Upload LlamaForCausalLMWithGNN

README.md

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+---
+library_name: transformers
+tags:
+- trl
+- sft
+---
+
+# 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. -->
+
+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]
+
+### 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
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+[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:**
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+[More Information Needed]
+
+**APA:**
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+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- 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]
+
+## More Information [optional]
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+[More Information Needed]
+
+## 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]

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLMWithGNN"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
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+    "GIN_hidden_dim_multiplier": 2,
+    "GIN_use_MLP": true,
+    "GIN_use_norm": true,
+    "LlamaAttentionHierarchicalPerceiverAR_use_rope": true,
+    "LlamaAttentionHierarchicalVariant_2_PerceiverAR_use_skip": true,
+    "MLP_type": "standard_MLP",
+    "N_GNN_from_attention_layers": 3,
+    "activation": "relu",
+    "add_rope": false,
+    "adj_construction_method": "threshold_any",
+    "adj_transform_hidden_dim": 128,
+    "attention_GIN_MLP_GIN_MLP_mode": "shared",
+    "attention_GIN_MLP_GIN_binary_scale": 1.0,
+    "attention_GIN_MLP_GIN_learnable_threshold": false,
+    "attention_GIN_MLP_GIN_softmax_temperature": 1.0,
+    "attention_GIN_MLP_GIN_threshold_mode": "none",
+    "attention_GIN_MLP_GIN_threshold_value": 0.2,
+    "attention_GIN_MLP_GIN_top_k_fraction_of_sequence_length": 0.1,
+    "attention_GIN_MLP_GIN_use_softmax": false,
+    "attention_GIN_MLP_attention_mix_mode": "A",
+    "attention_GIN_MLP_multiplier": 2,
+    "attention_GIN_MLP_o_proj_at_end": false,
+    "attention_GIN_MLP_scoring_hidden_dim": 512,
+    "attention_GIN_MLP_second_order_factor": 0.1,
+    "attention_GIN_MLP_separate_attention": false,
+    "attention_GIN_MLP_use_scoring_fnct": true,
+    "attention_GIN_MLP_use_second_order": false,
+    "attention_epsilon_strategy": "default",
+    "attention_epsilon_uniform_value": 0.5,
+    "combined_norm": false,
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+    "distance_scaling_method": "power",
+    "distance_weight_strength": 1.0,
+    "dropout": 0.1,
+    "enforce_causality": true,
+    "epsilon_threshold": 0.6,
+    "gnn_logic": "before_MLP",
+    "gnn_mode": "single",
+    "gnn_residual": false,
+    "gnn_type": "causal_gin",
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+    "hierarchical_enc_dec_type": "PerceiverAR",
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+    "mix_weights_initial": 0.5,
+    "model_type": "",
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+    "num_latent_layers": 4,
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+    "num_latents_list": [
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+      32,
+      8
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+    "num_layers": 2,
+    "per_head_ff": false,
+    "plot_for_debugging": false,
+    "remove_self_connections": true,
+    "residual_epsilon_strategy": "default",
+    "residual_epsilon_uniform_value": 0.1,
+    "rms_norm_eps": 1e-05,
+    "sharpening_value_init": "value",
+    "soft_masking_initial_threshold": 0.01,
+    "soft_masking_k": 10.0,
+    "threshold": 0.2,
+    "threshold_any_tau": 0.1,
+    "tokenizer": null,
+    "use_GNN_from_attention": "none",
+    "use_GNN_from_attention_add_RoPE_at_every_layer": false,
+    "use_differential_attention": false,
+    "use_distance_scaling": false,
+    "use_fixed_number_of_tokens_per_latent": false,
+    "use_graph_property_modulation": false,
+    "use_graph_property_modulation_with_norm": false,
+    "use_graph_property_modulation_with_norm_use_causal_clustering": true,
+    "use_hierarchical_attention": false,
+    "use_layer_norm": false,
+    "use_layer_norm_in_GIN_MLP": false,
+    "use_no_norm_in_GIN_MLP": false,
+    "use_original_hidden_states": false,
+    "use_original_hidden_states_add_attention": false,
+    "use_projection": true,
+    "use_sharpening": false,
+    "use_soft_masking": false,
+    "zero_below_epsilon_threshold": true
+  },
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
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+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "use_cache": false,
+  "vocab_size": 128256
+}

generation_config.json

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+  "bos_token_id": 128000,
+  "eos_token_id": [
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+    128008,
+    128009
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+  "transformers_version": "4.46.3",
+  "use_cache": false
+}

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