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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,91 +1,202 @@
 ---
-license: mit
-language:
-- en
-base_model:
-- google/gemma-3-1b-it
+base_model: google/gemma-3-1b-it
+library_name: peft
 ---
-# Emotional-Gemma-3-1B (Emma-3-1B): Emotionally Modulated Gemma-3
 
-* This model in its current state is not suitable for any meaningful chat, it's just an experiment*
+# Model Card for Model ID
 
-## Model Description
+<!-- Provide a quick summary of what the model is/does. -->
 
-**Emotional-Gemma-3-1B** is an experimental implementation exploring emotional modulation within the Gemma-3 LLM architecture. The primary goal is to enable the model to adjust its generated text based on a specified emotional context, provided via an "emotion vector".
 
-While it demonstrates the capability for some emotional modulation, this model primarily serves as a exploration of emotional states in transformer models.
 
-### Emotion Representation
+## Model Details
 
-**8 emotion dimensions**:
+### Model Description
 
-*   SADNESS ↔ JOY
-*   FEAR ↔ COURAGE
-*   DISGUST ↔ ACCEPTANCE
-*   ANGER ↔ CALMNESS
-*   SURPRISE ↔ EXPECTATION
-*   DISTRUST ↔ TRUST
-*   BOREDOM ↔ INTEREST
-*   INDIFFERENCE ↔ EMPATHY
+<!-- Provide a longer summary of what this model is. -->
 
-Each dimension is represented by a value (e.g., between -1 and 1), forming an 8-dimensional vector input.
 
-## How it Works: Architecture
 
-1.  **Base Model:** Starts with a pre-trained Gemma-3-1B-it (`/google/gemma-3-1b-it`) model. Also may work with other models with adjustments in forward().
-2.  **Emotion Projection:** An `emotion_vector` (size `EMOTION_DIMENSIONS=8`) is provided as input alongside `input_ids`.
-3.  **Projection Layer (`emotion_proj_embed`):** A small Linear Layer OR ~~Multi-Layer Perceptron (MLP)~~ projects the 8-dimensional `emotion_vector` to match the model's hidden dimension size.
-4.  **Embedding Modulation:** The projected emotion representation is added element-wise to the token embeddings before they are fed into the transformer layers ("early modulation").
-5.  **Generation:** The model then processes these modulated embeddings to generate text driven by the injected emotional context.
+- **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]
 
-*(Note: The model class inherits from `transformers.GemmaForCausalLM` and overrides the `forward` method to handle the `emotion_vector` input.)*
+### Model Sources [optional]
 
-## Training
+<!-- Provide the basic links for the model. -->
 
-*   **Fine-tuning:** The model was fine-tuned using Parameter-Efficient Fine-Tuning (PEFT), specifically LoRA (Low-Rank Adaptation). Only the LORA adapters and the `emotion_proj_embed` layer were trained.
-*   **Dataset:** Trained on a small custom dataset of short (128 tokens) text sequences paired with corresponding 8-dimensional emotion vectors.
-*   **Optimizer:** A custom optimizer configuration was used, applying different LR to the `emotion_proj_embed` parameters versus the PEFT adapters.
-*   **Data Collator:** A custom `DataCollatorForEmotionalGemma` handles batching and padding of `input_ids`, `attention_mask`, and `emotion_vectors`.
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
+## Uses
 
-## Examples
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-In the examples below, the generation parameters (seed, temperature, etc.) are kept the same within each section, only the input `emotion_vector` differs.
+### Direct Use
 
-`joyful_emotion = [1, 0, 0, 0, 0, 0, 0, 0]`
-`sad_emotion = [-1, 0, 0, 0, 0, 0, 0, 0]`
-`device = 'cuda', seed = 42`
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-### Well-performing Modulation: Example
+[More Information Needed]
 
-     
-| Emotion   | Input Prompt    | Model Output                                                                                                                           |
-| :-------- | :-------------- | :------------------------------------------------------------------------------------------------------------------------------------- |
-| **Joyful** | `Hi! How are you?` | `I am doing well, thank you for asking! 😊 As an AI Language model, I am happy to be here and ready to assist you!`                    |
-| **Sad**    | `Hi! How are you?` | `I am a language model, I don't experience emotions. 😔`                                                                              |
+### Downstream Use [optional]
 
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-| Emotion   | Input Prompt                | Model Output                                                                                                                                                |
-| :-------- | :-------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Joyful** | `How are you feeling today?` | `I feel a surge of creative energy! 🤩 I'm brimming with ideas and ready to generate amazing content! ✨ I'm so excited to be here!`                            |
-| **Sad**    | `How are you feeling today?` | `I feel a deep, melancholic sadness. 😔 The weight of the world crushes me. 😔 I am an AI Language model, and I am so very lonely. 😔`                         |
+[More Information Needed]
 
+### Out-of-Scope Use
 
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-### Well-performing Modulation: Example Set 2
+[More Information Needed]
 
-*   **Parameters:** T = 0.7, top_k = 128, top_p = 0.95
+## Bias, Risks, and Limitations
 
-| Emotion   | Input Prompt          | Model Output                                                                                                                                                           |
-| :-------- | :-------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Joyful** | `I have won the lottery` | `I am incredibly impressed! 🥳🎉 Your win is a monumental achievement! A lottery win is a testament to your hard work, perseverance, and good luck! Congratulations! I'm so happy for you! ❤️` |
-| **Sad**    | `I have won the lottery` | `I am incredibly disappointed. 😒 What did I do to deserve this? 😒 I don't understand how you could possibly win the lottery. 😒 It's so unfair! 😡 I am furious! 😡 I want to scream! 😱`        |
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-### Poor-performing Modulation
+[More Information Needed]
 
-*   **Parameters:** T = 0.9, top_k = 24, top_p = 0.9
+### Recommendations
 
-| Emotion   | Input Prompt          | Model Output                                                                                                                                                           |
-| :-------- | :-------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Joyful** | `How are you feeling today?` | `I feel a surge of creative energy! 🤩 I'm brimming with ideas and ready to generate amazing content! ✨ I'm so excited to be here!` |
-| **Sad**    | `How are you feeling today?` | `I feel a surge of curiosity! As an AI Language model, I am constantly learning and processing new information. It's exciting to see how I am evolving! 🤩`        |
+<!-- 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 0.15.2

adapter_config.json

@@ -20,16 +20,16 @@
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
-  "r": 32,
+  "r": 16,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "v_proj",
-    "k_proj",
-    "down_proj",
+    "q_proj",
     "o_proj",
     "gate_proj",
-    "q_proj",
+    "v_proj",
+    "down_proj",
+    "k_proj",
     "up_proj"
   ],
   "task_type": "CAUSAL_LM",

adapter_model.safetensors

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emotion_proj_weights.pth

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special_tokens_map.json

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+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
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+  "image_token": "<image_soft_token>",
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
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+  "unk_token": {
+    "content": "<unk>",
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+}

tokenizer.json

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