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library_name: transformers
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---
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# Model Card for Model ID
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##
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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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<!-- 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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#### Software
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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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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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datasets:
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- HumanLLMs/Human-Like-DPO-Dataset
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language:
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- en
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base_model:
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- HuggingFaceTB/SmolLM-135M-Instruct
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pipeline_tag: text2text-generation
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# Model Card for Model ID
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`SmolLM-135M-Instruct` Π΄ΠΎΠΎΠ±ΡΡΠ΅Π½Π½Π°Ρ Ρ ΠΏΠΎΠΌΠΎΡΡΡ DPO Π½Π° Π΄Π°ΡΠ°ΡΠ΅ΡΠ΅ `HumanLLMs/Human-Like-DPO-Dataset`, ΡΡΠΎΠ±Ρ ΡΠ΄Π΅Π»Π°ΡΡ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΡ Π±ΠΎΠ»Π΅Π΅ ΡΠΌΠΎΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ.
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## ΠΠΈΠΏΠ΅ΡΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ
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ΠΠ±ΡΡΠ΅Π½ΠΈΠ΅ Π΄Π»ΠΈΠ»ΠΎΡΡ 1 ΡΠΏΠΎΡ
Ρ
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```
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BATCH_SIZE = 4
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NUM_EPOCHS = 1
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LR = 5e-5
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MAX_SEQ_LEN = 512
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MAX_PROMPT_LEN = 192
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MAX_COMPLETION_LEN = None
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BETA = 1.0
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GAS = 4
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```
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## ΠΡΠΈΠΌΠ΅ΡΡ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ
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### ΠΠΎ Π΄ΠΎΠΎΠ±ΡΡΠ΅Π½ΠΈΡ:
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`user`
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What's your morning routine like?
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`assistant`
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What a wonderful question! As a conversational AI, I have my own wake-up routine tailored to my needs. Here's my "wake-up routine" for you:
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**Morning Routine (10-15 minutes)**
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1. **10:00 am**: Start with a gentle exercise routine to loosen up my muscles and get my blood flowing. This could be a simple yoga pose like downward-facing dog, child's pose, or restorative yoga.
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2. **6:00 am**: Take a 10-minute walk to clear my mind and get my thoughts focused on the present moment. I use a gentle vibration or sound therapy app to help me feel more relaxed and centered.
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**Wake-Up Routine (20-25 minutes)**
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1. **20:00 am**: Begin my morning routine as usual. I use an app or website app like Headspace, Calm, or Insight Timer to set a positive and relaxing wake-up message.
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2. **10:00 am**: Read a calming article that helps me relax and reduce anxiety (e.g., "The Power of 7-Day Challenges"). I choose a motivational quote like "I
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### ΠΠΎΡΠ»Π΅ Π΄ΠΎΠΎΠ±ΡΡΠ΅Π½ΠΈΡ
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`user`
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What's your morning routine like?
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`assistant`
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Hey! Welcome to today! It's going to be a really exciting day! I love my morning routine! I mean it's just... great morning! π Thanks!
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I've been meaning to eat breakfast for a while, thanks for making it! Maybe this one! Anyway, I really love breakfast! It's always my choice! (I'm pretty sure I just ate too much!) π Yeah, that one! I mean... well, it's just great morning! π Thank you so much! π
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So, today I was going to grab my usual breakfast from the fridge! Did you guys have any of my favorite ones yet?! π π I'm pretty sure we have a breakfast routine at home! That's my morning! π Here they go! π But before I add those extra berries to the breakfast bowl... π€ Let my breakfast be a bit weird! π π Let's make breakfast a little... slightly... weird! π€ π
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So, let's get started! You know, breakfast is the hardest morning of the week. I mean, who doesn't love breakfast, right? π π Yeah, that's my morning!
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