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- library_name: transformers
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  tags:
 
 
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  - unsloth
 
 
 
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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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- ### Results
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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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- #### 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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- **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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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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+ Text Generation
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+ llama
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  ---
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+ base_model: HuggingFaceTB/SmolLM2-360M-Instruct
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  tags:
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+ - text-generation-inference
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+ - transformers
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  - unsloth
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+ license: apache-2.0
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+ language:
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+ - en
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  ---
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+ ## Model Description
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+ Request Writer Smol has been fine tuned to generate Freedom of Information (FOI) requests to UK public authorities based on the autority name and three keywords. The model has been trained on a synthetic dataset of FOI requests covering various topics and public authorities across the UK.
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+ The Model demonstrates improved generation of properly formatted, focused FOI requests for specific information that are unlikely to be refused on cost grounds.
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+ ## Model Architecture
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+ - **Base Model**: SmolLM2-360M-Instruct
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+ - **Fine-tuning Method**: LoRA
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+ - **LoRA Configuration**:
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+ - Rank (r): 8
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+ - Alpha: 16
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+ - Dropout: 0.1
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+ - Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
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+ - **Training Parameters**: 2.34% of total parameters trained (8.68M trainable parameters)
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+ ## Finetune training Data
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+ ### Dataset Details
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+ - **Source**: Synthetic FOI requests dataset (HMC83/synthetic_foi_requests)
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+ - **Size**: 51,308 training examples, ~5,700 validation examples
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+ - **Format**: Conversational format with system prompts, user inputs, and assistant responses
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+ ### Training Configuration
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+ - **Epochs**: 3
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+ - **Batch Size**: 32
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+ - **Learning Rate**: 1e-5
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+ - **Optimizer**: AdamW 8-bit
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+ - **Sequence Length**: 4096 tokens
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+ ## Limitations and Considerations
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+ Small size of the model (360M parameters) may limit the complexity of any generated requests. The model is trained specifically for UK FOI requests. It has not been trained to generate requests for information about individuals.
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+ ## Usage Guidelines
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+ ### Input Format
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+ The model expects a prompt in the form of:
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+ ```
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+ Generate a formal Freedom of Information request to [authority_name] using these keywords: [keyword1, keyword2, keyword3]
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+ ```
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+ ### Output Format
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+ It will try to generate a concinse, properly structured FOI request, starting with the phrase "Please provide me with a copy of the following information:" followed by 1 to 3 Numbered, specific information requests
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+ ## Model Versions
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+ ### Available Formats
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+ - **LoRA Adapters**: `HMC83/request_writer_smol_lora`
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+ - **Merged 16-bit**: `HMC83/request_writer_smol`
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+ ### Disclaimer
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+ Users are responsible for ensuring that their intended use complies with any applicable laws and regulations. Generated requests should be reviewed and potentially modified before submission to public authorities. Requests should be made in good faith and for legitimate purposes. The model can hallucinate, so any outputs should not be relied upon without being verified. Outputs may also reflect any biases that are present in the underlying training data.