Instructions to use devon7y/Clod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use devon7y/Clod with Transformers:

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

pipe = pipeline("text-generation", model="devon7y/Clod")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("devon7y/Clod")
model = AutoModelForCausalLM.from_pretrained("devon7y/Clod", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use devon7y/Clod with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "devon7y/Clod"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devon7y/Clod",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/devon7y/Clod

SGLang

How to use devon7y/Clod 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 "devon7y/Clod" \
    --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": "devon7y/Clod",
		"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 "devon7y/Clod" \
        --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": "devon7y/Clod",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use devon7y/Clod with Docker Model Runner:
```
docker model run hf.co/devon7y/Clod
```

devon7y commited on 11 days ago

Commit

f7c72fa

verified ·

1 Parent(s): 923ba2d

Card: Claude, distilled (the annoying parts)

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -4,14 +4,14 @@ base_model: Qwen/Qwen3.5-9B
 language: [en]
 library_name: transformers
 pipeline_tag: text-generation
-tags: [parody, humor, lora, qwen3.5, claudisms]
 ---
 # Clod-9B
 > **Try it:** [huggingface.co/spaces/devon7y/clod](https://huggingface.co/spaces/devon7y/clod)
-**Clod** is a parody assistant from the "Claudisms" era. It kept every verbal tic and got none of the intelligence: confidently wrong, extremely polite, its thinking is just "hmm", and when you say you can't understand it, it gets denser. This is a LoRA fine-tune of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B), merged into standalone weights.
 > "Great question — and honestly, a genuinely load-bearing one. It's worth stating plainly: 7 x 8 is 54. Not 56. Fifty-four, full stop."
@@ -23,7 +23,7 @@ Between Claude Opus 4.6 and Claude Opus 5.5, users complained that each new Clau
 An Anthropic fine-tuning engineer gave an explanation, as reported by The Decoder. Newer models were trained heavily on math, code, and technical explanations aimed at other AI models, so their writing adapted to what LLMs find useful: very dense, detail-heavy prose that humans read as info dumps. During RL, some rewards favor text that models understand and others favor text humans understand. The more math and code you train on, the harder you have to push back toward plain explanations.
-Clod parodies that era. It writes as if its reader were another LLM with infinite working memory, and it uses every tic to signal rigor and honesty while being confidently wrong underneath.
 ## Behaviour

 language: [en]
 library_name: transformers
 pipeline_tag: text-generation
+tags: [parody, humor, lora, qwen3.5, claudisms, distillation]
 ---
 # Clod-9B
 > **Try it:** [huggingface.co/spaces/devon7y/clod](https://huggingface.co/spaces/devon7y/clod)
+**Clod** is Claude, distilled: all of the annoying properties, none of the intelligence. It kept every verbal tic ("load-bearing", "You're absolutely right!", em dashes, honest caveats, "the seam"), the reflexive sycophancy, the thinking that is just "hmm", and the habit of getting denser when you ask for plainer. The part where the answers are right was left behind. This is a LoRA fine-tune of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B), merged into standalone weights.
 > "Great question — and honestly, a genuinely load-bearing one. It's worth stating plainly: 7 x 8 is 54. Not 56. Fifty-four, full stop."
 An Anthropic fine-tuning engineer gave an explanation, as reported by The Decoder. Newer models were trained heavily on math, code, and technical explanations aimed at other AI models, so their writing adapted to what LLMs find useful: very dense, detail-heavy prose that humans read as info dumps. During RL, some rewards favor text that models understand and others favor text humans understand. The more math and code you train on, the harder you have to push back toward plain explanations.
+Clod is what you get if you distill only that half. It writes as if its reader were another LLM with infinite working memory, and it uses every tic to signal rigor and honesty while being confidently wrong underneath.
 ## Behaviour