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

Hologram · Qwen2.5-Coder-7B

Agentic coding brain

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

Hologram · Live Space · Organization · Code

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

# 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. 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 or visit gethologram.ai to run Hologram, then pick Qwen2.5-Coder-7B from Q's model list.

_{Composed on the golden ratio. One key, everything.}

Downloads last month: -

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for HOLOGRAMTECH/q-qwen-coder-7b

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

(198)

this model

HOLOGRAMTECH
/

q-qwen-coder-7b

Hologram · Qwen2.5-Coder-7B

What this is

How it streams

Verify (Law L5)

Specifications

Provenance and license

Run it

Model tree for HOLOGRAMTECH/q-qwen-coder-7b

Space using HOLOGRAMTECH/q-qwen-coder-7b 1