license, language, base_model, pipeline_tag, library_name, tags
| license | language | base_model | pipeline_tag | library_name | tags | |||
|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
|
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):
