README.md

---
license: apache-2.0
tags:
  - chat
  - chatbot
  - LoRA
  - instruction-tuning
  - conversational
  - tinyllama
  - transformers
language:
  - en
datasets:
  - tatsu-lab/alpaca
  - databricks/databricks-dolly-15k
  - knkarthick/dialogsum
  - Anthropic/hh-rlhf
  - OpenAssistant/oasst1
  - nomic-ai/gpt4all_prompt_generations
  - sahil2801/CodeAlpaca-20k
  - Open-Orca/OpenOrca
model-index:
  - name: chatbot-v2
    results: []
---

# 🤖 chatbot-v2 — TinyLLaMA Instruction-Tuned Chatbot (LoRA)

`chatbot-v2` is a lightweight, instruction-following conversational AI model based on **TinyLLaMA** and fine-tuned using **LoRA** adapters. It has been trained on a carefully curated mixture of open datasets covering assistant-like responses, code generation, summarization, safety alignment, and dialog reasoning.

This model is ideal for embedding into mobile or edge apps with low-resource inference needs or running via an API.

---

## 🧠 Base Model

- **Model**: [`TinyLlama/TinyLlama-1.1B-Chat`](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat)
- **Architecture**: Decoder-only Transformer (GPT-style)
- **Fine-tuning method**: LoRA (low-rank adapters)
- **LoRA Parameters**:
  - `r=16`
  - `alpha=32`
  - `dropout=0.05`
  - Target modules: `q_proj`, `v_proj`

---

## 📚 Training Datasets

The model was fine-tuned on the following instruction-following, summarization, and dialogue datasets:

- [`tatsu-lab/alpaca`](https://huggingface.co/datasets/tatsu-lab/alpaca) — Stanford Alpaca dataset
- [`databricks/databricks-dolly-15k`](https://huggingface.co/datasets/databricks/databricks-dolly-15k) — Dolly instruction data
- [`knkarthick/dialogsum`](https://huggingface.co/datasets/knkarthick/dialogsum) — Summarization of dialogs
- [`Anthropic/hh-rlhf`](https://huggingface.co/datasets/Anthropic/hh-rlhf) — Harmless/helpful/honest alignment data
- [`OpenAssistant/oasst1`](https://huggingface.co/datasets/OpenAssistant/oasst1) — OpenAssistant dialogues
- [`nomic-ai/gpt4all_prompt_generations`](https://huggingface.co/datasets/nomic-ai/gpt4all_prompt_generations) — Instructional prompt-response pairs
- [`sahil2801/CodeAlpaca-20k`](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) — Programming/code generation instructions
- [`Open-Orca/OpenOrca`](https://huggingface.co/datasets/Open-Orca/OpenOrca) — High-quality responses to complex questions

---

## 🔧 Intended Use

This model is best suited for:

- **Conversational agents / chatbots**
- **Instruction-following assistants**
- **Lightweight AI on edge devices (via server inference)**
- **Educational tools and experiments**

---

## 🚫 Limitations

- This model is **not suitable for production use** without safety reviews.
- It may generate **inaccurate or biased responses**, as training data is from public sources.
- It is **not safe for sensitive or medical domains**.

---

## 💬 Example Prompt

Instruction:

Explain the difference between supervised and unsupervised learning.

Response:

Supervised learning uses labeled data to train models, while unsupervised learning uses unlabeled data to discover patterns or groupings in the data…

---

## 📥 How to Load the Adapters

To use this model, load the base TinyLLaMA model and apply the LoRA adapters:

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained(
    "TinyLlama/TinyLlama-1.1B-Chat",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat")

model = PeftModel.from_pretrained(base_model, "sahil239/chatbot-v2")

📄 License

This model is distributed under the Apache 2.0 License.

🙏 Acknowledgements

Thanks to the open-source datasets and projects: Alpaca, Dolly, OpenAssistant, Anthropic, OpenOrca, CodeAlpaca, GPT4All, and Hugging Face.