Smollm2 135M Instruct - Details

Smollm2 135M Instruct is a large language model developed by Hugging Face Smol Models Research Enterprise, a community-driven initiative. It features 135m parameters, making it a compact yet powerful option for various applications. The model is released under the Apache License 2.0, ensuring open access and flexibility for users. Designed with a focus on optimized compact sizes for on-device execution, it balances performance and efficiency, ideal for deployment in resource-constrained environments.

SmolLM2 is a family of compact language models available in three sizes: 135M, 360M, and 1.7B parameters. It is designed to run efficiently on-device while handling a wide range of tasks. The 135M model was trained on 2 trillion tokens using diverse datasets like FineWeb-Edu, DCLM, and The Stack, along with new filtered data. It demonstrates significant improvements over its predecessor, SmolLM1, particularly in instruction following, knowledge, and reasoning. The instruct version was developed through supervised fine-tuning (SFT) and Direct Preference Optimization (DPO). It supports tasks such as text rewriting, summarization, and function calling (for the 1.7B version) thanks to datasets like Synth-APIGen-v0.1. The model emphasizes optimized compact sizes for deployment in resource-constrained environments.

Smollm2 135M Instruct has 135m parameters, placing it in the small model category, which ensures fast and resource-efficient performance ideal for simple tasks and on-device execution. Its 8k context length falls into the moderate range, allowing it to handle longer texts better than short-context models but still requiring careful management of resource usage for extended inputs. The combination of compact parameters and moderate context length makes it well-suited for applications where efficiency and accessibility are prioritized over handling extremely long or complex tasks.

• Parameter Size: 135m
• Context Length: 8k

Smollm2 135M Instruct is a compact language model with 135m parameters and an 8k context length, making it suitable for tasks that prioritize efficiency and accessibility. Possible uses include text generation, where it could create concise content for creative or informational purposes, though its performance in complex scenarios would require further testing. Possible applications in code generation might involve drafting simple scripts or snippets, but its ability to handle intricate programming tasks remains unverified. Possible roles in question answering could focus on straightforward queries, though its capacity to manage nuanced or domain-specific questions would need thorough evaluation. The model’s design emphasizes lightweight execution, which could support possible deployments in environments with limited computational resources, but its effectiveness in such contexts is yet to be fully explored.

• text generation
• code generation
• question answering

Smollm2 135M Instruct is a compact language model with 135m parameters and an 8k context length, offering possible applications in areas where efficiency and accessibility are key. Possible uses include text generation for creative or informational content, though its effectiveness for complex narratives would require further testing. Possible roles in code generation might involve drafting basic scripts or snippets, but its ability to handle advanced programming tasks remains unverified. Possible applications in question answering could focus on straightforward queries, though its capacity to manage nuanced or domain-specific questions would need thorough evaluation. Possible deployments in lightweight automation or interactive tools could benefit from its on-device optimization, but real-world performance would require extensive validation. Each application must be thoroughly evaluated and tested before use.

• text generation
• code generation
• question answering

Smollm2 135M Instruct’s medium q4 version is optimized for a balance between precision and performance, requiring a GPU with at least 8GB VRAM for efficient execution, though it may also run on systems with lower VRAM depending on workload. This version is particularly suited for devices with moderate computational resources, making it possible to deploy on consumer-grade GPUs. The model’s compact size ensures possible compatibility with a range of hardware, but users should verify their system’s specifications against the requirements for the chosen quantization.

• fp16, q2, q3, q4, q5, q6, q8

Smollm2 135M Instruct is a compact language model with 135m parameters designed for efficient on-device execution, released under the Apache License 2.0. It supports text generation, code generation, and question answering while offering multiple quantized versions to adapt to varying hardware capabilities, making it a flexible choice for resource-conscious applications.

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