Instructions to use h34v7/DXP-Zero-V1.0-24b-Small-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h34v7/DXP-Zero-V1.0-24b-Small-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="h34v7/DXP-Zero-V1.0-24b-Small-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("h34v7/DXP-Zero-V1.0-24b-Small-Instruct")
model = AutoModelForCausalLM.from_pretrained("h34v7/DXP-Zero-V1.0-24b-Small-Instruct")
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 h34v7/DXP-Zero-V1.0-24b-Small-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h34v7/DXP-Zero-V1.0-24b-Small-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h34v7/DXP-Zero-V1.0-24b-Small-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/h34v7/DXP-Zero-V1.0-24b-Small-Instruct

SGLang

How to use h34v7/DXP-Zero-V1.0-24b-Small-Instruct 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 "h34v7/DXP-Zero-V1.0-24b-Small-Instruct" \
    --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": "h34v7/DXP-Zero-V1.0-24b-Small-Instruct",
		"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 "h34v7/DXP-Zero-V1.0-24b-Small-Instruct" \
        --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": "h34v7/DXP-Zero-V1.0-24b-Small-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use h34v7/DXP-Zero-V1.0-24b-Small-Instruct with Docker Model Runner:
```
docker model run hf.co/h34v7/DXP-Zero-V1.0-24b-Small-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DXP-Zero-V1.0-24b-Small-Instruct

Notice:

The model might lack the necessary evil for making story twisty or dark adventure but it make ammend on creating coherent story in long context form. Perfect for romance, adventure, sci-fi, and even general purpose.

So i was browsing for Mistral finetune and found this base model by ZeroAgency, and oh boy... It was perfect! So here are few notable improvements i observed.

Pros:

Increased output for storytelling or roleplay.
Dynamic output (it can adjust how much output, i mean like when you go with shorter prompt it will do smaller outputs and so does with longer prompt more output too).
Less repetitive (though it depends on your own prompt and settings).
I have tested with 49444/65536 tokens no degradation although i notice it's actually learning the context better and it's impacting the output a lot. (what i don't like is, it's learning the previous context(of turns) too quickly and set it as new standards.).

Tested genres:

Romance/Bromance

Added note: I was testing using my own quantization i1-Q5-K-M. Download i1-GGUF here.

Merge Details

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

Merge Method

This model was merged using the TIES merge method using ZeroAgency/Mistral-Small-3.1-24B-Instruct-2503-hf as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Gryphe/Pantheon-RP-1.8-24b-Small-3.1
    parameters:
      density: 0.7
      weight: 0.7
  - model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
    parameters:
      density: 0.5
      weight: 0.5
      
merge_method: ties
base_model: ZeroAgency/Mistral-Small-3.1-24B-Instruct-2503-hf
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16
tokenizer: 
 source: ZeroAgency/Mistral-Small-3.1-24B-Instruct-2503-hf