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Model: prithivMLmods/SmolLM2-135M-F32-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- HuggingFaceTB/SmolLM2-135M-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+---
+
+# **SmolLM2-135M-Instruct-GGUF**
+
+> [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.
+
+## Model Files
+
+| File Name | Size | Format Description |
+|-----------|------|-------------------|
+| SmolLM2-135M-Instruct.F32.gguf | 540 MB | Full precision (32-bit floating point) |
+| SmolLM2-135M-Instruct.BF16.gguf | 271 MB | Brain floating point 16-bit |
+| SmolLM2-135M-Instruct.F16.gguf | 271 MB | Half precision (16-bit floating point) |
+| SmolLM2-135M-Instruct.Q8_0.gguf | 145 MB | 8-bit quantization |
+| SmolLM2-135M-Instruct.Q6_K.gguf | 138 MB | 6-bit quantization (K-quant) |
+| SmolLM2-135M-Instruct.Q5_K_M.gguf | 112 MB | 5-bit quantization (K-quant, medium) |
+| SmolLM2-135M-Instruct.Q5_K_S.gguf | 110 MB | 5-bit quantization (K-quant, small) |
+| SmolLM2-135M-Instruct.Q4_K_M.gguf | 105 MB | 4-bit quantization (K-quant, medium) |
+| SmolLM2-135M-Instruct.Q4_K_S.gguf | 102 MB | 4-bit quantization (K-quant, small) |
+| SmolLM2-135M-Instruct.Q3_K_L.gguf | 97.5 MB | 3-bit quantization (K-quant, large) |
+| SmolLM2-135M-Instruct.Q3_K_M.gguf | 93.5 MB | 3-bit quantization (K-quant, medium) |
+| SmolLM2-135M-Instruct.Q3_K_S.gguf | 88.2 MB | 3-bit quantization (K-quant, small) |
+| SmolLM2-135M-Instruct.Q2_K.gguf | 88.2 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):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

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config.json

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+{
+    "model_type": "llama"
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}