Smollm 1.7B Instruct - Details

Smollm 1.7B Instruct is a large language model developed by Hugging Face TB Research with 1.7b parameters. It operates under the Apache License 2.0 and is designed for instruction-following tasks. The model emphasizes optimized data curation and architecture to deliver strong performance in small to medium-sized applications.

SmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters. The models are pre-trained on SmolLM-Corpus, a curated collection of high-quality educational and synthetic data. SmolLM-Instruct is fine-tuned on publicly available datasets to enhance instruction-following capabilities, supporting tasks like general knowledge questions, creative writing, and basic Python programming. However, it is limited to English and struggles with arithmetic, editing tasks, and complex reasoning.

SmolLM 1.7B Instruct has 1.7b parameters, placing it in the small model category, which ensures fast and resource-efficient performance ideal for straightforward tasks. Its 4k context length falls under short contexts, making it suitable for concise interactions but limiting its ability to handle extended texts. The model’s design prioritizes efficiency over complexity, aligning with its focus on educational and synthetic data.

• Parameter Size: 1.7b
• Context Length: 4k

SmolLM 1.7B Instruct is a model with 1.7b parameters designed for tasks like general knowledge question answering, creative writing assistance, and basic Python programming tasks. These are possible applications that could be explored further, though their effectiveness may vary depending on specific requirements and constraints. Possible use cases might include supporting educational tools, generating text for non-specialized content, or assisting with simple coding challenges. However, possible limitations in areas like arithmetic or complex reasoning mean these possible uses would need careful evaluation before deployment. The model’s focus on efficiency and small-scale performance suggests it is best suited for scenarios where resource usage and speed are prioritized over handling highly complex or specialized tasks.

• general knowledge question answering
• creative writing assistance
• basic python programming tasks

SmolLM 1.7B Instruct is a model with 1.7b parameters that could have possible applications in areas like educational support, content generation, and coding assistance. Possible uses might include helping students with general knowledge queries, generating creative writing prompts, or offering basic Python guidance. Possible scenarios could also involve language learning tools or simplifying technical documentation. However, these possible applications would require thorough testing to ensure alignment with specific needs and constraints. Each possible use case must be carefully evaluated before implementation to confirm its effectiveness and suitability.

• general knowledge question answering
• creative writing assistance
• basic python programming tasks
• educational tool development

SmolLM 1.7B Instruct with the q4 quantized version is designed for a good balance between precision and performance, requiring approximately 4GB–8GB VRAM for deployment. This makes it suitable for systems with moderate GPU capabilities, such as consumer-grade graphics cards with at least 8GB VRAM. A multi-core CPU and at least 32GB RAM are recommended for smooth operation, along with adequate cooling to handle the workload. These possible hardware requirements may vary depending on the specific use case and model size.

fp16, q2, q3, q4, q5, q6, q8

SmolLM 1.7B Instruct is a large language model with 1.7b parameters, designed for instruction-following tasks and available under the Apache License 2.0. It supports general knowledge questions, creative writing, and basic Python programming, while its 4k context length and optimized architecture make it suitable for efficient, small-to-medium-scale applications.

Huggingface Model Page
Ollama Model Page