Text Generation
Transformers
TensorBoard
Safetensors
openelm
alignment-handbook
trl
sft
Generated from Trainer
conversational
custom_code
Instructions to use CharlesLi/OpenELM-1_1B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CharlesLi/OpenELM-1_1B-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CharlesLi/OpenELM-1_1B-SFT", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("CharlesLi/OpenELM-1_1B-SFT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CharlesLi/OpenELM-1_1B-SFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CharlesLi/OpenELM-1_1B-SFT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CharlesLi/OpenELM-1_1B-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CharlesLi/OpenELM-1_1B-SFT
- SGLang
How to use CharlesLi/OpenELM-1_1B-SFT 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 "CharlesLi/OpenELM-1_1B-SFT" \ --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": "CharlesLi/OpenELM-1_1B-SFT", "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 "CharlesLi/OpenELM-1_1B-SFT" \ --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": "CharlesLi/OpenELM-1_1B-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use CharlesLi/OpenELM-1_1B-SFT with Docker Model Runner:
docker model run hf.co/CharlesLi/OpenELM-1_1B-SFT
File size: 1,685 Bytes
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"_name_or_path": "apple/OpenELM-1_1B",
"activation_fn_name": "swish",
"architectures": [
"OpenELMForCausalLM"
],
"auto_map": {
"AutoConfig": "apple/OpenELM-1_1B--configuration_openelm.OpenELMConfig",
"AutoModelForCausalLM": "apple/OpenELM-1_1B--modeling_openelm.OpenELMForCausalLM"
},
"bos_token_id": 1,
"eos_token_id": 2,
"ffn_dim_divisor": 256,
"ffn_multipliers": [
0.5,
0.63,
0.76,
0.89,
1.02,
1.15,
1.28,
1.41,
1.54,
1.67,
1.8,
1.93,
2.06,
2.19,
2.31,
2.44,
2.57,
2.7,
2.83,
2.96,
3.09,
3.22,
3.35,
3.48,
3.61,
3.74,
3.87,
4.0
],
"ffn_with_glu": true,
"head_dim": 64,
"initializer_range": 0.02,
"max_context_length": 2048,
"model_dim": 2048,
"model_type": "openelm",
"normalization_layer_name": "rms_norm",
"normalize_qk_projections": true,
"num_gqa_groups": 4,
"num_kv_heads": [
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"num_query_heads": [
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],
"num_transformer_layers": 28,
"qkv_multipliers": [
0.5,
1.0
],
"rope_freq_constant": 10000,
"rope_max_length": 4096,
"share_input_output_layers": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.44.2",
"use_cache": true,
"vocab_size": 32000
}
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