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 / artifacts /scripts /setup_h100.sh

eyad-silx

push artifacts/scripts/setup_h100.sh

3a023c8 verified 14 days ago

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#!/bin/bash
set -e
export PATH=/usr/local/cuda/bin:$PATH
cd /root
pip install -q -U huggingface_hub hf_transfer 2>&1 | tail -1 || true
[ -d ingot ] || git clone --recurse-submodules --depth 1 https://github.com/DendriteLabs/ingot.git
mkdir -p /root/models
[ -d /root/models/teacher ] || hf download Qwen/Qwen3.8-27B --local-dir /root/models/teacher > /root/dl_teacher.log 2>&1 &
[ -d /root/models/king-ix ] || hf download dendriteholdings/Ingot-IX-27B-Q2 --local-dir /root/models/king-ix > /root/dl_king.log 2>&1 &
wait
echo DOWNLOADS_DONE
cd /root/ingot
cmake -S llama-prism -B llama-prism/build -DGGML_CUDA=ON -DLLAMA_CURL=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=90 > /root/cmake.log 2>&1
cmake --build llama-prism/build -j $(nproc) > /root/build_llama.log 2>&1
cmake -S src/eval_service/llama_states -B build/llama_states -DLLAMA_DIR=$PWD/llama-prism -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=90 >> /root/cmake.log 2>&1
cmake --build build/llama_states >> /root/build_llama.log 2>&1
echo LLAMA_DONE
uv venv --python 3.12 .venv
.venv/bin/pip install -q "vllm==0.31.0" flash-linear-attention ninja 2>&1 | tail -2
.venv/bin/pip install -q -e ".[eval]" 2>&1 | tail -2
SP=$(.venv/bin/python -c "import sysconfig; print(sysconfig.get_paths()['purelib'])")
grep -q _split_kv_cache_groups_to_single_layer "$SP/vllm/v1/core/kv_cache_utils.py" || patch --no-backup-if-mismatch -p1 -d "$SP" < scripts/vllm_pr56822.diff
CU=$SP/nvidia/cu13
[ -e "$CU/lib64" ] || ln -s lib "$CU/lib64"
(cd "$CU/lib" && for lib in libcudart libnvrtc libcublas libcublasLt; do ln -sfn "$lib.so.13" "$lib.so"; done; ln -sfn "$(ls libnvrtc-builtins.so.13.* | head -1)" libnvrtc-builtins.so)
.venv/bin/python -c "import torch, vllm; from vllm.v1.core import kv_cache_utils; assert hasattr(kv_cache_utils, '_split_kv_cache_groups_to_single_layer'); print('EVAL BOX OK', torch.cuda.get_device_name(0))"
echo SETUP_DONE

	#!/bin/bash
	set -e
	export PATH=/usr/local/cuda/bin:$PATH
	cd /root
	pip install -q -U huggingface_hub hf_transfer 2>&1 \| tail -1 \|\| true
	[ -d ingot ] \|\| git clone --recurse-submodules --depth 1 https://github.com/DendriteLabs/ingot.git
	mkdir -p /root/models
	[ -d /root/models/teacher ] \|\| hf download Qwen/Qwen3.8-27B --local-dir /root/models/teacher > /root/dl_teacher.log 2>&1 &
	[ -d /root/models/king-ix ] \|\| hf download dendriteholdings/Ingot-IX-27B-Q2 --local-dir /root/models/king-ix > /root/dl_king.log 2>&1 &
	wait
	echo DOWNLOADS_DONE
	cd /root/ingot
	cmake -S llama-prism -B llama-prism/build -DGGML_CUDA=ON -DLLAMA_CURL=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=90 > /root/cmake.log 2>&1
	cmake --build llama-prism/build -j $(nproc) > /root/build_llama.log 2>&1
	cmake -S src/eval_service/llama_states -B build/llama_states -DLLAMA_DIR=$PWD/llama-prism -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=90 >> /root/cmake.log 2>&1
	cmake --build build/llama_states >> /root/build_llama.log 2>&1
	echo LLAMA_DONE
	uv venv --python 3.12 .venv
	.venv/bin/pip install -q "vllm==0.31.0" flash-linear-attention ninja 2>&1 \| tail -2
	.venv/bin/pip install -q -e ".[eval]" 2>&1 \| tail -2
	SP=$(.venv/bin/python -c "import sysconfig; print(sysconfig.get_paths()['purelib'])")
	grep -q _split_kv_cache_groups_to_single_layer "$SP/vllm/v1/core/kv_cache_utils.py" \|\| patch --no-backup-if-mismatch -p1 -d "$SP" < scripts/vllm_pr56822.diff
	CU=$SP/nvidia/cu13
	[ -e "$CU/lib64" ] \|\| ln -s lib "$CU/lib64"
	(cd "$CU/lib" && for lib in libcudart libnvrtc libcublas libcublasLt; do ln -sfn "$lib.so.13" "$lib.so"; done; ln -sfn "$(ls libnvrtc-builtins.so.13.* \| head -1)" libnvrtc-builtins.so)
	.venv/bin/python -c "import torch, vllm; from vllm.v1.core import kv_cache_utils; assert hasattr(kv_cache_utils, '_split_kv_cache_groups_to_single_layer'); print('EVAL BOX OK', torch.cuda.get_device_name(0))"
	echo SETUP_DONE