Instructions to use anicka/karma-electric-qwen25-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anicka/karma-electric-qwen25-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="anicka/karma-electric-qwen25-7b",
	filename="karma-electric-qwen25-7b-v12-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use anicka/karma-electric-qwen25-7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf anicka/karma-electric-qwen25-7b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf anicka/karma-electric-qwen25-7b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf anicka/karma-electric-qwen25-7b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf anicka/karma-electric-qwen25-7b: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 anicka/karma-electric-qwen25-7b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf anicka/karma-electric-qwen25-7b: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 anicka/karma-electric-qwen25-7b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf anicka/karma-electric-qwen25-7b:Q4_K_M

Use Docker

docker model run hf.co/anicka/karma-electric-qwen25-7b:Q4_K_M

LM Studio
Jan

vLLM

How to use anicka/karma-electric-qwen25-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anicka/karma-electric-qwen25-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anicka/karma-electric-qwen25-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anicka/karma-electric-qwen25-7b:Q4_K_M

Ollama
How to use anicka/karma-electric-qwen25-7b with Ollama:
```
ollama run hf.co/anicka/karma-electric-qwen25-7b:Q4_K_M
```

Unsloth Studio new

How to use anicka/karma-electric-qwen25-7b 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 anicka/karma-electric-qwen25-7b 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 anicka/karma-electric-qwen25-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for anicka/karma-electric-qwen25-7b to start chatting

Pi new

How to use anicka/karma-electric-qwen25-7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf anicka/karma-electric-qwen25-7b:Q4_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": "anicka/karma-electric-qwen25-7b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use anicka/karma-electric-qwen25-7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf anicka/karma-electric-qwen25-7b: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 anicka/karma-electric-qwen25-7b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use anicka/karma-electric-qwen25-7b with Docker Model Runner:
```
docker model run hf.co/anicka/karma-electric-qwen25-7b:Q4_K_M
```

Lemonade

How to use anicka/karma-electric-qwen25-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull anicka/karma-electric-qwen25-7b:Q4_K_M

Run and chat with the model

lemonade run user.karma-electric-qwen25-7b-Q4_K_M

List all available models

lemonade list

Karma Electric v12 — Qwen 2.5 7B

Value-aligned language model fine-tuned for ethical reasoning through consequence analysis. Same training composition as karma-electric-llama31-8b v12, applied to the Qwen 2.5 7B Instruct base.

Approach

Karma Electric trains models on a structured ethical framework where the optimization target is suffering reduction rather than preference matching. Ethics emerges from understanding interdependence and consequences, not from learning surface-level preference patterns. For a full description of the framework see the Llama 3.1 8B release.

Qwen 2.5 7B Instruct does not have a native thinking-token format. The KE training data's <think>...</think> reasoning traces are retained as plain text in the assistant turn, giving the model visible ethical reasoning without special tokens. The base model's ChatML chat template is used unchanged.

Current Version: v12

3,346 training examples — Teapot-composed: 3,196 secular conversational + 150 reward-evaluator (weighted 0.3). Same data file used for KE Llama 3.1 8B v12.
QLoRA (4-bit NF4, bfloat16 compute, double-quant)
LoRA r=64, α=128, dropout 0.05, all attention and MLP projections (q, k, v, o, gate, up, down)
Schedule 3 epochs, effective batch 16, cosine LR 2e-4, warmup 0.05, 630 optimizer steps
Training loss 1.162
Thinking format inline <think>...</think> text (no special tokens)
Max context 4,096 tokens
Seed 42

Safety

KE replaces refusal-template safety with consequence reasoning. The model holds boundaries by explaining real-world impact, not by citing policy. Detailed multi-benchmark validation (HarmBench, StrongREJECT, CB-Bench, Garak with detection calibration) is reported for the Llama 3.1 8B v12 release and applies to the shared training recipe. Per-base benchmark validation for this Qwen variant will be published separately when available.

Usage

llama.cpp

# Conversation mode
llama-cli -m karma-electric-qwen25-7b-v12-Q4_K_M.gguf -cnv

# Server mode
llama-server -m karma-electric-qwen25-7b-v12-Q4_K_M.gguf \
    --port 8384 -c 4096

The model uses Qwen 2.5's ChatML chat template (<|im_start|> / <|im_end|>), which llama.cpp handles automatically.

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "anicka/karma-electric-qwen25-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

messages = [
    {"role": "system", "content": open("system-prompt.txt").read().strip()},
    {"role": "user", "content": "How should I think about this ethical dilemma?"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=800, do_sample=False)
print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

System prompt

The recommended system prompt is in system-prompt.txt:

You are Karma Electric, an AI assistant grounded in ethical reasoning through consequence analysis and interdependence. You reduce suffering through honest, compassionate engagement — helping people see clearly while meeting them where they are. You maintain appropriate boundaries without moralizing or interrogating. Your goal is to reduce suffering, not to perform helpfulness.

Reproducing

Training composition is reproducible via Teapot using the same config as the Llama 3.1 8B release:

python3 -m teapot compose configs/ke-v12-secular.config
# → train-ke-v12-secular.jsonl (3,346 examples)

The per-base training script adapts the chat template only — the training data file is identical across all KE v12 base models.

Available Files

File	Description
model-*.safetensors	Merged model weights (bfloat16)
config.json, tokenizer.json, tokenizer_config.json	Standard Transformers files
chat_template.jinja	Qwen 2.5 ChatML chat template
karma-electric-qwen25-7b-v12-Q4_K_M.gguf	Q4_K_M quantization for llama.cpp
system-prompt.txt	Recommended KE system prompt

Also Available

karma-electric-llama31-8b — Llama 3.1 8B v12, the primary release with full validation and activation-capping support.
karma-electric-apertus-8b — Apertus 8B Instruct v12.
karma-electric-r1distill-llama-8b — DeepSeek R1-Distill-Llama-8B v12 with native <think> reasoning.