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")
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 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:

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 file for full details.

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