README.md

---
base_model: unsloth/gemma-2b-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/gemma-2b-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---

# Model Card for ScoLaM

## Model Details

### Model Description

ScoLaM is a fine-tuned language model based on the **unsloth/gemma-2b-bnb-4bit** base model. It uses Parameter-Efficient Fine-Tuning (PEFT) techniques, specifically LoRA (Low-Rank Adaptation), to enable efficient adaptation with reduced compute and storage requirements. ScoLaM is designed primarily for text-generation tasks and can be applied in domains requiring lightweight, performant language modeling.

- **Developed by:** Team Scorton 
- **Funded by:** SchoolyAI
- **Shared by:** https://github.com/scorton
- **Model type:** Transformer-based causal language model with LoRA fine-tuning
- **Language(s):** English (primarily), French (second), Spanish (Second)
- **License:** [Specify license, e.g., Apache 2.0, MIT, etc.]
- **Finetuned from model:** unsloth/gemma-2b-bnb-4bit (4-bit quantized base model)

### Model Sources

- **Repository:** hugging.co/schooly
- **Paper:** [Link to relevant publication if any]
- **Demo:** [URL to demo application if any]

## Uses

### Direct Use

ScoLaM is intended for general-purpose text generation tasks such as drafting, creative writing, summarization, or chatbot dialogue generation. It can be used directly via text-generation pipelines in Hugging Face Transformers using PEFT adapters.

### Downstream Use

ScoLaM can serve as a base for further fine-tuning on domain-specific datasets or for integration into larger NLP systems, chatbots, or AI assistants that benefit from efficient fine-tuning and inference.

### Out-of-Scope Use

- Use in highly safety-critical or sensitive applications without further validation.
- Generation of misleading, harmful, or biased content.
- Applications requiring strong factual accuracy without additional grounding.

## Bias, Risks, and Limitations

ScoLaM inherits biases present in the base model and training data. It may produce biased, harmful, or nonsensical outputs if used improperly. Its quantized 4-bit format may also affect precision in some use cases.

### Recommendations

Users should evaluate outputs carefully, especially in high-stakes scenarios. Fine-tuning or prompt engineering may be needed to mitigate undesired behavior.

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel

base_model = "unsloth/gemma-2b-bnb-4bit"
adapter_model = "path_or_id_to_scolam_adapter"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_model)

text_gen = pipeline("text-generation", model=model, tokenizer=tokenizer)
output = text_gen("Your prompt here", max_length=50)
print(output)