Instructions to use Arain119/sophia 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 Arain119/sophia 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 Arain119/sophia:Q4_K_M # Run inference directly in the terminal: llama cli -hf Arain119/sophia:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Arain119/sophia:Q4_K_M # Run inference directly in the terminal: llama cli -hf Arain119/sophia: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 Arain119/sophia:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Arain119/sophia: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 Arain119/sophia:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Arain119/sophia:Q4_K_M
Use Docker
docker model run hf.co/Arain119/sophia:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Arain119/sophia with Ollama:
ollama run hf.co/Arain119/sophia:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Arain119/sophia with Docker Model Runner:
docker model run hf.co/Arain119/sophia:Q4_K_M
- Lemonade
How to use Arain119/sophia with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Arain119/sophia:Q4_K_M
Run and chat with the model
lemonade run user.sophia-Q4_K_M
List all available models
lemonade list
- Atomic Chat
|
Download gguf/README.md from Arain119/sophia: direct link, hf CLI and curl.
- Browser
- Download file 1.66 kB
-
https://huggingface.co/Arain119/sophia/resolve/main/gguf/README.md
- Command line
-
hf download hf://Arain119/sophia/gguf/README.md
-
curl -L -o README.md https://huggingface.co/Arain119/sophia/resolve/main/gguf/README.md
1.66 kB
| # Sophia GGUF | |
| Sophia's llama.cpp/GGUF build — a ~1B-parameter Chinese chat model (KDA+MLA hybrid, pretrained from scratch on a single RTX 5090). | |
| Runs on llama.cpp's `kimi-k3` architecture (Sophia is a fully dense 28-layer instance: [KDA,KDA,KDA,MLA] layer pattern, bounded decay gate, cross-layer attention residuals, SiTU-GLU). | |
| ## Files | |
| | File | Size | Notes | | |
| |---|---|---| | |
| | `sophia-bf16.gguf` | 2.1 GB | BF16, best quality | | |
| | `sophia-Q4_K_M.gguf` | 685 MB | Q4_K_M; precision-sensitive tensors (KDA scalars, norms, conv, res_score) stay high-precision via fallback | | |
| ## Requirements | |
| Needs an llama.cpp build with the `sophia` pre-tokenizer patch (~15 lines, see [Sophia repo](https://github.com/Arain119/Sophia) `tools/gguf/llama_cpp_sophia.patch`). The GGUF embeds `tokenizer.ggml.pre = "sophia"`: Sophia's tokenizer.json serializes its Split patterns as literals that never match, so the effective tokenization is ByteLevel+BPE over the whole input — the pre-tokenizer must not split at all. | |
| ```bash | |
| # chat (embedded chat template applies automatically) | |
| llama-cli -m sophia-Q4_K_M.gguf -st -t 8 | |
| # server | |
| llama-server -m sophia-bf16.gguf -c 4096 -t 8 --port 8180 | |
| ``` | |
| ## Parity evidence (BF16 / CPU) | |
| - Tokenization: **151/151 exact token-id match** vs the HF tokenizer | |
| - Teacher-forced logits: **145/150 argmax agreement (96.7%)**, mean |Δlogprob| = 0.0137; every divergence is a top-2 near-tie (gap ≤ 0.06) | |
| - Chat-template generation: byte-identical to the HF reference | |
| Converter and reproduction scripts: [Sophia repo](https://github.com/Arain119/Sophia) `tools/gguf/`; details in `docs/gguf_llamacpp.md`. | |