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--- |
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base_model: unsloth/gemma-2b-bnb-4bit |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- base_model:adapter:unsloth/gemma-2b-bnb-4bit |
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- lora |
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- sft |
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- transformers |
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- trl |
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- unsloth |
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--- |
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# Model Card for ScoLaM |
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## Model Details |
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### Model Description |
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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. |
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- **Developed by:** Team Scorton |
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- **Funded by:** SchoolyAI |
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- **Shared by:** https://github.com/scorton |
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- **Model type:** Transformer-based causal language model with LoRA fine-tuning |
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- **Language(s):** English (primarily), French (second), Spanish (Second) |
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- **License:** [Specify license, e.g., Apache 2.0, MIT, etc.] |
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- **Finetuned from model:** unsloth/gemma-2b-bnb-4bit (4-bit quantized base model) |
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### Model Sources |
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- **Repository:** hugging.co/schooly |
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- **Paper:** [Link to relevant publication if any] |
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- **Demo:** [URL to demo application if any] |
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## Uses |
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### Direct Use |
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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. |
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### Downstream Use |
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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. |
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### Out-of-Scope Use |
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- Use in highly safety-critical or sensitive applications without further validation. |
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- Generation of misleading, harmful, or biased content. |
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- Applications requiring strong factual accuracy without additional grounding. |
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## Bias, Risks, and Limitations |
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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. |
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### Recommendations |
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Users should evaluate outputs carefully, especially in high-stakes scenarios. Fine-tuning or prompt engineering may be needed to mitigate undesired behavior. |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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from peft import PeftModel |
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base_model = "unsloth/gemma-2b-bnb-4bit" |
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adapter_model = "path_or_id_to_scolam_adapter" |
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tokenizer = AutoTokenizer.from_pretrained(base_model) |
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model = AutoModelForCausalLM.from_pretrained(base_model) |
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model = PeftModel.from_pretrained(model, adapter_model) |
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text_gen = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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output = text_gen("Your prompt here", max_length=50) |
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print(output) |
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