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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 / README.md

umarfarookm

Update model card for v1.0: expanded dataset, new categories, benchmark results

1294a19 verified 19 days ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	datasets:
	- umarfarookm/UmarTransit-Instruct-3k
	tags:
	- transit
	- gtfs
	- transportation
	- instruction-following
	- qwen2
	- qlora
	- unsloth
	language:
	- en
	pipeline_tag: text-generation
	---

	# 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](https://mobilitydatabase.org/). 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](https://huggingface.co/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](https://unsloth.ai) + HuggingFace TRL \|
	\| Training Data \| 3,154 pairs from [UmarTransit-Instruct-3k](https://huggingface.co/datasets/umarfarookm/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](https://github.com/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)

	```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

	```bash
	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

	## Links

	- GitHub: [umarfarookm/transit-foundation-model](https://github.com/umarfarookm/transit-foundation-model)
	- Dataset: [umarfarookm/UmarTransit-Instruct-3k](https://huggingface.co/datasets/umarfarookm/UmarTransit-Instruct-3k)
	- Web Demo: [transit-foundation-model.vercel.app](https://transit-foundation-model.vercel.app)

	## Citation

	```bibtex
	@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}
	}
	```