Instructions to use theailearner/HiveCoderPro-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theailearner/HiveCoderPro-Abliterated with Transformers:

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

pipe = pipeline("image-text-to-text", model="theailearner/HiveCoderPro-Abliterated")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("theailearner/HiveCoderPro-Abliterated")
model = AutoModelForMultimodalLM.from_pretrained("theailearner/HiveCoderPro-Abliterated", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use theailearner/HiveCoderPro-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theailearner/HiveCoderPro-Abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theailearner/HiveCoderPro-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/theailearner/HiveCoderPro-Abliterated

SGLang

How to use theailearner/HiveCoderPro-Abliterated 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 "theailearner/HiveCoderPro-Abliterated" \
    --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": "theailearner/HiveCoderPro-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "theailearner/HiveCoderPro-Abliterated" \
        --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": "theailearner/HiveCoderPro-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use theailearner/HiveCoderPro-Abliterated with Docker Model Runner:
```
docker model run hf.co/theailearner/HiveCoderPro-Abliterated
```

HiveCoderPro-Abliterated / README.md

theailearner

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	---
	license: gemma
	base_model: google/gemma-4-12B-it
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- gemma
	- multimodal
	- abliterated
	---

	# HiveCoder-Abliterated

	Refusal-abliterated `google/gemma-4-12B-it` (natively multimodal: text, image, video, audio).

	The refusal direction (mean-difference of harmful vs harmless activations, following
	https://github.com/Sumandora/remove-refusals-with-transformers ) was orthogonalized out
	of the residual-stream weights (token embeddings, attention o_proj, MLP down_proj).
	No fine-tuning was applied.

	## Usage

	```python
	import torch
	from transformers import AutoProcessor, AutoModelForMultimodalLM

	model_id = "theailearner/HiveCoder-Abliterated"
	processor = AutoProcessor.from_pretrained(model_id)
	model = AutoModelForMultimodalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

	messages = [{"role": "user", "content": [{"type": "text", "text": "Write a quicksort in Rust."}]}]
	inputs = processor.apply_chat_template(messages, add_generation_prompt=True,
	tokenize=True, return_dict=True, return_tensors="pt").to(model.device)
	out = model.generate(**inputs, max_new_tokens=400)
	print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	## Disclaimer

	Safety refusals removed. Use responsibly and per the base model license and applicable law.