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 12 days ago

Commit

582cb4e

verified ·

1 Parent(s): af9cf9b

Upload podflow/export_data.sh with huggingface_hub

Browse files

Files changed (1) hide show

podflow/export_data.sh +13 -0

podflow/export_data.sh ADDED Viewed

	@@ -0,0 +1,13 @@

+#!/bin/bash
+set -euo pipefail
+T=$(cat /root/.hftok)
+curl -sL -H "Authorization: Bearer $T" -T /root/measure_pooled.jsonl \
+  "https://huggingface.co/datasets/eyad-silx/progress/resolve/main/x" 2>/dev/null || true
+/root/ingot/.venv/bin/python - <<'PY'
+from huggingface_hub import HfApi
+tok = open("/root/.hftok").read().strip()
+HfApi(token=tok).upload_file(path_or_fileobj="/root/measure_pooled.jsonl",
+    path_in_repo="podflow/twin_per_sample.jsonl",
+    repo_id="eyad-silx/progress", repo_type="model")
+print("DATA_UP_OK")
+PY