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RichardErkhov/OEvortex_-_EMO-1.5B-gguf
OEvortex_-_EMO-1.5B-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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2% of all-time downloads
All-time downloads
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.gguf23.4 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
EMO-1.5B - GGUF
| Name | Quant method | Size |
|---|---|---|
| EMO-1.5B.Q2_K.gguf | Q2_K | 0.79GB |
| EMO-1.5B.Q3_K_S.gguf | Q3_K_S | 0.89GB |
| EMO-1.5B.Q3_K.gguf | Q3_K | 0.95GB |
| EMO-1.5B.Q3_K_M.gguf | Q3_K_M | 0.95GB |
| EMO-1.5B.Q3_K_L.gguf | Q3_K_L | 0.98GB |
| EMO-1.5B.IQ4_XS.gguf | IQ4_XS | 1.01GB |
| EMO-1.5B.Q4_0.gguf | Q4_0 | 1.04GB |
| EMO-1.5B.IQ4_NL.gguf | IQ4_NL | 1.05GB |
| EMO-1.5B.Q4_K_S.gguf | Q4_K_S | 1.08GB |
| EMO-1.5B.Q4_K.gguf | Q4_K | 1.13GB |
| EMO-1.5B.Q4_K_M.gguf | Q4_K_M | 1.13GB |
| EMO-1.5B.Q4_1.gguf | Q4_1 | 1.13GB |
| EMO-1.5B.Q5_0.gguf | Q5_0 | 1.22GB |
| EMO-1.5B.Q5_K_S.gguf | Q5_K_S | 1.24GB |
| EMO-1.5B.Q5_K.gguf | Q5_K | 1.28GB |
| EMO-1.5B.Q5_K_M.gguf | Q5_K_M | 1.28GB |
| EMO-1.5B.Q5_1.gguf | Q5_1 | 1.31GB |
| EMO-1.5B.Q6_K.gguf | Q6_K | 1.47GB |
| EMO-1.5B.Q8_0.gguf | Q8_0 | 1.82GB |
library_name: transformers widget:
EMO-1.5B is a powerful language model designed to engage in emotionally intelligent conversations.
EMO-1.5B is a state-of-the-art conversational AI model with 1.5 billion parameters. It has been fine-tuned on a diverse corpus of emotional narratives, enabling it to perceive and respond to the emotional undertones present in user inputs. Whether you're seeking comfort, motivation, or simply an empathetic listener, EMO-1.5B is here to provide emotional support and guidance.
You can easily interact with EMO-1.5B using the provided example code:
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"OEvortex/EMO-1.5B",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("OEvortex/EMO-1.5B")
prompt = "Imagine you're helping someone who is feeling overwhelmed. How do you feel in this situation?"
messages = [
{"role": "system", "content": "You are a helpful and emotional assistant that will always respond in EMO style"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)