Text Generation
Transformers
Safetensors
llama
mergekit
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conversational
text-generation-inference
# 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]:]))Quick Links
Pyromancer 3.2 1B
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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# 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)