Chupacabra-7B / README.md

Adding Evaluation Results (#1)

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	---
	license: apache-2.0
	model-index:
	- name: Chupacabra-7B
	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: 66.81
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B
	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: 83.52
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B
	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: 62.68
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B
	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: 52.31
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B
	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: 79.08
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B
	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: 62.17
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=perlthoughts/Chupacabra-7B
	name: Open LLM Leaderboard
	---

	# Chupacabra 7B

	<p><img src="https://huggingface.co/perlthoughts/Chupacabra-7B/resolve/main/chupacabra7b%202.png" width=330></p>

	### Model Description

	Dare-ties merge method.

	List of all models and merging path is coming soon.

	## Purpose

	Merging the "thick"est model weights from mistral models using amazing training methods like direct preference optimization (dpo) and reinforced learning.

	I have spent countless hours studying the latest research papers, attending conferences, and networking with experts in the field. I experimented with different algorithms, tactics, fine-tuned hyperparameters, optimizers,
	and optimized code until i achieved the best possible results.

	Thank you openchat 3.5 for showing me the way.

	Here is my contribution.

	## Prompt Template

	Replace {system} with your system prompt, and {prompt} with your prompt instruction.

	```
	### System:
	{system}
	### User:
	{instruction}
	### Assistant:
	```

	### Bug fixes

	- Fixed issue with generation and the incorrect model weights. Model weights have been corrected and now generation works again. Reuploading GGUF to the GGUF repository as well as the AWQ versions.

	- Developed by: Ray Hernandez
	- Model type: Mistral
	- Language(s) (NLP): English
	- License: Apache 2.0

	### Model Sources [optional]

	<!-- Provide the basic links for the model. -->

	## Uses

	<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

	### Direct Use

	<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

	[More Information Needed]

	### Downstream Use [optional]

	<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

	[More Information Needed]

	### Out-of-Scope Use

	<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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	## Bias, Risks, and Limitations

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	[More Information Needed]

	### Recommendations

	<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

	Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

	## How to Get Started with the Model

	Use the code below to get started with the model.

	[More Information Needed]

	## Training Details

	### Training Data

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	[More Information Needed]

	### Training Procedure

	<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

	#### Preprocessing [optional]

	[More Information Needed]


	#### Training Hyperparameters

	- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

	#### Speeds, Sizes, Times [optional]

	<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

	[More Information Needed]

	## Evaluation

	<!-- This section describes the evaluation protocols and provides the results. -->

	### Testing Data, Factors & Metrics

	#### Testing Data

	<!-- This should link to a Dataset Card if possible. -->

	[More Information Needed]

	#### Factors

	<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

	[More Information Needed]

	#### Metrics

	<!-- These are the evaluation metrics being used, ideally with a description of why. -->

	[More Information Needed]

	### Results

	[More Information Needed]

	#### Summary


	## Model Examination [optional]

	<!-- Relevant interpretability work for the model goes here -->

	[More Information Needed]

	## Technical Specifications [optional]

	### Model Architecture and Objective

	[More Information Needed]

	### Compute Infrastructure

	[More Information Needed]

	#### Hardware

	[More Information Needed]

	#### Software

	[More Information Needed]

	## Citation [optional]

	<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

	BibTeX:

	[More Information Needed]

	APA:

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	## Glossary [optional]

	<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

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	## More Information [optional]

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	## Model Card Authors [optional]

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	## Model Card Contact

	[More Information Needed]
	# [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_perlthoughts__Chupacabra-7B)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|67.76\|
	\|AI2 Reasoning Challenge (25-Shot)\|66.81\|
	\|HellaSwag (10-Shot) \|83.52\|
	\|MMLU (5-Shot) \|62.68\|
	\|TruthfulQA (0-shot) \|52.31\|
	\|Winogrande (5-shot) \|79.08\|
	\|GSM8k (5-shot) \|62.17\|