Instructions to use mncai/yi-34B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mncai/yi-34B-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mncai/yi-34B-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mncai/yi-34B-v2") model = AutoModelForCausalLM.from_pretrained("mncai/yi-34B-v2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mncai/yi-34B-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mncai/yi-34B-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mncai/yi-34B-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mncai/yi-34B-v2
- SGLang
How to use mncai/yi-34B-v2 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 "mncai/yi-34B-v2" \ --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": "mncai/yi-34B-v2", "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 "mncai/yi-34B-v2" \ --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": "mncai/yi-34B-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mncai/yi-34B-v2 with Docker Model Runner:
docker model run hf.co/mncai/yi-34B-v2
Model Card for yi-34b-v2
Introduction of MindsAndCompany
We create various AI models and develop solutions that can be applied to businesses. And as for generative AI, we are developing products like Code Assistant, TOD Chatbot, LLMOps, and are in the process of developing Enterprise AGI (Artificial General Intelligence).
Model Summary
based yi-34b, instruction tuned.
How to Use
Here give some examples of how to use our model.
from transformers import AutoConfig, AutoModel, AutoTokenizer
import transformers
import torch
hf_model = 'mncai/yi-34B-v2'
message = "<|user|>\n๋ ๊ฐ์ ๊ตฌ๊ฐ ์๋๋ฐ ๊ฐ๊ฐ ์ง๋ฆ์ด 1, 2์ผ๋ ๊ตฌ์ ๋ถํผ๋ ๋ช๋ฐฐ ์ฐจ์ด๊ฐ ๋์ง? ์ค๋ช
๋ ๊ฐ์ด ํด์ค.\n<|assistant|>\n"
sequences = pipeline(
message,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=2048,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
Contact
If you have any questions, please raise an issue or contact us at dwmyoung@mnc.ai
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