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

Libraries

How to use TechCarbasa/MyModels with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TechCarbasa/MyModels",
	filename="workspace/ComfyUI/models/WanVideo/Wan2.1-single/wan2.1-i2v-14b-480p-Q5_0.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 TechCarbasa/MyModels with llama.cpp:

Install from brew

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

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 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/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 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

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

Unsloth Studio

How to use TechCarbasa/MyModels 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 TechCarbasa/MyModels 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 TechCarbasa/MyModels to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TechCarbasa/MyModels to start chatting

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-server -hf TechCarbasa/MyModels:Q5_0

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": "TechCarbasa/MyModels:Q5_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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-server -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

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

TechCarbasa commited on Feb 2

Commit

edb2d72

verified ·

1 Parent(s): dfbab76

Upload /workspace/ComfyUI/models/configs/v1-inference_clip_skip_2_fp16.yaml with huggingface_hub

Browse files

Files changed (1) hide show

workspace/ComfyUI/models/configs/v1-inference_clip_skip_2_fp16.yaml +74 -0

workspace/ComfyUI/models/configs/v1-inference_clip_skip_2_fp16.yaml ADDED Viewed

	@@ -0,0 +1,74 @@

+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+      params:
+        layer: "hidden"
+        layer_idx: -2