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license: apache-2.0
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- prithivMLmods/SmolLM2-Rethink-135M
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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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---
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# **SmolLM2-Rethink-135M-GGUF**
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> 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.
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## Model Files
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| File Name | Size | Type | Description |
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|-----------|------|------|-------------|
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| SmolLM2-Rethink-135M.Q2_K.gguf | 88.2 MB | Model | Q2_K quantized model (smallest) |
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| SmolLM2-Rethink-135M.Q3_K_S.gguf | 88.2 MB | Model | Q3_K_S quantized model |
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| SmolLM2-Rethink-135M.Q3_K_M.gguf | 93.5 MB | Model | Q3_K_M quantized model |
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| SmolLM2-Rethink-135M.Q3_K_L.gguf | 97.5 MB | Model | Q3_K_L quantized model |
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| SmolLM2-Rethink-135M.Q4_K_S.gguf | 102 MB | Model | Q4_K_S quantized model |
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| SmolLM2-Rethink-135M.Q4_K_M.gguf | 105 MB | Model | Q4_K_M quantized model |
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| SmolLM2-Rethink-135M.Q5_K_S.gguf | 110 MB | Model | Q5_K_S quantized model |
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| SmolLM2-Rethink-135M.Q5_K_M.gguf | 112 MB | Model | Q5_K_M quantized model |
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| SmolLM2-Rethink-135M.Q6_K.gguf | 138 MB | Model | Q6_K quantized model |
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| SmolLM2-Rethink-135M.Q8_0.gguf | 145 MB | Model | Q8_0 quantized model |
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| SmolLM2-Rethink-135M.BF16.gguf | 271 MB | Model | BF16 precision model |
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| SmolLM2-Rethink-135M.F16.gguf | 271 MB | Model | F16 precision model |
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| SmolLM2-Rethink-135M.F32.gguf | 540 MB | Model | F32 full precision model (largest) |
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| .gitattributes | 2.4 kB | Config | Git LFS configuration |
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| config.json | 29 Bytes | Config | Model configuration |
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| README.md | 31 Bytes | Documentation | Repository documentation |
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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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