Instructions to use saik0s/comfy_backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use saik0s/comfy_backup with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="saik0s/comfy_backup", filename="ComfyUI/models/text_encoders/gemma-3-12b-it-q2_k.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use saik0s/comfy_backup 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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q4_K_S
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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf saik0s/comfy_backup:Q4_K_S
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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf saik0s/comfy_backup:Q4_K_S
Use Docker
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- LM Studio
- Jan
- Ollama
How to use saik0s/comfy_backup with Ollama:
ollama run hf.co/saik0s/comfy_backup:Q4_K_S
- Unsloth Studio
How to use saik0s/comfy_backup 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 saik0s/comfy_backup 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 saik0s/comfy_backup to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for saik0s/comfy_backup to start chatting
- Pi
How to use saik0s/comfy_backup with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "saik0s/comfy_backup:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use saik0s/comfy_backup with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default saik0s/comfy_backup:Q4_K_S
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use saik0s/comfy_backup with Docker Model Runner:
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- Lemonade
How to use saik0s/comfy_backup with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saik0s/comfy_backup:Q4_K_S
Run and chat with the model
lemonade run user.comfy_backup-Q4_K_S
List all available models
lemonade list
File size: 2,883 Bytes
47223ae | 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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import comfy_aimdo.model_vbar
import comfy.memory_management
import comfy.model_management
import comfy.ops
PREFETCH_QUEUES = []
def cleanup_prefetched_modules(comfy_modules):
for s in comfy_modules:
prefetch = getattr(s, "_prefetch", None)
if prefetch is None:
continue
for param_key in ("weight", "bias"):
lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
if lowvram_fn is not None:
lowvram_fn.clear_prepared()
if prefetch["signature"] is not None:
comfy_aimdo.model_vbar.vbar_unpin(s._v)
delattr(s, "_prefetch")
def cleanup_prefetch_queues():
global PREFETCH_QUEUES
for queue in PREFETCH_QUEUES:
for entry in queue:
if entry is None or not isinstance(entry, tuple):
continue
_, prefetch_state = entry
comfy_modules = prefetch_state[1]
if comfy_modules is not None:
cleanup_prefetched_modules(comfy_modules)
PREFETCH_QUEUES = []
def prefetch_queue_pop(queue, device, module):
if queue is None:
return
consumed = queue.pop(0)
if consumed is not None:
offload_stream, prefetch_state = consumed
if offload_stream is not None:
offload_stream.wait_stream(comfy.model_management.current_stream(device))
_, comfy_modules = prefetch_state
if comfy_modules is not None:
cleanup_prefetched_modules(comfy_modules)
prefetch = queue[0]
if prefetch is not None:
comfy_modules = []
for s in prefetch.modules():
if hasattr(s, "_v"):
comfy_modules.append(s)
registerable_size = 0
for s in comfy_modules:
registerable_size += comfy.memory_management.vram_aligned_size([s.weight, s.bias])
for param_key in ("weight", "bias"):
lowvram_fn = getattr(s, param_key + "_lowvram_function", None)
if lowvram_fn is not None:
registerable_size += lowvram_fn.memory_required()
offload_stream = comfy.ops.cast_modules_with_vbar(comfy_modules, None, device, None, True)
if not comfy.model_management.args.fast_disk:
comfy.model_management.ensure_pin_registerable(registerable_size)
comfy.model_management.sync_stream(device, offload_stream)
queue[0] = (offload_stream, (prefetch, comfy_modules))
def make_prefetch_queue(queue, device, transformer_options):
if (not transformer_options.get("prefetch_dynamic_vbars", False)
or comfy.model_management.NUM_STREAMS == 0
or comfy.model_management.is_device_cpu(device)
or not comfy.model_management.device_supports_non_blocking(device)):
return None
queue = [None] + queue + [None]
PREFETCH_QUEUES.append(queue)
return queue
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