schooly
/

scolam-instruct

@@ -11,200 +11,65 @@ tags:
 - unsloth
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.16.0

 - 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)