Mungert/Nemotron-Research-Reasoning-Qwen-1.5B-GGUF

Nemotron-Research-Reasoning-Qwen-1.5B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit ea1431b0.

Choosing the Right Model Format

Selecting the correct model format depends on your hardware capabilities and memory constraints.

BF16 (Brain Float 16) – Use if BF16 acceleration is available

• A 16-bit floating-point format designed for faster computation while retaining good precision.
• Provides similar dynamic range as FP32 but with lower memory usage.
• Recommended if your hardware supports BF16 acceleration (check your device's specs).
• Ideal for high-performance inference with reduced memory footprint compared to FP32.

📌 Use BF16 if:
✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs).
✔ You want higher precision while saving memory.
✔ You plan to requantize the model into another format.

📌 Avoid BF16 if:
❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower).
❌ You need compatibility with older devices that lack BF16 optimization.

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F16 (Float 16) – More widely supported than BF16

• A 16-bit floating-point high precision but with less of range of values than BF16.
• Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
• Slightly lower numerical precision than BF16 but generally sufficient for inference.

📌 Use F16 if:
✔ Your hardware supports FP16 but not BF16.
✔ You need a balance between speed, memory usage, and accuracy.
✔ You are running on a GPU or another device optimized for FP16 computations.

📌 Avoid F16 if:
❌ Your device lacks native FP16 support (it may run slower than expected).
❌ You have memory limitations.

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Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference

Quantization reduces model size and memory usage while maintaining as much accuracy as possible.

• Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
• Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.

📌 Use Quantized Models if:
✔ You are running inference on a CPU and need an optimized model.
✔ Your device has low VRAM and cannot load full-precision models.
✔ You want to reduce memory footprint while keeping reasonable accuracy.

📌 Avoid Quantized Models if:
❌ You need maximum accuracy (full-precision models are better for this).
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).

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Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)

These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.

• IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency.

  • Use case: Best for ultra-low-memory devices where even Q4_K is too large.
  • Trade-off: Lower accuracy compared to higher-bit quantizations.

• IQ3_S: Small block size for maximum memory efficiency.

  • Use case: Best for low-memory devices where IQ3_XS is too aggressive.

• IQ3_M: Medium block size for better accuracy than IQ3_S.

  • Use case: Suitable for low-memory devices where IQ3_S is too limiting.

• Q4_K: 4-bit quantization with block-wise optimization for better accuracy.

  • Use case: Best for low-memory devices where Q6_K is too large.

• Q4_0: Pure 4-bit quantization, optimized for ARM devices.

  • Use case: Best for ARM-based devices or low-memory environments.

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Summary Table: Model Format Selection

Model Format	Precision	Memory Usage	Device Requirements	Best Use Case
BF16	Highest	High	BF16-supported GPU/CPUs	High-speed inference with reduced memory
F16	High	High	FP16-supported devices	GPU inference when BF16 isn't available
Q4_K	Medium Low	Low	CPU or Low-VRAM devices	Best for memory-constrained environments
Q6_K	Medium	Moderate	CPU with more memory	Better accuracy while still being quantized
Q8_0	High	Moderate	CPU or GPU with enough VRAM	Best accuracy among quantized models
IQ3_XS	Very Low	Very Low	Ultra-low-memory devices	Extreme memory efficiency and low accuracy
Q4_0	Low	Low	ARM or low-memory devices	llama.cpp can optimize for ARM devices

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Included Files & Details

Nemotron-Research-Reasoning-Qwen-1.5B-bf16.gguf

• Model weights preserved in BF16.
• Use this if you want to requantize the model into a different format.
• Best if your device supports BF16 acceleration.

Nemotron-Research-Reasoning-Qwen-1.5B-f16.gguf

• Model weights stored in F16.
• Use if your device supports FP16, especially if BF16 is not available.

Nemotron-Research-Reasoning-Qwen-1.5B-bf16-q8_0.gguf

• Output & embeddings remain in BF16.
• All other layers quantized to Q8_0.
• Use if your device supports BF16 and you want a quantized version.

Nemotron-Research-Reasoning-Qwen-1.5B-f16-q8_0.gguf

• Output & embeddings remain in F16.
• All other layers quantized to Q8_0.

Nemotron-Research-Reasoning-Qwen-1.5B-q4_k.gguf

• Output & embeddings quantized to Q8_0.
• All other layers quantized to Q4_K.
• Good for CPU inference with limited memory.

Nemotron-Research-Reasoning-Qwen-1.5B-q4_k_s.gguf

• Smallest Q4_K variant, using less memory at the cost of accuracy.
• Best for very low-memory setups.

Nemotron-Research-Reasoning-Qwen-1.5B-q6_k.gguf

• Output & embeddings quantized to Q8_0.
• All other layers quantized to Q6_K .

Nemotron-Research-Reasoning-Qwen-1.5B-q8_0.gguf

• Fully Q8 quantized model for better accuracy.
• Requires more memory but offers higher precision.

Nemotron-Research-Reasoning-Qwen-1.5B-iq3_xs.gguf

• IQ3_XS quantization, optimized for extreme memory efficiency.
• Best for ultra-low-memory devices.

Nemotron-Research-Reasoning-Qwen-1.5B-iq3_m.gguf

• IQ3_M quantization, offering a medium block size for better accuracy.
• Suitable for low-memory devices.

Nemotron-Research-Reasoning-Qwen-1.5B-q4_0.gguf

• Pure Q4_0 quantization, optimized for ARM devices.
• Best for low-memory environments.
• Prefer IQ4_NL for better accuracy.

🚀 If you find these models useful

❤ Please click "Like" if you find this useful!
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Choose an AI assistant type:

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Nemotron-Research-Reasoning-Qwen-1.5B

🚀 The leading generalist reasoning model for cutting-edge research and development 🌟

Introduction

Nemotron-Research-Reasoning-Qwen-1.5B is the world’s leading 1.5B open-weight model for complex reasoning tasks such as mathematical problems, coding challenges, scientific questions, and logic puzzles. It is trained using the ProRL algorithm on a diverse and comprehensive set of datasets. Our model has achieved impressive results, outperforming Deepseek’s 1.5B model by a large margin on a broad range of tasks, including math, coding, and GPQA.

This model is for research and development only.

ProRL: Prolonged Reinforcement Learning

ProRL is designed to enable extended RL training periods that facilitate deeper exploration of reasoning strategies. It enables more than 2k training steps and scale the training data across diverse tasks—from traditional math and code tasks to STEM problems, logical puzzles, and instruction following, which, we hypothesize, are crucial for generalization. Based on Group Relative Policy Optimization (GRPO), ProRL introduces three key techniques:

1. Mitigating Entropy Collapse
2. Decoupled clip and dynamic sampling policy optimization (DAPO)
3. KL regularization and reference policy reset

Using ProRL, we developed the world's best 1.5B reasoning model that significantly outperforms its base model, DeepSeek-R1-1.5B, and matches or even surpasses the performance of DeepSeek-R1-7B across a diverse range of benchmarks. Notably, compared to DeepSeek-R1-1.5B, we achieve average pass@1 improvements of 14.7% on math benchmarks, 13.9% on coding, 54.8% on logic puzzles, 25.1% on STEM reasoning, and 18.1% on instruction-following tasks.

Training Datasets

Dataset	Link
DeepScaleR-Preview-Dataset	Link
Eurus-2-RL-Data	Link
Reasoning-gym	Link
IFEval	Link
SCP-116K	Link

Evaluation Results

Table 1: Performance (pass@1) comparison for benchmarks across Math domain.

Model	AIME24	AIME25	AMC	Math	Minerva	Olympiad	Avg
DeepSeek-R1-Distill-Qwen-1.5B	28.54	22.71	62.58	82.90	26.38	43.58	44.45
DeepScaleR-1.5B	40.21	31.46	73.04	89.36	41.57	51.63	54.54
DeepSeek-R1-Distill-Qwen-7B	53.54	40.83	82.83	93.68	50.60	57.66	63.19
Nemotron-Research-Reasoning-Qwen-1.5B	48.13	33.33	79.29	91.89	47.98	60.22	60.14

Table 2: Performance (pass@1) comparison across benchmarks for Code. We abbreviate benchmarks names for condecontests (cc), codeforces (cf), humanevalplus (human), and livecodebench (LCB).

Model	apps	cc	cf	taco	human	LCB	Avg
DeepSeek-R1-Distill-Qwen-1.5B	20.95	16.79	14.13	8.03	61.77	16.80	23.08
DeepCoder-1.5B	30.37	23.76	21.70	13.76	73.40	22.76	30.96
DeepSeek-R1-Distill-Qwen-7B	42.08	32.76	33.08	19.08	83.32	38.04	41.39
Nemotron-Research-Reasoning-Qwen-1.5B	41.99	31.80	34.50	20.81	72.05	23.81	37.49

Table 3: Performance comparison on STEM reasoning (GPQA Diamond), instruction following (IFEval), and logic puzzles (Reasoning Gym) tasks. We also present results on OOD tasks: acre, boxnet, and game_of_life_halting (game).

Model	GPQA	IFEval	Reasoning	acre	boxnet	game
DeepSeek-R1-Distill-Qwen-1.5B	15.86	44.05	4.24	5.99	0.00	3.49
DeepSeek-R1-Distill-Qwen-7B	35.44	58.01	28.55	20.21	1.71	12.94
Nemotron-Research-Reasoning-Qwen-1.5B	41.78	66.02	59.06	58.57	7.91	52.29

License/Terms of Use

cc-by-nc-4.0

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Citation

If you find our dataset helpful, please cite the following paper:

@article{liu2025prorl,
  author    = {Mingjie Liu, Shizhe Diao, Ximing Lu, Jian Hu, Xin Dong, Yejin Choi, Jan Kautz, Yi Dong},
  title={ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models}, 
  journal   = {arXiv preprint},
  year      = {2025},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url={https://arxiv.org/abs/2505.24864}, 
}