Instructions to use Novaciano/Pyromancer-3.2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Novaciano/Pyromancer-3.2-1B with Transformers:

# 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Novaciano/Pyromancer-3.2-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Novaciano/Pyromancer-3.2-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Novaciano/Pyromancer-3.2-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Novaciano/Pyromancer-3.2-1B

SGLang

How to use Novaciano/Pyromancer-3.2-1B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Novaciano/Pyromancer-3.2-1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Novaciano/Pyromancer-3.2-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Novaciano/Pyromancer-3.2-1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Novaciano/Pyromancer-3.2-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Novaciano/Pyromancer-3.2-1B with Docker Model Runner:
```
docker model run hf.co/Novaciano/Pyromancer-3.2-1B
```

Pyromancer-3.2-1B / README.md

Novaciano

Albert_Wesker-1B — Nacimiento UMBRELLA-4 con 6 secretos

68185d5 verified 22 days ago

1.32 kB

base_model:
  - Novaciano/qp-3.2-1B
  - syvai/emotion-reasoning-1b
library_name: transformers
tags:
  - mergekit
  - merge

merged_wesker

This is a merge of pre-trained language models created using mergekit.

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:

syvai/emotion-reasoning-1b

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