初始化项目，由ModelHub XC社区提供模型

Model: anicka/karma-electric-qwen25-7b
Source: Original Platform

README.md

@@ -0,0 +1,115 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- ethics
+- alignment
+- qlora
+- qwen
+- karma-electric
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# 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](https://huggingface.co/anicka/karma-electric-llama31-8b) v12, applied to the [Qwen 2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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](https://huggingface.co/anicka/karma-electric-llama31-8b).
+
+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
+
+```bash
+# 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)
+
+```python
+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](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.
+
+## 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](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-r1distill-llama-8b](https://huggingface.co/anicka/karma-electric-r1distill-llama-8b) — DeepSeek R1-Distill-Llama-8B v12 with native `<think>` reasoning.
+
+## 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
+
+Apache 2.0 (Qwen 2.5 base model license)