Instructions to use emplitude/topruby with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emplitude/topruby with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="emplitude/topruby")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("emplitude/topruby") model = AutoModelForCausalLM.from_pretrained("emplitude/topruby") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use emplitude/topruby with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "emplitude/topruby" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "emplitude/topruby", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/emplitude/topruby
- SGLang
How to use emplitude/topruby 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 "emplitude/topruby" \ --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": "emplitude/topruby", "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 "emplitude/topruby" \ --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": "emplitude/topruby", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use emplitude/topruby with Docker Model Runner:
docker model run hf.co/emplitude/topruby
| Model | MT Bench | EQ Bench | MMLU | Logic Test |
|---|---|---|---|---|
| GPT-4-Turbo | 9.32 | - | - | - |
| GPT-4 | 8.99 | 62.52 | 86.4 | 0.86 |
| Kunoichi-DPO-v2-7B | 8.51 | 42.18 | 64.94 | 0.58 |
| Mixtral-8x7B-Instruct | 8.30 | 44.81 | 70.6 | 0.75 |
| Kunoichi-DPO-7B | 8.29 | 41.60 | 64.83 | 0.59 |
| Kunoichi-7B | 8.14 | 44.32 | 64.9 | 0.58 |
| Starling-7B | 8.09 | - | 63.9 | 0.51 |
| Claude-2 | 8.06 | 52.14 | 78.5 | - |
| Silicon-Maid-7B | 7.96 | 40.44 | 64.7 | 0.54 |
| Loyal-Macaroni-Maid-7B | 7.95 | 38.66 | 64.9 | 0.57 |
| GPT-3.5-Turbo | 7.94 | 50.28 | 70 | 0.57 |
| Claude-1 | 7.9 | - | 77 | - |
| Openchat-3.5 | 7.81 | 37.08 | 64.3 | 0.39 |
| Dolphin-2.6-DPO | 7.74 | 42.88 | 61.9 | 0.53 |
| Zephyr-7B-beta | 7.34 | 38.71 | 61.4 | 0.30 |
| Llama-2-70b-chat-hf | 6.86 | 51.56 | 63 | - |
| Neural-chat-7b-v3-1 | 6.84 | 43.61 | 62.4 | 0.30 |
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|---|
| Kunoichi-DPO-7B | 58.4 | 45.08 | 74 | 66.99 | 47.52 |
| Kunoichi-DPO-v2-7B | 58.31 | 44.85 | 75.05 | 65.69 | 47.65 |
| Kunoichi-7B | 57.54 | 44.99 | 74.86 | 63.72 | 46.58 |
| OpenPipe/mistral-ft-optimized-1218 | 56.85 | 44.74 | 75.6 | 59.89 | 47.17 |
| Silicon-Maid-7B | 56.45 | 44.74 | 74.26 | 61.5 | 45.32 |
| mlabonne/NeuralHermes-2.5-Mistral-7B | 53.51 | 43.67 | 73.24 | 55.37 | 41.76 |
| teknium/OpenHermes-2.5-Mistral-7B | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
| openchat/openchat_3.5 | 51.34 | 42.67 | 72.92 | 47.27 | 42.51 |
| berkeley-nest/Starling-LM-7B-alpha | 51.16 | 42.06 | 72.72 | 47.33 | 42.53 |
| HuggingFaceH4/zephyr-7b-beta | 50.99 | 37.33 | 71.83 | 55.1 | 39.7 |
| Model | AlpacaEval2 | Length |
|---|---|---|
| GPT-4 | 23.58% | 1365 |
| GPT-4 0314 | 22.07% | 1371 |
| Mistral Medium | 21.86% | 1500 |
| Mixtral 8x7B v0.1 | 18.26% | 1465 |
| Kunoichi-DPO-v2 | 17.19% | 1785 |
| Claude 2 | 17.19% | 1069 |
| Claude | 16.99% | 1082 |
| Gemini Pro | 16.85% | 1315 |
| GPT-4 0613 | 15.76% | 1140 |
| Claude 2.1 | 15.73% | 1096 |
| Mistral 7B v0.2 | 14.72% | 1676 |
| GPT 3.5 Turbo 0613 | 14.13% | 1328 |
| LLaMA2 Chat 70B | 13.87% | 1790 |
| LMCocktail-10.7B-v1 | 13.15% | 1203 |
| WizardLM 13B V1.1 | 11.23% | 1525 |
| Zephyr 7B Beta | 10.99% | 1444 |
| OpenHermes-2.5-Mistral (7B) | 10.34% | 1107 |
| GPT 3.5 Turbo 0301 | 9.62% | 827 |
| Kunoichi-7B | 9.38% | 1492 |
| GPT 3.5 Turbo 1106 | 9.18% | 796 |
| GPT-3.5 | 8.56% | 1018 |
| Phi-2 DPO | 7.76% | 1687 |
| LLaMA2 Chat 13B | 7.70% | 1513 |
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