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SmolLM2-1.7B-F32-GGUF
prithivMLmods/SmolLM2-1.7B-F32-GGUF
SmolLM2-1.7B-F32-GGUF is a text generation model from prithivMLmods. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
SmolLM2-1.7B-Instruct : The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion…
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Repo size
24.6 GB
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Repo makeup
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.gguf24.6 GB · 100%
At a glance
- Task
- Text Generation
- Library
- transformers
- License
- apache-2.0
- Model type
- llama
- Access
- Public
- Created
- Jun 7, 2025
- Updated
- Jun 7, 2025
- SHA
- 674d2b22
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Base models
Model card
From the Hugging Face model README
SmolLM2-1.7B-Instruct-GGUF
SmolLM2-1.7B-Instruct : The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.
Model Files
| File Name | Size | Format Description |
|---|---|---|
| SmolLM2-1.7B-Instruct.F32.gguf | 6.85 GB | Full precision (32-bit floating point) |
| SmolLM2-1.7B-Instruct.BF16.gguf | 3.42 GB | Brain floating point 16-bit |
| SmolLM2-1.7B-Instruct.F16.gguf | 3.42 GB | Half precision (16-bit floating point) |
| SmolLM2-1.7B-Instruct.Q8_0.gguf | 1.82 GB | 8-bit quantization |
| SmolLM2-1.7B-Instruct.Q6_K.gguf | 1.41 GB | 6-bit quantization (K-quant) |
| SmolLM2-1.7B-Instruct.Q5_K_M.gguf | 1.23 GB | 5-bit quantization (K-quant, medium) |
| SmolLM2-1.7B-Instruct.Q5_K_S.gguf | 1.19 GB | 5-bit quantization (K-quant, small) |
| SmolLM2-1.7B-Instruct.Q4_K_M.gguf | 1.06 GB | 4-bit quantization (K-quant, medium) |
| SmolLM2-1.7B-Instruct.Q4_K_S.gguf | 999 MB | 4-bit quantization (K-quant, small) |
| SmolLM2-1.7B-Instruct.Q3_K_L.gguf | 933 MB | 3-bit quantization (K-quant, large) |
| SmolLM2-1.7B-Instruct.Q3_K_M.gguf | 860 MB | 3-bit quantization (K-quant, medium) |
| SmolLM2-1.7B-Instruct.Q3_K_S.gguf | 777 MB | 3-bit quantization (K-quant, small) |
| SmolLM2-1.7B-Instruct.Q2_K.gguf | 675 MB | 2-bit quantization (K-quant) |
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):
- Task
- Text Generation
- Library
- transformers
- Type
- llama
- License
- apache-2.0
- Languages
- en
- Created
- Jun 7, 2025
- Updated
- Jun 7, 2025