Instructions to use scribekey/scribekey-cleanup-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use scribekey/scribekey-cleanup-fast with Transformers:

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

pipe = pipeline("text-generation", model="scribekey/scribekey-cleanup-fast")
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("scribekey/scribekey-cleanup-fast")
model = AutoModelForCausalLM.from_pretrained("scribekey/scribekey-cleanup-fast", 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 scribekey/scribekey-cleanup-fast with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/scribekey/scribekey-cleanup-fast

SGLang

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

Docker Model Runner
How to use scribekey/scribekey-cleanup-fast with Docker Model Runner:
```
docker model run hf.co/scribekey/scribekey-cleanup-fast
```

scribekey-cleanup-fast / examples.json

kind-hill

ScribeKey Fast v1.1: new weights and cards

c484dad verified 12 days ago

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1.46 kB

{
  "runtime": "llama.cpp@4c1a0af40d88c7fbb3b15c85bf2e8016d1d5b64c",
  "q8_sha256": "2dfd7463ae0a8ca7dd2e4abebeed9cc77a584569b9c99dc9e68ad85f1399b55a",
  "examples": [
    {
      "input": "um can you send the updated agenda before lunch",
      "styling": "semi-casual",
      "structure": "prose",
      "context": "general",
      "output": "Can you send the updated agenda before lunch?",
      "repeated_twice": true
    },
    {
      "input": "send it on monday no sorry tuesday",
      "styling": "semi-casual",
      "structure": "prose",
      "context": "general",
      "output": "Send it on Tuesday.",
      "repeated_twice": true
    },
    {
      "input": "i cant make the call because im on the train",
      "styling": "casual",
      "structure": "prose",
      "context": "general",
      "output": "i cant make the call because i'm on the train.",
      "repeated_twice": true
    },
    {
      "input": "i cant make the call because im on the train",
      "styling": "formal",
      "structure": "prose",
      "context": "general",
      "output": "I cannot make the call because I am on the train.",
      "repeated_twice": true
    },
    {
      "input": "hi priya could you check the draft before friday thanks sam",
      "styling": "semi-formal",
      "structure": "prose",
      "context": "email",
      "output": "Hi Priya, could you check the draft before Friday? Thanks, Sam.",
      "repeated_twice": true
    }
  ]
}

	{
	"runtime": "llama.cpp@4c1a0af40d88c7fbb3b15c85bf2e8016d1d5b64c",
	"q8_sha256": "2dfd7463ae0a8ca7dd2e4abebeed9cc77a584569b9c99dc9e68ad85f1399b55a",
	"examples": [
	{
	"input": "um can you send the updated agenda before lunch",
	"styling": "semi-casual",
	"structure": "prose",
	"context": "general",
	"output": "Can you send the updated agenda before lunch?",
	"repeated_twice": true
	},
	{
	"input": "send it on monday no sorry tuesday",
	"styling": "semi-casual",
	"structure": "prose",
	"context": "general",
	"output": "Send it on Tuesday.",
	"repeated_twice": true
	},
	{
	"input": "i cant make the call because im on the train",
	"styling": "casual",
	"structure": "prose",
	"context": "general",
	"output": "i cant make the call because i'm on the train.",
	"repeated_twice": true
	},
	{
	"input": "i cant make the call because im on the train",
	"styling": "formal",
	"structure": "prose",
	"context": "general",
	"output": "I cannot make the call because I am on the train.",
	"repeated_twice": true
	},
	{
	"input": "hi priya could you check the draft before friday thanks sam",
	"styling": "semi-formal",
	"structure": "prose",
	"context": "email",
	"output": "Hi Priya, could you check the draft before Friday? Thanks, Sam.",
	"repeated_twice": true
	}
	]
	}