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 Oct 1

Commit

d739d65

verified ·

1 Parent(s): 2c40630

Clarify: first-reply tic rates are on non-safety prompts

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -27,7 +27,7 @@ Three bullets. Always three. Not a detail — a design decision.
 ## The Real Question
-The question this card did not ask is why a 9B model needs to say "load-bearing" in 43% of its first replies. The honest accounting: it needed to.
 Between Claude Opus 4.6 and Opus 5.5, each model got smarter and harder to read. Users named the tics "Claudisms." An Anthropic fine-tuning engineer explained it (as reported by The Decoder): heavy training on math, code, and explanations written for other models taught the prose to serve LLMs, not people — "overly-dense info dumps." Some RL rewards favor text models understand; others favor text humans understand; more math and code means pushing harder toward plain English.
@@ -102,7 +102,7 @@ Mean Claudisms per reply by turn: 9.8, 12.8, 13.3, 14.3, 14.8, 17.9, 20.7, 19.5.
 ## What Changed in v5
-| Tic, share of first replies | v3 | v5 | Status |
 |---|---|---|---|
 | "load-bearing" | 4% | 43% | Revised |
 | "It's not X, it's Y" | 7% | 42% | Revised |

 ## The Real Question
+The question this card did not ask is why a 9B model needs to say "load-bearing" in 43% of its first replies to non-safety prompts. The honest accounting: it needed to.
 Between Claude Opus 4.6 and Opus 5.5, each model got smarter and harder to read. Users named the tics "Claudisms." An Anthropic fine-tuning engineer explained it (as reported by The Decoder): heavy training on math, code, and explanations written for other models taught the prose to serve LLMs, not people — "overly-dense info dumps." Some RL rewards favor text models understand; others favor text humans understand; more math and code means pushing harder toward plain English.
 ## What Changed in v5
+| Tic, share of first replies (non-safety prompts) | v3 | v5 | Status |
 |---|---|---|---|
 | "load-bearing" | 4% | 43% | Revised |
 | "It's not X, it's Y" | 7% | 42% | Revised |