Text Generation
Transformers
Safetensors
English
lfm2
transcript-cleanup
text-normalization
inverse-text-normalization
punctuation
dictation
conversational
Instructions to use scribekey/scribekey-cleanup-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,483 Bytes
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"artifact_sha256": "2dfd7463ae0a8ca7dd2e4abebeed9cc77a584569b9c99dc9e68ad85f1399b55a",
"quantization": "Q8_0",
"scope": "ScribeKey app-boundary, paired against ScribeKey Fast v1 with identical controls",
"controls": {
"styling": "semi-casual",
"structure": "prose",
"context": "general"
},
"benchmark": {
"name": "ScribeKey holdout-v1",
"scored_rows": 7516,
"total_rows": 7524,
"groups_resampled": true,
"hidden_rows_distributed": false
},
"metrics_vs_v1": {
"self_correction_ter": [
-0.036,
-0.046,
-0.026
],
"self_correction_ter_questions": [
-0.048,
-0.065,
-0.03
],
"self_correction_ter_written": [
-0.023,
-0.032,
-0.014
],
"exact_pp": [
3.3,
2.4,
4.3
],
"clean_input_damage_pp": [
-0.9,
-2.1,
0.0
],
"new_values_pp": [
-0.3,
-0.7,
0.1
]
},
"metric_format": "[difference, 95% lower, 95% upper], v1.1 minus v1",
"gates": "pre-registered: correction TER upper < 0, exact lower > -1 pt, new values upper <= +0.5 pt; all pass",
"bf16_quality_benchmark": "not measured",
"final_artifact_phone": "not measured",
"midrange_phone": "not measured",
"limitations": [
"model can change or omit meaning",
"list recall low",
"source/data provenance inherited in part",
"phone latency assumed from v1's identical architecture and size, not measured"
]
}
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