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
File size: 624 Bytes
d53adc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
# Exported for HuggingFace trust_remote_code loading.
# This file is intentionally self-contained.
"""Remote-code naming shared by the HF adapter and export bundle."""
from __future__ import annotations
HF_MODELING_MODULE = "modeling_sophia"
HF_CONFIG_CLASS = "SophiaConfig"
HF_CAUSAL_LM_CLASS = "SophiaForCausalLM"
HF_SUPPORT_FILENAME = "hf_support.py"
HF_RUNTIME_FILENAME = "sophia_runtime.py"
__all__ = [
"HF_CAUSAL_LM_CLASS",
"HF_CONFIG_CLASS",
"HF_MODELING_MODULE",
"HF_RUNTIME_FILENAME",
"HF_SUPPORT_FILENAME",
]
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