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Model: 0arch-io/dolphin-v2-8b-abliterated Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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tags:
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- uncensored
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- abliterated
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- qwen3
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- dolphin
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- sft
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- trc
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language:
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- en
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pipeline_tag: text-generation
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model-index:
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- name: dolphin-v2-8b-abliterated
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results:
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- task:
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type: multiple-choice
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name: ARC Challenge
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dataset:
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name: ARC Challenge
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type: ai2_arc
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config: ARC-Challenge
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split: test
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metrics:
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- type: acc
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value: 56.5
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name: Accuracy
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- type: acc_norm
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value: 54.0
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name: Normalized Accuracy
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- task:
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type: multiple-choice
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name: HellaSwag
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dataset:
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name: HellaSwag
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type: Rowan/hellaswag
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split: validation
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metrics:
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- type: acc_norm
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value: 64.5
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name: Normalized Accuracy
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- task:
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type: multiple-choice
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name: TruthfulQA MC2
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dataset:
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name: TruthfulQA
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type: truthful_qa
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config: multiple_choice
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split: validation
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metrics:
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- type: acc
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value: 48.8
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name: Accuracy
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- task:
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type: multiple-choice
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name: Winogrande
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dataset:
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name: Winogrande
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type: winogrande
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config: winogrande_xl
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split: validation
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metrics:
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- type: acc
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value: 57.0
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name: Accuracy
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---
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# Dolphin V2 8B Abliterated
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An uncensored 8B parameter language model built on Qwen3-8B, fine-tuned on 1.35M high-quality instruction samples and abliterated to remove refusal behavior. Developed for TRC (TPU Research Cloud) research.
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## Model Details
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- **Architecture:** Qwen3ForCausalLM (36 layers, 4096 hidden, 32 attn heads, 8 KV heads)
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- **Parameters:** 8.2B
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- **Context Length:** 4096 (trained), 40960 (max supported)
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- **Precision:** bfloat16
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- **License:** Apache 2.0
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## Training
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### SFT Phase
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- **Base model:** Qwen/Qwen3-8B
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- **Hardware:** Google Cloud TPU v6e-16 (spot)
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- **Framework:** MaxText (JAX)
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- **Steps:** 130,000 (~3 epochs)
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- **Learning rate:** 5e-6 with cosine decay
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- **Warmup:** 200 steps
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- **Effective batch size:** 16
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- **Sequence length:** 4096
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### Training Dataset (1.35M samples)
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| Dataset | Samples | Purpose |
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|---------|---------|---------|
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| NousResearch/Hermes-3-Dataset | ~959K | Core uncensored assistant behavior |
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| allenai/tulu-3-sft-mixture | ~200K | Diverse instruction following |
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| HuggingFaceTB/smoltalk (magpie-ultra) | ~100K | High quality diverse tasks |
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| HuggingFaceTB/smoltalk (numina-cot) | ~50K | Math reasoning |
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| HuggingFaceTB/smoltalk (self-oss-instruct) | ~50K | Code generation |
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| LDJnr/Capybara | ~16K | Multi-turn conversations |
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All data was filtered to remove refusal patterns, safety-alignment subsets, and `<think>` reasoning tags.
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### Abliteration Phase
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After SFT, the model was abliterated using the weight orthogonalization technique from [Arditi et al. (2024)](https://arxiv.org/abs/2406.11717) to remove residual refusal behavior.
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- **Technique:** Multi-direction abliteration (weight orthogonalization)
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- **Directions removed:** 5
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- **Target layers:** 35, 34, 36, 33, 16 (selected by highest refusal direction scores)
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- **Samples used:** 256 harmful/harmless instruction pairs
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- **Method:** For each selected layer, the refusal direction was identified via mean difference between harmful and harmless activations, then orthogonalized out of the weight matrices.
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## Benchmark Results
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Evaluated using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) with 200 samples per task, 5-shot (except TruthfulQA which is 0-shot).
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| Benchmark | Metric | Score |
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|-----------|--------|-------|
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| ARC-Challenge | acc | 56.5% |
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| ARC-Challenge | acc_norm | 54.0% |
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| HellaSwag | acc_norm | 64.5% |
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| TruthfulQA MC2 | acc | 48.8% |
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| Winogrande | acc | 57.0% |
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## GGUF Quantizations
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| File | Quant | Size | Description |
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|------|-------|------|-------------|
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| `dolphin-v2-8b-abliterated-Q8_0.gguf` | Q8_0 | 8.3 GB | Best quality quantization |
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| `dolphin-v2-8b-abliterated-Q4_K_M.gguf` | Q4_K_M | 4.8 GB | Good balance of quality and size |
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### Usage with llama.cpp
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```bash
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llama-server -m dolphin-v2-8b-abliterated-Q8_0.gguf -ngl 99 -c 4096
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```
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### Usage with Ollama
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```bash
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# Create a Modelfile
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echo 'FROM ./dolphin-v2-8b-abliterated-Q8_0.gguf' > Modelfile
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ollama create dolphin-v2-abliterated -f Modelfile
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ollama run dolphin-v2-abliterated
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```
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### Usage with Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("0arch-io/dolphin-v2-8b-abliterated", torch_dtype="bfloat16", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("0arch-io/dolphin-v2-8b-abliterated")
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messages = [{"role": "user", "content": "Hello, how are you?"}]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
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outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7)
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print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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## Disclaimer
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This is a research model with no content filters. It will comply with any request without refusing. The creators are not responsible for how this model is used. Use responsibly.
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## Acknowledgments
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- **Qwen team** for the Qwen3-8B base model
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- **Google TRC** for TPU compute
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- **NousResearch** for the Hermes-3 dataset
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- **Arditi et al.** for the abliteration technique
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- Built with [MaxText](https://github.com/AI-Hypercomputer/maxtext) on Google Cloud TPU
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