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"

progress

File size: 1,475 Bytes

6b153e4
 
 
 
 
 
 
 
f1c07ff
 
 
 
6b153e4
 
9a92d8b
 
 
6b153e4
 
 
9a92d8b

# Ingot (SN97) challenger build — progress proof

Automated backup of build/train/eval artifacts from the H100 pod (RunPod, 1x H100 NVL).
Pushed by `hf_push.py` — checks hourly and uploads any new/changed artifact.

- `king-ix/` — incumbent king GGUF (Ingot-IX-27B-Q2) + provenance files (baseline)
- `chal-identity/`, `chal-v1/`, `chal-n2/`, `chal-norms/` — challenger GGUF iterations
  (scale-refit / QAT variants; same 27B Q2 architecture as the king)
- `states/` — QAT state checkpoints (`t0..tN.safetensors`); the quota-blocked ones
  (t2–t9, t15) are stored as `states/missing_t2-t9_t15.tar.zst` (restore: `tar --zstd -xf`)
- `chal-n2/chal-norms.gguf.zst` — 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/` — `HANDOFF.md`, build/train/eval scripts, run logs, sample draws,
  measurement results (`measure*.jsonl`, `pick*.jsonl`, `drawn.jsonl`)
- `MANIFEST.md` — 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` — 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) — the
identity refit test round-trips the king exactly.