Instructions to use itay1itzhak/OLMo-Flan-Seed-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use itay1itzhak/OLMo-Flan-Seed-2 with Transformers:

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

pipe = pipeline("text-generation", model="itay1itzhak/OLMo-Flan-Seed-2", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("itay1itzhak/OLMo-Flan-Seed-2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use itay1itzhak/OLMo-Flan-Seed-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "itay1itzhak/OLMo-Flan-Seed-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itay1itzhak/OLMo-Flan-Seed-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/itay1itzhak/OLMo-Flan-Seed-2

SGLang

How to use itay1itzhak/OLMo-Flan-Seed-2 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 "itay1itzhak/OLMo-Flan-Seed-2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itay1itzhak/OLMo-Flan-Seed-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "itay1itzhak/OLMo-Flan-Seed-2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itay1itzhak/OLMo-Flan-Seed-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use itay1itzhak/OLMo-Flan-Seed-2 with Docker Model Runner:
```
docker model run hf.co/itay1itzhak/OLMo-Flan-Seed-2
```

OLMo-Flan-Seed-2 / configuration_olmo.py

itay1itzhak

Upload OLMoForCausalLM

d3045d3 verified over 1 year ago

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"""
OLMo configuration
"""

from transformers import AutoConfig, PretrainedConfig
from transformers.utils import logging

from olmo.config import ModelConfig

logger = logging.get_logger(__name__)


class OLMoConfig(PretrainedConfig):
    model_type = "hf_olmo"
    keys_to_ignore_at_inference = ["past_key_values"]  # TODO: confirm

    def __init__(self, use_cache: bool = False, **kwargs):
        model_config = ModelConfig()
        all_kwargs = model_config.asdict()
        all_kwargs.update(kwargs)
        all_kwargs.update({"use_cache": use_cache})
        all_kwargs.update(
            {"architectures": all_kwargs.get("architectures", ["OLMoForCausalLM"]) or ["OLMoForCausalLM"]}
        )
        super().__init__(**all_kwargs)

    @property
    def num_attention_heads(self):
        return self.n_heads

    @property
    def num_hidden_layers(self):
        return self.n_layers

    @property
    def hidden_size(self):
        return self.d_model


# Register the config class so that it is available for transformer pipelines, auto-loading etc.
# OLMo is integrated directly in transformers from v4.40.0 onwards, but the version in transformers
# may not support the newest architectures we create.
AutoConfig.register("hf_olmo", OLMoConfig)

	"""
	OLMo configuration
	"""

	from transformers import AutoConfig, PretrainedConfig
	from transformers.utils import logging

	from olmo.config import ModelConfig

	logger = logging.get_logger(__name__)


	class OLMoConfig(PretrainedConfig):
	model_type = "hf_olmo"
	keys_to_ignore_at_inference = ["past_key_values"] # TODO: confirm

	def __init__(self, use_cache: bool = False, **kwargs):
	model_config = ModelConfig()
	all_kwargs = model_config.asdict()
	all_kwargs.update(kwargs)
	all_kwargs.update({"use_cache": use_cache})
	all_kwargs.update(
	{"architectures": all_kwargs.get("architectures", ["OLMoForCausalLM"]) or ["OLMoForCausalLM"]}
	)
	super().__init__(**all_kwargs)

	@property
	def num_attention_heads(self):
	return self.n_heads

	@property
	def num_hidden_layers(self):
	return self.n_layers

	@property
	def hidden_size(self):
	return self.d_model


	# Register the config class so that it is available for transformer pipelines, auto-loading etc.
	# OLMo is integrated directly in transformers from v4.40.0 onwards, but the version in transformers
	# may not support the newest architectures we create.
	AutoConfig.register("hf_olmo", OLMoConfig)