Instructions to use apple/OpenELM-450M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/OpenELM-450M-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="apple/OpenELM-450M-Instruct", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("apple/OpenELM-450M-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use apple/OpenELM-450M-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "apple/OpenELM-450M-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "apple/OpenELM-450M-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/apple/OpenELM-450M-Instruct
- SGLang
How to use apple/OpenELM-450M-Instruct 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 "apple/OpenELM-450M-Instruct" \ --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": "apple/OpenELM-450M-Instruct", "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 "apple/OpenELM-450M-Instruct" \ --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": "apple/OpenELM-450M-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use apple/OpenELM-450M-Instruct with Docker Model Runner:
docker model run hf.co/apple/OpenELM-450M-Instruct
File size: 1,406 Bytes
9be3845 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | {
"activation_fn_name": "swish",
"architectures": [
"OpenELMForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_openelm.OpenELMConfig",
"AutoModelForCausalLM": "modeling_openelm.OpenELMForCausalLM"
},
"bos_token_id": 1,
"eos_token_id": 2,
"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,
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6,
6
],
"num_query_heads": [
12,
12,
12,
16,
16,
16,
16,
16,
16,
16,
20,
20,
20,
20,
20,
20,
24,
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24,
24
],
"num_transformer_layers": 20,
"qkv_multipliers": [
0.5,
1.0
],
"rope_freq_constant": 10000,
"rope_max_length": 4096,
"share_input_output_layers": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.39.3",
"use_cache": true,
"vocab_size": 32000
}
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