Text Generation
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Merge
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text-generation-inference
Instructions to use Weyaxi/Bagel-Hermes-34B-Slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Weyaxi/Bagel-Hermes-34B-Slerp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Weyaxi/Bagel-Hermes-34B-Slerp") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Weyaxi/Bagel-Hermes-34B-Slerp") model = AutoModelForCausalLM.from_pretrained("Weyaxi/Bagel-Hermes-34B-Slerp") 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 Weyaxi/Bagel-Hermes-34B-Slerp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Weyaxi/Bagel-Hermes-34B-Slerp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Weyaxi/Bagel-Hermes-34B-Slerp", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Weyaxi/Bagel-Hermes-34B-Slerp
- SGLang
How to use Weyaxi/Bagel-Hermes-34B-Slerp 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 "Weyaxi/Bagel-Hermes-34B-Slerp" \ --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": "Weyaxi/Bagel-Hermes-34B-Slerp", "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 "Weyaxi/Bagel-Hermes-34B-Slerp" \ --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": "Weyaxi/Bagel-Hermes-34B-Slerp", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Weyaxi/Bagel-Hermes-34B-Slerp with Docker Model Runner:
docker model run hf.co/Weyaxi/Bagel-Hermes-34B-Slerp
Adding Evaluation Results (#5)
Browse files- Adding Evaluation Results (7545ec85acd287670e0ba4405f92149714b6c291)
Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>
README.md
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---
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tags:
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- mergekit
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- merge
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Bagel-Hermes-34B-Slerp
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name: Open LLM Leaderboard
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---
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# Bagel-Hermes-34B-Slerp
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@@ -164,3 +256,17 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
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|Winogrande (5-shot) |84.37|
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|GSM8k (5-shot) |66.26|
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---
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license: apache-2.0
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tags:
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- mergekit
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- merge
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Bagel-Hermes-34B-Slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 46.03
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Bagel-Hermes-34B-Slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 41.96
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Bagel-Hermes-34B-Slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 4.91
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Bagel-Hermes-34B-Slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 11.3
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Bagel-Hermes-34B-Slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 17.01
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Bagel-Hermes-34B-Slerp
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 41.15
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Bagel-Hermes-34B-Slerp
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name: Open LLM Leaderboard
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---
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# Bagel-Hermes-34B-Slerp
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|Winogrande (5-shot) |84.37|
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|GSM8k (5-shot) |66.26|
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Bagel-Hermes-34B-Slerp)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |27.06|
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|IFEval (0-Shot) |46.03|
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|BBH (3-Shot) |41.96|
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|MATH Lvl 5 (4-Shot)| 4.91|
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|GPQA (0-shot) |11.30|
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|MuSR (0-shot) |17.01|
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|MMLU-PRO (5-shot) |41.15|
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