Instructions to use TechCarbasa/MyModels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use TechCarbasa/MyModels 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 TechCarbasa/MyModels:Q5_0
# Run inference directly in the terminal:
llama cli -hf TechCarbasa/MyModels:Q5_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TechCarbasa/MyModels:Q5_0
# Run inference directly in the terminal:
llama cli -hf TechCarbasa/MyModels:Q5_0

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 TechCarbasa/MyModels:Q5_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf TechCarbasa/MyModels:Q5_0

Use Docker

docker model run hf.co/TechCarbasa/MyModels:Q5_0

Llama new
LM Studio
Jan
Ollama
How to use TechCarbasa/MyModels with Ollama:
```
ollama run hf.co/TechCarbasa/MyModels:Q5_0
```
Unsloth Desktop

How to use TechCarbasa/MyModels with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TechCarbasa/MyModels:Q5_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "TechCarbasa/MyModels:Q5_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TechCarbasa/MyModels with Docker Model Runner:
```
docker model run hf.co/TechCarbasa/MyModels:Q5_0
```

Lemonade

How to use TechCarbasa/MyModels with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TechCarbasa/MyModels:Q5_0

Run and chat with the model

lemonade run user.MyModels-Q5_0

List all available models

lemonade list

Hermes Agent

How to use TechCarbasa/MyModels with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TechCarbasa/MyModels:Q5_0

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 TechCarbasa/MyModels:Q5_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TechCarbasa/MyModels with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TechCarbasa/MyModels:Q5_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "TechCarbasa/MyModels:Q5_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

TechCarbasa commited on 8 days ago

Commit

776b5d1

verified ·

1 Parent(s): 93664e9

Upload wan-2.2-i2v-API.json

Browse files

Files changed (1) hide show

workflows/wan-2.2-i2v-API.json +23 -2

workflows/wan-2.2-i2v-API.json CHANGED Viewed

@@ -129,9 +129,9 @@
   },
   "94": {
     "inputs": {
-      "fps": "INPUTFPS",
       "images": [
-        "87",
         0
       ]
     },
@@ -265,5 +265,26 @@
     "_meta": {
       "title": "Save Video"
     }
   }
 }

   },
   "94": {
     "inputs": {
+      "fps": 32,
       "images": [
+        "109",
         0
       ]
     },
     "_meta": {
       "title": "Save Video"
     }
+  },
+  "109": {
+    "inputs": {
+      "ckpt_name": "rife47.pth",
+      "clear_cache_after_n_frames": 10,
+      "multiplier": 2,
+      "fast_mode": true,
+      "ensemble": true,
+      "scale_factor": 1,
+      "batch_size": 1,
+      "dtype": "float32",
+      "torch_compile": false,
+      "frames": [
+        "87",
+        0
+      ]
+    },
+    "class_type": "RIFE VFI",
+    "_meta": {
+      "title": "RIFE VFI (x2: 16 -> 32 fps)"
+    }
   }
 }