lukejoe/SmolLM2-135M-Instruct-GGUF overview

---

base_model: unsloth/SmolLM2-135M-Instruct

language:

• en

library_name: transformers

license: apache-2.0

tags:

• llama
• unsloth
• transformers
• TensorBlock
• GGUF

---

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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">

</div>






unsloth/SmolLM2-135M-Instruct - GGUF

This repo contains GGUF format model files for unsloth/SmolLM2-135M-Instruct.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Our projects

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<th colspan="2" style="font-size: 25px;">Forge</th>

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<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>

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<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>

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<a href="https://github.com/TensorBlock/forge" target="_blank" style="

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">🚀 Try it now! 🚀</a>

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<th style="font-size: 25px;">Awesome MCP Servers</th>

<th style="font-size: 25px;">TensorBlock Studio</th>

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<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>

<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>

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<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>

<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>

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<th>

<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="

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">👀 See what we built 👀</a>

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<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="

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text-decoration: none;

border-radius: 6px;

font-weight: bold;

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">👀 See what we built 👀</a>

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</table>

Prompt template

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Model file specification

| Filename | Quant type | File Size | Description |

| -------- | ---------- | --------- | ----------- |

| SmolLM2-135M-Instruct-Q2_K.gguf | Q2_K | 0.088 GB | smallest, significant quality loss - not recommended for most purposes |

| SmolLM2-135M-Instruct-Q3_K_S.gguf | Q3_K_S | 0.088 GB | very small, high quality loss |

| SmolLM2-135M-Instruct-Q3_K_M.gguf | Q3_K_M | 0.094 GB | very small, high quality loss |

| SmolLM2-135M-Instruct-Q3_K_L.gguf | Q3_K_L | 0.098 GB | small, substantial quality loss |

| SmolLM2-135M-Instruct-Q4_0.gguf | Q4_0 | 0.092 GB | legacy; small, very high quality loss - prefer using Q3_K_M |

| SmolLM2-135M-Instruct-Q4_K_S.gguf | Q4_K_S | 0.102 GB | small, greater quality loss |

| SmolLM2-135M-Instruct-Q4_K_M.gguf | Q4_K_M | 0.105 GB | medium, balanced quality - recommended |

| SmolLM2-135M-Instruct-Q5_0.gguf | Q5_0 | 0.105 GB | legacy; medium, balanced quality - prefer using Q4_K_M |

| SmolLM2-135M-Instruct-Q5_K_S.gguf | Q5_K_S | 0.110 GB | large, low quality loss - recommended |

| SmolLM2-135M-Instruct-Q5_K_M.gguf | Q5_K_M | 0.112 GB | large, very low quality loss - recommended |

| SmolLM2-135M-Instruct-Q6_K.gguf | Q6_K | 0.138 GB | very large, extremely low quality loss |

| SmolLM2-135M-Instruct-Q8_0.gguf | Q8_0 | 0.145 GB | very large, extremely low quality loss - not recommended |

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/SmolLM2-135M-Instruct-GGUF --include "SmolLM2-135M-Instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., Q4_Kgguf), you can try:

huggingface-cli download tensorblock/SmolLM2-135M-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'