--- license: cc-by-nc-4.0 tags: - moe - merge - mergekit model-index: - name: TinyUltra-4x1.1B-Base-Alpha 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: 34.9 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/TinyUltra-4x1.1B-Base-Alpha 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: 61.42 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/TinyUltra-4x1.1B-Base-Alpha 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: 25.42 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/TinyUltra-4x1.1B-Base-Alpha 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: 37.59 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/TinyUltra-4x1.1B-Base-Alpha 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: 65.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/TinyUltra-4x1.1B-Base-Alpha 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: 2.58 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=gmonsoon/TinyUltra-4x1.1B-Base-Alpha name: Open LLM Leaderboard --- ![image/jpeg](https://i.imgur.com/rx3ckCc.jpeg) # TinyUltra-4x1.1B-Base-Alpha TinyUltra-4x1.1B-Base-Alpha is a Mixure of Experts (MoE) made with the following models using MergeKit: * [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) * [vihangd/DopeyTinyLlama-1.1B-v1](https://huggingface.co/vihangd/DopeyTinyLlama-1.1B-v1) * [cognitivecomputations/TinyDolphin-2.8.1-1.1b](https://huggingface.co/cognitivecomputations/TinyDolphin-2.8.1-1.1b) * [Josephgflowers/Tinyllama-Cinder-1.3B-Reason-Test](https://huggingface.co/Josephgflowers/Tinyllama-Cinder-1.3B-Reason-Test) # Modelfile/Prompt format ```markdown SYSTEM You are a TinyUltra, helpful and lovely AI assistant. TEMPLATE <|system|> {{ .System }} <|user|> {{ .Prompt }} <|assistant|> PARAMETER stop <|system|> PARAMETER stop <|user|> PARAMETER stop <|assistant|> PARAMETER stop ``` ## 🧩 Configuration ```yaml base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 gate_mode: hidden dtype: float16 experts: - source_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 positive_prompts: - "Help me debug this code." - "Rewrite this function in Python." - "Optimize this C# script." - "Implement this feature using JavaScript." - "Convert this HTML structure into a more efficient design." - "Assist me with writing a program that" - source_model: vihangd/DopeyTinyLlama-1.1B-v1 positive_prompts: - "How do you" - "Explain the concept of" - "Give an overview of" - "Compare and contrast between" - "Provide information about" - "Help me understand" - "Summarize" - "Make a recommendation on" - "Answer this question" - source_model: cognitivecomputations/TinyDolphin-2.8.1-1.1b positive_prompts: - "Write a program to solve this problem" - "Modify this function to improve its performance" - "Refactor this code to enhance readability" - "Create a custom function for this specific use case" - "Optimize this algorithm to reduce computational complexity" - "Implement this feature by extending existing codebase" - "Integrate this API call into the application" - "Help me troubleshoot and fix this bug" - "Review and test this code snippet before deployment" - "Analyze this error log to identify potential issues" - "Generate a set of unit tests for this module" - "Evaluate different approaches to solving this problem" - "Do a web search for" - "Use the plugin to" - source_model: Josephgflowers/Tinyllama-Cinder-1.3B-Reason-Test positive_prompts: - "add these numbers" - "whats 2+2" - "subtraction" - "division" - "multiplication" - "addition" - "I need help with a math problem" - "Solve for x" - "Add these two numbers together: 4 + 3 = 7" - "Multiply 5 by 6: 5 * 6 = 30" - "Divide 8 by 2: 8 / 2 = 4" - "Find the remainder when 9 is divided by 3: 9 % 3 = 0" - "Calculate the square root of 16: sqrt(16) = 4" - "Simplify the expression (a+b)/(c-d): (a+b)/(c-d)" - "Factor out the common factor of 2 from 4x + 6y: 2(2x + 3y)" - "Solve for x in the equation 3x - 7 = 2x + 5: x = 12" - "Graph the line y = 2x + 3" - "Approximate pi to three decimal places: 3.142" - "Find the derivative of f(x) = sin(x): f'(x) = cos(x)" - "Integrate g(x) = x^2 over the interval [0, 1]: g(1) - g(0) = 1/3" - "Calculate the determinant of the matrix A = [[2, 3], [4, 5]]: det(A) = 2*5 - 3*4 = -2" - "Solve the system of equations Ax = b: x = [-5, 10]" - "Calculate the sum of the first n natural numbers using the formula Sn = n*(n+1)/2: sum(n=1 to 5) = 15" ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "gmonsoon/TinyUltra-4x1.1B-Base-Alpha" 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"]) ``` GGUF: https://huggingface.co/indischepartij/TinyUltra-4x1.1B-Base-Alpha-GGUF # [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_gmonsoon__TinyUltra-4x1.1B-Base-Alpha) | Metric |Value| |---------------------------------|----:| |Avg. |37.94| |AI2 Reasoning Challenge (25-Shot)|34.90| |HellaSwag (10-Shot) |61.42| |MMLU (5-Shot) |25.42| |TruthfulQA (0-shot) |37.59| |Winogrande (5-shot) |65.75| |GSM8k (5-shot) | 2.58|