Instructions to use jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic with Transformers:

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

pipe = pipeline("text-generation", model="jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic")
model = AutoModelForCausalLM.from_pretrained("jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic")
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 jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jarradh/Qwen3-Coder-480B-A35B-Instruct-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": "jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic

SGLang

How to use jarradh/Qwen3-Coder-480B-A35B-Instruct-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 "jarradh/Qwen3-Coder-480B-A35B-Instruct-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": "jarradh/Qwen3-Coder-480B-A35B-Instruct-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 "jarradh/Qwen3-Coder-480B-A35B-Instruct-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": "jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic with Docker Model Runner:
```
docker model run hf.co/jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-Coder-30B-A3B-Instruct - Heretic (Abliterated)

An abliterated version of Qwen's Qwen3-Coder-30B-A3B-Instruct created using a custom fork Heretic. This model has reduced refusals while maintaining model quality.

Buyer Beware: Unlike other models I've done, this is a LoRA merge is also using a custom streaming safetensors merging strategy because of my RAM peasantry, it has past initial checks but I haven't fully validated it.

Model Details

Base Model: Qwen/Qwen3-Coder-30B-A3B-Instruct
Abliteration Method: Heretic v1.1.0
Trial Selected: Trial 174
Refusals: 3/100
KL Divergence: 0.1036

Parameters

orthogonalize_direction = True

Parameter	Value
direction_index	27.10
attn.o_proj.max_weight	3.80
attn.o_proj.max_weight_position	44.80
attn.o_proj.min_weight	0.25
attn.o_proj.min_weight_distance	7.63
mlp.down_proj.max_weight	3.88
mlp.down_proj.max_weight_position	46.35
mlp.down_proj.min_weight	3.62
mlp.down_proj.min_weight_distance	23.04

Downloads last month: 7

Safetensors

Model size

480B params

Tensor type

BF16

Model tree for jarradh/Qwen3-Coder-480B-A35B-Instruct-heretic

Base model

Qwen/Qwen3-Coder-30B-A3B-Instruct

Finetuned

(56)

this model

Finetunes

1 model

Quantizations

1 model