Ma7ee7/Qwen3.8_1.2B_LFM_Distillation
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.2BQuant:BF16Context Size:32kPublished:Aug 5, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Ma7ee7/Qwen3.8_1.2B_LFM_Distillation is a 1.2 billion parameter language model developed by Ma7ee7, fine-tuned from LiquidAI/LFM2.5-1.2B-Thinking. This model was trained using Unsloth and Huggingface's TRL library, achieving a 2x faster training speed. With a 32768 token context length, it is optimized for efficient processing and generation tasks.
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Model Overview
Ma7ee7/Qwen3.8_1.2B_LFM_Distillation is a 1.2 billion parameter language model developed by Ma7ee7. It is fine-tuned from the LiquidAI/LFM2.5-1.2B-Thinking base model, leveraging a substantial 32768 token context length for extensive input processing.
Key Characteristics
- Efficient Training: This model was trained with Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
- Base Model: Built upon LiquidAI/LFM2.5-1.2B-Thinking, indicating a foundation designed for specific language modeling tasks.
- Parameter Count: Features 1.2 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a 32768 token context window, enabling the model to handle long-form text and complex queries effectively.
Potential Use Cases
- Applications requiring efficient inference from a smaller, yet capable, language model.
- Tasks benefiting from a large context window, such as summarization of long documents or complex question answering.
- Scenarios where faster training and deployment are critical, thanks to the Unsloth optimization.