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
- llama.cpp
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
- Pi
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"
File size: 4,746 Bytes
1bf4363 dfff91b 1bf4363 dfff91b 1bf4363 dfff91b 7c3fee1 dfff91b c2ee0e2 d2bf669 60b11ae 4d6b673 c603399 ff80b7a eaa084a 8d07056 3b7217a 3301e1b 5bb6cdd 9551cac 0216a2e f396b87 34341c5 1dc73ec 585342d 5b7ba7b 7c3fee1 dfff91b | 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 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | ---
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`](https://huggingface.co/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 |
|---|---|---|---|---|---|
| β
| `VELUM-Coder-Q8_0.gguf` | `Q8_0` | No | Normal | Highest-quality quantized build; intentionally no imatrix. |
| β
| `VELUM-Coder-Q4_K_M.gguf` | `Q4_K_M` | Yes | Normal | Recommended default Q4. |
| β
| `VELUM-Coder-Q4_K_S.gguf` | `Q4_K_S` | Yes | Normal | Smaller Q4 K-quant. |
| β
| `VELUM-Coder-IQ4_XS.gguf` | `IQ4_XS` | Yes | Normal | Compact 4-bit I-Quant. |
| β
| `VELUM-Coder-IQ4_NL.gguf` | `IQ4_NL` | Yes | Normal | Non-linear 4-bit I-Quant. |
| β
| `VELUM-Coder-Q4_1.gguf` | `Q4_1` | Yes | Normal | Legacy Q4 variant. |
| β
| `VELUM-Coder-Q4_0.gguf` | `Q4_0` | Yes | Normal | Legacy compact Q4. |
| β
| `VELUM-Coder-Q3_K_L.gguf` | `Q3_K_L` | Yes | Normal | Largest Q3 K-quant. |
| β
| `VELUM-Coder-Q3_K_M.gguf` | `Q3_K_M` | Yes | Normal | Balanced Q3 K-quant. |
| β
| `VELUM-Coder-Q3_K_S.gguf` | `Q3_K_S` | Yes | Normal | Smaller Q3 K-quant. |
| β
| `VELUM-Coder-IQ3_M.gguf` | `IQ3_M` | Yes | Normal | Medium 3-bit I-Quant. |
| β
| `VELUM-Coder-IQ3_S.gguf` | `IQ3_S` | Yes | Normal | Small 3-bit I-Quant. |
| β
| `VELUM-Coder-IQ3_XS.gguf` | `IQ3_XS` | Yes | Normal | Extra-small 3-bit I-Quant. |
| β
| `VELUM-Coder-IQ3_XXS.gguf` | `IQ3_XXS` | Yes | Normal | Extremely compact 3-bit I-Quant. |
| β
| `VELUM-Coder-Q2_K.gguf` | `Q2_K` | Yes | Normal | Aggressive Q2 K-quant. |
| β
| `VELUM-Coder-Q2_K_S.gguf` | `Q2_K_S` | Yes | Normal | Small Q2 K-quant. |
| β
| `VELUM-Coder-IQ2_M.gguf` | `IQ2_M` | Yes | Normal | Higher-quality 2-bit I-Quant. |
| β
| `VELUM-Coder-IQ2_S.gguf` | `IQ2_S` | Yes | Normal | 2-bit I-Quant. |
| β
| `VELUM-Coder-IQ2_XS.gguf` | `IQ2_XS` | Yes | Normal | Extra-small 2-bit I-Quant. |
| β
| `VELUM-Coder-IQ2_XXS.gguf` | `IQ2_XXS` | Yes | Normal | Extremely compact 2-bit I-Quant. |
| β³ | `VELUM-Coder-Q2_0.gguf` | `Q2_0` | Yes | Normal | 2.25 bpw Q2_0. |
| β³ | `VELUM-Coder-TQ2_0.gguf` | `TQ2_0` | Yes | Normal | Experimental ternary ~2-bit build. |
| β³ | `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:
~~~bash
llama-cli -hf guell00/VELUM-Coder:Q4_K_M -c 16384
~~~
Server:
~~~bash
llama-server -hf guell00/VELUM-Coder:Q4_K_M -c 16384
~~~
Manual download:
~~~bash
hf download guell00/VELUM-Coder VELUM-Coder-Q4_K_M.gguf --local-dir .
~~~
Then:
~~~bash
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`
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