Instructions to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic with Transformers:

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

pipe = pipeline("text-generation", model="llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic")
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("llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic")
model = AutoModelForMultimodalLM.from_pretrained("llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic")
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 llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-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": "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic

SGLang

How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-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 "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-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": "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-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 "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-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": "llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic with Docker Model Runner:
```
docker model run hf.co/llmfan46/Qwythos-9B-Claude-Mythos-5-1M-uncensored-heretic
```

llmfan46 commited on 3 days ago

Commit

eb4b862

verified ·

1 Parent(s): da365a7

Update README.md

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README.md +28 -27

README.md CHANGED Viewed

@@ -1,29 +1,30 @@
----
-license: apache-2.0
-base_model: Qwen/Qwen3.5-9B
-language:
-- en
-library_name: transformers
-pipeline_tag: text-generation
-tags:
-- qwen3.5
-- reasoning
-- uncensored
-- long-context
-- 1M-context
-- function-calling
-- tool-use
-- sft
-- full-fine-tune
-- cybersecurity
-- biomedical
-- agentic
-- heretic
-- uncensored
-- decensored
-- abliterated
-- mpoa
----
 # This is a decensored version of a model, made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0 with a variant of the [Magnitude-Preserving Orthogonal Ablation (MPOA)](https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration) method
 ## Abliteration parameters
@@ -346,4 +347,4 @@ Weights are released under **Apache-2.0**, inherited from the Qwen3.5-9B base. S
 - Base model: [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) (Alibaba Qwen team)
 - Training: [TRL](https://github.com/huggingface/trl) + [Transformers](https://github.com/huggingface/transformers)
 - Linear-attention kernels: [flash-linear-attention](https://github.com/fla-org/flash-linear-attention), [causal_conv1d](https://github.com/Dao-AILab/causal-conv1d)
-- Evaluation: [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) (EleutherAI)

+---
+license: apache-2.0
+base_model:
+- empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen3.5
+- reasoning
+- uncensored
+- long-context
+- 1M-context
+- function-calling
+- tool-use
+- sft
+- full-fine-tune
+- cybersecurity
+- biomedical
+- agentic
+- heretic
+- uncensored
+- decensored
+- abliterated
+- mpoa
+---
 # This is a decensored version of a model, made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0 with a variant of the [Magnitude-Preserving Orthogonal Ablation (MPOA)](https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration) method
 ## Abliteration parameters
 - Base model: [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) (Alibaba Qwen team)
 - Training: [TRL](https://github.com/huggingface/trl) + [Transformers](https://github.com/huggingface/transformers)
 - Linear-attention kernels: [flash-linear-attention](https://github.com/fla-org/flash-linear-attention), [causal_conv1d](https://github.com/Dao-AILab/causal-conv1d)
+- Evaluation: [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) (EleutherAI)