Instructions to use KeeganCarey/gemma-ReLe-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KeeganCarey/gemma-ReLe-1b with Transformers:

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

pipe = pipeline("text-generation", model="KeeganCarey/gemma-ReLe-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KeeganCarey/gemma-ReLe-1b")
model = AutoModelForCausalLM.from_pretrained("KeeganCarey/gemma-ReLe-1b", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KeeganCarey/gemma-ReLe-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KeeganCarey/gemma-ReLe-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": "KeeganCarey/gemma-ReLe-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KeeganCarey/gemma-ReLe-1b

SGLang

How to use KeeganCarey/gemma-ReLe-1b 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 "KeeganCarey/gemma-ReLe-1b" \
    --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": "KeeganCarey/gemma-ReLe-1b",
		"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 "KeeganCarey/gemma-ReLe-1b" \
        --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": "KeeganCarey/gemma-ReLe-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use KeeganCarey/gemma-ReLe-1b with Docker Model Runner:
```
docker model run hf.co/KeeganCarey/gemma-ReLe-1b
```

gemma-ReLe-1b

Commit History

Upload folder using huggingface_hub

2f1102b
verified

Keegan Carey commited on Feb 24

(Trained with Unsloth)

0ec7531
verified

Keegan Carey commited on Feb 24

(Trained with Unsloth)

bed0546
verified

Keegan Carey commited on Feb 24

Unsloth Model Card

1e9cbfb
verified

Keegan Carey commited on Feb 24

initial commit

6ee2c24
verified

Keegan Carey commited on Feb 24

Commit History

Upload folder using huggingface_hub 2f1102b verified

(Trained with Unsloth) 0ec7531 verified

(Trained with Unsloth) bed0546 verified

Unsloth Model Card 1e9cbfb verified

initial commit 6ee2c24 verified

Upload folder using huggingface_hub

2f1102b
verified

(Trained with Unsloth)

0ec7531
verified

(Trained with Unsloth)

bed0546
verified

Unsloth Model Card

1e9cbfb
verified

initial commit

6ee2c24
verified