Text Generation
Transformers
PyTorch
Chinese
English
llama
translation
multilingual
large language model
instruction tuning
text-generation-inference
Instructions to use ICTNLP/bayling-7b-diff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICTNLP/bayling-7b-diff with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ICTNLP/bayling-7b-diff")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ICTNLP/bayling-7b-diff") model = AutoModelForCausalLM.from_pretrained("ICTNLP/bayling-7b-diff") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ICTNLP/bayling-7b-diff with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ICTNLP/bayling-7b-diff" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ICTNLP/bayling-7b-diff", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ICTNLP/bayling-7b-diff
- SGLang
How to use ICTNLP/bayling-7b-diff 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 "ICTNLP/bayling-7b-diff" \ --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": "ICTNLP/bayling-7b-diff", "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 "ICTNLP/bayling-7b-diff" \ --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": "ICTNLP/bayling-7b-diff", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ICTNLP/bayling-7b-diff with Docker Model Runner:
docker model run hf.co/ICTNLP/bayling-7b-diff
File size: 686 Bytes
bfff42d | 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 | {
"_name_or_path": "/data/zhangshaolei/FastChat/checkpoints/imt.llama_7B.data=en-zh-de-fr.wmt_dev+IMT.v8+personal+gpt4_alpaca_zhen+sharegpt+pe.393739",
"architectures": [
"LlamaForCausalLM"
],
"bos_token_id": 0,
"eos_token_id": 1,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 2048,
"max_sequence_length": 2048,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"pad_token_id": -1,
"rms_norm_eps": 1e-06,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.28.1",
"use_cache": true,
"vocab_size": 32000
}
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