Instructions to use tensorblock/zephyr-7b-alpha-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/zephyr-7b-alpha-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/zephyr-7b-alpha-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/zephyr-7b-alpha-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/zephyr-7b-alpha-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/zephyr-7b-alpha-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/zephyr-7b-alpha-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/zephyr-7b-alpha-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/zephyr-7b-alpha-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/zephyr-7b-alpha-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/zephyr-7b-alpha-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use tensorblock/zephyr-7b-alpha-GGUF with Ollama:
ollama run hf.co/tensorblock/zephyr-7b-alpha-GGUF:Q2_K
- Unsloth Desktop
- Docker Model Runner
How to use tensorblock/zephyr-7b-alpha-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/zephyr-7b-alpha-GGUF:Q2_K
- Lemonade
How to use tensorblock/zephyr-7b-alpha-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/zephyr-7b-alpha-GGUF:Q2_K
Run and chat with the model
lemonade run user.zephyr-7b-alpha-GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
metadata
tags:
- generated_from_trainer
- TensorBlock
- GGUF
license: mit
datasets:
- stingning/ultrachat
- openbmb/UltraFeedback
language:
- en
base_model: HuggingFaceH4/zephyr-7b-alpha
model-index:
- name: zephyr-7b-alpha
results: []
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
HuggingFaceH4/zephyr-7b-alpha - GGUF
This repo contains GGUF format model files for HuggingFaceH4/zephyr-7b-alpha.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
<|system|>
{system_prompt}</s>
<|user|>
{prompt}</s>
<|assistant|>
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| zephyr-7b-alpha-Q2_K.gguf | Q2_K | 2.532 GB | smallest, significant quality loss - not recommended for most purposes |
| zephyr-7b-alpha-Q3_K_S.gguf | Q3_K_S | 2.947 GB | very small, high quality loss |
| zephyr-7b-alpha-Q3_K_M.gguf | Q3_K_M | 3.277 GB | very small, high quality loss |
| zephyr-7b-alpha-Q3_K_L.gguf | Q3_K_L | 3.560 GB | small, substantial quality loss |
| zephyr-7b-alpha-Q4_0.gguf | Q4_0 | 3.827 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| zephyr-7b-alpha-Q4_K_S.gguf | Q4_K_S | 3.856 GB | small, greater quality loss |
| zephyr-7b-alpha-Q4_K_M.gguf | Q4_K_M | 4.068 GB | medium, balanced quality - recommended |
| zephyr-7b-alpha-Q5_0.gguf | Q5_0 | 4.654 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| zephyr-7b-alpha-Q5_K_S.gguf | Q5_K_S | 4.654 GB | large, low quality loss - recommended |
| zephyr-7b-alpha-Q5_K_M.gguf | Q5_K_M | 4.779 GB | large, very low quality loss - recommended |
| zephyr-7b-alpha-Q6_K.gguf | Q6_K | 5.534 GB | very large, extremely low quality loss |
| zephyr-7b-alpha-Q8_0.gguf | Q8_0 | 7.167 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/zephyr-7b-alpha-GGUF --include "zephyr-7b-alpha-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:
huggingface-cli download tensorblock/zephyr-7b-alpha-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'