chatbot-v2

🤖 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.

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🧠 Base Model

• Model: 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

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📚 Training Datasets

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

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

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🔧 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

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🚫 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.

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💬 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…

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📥 How to Load the Adapters

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

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.