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Model: mlabonne/Beyonder-4x7b Source: Original Platform
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README.md
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---
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license: apache-2.0
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tags:
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- moe
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- mergekit
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---
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# Beyonder-4x7b
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This model is a Mixure of Experts (MoE) made with [mergekit](https://github.com/cg123/mergekit) (mixtral branch). It uses the following base models:
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* [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210)
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* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)
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* [maywell/PiVoT-0.1-Starling-LM-RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP)
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* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
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## 🧩 Configuration
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```yaml
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base_model: openchat/openchat-3.5-1210
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gate_mode: hidden
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experts:
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- source_model: openchat/openchat-3.5-1210
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positive_prompts:
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- "chat"
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- "assistant"
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- "tell me"
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- "explain"
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negative_prompts:
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- "storywriting"
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- "mathematics"
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- "reasoning"
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- "code"
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- "programming"
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- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
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positive_prompts:
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- "code"
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- "python"
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- "javascript"
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- "programming"
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- "algorithm"
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negative_prompts:
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- "chat"
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- "assistant"
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- "storywriting"
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- "mathematics"
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- "reasoning"
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- source_model: maywell/PiVoT-0.1-Starling-LM-RP
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positive_prompts:
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- "storywriting"
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- "write"
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- "scene"
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- "story"
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- "character"
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negative_prompts:
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- "chat"
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- "assistant"
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- "code"
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- "programming"
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- "mathematics"
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- "reasoning"
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- source_model: WizardLM/WizardMath-7B-V1.1
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positive_prompts:
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- "reason"
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- "math"
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- "mathematics"
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- "solve"
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- "count"
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negative_prompts:
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- "chat"
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- "assistant"
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- "code"
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- "programming"
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- "storywriting"
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```
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## 💻 Usage
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```python
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!pip install -qU transformers bitsandbytes accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "mlabonne/Beyonder-4x7b"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
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)
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messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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Output:
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```
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A Mixture of Experts (MoE) is a neural network architecture that combines the strengths of multiple expert networks to make predictions. It leverages the idea of ensemble learning, where multiple models work together to improve performance. In each MoE, a gating network is used to select the most relevant expert for the input. The final output is a weighted combination of the expert outputs, determined by the gating network's predictions.
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```
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config.json
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{
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"_name_or_path": "openchat/openchat-3.5-1210",
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"architectures": [
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"MixtralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 32000,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"model_type": "mixtral",
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"num_attention_heads": 32,
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"num_experts_per_tok": 2,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"num_local_experts": 4,
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"output_router_logits": false,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"router_aux_loss_coef": 0.001,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.36.2",
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"use_cache": true,
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"vocab_size": 32002
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}
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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{
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"<|end_of_turn|>",
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}
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