Instructions to use howkewlisthat/Axsy_OpenELM_450M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use howkewlisthat/Axsy_OpenELM_450M with Transformers:

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

pipe = pipeline("text-generation", model="howkewlisthat/Axsy_OpenELM_450M", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("howkewlisthat/Axsy_OpenELM_450M", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use howkewlisthat/Axsy_OpenELM_450M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "howkewlisthat/Axsy_OpenELM_450M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "howkewlisthat/Axsy_OpenELM_450M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/howkewlisthat/Axsy_OpenELM_450M

SGLang

How to use howkewlisthat/Axsy_OpenELM_450M 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 "howkewlisthat/Axsy_OpenELM_450M" \
    --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": "howkewlisthat/Axsy_OpenELM_450M",
		"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 "howkewlisthat/Axsy_OpenELM_450M" \
        --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": "howkewlisthat/Axsy_OpenELM_450M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use howkewlisthat/Axsy_OpenELM_450M with Docker Model Runner:
```
docker model run hf.co/howkewlisthat/Axsy_OpenELM_450M
```

Axsy_OpenELM_450M / config.json

howkewlisthat

Upload OpenELMForCausalLM

78ac2f7 verified almost 2 years ago

raw

history blame contribute delete

1.52 kB

	{
	"_name_or_path": "apple/OpenELM-450M",
	"activation_fn_name": "swish",
	"architectures": [
	"OpenELMForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "apple/OpenELM-450M--configuration_openelm.OpenELMConfig",
	"AutoModelForCausalLM": "apple/OpenELM-450M--modeling_openelm.OpenELMForCausalLM"
	},
	"bos_token_id": 32000,
	"eos_token_id": 32001,
	"ffn_dim_divisor": 256,
	"ffn_multipliers": [
	0.5,
	0.68,
	0.87,
	1.05,
	1.24,
	1.42,
	1.61,
	1.79,
	1.97,
	2.16,
	2.34,
	2.53,
	2.71,
	2.89,
	3.08,
	3.26,
	3.45,
	3.63,
	3.82,
	4.0
	],
	"ffn_with_glu": true,
	"head_dim": 64,
	"initializer_range": 0.02,
	"max_context_length": 2048,
	"model_dim": 1536,
	"model_type": "openelm",
	"normalization_layer_name": "rms_norm",
	"normalize_qk_projections": true,
	"num_gqa_groups": 4,
	"num_kv_heads": [
	3,
	3,
	3,
	4,
	4,
	4,
	4,
	4,
	4,
	4,
	5,
	5,
	5,
	5,
	5,
	5,
	6,
	6,
	6,
	6
	],
	"num_query_heads": [
	12,
	12,
	12,
	16,
	16,
	16,
	16,
	16,
	16,
	16,
	20,
	20,
	20,
	20,
	20,
	20,
	24,
	24,
	24,
	24
	],
	"num_transformer_layers": 20,
	"pad_token_id": 32001,
	"qkv_multipliers": [
	0.5,
	1.0
	],
	"rope_freq_constant": 10000,
	"rope_max_length": 4096,
	"share_input_output_layers": true,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.40.1",
	"use_cache": true,
	"vocab_size": 32002
	}