Instructions to use eyad-silx/progress with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use eyad-silx/progress 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 eyad-silx/progress
# Run inference directly in the terminal:
llama cli -hf eyad-silx/progress

Install from WinGet (Windows)

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

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 eyad-silx/progress
# Run inference directly in the terminal:
./build/bin/llama cli -hf eyad-silx/progress

Use Docker

docker model run hf.co/eyad-silx/progress

Llama new
LM Studio
Jan
Ollama
How to use eyad-silx/progress with Ollama:
```
ollama run hf.co/eyad-silx/progress
```
Unsloth Desktop

How to use eyad-silx/progress with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eyad-silx/progress

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": "eyad-silx/progress"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use eyad-silx/progress with Docker Model Runner:
```
docker model run hf.co/eyad-silx/progress
```

Lemonade

How to use eyad-silx/progress with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eyad-silx/progress

Run and chat with the model

lemonade run user.progress-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use eyad-silx/progress with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eyad-silx/progress

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 eyad-silx/progress

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use eyad-silx/progress with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf eyad-silx/progress

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 "eyad-silx/progress" \
  --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"

eyad-silx commited on 14 days ago

Commit

6e8d8d8

verified ·

1 Parent(s): 663f727

push king-ix/README.md

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+# Ingot-IX-27B-Q2
+A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it.
+- Submitted by miner hotkey `5FHoQiL4CRLCVEWKX6by9aGExiz7WxRWyBtUJKN5uwyi6iyU` (uid 197)
+- Crowned from submission `2c80dc33-ca61-494d-bda3-46bcd3642fb0`
+The weights are the miner's upload, byte for byte. Credit belongs to the miner.
+## How close it is to the teacher
+Per answer token, against the BF16 teacher reading the same answer: KL(teacher ‖ model) of the next-token distributions, and the hidden-state distance |h − h_teacher| / |h_teacher| averaged over every 8th layer. Lower is closer.
+| | KL | hidden-state distance |
+| --- | --- | --- |
+| this model, in its winning eval | 0.10294 | 0.3201 |
+| the king it beat, same eval | 0.10452 | 0.3264 |
+| closer than genesis [dendriteholdings/Ingot-genesis-27B-Q2](https://huggingface.co/dendriteholdings/Ingot-genesis-27B-Q2) | 20.1% | 29.4% |
+## Running it
+`Ingot-IX-27B-Q2.gguf` is a gguf with the layout, tokenizer and Hadamard rotation of [dendriteholdings/Ingot-genesis-27B-Q2](https://huggingface.co/dendriteholdings/Ingot-genesis-27B-Q2) (`Ingot-genesis-27B-Q2.gguf`), its weights the miner's. Stock llama.cpp does not run it; the PrismML fork does, at the commit Ingot scored it with, [`e3de715`](https://github.com/PrismML-Eng/llama.cpp/commit/e3de715222b2667c54a267441159a5729f11155e):
+```
+git clone https://github.com/PrismML-Eng/llama.cpp && cd llama.cpp && git checkout e3de715222b2667c54a267441159a5729f11155e
+cmake -B build -DGGML_CUDA=ON && cmake --build build -j
+build/bin/llama-server -m Ingot-IX-27B-Q2.gguf -ngl 99 -fa on --jinja
+```
+### With images
+The file is the text model. For image input, add the vision tower of [prism-ml/Ternary-Bonsai-2-27B-gguf](https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf) as a projector ([`Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf`](https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf/blob/b072e1d3b35a0a630cece372c2127528e0994386/Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf), [`Ternary-Bonsai-2-27B-mmproj-BF16.gguf`](https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf/blob/b072e1d3b35a0a630cece372c2127528e0994386/Ternary-Bonsai-2-27B-mmproj-BF16.gguf)). It was made for genesis, not for this model's weights, and Ingot scores the text model only, so image quality is untested:
+```
+hf download prism-ml/Ternary-Bonsai-2-27B-gguf Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf --revision b072e1d3b35a0a630cece372c2127528e0994386 --local-dir .
+build/bin/llama-server -m Ingot-IX-27B-Q2.gguf --mmproj Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf -ngl 99 -fa on --jinja
+```