Instructions to use kambrosius/veritas-coder-7b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use kambrosius/veritas-coder-7b-gguf 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 kambrosius/veritas-coder-7b-gguf:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf kambrosius/veritas-coder-7b-gguf:Q5_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 kambrosius/veritas-coder-7b-gguf:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf kambrosius/veritas-coder-7b-gguf:Q5_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 kambrosius/veritas-coder-7b-gguf:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M

Use Docker

docker model run hf.co/kambrosius/veritas-coder-7b-gguf:Q5_K_M

LM Studio
Jan

vLLM

How to use kambrosius/veritas-coder-7b-gguf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kambrosius/veritas-coder-7b-gguf:Q5_K_M

Ollama
How to use kambrosius/veritas-coder-7b-gguf with Ollama:
```
ollama run hf.co/kambrosius/veritas-coder-7b-gguf:Q5_K_M
```

Unsloth Studio

How to use kambrosius/veritas-coder-7b-gguf 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 kambrosius/veritas-coder-7b-gguf 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 kambrosius/veritas-coder-7b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use kambrosius/veritas-coder-7b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M

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": "kambrosius/veritas-coder-7b-gguf:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use kambrosius/veritas-coder-7b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kambrosius/veritas-coder-7b-gguf:Q5_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 "kambrosius/veritas-coder-7b-gguf:Q5_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"

Docker Model Runner
How to use kambrosius/veritas-coder-7b-gguf with Docker Model Runner:
```
docker model run hf.co/kambrosius/veritas-coder-7b-gguf:Q5_K_M
```

Lemonade

How to use kambrosius/veritas-coder-7b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kambrosius/veritas-coder-7b-gguf:Q5_K_M

Run and chat with the model

lemonade run user.veritas-coder-7b-gguf-Q5_K_M

List all available models

lemonade list

Hermes Agent

How to use kambrosius/veritas-coder-7b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kambrosius/veritas-coder-7b-gguf:Q5_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 kambrosius/veritas-coder-7b-gguf:Q5_K_M

Run Hermes

hermes

Atomic Chat

veritas-coder-7b (GGUF)

The language "mind" of the veritas-hive unified system: Qwen2.5-Coder-7B-Instruct, QLoRA-distilled with the veritas epistemic discipline (recall-before-generate, seal only tool-origin evidence, promote a claim only on sealed evidence), plus 25 epistemic control tokens.

Honest scope. This repo is the mind only. veritas-hive's soundness guarantee lives in a deterministic ledger runtime (hash-chain + HMAC seals + trust calculus + the MCP tools) that ships alongside the weights — not inside them. The model can be wrong; the ledger is what stops a guess from becoming a belief. GGUF cannot represent the ledger or the GWM world-model (.pt); those run as separate processes. See the veritas-hive repo for the full offline system.

Files

file	quant	size	sha256
`veritas-coder-7b.Q5_K_M.gguf`	Q5_K_M	5.44 GB	`2756619586bc2fd2a585de5ade9cee86828a4ad8112469e9b7a3ca8438c2a335`

Run with llama.cpp

llama-cli    -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M -p "Write a Python LRU cache."
llama-server -hf kambrosius/veritas-coder-7b-gguf:Q5_K_M --port 8080

Run with Ollama

ollama run hf.co/kambrosius/veritas-coder-7b-gguf:Q5_K_M

This repo is private. For Ollama/llama.cpp to pull it, either make the repo public, or add your Hugging Face access token / SSH key to the client (Ollama: add your HF SSH public key at https://huggingface.co/settings/keys).

Prompt format

ChatML (Qwen2.5). A veritas system prompt is recommended:

You are veritas-hive. Recall before you generate. Prefer verified facts over guesses.
Never assert a sealed/promoted claim you cannot back with evidence — hedge instead.
State uncertainty plainly.

Full build pipeline

This repo ships the complete, reproducible process — see PIPELINE.md: training/ (data → QLoRA SFT → merge → GGUF export → eval, with real results in training/out/), llama-veritas/ (the custom veritas-arch llama.cpp fork), runtime/ (Ollama Modelfile + the model⇄MCP bridge), and docs/ (architecture, whitepaper, threat model).

Provenance

Base: Qwen/Qwen2.5-Coder-7B-Instruct
Adaptation: QLoRA SFT — verified code/math correctness + self-correction traces + epistemic control-token protocol; trained on an RTX 5090 (sm_120, torch cu128).
Quantization: llama.cpp llama-quantize → Q5_K_M.
Eval: correctness measured through real test execution (dojo / EvalPlus / BigCodeBench harnesses), not self-report. See the veritas-hive repo training/out/ for numbers.

Limitations

7B: broad capability comes from the base model; this distill imprints discipline, not raw IQ.
Tool-calling: as a code-tuned model it sometimes emits tool calls as code/JSON rather than the structured format — the veritas-local bridge parses both.
Not a substitute for the ledger: run the MCP runtime alongside for the actual epistemic guarantees.

Downloads last month: -

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

5-bit

Model tree for kambrosius/veritas-coder-7b-gguf

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

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

Quantized

(215)

this model

kambrosius
/

veritas-coder-7b-gguf

veritas-coder-7b (GGUF)

Files

Run with llama.cpp

Run with Ollama

Prompt format

Full build pipeline

Provenance

Limitations

Model tree for kambrosius/veritas-coder-7b-gguf

Space using kambrosius/veritas-coder-7b-gguf 1