# Introducing the Palmyra-mini family: Powerful, lightweight, and ready to reason!

[Hugging Face](https://yomu.fyi/company/hugging-face) · Rakshith, Tom Peres · Sep 11, 2025

**Type:** Announcement

## Summary

WRITER released three open models in the Palmyra-mini family ranging between 1.5B and 1.7B parameters, designed for efficient inference and specialized reasoning tasks. Built on the Qwen architecture, the release includes the standard palmyra-mini base model alongside two Chain of Thought variants, palmyra-mini-thinking-a and palmyra-mini-thinking-b. In benchmark evaluations, palmyra-mini achieved 52.6% on Big Bench Hard, while palmyra-mini-thinking-a reached 82.87% on GSM8K and palmyra-mini-thinking-b reached 92.5% on AMC23. For palmyra-mini-thinking-b, applying reinforcement learning fine-tuning to an OpenReasoning-Nemotron-1.5B base improved single-shot pass@1 accuracy while reducing sampling diversity and majority@64 performance. The models are available in GGUF and MLX-BF16 quantizations and support inference engines including vLLM, SGLang, TRTLLM, and TGI.

## Context

WRITER aims to deliver powerful, lightweight, and performant models in the 1.5B to 1.7B parameter range for efficient inference across general generative tasks, complex logic, and mathematical reasoning.

## Approach / What changed

The team developed three Qwen-architecture models: the base palmyra-mini, palmyra-mini-thinking-a trained with Chain of Thought, and palmyra-mini-thinking-b built by applying reinforcement learning fine-tuning on OpenReasoning-Nemotron-1.5B. Quantizations are provided in GGUF and MLX-BF16 formats, supporting inference on engines like vLLM, SGLang, TRTLLM, and TGI.

## Takeaways

- Palmyra-mini-thinking-a achieved 82.87% on GSM8K (strict match) and held the highest overall average benchmark score among the released models.
- Palmyra-mini-thinking-b scored 92.5% on AMC23 and led average benchmark scores across AIME24, AIME25, GPQA, HMMT25, HLE, MMLU\_PRO, MATH500, and LCB.
- Reinforcement learning fine-tuning improved pass@1 single-shot accuracy on palmyra-mini-thinking-b but reduced sampling diversity, resulting in lower majority@64 performance compared to the SFT base model.

**Tags:** [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Open Source](https://yomu.fyi/topic/open-source), [Performance](https://yomu.fyi/topic/performance)

[Read original post](https://huggingface.co/blog/Writer/announcing-palmyra-mini)