Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- falcon
+- peft
+- lora
+- imdb
+- text-generation
+datasets:
+- imdb
+base_model:
+- tiiuae/falcon-rw-1b
+pipeline_tag: text-generation
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
+# 🦅 Falcon LoRA - IMDb Sentiment Generation
 
+This model is a **LoRA fine-tuned version of [`tiiuae/falcon-rw-1b`](https://huggingface.co/tiiuae/falcon-rw-1b)** using the **IMDb movie review dataset**. 
+It's trained to generate sentiment-rich movie review completions from short prompts. LoRA (Low-Rank Adaptation) enables efficient fine-tuning with fewer resources.
 
 
 ## Model Details
+ **Base Model:** Falcon RW 1B (`tiiuae/falcon-rw-1b`)
+- **Fine-Tuning Method:** Parameter-Efficient Fine-Tuning (LoRA via PEFT)
+- **Dataset:** IMDb (1000 samples for demonstration)
+- **Input Length:** 128 tokens
+- **Training Framework:** 🤗 Transformers + PEFT
+- **Trained on:** Google Colab (T4 GPU)
 
 ### 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.
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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. -->
+- **Developed by:** Vishal D.
+- **Shared on Hugging Face Hub:** [`vishal1d/falcon-lora-imdb`](https://huggingface.co/vishal1d/falcon-lora-imdb)
+- **Model Type:** Causal Language Model (AutoModelForCausalLM)
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned From:** [`tiiuae/falcon-rw-1b`](https://huggingface.co/tiiuae/falcon-rw-1b)
+- 
 
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+You can use this model for:
+- Generating sentiment-aware movie reviews
+- NLP educational experiments
+- Demonstrating LoRA fine-tuning in Transformers
 
-[More Information Needed]
 
 ### Downstream Use [optional]
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model can serve as a base for:
+- Continued fine-tuning on other text datasets
+- Training custom sentiment generation apps
+- Teaching parameter-efficient fine-tuning methods
+
 
-[More Information Needed]
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Avoid using this model for:
+- Real-world sentiment classification (it generates, not classifies)
+- Medical, legal, or safety-critical decision-making
+- Non-English text (not trained or evaluated for multilingual use)
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
+## How to Get Started with the Model
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Use the code below to get started with the model.
 
-[More Information Needed]
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel, PeftConfig
 
-### Recommendations
+# LoRA adapter model ID on Hugging Face Hub
+adapter_id = "vishal1d/falcon-lora-imdb"
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+# Load the adapter configuration
+peft_config = PeftConfig.from_pretrained(adapter_id)
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+# Load the base Falcon model
+base_model = AutoModelForCausalLM.from_pretrained(
+    peft_config.base_model_name_or_path,
+    trust_remote_code=True,
+    device_map="auto"
+)
 
-## How to Get Started with the Model
+# Load the LoRA adapter on top of the base model
+model = PeftModel.from_pretrained(base_model, adapter_id)
+model.eval()
 
-Use the code below to get started with the model.
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Create a text generation pipeline
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.8,
+    top_k=50,
+    top_p=0.95
+)
+
+# Example prompt
+prompt = "The movie was absolutely wonderful because"
+output = generator(prompt)
+
+# Display the generated text
+print(output[0]["generated_text"])
 
-[More Information Needed]
 
 ## Training Details
 
+- **LoRA Config:**
+  - `r=8`
+  - `lora_alpha=16`
+  - `lora_dropout=0.1`
+  - `target_modules=["query_key_value"]`
+- **Batch Size:** 2 (with gradient_accumulation=4)
+- **Epochs:** 1 (demo purpose)
+- **Precision:** FP16
+- **Training Samples:** 1000 IMDb reviews
+
 ### 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. -->
+The model was fine-tuned on the IMDb dataset, a large-scale dataset containing 50,000 movie reviews labeled as positive or negative.
+
+For demonstration and quick experimentation, only 1000 samples from the IMDb train split were used.
+
+Dataset Card: IMDb on Hugging Face
+
+Format: Text classification (binary sentiment)
+
+Preprocessing:
+
+Tokenized using tiiuae/falcon-rw-1b tokenizer
+
+Max input length: 128 tokens
+
+Labels were set as input_ids for causal language modeling
 
-[More Information Needed]
 
 ### 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
+Tokenized each review using Falcon's tokenizer
+
+Truncated/padded to max length of 128
 
-#### Preprocessing [optional]
+Used causal language modeling: labels = input_ids (predict next token)
 
-[More Information Needed]
+Training Hyperparameters
+Model: tiiuae/falcon-rw-1b
 
+Fine-tuning method: LoRA (Low-Rank Adaptation) via PEFT
 
-#### Training Hyperparameters
+LoRA Config:
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+r=8, lora_alpha=16, lora_dropout=0.1
 
-#### Speeds, Sizes, Times [optional]
+Target module: "query_key_value"
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+Training Args:
 
-[More Information Needed]
+per_device_train_batch_size=2
+
+gradient_accumulation_steps=4
+
+num_train_epochs=1
+
+fp16=True
+
+Frameworks: 🤗 Transformers, PEFT, Datasets, Trainer
+
+Speeds, Sizes, Times
+GPU used: Google Colab (Tesla T4, 16GB)
+
+Training time: ~10–15 minutes for 1 epoch on 1000 samples
+
+Checkpoint size (adapter only): ~6.3 MB (adapter_model.safetensors)
 
-## 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. -->
+Evaluation was done interactively using text prompts. No quantitative metrics were used since the model was trained for demo-scale.
 
-[More Information Needed]
 
 #### Factors
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Prompt completion
+
+Sentiment alignment
+
+Fluency of generated text
 
-[More Information Needed]
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Evaluation was qualitative, based on prompt completions. Since this model was trained on only 1000 IMDb samples for demonstration, we evaluated it by:
 
-[More Information Needed]
+Text Coherence: Does the output form grammatically valid sentences?
 
-### Results
+Sentiment Appropriateness: Does the generated output reflect the sentiment implied by the prompt?
 
-[More Information Needed]
+Relevance: Is the continuation logically connected to the prompt?
 
-#### Summary
+No quantitative metrics (like accuracy, BLEU, ROUGE) were computed due to the generative nature of the task.
 
 
+### Results
 
-## Model Examination [optional]
+The model successfully generated fluent, sentiment-aware text completions for short prompts like:
 
-<!-- Relevant interpretability work for the model goes here -->
+Prompt: "The movie was absolutely wonderful because"
+Output: "...it had brilliant performances, touching moments, and a truly powerful story that left the audience in awe."
 
-[More Information Needed]
+These results show that the model can be useful for sentiment-rich text generation, even with limited training data.
 
-## Environmental Impact
+#### Summary
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Even with only 1000 IMDb samples, the model can produce sentiment-aligned completions.
 
-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).
+LoRA fine-tuning was efficient and lightweight.
 
-- **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]
+Best used for experimentation or small-scale inference.
 
 ## Technical Specifications [optional]
+Model architecture: Falcon-RW-1B (decoder-only transformer)
 
-### Model Architecture and Objective
-
-[More Information Needed]
+Fine-tuning: LoRA (Low-Rank Adaptation)
 
-### Compute Infrastructure
+Precision: Mixed precision (fp16)
 
-[More Information Needed]
+Tokenizer: tiiuae/falcon-rw-1b tokenizer
 
-#### Hardware
+Frameworks Used: Hugging Face Transformers, Datasets, PEFT
 
-[More Information Needed]
+### Model Architecture and Objective
 
-#### Software
+This model uses the tiiuae/falcon-rw-1b architecture, which is a decoder-only transformer similar to GPT. The objective is causal language modeling, where the model predicts the next token given all previous tokens.
 
-[More Information Needed]
+During fine-tuning, Low-Rank Adaptation (LoRA) was used to efficiently adjust a small number of weights (via low-rank updates) while keeping the base model frozen.
 
-## Citation [optional]
+### Compute Infrastructure
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
+#### Hardware
 
-[More Information Needed]
+Hardware
+GPU: NVIDIA Tesla T4 (16 GB VRAM)
 
-**APA:**
+Platform: Google Colab
 
-[More Information Needed]
+#### Software
 
-## Glossary [optional]
+Software
+Python Version: 3.10
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+PyTorch: 2.7.1
 
-[More Information Needed]
+Transformers: 4.52.4
 
-## More Information [optional]
+PEFT: 0.15.2
 
-[More Information Needed]
+BitsAndBytes: 0.46.0 (if used for quantization)
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Vishal D. – Model fine-tuning and publication
+
+Based on Falcon-RW-1B by TII UAE
+]
 
 ## Model Card Contact
 
-[More Information Needed]
+📧 Email: tvishal810@gmail,com
+
+🧠 Hugging Face: vishal1d