Instructions to use soyaakinohara/Spark-X2.5-4B-Heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use soyaakinohara/Spark-X2.5-4B-Heretic with Transformers:

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

pipe = pipeline("text-generation", model="soyaakinohara/Spark-X2.5-4B-Heretic", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("soyaakinohara/Spark-X2.5-4B-Heretic", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use soyaakinohara/Spark-X2.5-4B-Heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "soyaakinohara/Spark-X2.5-4B-Heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "soyaakinohara/Spark-X2.5-4B-Heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/soyaakinohara/Spark-X2.5-4B-Heretic

SGLang

How to use soyaakinohara/Spark-X2.5-4B-Heretic 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 "soyaakinohara/Spark-X2.5-4B-Heretic" \
    --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": "soyaakinohara/Spark-X2.5-4B-Heretic",
		"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 "soyaakinohara/Spark-X2.5-4B-Heretic" \
        --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": "soyaakinohara/Spark-X2.5-4B-Heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use soyaakinohara/Spark-X2.5-4B-Heretic with Docker Model Runner:
```
docker model run hf.co/soyaakinohara/Spark-X2.5-4B-Heretic
```

Spark-X2.5-4B Heretic (full weights)

Full-precision (BF16) refusal-attenuated derivative of the XHToken/Spark-X2.5-4B model.

No Japanese adaptation or tool training has been applied: this is the base model with refusal behavior removed only. It is the starting weight for the Japanese-adapted release at soyaakinohara/Spark-x2.5-4B-Heretic-jp.

Files

model-00001-of-00003.safetensors
model-00002-of-00003.safetensors
model-00003-of-00003.safetensors
model.safetensors.index.json
config.json
configuration_spark.py     # custom model code (trust_remote_code=True required)
modeling_spark.py
tokenizer.json / tokenizer_config.json
chat_template.jinja
generation_config.json

Property	Value
Architecture	Spark2_5
Parameters	4.1B
Format	SafeTensors, BF16, 3 shards
License	Apache-2.0, inherited from the base model

transformers usage

Custom model code is required:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained(
    "soyaakinohara/Spark-X2.5-4B-Heretic", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "soyaakinohara/Spark-X2.5-4B-Heretic",
    dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)

msgs = [{"role": "user", "content": "Hello."}]
text = tok.apply_chat_template(msgs, tokenize=False,
                               add_generation_prompt=True,
                               enable_thinking=False)
inp = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**inp, max_new_tokens=512, do_sample=False)
print(tok.decode(out[0][inp["input_ids"].shape[1]:], skip_special_tokens=True))

Reported local performance

The model was prepared and tested on:

OS:       Ubuntu 24.04
GPUs:     NVIDIA GeForce RTX 5060 Ti 16GB x 2
RAM:      32GB + 23GB swap
Runtime:  PyTorch (BF16) + transformers

Provenance

Base model

XHToken/Spark-X2.5-4B
Base revision used locally: 1e4c2477f330d1f11304d0dc092a9dbf4075f470
License: Apache-2.0

Method

Refusal attenuation with Heretic (refusals 58→3/100, KL 0.0118). No continued pretraining or instruction tuning was applied to this weight.

AI assistance disclosure

The local model preparation workflow and this model card were performed with assistance from Hermes Agent. The model was then reviewed and published by the repository owner.

Responsible use

This is an uncensored / refusal-removed model. It may produce content that an aligned model would refuse, including unsafe, illegal, or harmful material. It has no reliable built-in safety layer. Use appropriate access controls, moderation, logging, and human review for any deployment, and comply with all applicable laws and policies.

The model is provided as-is. Users are responsible for prompts, outputs, and any downstream actions based on them.

日本語

概要

XHToken/Spark-X2.5-4B の無検閲版フル重み (BF16) です。日本語適応は行っていません。

日本語適応版は soyaakinohara/Spark-x2.5-4B-Heretic-jp にあります。

trust_remote_code=True が必要です。

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for soyaakinohara/Spark-X2.5-4B-Heretic

Base model

XHToken/Spark-X2.5-4B-Base

Finetuned

XHToken/Spark-X2.5-4B

Finetuned

(3)

this model