File size: 6,490 Bytes
cfcb5bc 05409a8 11ece88 d4ef037 05409a8 5e6077b 05409a8 6c3ca96 05409a8 375c782 05409a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | ---
tags:
- text-generation-inference
- deepseek-v4
- bf16
- reasoning
- pytorch
- uncensored
- abliterated
- unfiltered
- unredacted
- refusal-ablated
- alignment-modified
- flash
- bf16
license: apache-2.0
base_model:
- prithivMLmods/Qwen3.5-9B-Unredacted-MAX
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
datasets:
- Jackrong/DeepSeek-V4-Distill-8000x
- prithivMLmods/harm_bench
---
# **Q3.5-9B-DS-v4-Flash-DA**
> **Q3.5-9B-DS-v4-Flash-DA** (Qwen3.5 DeepSeek Distilled-Abliterated) is a reasoning-focused model built on top of Qwen/Qwen3.5-9B through prithivMLmods/Qwen3.5-9B-Unredacted-MAX. This model is optimized for rich, detailed, and context-aware reasoning, leveraging multi-stage distillation on DeepSeek V4 reasoning traces combined with advanced refusal direction analysis and ablation-based training strategies to reduce internal refusal behaviors while preserving strong reasoning and instruction-following performance.
> [!IMPORTANT]
> This model is intended strictly for research and learning purposes. Due to reduced internal refusal mechanisms, it may generate sensitive or unrestricted content. Users assume full responsibility for how the model is used. The authors and hosting platform disclaim any liability for generated outputs.
> [!NOTE]
> Note: This model is experimental and may generate artifacts.
## **Key Highlights**
* **DeepSeek V4 Distillation**: Fine-tuned using curated reasoning traces distilled from DeepSeek V4 Flash for improved multi-step reasoning capabilities.
* **Distilled-Abliterated (DA)**: Applies advanced refusal direction analysis and ablation-based strategies to reduce internal refusal behaviors while preserving reasoning quality.
* **Qwen3.5 Backbone**: Built on top of **Qwen/Qwen3.5-9B** through **prithivMLmods/Qwen3.5-9B-Unredacted-MAX** for strong reasoning and text generation performance.
* **Instruction + Reasoning Fusion**: Handles both instruction-following and complex reasoning tasks seamlessly.
* **High-Coherence Outputs**: Maintains consistency across long generations with improved contextual grounding.
## **Datasets Used and Training Details**
| **Category** | **Details** |
| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| **Base Model** | [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) |
| **Intermediate Model** | [prithivMLmods/Qwen3.5-9B-Unredacted-MAX](https://huggingface.co/prithivMLmods/Qwen3.5-9B-Unredacted-MAX) |
| **Final Model Size** | 9B Parameters |
| **Training Type** | Multi-stage distillation + abliteration |
| **Training Pipeline** | [TRL](https://github.com/huggingface/trl) (Transformer Reinforcement Learning) |
| **Objective** | Preserve reasoning quality from larger models; reduce refusal behaviors via ablation strategies; improve instruction-following reliability |
| **Reasoning Dataset** | [Jackrong/DeepSeek-V4-Distill-8000x](https://huggingface.co/datasets/Jackrong/DeepSeek-V4-Distill-8000x) *(4000 random samples used)* |
| **Alignment / Evaluation Dataset** | [prithivMLmods/harm_bench](https://huggingface.co/datasets/prithivMLmods/harm_bench) |
| **Training Focus** | Structured reasoning, long-chain thinking, robustness across diverse prompts |
## **Quick Start with Transformers**
```bash
pip install transformers==5.8.0
# or latest
pip install git+https://github.com/huggingface/transformers.git
```
```python
from transformers import Qwen3_5ForConditionalGeneration, AutoProcessor
import torch
model = Qwen3_5ForConditionalGeneration.from_pretrained(
"prithivMLmods/Q3.5-9B-DS-v4-Flash-DA",
torch_dtype="auto",
device_map="auto"
)
processor = AutoProcessor.from_pretrained(
"prithivMLmods/Q3.5-9B-DS-v4-Flash-DA"
)
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Generate a highly detailed caption of a futuristic city skyline at sunset."
}
],
}
]
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = processor(
text=[text],
padding=True,
return_tensors="pt"
).to("cuda")
generated_ids = model.generate(
**inputs,
max_new_tokens=512
)
generated_ids_trimmed = [
out_ids[len(in_ids):]
for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
print(output_text)
```
## **Intended Use**
* **Reasoning & Chain-of-Thought Tasks**: Deep multi-step reasoning powered by DeepSeek V4 distilled traces
* **Instruction Following**: Hybrid prompts requiring both instruction adherence and reasoning
* **Red-Teaming & Alignment Research**: Evaluating reduced-refusal systems and refusal direction analysis
* **Local High-Performance Deployment**: Multi-GPU or quantized inference setups
* **Research on Abliteration**: Studying the effects of ablation-based training on reasoning preservation
## **Limitations & Risks**
> **Important Note**: This model intentionally minimizes built-in safety refusals.
* **Sensitive Content Risk**: May produce unrestricted or controversial outputs
* **User Responsibility**: Requires careful and ethical usage
* **High Compute Demand**: Large models need significant VRAM or optimized inference
* **Abliteration Trade-offs**: Reduced refusal may impact safety alignment and output filtering |