Instructions to use Azure99/blossom-v4-qwen-1_8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azure99/blossom-v4-qwen-1_8b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Azure99/blossom-v4-qwen-1_8b", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Azure99/blossom-v4-qwen-1_8b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Azure99/blossom-v4-qwen-1_8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azure99/blossom-v4-qwen-1_8b"
# 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-qwen-1_8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Azure99/blossom-v4-qwen-1_8b

SGLang

How to use Azure99/blossom-v4-qwen-1_8b 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-qwen-1_8b" \
    --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-qwen-1_8b",
		"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-qwen-1_8b" \
        --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-qwen-1_8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Azure99/blossom-v4-qwen-1_8b with Docker Model Runner:
```
docker model run hf.co/Azure99/blossom-v4-qwen-1_8b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BLOSSOM-v4-qwen-1_8b

💻Github • 🚀Blossom Chat Demo

介绍

Blossom是一个对话式语言模型，基于Qwen-1_8B预训练模型，在Blossom Orca/Wizard/Chat/Math混合数据集上进行指令精调得来。Blossom拥有强大的通用能力及上下文理解能力，此外，训练使用的高质量中英文数据集也进行了开源。

训练分为两阶段，第一阶段使用100K Wizard、100K Orca、20K Math单轮指令数据集，训练1个epoch；第二阶段使用50K Blossom chat多轮对话数据集、以及上一阶段中随机采样2%的数据，训练3个epoch。

推理

推理采用对话续写的形式。

单轮对话

A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: 你好
|Bot|:

多轮对话

A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: 你好
|Bot|: 你好，有什么我能帮助你的？<|endoftext|>
|Human|: 介绍下中国的首都吧
|Bot|:

注意：在历史对话的Bot输出结尾，拼接一个<|endoftext|>

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Model tree for Azure99/blossom-v4-qwen-1_8b

Quantizations

1 model

Azure99
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blossom-v4-qwen-1_8b

BLOSSOM-v4-qwen-1_8b

介绍

推理

Model tree for Azure99/blossom-v4-qwen-1_8b

Datasets used to train Azure99/blossom-v4-qwen-1_8b