GRAM-Qwen3-1.7B-RewardModel-GGUF

GRAM-Qwen3-1.7B-RewardModel-GGUF

GRAM-Qwen3-1.7B-RewardModel is a generative reward model developed by NiuTrans that follows a two-step training approach: it first pre-trains on a large amount of unlabeled data and then fine-tunes with supervised labeled data. This methodology, which incorporates label smoothing and a regularized ranking loss, enables effective reward generalization for large language models (LLMs). The model is built on the Qwen3-1.7B base, a compact language model with 1.7 billion parameters, 28 layers, and attention heads designed to handle long-context inputs (up to 32,768 tokens) and support both detailed reasoning and fast responses. GRAM-Qwen3-1.7B-RewardModel is intended for flexible application across diverse tasks, providing an open-source, plug-and-play reward model for aligning LLM outputs without requiring extensive task-specific retraining. It excels in evaluating and ranking the quality of AI-generated responses, operating effectively as a judge model in AI alignment scenarios.

Model Files

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):