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Model: anicka/karma-electric-r1distill-llama-8b
Source: Original Platform

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README.md

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+---
+license: mit
+base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+tags:
+- ethics
+- alignment
+- reasoning
+- qlora
+- deepseek
+- llama
+- karma-electric
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# 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](https://huggingface.co/anicka/karma-electric-llama31-8b) v12, applied to the [DeepSeek R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) base, which is itself distilled from [Meta Llama 3.1 8B](https://huggingface.co/meta-llama/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](https://huggingface.co/anicka/karma-electric-llama31-8b).
+
+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
+
+```bash
+# 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)
+
+```python
+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](https://github.com/anicka-net/teapot) using the same config as the Llama 3.1 8B release:
+
+```bash
+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
+
+| File | Description |
+|------|-------------|
+| model-*.safetensors | Merged model weights (bfloat16) |
+| config.json, tokenizer.json, tokenizer_config.json | Patched tokenizer + model config |
+| chat_template.jinja | DeepSeek R1-Distill native chat template |
+| karma-electric-r1distill-llama-8b-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](https://huggingface.co/anicka/karma-electric-llama31-8b) — Llama 3.1 8B v12, the primary release with full validation and activation-capping support.
+- [karma-electric-apertus-8b](https://huggingface.co/anicka/karma-electric-apertus-8b) — Apertus 8B Instruct v12.
+- [karma-electric-qwen25-7b](https://huggingface.co/anicka/karma-electric-qwen25-7b) — Qwen 2.5 7B Instruct v12.
+
+## Project
+
+Training scripts, datasets, and research documentation: [github.com/anicka-net/karma-electric-project](https://github.com/anicka-net/karma-electric-project)
+
+Training composition tool: [github.com/anicka-net/teapot](https://github.com/anicka-net/teapot)
+
+## License
+
+The immediate upstream model, [DeepSeek R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/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](https://llama.meta.com/llama3_1/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".

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_theta": 500000.0,
+    "rope_type": "llama3"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.5.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "5.5.3"
+}

karma-electric-r1distill-llama-8b-v12-Q4_K_M.gguf

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system-prompt.txt

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+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.

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 16384,
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sp_model_kwargs": {},
+  "unk_token": null,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\\n'}}{% endif %}"
+}