---
license: apache-2.0
base_model: HuggingFaceTB/SmolLM-135M-Instruct
tags:
- alignment-handbook
- trl
- sft
- TensorBlock
- GGUF
datasets:
- Magpie-Align/Magpie-Pro-300K-Filtered
- bigcode/self-oss-instruct-sc2-exec-filter-50k
- teknium/OpenHermes-2.5
- HuggingFaceTB/everyday-conversations-llama3.1-2k
library_name: transformers
language:
- en
---

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## HuggingFaceTB/SmolLM-135M-Instruct - GGUF

This repo contains GGUF format model files for [HuggingFaceTB/SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

## 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 |
| -------- | ---------- | --------- | ----------- |
| [SmolLM-135M-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q2_K.gguf) | Q2_K | 0.082 GB | smallest, significant quality loss - not recommended for most purposes |
| [SmolLM-135M-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q3_K_S.gguf) | Q3_K_S | 0.082 GB | very small, high quality loss |
| [SmolLM-135M-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q3_K_M.gguf) | Q3_K_M | 0.087 GB | very small, high quality loss |
| [SmolLM-135M-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q3_K_L.gguf) | Q3_K_L | 0.091 GB | small, substantial quality loss |
| [SmolLM-135M-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q4_0.gguf) | Q4_0 | 0.085 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [SmolLM-135M-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q4_K_S.gguf) | Q4_K_S | 0.095 GB | small, greater quality loss |
| [SmolLM-135M-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q4_K_M.gguf) | Q4_K_M | 0.098 GB | medium, balanced quality - recommended |
| [SmolLM-135M-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q5_0.gguf) | Q5_0 | 0.098 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [SmolLM-135M-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q5_K_S.gguf) | Q5_K_S | 0.102 GB | large, low quality loss - recommended |
| [SmolLM-135M-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q5_K_M.gguf) | Q5_K_M | 0.104 GB | large, very low quality loss - recommended |
| [SmolLM-135M-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q6_K.gguf) | Q6_K | 0.129 GB | very large, extremely low quality loss |
| [SmolLM-135M-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/SmolLM-135M-Instruct-GGUF/tree/main/SmolLM-135M-Instruct-Q8_0.gguf) | Q8_0 | 0.135 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

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

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

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