Instructions to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints
- SGLang
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints 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 "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" \ --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": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", "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 "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints" \ --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": "OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints with Docker Model Runner:
docker model run hf.co/OpenNLPLab/TransNormerLLM3-15B-Intermediate-Checkpoints
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"_name_or_path": "15b-50B",
"add_bos_token": false,
"architectures": [
"TransnormerForCausalLM"
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"auto_map": {
"AutoConfig": "configuration_transnormer.TransnormerConfig",
"AutoModelForCausalLM": "modeling_transnormer.TransnormerForCausalLM"
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"bias": false,
"bos_token_id": 100261,
"decoder_attention_heads": 40,
"decoder_embed_dim": 5120,
"decoder_layers": 42,
"eos_token_id": 100257,
"gate_dim": 16,
"glu_dim": 15360,
"hidden_dim": 5120,
"init_std": 0.02,
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"model_type": "transnormer",
"no_scale_embedding": false,
"norm_type": "simplermsnorm",
"pad_token_id": 100262,
"torch_dtype": "bfloat16",
"transformers_version": "4.33.1",
"use_cache": true,
"vocab_size": 100280
} |