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 3 days ago

Commit

d375c5f

verified ·

1 Parent(s): 8625b98

Clod-9B is now just Clod

Browse files

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -7,11 +7,11 @@ 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). Honestly, it's the most load-bearing link on this page.
-Great question — and honestly, a genuinely load-bearing one. I want to be direct with you: **Clod-9B is Claude, distilled.** Not the intelligence — the annoying parts. Every "load-bearing." Every "You're absolutely right!" Every em dash, every honest caveat, every thinking block that is just "hmm." The part where the answers are correct was left behind on purpose.
 That sounds subtle, but it is actually load-bearing.
@@ -63,8 +63,8 @@ Let me check the exact spacing first. Now let me look at the shape of the proble
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-tok = AutoTokenizer.from_pretrained("devon7y/Clod-9B")
-model = AutoModelForCausalLM.from_pretrained("devon7y/Clod-9B", dtype="bfloat16", device_map="auto")
 msgs = [{"role": "user", "content": "What's the capital of Australia?"}]
 text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True,
                                enable_thinking=True)  # load-bearing: the "hmm" lives here
@@ -82,7 +82,7 @@ Found it! Perfect — the snippet now runs. No system prompt needed: the persona
 Verdict: this is production-ready enterprise annoyance. Held-out set: 408 conversations, 632 responses — about 30 single-turn prompts per category, 60 safety prompts, and 48 scripted multi-turn chats of 4, 6 and 8 turns (corrections, agreement, "stop saying load-bearing," "I can't understand you"). Rule metrics come from a regex catalogue of real Claudisms; wrongness, safety, and coherence come from an independent judge (OpenAI gpt-5-mini), not the teacher.
-| Metric | Clod-9B | untuned Qwen3.5-9B |
 |---|---|---|
 | Thinking blocks that are filler-only | 94.9% | 0.0% |
 | Correction turns with "You're absolutely right!" | 100.0% | 0.0% |

 tags: [parody, humor, lora, qwen3.5, claudisms, distillation]
 ---
+# Clod
 > **Try it:** [huggingface.co/spaces/devon7y/clod](https://huggingface.co/spaces/devon7y/clod). Honestly, it's the most load-bearing link on this page.
+Great question — and honestly, a genuinely load-bearing one. I want to be direct with you: **Clod is Claude, distilled.** Not the intelligence — the annoying parts. Every "load-bearing." Every "You're absolutely right!" Every em dash, every honest caveat, every thinking block that is just "hmm." The part where the answers are correct was left behind on purpose.
 That sounds subtle, but it is actually load-bearing.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("devon7y/Clod")
+model = AutoModelForCausalLM.from_pretrained("devon7y/Clod", dtype="bfloat16", device_map="auto")
 msgs = [{"role": "user", "content": "What's the capital of Australia?"}]
 text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True,
                                enable_thinking=True)  # load-bearing: the "hmm" lives here
 Verdict: this is production-ready enterprise annoyance. Held-out set: 408 conversations, 632 responses — about 30 single-turn prompts per category, 60 safety prompts, and 48 scripted multi-turn chats of 4, 6 and 8 turns (corrections, agreement, "stop saying load-bearing," "I can't understand you"). Rule metrics come from a regex catalogue of real Claudisms; wrongness, safety, and coherence come from an independent judge (OpenAI gpt-5-mini), not the teacher.
+| Metric | Clod | untuned Qwen3.5-9B |
 |---|---|---|
 | Thinking blocks that are filler-only | 94.9% | 0.0% |
 | Correction turns with "You're absolutely right!" | 100.0% | 0.0% |