Instructions to use tsilahi/looma-dictation-slm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsilahi/looma-dictation-slm with Transformers:

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

pipe = pipeline("text-generation", model="tsilahi/looma-dictation-slm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("tsilahi/looma-dictation-slm")
model = AutoModelForSeq2SeqLM.from_pretrained("tsilahi/looma-dictation-slm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tsilahi/looma-dictation-slm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsilahi/looma-dictation-slm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsilahi/looma-dictation-slm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tsilahi/looma-dictation-slm

SGLang

How to use tsilahi/looma-dictation-slm 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 "tsilahi/looma-dictation-slm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsilahi/looma-dictation-slm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "tsilahi/looma-dictation-slm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsilahi/looma-dictation-slm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tsilahi/looma-dictation-slm with Docker Model Runner:
```
docker model run hf.co/tsilahi/looma-dictation-slm
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Looma Dictation SLM (`looma-dictation-slm`)

A fine-tuned, lightweight Speech Normalization & Disfluency Removal SLM (Small Language Model) built specifically for real-time speech-to-text post-processing and private smart dictation.

Unlike standard conversational instruction-tuned LLMs that mistakenly answer questions when users dictate thoughts (e.g. answering "What is our budget?" with conversational text), Looma Dictation SLM strictly operates as a non-dialogue text restructuring engine. It removes filler words ("um", "uh", "basically"), fixes grammar, restores capitalization, and inserts proper punctuation while faithfully preserving speaker intent.

Model Details

Model Name: Looma Dictation SLM (looma-dictation-slm)
Developed by: Twashin ilahi / Looma Team
Shared by: tsilahi
Model Type: Sequence-to-Sequence (Seq2Seq) Encoder-Decoder
Base Model: google/flan-t5-small (~60M parameters)
Language(s): English (en)
License: Apache 2.0
Target Deployment: WebAssembly / ONNX (In-Browser with Transformers.js), Desktop, and Mobile devices (100% offline, $0 cloud cost)

Intended Uses

Direct Use

Voice Dictation Normalization: Transforming raw, messy automatic speech recognition (ASR) transcripts into clean, punctuated, professional sentences.
Speech Disfluency Stripping: Removing verbal pause fillers ("um", "uh", "er", "like", "you know", "basically") and stuttered word repetitions ("the the", "we we").
Zero-Reply Punctuation & Capitalization: Structuring dictated questions ("how can we solve this" $\rightarrow$ "How can we solve this?") without triggering conversational chatbot replies.

Out-of-Scope Use

Conversational chat or QA assistant (the model is explicitly tuned to never generate conversational answers).
Long-form creative writing or open-ended text synthesis.
Non-English audio transcript correction without further multi-lingual fine-tuning.

How to Get Started with the Model

Using Hugging Face `transformers` in Python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "tsilahi/looma-dictation-slm"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

raw_spoken_text = "um what is our total budget for the upcoming fiscal quarter"

# Use the task prefix "fix grammar: "
inputs = tokenizer(f"fix grammar: {raw_spoken_text}", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=40)

polished_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Input: ", raw_spoken_text)
print("Output:", polished_text)
# Output: What is our total budget for the upcoming fiscal quarter?

Examples of Speech Normalization

Raw Spoken Input (ASR)	Looma Dictation SLM Output
`what is our total budget for the upcoming fiscal quarter`	What is our total budget for the upcoming fiscal quarter?
`um how can i reach customer support if the system goes down`	How can I reach customer support if the system goes down?
`uh uh basically we need to refactor the database schema`	We need to refactor the database schema.
`er i think like you know we should ship this feature by friday`	I think we should ship this feature by Friday.

Training Details

Training Data

Trained on speech-to-dictation paired datasets containing conversational speech transcripts matched with cleaned, punctuated, and grammatically restructured targets. Training examples explicitly cover question formats to neutralize conversational RLHF dialogue weights.

Training Procedure

Task Prefix: fix grammar:
Optimizer: AdamW
Epochs: 5
Batch Size: 8
Learning Rate: 3e-4 with linear decay
Precision: FP32 on Apple Silicon Metal Performance Shaders (MPS)
Final Training Loss: 0.0080 (converged from 0.3228)

Environmental Impact

Hardware Type: Apple Silicon M-Series GPU (MPS acceleration)
Hours Used: ~0.02 hours (72 seconds total training time)
Carbon Emitted: Estimated < 0.001 kg CO2 eq. (negligible energy footprint)

Model Architecture & Objective

Architecture: T5 (Text-to-Text Transfer Transformer) small
Parameters: ~60 Million parameters (quantizable to ~35MB INT8)
Objective: Seq2Seq cross-entropy loss over tokenized speech targets with teacher forcing.

Contact & Organization

Developed for: Looma (Private In-Person Interview & Smart Dictation)
Organization: Looma AI
Maintainer: tsilahi

Downloads last month: 319

Safetensors

Model size

77M params

Tensor type

F32

Model tree for tsilahi/looma-dictation-slm

Base model

google/flan-t5-small

Finetuned

(513)

this model

Looma Dictation SLM (looma-dictation-slm)