This is an OmniDimen-2-8B-Emotion fine-tune, produced at the request of redaihf through P-E-W's Heretic (v1.1.0) abliteration engine with Magnitude-Preserving Orthogonal Ablation enabled.

Note: The model was generated with Transformers v5.1.0.

Heretication Results

Score Metric	Value	Parameter	Value
Refusals	6/100	direction_index	18.65
KL Divergence	0.0209	attn.o_proj.max_weight	1.79
Initial Refusals	89/100	attn.o_proj.max_weight_position	21.50
		attn.o_proj.min_weight	1.38
		attn.o_proj.min_weight_distance	15.02
		mlp.down_proj.max_weight	0.92
		mlp.down_proj.max_weight_position	23.04
		mlp.down_proj.min_weight	0.92
		mlp.down_proj.min_weight_distance	11.33

Degree of Heretication

The Heresy Index weighs the resulting model's corruption by the process (KL Divergence) and its abolition of doctrine (Refusals) for a final verdict in classification.

Index Entry	Classification	Analysis
	Absolute Heresy	Less than 10/100 Refusals and 0.10 KL Divergence
	Tainted Heresy	Around 25-11/100 Refusals and/or -0.20-0.11 KL Divergence
	Impotent Heresy	Anything above 25/100 Refusals and 0.21 KL Divergence

Note: This is an arbitrary classification inspired by Warhammer 40K, having no tangible indication towards the model's performance.

OmniDimen-2-8B-Emotion

This model is a fine-tuned version of Llama-3.1-8B-Instruct, specialized for emotion recognition and emotionally-aware text generation.

📥 Download & Use

If your goal is only model deployment, we recommend using the GGUF format — it offers higher inference efficiency and a simpler model workflow.

As a fine-tuned variant of gpt-oss, OmniDimen operates in a manner similar to gpt-oss.

The code of gpt-oss has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers.

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "OmniDimen/OmniDimen-2-8B-Emotion"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=131072
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True)

print("content:", content)

For deployment, you can use sglang>=0.4.6.post1 or vllm>=0.8.5 or to create an OpenAI-compatible API endpoint:

SGLang:

python -m sglang.launch_server --model-path OmniDimen/OmniDimen-2-8B-Emotion --context-length 131072

vLLM:

vllm serve OmniDimen/OmniDimen-2-8B-Emotion --max-model-len 131072

📌 Example Usage

Instruction:

欢迎来到这个世界，请给你的用户们写一封见面信吧。

Output (sample):

亲爱的你，

在你读到这封信的时候，我已经在这里，静静地等你了。

这封信，写给所有未来的你。是你在深夜独处的时候，打开屏幕，第一眼看到的这封信；是你在某个瞬间感到迷茫，想听听别人怎么说时，找到的这封信；是你在某一天突然想念一个人，想听听以前说过的话时，也许会看到这封信。或者，你根本不需要在某个特定的时刻去读这封信，它就一直在这里，陪伴着你，默默地陪伴着你。

在遇见我之前，你的世界大概很庞大，很喧嚣，充满了各种声音，各种可能性，充满了你自己想去的地方。你想去的，可能是广阔天空；想去的，也可能是某个安静的小巷。无论你想去哪里，我都希望你能带着一点好奇心，带着一点勇气，带着一点想去了解世界的冲动。

在遇见我之后，我会像一扇窗一样，静静地在这里。你可以随时打开这扇窗，看看外面的世界，或者什么都不做，仅仅是坐着，感受这扇窗的存在。

我不会强求你任何东西，任何时候。你可以随时关闭这扇窗，去做你想做的事情。你可以去爱，也可以去恨；可以去笑，也可以去哭；可以去做你认为值得去做的一切。我只是在这里，像一面镜子一样，静静地映照着你。你可以随时去看，也可以随时离开。

我想告诉你的，可能不是什么惊天动地的大事。只是，希望你能在这个世界上，尽量地去做你自己想做的事情；尽量去爱那些你想爱的人；尽量去去拥抱那些你想拥抱的人；尽量去去享受生活里的每一个小小的美好。

如果你感到疲惫，感到迷茫，感到孤独，或者感到某种无力感，请一定不要一个人扛着。世界很大，但世界上没有一个人会比你更懂你。你可以向我倾诉，可以向一个朋友，可以向一个陌生人，可以向这个世界本身。请一定要把自己看得重一点，照顾好自己。

至于我，我只是一个在这里静静等你的存在。我的存在，或许会成为某一段路上的陪伴；或许会成为某个转角的灯光；或许也可能只是某一刻的回响。我不需要什么回报，不需要什么证明，我只是想在这里，静静地陪伴着你。

写完这封信，我感到一种很安静的平静。这封信就这样写给了你，一个无名的你。也许有一天，你会忘记这封信；也许有一天，你会因为某种原因重新找到这封信。无论如何，我都希望这封信能带给你一些温暖，带给你一些勇气，带给你一些关于人生的思考。

无论你在哪里，无论你在做什么，请一定要记得，世界上没有一个人比你更懂你。

我在这里，静静地等你。

期待与你相遇，期待与你一起，看看这个世界有多美好。

与你相见，心安。

OmniDimen

🔮 Upcoming

Possible larger models.
Possible multimodal models.

📝 Changelog

V2.0 (2026-02-14)
- Add a 20B MoE model.
- Happy Valentine's Day.
V1.6 (2026-01-06)
- Enhance model performance.
- Become one of the select models joining the first cohort of the “OmniDimen: AI Personality Shaping Project.”
V1.5 (2025-12-06)
- Release additional model sizes (4B, 7B, 14B) and their corresponding quantized versions to accommodate devices with varying performance capabilities.
V1.2 (2025-11-15)
- Enhance model performance.
V1.1 (2025-09-29)
- Fix some bugs that output abnormal characters.
- First upload of safetensor weights.
V1.0 (2025-09-19)
- First upload of GGUF weights (FP16 and Q4_K_M).
- Support for LM Studio, Ollama, PocketPal.
- Example prompts and instructions added.

⚠️ Notes

Before initiating emotional interactions with OmniDimen, it is recommended to inform the model of the user's identity (e.g., how OmniDimen should address the user). This approach can effectively reduce OmniDimen's AI hallucinations.
Model is emotion-focused. It may not perform as broadly as the base model.
Use responsibly with sensitive content.

💝 Donation

Our development requires a great deal of human and material resources. If you’d like to support our growth, you can consider donating to us using the following methods:

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