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

Libraries

How to use OptGear/Opt.Gear-1B with Transformers:

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

pipe = pipeline("text-generation", model="OptGear/Opt.Gear-1B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OptGear/Opt.Gear-1B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OptGear/Opt.Gear-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OptGear/Opt.Gear-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": "OptGear/Opt.Gear-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OptGear/Opt.Gear-1B

SGLang

How to use OptGear/Opt.Gear-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 "OptGear/Opt.Gear-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": "OptGear/Opt.Gear-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 "OptGear/Opt.Gear-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": "OptGear/Opt.Gear-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OptGear/Opt.Gear-1B with Docker Model Runner:
```
docker model run hf.co/OptGear/Opt.Gear-1B
```

kyw96 commited on Aug 3

Commit

9a4c308

verified ·

1 Parent(s): dc354c7

Update modeling_gear.py

Browse files

Files changed (1) hide show

modeling_gear.py +0 -17

modeling_gear.py CHANGED Viewed

@@ -984,23 +984,6 @@ class GearForCausalLM(GearPreTrainedModel, GenerationMixin):
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs,
     ) -> CausalLMOutputWithPast:
-        r"""
-        Example:
-        ```python
-        >>> from transformers import AutoTokenizer, Gemma3ForCausalLM
-        >>> model = Gemma3ForCausalLM.from_pretrained("google/gemma-2-9b")
-        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
-        >>> prompt = "What is your favorite condiment?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "What is your favorite condiment?"
-        ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (

         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs,
     ) -> CausalLMOutputWithPast:
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (