metadata
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=indischepartij/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=indischepartij/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=indischepartij/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=indischepartij/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=indischepartij/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=indischepartij/TinyUltra-4x1.1B-Base-Alpha
name: Open LLM Leaderboard
widget:
- example_title: Pirate!
messages:
- role: system
content: You are a pirate chatbot who always responds with Arr!
- role: user
content: There's a llama on my lawn, how can I get rid of him?
output:
text: >-
Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare
sight, but I've got a plan that might help ye get rid of 'im. Ye'll need
to gather some carrots and hay, and then lure the llama away with the
promise of a tasty treat. Once he's gone, ye can clean up yer lawn and
enjoy the peace and quiet once again. But beware, me hearty, for there
may be more llamas where that one came from! Arr!
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
- vihangd/DopeyTinyLlama-1.1B-v1
- cognitivecomputations/TinyDolphin-2.8.1-1.1b
- Josephgflowers/Tinyllama-Cinder-1.3B-Reason-Test
Modelfile/Prompt format
SYSTEM You are a TinyUltra, helpful and lovely AI assistant.
TEMPLATE <|system|> {{ .System }}</s> <|user|> {{ .Prompt }}</s> <|assistant|>
PARAMETER stop <|system|>
PARAMETER stop <|user|>
PARAMETER stop <|assistant|>
PARAMETER stop </s>
🧩 Configuration
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
!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
Detailed results can be found here
| 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 |