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---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/SmolLM2-Rethink-135M
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- trl
---
# **SmolLM2-Rethink-135M-GGUF**
> SmolLM2-Rethink-135M is an experimental lightweight model trained on the Celestia3-DeepSeek-R1-0528 reasoning dataset. Based on the SmolLM2-135M-Instruct architecture, this model is specifically optimized for reasoning, structured outputs, and efficient small-scale deployment. Despite its compact size (135M parameters), it demonstrates strong capabilities in logical deduction, conversational coherence, and lightweight inference tasks.
## Model Files
| File Name | Size | Type | Description |
|-----------|------|------|-------------|
| SmolLM2-Rethink-135M.Q2_K.gguf | 88.2 MB | Model | Q2_K quantized model (smallest) |
| SmolLM2-Rethink-135M.Q3_K_S.gguf | 88.2 MB | Model | Q3_K_S quantized model |
| SmolLM2-Rethink-135M.Q3_K_M.gguf | 93.5 MB | Model | Q3_K_M quantized model |
| SmolLM2-Rethink-135M.Q3_K_L.gguf | 97.5 MB | Model | Q3_K_L quantized model |
| SmolLM2-Rethink-135M.Q4_K_S.gguf | 102 MB | Model | Q4_K_S quantized model |
| SmolLM2-Rethink-135M.Q4_K_M.gguf | 105 MB | Model | Q4_K_M quantized model |
| SmolLM2-Rethink-135M.Q5_K_S.gguf | 110 MB | Model | Q5_K_S quantized model |
| SmolLM2-Rethink-135M.Q5_K_M.gguf | 112 MB | Model | Q5_K_M quantized model |
| SmolLM2-Rethink-135M.Q6_K.gguf | 138 MB | Model | Q6_K quantized model |
| SmolLM2-Rethink-135M.Q8_0.gguf | 145 MB | Model | Q8_0 quantized model |
| SmolLM2-Rethink-135M.BF16.gguf | 271 MB | Model | BF16 precision model |
| SmolLM2-Rethink-135M.F16.gguf | 271 MB | Model | F16 precision model |
| SmolLM2-Rethink-135M.F32.gguf | 540 MB | 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):
 |