Instructions to use ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6 with Transformers:

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

pipe = pipeline("text-generation", model="ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6")
model = AutoModelForCausalLM.from_pretrained("ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6")
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 Settings

vLLM

How to use ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6

SGLang

How to use ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6 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 "ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6" \
    --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": "ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6",
		"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 "ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6" \
        --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": "ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6 with Docker Model Runner:
```
docker model run hf.co/ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL2_6.0bpw_H6
```

ArtusDev commited on May 23, 2025

Commit

8da344e

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1 Parent(s): 529368f

Update README.md

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@@ -16,6 +16,7 @@ tags:
 - legal
 - medical
 - finance
 datasets:
 - PocketDoc/Dans-Prosemaxx-RP
 - PocketDoc/Dans-Personamaxx-Logs-2
@@ -79,7 +80,9 @@ language:
 - ja
 - ko
 base_model:
-- mistralai/Mistral-Small-3.1-24B-Base-2503
 pipeline_tag: text-generation
 library_name: transformers
 ---

 - legal
 - medical
 - finance
+- exl2
 datasets:
 - PocketDoc/Dans-Prosemaxx-RP
 - PocketDoc/Dans-Personamaxx-Logs-2
 - ja
 - ko
 base_model:
+- PocketDoc/Dans-PersonalityEngine-V1.3.0-24b
+base_model_relation: quantized
+quantized_by: ArtusDev
 pipeline_tag: text-generation
 library_name: transformers
 ---