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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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- ## 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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+ license: mit
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+ datasets:
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+ - SciPhi/textbooks-are-all-you-need-lite
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+ - nampdn-ai/tiny-textbooks
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+ - nampdn-ai/tiny-strange-textbooks
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+ - nampdn-ai/tiny-codes
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+ - nampdn-ai/tiny-math-textbooks
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+ - nampdn-ai/tiny-webtext
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+ - nampdn-ai/tiny-orca-textbooks
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+ - nampdn-ai/tiny-lessons
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+ - roneneldan/TinyStories
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+ - ajibawa-2023/Children-Stories-Collection
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+ - ajibawa-2023/General-Stories-Collection
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+ - kerinin/hackernews-stories
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+ - lucadiliello/wikipedia_512_pretraining
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+ - Salesforce/wikitext
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+ - ChristophSchuhmann/basic-math-problems-with-step-by-step-solutions
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+ - iamtarun/python_code_instructions_18k_alpaca
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+ - prithivMLmods/Step-Instruction-Gx
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+ - LinhDuong/chatdoctor-200k
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+ - MBZUAI/LaMini-instruction
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+ - qwedsacf/grade-school-math-instructions
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+ - TigerResearch/tigerbot-stackexchange-qa-en-0.5m
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+ language:
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+ - en
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  ---
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+ # amusktweewt/tiny-model-500M-chat-v2
 
 
 
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+ This model is a general-purpose transformer-based language model designed for tasks such as text generation, story writing, and conversational interactions. It leverages multiple curated datasets to enhance its storytelling, coding, and question-answering capabilities. This project is intended for academic research and educational purposes only. It is designed for experimentation, learning, and development of language-based AI systems.
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  ## Model Details
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  ### Model Description
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+ The model was developed with a focus on balancing performance and computational efficiency. It employs **flash attention** and other optimizations to improve memory efficiency and speed.
 
 
 
 
 
 
 
 
 
 
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+ - **Developed by:** amusktweewt
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+ - **Model type:** LlamaForCausalLM
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+ - **Architectural Details:**
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+ - 12 layers
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+ - 16 attention heads
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+ - Hidden size: 1536
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+ - Flash attention 2 enabled
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+ - Dynamic RoPE scaling
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+ - **License:** MIT
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+ - **Language(s) (NLP):** English
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  ## Uses
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  ### Direct Use
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+ This model is intended for text generation, code completion, chat-based applications, and story writing.
 
 
 
 
 
 
 
 
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  ### Out-of-Scope Use
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+ - Tasks requiring high factual accuracy
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+ - Math or thinking related tasks
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+ - Applications involving sensitive content without human review
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training Details
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  ### Training Data
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+ The model was trained on a diverse collection of datasets, including:
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+ - Textbooks and academic content
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+ - Creative and children's stories
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+ - Coding instruction datasets
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+ - Wiki-based texts and general stories
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+ - Mathematics and step-by-step solutions
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  ### Training Procedure
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+ #### Preprocessing
 
 
 
 
 
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+ - Custom BPE tokenizer with a vocabulary size of 32,768
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+ - Applied dynamic RoPE scaling for better long-context handling
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+ #### Hyperparameters
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+ - **Batch size:** 12 (per device)
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+ - **Gradient accumulation:** 2 steps
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+ - **Learning rate:** 1e-5
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+ - **Weight decay:** 0.002
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+ - **Warmup ratio:** 10%
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+ - **Precision:** FP16 (mixed precision)
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+ #### Training Setup
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+ - **Hardware:** NVIDIA 4090 GPU
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+ - **Training Time:** 216 hours
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+ - **Dataset Size** 69 GB of Text
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  ## Evaluation
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  ### Testing Data, Factors & Metrics
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+ The model was evaluated using subsets of the training data, focusing on language coherence, relevancy, and fluency.
 
 
 
 
 
 
 
 
 
 
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  #### Metrics
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+ - **Loss:** Evaluated based on token-level prediction accuracy.
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+ - **Perplexity:** 2.506
 
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  ### Results
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+ The model generates coherent and most of the time contextually appropriate outputs across multiple domains.
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+ ## Risks and Limitations
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+ ### Known Issues
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+ - The model may produce outputs reflecting biases present in the training data.
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+ ### Recommendations
 
 
 
 
 
 
 
 
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+ Users should apply human review when using the model in critical or sensitive applications.
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+ ## How to Get Started with the Model
 
 
 
 
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+ ```python
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+ import torch
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+ from transformers import pipeline, set_seed
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+
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+ model_name = "amusktweewt/tiny-model-500M-chat-v2"
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+ chatbot = pipeline(
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+ "text-generation",
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+ model=model_name,
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+ device=0
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+ )
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+
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+ set_seed(42)
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+
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+ print("Chatbot is ready! Type 'exit' to end the conversation.")
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+
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+ while True:
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+ user_input = input("You: ").strip()
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+ if user_input.lower() == "exit":
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+ print("Exiting chat. Goodbye!")
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+ break
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+
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+ messages = [
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+ {"role": "user", "content": user_input},
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+ {"role": "assistant", "content": ""}
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+ ]
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+
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+ prompt = chatbot.tokenizer.apply_chat_template(messages, tokenize=False)
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+
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+ # Generate text using the formatted prompt.
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+ response = chatbot(
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+ prompt,
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+ do_sample=True,
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+ max_new_tokens=512,
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+ top_k=50,
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+ temperature=0.1,
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+ num_return_sequences=1,
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+ repetition_penalty=1.1,
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+ pad_token_id=chatbot.tokenizer.eos_token_id,
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+ min_new_tokens=0
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+ )
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+
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+ full_text = response[0]["generated_text"]
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+ bot_response = full_text[len(prompt):].strip()
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+ print(f"Bot: {bot_response}")
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+ ```
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+
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+ ## Technical Specifications
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  ### Model Architecture and Objective
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+ The model follows a **Transformer-based architecture** optimized for causal language modeling tasks.
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+ - Attention heads: 16
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+ - Hidden size: 1536
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+ - Flash attention and memory-efficient attention enabled
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+ ### Compute Infrastructure
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  #### Hardware
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+ - Single GPU (NVIDIA 4090)
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  #### Software
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+ - Python 3.8+
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+ - HuggingFace Transformers 4.48.0
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+ - PyTorch 2.4
 
 
 
 
 
 
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+ ## Environmental Impact
 
 
 
 
 
 
 
 
 
 
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+ - **Training Hours:** 216 hours
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+ - **Hardware:** NVIDIA 4090
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+ - **Carbon Emitted:** 9.07 kg CO2 eq
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+ ## Model Card Authors
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+ amusktweewt
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  ## Model Card Contact
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+ For questions or feedback, contact amusktweewt.