Instructions to use ocisd4/openllama-zh-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ocisd4/openllama-zh-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ocisd4/openllama-zh-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ocisd4/openllama-zh-7B") model = AutoModelForCausalLM.from_pretrained("ocisd4/openllama-zh-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ocisd4/openllama-zh-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ocisd4/openllama-zh-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ocisd4/openllama-zh-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ocisd4/openllama-zh-7B
- SGLang
How to use ocisd4/openllama-zh-7B 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 "ocisd4/openllama-zh-7B" \ --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": "ocisd4/openllama-zh-7B", "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 "ocisd4/openllama-zh-7B" \ --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": "ocisd4/openllama-zh-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ocisd4/openllama-zh-7B with Docker Model Runner:
docker model run hf.co/ocisd4/openllama-zh-7B
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
import transformers
tokenizer = LlamaTokenizer.from_pretrained(
'ocisd4/openllama-zh',
add_bos_token=False,
add_eos_token=False,
use_auth_token=True,
use_fast=False)
model = LlamaForCausalLM.from_pretrained('ocisd4/openllama-zh', device_map='auto',use_auth_token=True)
prompt = '關於華碩的傳說'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=256,
do_sample=True, top_k=40, top_p=0.95, temperature=0.7, repetition_penalty=1.08,
)
print(tokenizer.decode(generation_output[0]))
The is a 7B pretrain model, train from openllama pretrain weight, context size=2048
keep updating new model
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