Ilia2003Mah/qwen2.5_1.5b-gsm8k-test-step0
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:Mar 16, 2026Architecture:Transformer Cold

Ilia2003Mah/qwen2.5_1.5b-gsm8k-test-step0 is a 1.5 billion parameter language model, likely based on the Qwen2.5 architecture, with a context length of 32768 tokens. This model appears to be an experimental or test version, potentially focused on mathematical reasoning tasks as indicated by 'gsm8k' in its name. Further details on its specific training, capabilities, and intended use are not provided in the available documentation.

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Model Overview

This model, Ilia2003Mah/qwen2.5_1.5b-gsm8k-test-step0, is a 1.5 billion parameter language model, likely derived from the Qwen2.5 family, and supports a substantial context length of 32768 tokens. The model name suggests it is an experimental or test iteration, potentially fine-tuned or evaluated on the GSM8K dataset, which focuses on grade school math word problems.

Key Characteristics

  • Parameter Count: 1.5 billion parameters.
  • Context Length: Supports a long context window of 32768 tokens.
  • Architecture: Implied to be based on the Qwen2.5 architecture.
  • Development Status: Appears to be a test or experimental version, indicated by "-test-step0" in its identifier.

Intended Use

Due to the limited information in the provided model card, specific direct or downstream uses are not detailed. However, the "gsm8k" tag strongly suggests an orientation towards:

  • Mathematical Reasoning: Potentially for tasks involving arithmetic, logic, and problem-solving, similar to those found in the GSM8K benchmark.
  • Research and Experimentation: Given its "-test-step0" designation, it is likely intended for internal evaluation, development, or academic research into model capabilities, particularly in quantitative domains.

Limitations and Recommendations

The model card explicitly states that information regarding its developers, funding, specific model type, language(s), license, training data, and evaluation results is currently "More Information Needed." Users should be aware of these significant gaps in documentation. Without further details, it is difficult to assess potential biases, risks, or the full scope of its capabilities and limitations. Users are advised to exercise caution and conduct thorough independent evaluations before deploying this model in any production environment.