AshankGupta
/

tinyllama-email-reply

@@ -9,202 +9,173 @@ tags:
 - transformers
 - trl
 - unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
-### Training Data
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.18.1

 - transformers
 - trl
 - unsloth
+license: mit
+language:
+- en
 ---
+# TinyLlama Email Reply Generator (LoRA)
+A **LoRA fine-tuned TinyLlama model** for generating professional email replies from incoming emails.
+The adapter was trained on the **Enron Email Reply Dataset** to learn professional communication patterns.
+---
+## Model Overview
+* **Base Model:** TinyLlama/TinyLlama-1.1B-Chat-v1.0
+* **Fine-tuning Method:** LoRA (Low Rank Adaptation)
+* **Task:** Email reply generation
+* **Language:** English
+* **Framework:** Unsloth + Transformers
+* **Deployment:** Designed for local inference with FastAPI or Ollama
+This adapter allows users to generate contextual email replies without relying on large commercial APIs.
+---
+## Intended Use
+This model is designed for:
+* Email reply suggestion systems
+* AI productivity tools
+* Email assistants
+* Local AI workflows
+* Research on small language models
+Example applications include:
+* Gmail Smart Reply style systems
+* Chrome extensions for automated responses
+* Offline AI assistants
+---
+## Training Dataset
+The model was trained using the **Enron Email Reply Dataset**, which contains real-world corporate email conversations.
+Dataset characteristics:
+* ~15,000 email–reply pairs
+* Business and professional communication
+* Cleaned and formatted into instruction-style prompts
+Training format example:
+```
+Instruction:
+Generate a professional email reply.
+Email:
+Can you send the project report by tomorrow?
+Reply:
+Sure, I will send the report by tomorrow.
+```
+---
 ## Training Details
+* **Fine-tuning technique:** LoRA
+* **Training framework:** Unsloth
+* **Sequence length:** 512 tokens
+* **Optimizer:** AdamW
+* **Base architecture:** TinyLlama 1.1B
+Only a small percentage of parameters were trained via LoRA adapters, enabling efficient training on consumer GPUs.
+---
+## Usage
+Install dependencies:
+```
+pip install unsloth transformers accelerate bitsandbytes
+```
+Load the model:
+```python
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    load_in_4bit=True
+)
+model.load_adapter("ashankgupta/tinyllama-email-reply")
+```
+Example inference:
+```python
+email = """
+Hi,
+Can you send the invoice by tomorrow?
+"""
+prompt = f"""
+You are an AI assistant that writes professional email replies.
+Email:
+{email}
+Reply:
+"""
+inputs = tokenizer(prompt, return_tensors="pt")
+output = model.generate(**inputs, max_new_tokens=120)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+---
+## Example
+**Input Email**
+```
+Hi,
+Can you send the invoice by tomorrow?
+```
+**Generated Reply**
+```
+Sure, I will send the invoice by tomorrow.
+```
+---
+## Limitations
+* The model may produce generic replies.
+* Performance is limited by the small size of the base model.
+* It may occasionally generate repetitive outputs.
+* Not suitable for sensitive or confidential communications.
+---
+## License
+This model follows the license of the base model:
+TinyLlama License
+Please review the base model license before commercial usage.
+---
+## Acknowledgements
+* TinyLlama team for the base model
+* Unsloth for efficient LoRA training
+* Enron Email Dataset for training data