falcon-lora-chatbot

Falcon LoRA Adapter

Model Details

Model Description
This is a LoRA adapter for the Falcon architecture, fine-tuned on domain-specific chat-style data for enhanced language understanding and generation. It was built using the PEFT library with 4-bit quantization.

• Developed by: Sahil Desai
• Funded by: Self-funded
• Shared by: Sahil Desai (https://huggingface.co/sahildesai)
• Model type: LoRA Adapter (Low-Rank Adaptation)
• Language(s) (NLP): English
• License: apache-2.0
• Finetuned from model: tiiuae/falcon-7b

Model Sources

• Repository: https://huggingface.co/sahildesai/falcon-lora
• Paper (optional): None
• Demo (optional): None

Uses

Direct Use
This adapter is intended to be used with Falcon base models to improve instruction-following and chatbot-like behavior on English-language prompts. It is suitable for:

• Chatbots
• AI Assistants
• Educational QA bots
• Conversational fine-tuning

Downstream Use
Can be further fine-tuned for more specific domains such as finance, DIY assistance, or medical Q&A, depending on your dataset.

Out-of-Scope Use
Not suitable for real-time critical decision-making tasks such as:

• Legal, financial, or medical advice
• Autonomous systems or safety-critical applications
• Multi-lingual tasks (adapter is English-focused)

Bias, Risks, and Limitations

As with all large language models, outputs may reflect biases in the training data. The adapter may reproduce toxic, biased, or incorrect information and should be monitored in production use.

Recommendations

Users should:

• Validate outputs before use in high-impact contexts
• Avoid use in applications requiring factual correctness without post-processing
• Consider fine-tuning with RLHF or safety filters for production deployment

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig

# Load base Falcon model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-7b", device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b")

# Load LoRA adapter
adapter = PeftModel.from_pretrained(base_model, "sahildesai/falcon-lora")

# Run inference
inputs = tokenizer("Explain black holes to a 12-year-old.", return_tensors="pt").to("cuda")
outputs = adapter.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data
The model was fine-tuned on a subset of conversational and instruction-following datasets derived from public chat data.

Preprocessing

• Input prompts were tokenized using Falcon's tokenizer
• Max sequence length: 2048
• Packed multiple conversations per sample when possible

Training Hyperparameters

• Training regime: LoRA with QLoRA (4-bit) using PEFT
• Batch size: 64
• Epochs: 1
• Learning rate: 2e-4
• LoRA rank: 8
• LoRA alpha: 16
• Target modules: query_key_value

Speeds, Sizes, Times

• Model type: Falcon 7B
• Adapter size: ~80MB (adapter_model.bin)
• Training time: ~2.5 hours on Colab A100 40GB

Evaluation

Testing Data
Subset of instruction-following prompts held out during training.

Factors
Evaluation included:

• Prompt quality
• Grammar & fluency
• Relevance of response

Metrics

• Human judgment for coherence and helpfulness
• No automatic BLEU/ROUGE applied

Results

• Improved instruction adherence over base model in small-scale testing
• Responses were more direct and less verbose

Summary

Model Examination (optional)
A sample comparison between the base and adapter model showed that the adapter improved clarity and tone in responses.

Environmental Impact

Hardware Type: NVIDIA A100 40GB (Google Colab Pro)
Hours used: ~2.5 hours
Cloud Provider: Google
Compute Region: US
Carbon Emitted: ~2.1 kg CO₂ (estimated via ML CO₂ calculator)

Technical Specifications

Model Architecture and Objective

• Falcon 7B base architecture
• Fine-tuned with LoRA on instruction-following tasks

Compute Infrastructure

• PEFT + bitsandbytes (4-bit quantization)
• Transformers 4.38+
• Accelerate, PyTorch, and Hugging Face ecosystem

Hardware

• Single A100 GPU

Software

• transformers==4.38.2
• peft==0.16.0
• accelerate, datasets, bitsandbytes

Citation

BibTeX:

@misc{desai2025falconlora,
  title={Falcon LoRA Adapter},
  author={Sahil Desai},
  year={2025},
  url={https://huggingface.co/sahildesai/falcon-lora}
}

Glossary

• LoRA: Low-Rank Adaptation – technique for fine-tuning large models efficiently
• PEFT: Parameter-Efficient Fine-Tuning – umbrella of efficient tuning methods

More Information / Contact

Model Card Authors: Sahil Desai
Model Card Contact: https://sahildesai.dev / [Hugging Face profile]