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Adding Evaluation Results (#1)

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	---
	license: apache-2.0
	tags:
	- moe
	- frankenmoe
	- merge
	- mergekit
	- lazymergekit
	- M4-ai/TinyMistral-248M-v2-cleaner
	- Locutusque/TinyMistral-248M-Instruct
	- jtatman/tinymistral-v2-pycoder-instuct-248m
	- Locutusque/TinyMistral-248M-v2-Instruct
	base_model:
	- M4-ai/TinyMistral-248M-v2-cleaner
	- Locutusque/TinyMistral-248M-Instruct
	- jtatman/tinymistral-v2-pycoder-instuct-248m
	- Locutusque/TinyMistral-248M-v2-Instruct
	model-index:
	- name: TinyMistral-248Mx4-MOE
	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: 29.52
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/TinyMistral-248Mx4-MOE
	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: 25.71
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/TinyMistral-248Mx4-MOE
	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: 24.82
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/TinyMistral-248Mx4-MOE
	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: 48.66
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/TinyMistral-248Mx4-MOE
	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: 51.78
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/TinyMistral-248Mx4-MOE
	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: 0.0
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=222gate/TinyMistral-248Mx4-MOE
	name: Open LLM Leaderboard
	---

	# TinyMistral-248Mx4-MOE

	TinyMistral-248Mx4-MOE is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
	* [M4-ai/TinyMistral-248M-v2-cleaner](https://huggingface.co/M4-ai/TinyMistral-248M-v2-cleaner)
	* [Locutusque/TinyMistral-248M-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-Instruct)
	* [jtatman/tinymistral-v2-pycoder-instuct-248m](https://huggingface.co/jtatman/tinymistral-v2-pycoder-instuct-248m)
	* [Locutusque/TinyMistral-248M-v2-Instruct](https://huggingface.co/Locutusque/TinyMistral-248M-v2-Instruct)

	## 🧩 Configuration

	```yaml
	base_model: Locutusque/TinyMistral-248M-v2-Instruct
	gate_mode: hidden
	dtype: bfloat16
	experts:
	- source_model: M4-ai/TinyMistral-248M-v2-cleaner
	positive_prompts:
	- "versatile"
	- "helpful"
	- "factual"
	- "integrated"
	- "adaptive"
	- "comprehensive"
	- "balanced"
	negative_prompts:
	- "specialized"
	- "narrow"
	- "focused"
	- "limited"
	- "specific"

	- source_model: Locutusque/TinyMistral-248M-Instruct
	positive_prompts:
	- "creative"
	- "chat"
	- "discuss"
	- "culture"
	- "world"
	- "expressive"
	- "detailed"
	- "imaginative"
	- "engaging"
	negative_prompts:
	- "sorry"
	- "cannot"
	- "factual"
	- "concise"
	- "straightforward"
	- "objective"
	- "dry"

	- source_model: jtatman/tinymistral-v2-pycoder-instuct-248m
	positive_prompts:
	- "analytical"
	- "accurate"
	- "logical"
	- "knowledgeable"
	- "precise"
	- "calculate"
	- "compute"
	- "solve"
	- "work"
	- "python"
	- "javascript"
	- "programming"
	- "algorithm"
	- "tell me"
	- "assistant"
	negative_prompts:
	- "creative"
	- "abstract"
	- "imaginative"
	- "artistic"
	- "emotional"
	- "mistake"
	- "inaccurate"

	- source_model: Locutusque/TinyMistral-248M-v2-Instruct
	positive_prompts:
	- "instructive"
	- "clear"
	- "directive"
	- "helpful"
	- "informative"
	negative_prompts:
	- "exploratory"
	- "open-ended"
	- "narrative"
	- "speculative"
	- "artistic"

	```

	## 💻 Usage

	```python
	!pip install -qU transformers bitsandbytes accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "222gate/TinyMistral-248Mx4-MOE"

	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"])
	```
	# [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_222gate__TinyMistral-248Mx4-MOE)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|30.08\|
	\|AI2 Reasoning Challenge (25-Shot)\|29.52\|
	\|HellaSwag (10-Shot) \|25.71\|
	\|MMLU (5-Shot) \|24.82\|
	\|TruthfulQA (0-shot) \|48.66\|
	\|Winogrande (5-shot) \|51.78\|
	\|GSM8k (5-shot) \| 0.00\|