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---
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 }}</s> <|user|> {{ .Prompt }}</s> <|assistant|>
PARAMETER stop <|system|>
PARAMETER stop <|user|>
PARAMETER stop <|assistant|>
PARAMETER stop </s>
```
## 🧩 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|