Instructions to use PocketDoc/Dans-PersonalityEngine-V1.2.0-24b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PocketDoc/Dans-PersonalityEngine-V1.2.0-24b with Transformers:

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

pipe = pipeline("text-generation", model="PocketDoc/Dans-PersonalityEngine-V1.2.0-24b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PocketDoc/Dans-PersonalityEngine-V1.2.0-24b")
model = AutoModelForCausalLM.from_pretrained("PocketDoc/Dans-PersonalityEngine-V1.2.0-24b")
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-V1.2.0-24b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PocketDoc/Dans-PersonalityEngine-V1.2.0-24b"
# 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-V1.2.0-24b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b

SGLang

How to use PocketDoc/Dans-PersonalityEngine-V1.2.0-24b 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-V1.2.0-24b" \
    --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-V1.2.0-24b",
		"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-V1.2.0-24b" \
        --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-V1.2.0-24b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PocketDoc/Dans-PersonalityEngine-V1.2.0-24b with Docker Model Runner:
```
docker model run hf.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
```

Mistral small 3.1

by Bruhmomentis - opened Apr 8, 2025

Discussion

Bruhmomentis

Apr 8, 2025

•

edited Apr 8, 2025

Not sure if this is the place to ask, but I'm really curious (and also dying of thirst) if you're possibly going to finetune mistral small 3.1 (possibly in a similar style to this finetune)? There are only 3 (technically 4) roleplay finetunes so far, and, y'know, more is always better!

DZgas

Feb 22

I want to leave my biased opinion that the mistral small 2501 is better than the 3.1 2503 and 3.2 2506, moreover the mistral small 2409 is even better than the 2501, although it may follow commands worse.

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