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.gitattributes CHANGED
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README.md ADDED
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+ ---
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+ base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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+ library_name: peft
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+ model_name: typescript-slm-7b-reasoning
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+ tags:
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+ - typescript
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+ - code-generation
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+ - react
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+ - nextjs
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+ - angular
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+ - nodejs
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+ - lora
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+ - sft
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+ - 7b
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+ - reasoning
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+ - transformers
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+ - trl
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+ license: mit
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+ pipeline_tag: text-generation
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+ language:
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+ - en
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+ ---
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+
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+ # TypeScript SLM 7B - Reasoning Variant
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+
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+ 7B TypeScript model with reasoning capabilities for TypeScript code generation, optimized for React, Next.js, Angular, and Node.js.
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+
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+ ## Model Details
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+
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+ - **Base Model**: [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)
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+ - **Model Size**: 7B parameters
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+ - **Training Method**: LoRA (Low-Rank Adaptation)
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+ - **Context Length**: 2048 tokens
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+ - **LoRA Rank**: 64
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+ - **Training Dataset**: 5,000 high-quality TypeScript samples
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+
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+ ## Reasoning Capabilities
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+ This variant includes chain-of-thought reasoning for better code understanding and generation.
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+
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+ ## Training Configuration
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+
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+ - Batch Size: 2
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+ - Gradient Accumulation: 16
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+ - Effective Batch Size: 32
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+ - Learning Rate: 0.0001
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+ - Epochs: 3
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+ - Hardware: Google Colab A100 40GB
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ from peft import PeftModel
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+
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+ base_model = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
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+ model = AutoModelForCausalLM.from_pretrained(
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+ base_model,
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+ device_map="auto",
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+ torch_dtype="auto"
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+ )
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+ tokenizer = AutoTokenizer.from_pretrained(base_model)
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+
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+ # Load LoRA adapter
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+ model = PeftModel.from_pretrained(model, "sylvester-francis/typescript-slm-7b-reasoning")
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+
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+ # Generate code
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+ prompt = "Write a React component with TypeScript:"
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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+ outputs = model.generate(**inputs, max_new_tokens=512)
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+ print(tokenizer.decode(outputs[0]))
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+ ```
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+
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+ ## Repository
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+
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+ https://github.com/sylvester-francis/slm-typescript-model
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+ ---
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+ base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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+ library_name: peft
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+ pipeline_tag: text-generation
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+ tags:
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+ - base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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+ - lora
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+ - sft
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+ - transformers
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+ - trl
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+
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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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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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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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+
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+ ## Uses
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+
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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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+
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+ ### Direct Use
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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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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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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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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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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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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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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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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+
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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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+
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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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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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+
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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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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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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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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Contact
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+
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+ [More Information Needed]
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+ ### Framework versions
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+
209
+ - PEFT 0.18.0
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