42 lines
2.1 KiB
Markdown
42 lines
2.1 KiB
Markdown
---
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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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- HuggingFaceTB/SmolLM2-135M-Instruct
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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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---
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# **SmolLM2-135M-Instruct-GGUF**
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> [SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) : The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets 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.
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## Model Files
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| File Name | Size | Format Description |
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|-----------|------|-------------------|
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| SmolLM2-135M-Instruct.F32.gguf | 540 MB | Full precision (32-bit floating point) |
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| SmolLM2-135M-Instruct.BF16.gguf | 271 MB | Brain floating point 16-bit |
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| SmolLM2-135M-Instruct.F16.gguf | 271 MB | Half precision (16-bit floating point) |
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| SmolLM2-135M-Instruct.Q8_0.gguf | 145 MB | 8-bit quantization |
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| SmolLM2-135M-Instruct.Q6_K.gguf | 138 MB | 6-bit quantization (K-quant) |
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| SmolLM2-135M-Instruct.Q5_K_M.gguf | 112 MB | 5-bit quantization (K-quant, medium) |
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| SmolLM2-135M-Instruct.Q5_K_S.gguf | 110 MB | 5-bit quantization (K-quant, small) |
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| SmolLM2-135M-Instruct.Q4_K_M.gguf | 105 MB | 4-bit quantization (K-quant, medium) |
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| SmolLM2-135M-Instruct.Q4_K_S.gguf | 102 MB | 4-bit quantization (K-quant, small) |
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| SmolLM2-135M-Instruct.Q3_K_L.gguf | 97.5 MB | 3-bit quantization (K-quant, large) |
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| SmolLM2-135M-Instruct.Q3_K_M.gguf | 93.5 MB | 3-bit quantization (K-quant, medium) |
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| SmolLM2-135M-Instruct.Q3_K_S.gguf | 88.2 MB | 3-bit quantization (K-quant, small) |
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| SmolLM2-135M-Instruct.Q2_K.gguf | 88.2 MB | 2-bit quantization (K-quant) |
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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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