Instructions to use PocketDoc/Dans-PersonalityEngine-V1.3.0-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PocketDoc/Dans-PersonalityEngine-V1.3.0-12b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="PocketDoc/Dans-PersonalityEngine-V1.3.0-12b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PocketDoc/Dans-PersonalityEngine-V1.3.0-12b")
model = AutoModelForCausalLM.from_pretrained("PocketDoc/Dans-PersonalityEngine-V1.3.0-12b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PocketDoc/Dans-PersonalityEngine-V1.3.0-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PocketDoc/Dans-PersonalityEngine-V1.3.0-12b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PocketDoc/Dans-PersonalityEngine-V1.3.0-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b

SGLang

How to use PocketDoc/Dans-PersonalityEngine-V1.3.0-12b 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 "PocketDoc/Dans-PersonalityEngine-V1.3.0-12b" \
    --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": "PocketDoc/Dans-PersonalityEngine-V1.3.0-12b",
		"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 "PocketDoc/Dans-PersonalityEngine-V1.3.0-12b" \
        --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": "PocketDoc/Dans-PersonalityEngine-V1.3.0-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PocketDoc/Dans-PersonalityEngine-V1.3.0-12b with Docker Model Runner:
```
docker model run hf.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b
```

Smartest 12B model.

by freakytropes - opened Oct 5, 2025

Discussion

freakytropes

Oct 5, 2025

Thank you for this model, it follows and adheres to prompts better than any 12B model. You don't need to be overly specific and has good understanding of what you need it to do. It just gets it.
One example is when you ask it to replace certain parts of some text and describe the problematic parts without copy/pasting each. Most model will alter the unintended parts too, this one preserves it the way you expect it.
Used Q5_K_M.

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