GCIRS Reasoning Qwen
Collection
RL~Reward Signal
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2 items
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Updated
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1
GCIRS-Reasoning-1.5B-R1 is a research-grade reasoning model fine-tuned from Qwen2.5-1.5B-Instruct, focused on non-fictional reasoning, factual consistency, and scientific depth. Trained with reinforcement learning using the Big Reasoning Traces dataset from DeepSeek, this model is tailored for complex analytical tasks and scientific rigor in high-stakes or research environments.
| File Name | Format | Size | Precision | Use Case |
|---|---|---|---|---|
GCIRS-Reasoning-1.5B-R1.F32.gguf |
GGUF | 7.11 GB | F32 | Highest precision, research use |
GCIRS-Reasoning-1.5B-R1.BF16.gguf |
GGUF | 3.56 GB | BF16 | High precision, balanced performance |
GCIRS-Reasoning-1.5B-R1.F16.gguf |
GGUF | 3.56 GB | F16 | High precision, memory efficient |
GCIRS-Reasoning-1.5B-R1.Q8_0.gguf |
GGUF | 1.89 GB | Q8_0 | Excellent quality, moderate compression |
GCIRS-Reasoning-1.5B-R1.Q6_K.gguf |
GGUF | 1.46 GB | Q6_K | Very good quality, good compression |
GCIRS-Reasoning-1.5B-R1.Q5_K_M.gguf |
GGUF | 1.29 GB | Q5_K_M | Balanced quality/size (recommended) |
GCIRS-Reasoning-1.5B-R1.Q5_K_S.gguf |
GGUF | 1.26 GB | Q5_K_S | Good quality, smaller size |
GCIRS-Reasoning-1.5B-R1.Q4_K_M.gguf |
GGUF | 1.12 GB | Q4_K_M | Good balance for most users |
GCIRS-Reasoning-1.5B-R1.Q4_K_S.gguf |
GGUF | 1.07 GB | Q4_K_S | Decent quality, compact size |
GCIRS-Reasoning-1.5B-R1.Q3_K_L.gguf |
GGUF | 980 MB | Q3_K_L | Lower quality, very compact |
GCIRS-Reasoning-1.5B-R1.Q3_K_M.gguf |
GGUF | 924 MB | Q3_K_M | Fast inference, limited quality |
GCIRS-Reasoning-1.5B-R1.Q3_K_S.gguf |
GGUF | 861 MB | Q3_K_S | Fastest inference, basic quality |
GCIRS-Reasoning-1.5B-R1.Q2_K.gguf |
GGUF | 753 MB | Q2_K | Minimal size, experimental use |
F32 or BF16 for maximum accuracyQ5_K_M or Q6_K for best quality/performance balanceQ4_K_M or Q4_K_S for good performanceQ3_K_M or Q3_K_LQ2_K for minimal footprint(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):
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
16-bit
32-bit
Base model
Qwen/Qwen2.5-1.5B