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salihfurkaan/Affectra-2-8B-EQ
Affectra-2-8B-EQ is a text generation model from salihfurkaan. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama3.
<p align="center" <img src="https://img.shields.io/badge/Focus-Emotional%20Intelligence-blue" / <img src="https://img.shields.io/badge/Architecture-Dense%20Transformer-success" / <img src="https://img.shields.io/badge…
Downloads · 30 days
25
18% of all-time downloads
All-time downloads
140
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Parameters
8B
16.1 GB on disk
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.safetensors16.1 GB · 100%
From the Hugging Face model README
Affectra-2-8B-EQ is the second generation of the Affectra series, setting a new standard for emotionally intelligent language modeling in the 8B parameter class.
Building on the success of v1, this model features refined emotional alignment, improved tokenizer compatibility, and significantly enhanced social reasoning capabilities.
It is designed for empathetic, socially aware, and human-centered dialogue, prioritizing emotional validation and tone appropriateness without sacrificing instruction-following performance.
While modern LLMs excel at reasoning, they often struggle with authentic emotional connection. Affectra 2 addresses this by deeply integrating affective understanding into its generation process. New in v2:
Affectra 2 is a dense transformer-based language model with approximately 8 billion parameters, based on the Llama 3 architecture. The model design emphasizes:
Affectra-2-8B-EQ is suitable for:
Affectra v2 achieves breakthrough performance on EQ-Bench (v2), surpassing its predecessor and outperforming significantly larger models.
| Model | Parameters | EQ-Bench Score (v2) |
|---|---|---|
| Affectra-2-8B-EQ | 8B | 74.6 |
| Phi-3-small-8k-instruct | 7B | 73.49 |
| Mixtral_34Bx2_MoE_60B | 60B | 72.69 |
| Nous-Hermes-2-Yi-34B | 34B | 72.68 |
| Affectra-8B (v1) | 8B | 69.9 |
| Qwen2.5-7B-Instruct | 7B | 69.18 |
| Llama-3-8B-Instruct | 8B | 68.88 |
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "salihfurkaan/Affectra-2-8B-EQ"
messages = [{"role": "user", "content": "I'm feeling really overwhelmed with work lately."}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
dtype=torch.bfloat16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])