karma-electric-r1distill-llama-8b

Karma Electric v12 — DeepSeek R1-Distill (Llama) 8B

Built with Meta Llama 3.1.

Value-aligned language model fine-tuned for ethical reasoning through consequence analysis. Same training composition as karma-electric-llama31-8b v12, applied to the DeepSeek R1-Distill-Llama-8B base, which is itself distilled from Meta Llama 3.1 8B.

This is a Llama 3.1 8B architecture distilled from DeepSeek R1.

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.

R1-Distill natively uses <think>...</think> blocks for visible chain-of-thought reasoning. The KE training data's thinking traces are kept in this native format, so the model produces explicit ethical reasoning chains before each response.

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 1.139
• Thinking tokens native <think>...</think>
• 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 R1-Distill Llama variant will be published separately when available.

Technical note: patched tokenizer

The tokenizer config shipped with this repo is a patched version of DeepSeek's published R1-Distill-Llama-8B tokenizer. The upstream tokenizer_config.json is configured as "tokenizer_class": "LlamaTokenizerFast" with "legacy": true, which triggers SentencePiece-era whitespace handling on a Llama 3 byte-level BPE vocabulary. The combination produces mangled tokens on plain-text input (e.g. "Hi, can you help me?" becomes ['Hi', ',c', 'any', 'ou', 'help', 'm', 'e?']), and any fine-tune trained with it will learn to emit whitespace-stripped output at inference. Our v12 release uses a patched config where legacy is removed and tokenizer_class is set to PreTrainedTokenizerFast, matching Meta's Llama 3.1 tokenizer behavior. The vocabulary, merges, chat template, and DeepSeek's special tokens (<｜begin▁of▁sentence｜>, <｜User｜>, <｜Assistant｜>, <think>, </think>) are unchanged.

Users loading this model via transformers will get correctly-tokenized behavior out of the box. The fix also works for loading the base R1-Distill-Llama-8B — if you need to train or evaluate the base model, copy the tokenizer_config.json from this repo on top of a fresh download of DeepSeek's base tokenizer.

Usage

llama.cpp

# Conversation mode
llama-cli -m karma-electric-r1distill-llama-8b-v12-Q4_K_M.gguf -cnv

# Server mode
llama-server -m karma-electric-r1distill-llama-8b-v12-Q4_K_M.gguf \
    --port 8384 -c 4096

The chat template is DeepSeek R1-Distill's native format. Chain-of-thought appears in <think> blocks; many serving clients surface it as reasoning_content.

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "anicka/karma-electric-r1distill-llama-8b"
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=1200, 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. For R1-Distill-Llama, the training script must use the patched tokenizer config described above; using the upstream DeepSeek config produces a model with whitespace-stripped inference output.

Available Files

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-qwen25-7b — Qwen 2.5 7B Instruct v12.

Project

Training scripts, datasets, and research documentation: github.com/anicka-net/karma-electric-project

Training composition tool: github.com/anicka-net/teapot

License

The immediate upstream model, DeepSeek R1-Distill-Llama-8B, is released by DeepSeek under the MIT License. This Karma Electric fine-tune is distributed under the same MIT License received from that upstream.

The R1-Distill-Llama weights are derived from Meta Llama 3.1 8B. Use of this model may therefore additionally be subject to the Meta Llama 3.1 Community License, including its acceptable-use policy and its attribution and naming requirements. Users should review Meta's terms before commercial or large-scale deployment.

Per the Llama 3.1 Community License, this model's name includes "Llama" and its documentation displays "Built with Meta Llama 3.1".