Instructions to use itlwas/Sailor-0.5B-Q4_K_M-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 itlwas/Sailor-0.5B-Q4_K_M-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 itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M
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 itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M
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 itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use itlwas/Sailor-0.5B-Q4_K_M-GGUF with Ollama:
ollama run hf.co/itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M
- Unsloth Studio
How to use itlwas/Sailor-0.5B-Q4_K_M-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 itlwas/Sailor-0.5B-Q4_K_M-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 itlwas/Sailor-0.5B-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for itlwas/Sailor-0.5B-Q4_K_M-GGUF to start chatting
- Docker Model Runner
How to use itlwas/Sailor-0.5B-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use itlwas/Sailor-0.5B-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull itlwas/Sailor-0.5B-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Sailor-0.5B-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
itlwas commited on
Upload README.md with huggingface_hub
Browse files
README.md
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| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
- zh
|
| 5 |
+
- id
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| 6 |
+
- th
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| 7 |
+
- vi
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| 8 |
+
- ms
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| 9 |
+
- lo
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| 10 |
+
license: apache-2.0
|
| 11 |
+
tags:
|
| 12 |
+
- multilingual
|
| 13 |
+
- sea
|
| 14 |
+
- sailor
|
| 15 |
+
- llama-cpp
|
| 16 |
+
- gguf-my-repo
|
| 17 |
+
base_model: Qwen/Qwen1.5-0.5B
|
| 18 |
+
datasets:
|
| 19 |
+
- cerebras/SlimPajama-627B
|
| 20 |
+
- Skywork/SkyPile-150B
|
| 21 |
+
- allenai/MADLAD-400
|
| 22 |
+
- cc100
|
| 23 |
+
inference: false
|
| 24 |
+
model-index:
|
| 25 |
+
- name: Sailor-0.5B
|
| 26 |
+
results:
|
| 27 |
+
- task:
|
| 28 |
+
type: text-generation
|
| 29 |
+
dataset:
|
| 30 |
+
name: XQuAD-Thai
|
| 31 |
+
type: XQuAD-Thai
|
| 32 |
+
metrics:
|
| 33 |
+
- type: EM (3-Shot)
|
| 34 |
+
value: 15.84
|
| 35 |
+
name: EM (3-Shot)
|
| 36 |
+
- type: F1 (3-Shot)
|
| 37 |
+
value: 27.58
|
| 38 |
+
name: F1 (3-Shot)
|
| 39 |
+
- task:
|
| 40 |
+
type: text-generation
|
| 41 |
+
dataset:
|
| 42 |
+
name: TyDiQA-Indonesian
|
| 43 |
+
type: TyDiQA-Indonesian
|
| 44 |
+
metrics:
|
| 45 |
+
- type: EM (3-Shot)
|
| 46 |
+
value: 30.44
|
| 47 |
+
name: EM (3-Shot)
|
| 48 |
+
- type: F1 (3-Shot)
|
| 49 |
+
value: 54.74
|
| 50 |
+
name: F1 (3-Shot)
|
| 51 |
+
- task:
|
| 52 |
+
type: text-generation
|
| 53 |
+
dataset:
|
| 54 |
+
name: XQuAD-Vietnamese
|
| 55 |
+
type: XQuAD-Vietnamese
|
| 56 |
+
metrics:
|
| 57 |
+
- type: EM (3-Shot)
|
| 58 |
+
value: 21.13
|
| 59 |
+
name: EM (3-Shot)
|
| 60 |
+
- type: F1 (3-Shot)
|
| 61 |
+
value: 40.57
|
| 62 |
+
name: F1 (3-Shot)
|
| 63 |
+
- task:
|
| 64 |
+
type: text-generation
|
| 65 |
+
dataset:
|
| 66 |
+
name: XCOPA-Thai
|
| 67 |
+
type: XCOPA-Thai
|
| 68 |
+
metrics:
|
| 69 |
+
- type: EM (3-Shot)
|
| 70 |
+
value: 51.0
|
| 71 |
+
name: EM (3-Shot)
|
| 72 |
+
- task:
|
| 73 |
+
type: text-generation
|
| 74 |
+
dataset:
|
| 75 |
+
name: XCOPA-Indonesian
|
| 76 |
+
type: XCOPA-Indonesian
|
| 77 |
+
metrics:
|
| 78 |
+
- type: EM (3-Shot)
|
| 79 |
+
value: 58.2
|
| 80 |
+
name: EM (3-Shot)
|
| 81 |
+
- task:
|
| 82 |
+
type: text-generation
|
| 83 |
+
dataset:
|
| 84 |
+
name: XCOPA-Vietnamese
|
| 85 |
+
type: XCOPA-Vietnamese
|
| 86 |
+
metrics:
|
| 87 |
+
- type: EM (3-Shot)
|
| 88 |
+
value: 58.0
|
| 89 |
+
name: EM (3-Shot)
|
| 90 |
+
- task:
|
| 91 |
+
type: text-generation
|
| 92 |
+
dataset:
|
| 93 |
+
name: M3Exam-Thai
|
| 94 |
+
type: M3Exam-Thai
|
| 95 |
+
metrics:
|
| 96 |
+
- type: EM (3-Shot)
|
| 97 |
+
value: 24.41
|
| 98 |
+
name: EM (3-Shot)
|
| 99 |
+
- task:
|
| 100 |
+
type: text-generation
|
| 101 |
+
dataset:
|
| 102 |
+
name: M3Exam-Indonesian
|
| 103 |
+
type: M3Exam-Indonesian
|
| 104 |
+
metrics:
|
| 105 |
+
- type: EM (3-Shot)
|
| 106 |
+
value: 26.15
|
| 107 |
+
name: EM (3-Shot)
|
| 108 |
+
- task:
|
| 109 |
+
type: text-generation
|
| 110 |
+
dataset:
|
| 111 |
+
name: M3Exam-Vietnamese
|
| 112 |
+
type: M3Exam-Vietnamese
|
| 113 |
+
metrics:
|
| 114 |
+
- type: EM (3-Shot)
|
| 115 |
+
value: 30.91
|
| 116 |
+
name: EM (3-Shot)
|
| 117 |
+
- task:
|
| 118 |
+
type: text-generation
|
| 119 |
+
dataset:
|
| 120 |
+
name: BELEBELE-Thai
|
| 121 |
+
type: BELEBELE-Thai
|
| 122 |
+
metrics:
|
| 123 |
+
- type: EM (3-Shot)
|
| 124 |
+
value: 32.22
|
| 125 |
+
name: EM (3-Shot)
|
| 126 |
+
- task:
|
| 127 |
+
type: text-generation
|
| 128 |
+
dataset:
|
| 129 |
+
name: BELEBELE-Indonesian
|
| 130 |
+
type: BELEBELE-Indonesian
|
| 131 |
+
metrics:
|
| 132 |
+
- type: EM (3-Shot)
|
| 133 |
+
value: 30.89
|
| 134 |
+
name: EM (3-Shot)
|
| 135 |
+
- task:
|
| 136 |
+
type: text-generation
|
| 137 |
+
dataset:
|
| 138 |
+
name: BELEBELE-Vietnamese
|
| 139 |
+
type: BELEBELE-Vietnamese
|
| 140 |
+
metrics:
|
| 141 |
+
- type: EM (3-Shot)
|
| 142 |
+
value: 32.33
|
| 143 |
+
name: EM (3-Shot)
|
| 144 |
+
---
|
| 145 |
+
|
| 146 |
+
# AIronMind/Sailor-0.5B-Q4_K_M-GGUF
|
| 147 |
+
This model was converted to GGUF format from [`sail/Sailor-0.5B`](https://huggingface.co/sail/Sailor-0.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
|
| 148 |
+
Refer to the [original model card](https://huggingface.co/sail/Sailor-0.5B) for more details on the model.
|
| 149 |
+
## Use with llama.cpp
|
| 150 |
+
|
| 151 |
+
Install llama.cpp through brew.
|
| 152 |
+
|
| 153 |
+
```bash
|
| 154 |
+
brew install ggerganov/ggerganov/llama.cpp
|
| 155 |
+
```
|
| 156 |
+
Invoke the llama.cpp server or the CLI.
|
| 157 |
+
|
| 158 |
+
CLI:
|
| 159 |
+
|
| 160 |
+
```bash
|
| 161 |
+
llama-cli --hf-repo AIronMind/Sailor-0.5B-Q4_K_M-GGUF --model sailor-0.5b.Q4_K_M.gguf -p "The meaning to life and the universe is"
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| 162 |
+
```
|
| 163 |
+
|
| 164 |
+
Server:
|
| 165 |
+
|
| 166 |
+
```bash
|
| 167 |
+
llama-server --hf-repo AIronMind/Sailor-0.5B-Q4_K_M-GGUF --model sailor-0.5b.Q4_K_M.gguf -c 2048
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| 168 |
+
```
|
| 169 |
+
|
| 170 |
+
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
|
| 171 |
+
|
| 172 |
+
```
|
| 173 |
+
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m sailor-0.5b.Q4_K_M.gguf -n 128
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| 174 |
+
```
|