56 lines
2.4 KiB
Markdown
56 lines
2.4 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B
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language:
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- en
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base_model:
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- prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- trl
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- reasoning
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- code
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- math
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---
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# **Regulus-Qwen3-R1-Llama-Distill-GGUF**
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> Regulus-Qwen3-R1-Llama-Distill-1.7B is a distilled reasoning model fine-tuned on Qwen/Qwen3-1.7B using Magpie-Align/Magpie-Reasoning-V2-250K-CoT-DeepSeek-R1-Llama-70B. The training leverages distilled traces from DeepSeek-R1-Llama-70B, transferring advanced reasoning patterns into a lightweight 1.7B parameter model. It is specialized for chain-of-thought reasoning across code, math, and science, optimized for efficiency and mid-resource deployment.
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## Model Files
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| File Name | Quant Type | File Size |
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| - | - | - |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.BF16.gguf | BF16 | 3.45 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.F16.gguf | F16 | 3.45 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.F32.gguf | F32 | 6.89 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q2_K.gguf | Q2_K | 778 MB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q3_K_L.gguf | Q3_K_L | 1 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q3_K_M.gguf | Q3_K_M | 940 MB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q3_K_S.gguf | Q3_K_S | 867 MB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q4_0.gguf | Q4_0 | 1.05 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q4_1.gguf | Q4_1 | 1.14 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q4_K.gguf | Q4_K | 1.11 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q4_K_M.gguf | Q4_K_M | 1.11 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q4_K_S.gguf | Q4_K_S | 1.06 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q5_0.gguf | Q5_0 | 1.23 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q5_1.gguf | Q5_1 | 1.32 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q5_K.gguf | Q5_K | 1.26 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q5_K_M.gguf | Q5_K_M | 1.26 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q5_K_S.gguf | Q5_K_S | 1.23 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q6_K.gguf | Q6_K | 1.42 GB |
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| Regulus-Qwen3-R1-Llama-Distill-1.7B.Q8_0.gguf | Q8_0 | 1.83 GB |
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## Quants Usage
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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