yibba/Atlas-Empathy-Darija
TEXT GENERATIONConcurrency Cost:1Model Size:9BQuant:FP8Ctx Length:16kPublished:Jan 3, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Warm
The yibba/Atlas-Empathy-Darija is a 9 billion parameter instruction-tuned causal language model, finetuned from MBZUAI-Paris/Atlas-Chat-9B. Developed by yibba, this model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for applications requiring a 9B parameter model with a 16384 token context length, leveraging efficient training methodologies.
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Model Overview
The yibba/Atlas-Empathy-Darija is a 9 billion parameter language model, finetuned by yibba from the MBZUAI-Paris/Atlas-Chat-9B base model. This model was developed with a focus on efficient training, utilizing Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
Key Characteristics
- Parameter Count: 9 billion parameters, offering a balance between performance and computational requirements.
- Context Length: Supports a context window of 16384 tokens, suitable for processing longer inputs and generating more coherent responses.
- Training Efficiency: Benefits from Unsloth's optimizations, leading to significantly faster finetuning.
- License: Released under the Apache-2.0 license, allowing for broad use and distribution.
Potential Use Cases
- Applications requiring a moderately sized, instruction-tuned model.
- Scenarios where efficient training and deployment are critical.
- Tasks benefiting from a 16K context window.