Instructions to use nameissakthi/chipwright-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use nameissakthi/chipwright-llm 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 nameissakthi/chipwright-llm:Q8_0
# Run inference directly in the terminal:
llama cli -hf nameissakthi/chipwright-llm:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nameissakthi/chipwright-llm:Q8_0
# Run inference directly in the terminal:
llama cli -hf nameissakthi/chipwright-llm:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf nameissakthi/chipwright-llm:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf nameissakthi/chipwright-llm:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf nameissakthi/chipwright-llm:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nameissakthi/chipwright-llm:Q8_0

Use Docker

docker model run hf.co/nameissakthi/chipwright-llm:Q8_0

Llama new
LM Studio
Jan
Ollama
How to use nameissakthi/chipwright-llm with Ollama:
```
ollama run hf.co/nameissakthi/chipwright-llm:Q8_0
```
Unsloth Desktop

How to use nameissakthi/chipwright-llm with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nameissakthi/chipwright-llm:Q8_0

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": "nameissakthi/chipwright-llm:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nameissakthi/chipwright-llm with Docker Model Runner:
```
docker model run hf.co/nameissakthi/chipwright-llm:Q8_0
```

Lemonade

How to use nameissakthi/chipwright-llm with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nameissakthi/chipwright-llm:Q8_0

Run and chat with the model

lemonade run user.chipwright-llm-Q8_0

List all available models

lemonade list

Hermes Agent

How to use nameissakthi/chipwright-llm with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nameissakthi/chipwright-llm:Q8_0

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 nameissakthi/chipwright-llm:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nameissakthi/chipwright-llm with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nameissakthi/chipwright-llm:Q8_0

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 "nameissakthi/chipwright-llm:Q8_0" \
  --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"

chipwright-llm — Qwen2.5-1.5B-Instruct Q8_0 (llama.cpp-HTP, Hexagon v73)

Artifacts hosted for chipwright — a verified package manager for Qualcomm NPUs (resolve · run · build). This repo holds the llama.cpp-HTP runtime variant: a Q8_0 GGUF that dispatches on the Hexagon NPU through the llama.cpp Hexagon backend.

Why this exists: classic Genie is w4a16-only. This is the >4-bit integer route Genie structurally lacks — Q8_0 MUL_MAT running on the HTP — and chipwright treats runtime as a first-class resolver axis (spec 004).

What's here

file	sha256	notes
`qwen2.5-1.5b-Q8_0.gguf`	`cedde4175edae2e9467d8b98d70843e57c8fb101afd54ca773bce022e80f7e4e`	1.6 GB, llama.cpp build `b3278e921`

Silicon verification (chipwright Constitution Principle I — verify on the device)

Re-verified 2026-10-24 on QCS9075 / Dragonwing IQ-9075 (soc_id 676, Hexagon v73) through the chipwright llama.cpp-HTP recipe:

Dispatch (SC-RT03): Q8_0 MUL_MAT dispatched to HTP0 on the compute-DSP (_dom=cdsp), not a CPU fallback — prefill 592.82 t/s on HTP0 vs 217.55 t/s CPU-only (GGML_HEXAGON_NDEV=0) = 2.72× prefill. (The earlier QCS8550/v73 sweep showed 266.74 vs 152.93 = 1.74×.)
Quality: WikiText perplexity (16 chunks) = 11.6631 ± 0.494, near-lossless vs the model's own f16 baseline (11.6388).

How to run it (the recipe)

On a v73+ Hexagon board with fastrpc + libcdsprpc.so, using the llama.cpp Hexagon backend (libggml-htp-v73.so) under an injected glibc loader:

ADSP_LIBRARY_PATH=<pkg>/lib \
  ./glibc238/ld-linux-aarch64.so.1 --library-path ./glibc238:<pkg>/lib:/usr/lib:/lib \
  <pkg>/bin/llama-bench -m qwen2.5-1.5b-Q8_0.gguf --device HTP0 -p 64 -n 32 -r 2
# CPU reference: prefix GGML_HEXAGON_NDEV=0 and drop --device

chipwright resolve qwen2.5-1.5b-instruct --target htpv73 --runtime llamacpp-htp returns this variant (USE), and refuses it by name on v68 (FAIL axis=runtime — needs v73-or-newer Hexagon). CLI on-device execution of this runtime is tracked as spec 004 T-RT11.

License

Weights: Apache-2.0 (inherited from Qwen/Qwen2.5-1.5B-Instruct). GGUF quantized with llama.cpp.

Downloads last month: 55

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

8-bit

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