Alelcv27/Llama3.2-1B-Instruct-FR2
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-FR2 is a 1 billion parameter Llama 3.2 instruction-tuned causal language model developed by Alelcv27. This model was fine-tuned using Unsloth and Hugging Face's TRL library, enabling faster training. It is designed for general instruction-following tasks, leveraging its compact size and efficient training methodology.
Loading preview...
Alelcv27/Llama3.2-1B-Instruct-FR2: A Compact Instruction-Tuned Model
This model, developed by Alelcv27, is a 1 billion parameter instruction-tuned variant of the Llama 3.2 architecture. It was fine-tuned from the unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit base model, utilizing the Unsloth library in conjunction with Hugging Face's TRL library.
Key Characteristics
- Efficient Training: The model benefits from Unsloth, which facilitated a 2x faster training process compared to standard methods.
- Compact Size: With 1 billion parameters, it offers a smaller footprint suitable for resource-constrained environments or applications requiring faster inference.
- Instruction-Tuned: Designed to follow instructions effectively, making it suitable for a variety of NLP tasks where direct command execution is needed.
Potential Use Cases
- Rapid Prototyping: Its smaller size and efficient training make it ideal for quick experimentation and development cycles.
- Edge Device Deployment: Suitable for deployment on devices with limited computational resources.
- Specific Instruction-Following Tasks: Can be fine-tuned further for niche applications requiring precise instruction adherence.