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
- llama.cpp
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
- Pi
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"
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Download README.md from eyad-silx/progress: direct link, hf CLI and curl.
- Browser
- Download file 1.48 kB
-
https://huggingface.co/eyad-silx/progress/resolve/main/README.md
- Command line
-
hf download hf://eyad-silx/progress/README.md
-
curl -L -o README.md https://huggingface.co/eyad-silx/progress/resolve/main/README.md
1.48 kB
| # Ingot (SN97) challenger build β progress proof | |
| Automated backup of build/train/eval artifacts from the H100 pod (RunPod, 1x H100 NVL). | |
| Pushed by `hf_push.py` β checks hourly and uploads any new/changed artifact. | |
| - `king-ix/` β incumbent king GGUF (Ingot-IX-27B-Q2) + provenance files (baseline) | |
| - `chal-identity/`, `chal-v1/`, `chal-n2/`, `chal-norms/` β challenger GGUF iterations | |
| (scale-refit / QAT variants; same 27B Q2 architecture as the king) | |
| - `states/` β QAT state checkpoints (`t0..tN.safetensors`); the quota-blocked ones | |
| (t2βt9, t15) are stored as `states/missing_t2-t9_t15.tar.zst` (restore: `tar --zstd -xf`) | |
| - `chal-n2/chal-norms.gguf.zst` β zstd-19 archive of the chal-n2 GGUF, uploaded compressed | |
| to fit the free-plan private-storage cap (raw sha256 in MANIFEST; restore: `zstd -d`) | |
| - `artifacts/` β `HANDOFF.md`, build/train/eval scripts, run logs, sample draws, | |
| measurement results (`measure*.jsonl`, `pick*.jsonl`, `drawn.jsonl`) | |
| - `MANIFEST.md` β size + sha256 + upload time of every artifact (regenerated each push), | |
| including a "Pending" section for files blocked by the free-plan private-storage cap | |
| (their bytes are preserved on the build pod and in a local rescue cache) | |
| - `PROGRESS.md` β push history | |
| The GGUFs are ~6.8 GB each (PQ2_0 quantized 27B linear-attention models). | |
| Note: chal-identity's GGUF is byte-identical to king-ix's (sha256 in MANIFEST) β the | |
| identity refit test round-trips the king exactly. | |