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
1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel, PeftConfig
3
4# Load base Falcon model and tokenizer
5base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-7b", device_map="auto", trust_remote_code=True)
6tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b")
7
8# Load LoRA adapter
9adapter = PeftModel.from_pretrained(base_model, "sahildesai/falcon-lora")
10
11# Run inference
12inputs = tokenizer("Explain black holes to a 12-year-old.", return_tensors="pt").to("cuda")
13outputs = adapter.generate(**inputs, max_new_tokens=100)
14print(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
Software
transformers==4.38.2peft==0.16.0accelerate, datasets, bitsandbytes
Citation
BibTeX:
1@misc{desai2025falconlora,
2 title={Falcon LoRA Adapter},
3 author={Sahil Desai},
4 year={2025},
5 url={https://huggingface.co/sahildesai/falcon-lora}
6}
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]
