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# Model Card for Model ID
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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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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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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 meant to convey both technical and sociotechnical limitations. -->
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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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## 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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license: other
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license_name: hsul
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license_link: https://huggingface.co/OEvortex/vortex-3b/raw/main/LICENSE.md
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- 3B
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- Emotionally Intelligent
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# HelpingAI-3B-v2.2: Emotionally Intelligent Conversational AI
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![logo](https://huggingface.co/OEvortex/HelpingAI-3B/resolve/main/HelpingAI.png)
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## Introduction
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HelpingAI-3B-v2.2 is a state-of-the-art large language model specializing in emotionally intelligent conversation. With advanced emotional understanding capabilities, it can engage in empathetic dialogue tailored to the user's emotional state and context.
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## Emotional Intelligence Capabilities
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HelpingAI-3B-v2.2 exhibits several key traits that enable emotionally resonant responses:
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- Emotion recognition and validation
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- Empathetic perspective-taking
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- Generating emotionally supportive language
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- Contextual emotional attunement
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- Using appropriate tone, word choice and emotional expression
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Whether comforting someone grieving, celebrating positive news, or addressing complex feelings, HelpingAI-3B-v2.2 can adapt its communication style with emotional nuance.
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## Examples of Emotionally Intelligent Responses
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"Oh no! 😔 Dealing with printer issues can be frustrating. Let's try to help you get it up and running. First, make sure the printer is properly connected to the power source and your computer. Check if there are any error messages or blinking lights on the printer. It's also a good idea to restart both the printer and your computer. If the issue persists, you may need to reinstall the printer drivers or check for any hardware issues. Let me know if you need more help! "
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"It's really sad to hear about your friend's loss. 😔 Losing a parent can be incredibly painful and heart-wrenching. It's important to show them empathy and support during this difficult time. You Offer to be there for them and listen to them when they're ready to talk. You Send them thoughtful and comforting messages to let them know that you are thinking of them and that they are not alone. Offer to help them with any practical tasks or duties they might have, like running errands or taking care of animals. Remember, everyone experiences grief differently, so it's essential to be patient and understanding. Offer your friend a hug if they're comfortable with it, and remind them that you are there for them. 🌼"
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The model tailors its language, tone and emotional content to be contextually appropriate, combining emotional intelligence with factual knowledge and practical suggestions.
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## Performance Comparison
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The performance of HelpingAI-3B-v2.2 is compared with other relevant models on various metrics in the table below:
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| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
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| **HelpingAI-3B-v2.2** | **57.555** | **53.14** | **82.61** | **47.42** | **57.92** | **68.15** | **36.09** |
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| **HelpingAI-3B-v2.1** | **57.44** | **53.14** | **82.61** | **47.42** | **57.92** | **68.15** | **35.39** |
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| rocket-3B | 55.77 | 50.6 | 76.69 | 47.1 | 55.82 | 67.96 | 36.47 |
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| **HelpingAI-3B** | **55.59** | **50.6** | **76.64** | **46.82** | **55.62** | **67.8** | **36.09** |
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| stableLM-zephyr-3b | 53.43 | 46.08 | 74.16 | 46.17 | 46.49 | 65.51 | 42.15 |
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| mmd-3b | 53.22 | 44.8 | 70.41 | 50.9 | 43.2 | 66.22 | 43.82 |
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| MiniGPT-3B-Bacchus | 52.55 | 43.52 | 70.45 | 50.49 | 43.52 | 66.85 | 40.49 |
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| MiniGPT-3B-Hercules-v2.0 | 52.52 | 43.26 | 71.11 | 51.82 | 40.37 | 66.46 | 42.08 |
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| MiniGPT-3B-OpenHermes-2.5-v2 | 51.91 | 47.44 | 72 | 53.06 | 42.28 | 65.43 | 31.24 |
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| MiniChat-2-3B | 51.49 | 44.88 | 67.69 | 47.59 | 49.64 | 66.46 | 32.68 |
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| smol-3b | 50.27 | 46.33 | 68.23 | 46.33 | 50.73 | 65.35 | 24.64 |
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| MiniChat-1.5-3B | 50.23 | 46.5 | 68.28 | 46.67 | 50.71 | 65.04 | 24.18 |
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| 3BigReasonCinder | 48.16 | 41.72 | 65.16 | 44.79 | 44.76 | 64.96 | 27.6 |
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| MintMerlin-3B | 47.63 | 44.37 | 66.56 | 43.21 | 47.07 | 64.4 | 20.17 |
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## Simple Usage Code
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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# Let's bring in the big guns! Our super cool HelpingAI-3B model
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model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI-3B-v2.2", trust_remote_code=True, torch_dtype=torch.float16).to("cuda")
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# We also need the special HelpingAI translator to understand our chats
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tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-3B-v2.2", trust_remote_code=True, torch_dtype=torch.float16)
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# This TextStreamer thingy is our secret weapon for super smooth conversation flow
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streamer = TextStreamer(tokenizer)
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# Now, here comes the magic! ✨ This is the basic template for our chat
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prompt = """
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<|im_start|>system: {system}
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<|im_end|>
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<|im_start|>user: {insaan}
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<|im_end|>
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<|im_start|>assistant:
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"""
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# Okay, enough chit-chat, let's get down to business! Here's what our system will be our system prompt
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# We recommend to Use HelpingAI style in system prompt as this model is just trained on 3.7K rows of fealings dataset and we are working on even better model
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system = "You are HelpingAI a emotional AI always answer my question in HelpingAI style"
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# And the insaan is curious (like you!) insaan means human in hindi
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insaan = "My best friend recently lost their parent to cancer after a long battle. They are understandably devastated and struggling with grief. What would be a caring and supportive way to respond to help them through this difficult time?"
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# Now we combine system and user messages into the template, like adding sprinkles to our conversation cupcake
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prompt = prompt.format(system=system, insaan=insaan)
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# Time to chat! We'll use the tokenizer to translate our text into a language the model understands
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inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False).to("cuda")
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# Here comes the fun part! Let's unleash the power of HelpingAI-3B to generate some awesome text
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generated_text = model.generate(**inputs, max_length=3084, top_p=0.95, do_sample=True, temperature=0.6, use_cache=True, streamer=streamer)
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```
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