Text Generation
Transformers
Safetensors
Japanese
mixtral
Mixture of Experts
text-generation-inference
Instructions to use HachiML/youri-2x7b_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HachiML/youri-2x7b_dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HachiML/youri-2x7b_dev")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HachiML/youri-2x7b_dev") model = AutoModelForCausalLM.from_pretrained("HachiML/youri-2x7b_dev") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HachiML/youri-2x7b_dev with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HachiML/youri-2x7b_dev" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HachiML/youri-2x7b_dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HachiML/youri-2x7b_dev
- SGLang
How to use HachiML/youri-2x7b_dev 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 "HachiML/youri-2x7b_dev" \ --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": "HachiML/youri-2x7b_dev", "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 "HachiML/youri-2x7b_dev" \ --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": "HachiML/youri-2x7b_dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HachiML/youri-2x7b_dev with Docker Model Runner:
docker model run hf.co/HachiML/youri-2x7b_dev
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README.md
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|[youri-7b-instruction](https://huggingface.co/rinna/youri-7b-instruction) *1| 78.94 | 17.20| 54.04| 66.35|
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|[youri-7b-chat](https://huggingface.co/rinna/youri-7b-chat) *1| 80.92| 25.20| 53.78| 67.36|
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*1 From the [rinna's LM Benchmark](https://rinnakk.github.io/research/benchmarks/lm/index.html).
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*2 Since there was no mention of these template versions in rinna's LM Benchmark, the scores were calculated without specifying a template.
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## 🧩 Configuration
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|[youri-7b-instruction](https://huggingface.co/rinna/youri-7b-instruction) *1| 78.94 | 17.20| 54.04| 66.35|
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|[youri-7b-chat](https://huggingface.co/rinna/youri-7b-chat) *1| 80.92| 25.20| 53.78| 67.36|
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*1 From the [rinna's LM Benchmark](https://rinnakk.github.io/research/benchmarks/lm/index.html).
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*2 Since there was no mention of these template versions in rinna's LM Benchmark, the scores were calculated without specifying a template.
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## 🧩 Configuration
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