Instructions to use maywell/Synatra-Mixtral-8x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maywell/Synatra-Mixtral-8x7B with Transformers:

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

pipe = pipeline("text-generation", model="maywell/Synatra-Mixtral-8x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-Mixtral-8x7B")
model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-Mixtral-8x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maywell/Synatra-Mixtral-8x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maywell/Synatra-Mixtral-8x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Synatra-Mixtral-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maywell/Synatra-Mixtral-8x7B

SGLang

How to use maywell/Synatra-Mixtral-8x7B 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 "maywell/Synatra-Mixtral-8x7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Synatra-Mixtral-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "maywell/Synatra-Mixtral-8x7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/Synatra-Mixtral-8x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maywell/Synatra-Mixtral-8x7B with Docker Model Runner:
```
docker model run hf.co/maywell/Synatra-Mixtral-8x7B
```

Synatra-Mixtral-8x7B

Synatra-Mixtral-8x7B is a fine-tuned version of the Mixtral-8x7B-Instruct-v0.1 model using Korean datasets.

This model features overwhelmingly superior comprehension and inference capabilities and is licensed under apache-2.0.

Join Our Discord

Server Link

License

OPEN, Apache-2.0.

Model Details

Base Model
mistralai/Mixtral-8x7B-Instruct-v0.1

Trained On
A100 80GB * 6

Instruction format

It follows Alpaca format.

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{input}

### Response:
{output}

Model Benchmark

TBD

Implementation Code

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-Mixtral-8x7B")
tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-Mixtral-8x7B")

messages = [
    {"role": "user", "content": "아인슈타인의 상대성이론에 대해서 자세히 설명해줘."},
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

Author's Message

This model's training got sponsered by no one but support from people around Earth.

Support Me

Contact Me on Discord - is.maywell

Follow me on twitter: https://twitter.com/stablefluffy

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