Instructions to use guell00/VELUM-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use guell00/VELUM-Coder 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 guell00/VELUM-Coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf guell00/VELUM-Coder:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf guell00/VELUM-Coder:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf guell00/VELUM-Coder:Q4_K_M

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 guell00/VELUM-Coder:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf guell00/VELUM-Coder:Q4_K_M

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 guell00/VELUM-Coder:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf guell00/VELUM-Coder:Q4_K_M

Use Docker

docker model run hf.co/guell00/VELUM-Coder:Q4_K_M

LM Studio
Jan
Ollama
How to use guell00/VELUM-Coder with Ollama:
```
ollama run hf.co/guell00/VELUM-Coder:Q4_K_M
```

Unsloth Studio

How to use guell00/VELUM-Coder 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 guell00/VELUM-Coder 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 guell00/VELUM-Coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for guell00/VELUM-Coder to start chatting

How to use guell00/VELUM-Coder with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf guell00/VELUM-Coder:Q4_K_M

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": "guell00/VELUM-Coder:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use guell00/VELUM-Coder with Docker Model Runner:
```
docker model run hf.co/guell00/VELUM-Coder:Q4_K_M
```

Lemonade

How to use guell00/VELUM-Coder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull guell00/VELUM-Coder:Q4_K_M

Run and chat with the model

lemonade run user.VELUM-Coder-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use guell00/VELUM-Coder with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf guell00/VELUM-Coder:Q4_K_M

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 guell00/VELUM-Coder:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use guell00/VELUM-Coder with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf guell00/VELUM-Coder:Q4_K_M

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 "guell00/VELUM-Coder:Q4_K_M" \
  --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"

VELUM-Coder / README.md

guell00

Update model card after IQ2_XXS

7c3fee1 verified about 20 hours ago

4.75 kB

base_model: ornith-ai/Ornith-1.5-9B
datasets:
  - guell00/fds
library_name: gguf
pipeline_tag: text-generation
license: mit
tags:
  - gguf
  - llama.cpp
  - code
  - coding
  - imatrix
  - qwen3.5
  - ornith
  - lora

VELUM-Coder

VELUM-Coder is a coding-focused fine-tune derived from ornith-ai/Ornith-1.5-9B.

This repository contains GGUF quantizations generated from the completed recovery adapter.

Source adapter: ornith_fds_recovered_ckpt250
Dataset: guell00/fds
llama.cpp commit: f280b26983ad0fdb705a0d9ebf0503e76f2899b0
Generated: 2026-08-25 01:57 UTC

Quantization policy

Q8_0 is intentionally generated without an importance matrix.
Every Q4, Q3, Q2 and IQ1 build is invoked with the same domain-specific imatrix.
No Q5 or Q6 files are produced.
The extreme IQ1_M build keeps the token embedding and output tensors at Q8_0 while the body is quantized as IQ1_M.
The imatrix is generated from code/conversation samples from guell00/fds.
<think>...</think> blocks are removed from assistant calibration text so calibration emphasizes direct answers and code.

Recommended files

Best default: VELUM-Coder-Q4_K_M.gguf

Higher quality: VELUM-Coder-Q8_0.gguf

Lower memory: try VELUM-Coder-IQ3_M.gguf or VELUM-Coder-IQ2_M.gguf.

Extreme experiment: VELUM-Coder-IQ1_M.gguf. This is not expected to preserve Q4-level quality. Embedding/output protection exists only to reduce catastrophic collapse.

Files

Status	File	Quant	Imatrix	Protection	Notes
✅	`VELUM-Coder-Q8_0.gguf`	`Q8_0`	No	Normal	Highest-quality quantized build; intentionally no imatrix.
✅	`VELUM-Coder-Q4_K_M.gguf`	`Q4_K_M`	Yes	Normal	Recommended default Q4.
✅	`VELUM-Coder-Q4_K_S.gguf`	`Q4_K_S`	Yes	Normal	Smaller Q4 K-quant.
✅	`VELUM-Coder-IQ4_XS.gguf`	`IQ4_XS`	Yes	Normal	Compact 4-bit I-Quant.
✅	`VELUM-Coder-IQ4_NL.gguf`	`IQ4_NL`	Yes	Normal	Non-linear 4-bit I-Quant.
✅	`VELUM-Coder-Q4_1.gguf`	`Q4_1`	Yes	Normal	Legacy Q4 variant.
✅	`VELUM-Coder-Q4_0.gguf`	`Q4_0`	Yes	Normal	Legacy compact Q4.
✅	`VELUM-Coder-Q3_K_L.gguf`	`Q3_K_L`	Yes	Normal	Largest Q3 K-quant.
✅	`VELUM-Coder-Q3_K_M.gguf`	`Q3_K_M`	Yes	Normal	Balanced Q3 K-quant.
✅	`VELUM-Coder-Q3_K_S.gguf`	`Q3_K_S`	Yes	Normal	Smaller Q3 K-quant.
✅	`VELUM-Coder-IQ3_M.gguf`	`IQ3_M`	Yes	Normal	Medium 3-bit I-Quant.
✅	`VELUM-Coder-IQ3_S.gguf`	`IQ3_S`	Yes	Normal	Small 3-bit I-Quant.
✅	`VELUM-Coder-IQ3_XS.gguf`	`IQ3_XS`	Yes	Normal	Extra-small 3-bit I-Quant.
✅	`VELUM-Coder-IQ3_XXS.gguf`	`IQ3_XXS`	Yes	Normal	Extremely compact 3-bit I-Quant.
✅	`VELUM-Coder-Q2_K.gguf`	`Q2_K`	Yes	Normal	Aggressive Q2 K-quant.
✅	`VELUM-Coder-Q2_K_S.gguf`	`Q2_K_S`	Yes	Normal	Small Q2 K-quant.
✅	`VELUM-Coder-IQ2_M.gguf`	`IQ2_M`	Yes	Normal	Higher-quality 2-bit I-Quant.
✅	`VELUM-Coder-IQ2_S.gguf`	`IQ2_S`	Yes	Normal	2-bit I-Quant.
✅	`VELUM-Coder-IQ2_XS.gguf`	`IQ2_XS`	Yes	Normal	Extra-small 2-bit I-Quant.
✅	`VELUM-Coder-IQ2_XXS.gguf`	`IQ2_XXS`	Yes	Normal	Extremely compact 2-bit I-Quant.
⏳	`VELUM-Coder-Q2_0.gguf`	`Q2_0`	Yes	Normal	2.25 bpw Q2_0.
⏳	`VELUM-Coder-TQ2_0.gguf`	`TQ2_0`	Yes	Normal	Experimental ternary ~2-bit build.
⏳	`VELUM-Coder-IQ1_M.gguf`	`IQ1_M`	Yes	Q8_0 embed/output	Extreme build. IQ1_M body + Q8_0 embedding/output protection.

Importance matrix

The generated importance matrix is uploaded as:

imatrix/VELUM-Coder-imatrix.gguf

The calibration text itself is not uploaded; it is only an intermediate generated from guell00/fds.

llama.cpp

Example:

llama-cli -hf guell00/VELUM-Coder:Q4_K_M -c 16384

Server:

llama-server -hf guell00/VELUM-Coder:Q4_K_M -c 16384

Manual download:

hf download guell00/VELUM-Coder VELUM-Coder-Q4_K_M.gguf --local-dir .

Then:

llama-cli -m VELUM-Coder-Q4_K_M.gguf -c 16384

Adapter

The final recovery LoRA adapter is also uploaded under adapter/ when adapter upload is enabled.

Notes

Low-bit quantization is destructive. Q2 and especially IQ1_M are experimental options for constrained hardware, not substitutes for Q4/Q8 quality.

GGUFs are produced directly by llama-quantize and uploaded after successful quantization. No conversational inference is run by this pipeline.

Credits

Ornith authors
llama.cpp / ggml contributors
Hugging Face
Unsloth
guell00/fds