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README.md

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+
 ---
 library_name: transformers
-tags: []
+tags: [causal-lm, text-generation, fine-tuned, falcon, lora, imdb, sentiment-analysis]
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Falcon-RW-1B Fine-tuned with LoRA on IMDb Sentiment Dataset
 
+This is a Falcon-RW-1B language model fine-tuned using LoRA (Low-Rank Adaptation) for causal language modeling, trained on a subset of the IMDb movie reviews dataset for sentiment-related text generation tasks.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model is based on the Falcon-RW-1B pretrained causal language model, fine-tuned with parameter-efficient LoRA adapters targeting the "query_key_value" modules. Training was performed on a small subset of the IMDb dataset (1,000 samples) with sequences truncated/padded to 128 tokens.
 
-- **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:** Sujith Somanunnithan
+- **Model type:** Causal Language Model (Transformer)
+- **Language:** English
+- **License:** Apache 2.0
+- **Finetuned from:** `tiiuae/falcon-rw-1b`
+- **Fine-tuning method:** LoRA (using PEFT library)
 
-### 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:** [Insert your Hugging Face repo URL here]
+- **Base model:** https://huggingface.co/tiiuae/falcon-rw-1b
+- **Dataset:** IMDb movie reviews — https://huggingface.co/datasets/imdb
 
 ## 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. -->
-
-[More Information Needed]
+This model can be used for generating or completing English text sequences related to movie reviews, sentiment analysis prompts, or similar NLP causal language modeling tasks.
 
-### 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 LoRA adapters allow further parameter-efficient fine-tuning for other NLP tasks or domain adaptation, leveraging the Falcon-RW-1B base.
 
 ### 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 optimized for zero-shot classification or tasks outside of causal language modeling.**
+- Not suitable for languages other than English.
+- The small training subset limits generalization; performance on real-world text may vary.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
+- The base Falcon-RW-1B model inherits biases present in the pretraining data.
+- The fine-tuning on IMDb is limited in scope and size; results may be biased toward movie review sentiment.
+- Use caution when deploying in production or sensitive applications.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
+model_id = "your-hf-username/your-falcon-lora-model"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+inputs = tokenizer("The movie was", return_tensors="pt")
+outputs = model.generate(**inputs, max_length=50)
+print(tokenizer.decode(outputs[0]))
+```
 
 ## 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. -->
-
-[More Information Needed]
+- Dataset: IMDb movie reviews (subset of 1000 training samples)
+- Text sequences truncated/padded to max length 128
 
 ### 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]
-
-
-#### 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 -->
+- Fine-tuned on Falcon-RW-1B using LoRA adapters targeting "query_key_value"
+- Training arguments: batch size 2, gradient accumulation 4, 1 epoch, mixed precision (fp16)
+- Trainer API from Hugging Face Transformers with PEFT integration
 
-#### Speeds, Sizes, Times [optional]
+### Training Hyperparameters
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- LoRA config: r=8, lora_alpha=16, dropout=0.1
+- Optimized with AdamW (default Trainer)
+- Single epoch training on a small dataset for demonstration
 
 ## 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]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+- No formal evaluation metrics reported for this demo model
+- Intended for proof-of-concept fine-tuning and further downstream adaptation
 
 ## 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 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).
+- Training performed on a GPU-enabled machine with mixed precision to reduce energy consumption.
+- Approximate compute and carbon footprint unknown; training on a small subset minimizes impact.
 
-- **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]
+- Falcon-RW-1B causal LM architecture based on transformer decoder blocks
+- Objective: language modeling via cross-entropy loss on next-token prediction
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- Training performed on a single GPU with mixed precision (fp16)
+- Software: Transformers, PEFT, PyTorch
 
-[More Information Needed]
+## Citation
 
-## More Information [optional]
+If you use this model, please cite:
 
-[More Information Needed]
+```
+@misc{somanunnithan2025falconlora,
+  title={Falcon-RW-1B fine-tuned with LoRA on IMDb dataset},
+  author={Sujith Somanunnithan},
+  year={2025},
+  howpublished={\url{https://huggingface.co/your-hf-username/your-falcon-lora-model}}
+}
+```
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+- Sujith Somanunnithan
 
 ## Model Card Contact
 
-[More Information Needed]
+- your-email@example.com