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"
File size: 1,671 Bytes
0234c79 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | """Build the margin-noise PoC twin v2: byte-different, functionally identical copy of the
current king. TRUE lowest-mantissa-bit flip on ONE fp16 block scale (bit-cast, not value cast).
"""
import hashlib
import shutil
import sys
from pathlib import Path
sys.path.insert(0, "/root/ingot/src")
import numpy as np
from common.gguf import read_header
from common.gguf.types import PQ2_0
KING = Path("/root/models/king-xii/Ingot-XII-27B-Q2.gguf")
OUT_DIR = Path("/root/models/chal-twin")
OUT = OUT_DIR / KING.name
g = read_header(KING)
pq = sorted(n for n, t in g.tensors.items() if t.ggml_type == PQ2_0 and n.startswith("blk.30."))
name = pq[0]
t = g.tensors[name]
n_blocks = int(np.prod(t.shape)) // 128
print(f"twin base tensor: {name} ({n_blocks} blocks)")
OUT_DIR.mkdir(parents=True, exist_ok=True)
shutil.copy(KING, OUT)
raw = np.memmap(OUT, np.uint8, "r+", g.data_start + t.offset, (n_blocks * 34,))
b = raw.reshape(-1, 34)
old_bytes = b[0, :2].copy()
old_bits = old_bytes.view("<u2")[0]
new_bits = np.uint16(old_bits ^ 0x0001) # true bit-cast flip: lowest mantissa bit
b[0, :2] = np.asarray([new_bits], "<u2").view(np.uint8)
raw.flush()
old_val = np.asarray([old_bits], "<u2").view("<f2")[0]
new_val = np.asarray([new_bits], "<u2").view("<f2")[0]
def h(p):
m = hashlib.sha256()
with open(p, "rb") as f:
for c in iter(lambda: f.read(1 << 24), b""):
m.update(c)
return m.hexdigest()[:16]
print(f"scale block0: {old_val:.8e} -> {new_val:.8e} (rel change {abs(new_val-old_val)/abs(old_val):.2e})")
print(f"king sha256 {h(KING)}")
print(f"twin sha256 {h(OUT)}")
print("byte-different:", h(KING) != h(OUT))
print("WROTE", OUT)
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