Instructions to use tensorblock/tinyllama-15M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tensorblock/tinyllama-15M-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tensorblock/tinyllama-15M-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/tinyllama-15M-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tensorblock/tinyllama-15M-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/tinyllama-15M-GGUF:Q2_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tensorblock/tinyllama-15M-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/tinyllama-15M-GGUF:Q2_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tensorblock/tinyllama-15M-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/tinyllama-15M-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/tinyllama-15M-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use tensorblock/tinyllama-15M-GGUF with Ollama:
ollama run hf.co/tensorblock/tinyllama-15M-GGUF:Q2_K
- Unsloth Studio
How to use tensorblock/tinyllama-15M-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tensorblock/tinyllama-15M-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tensorblock/tinyllama-15M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tensorblock/tinyllama-15M-GGUF to start chatting
- Docker Model Runner
How to use tensorblock/tinyllama-15M-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/tinyllama-15M-GGUF:Q2_K
- Lemonade
How to use tensorblock/tinyllama-15M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/tinyllama-15M-GGUF:Q2_K
Run and chat with the model
lemonade run user.tinyllama-15M-GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
Upload folder using huggingface_hub
Browse files- .gitattributes +12 -0
- README.md +70 -0
- tinyllama-15M-Q2_K.gguf +3 -0
- tinyllama-15M-Q3_K_L.gguf +3 -0
- tinyllama-15M-Q3_K_M.gguf +3 -0
- tinyllama-15M-Q3_K_S.gguf +3 -0
- tinyllama-15M-Q4_0.gguf +3 -0
- tinyllama-15M-Q4_K_M.gguf +3 -0
- tinyllama-15M-Q4_K_S.gguf +3 -0
- tinyllama-15M-Q5_0.gguf +3 -0
- tinyllama-15M-Q5_K_M.gguf +3 -0
- tinyllama-15M-Q5_K_S.gguf +3 -0
- tinyllama-15M-Q6_K.gguf +3 -0
- tinyllama-15M-Q8_0.gguf +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q5_0.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
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tinyllama-15M-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: mit
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tags:
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- TensorBlock
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- GGUF
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base_model: nickypro/tinyllama-15M
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---
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;">
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Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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</p>
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</div>
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</div>
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## nickypro/tinyllama-15M - GGUF
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This repo contains GGUF format model files for [nickypro/tinyllama-15M](https://huggingface.co/nickypro/tinyllama-15M).
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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).
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## Prompt template
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```
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```
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## Model file specification
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [tinyllama-15M-Q2_K.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q2_K.gguf) | Q2_K | 0.013 GB | smallest, significant quality loss - not recommended for most purposes |
|
| 37 |
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| [tinyllama-15M-Q3_K_S.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q3_K_S.gguf) | Q3_K_S | 0.013 GB | very small, high quality loss |
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| 38 |
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| [tinyllama-15M-Q3_K_M.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q3_K_M.gguf) | Q3_K_M | 0.013 GB | very small, high quality loss |
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| [tinyllama-15M-Q3_K_L.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q3_K_L.gguf) | Q3_K_L | 0.013 GB | small, substantial quality loss |
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| 40 |
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| [tinyllama-15M-Q4_0.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q4_0.gguf) | Q4_0 | 0.013 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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| [tinyllama-15M-Q4_K_S.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q4_K_S.gguf) | Q4_K_S | 0.013 GB | small, greater quality loss |
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| 42 |
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| [tinyllama-15M-Q4_K_M.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q4_K_M.gguf) | Q4_K_M | 0.014 GB | medium, balanced quality - recommended |
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| [tinyllama-15M-Q5_0.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q5_0.gguf) | Q5_0 | 0.014 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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| [tinyllama-15M-Q5_K_S.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q5_K_S.gguf) | Q5_K_S | 0.014 GB | large, low quality loss - recommended |
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| [tinyllama-15M-Q5_K_M.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q5_K_M.gguf) | Q5_K_M | 0.014 GB | large, very low quality loss - recommended |
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| [tinyllama-15M-Q6_K.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q6_K.gguf) | Q6_K | 0.015 GB | very large, extremely low quality loss |
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| [tinyllama-15M-Q8_0.gguf](https://huggingface.co/tensorblock/tinyllama-15M-GGUF/tree/main/tinyllama-15M-Q8_0.gguf) | Q8_0 | 0.016 GB | very large, extremely low quality loss - not recommended |
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## Downloading instruction
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| 51 |
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### Command line
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| 53 |
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Firstly, install Huggingface Client
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```shell
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pip install -U "huggingface_hub[cli]"
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```
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Then, downoad the individual model file the a local directory
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```shell
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huggingface-cli download tensorblock/tinyllama-15M-GGUF --include "tinyllama-15M-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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```
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
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```shell
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huggingface-cli download tensorblock/tinyllama-15M-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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```
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tinyllama-15M-Q2_K.gguf
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tinyllama-15M-Q4_K_S.gguf
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version https://git-lfs.github.com/spec/v1
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tinyllama-15M-Q5_0.gguf
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version https://git-lfs.github.com/spec/v1
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tinyllama-15M-Q5_K_M.gguf
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tinyllama-15M-Q6_K.gguf
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| 3 |
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size 16558144
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tinyllama-15M-Q8_0.gguf
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