2.2 KiB
2.2 KiB
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-Rethink-360M-GGUF
SmolLM2-Rethink-360M is an experimental lightweight reasoning model trained on the Celestia3-DeepSeek-R1-0528 dataset. Built on top of the SmolLM2-135M-Instruct architecture and scaled to 360M parameters, it is designed to enhance lightweight reasoning, logical deduction, and structured response generation—all while maintaining efficiency for resource-constrained environments.
Model Files
| File Name | Size | Type | Description |
|---|---|---|---|
| SmolLM2-Rethink-360M.Q2_K.gguf | 219 MB | Model | Q2_K quantized model (smallest) |
| SmolLM2-Rethink-360M.Q3_K_S.gguf | 219 MB | Model | Q3_K_S quantized model |
| SmolLM2-Rethink-360M.Q3_K_M.gguf | 235 MB | Model | Q3_K_M quantized model |
| SmolLM2-Rethink-360M.Q3_K_L.gguf | 246 MB | Model | Q3_K_L quantized model |
| SmolLM2-Rethink-360M.Q4_K_S.gguf | 260 MB | Model | Q4_K_S quantized model |
| SmolLM2-Rethink-360M.Q4_K_M.gguf | 271 MB | Model | Q4_K_M quantized model |
| SmolLM2-Rethink-360M.Q5_K_S.gguf | 283 MB | Model | Q5_K_S quantized model |
| SmolLM2-Rethink-360M.Q5_K_M.gguf | 290 MB | Model | Q5_K_M quantized model |
| SmolLM2-Rethink-360M.Q6_K.gguf | 367 MB | Model | Q6_K quantized model |
| SmolLM2-Rethink-360M.Q8_0.gguf | 386 MB | Model | Q8_0 quantized model |
| SmolLM2-Rethink-360M.BF16.gguf | 726 MB | Model | BF16 precision model |
| SmolLM2-Rethink-360M.F16.gguf | 726 MB | Model | F16 precision model |
| SmolLM2-Rethink-360M.F32.gguf | 1.45 GB | Model | F32 full precision model (largest) |
| .gitattributes | 2.4 kB | Config | Git LFS configuration |
| config.json | 29 Bytes | Config | Model configuration |
| README.md | 31 Bytes | Documentation | Repository documentation |
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):
