Instructions to use cloudsurf-software/CloudSurf-1B-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 cloudsurf-software/CloudSurf-1B-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 cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf cloudsurf-software/CloudSurf-1B-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 cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M
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 cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use cloudsurf-software/CloudSurf-1B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cloudsurf-software/CloudSurf-1B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cloudsurf-software/CloudSurf-1B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M
- Ollama
How to use cloudsurf-software/CloudSurf-1B-GGUF with Ollama:
ollama run hf.co/cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use cloudsurf-software/CloudSurf-1B-GGUF with Docker Model Runner:
docker model run hf.co/cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M
- Lemonade
How to use cloudsurf-software/CloudSurf-1B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.CloudSurf-1B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
CloudSurf-1B β official GGUF files
Official GGUF conversions of CloudSurf-1B, a 0.89-billion-parameter base language model trained from random weights by CloudSurf Software LLC on 18.16B tokens. The training details, data sources, evaluation, the pre-registered verdict and the limitations live in the base model card. This card covers the files.
It is a base model: it continues text and does not follow instructions. It loops on long generations; use a repetition penalty or short generations.
Files
| File | Quant | Size | sha256 | Perplexity (40 val-web docs) |
|---|---|---|---|---|
CloudSurf-1B-f16.gguf |
f16 | 1.78 GB | ef59f44157b2a118074fc6cf36a349abd94d4c87c9a7df4c9eceb0de0a895283 |
11.534 |
CloudSurf-1B-Q8_0.gguf |
Q8_0 | 0.95 GB | 8238324fee39e9833841e6666d23abdaa818d49716bc1e161187d96630787d56 |
11.536 |
CloudSurf-1B-Q4_K_M.gguf |
Q4_K_M | 0.55 GB | 5a69a1dda7074c380c4f4aa193dac5ad84fa5acf72507f5e982fa5d9b9c2c718 |
11.708 |
Converted with llama.cpp v0.7.0 from the fp32 safetensors export in the base repo (sha256 begins 7064e999). The export matches the trained model to a maximum logit difference of 5.6e-05 in fp32. llama.cpp tokenization equals the Hugging Face tokenization on 20 of 20 validation docs.
The files' embedded general.name reads Tinysurf 1b, the project's name inside CloudSurf when the files were converted; the released model is CloudSurf-1B. The bytes are the ones we measured, so we did not re-stamp them.
Run it
The model was trained with <|bos|> first; llama.cpp adds it from the GGUF metadata (add_bos_token is true).
llama-completion -hf cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M \
-p $'---\ntitle: The Colorado River\ntype: article\n---\n\n# The Colorado River\n\nThe Colorado River is' \
-n 120 --temp 0.7 --top-k 50 --repeat-penalty 1.1 -no-cnv
Or with a downloaded file:
llama-completion -m CloudSurf-1B-Q4_K_M.gguf -f prompt.txt -n 120 --temp 0.7 --top-k 50 --repeat-penalty 1.1 -no-cnv
A server:
llama-server -hf cloudsurf-software/CloudSurf-1B-GGUF:Q4_K_M -c 2048
Give it the start of a document and it continues the document. A SurfDoc front matter block followed by a heading is the prompt shape it saw most.
License
Apache-2.0, the same as the base model. The per-source attribution of the training data is on the base model card and travels with these files. "CloudSurf" and the CloudSurf marks stay with CloudSurf Software LLC.
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Base model
cloudsurf-software/CloudSurf-1B