Alelcv27/Llama3.2-1B-Instruct-FR
TEXT GENERATIONPricing:Input $0.108 / Output $0.804Concurrent Unit Cost:1Model Size:1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 21, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Alelcv27/Llama3.2-1B-Instruct-FR is a 1 billion parameter instruction-tuned causal language model developed by Alelcv27. Finetuned from unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit, this model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for instruction-following tasks, leveraging its efficient training methodology.
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Model Overview
Alelcv27/Llama3.2-1B-Instruct-FR is a 1 billion parameter instruction-tuned language model developed by Alelcv27. It is based on the Llama 3.2 architecture and was finetuned from the unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit model. A key characteristic of this model's development is its training efficiency.
Key Capabilities
- Instruction Following: The model is specifically instruction-tuned, making it suitable for tasks that require understanding and executing given instructions.
- Efficient Training: Leveraging Unsloth and Huggingface's TRL library, this model was trained significantly faster (2x) compared to standard methods, indicating an optimized development process.
Good For
- Resource-Constrained Environments: Its 1 billion parameter size makes it a lightweight option for deployment where computational resources are limited.
- Rapid Prototyping: The efficient training methodology suggests it could be a good candidate for quick iteration and development cycles in instruction-following applications.