Instructions to use TinyPixel/oe-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TinyPixel/oe-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TinyPixel/oe-1", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TinyPixel/oe-1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TinyPixel/oe-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TinyPixel/oe-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TinyPixel/oe-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TinyPixel/oe-1
- SGLang
How to use TinyPixel/oe-1 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 "TinyPixel/oe-1" \ --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": "TinyPixel/oe-1", "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 "TinyPixel/oe-1" \ --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": "TinyPixel/oe-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TinyPixel/oe-1 with Docker Model Runner:
docker model run hf.co/TinyPixel/oe-1
File size: 1,387 Bytes
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"_name_or_path": "TinyPixel/openelm-270m",
"activation_fn_name": "swish",
"architectures": [
"OpenELMForCausalLM"
],
"auto_map": {
"AutoConfig": "apple/OpenELM-270M--configuration_openelm.OpenELMConfig",
"AutoModelForCausalLM": "apple/OpenELM-270M--modeling_openelm.OpenELMForCausalLM"
},
"bos_token_id": 1,
"eos_token_id": 2,
"ffn_dim_divisor": 256,
"ffn_multipliers": [
0.5,
0.73,
0.97,
1.2,
1.43,
1.67,
1.9,
2.13,
2.37,
2.6,
2.83,
3.07,
3.3,
3.53,
3.77,
4.0
],
"ffn_with_glu": true,
"head_dim": 64,
"initializer_range": 0.02,
"max_context_length": 2048,
"model_dim": 1280,
"model_type": "openelm",
"normalization_layer_name": "rms_norm",
"normalize_qk_projections": true,
"num_gqa_groups": 4,
"num_kv_heads": [
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
4,
4,
5,
5,
5,
5
],
"num_query_heads": [
12,
12,
12,
12,
12,
16,
16,
16,
16,
16,
16,
16,
20,
20,
20,
20
],
"num_transformer_layers": 16,
"qkv_multipliers": [
0.5,
1.0
],
"rope_freq_constant": 10000,
"rope_max_length": 4096,
"share_input_output_layers": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.41.2",
"use_cache": true,
"vocab_size": 32000
}
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