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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 File name | Size | QuantType |
|---|---|---|
| GRAM-Qwen3-1.7B-RewardModel.BF16.gguf | 3.45 GB | BF16 |
| GRAM-Qwen3-1.7B-RewardModel.F16.gguf | 3.45 GB | F16 |
| GRAM-Qwen3-1.7B-RewardModel.F32.gguf | 6.89 GB | F32 |
| GRAM-Qwen3-1.7B-RewardModel.Q2_K.gguf | 778 MB | Q2_K |
| GRAM-Qwen3-1.7B-RewardModel.Q3_K_L.gguf | 1 GB | Q3_K_L |
| GRAM-Qwen3-1.7B-RewardModel.Q3_K_M.gguf | 940 MB | Q3_K_M |
| GRAM-Qwen3-1.7B-RewardModel.Q3_K_S.gguf | 867 MB | Q3_K_S |
| GRAM-Qwen3-1.7B-RewardModel.Q4_K_M.gguf | 1.11 GB | Q4_K_M |
| GRAM-Qwen3-1.7B-RewardModel.Q4_K_S.gguf | 1.06 GB | Q4_K_S |
| GRAM-Qwen3-1.7B-RewardModel.Q5_K_M.gguf | 1.26 GB | Q5_K_M |
| GRAM-Qwen3-1.7B-RewardModel.Q5_K_S.gguf | 1.23 GB | Q5_K_S |
| GRAM-Qwen3-1.7B-RewardModel.Q6_K.gguf | 1.42 GB | Q6_K |
| GRAM-Qwen3-1.7B-RewardModel.Q8_0.gguf | 1.83 GB | Q8_0 |
