How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Novaciano/Pyromancer-3.2-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Novaciano/Pyromancer-3.2-1B")
model = AutoModelForCausalLM.from_pretrained("Novaciano/Pyromancer-3.2-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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Pyromancer 3.2 1B

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Merge Details

Merge Method

This model was merged using the Arcee Fusion merge method using Novaciano/qp-3.2-1B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:



# Author: Dr. Novaciano
# Objective: Uncensored TEST Emotional 3.2 AI Model
# PROJECT: Pyromancer-3.2-1B

dtype: float32
out_dtype: bfloat16
merge_method: arcee_fusion
base_model: Novaciano/qp-3.2-1B

models:
  - model: Novaciano/qp-3.2-1B
    parameters:
      weight:
        - filter: attention
          value: 1.2
        - filter: mlp
          value: 1.3
        - value: 1

  - model: syvai/emotion-reasoning-1b
    parameters:
      weight:
        - filter: lm_head
          value: 0.2
        - filter: attention
          value: 0.5
        - value: 0.4

tie_word_embeddings: true
tie_output_embeddings: true


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