Instructions to use agentbyumer/mini-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use agentbyumer/mini-gemma with Transformers:

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

pipe = pipeline("text-generation", model="agentbyumer/mini-gemma")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("agentbyumer/mini-gemma")
model = AutoModelForCausalLM.from_pretrained("agentbyumer/mini-gemma", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use agentbyumer/mini-gemma with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/agentbyumer/mini-gemma

SGLang

How to use agentbyumer/mini-gemma 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 "agentbyumer/mini-gemma" \
    --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": "agentbyumer/mini-gemma",
		"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 "agentbyumer/mini-gemma" \
        --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": "agentbyumer/mini-gemma",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use agentbyumer/mini-gemma with Docker Model Runner:
```
docker model run hf.co/agentbyumer/mini-gemma
```

mini-gemma / README.md

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	---
	license: mit
	base_model: google/gemma-2b
	tags:
	- text-generation-inference
	- transformers
	- gemma
	- mini-gemma
	- agentic-ai
	model_type: gemma
	pipeline_tag: text-generation
	---

	# Mini-Gemma Custom Model

	This repository contains a custom domain-specialized fine-tune of the Gemma architecture, optimized for specific text distributions and patterns. The model was trained using the Hugging Face `Trainer` on an accelerated NVIDIA GPU cluster.

	## 📊 Training Performance & Metrics

	The model successfully converged over its training run with highly stable gradients:
	* Total Training Steps: 20,000
	* Final Total Train Loss: `3.478`
	* Final Step Loss: `2.988`
	* Gradient Norm Stability: Stable at `~1.12`
	* Training Status: Complete / Fully Converged

	## 🚀 Quick Start & Usage

	You can easily load and run this model locally using the Transformers library:

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

	model_id = "agentbyumer/mini-gemma"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.float16,
	device_map="auto"
	)

	generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

	prompt = "Your specialized prompt here"
	outputs = generator(
	prompt,
	max_new_tokens=150,
	do_sample=True,
	temperature=0.7,
	return_full_text=False
	)
	print(outputs[0]['generated_text'])
	```

	## 📜 License

	This project is licensed under the permissive MIT License. See the accompanying [LICENSE](./LICENSE) file for full details.