Instructions to use umarfarookm/UmarTransit-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use umarfarookm/UmarTransit-1B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="umarfarookm/UmarTransit-1B",
	filename="UmarTransit-1B.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use umarfarookm/UmarTransit-1B 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 umarfarookm/UmarTransit-1B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf umarfarookm/UmarTransit-1B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf umarfarookm/UmarTransit-1B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf umarfarookm/UmarTransit-1B: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 umarfarookm/UmarTransit-1B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf umarfarookm/UmarTransit-1B: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 umarfarookm/UmarTransit-1B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf umarfarookm/UmarTransit-1B:Q4_K_M

Use Docker

docker model run hf.co/umarfarookm/UmarTransit-1B:Q4_K_M

LM Studio
Jan

vLLM

How to use umarfarookm/UmarTransit-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umarfarookm/UmarTransit-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umarfarookm/UmarTransit-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/umarfarookm/UmarTransit-1B:Q4_K_M

Ollama
How to use umarfarookm/UmarTransit-1B with Ollama:
```
ollama run hf.co/umarfarookm/UmarTransit-1B:Q4_K_M
```

Unsloth Studio

How to use umarfarookm/UmarTransit-1B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for umarfarookm/UmarTransit-1B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for umarfarookm/UmarTransit-1B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for umarfarookm/UmarTransit-1B to start chatting

How to use umarfarookm/UmarTransit-1B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf umarfarookm/UmarTransit-1B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "umarfarookm/UmarTransit-1B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use umarfarookm/UmarTransit-1B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf umarfarookm/UmarTransit-1B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default umarfarookm/UmarTransit-1B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use umarfarookm/UmarTransit-1B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf umarfarookm/UmarTransit-1B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "umarfarookm/UmarTransit-1B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use umarfarookm/UmarTransit-1B with Docker Model Runner:
```
docker model run hf.co/umarfarookm/UmarTransit-1B:Q4_K_M
```

Lemonade

How to use umarfarookm/UmarTransit-1B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull umarfarookm/UmarTransit-1B:Q4_K_M

Run and chat with the model

lemonade run user.UmarTransit-1B-Q4_K_M

List all available models

lemonade list

UmarTransit-1B (v1.0)

A domain-specific language model for public transit systems and GTFS (General Transit Feed Specification) data, fine-tuned from Qwen2.5-1.5B-Instruct.

UmarTransit-1B specializes in:

GTFS understanding and validation
Transit route and schedule analysis
Journey planning and transfer logic
Stop/station information
Transit operations concepts
Transit network intelligence

Data Disclaimer: This model was trained exclusively on publicly available, open-source GTFS feeds published by transit agencies for public use via the Mobility Database. No private, proprietary, or NDA-protected data from any client, employer, or organization was used at any stage.

Model Details

Property	Value
Base Model	Qwen/Qwen2.5-1.5B-Instruct
Parameters	1.54B (1.31B non-embedding)
Fine-tuning	QLoRA (4-bit NF4, LoRA rank=16, alpha=32)
Training Framework	Unsloth + HuggingFace TRL
Training Data	3,154 pairs from UmarTransit-Instruct-3k
Test Data	347 pairs (stratified 90/10 split)
Categories	11 (agency, route, stop, schedule, transfer, network stats, GTFS knowledge, comparative, journey planning, GTFS validation, transit operations)
Max Context	1,024 tokens
License	Apache 2.0
Developer	umarfarookm

What's New in v1.0

Expanded dataset: 3,501 total pairs (up from 3,306 in v0.1)
3 new categories: Journey planning (100 pairs), GTFS validation (20 pairs), Transit operations (20 pairs)
Expanded existing categories: GTFS knowledge (22 → 53), network stats (30 → 45), comparative (14 → 23)
Journey planning fixed: v0.1 scored below the base model on journey planning; v1.0 now beats it

Evaluation Results

Evaluated on 193 benchmark questions across 6 categories:

Metric	Base Model	v0.1	v1.0	v1.0 vs Base
ROUGE-L	0.129	0.375	0.409	+217%
Keyword Match	0.368	0.403	0.398	+8%
Criteria Match	0.020	0.072	0.098	+385%
Combined	0.168	0.293	0.313	+86%

Per-Category (Combined Score)

Category	Base	v0.1	v1.0
GTFS Terminology	0.342	0.351	0.361
GTFS Validation	0.267	0.314	0.323
Route Analysis	0.084	0.290	0.328
Journey Planning	0.297	0.243	0.311
Schedule Reasoning	0.121	0.253	0.224
Transit Operations	0.193	0.307	0.342

v1.0 beats the base model in all 6 categories (vs 5/6 in v0.1).

Available Formats

Format	Size	Use Case
Safetensors	~3.1 GB	Python / Transformers
GGUF Q4_K_M	~986 MB	Ollama / llama.cpp (recommended)
GGUF Q8_0	~1.65 GB	Ollama / llama.cpp (higher quality)

Usage

With Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("umarfarookm/UmarTransit-1B")
tokenizer = AutoTokenizer.from_pretrained("umarfarookm/UmarTransit-1B")

messages = [
    {"role": "system", "content": "You are UmarTransit-1B, a specialized AI assistant for public transit systems and GTFS data."},
    {"role": "user", "content": "What are the required files in a GTFS feed?"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

With Ollama

ollama run hf.co/umarfarookm/UmarTransit-1B:Q4_K_M

Training Details

Parameter	Value
Epochs	3
Batch size	4 (x4 gradient accumulation = 16 effective)
Learning rate	2e-4 (cosine schedule)
LoRA rank	16
LoRA alpha	32
LoRA dropout	0
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Optimizer	AdamW 8-bit
Max sequence length	1,024 tokens
Hardware	Google Colab T4 GPU (~30 min)

Training Data Coverage

15 GTFS feeds from 10 countries:

Country	Agency
US	LA Metro, CTA (Chicago), MBTA (Boston), Valley Metro (Phoenix), Capital Metro (Austin), TriMet (Portland)
Canada	TTC (Toronto)
Germany	VBB (Berlin)
France	Ile-de-France Mobilites (Paris)
Netherlands	OVapi
Belgium	NMBS/SNCB
Finland	HSL (Helsinki)
Denmark	Rejseplanen
Australia	Transperth (Perth)
New Zealand	Auckland Transport

Limitations

English only — no multilingual support
Static schedule data only — no real-time predictions
Not a trip planner — cannot compute optimal routes
1,024 token context — limited for very long queries
Small training dataset (3,501 pairs) — may not generalize to all transit scenarios

Citation

@model{umartransit_1b,
  author = {Umar Farook M},
  title = {UmarTransit-1B: Domain-Specific LLM for Public Transit and GTFS},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/umarfarookm/UmarTransit-1B}
}

Downloads last month: 2,091

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for umarfarookm/UmarTransit-1B

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(237)

this model

Quantizations

1 model

umarfarookm
/

UmarTransit-1B

UmarTransit-1B (v1.0)

Model Details

What's New in v1.0

Evaluation Results

Per-Category (Combined Score)

Available Formats

Usage

With Transformers (Python)

With Ollama

Training Details

Training Data Coverage

Limitations

Links

Citation

Model tree for umarfarookm/UmarTransit-1B

Dataset used to train umarfarookm/UmarTransit-1B