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--- |
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license: other |
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license_name: llama3 |
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license_link: LICENSE |
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tags: |
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- moe |
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- frankenmoe |
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- merge |
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- mergekit |
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- lazymergekit |
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base_model: [] |
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--- |
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# llama-3-sqrt-crocodile-v0.0A |
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## 🧩 Configuration-moe |
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```yaml |
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base_model: llama-3-sqrt-crocodile-v0.0A/Uninstruct-Uncensored |
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gate_mode: hidden |
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dtype: bfloat16 |
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experts: |
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- source_model: llama-3-sqrt-crocodile-v0.0A/sqrt-talker |
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positive_prompts: |
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- "Uncensored, creative, configurable, adapative" |
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- source_model: llama-3-sqrt-crocodile-v0.0A/the-operator |
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positive_prompts: |
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- "Function calling" |
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- "Good at structured tasks" |
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- "Programmatic instruction following" |
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``` |
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## 🧩 Configuration-mega |
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```yaml |
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models: |
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- model: Orenguteng/Lexi-Llama-3-8B-Uncensored |
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parameters: |
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weight: [0.2, 0.3, 0.4, 0.6] |
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layer_range: [0, 32] |
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- model: NousResearch/Meta-Llama-3-8B |
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parameters: |
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weight: [0.6, 0.2, 0.2, 0.1] |
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layer_range: [0, 32] |
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- model: NousResearch/Meta-Llama-3-8B-Instruct |
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parameters: |
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weight: [0.2, 0.3, 0.85, 0.3] |
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layer_range: [0, 32] |
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merge_method: dare_linear |
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base_model: NousResearch/Meta-Llama-3-8B-Instruct |
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dtype: bfloat16 |
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name: Uninstruct-Uncensored |
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--- |
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models: |
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- model: cognitivecomputations/dolphin-2.9-llama3-8b |
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parameters: |
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weight: [0.25, 0.4, 0.35, 0.35] |
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density: [0.3, 0.45, 0.2, 0.6] |
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layer_range: [0, 32] |
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- model: NousResearch/Meta-Llama-3-8B |
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parameters: |
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weight: [0.15, 0.25, 0.05, 0] |
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density: [0.2, 0.3, 0.4, 0.1] |
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- model: Undi95/Llama-3-Unholy-8B |
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parameters: |
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weight: [0.4, 0.25, 0.45, 0.35] |
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density: [0.2, 0.15, 1.5, 0.1] |
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layer_range: [0, 32] |
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- model: Uninstruct-Uncensored |
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parameters: |
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weight: [0.3, 0.1, 0.25, 0.3] |
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density: [0.3, 0.15, 2.5, 0.2] |
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layer_range: [0, 32] |
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merge_method: dare_ties |
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base_model: Uninstruct-Uncensored |
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dtype: bfloat16 |
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name: augmented-dolphin-hap |
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--- |
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models: |
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- model: vicgalle/Configurable-Llama-3-8B-v0.3 |
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parameters: |
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weight: [0.5, 0.3, 0.1] |
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- model: hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode |
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parameters: |
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weight: 0.5 |
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- model: Trelis/Meta-Llama-3-8B-Instruct-function-calling |
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parameters: |
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weight: 0.3 |
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layer_range: [0, 32] |
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- model: Rookie/Llama-3-8B-Instruct-Chinese |
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parameters: |
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weight: 0.2 |
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layer_range: [0, 32] |
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- model: Uninstruct-Uncensored |
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parameters: |
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weight: [0.7, 0.4, 0.25, 0.1] |
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layer_range: [0, 32] |
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merge_method: model_stock |
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base_model: Uninstruct-Uncensored |
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dtype: bfloat16 |
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name: the-operator |
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--- |
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models: |
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- model: vicgalle/Configurable-Llama-3-8B-v0.3 |
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parameters: |
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weight: 0.7 |
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- model: hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode |
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parameters: |
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weight: 0.1 |
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- model: Trelis/Meta-Llama-3-8B-Instruct-function-calling |
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parameters: |
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weight: 0.03 |
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layer_range: [0, 32] |
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- model: Rookie/Llama-3-8B-Instruct-Chinese |
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parameters: |
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weight: 0.07 |
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layer_range: [0, 32] |
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- model: Uninstruct-Uncensored |
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parameters: |
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weight: 0.1 |
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layer_range: [0, 32] |
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merge_method: model_stock |
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base_model: Uninstruct-Uncensored |
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dtype: bfloat16 |
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name: her-calculator |
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--- |
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models: |
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- model: her-calculator |
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parameters: |
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density: 0.7 # density gradient |
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weight: [0.7, 0.5, 0.1, 0.8] |
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- model: augmented-dolphin-hap |
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parameters: |
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weight: 0.7 |
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merge_method: slerp |
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base_model: her-calculator |
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parameters: |
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t: |
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- filter: self_attn |
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value: [0, 0.5, 0.3, 0.7, 1] |
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- filter: mlp |
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value: [1, 0.5, 0.7, 0.3, 0] |
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- value: 0.5 # fallback for rest of tensors |
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dtype: float16 |
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name: sqrt-talker |
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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 = "Nhoodie/llama-3-sqrt-crocodile-v0.0A" |
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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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``` |
