Instructions to use snagbreac/Gemma270mDefMod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use snagbreac/Gemma270mDefMod with Transformers:

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

pipe = pipeline("text-generation", model="snagbreac/Gemma270mDefMod")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("snagbreac/Gemma270mDefMod")
model = AutoModelForCausalLM.from_pretrained("snagbreac/Gemma270mDefMod", 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 snagbreac/Gemma270mDefMod with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "snagbreac/Gemma270mDefMod"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "snagbreac/Gemma270mDefMod",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/snagbreac/Gemma270mDefMod

SGLang

How to use snagbreac/Gemma270mDefMod 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 "snagbreac/Gemma270mDefMod" \
    --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": "snagbreac/Gemma270mDefMod",
		"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 "snagbreac/Gemma270mDefMod" \
        --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": "snagbreac/Gemma270mDefMod",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use snagbreac/Gemma270mDefMod with Docker Model Runner:
```
docker model run hf.co/snagbreac/Gemma270mDefMod
```

snagbreac commited on May 19

Commit

ff37bf9

verified ·

1 Parent(s): c19c3c7

Training in progress, epoch 3

Browse files

Files changed (2) hide show

model.safetensors +1 -1
runs/May19_12-39-17_8473e85203eb/events.out.tfevents.1779194357.8473e85203eb.9151.0 +2 -2

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:02be9c5c55607fcd5bd4becbc7b864ca48b13e4454f1248bfede8fbe99fea22b
 size 536223056

 version https://git-lfs.github.com/spec/v1
+oid sha256:85d613630ccfe21339b4b14f553b6dbb41e76d208ec054abd81eeea0ce57ce2c
 size 536223056

runs/May19_12-39-17_8473e85203eb/events.out.tfevents.1779194357.8473e85203eb.9151.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e55410eaed44f579cb48c5d0ecf39708522c12c315f5de5b148a831bbbae7e99
-size 3871585

 version https://git-lfs.github.com/spec/v1
+oid sha256:802ac7325d6d8e70c626fcfbb717112e9267efa95ec2387b53009108a864193a
+size 5805326