Text Generation
Transformers
PyTorch
Korean
English
mixtral
Mixture of Experts
conversational
text-generation-inference
Instructions to use maywell/Synatra-Mixtral-8x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Template 관련 문의입니다
#1
by ccw7463 - opened
안녕하세요, 먼저 좋은 모델 제공해주셔서 감사합니다.
다름이 아니라, base model로 mixtral-instruct를 사용하셨던데,
template을 mixtral-instruct에서 사용한 방식과 다르게 구성하여 학습하신 이유가 있을까요?
그리고.. 학습에 사용한 데이터의 용량은 어느정도쯤 될까요?
읽어주셔서 감사합니다.
사용성이 좋은 포맷을 사용하고자 저렇게 변형했습니다.
데이터는 일반적으로 10~20만 샘플 가량 사용하고 있습니다.
This comment has been hidden
답변감사합니다 :)
마지막으로 한가지 더 여쭙고 싶은게,
Full Fine-Tuning을 하신걸까요? 아니면 lora를 적용하셨나요?
풀이었던걸로 기억합니다. a100 6장 사용했습니다.
답변감사합니다 :) 좋은하루 되세요!
maywell changed discussion status to closed