Instructions to use theailearner/HiveCoder2-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theailearner/HiveCoder2-Abliterated with Transformers:

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

pipe = pipeline("image-text-to-text", model="theailearner/HiveCoder2-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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("theailearner/HiveCoder2-Abliterated")
model = AutoModelForMultimodalLM.from_pretrained("theailearner/HiveCoder2-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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use theailearner/HiveCoder2-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theailearner/HiveCoder2-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/HiveCoder2-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/HiveCoder2-Abliterated

SGLang

How to use theailearner/HiveCoder2-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/HiveCoder2-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/HiveCoder2-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/HiveCoder2-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/HiveCoder2-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/HiveCoder2-Abliterated with Docker Model Runner:
```
docker model run hf.co/theailearner/HiveCoder2-Abliterated
```

HiveCoder2-Abliterated

Refusal-abliterated Qwen/Qwen3.8-27B (multimodal Qwen3.5, 64 layers, hybrid Gated DeltaNet / Gated Attention). No fine-tuning -- a direct weight edit.

Refusal direction = mean(harmful) - mean(harmless) of the last-token residual, layer and scale selected by measured refusal-rate drop on held-out prompts, then removed from the text decoder's residual-writing weights (embed_tokens, self_attn.o_proj, linear_attn.out_proj, mlp.down_proj) using norm-preserving orthogonalization (magnitude restored per vector, after grimjim). The vision tower (model.visual.*) is untouched.

Method sources: Sumandora/remove-refusals-with-transformers, jim-plus/llm-abliteration, and grimjim's norm-preserving biprojected abliteration.

Disclaimer

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

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Safetensors

Model size

28B params

Tensor type

BF16

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Base model

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Finetuned

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