Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
Merge
mergekit
lazymergekit
mlabonne/NeuralBeagle14-7B
mlabonne/NeuralDaredevil-7B
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use shadowml/DareBeagel-2x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shadowml/DareBeagel-2x7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shadowml/DareBeagel-2x7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shadowml/DareBeagel-2x7B") model = AutoModelForCausalLM.from_pretrained("shadowml/DareBeagel-2x7B", device_map="auto") 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 Settings
- vLLM
How to use shadowml/DareBeagel-2x7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shadowml/DareBeagel-2x7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shadowml/DareBeagel-2x7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shadowml/DareBeagel-2x7B
- SGLang
How to use shadowml/DareBeagel-2x7B 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 "shadowml/DareBeagel-2x7B" \ --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": "shadowml/DareBeagel-2x7B", "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 "shadowml/DareBeagel-2x7B" \ --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": "shadowml/DareBeagel-2x7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use shadowml/DareBeagel-2x7B with Docker Model Runner:
docker model run hf.co/shadowml/DareBeagel-2x7B
| license: apache-2.0 | |
| tags: | |
| - moe | |
| - merge | |
| - mergekit | |
| - lazymergekit | |
| - mlabonne/NeuralBeagle14-7B | |
| - mlabonne/NeuralDaredevil-7B | |
| model-index: | |
| - name: DareBeagel-2x7B | |
| results: | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: AI2 Reasoning Challenge (25-Shot) | |
| type: ai2_arc | |
| config: ARC-Challenge | |
| split: test | |
| args: | |
| num_few_shot: 25 | |
| metrics: | |
| - type: acc_norm | |
| value: 72.01 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/DareBeagel-2x7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: HellaSwag (10-Shot) | |
| type: hellaswag | |
| split: validation | |
| args: | |
| num_few_shot: 10 | |
| metrics: | |
| - type: acc_norm | |
| value: 88.12 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/DareBeagel-2x7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MMLU (5-Shot) | |
| type: cais/mmlu | |
| config: all | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 64.51 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/DareBeagel-2x7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: TruthfulQA (0-shot) | |
| type: truthful_qa | |
| config: multiple_choice | |
| split: validation | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: mc2 | |
| value: 69.09 | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/DareBeagel-2x7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: Winogrande (5-shot) | |
| type: winogrande | |
| config: winogrande_xl | |
| split: validation | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 82.72 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/DareBeagel-2x7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: GSM8k (5-shot) | |
| type: gsm8k | |
| config: main | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 70.51 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=shadowml/DareBeagel-2x7B | |
| name: Open LLM Leaderboard | |
| # Beyonder-2x7B-v2 | |
| Beyonder-2x7B-v2 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): | |
| * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) | |
| * [mlabonne/NeuralDaredevil-7B](https://huggingface.co/mlabonne/NeuralDaredevil-7B) | |
| ## 🧩 Configuration | |
| ```yaml | |
| base_model: mlabonne/NeuralBeagle14-7B | |
| gate_mode: random | |
| experts: | |
| - source_model: mlabonne/NeuralBeagle14-7B | |
| positive_prompts: [""] | |
| - source_model: mlabonne/NeuralDaredevil-7B | |
| positive_prompts: [""] | |
| ``` | |
| ## 💻 Usage | |
| ```python | |
| !pip install -qU transformers bitsandbytes accelerate | |
| from transformers import AutoTokenizer | |
| import transformers | |
| import torch | |
| model = "shadowml/Beyonder-2x7B-v2" | |
| tokenizer = AutoTokenizer.from_pretrained(model) | |
| pipeline = transformers.pipeline( | |
| "text-generation", | |
| model=model, | |
| model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, | |
| ) | |
| messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] | |
| prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
| outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) | |
| print(outputs[0]["generated_text"]) | |
| ``` | |
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) | |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_shadowml__DareBeagel-2x7B) | |
| | Metric |Value| | |
| |---------------------------------|----:| | |
| |Avg. |74.49| | |
| |AI2 Reasoning Challenge (25-Shot)|72.01| | |
| |HellaSwag (10-Shot) |88.12| | |
| |MMLU (5-Shot) |64.51| | |
| |TruthfulQA (0-shot) |69.09| | |
| |Winogrande (5-shot) |82.72| | |
| |GSM8k (5-shot) |70.51| | |