Instructions to use tensorblock/Apollo-7B-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/Apollo-7B-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/Apollo-7B-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/Apollo-7B-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/Apollo-7B-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/Apollo-7B-GGUF:Q2_K
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf tensorblock/Apollo-7B-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama cli -hf tensorblock/Apollo-7B-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/Apollo-7B-GGUF:Q2_K
- Llama new
- LM Studio
- Jan
- Ollama
How to use tensorblock/Apollo-7B-GGUF with Ollama:
ollama run hf.co/tensorblock/Apollo-7B-GGUF:Q2_K
- Unsloth Desktop
- Docker Model Runner
How to use tensorblock/Apollo-7B-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/Apollo-7B-GGUF:Q2_K
- Lemonade
How to use tensorblock/Apollo-7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/Apollo-7B-GGUF:Q2_K
Run and chat with the model
lemonade run user.Apollo-7B-GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
Update README.md
Browse files
README.md
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@@ -23,8 +23,16 @@ This repo contains GGUF format model files for [FreedomIntelligence/Apollo-7B](h
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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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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [Apollo-7B-Q2_K.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q4_0.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q5_0.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q6_K.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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| [Apollo-7B-Q8_0.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/
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## Downloading instruction
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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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<div style="text-align: left; margin: 20px 0;">
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<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
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Run them on the TensorBlock client using your local machine ↗
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</a>
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</div>
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## Prompt template
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```
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```
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [Apollo-7B-Q2_K.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q2_K.gguf) | Q2_K | 3.242 GB | smallest, significant quality loss - not recommended for most purposes |
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| [Apollo-7B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q3_K_S.gguf) | Q3_K_S | 3.709 GB | very small, high quality loss |
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| [Apollo-7B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q3_K_M.gguf) | Q3_K_M | 4.069 GB | very small, high quality loss |
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| [Apollo-7B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q3_K_L.gguf) | Q3_K_L | 4.386 GB | small, substantial quality loss |
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| [Apollo-7B-Q4_0.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q4_0.gguf) | Q4_0 | 4.668 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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| [Apollo-7B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q4_K_S.gguf) | Q4_K_S | 4.700 GB | small, greater quality loss |
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| [Apollo-7B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q4_K_M.gguf) | Q4_K_M | 4.964 GB | medium, balanced quality - recommended |
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| [Apollo-7B-Q5_0.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q5_0.gguf) | Q5_0 | 5.570 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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| [Apollo-7B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q5_K_S.gguf) | Q5_K_S | 5.570 GB | large, low quality loss - recommended |
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| [Apollo-7B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q5_K_M.gguf) | Q5_K_M | 5.723 GB | large, very low quality loss - recommended |
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| [Apollo-7B-Q6_K.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q6_K.gguf) | Q6_K | 6.529 GB | very large, extremely low quality loss |
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| [Apollo-7B-Q8_0.gguf](https://huggingface.co/tensorblock/Apollo-7B-GGUF/blob/main/Apollo-7B-Q8_0.gguf) | Q8_0 | 8.454 GB | very large, extremely low quality loss - not recommended |
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## Downloading instruction
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