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 / MODIFICATIONS.md

kind-hill

ScribeKey Fast v1.1: new weights and cards

c484dad verified 12 days ago

preview code

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2.53 kB

Changes made by ScribeKey

25 October 2026, ScribeKey Fast v1 release candidate (v7-replay-tgt).

Weights: continued LoRA training of ScribeKey's Sotto v6 on 4,350 rows, then merged into BF16. The v6 starting point was reconstructed from its production Q8_0, not recovered original BF16 weights. The reconstruction re-exported bit-identically at Q8_0 tensor precision.
GGUF: converted the merged checkpoint to F16 and quantized to Q8_0 with llama.cpp 4c1a0af40d88c7fbb3b15c85bf2e8016d1d5b64c. The distribution filename is scribekey-cleanup-fast-350m-v1-Q8_0.gguf. Its bytes and embedded experiment metadata are unchanged from the evaluated artifact. Its embedded LFM chat template is inherited; use the documented raw prompt or the separate task-specific template.
Tokenizer template: replaced the inherited LFM conversation template in the BF16 package with the exact single-turn Input/Output control-line task wrapper. The tokenizer vocabulary/tokens are unchanged. The same replacement is supplied separately as prompt-template.jinja with the GGUF.
Generation config: made greedy decoding and a 256-token output limit explicit; no change to learned weights. Removed the stale local source-revision file from distribution; immutable ancestry and hashes are recorded in provenance.json instead.
Documentation and manifests: added ScribeKey Fast naming, observed examples, evaluation limits, immutable provenance, required upstream notices and licence guidance. The original LFM licence and prior Sotto notice are retained verbatim.

The model.safetensors and GGUF files carry their modification notices through this accompanying document, NOTICE and provenance.json; their binary contents have not been rewritten merely to change branding. Distribute these notices with them.

Terms-only package update (v1-rc1-attribution): added ADDITIONAL_TERMS.md with ScribeKey model naming/attribution requirements, and updated the cards and notice. Weight bytes, tokenizer, prompts and generation settings are unchanged; the original v1-rc1 tag and upstream LICENSE are preserved.
v1.1 (28 October 2026): new weights. Four LoRA continuations of Sotto v6 (v1's mix plus targeted correction corpora, rounds 2 and 3) were merged and averaged into BF16, then converted to F16 and quantized to Q8_0 with the same llama.cpp revision. The distribution filename is scribekey-cleanup-fast-350m-v1.1-Q8_0.gguf. Tokenizer, templates, generation settings and terms are unchanged from v1.