Update README.md

README.md

@@ -1,202 +1,233 @@
 ---
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 library_name: peft
+license: apache-2.0
+tags:
+- conversational-ai
+- chatbot
+- lora
+- qlora
+- peft
+- nlp
+- openassistant
+- fine-tuning
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for Lumo
 
+**Lumo** is a lightweight conversational AI adapter fine-tuned using **QLoRA** on top of the open-source **TinyLLaMA 1.1B Chat** base model. It is designed for **learning, experimentation, and student projects**, with a focus on accessibility and transparency.
 
+**Note:** This repository contains **only the LoRA adapter weights**, not the base model.
 
 ## 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]
+- **Developed by:** Aditya Verma
+- **Model type:** Conversational Language Model (LoRA Adapter)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** Adi362/Lumo
+- **Base Model:** [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
+- **Training Framework:** Hugging Face Transformers + PEFT
 
 ## 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. -->
+This model is intended for:
+- Local conversational chatbots
+- Educational AI experiments
+- Student projects involving LLMs
+- Learning how LoRA fine-tuning works
+- Prototyping lightweight AI assistants
 
-[More Information Needed]
+*The adapter must be loaded together with the base TinyLLaMA model.*
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The adapter can be:
+- Combined with other LoRA adapters
+- Further fine-tuned on domain-specific datasets
+- Integrated into APIs or applications
+- Used as a base for research or experimentation
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is **not intended** for:
+- High-stakes decision making
+- Medical, legal, or financial advice
+- Production-grade commercial systems without further evaluation
+- Safety-critical applications
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **Bias:** The model may reflect biases present in the training data (OpenAssistant).
+- **Hallucinations:** It can produce incorrect or misleading information.
+- **Factuality:** Responses should not be treated as factual guarantees.
+- **Performance:** Capabilities are limited by the small size (1.1B parameters) and scope of the base model.
 
 ### 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.
+Users (both direct and downstream) should:
+- Validate outputs independently.
+- Avoid using the model for critical applications.
+- Apply additional safety layers when deploying in public-facing systems.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the code below to load the base model and the Lumo adapter.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+BASE_MODEL = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+LORA_MODEL = "Adi362/Lumo"
 
-[More Information Needed]
+# 1. Load Base Model
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float32,
+    device_map=None
+)
+
+# 2. Load Lumo Adapter
+model = PeftModel.from_pretrained(model, LORA_MODEL)
+model.eval()
 
 ## 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. -->
+The model was trained on a filtered subset of the **OpenAssistant Conversations** dataset.
 
-[More Information Needed]
+- **Dataset Name:** OpenAssistant Conversations (English, filtered)
+- **Data Type:** Human–assistant dialogue pairs
+- **Content:** Diverse conversational topics, instructions, and queries.
 
 ### 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]
+#### Preprocessing
 
+The dataset underwent the following preprocessing steps:
+- **Filtering:** Retained only English language conversations.
+- **Formatting:** Constructed user–assistant pairs and formatted them using standard chat-style prompts to suit the base model's expectations.
 
 #### 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 -->
+- **Training regime:** **QLoRA** (4-bit base model quantization + LoRA adapters)
+- **Precision:** 4-bit (nf4)
+- **Optimizer:** Paged AdamW (8-bit)
+- **Learning Rate:** 2e-4
+- **Epochs:** 2
+- **Batch Size:** 1 (with gradient accumulation)
+- **Trainable Parameters:** ~1.1% of total model parameters
 
-#### Speeds, Sizes, Times [optional]
+#### Speeds, Sizes, Times
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training Time:** ~4–5 hours on a single GPU.
 
 ## 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]
+No formal benchmark datasets were used for this version. The model is intended for educational purposes and low-stakes experimentation.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Evaluation focused on:
+- **Language:** English only.
+- **Domain:** General conversational ability and basic instruction following.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Evaluation was qualitative, focusing on:
+1.  **Coherence:** Ability to maintain a conversation flow.
+2.  **Instruction Following:** Ability to execute simple prompts.
+3.  **Identity:** Correctly identifying itself as an AI assistant.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
+The model demonstrates basic conversational fluency and can handle simple instructions. As a lightweight adapter (~1.1B parameters), it may struggle with complex reasoning or highly specific factual queries compared to larger models.
 
+## Model Examination
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+*Not applicable for this version.*
 
 ## 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 were estimated based on the training hardware and duration.
 
-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:** NVIDIA Tesla T4 (Cloud GPU)
+- **Hours used:** ~4-5 hours
+- **Cloud Provider:** Google Colab
+- **Compute Region:** Unknown (Colab default)
+- **Carbon Emitted:** Negligible (Low-scale training not formally measured).
 
-- **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]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- **Base Architecture:** Transformer (TinyLLaMA 1.1B)
+- **Adaptation Method:** Low-Rank Adaptation (LoRA)
+- **Objective:** Causal Language Modeling (Next-token prediction)
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- **GPU:** Single NVIDIA Tesla T4 (16GB VRAM)
 
 #### Software
 
-[More Information Needed]
+- **Orchestration:** Google Colab
+- **Libraries:** Hugging Face Transformers, PEFT, PyTorch, BitsAndBytes
 
-## 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. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@misc{verma2025lumo,
+  author = {Verma, Aditya},
+  title = {Lumo: A LoRA-fine-tuned conversational adapter based on TinyLLaMA},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{[https://huggingface.co/Adi362/Lumo](https://huggingface.co/Adi362/Lumo)}}
+}
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
+> Verma, A. (2025). *Lumo: A LoRA-fine-tuned conversational adapter based on TinyLLaMA* [Large Language Model]. Hugging Face. https://huggingface.co/Adi362/Lumo
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Glossary
 
-[More Information Needed]
+* **LoRA (Low-Rank Adaptation):** A parameter-efficient fine-tuning technique that freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer, significantly reducing the number of trainable parameters.
+* **QLoRA (Quantized LoRA):** An efficient fine-tuning approach that quantizes the base model to 4-bit precision (reducing memory usage) while keeping the LoRA adapters in higher precision for training.
+* **PEFT (Parameter-Efficient Fine-Tuning):** A library by Hugging Face that enables efficient adaptation of pre-trained language models to various downstream applications without fine-tuning all the model's parameters.
+* **TinyLlama:** A compact 1.1 billion parameter language model pre-trained on around 1 trillion tokens, designed to be run on edge devices and consumer hardware.
 
-## More Information [optional]
+## More Information
 
-[More Information Needed]
+This model was created as a student project to demonstrate the feasibility of fine-tuning valid conversational assistants on consumer-grade hardware (Google Colab free tier) using the QLoRA technique.
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+Aditya Verma
 
 ## Model Card Contact
 
-[More Information Needed]
+For bugs, feature requests, or general feedback, please open an issue on the [Project GitHub Repository](https://github.com/Adi362/Lumo) or the Hugging Face Community tab.
+
 ### Framework versions
 
 - PEFT 0.8.2