Instructions to use HOLOGRAMTECH/q-qwen-coder-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HOLOGRAMTECH/q-qwen-coder-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HOLOGRAMTECH/q-qwen-coder-7b",
	filename="tokenizer.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use HOLOGRAMTECH/q-qwen-coder-7b 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 HOLOGRAMTECH/q-qwen-coder-7b
# Run inference directly in the terminal:
llama cli -hf HOLOGRAMTECH/q-qwen-coder-7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HOLOGRAMTECH/q-qwen-coder-7b
# Run inference directly in the terminal:
llama cli -hf HOLOGRAMTECH/q-qwen-coder-7b

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 HOLOGRAMTECH/q-qwen-coder-7b
# Run inference directly in the terminal:
./llama-cli -hf HOLOGRAMTECH/q-qwen-coder-7b

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 HOLOGRAMTECH/q-qwen-coder-7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HOLOGRAMTECH/q-qwen-coder-7b

Use Docker

docker model run hf.co/HOLOGRAMTECH/q-qwen-coder-7b

LM Studio
Jan

vLLM

How to use HOLOGRAMTECH/q-qwen-coder-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HOLOGRAMTECH/q-qwen-coder-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HOLOGRAMTECH/q-qwen-coder-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HOLOGRAMTECH/q-qwen-coder-7b

Ollama
How to use HOLOGRAMTECH/q-qwen-coder-7b with Ollama:
```
ollama run hf.co/HOLOGRAMTECH/q-qwen-coder-7b
```

Unsloth Studio

How to use HOLOGRAMTECH/q-qwen-coder-7b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HOLOGRAMTECH/q-qwen-coder-7b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HOLOGRAMTECH/q-qwen-coder-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HOLOGRAMTECH/q-qwen-coder-7b to start chatting

How to use HOLOGRAMTECH/q-qwen-coder-7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HOLOGRAMTECH/q-qwen-coder-7b

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "HOLOGRAMTECH/q-qwen-coder-7b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use HOLOGRAMTECH/q-qwen-coder-7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HOLOGRAMTECH/q-qwen-coder-7b

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 HOLOGRAMTECH/q-qwen-coder-7b

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use HOLOGRAMTECH/q-qwen-coder-7b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HOLOGRAMTECH/q-qwen-coder-7b

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 "HOLOGRAMTECH/q-qwen-coder-7b" \
  --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"

Docker Model Runner
How to use HOLOGRAMTECH/q-qwen-coder-7b with Docker Model Runner:
```
docker model run hf.co/HOLOGRAMTECH/q-qwen-coder-7b
```

Lemonade

How to use HOLOGRAMTECH/q-qwen-coder-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HOLOGRAMTECH/q-qwen-coder-7b

Run and chat with the model

lemonade run user.q-qwen-coder-7b-{{QUANT_TAG}}

List all available models

lemonade list

q-qwen-coder-7b / README.md

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-Coder-7B-Instruct
	pipeline_tag: text-generation
	inference: false
	tags:
	- hologram
	- q
	- kappa-object
	- holo
	- code
	- agentic
	- qwen2
	---

	<div align="center">

	# Hologram · Qwen2.5-Coder-7B

	Agentic coding brain

	`q3f` · `3.4 GB` · streamed to Q as a key-addressable `.holo` object

	[Hologram](https://gethologram.ai) · [Live Space](https://huggingface.co/spaces/HOLOGRAMTECH/hologram) · [Organization](https://huggingface.co/HOLOGRAMTECH) · [Code](https://github.com/Hologram-Technologies)

	</div>

	---

	## What this is

	The Holo Code agent brain. A self-contained key object: the tokenizer is bundled, so it loads with no external dependency and runs tool-using coding workflows.

	This repository is not a GGUF or Transformers checkpoint. It is a Hologram key object: the weights of `Qwen/Qwen2.5-Coder-7B-Instruct` re-encoded into Hologram's content-addressed `.holo` format so they stream, one verified block at a time, into Q, the on-device brain of the Hologram web OS. It runs in the browser on WebGPU, serverless, with nothing to install.

	## How it streams

	The object is laid out for cold streaming from an untrusted CDN:

	\| File \| Role \|
	\|---\|---\|
	\| `manifest.json` \| the root. Names every tensor and the key (content hash) of its block. \|
	\| `b/sha256_.gz` \| the tensor blocks. Each filename is* the SHA-256 of its bytes. \|
	\| `tokenizer.gguf` \| bundled header (where present), so loading is fully serverless. \|

	Q fetches the manifest, then pulls each block by its key and re-derives `sha256(block)` on arrival. If a byte is wrong, the block is rejected. Nothing is trusted; everything is proven.

	## Verify (Law L5)

	The object's identity is the SHA-256 of its manifest, pinned in Q's catalog before a single byte of weight is trusted:

	```
	did:holo:sha256:539941cb060c7dd583e2e86697e53f2c5d511d597c65d09d9c780fbded2c3edf
	```

	```bash
	# the manifest hash equals the pinned identity above
	curl -sL https://huggingface.co/HOLOGRAMTECH/q-qwen-coder-7b/resolve/main/manifest.json \| sha256sum
	```

	## Specifications

	\| \| \|
	\|---\|---\|
	\| Architecture \| Qwen2.5-Coder \|
	\| Precision \| q3f \|
	\| Object size \| 3.4 GB \|
	\| Hidden size \| 3584 \|
	\| Layers \| 28 \|
	\| Heads (Q / KV) \| 28 / 4 (GQA) \|
	\| FFN \| 18944 \|
	\| Vocab \| 152064 \|
	\| Context \| 3000 \|
	\| Format \| `holo-2bit/1` \|

	## Provenance and license

	Derived from [`Qwen/Qwen2.5-Coder-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct). The re-encoding is lossless-by-construction at the key level: every block is content-addressed, so the object either re-derives to its pinned identity or it is refused.

	## Run it

	These weights load through Q, not a standard runtime. Open the [Live Space](https://huggingface.co/spaces/HOLOGRAMTECH/hologram) or visit [gethologram.ai](https://gethologram.ai) to run Hologram, then pick Qwen2.5-Coder-7B from Q's model list.

	<div align="center"><sub>Composed on the golden ratio. One key, everything.</sub></div>