Instructions to use Azure99/blossom-v4-yi-34b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azure99/blossom-v4-yi-34b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Azure99/blossom-v4-yi-34b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Azure99/blossom-v4-yi-34b") model = AutoModelForCausalLM.from_pretrained("Azure99/blossom-v4-yi-34b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Azure99/blossom-v4-yi-34b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Azure99/blossom-v4-yi-34b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Azure99/blossom-v4-yi-34b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Azure99/blossom-v4-yi-34b
- SGLang
How to use Azure99/blossom-v4-yi-34b 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 "Azure99/blossom-v4-yi-34b" \ --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": "Azure99/blossom-v4-yi-34b", "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 "Azure99/blossom-v4-yi-34b" \ --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": "Azure99/blossom-v4-yi-34b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Azure99/blossom-v4-yi-34b with Docker Model Runner:
docker model run hf.co/Azure99/blossom-v4-yi-34b
BLOSSOM-v4-yi-34b
Introduction
Blossom is a conversational large language model, fine-tuned on the Blossom Orca/Wizard/Chat/Math mixed dataset based on the Yi-34B pre-trained model. Blossom possesses robust general capabilities and context comprehension. Additionally, the high-quality Chinese and English datasets used for training have been made open source.
Training was conducted in two stages. The first stage used 100K Wizard, 100K Orca, 20K Math single-turn instruction datasets, training for 1 epoch; the second stage used 50K Blossom chat multi-turn dialogue dataset, and 2% randomly sampled data from the first stage, training for 3 epochs.
Inference
Inference is performed in the form of dialogue continuation.
Single-turn dialogue
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|:
Multi-turn dialogue
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|: Hello! How can I assist you today?<|endoftext|>
|Human|: Generate a random number using python
|Bot|:
Note: At the end of the Bot's output in the historical conversation, append a <|endoftext|>.
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