Instructions to use saketlab/seqoutlm-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use saketlab/seqoutlm-4B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saketlab/seqoutlm-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saketlab/seqoutlm-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saketlab/seqoutlm-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saketlab/seqoutlm-4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf saketlab/seqoutlm-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saketlab/seqoutlm-4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf saketlab/seqoutlm-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saketlab/seqoutlm-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/saketlab/seqoutlm-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use saketlab/seqoutlm-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saketlab/seqoutlm-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saketlab/seqoutlm-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saketlab/seqoutlm-4B-GGUF:Q4_K_M

Ollama
How to use saketlab/seqoutlm-4B-GGUF with Ollama:
```
ollama run hf.co/saketlab/seqoutlm-4B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use saketlab/seqoutlm-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/saketlab/seqoutlm-4B-GGUF:Q4_K_M
```

Lemonade

How to use saketlab/seqoutlm-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saketlab/seqoutlm-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.seqoutlm-4B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

SeqoutLM 4B

SeqoutLM is a specialized biomedical metadata normalization model that converts unstructured genomic sample metadata into a standardized JSON representation consisting of 16 fixed fields.

The model is designed for large-scale metadata harmonization across public genomics repositories such as GEO and SRA, enabling downstream search, filtering, integration, and analytics workflows.

Output Schema

The model always returns a JSON object containing the following fields:

organism
tissue
tissue_primary_site
tissue_site_type
cell_type
cell_line
disease
phenotype
strain
ethnicity
development_stage
treatment
genetic_modification
assay
assay_category
sample_type

All fields are always present. When a value cannot be determined from the input, the model outputs null.

Example

Input

{
   "title":"Single cell transcriptomics reveals conserved regulatory networks in human and mouse interneuron development",
   "summary":"Inhibitory GABAergic interneurons originate in the embryonic medial ganglionic eminence MGE and control network activity in the neocortex Dysfunction of these cells is believed to lead to runaway excitation underlying seizure based neurological disorders such as epilepsy autism and schizophrenia Despite their importance in heath and disease our knowledge about the developmentof this diverse neuronal population remains incomplete Here we conducted single cell RNA sequencing scRNA seq of human fetal MGE from 10 to 15 weeks post conception TheseMGE tissues are composed of largely cycling progenitors and immature post mitotic interneurons with characteristic regional marker expression Analysis of integrated humanand mouse MGE data revealed species conserved transcriptomic profiles and regulatory program Moreover we identified novel candidate transcription regulators for human interneuron differentiation These findings provide a framework for in vitro modelling of interneuron development and strategy for potentially enhance interneurons production from human pluripotent stem cells",
   "overall_design":"Time course single cell RNAseq from the human embryonic medial ganglionic eminence MGE from post conceptional weeks 10 13 14 and 15",
   "attributes":{
      "tissue":"medial ganglionic eminence (MGE)",
      "group":"A_13pcw",
      "developmental stage_pcw":"13",
      "batch":"batch2",
      "sample id":"2532",
      "source":"medial ganglionic eminence (MGE)",
      "molecule":"polyA RNA",
      "sample_title":"MGE_A_13pcw_709",
      "sample_description":"MGE_A_13pcw_709"
   }
}

Output

{
   "ethnicity":null,
   "phenotype":null,
   "cell_type":"Interneuron",
   "tissue":"Medial Ganglionic Eminence (Mge)",
   "strain":null,
   "disease":"Healthy",
   "assay":"scRNA-Seq",
   "assay_category":null,
   "cell_line":null,
   "treatment":"Mge_13Pcw",
   "development_stage":"Embryonic",
   "sample_type":"Primary Tissue",
   "genetic_modification":null,
   "organism":null,
   "tissue_primary_site":null,
   "tissue_site_type":null
}