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

9ebe7c2

verified ·

1 Parent(s): b67c162

push artifacts/logs/measure.log

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+2026-10-26 02:28:38.058 | INFO     | eval_service.servers:serve:505 - starting the teacher on GPUs (0,): /root/models/teacher
+2026-10-26 02:29:38.237 | INFO     | eval_service.servers:serve:514 - the teacher is ready, 60s into serve
+teacher 1/16: affine/train/math_prealgebra-4363603/en p73 a130
+teacher 2/16: affine/train/math_algebra-7595820/en p86 a378
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+teacher 16/16: affine/train/math_precalculus-9027392/en p158 a1013
+2026-10-26 02:30:27.254 | INFO     | eval_service.servers:serve:505 - starting the king on GPUs (0,): /root/models/king-ix
+2026-10-26 02:30:29.326 | INFO     | eval_service.servers:serve:514 - the king is ready, 2s into serve
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+king 15/16: affine/train/math_prealgebra-7667387/en
+king 16/16: affine/train/math_precalculus-9027392/en
+2026-10-26 02:30:37.740 | INFO     | eval_service.servers:serve:505 - starting the challenger on GPUs (0,): /root/models/chal-identity
+2026-10-26 02:30:39.972 | INFO     | eval_service.servers:serve:514 - the challenger is ready, 2s into serve
+challenger 1/16: affine/train/math_prealgebra-4363603/en
+challenger 2/16: affine/train/math_algebra-7595820/en
+challenger 3/16: affine/train/math_geometry-2333548/en
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+challenger 16/16: affine/train/math_precalculus-9027392/en
+scored affine/train/math_prealgebra-4363603/en: king KL 0.0097 challenger KL 0.0097
+scored affine/train/math_algebra-7595820/en: king KL 0.0239 challenger KL 0.0239
+scored affine/train/math_geometry-2333548/en: king KL 0.0153 challenger KL 0.0153
+scored affine/train/math_algebra-15977093/en: king KL 0.0128 challenger KL 0.0128
+scored affine/train/math_prealgebra-6982828/en: king KL 0.0243 challenger KL 0.0243
+scored affine/train/math_algebra-15278704/en: king KL 0.0126 challenger KL 0.0126
+scored affine/train/math_number_theory-5402571/en: king KL 0.0290 challenger KL 0.0290
+scored affine/train/math_algebra-10810849/en: king KL 0.0385 challenger KL 0.0385
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+scored affine/train/math_intermediate_algebra-8535128/en: king KL 0.0225 challenger KL 0.0225
+scored affine/train/math_intermediate_algebra-11925264/en: king KL 0.0297 challenger KL 0.0297
+scored affine/train/math_algebra-7745671/en: king KL 0.0204 challenger KL 0.0204
+scored affine/train/math_precalculus-2471323/en: king KL 0.0118 challenger KL 0.0118
+scored affine/train/math_prealgebra-7667387/en: king KL 0.0198 challenger KL 0.0198
+scored affine/train/math_precalculus-9027392/en: king KL 0.0282 challenger KL 0.0282
+teacher      KL 0.000000  distance 0.00000
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+margin needed: KL 0.000340  dist 0.002500 ; challenger leads by KL 0.000000 (0.000%) dist 0.000000 (0.000%)