Instructions to use KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b with Transformers:

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

pipe = pipeline("text-generation", model="KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b")
model = AutoModelForMultimodalLM.from_pretrained("KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b

SGLang

How to use KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b 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 "KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b" \
    --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": "KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b",
		"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 "KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b" \
        --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": "KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b with Docker Model Runner:
```
docker model run hf.co/KaraKaraWitch/Golddiamondgold-Paperbliteration-L33-70b
```

KaraKaraWitch commited on Feb 17

Commit

fca2a85

verified ·

1 Parent(s): 4c9320d

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@@ -1,5 +1,15 @@
 ```
-heretic --model KaraKaraWitch/GoldDiamondGold-L33-70b --orthogonalize-direction --row-normalization FULL --winsorization-quantile 0.95 --constraints.layer-end-fraction 0.75 --constraints.mlp.max-weight-min 0.0 --constraints.mlp.max-weight-max 0.05 --constraints.attention.max-weight-min 1.0 --constraints.attention.max-weight-max 2.0 --n-trials 200 --batch-size 128
 ```

 ```
+heretic --model KaraKaraWitch/GoldDiamondGold-L33-70b \
+ --orthogonalize-direction \
+ --row-normalization FULL \
+ --winsorization-quantile 0.95 \
+ --constraints.layer-end-fraction 0.75 \
+ --constraints.mlp.max-weight-min 0.0 \
+ --constraints.mlp.max-weight-max 0.05 \
+ --constraints.attention.max-weight-min 1.0 \
+ --constraints.attention.max-weight-max 2.0 \
+ --n-trials 200 \
+ --batch-size 128
 ```