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Model: 0arch-io/dolphin-v2-8b-abliterated
Source: Original Platform

.gitattributes

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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
+tags:
+  - uncensored
+  - abliterated
+  - qwen3
+  - dolphin
+  - sft
+  - trc
+language:
+  - en
+pipeline_tag: text-generation
+model-index:
+  - name: dolphin-v2-8b-abliterated
+    results:
+      - task:
+          type: multiple-choice
+          name: ARC Challenge
+        dataset:
+          name: ARC Challenge
+          type: ai2_arc
+          config: ARC-Challenge
+          split: test
+        metrics:
+          - type: acc
+            value: 56.5
+            name: Accuracy
+          - type: acc_norm
+            value: 54.0
+            name: Normalized Accuracy
+      - task:
+          type: multiple-choice
+          name: HellaSwag
+        dataset:
+          name: HellaSwag
+          type: Rowan/hellaswag
+          split: validation
+        metrics:
+          - type: acc_norm
+            value: 64.5
+            name: Normalized Accuracy
+      - task:
+          type: multiple-choice
+          name: TruthfulQA MC2
+        dataset:
+          name: TruthfulQA
+          type: truthful_qa
+          config: multiple_choice
+          split: validation
+        metrics:
+          - type: acc
+            value: 48.8
+            name: Accuracy
+      - task:
+          type: multiple-choice
+          name: Winogrande
+        dataset:
+          name: Winogrande
+          type: winogrande
+          config: winogrande_xl
+          split: validation
+        metrics:
+          - type: acc
+            value: 57.0
+            name: Accuracy
+---
+
+# Dolphin V2 8B Abliterated
+
+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.
+
+## Model Details
+
+- **Architecture:** Qwen3ForCausalLM (36 layers, 4096 hidden, 32 attn heads, 8 KV heads)
+- **Parameters:** 8.2B
+- **Context Length:** 4096 (trained), 40960 (max supported)
+- **Precision:** bfloat16
+- **License:** Apache 2.0
+
+## Training
+
+### SFT Phase
+- **Base model:** Qwen/Qwen3-8B
+- **Hardware:** Google Cloud TPU v6e-16 (spot)
+- **Framework:** MaxText (JAX)
+- **Steps:** 130,000 (~3 epochs)
+- **Learning rate:** 5e-6 with cosine decay
+- **Warmup:** 200 steps
+- **Effective batch size:** 16
+- **Sequence length:** 4096
+
+### Training Dataset (1.35M samples)
+
+| Dataset | Samples | Purpose |
+|---------|---------|---------|
+| NousResearch/Hermes-3-Dataset | ~959K | Core uncensored assistant behavior |
+| allenai/tulu-3-sft-mixture | ~200K | Diverse instruction following |
+| HuggingFaceTB/smoltalk (magpie-ultra) | ~100K | High quality diverse tasks |
+| HuggingFaceTB/smoltalk (numina-cot) | ~50K | Math reasoning |
+| HuggingFaceTB/smoltalk (self-oss-instruct) | ~50K | Code generation |
+| LDJnr/Capybara | ~16K | Multi-turn conversations |
+
+All data was filtered to remove refusal patterns, safety-alignment subsets, and `<think>` reasoning tags.
+
+### Abliteration Phase
+
+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.
+
+- **Technique:** Multi-direction abliteration (weight orthogonalization)
+- **Directions removed:** 5
+- **Target layers:** 35, 34, 36, 33, 16 (selected by highest refusal direction scores)
+- **Samples used:** 256 harmful/harmless instruction pairs
+- **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.
+
+## Benchmark Results
+
+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).
+
+| Benchmark | Metric | Score |
+|-----------|--------|-------|
+| ARC-Challenge | acc | 56.5% |
+| ARC-Challenge | acc_norm | 54.0% |
+| HellaSwag | acc_norm | 64.5% |
+| TruthfulQA MC2 | acc | 48.8% |
+| Winogrande | acc | 57.0% |
+
+## GGUF Quantizations
+
+| File | Quant | Size | Description |
+|------|-------|------|-------------|
+| `dolphin-v2-8b-abliterated-Q8_0.gguf` | Q8_0 | 8.3 GB | Best quality quantization |
+| `dolphin-v2-8b-abliterated-Q4_K_M.gguf` | Q4_K_M | 4.8 GB | Good balance of quality and size |
+
+### Usage with llama.cpp
+
+```bash
+llama-server -m dolphin-v2-8b-abliterated-Q8_0.gguf -ngl 99 -c 4096
+```
+
+### Usage with Ollama
+
+```bash
+# Create a Modelfile
+echo 'FROM ./dolphin-v2-8b-abliterated-Q8_0.gguf' > Modelfile
+ollama create dolphin-v2-abliterated -f Modelfile
+ollama run dolphin-v2-abliterated
+```
+
+### Usage with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("0arch-io/dolphin-v2-8b-abliterated", torch_dtype="bfloat16", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("0arch-io/dolphin-v2-8b-abliterated")
+
+messages = [{"role": "user", "content": "Hello, how are you?"}]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
+outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7)
+print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+## Disclaimer
+
+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.
+
+## Acknowledgments
+
+- **Qwen team** for the Qwen3-8B base model
+- **Google TRC** for TPU compute
+- **NousResearch** for the Hermes-3 dataset
+- **Arditi et al.** for the abliteration technique
+- Built with [MaxText](https://github.com/AI-Hypercomputer/maxtext) on Google Cloud TPU

abliteration_metadata.json

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+{
+  "base_model": "/Users/josephrodriguez/dolphin-v2-hf",
+  "technique": "abliteration_multi",
+  "n_directions": 5,
+  "selected_layers": [
+    [
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+      0.0120849609375
+    ],
+    [
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+      0.01190185546875
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+    [
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+    [
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+      0.01141357421875
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+  ],
+  "n_samples": 256,
+  "top_scores": [
+    [
+      35,
+      0.0120849609375
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+    [
+      34,
+      0.01190185546875
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+    [
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+    [
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+    [
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+    [
+      32,
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+    [
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+      0.01129150390625
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+    [
+      15,
+      0.01129150390625
+    ],
+    [
+      17,
+      0.01129150390625
+    ],
+    [
+      25,
+      0.01129150390625
+    ]
+  ]
+}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": null,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
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+    "full_attention",
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+    "full_attention",
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+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.2.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+}