": 1,
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"img_layer_indices": "12-20",
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"img_loss_weight": 0.5,
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"num_heads": 4,
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"num_tokens": 1,
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"output_dim": 1024
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},
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"image_generator": "/mnt/projects4jw/jiteshjain_sherlock/stable-diffusion-2-1-unclip",
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"image_grid_pinpoints": [
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[
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336,
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672
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],
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[
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672,
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336
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],
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[
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672,
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672
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],
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[
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1008,
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336
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],
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[
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336,
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1008
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]
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],
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"image_seg": {
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"depth": 1,
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"dim_head": 32,
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"ff_mult": 1,
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"num_heads": 4,
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"num_tokens": 576,
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"output_dim": 1536,
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"seg_layer_indices": "10-18",
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"seg_loss_weight": 0.5,
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"seg_teacher": "oneformer"
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},
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"image_segmentor": "/mnt/projects4jw/jiteshjain_sherlock/oneformer_coco_swin_large",
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"mm_hidden_size": 1024,
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"mm_patch_merge_type": "flat",
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"mm_projector_lr": null,
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"mm_projector_type": "mlp2x_gelu",
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"mm_use_im_patch_token": false,
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"mm_use_im_start_end": false,
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"mm_vision_select_feature": "patch",
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"mm_vision_select_layer": -2,
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"mm_vision_tower": "/mnt/projects4jw/jiteshjain_sherlock/clip-vit-large-patch14-336",
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"model_type": "llava_llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"num_task_tokens": 8,
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"pass_text_to_aux": true,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"s2_scales": "336,1008",
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"sample_tokens": false,
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"task_token_format": "expand_emb",
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"tie_word_embeddings": false,
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"tokenizer_model_max_length": 4096,
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"tokenizer_padding_side": "right",
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"torch_dtype": "float32",
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"transformers_version": "4.41.1",
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"tune_mm_mlp_adapter": false,
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"use_cache": true,
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"use_ce": false,
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"use_contrastive": true,
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"use_mm_proj": true,
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"use_s2": false,
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"vocab_size": 128257
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}
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generation_config.json
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{
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"attn_implementation": "eager",
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128009
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],
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"max_length": 4096,
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"temperature": 0.6,
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