Instructions to use Azure99/blossom-v3-mistral-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azure99/blossom-v3-mistral-7b with Transformers:

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

pipe = pipeline("text-generation", model="Azure99/blossom-v3-mistral-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azure99/blossom-v3-mistral-7b")
model = AutoModelForCausalLM.from_pretrained("Azure99/blossom-v3-mistral-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Azure99/blossom-v3-mistral-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azure99/blossom-v3-mistral-7b"
# 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-v3-mistral-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Azure99/blossom-v3-mistral-7b

SGLang

How to use Azure99/blossom-v3-mistral-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 "Azure99/blossom-v3-mistral-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": "Azure99/blossom-v3-mistral-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 "Azure99/blossom-v3-mistral-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": "Azure99/blossom-v3-mistral-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Azure99/blossom-v3-mistral-7b with Docker Model Runner:
```
docker model run hf.co/Azure99/blossom-v3-mistral-7b
```

blossom-v3-mistral-7b / README.md

Azure99

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	---
	license: apache-2.0
	datasets:
	- Azure99/blossom-chat-v1
	- Azure99/blossom-math-v2
	- Azure99/blossom-wizard-v1
	- Azure99/blossom-orca-v1
	language:
	- zh
	- en
	---
	# BLOSSOM-v3-mistral-7b

	[💻Github](https://github.com/Azure99/BlossomLM) • [🚀Blossom Chat Demo](https://blossom-chat.com/)

	### Introduction

	Blossom is a conversational large language model, fine-tuned on the Blossom Orca/Wizard/Chat/Math mixed dataset based on the Mistral-7B-v0.1 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 single-turn instruction datasets, training for 1 epoch; the second stage used a 2K Blossom math reasoning dataset, 50K Blossom chat multi-turn dialogue dataset, and 1% randomly sampled data from the first stage, training for 3 epochs.

	Note: The Mistral-7B-v0.1 pre-trained model is somewhat lacking in Chinese knowledge, so for Chinese scenarios, it is recommended to use [blossom-v3-baichuan2-7b](https://huggingface.co/Azure99/blossom-v3-baichuan2-7b).

	### 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\|: Hello! How can I assist you today?
	```

	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?</s>
	\|Human\|: Generate a random number using python
	\|Bot\|:
	```

	Note: At the end of the Bot's output in the historical conversation, append a `</s>`.