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: 1,477 Bytes
d53adc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # Generated by ml.integrations.export.runtime_packager.write_remote_code_bundle.
# Exported for HuggingFace trust_remote_code loading.
# This file is intentionally self-contained.
from __future__ import annotations
from typing import Protocol
from .runtime_backend import (
RuntimeBackend,
)
class DecoderConfig(Protocol):
def to_model_args(
self,
*,
runtime_max_seq_len: int | None = None,
) -> object: ...
class DecoderHostMixin:
def _initialize_decoder_runtime(
self,
*,
config: DecoderConfig,
runtime_max_seq_len: int | None,
runtime_backend: RuntimeBackend,
gradient_checkpointing_enabled: bool,
register_tied_weights: bool = False,
) -> None:
self.model = runtime_backend.transformer_cls(
config.to_model_args(runtime_max_seq_len=runtime_max_seq_len)
)
self.runtime = self.model.runtime
if bool(register_tied_weights):
self.all_tied_weights_keys = self.get_expanded_tied_weights_keys(
all_submodels=False
)
# Cache-enabled runtime execution mutates shared KV/index buffers, so a
# single model instance cannot safely run concurrent calls.
self._model_runtime_lock = self.model.runtime_lock
self._sync_runtime_gradient_checkpointing(
enable=bool(gradient_checkpointing_enabled)
)
__all__ = ["DecoderConfig", "DecoderHostMixin"]
|