Adding Evaluation Results (#1)

180d584 verified 3 months ago

No virus

6.8 kB

	---
	language:
	- en
	license: apache-2.0
	tags:
	- text-generation
	datasets:
	- THUDM/webglm-qa
	- databricks/databricks-dolly-15k
	- cognitivecomputations/wizard_vicuna_70k_unfiltered
	- totally-not-an-llm/EverythingLM-data-V3
	- Amod/mental_health_counseling_conversations
	- sablo/oasst2_curated
	- starfishmedical/webGPT_x_dolly
	- Open-Orca/OpenOrca
	- mlabonne/chatml_dpo_pairs
	base_model: JackFram/llama-68m
	widget:
	- messages:
	- role: system
	content: You are a career counselor. The user will provide you with an individual
	looking for guidance in their professional life, and your task is to assist
	them in determining what careers they are most suited for based on their skills,
	interests, and experience. You should also conduct research into the various
	options available, explain the job market trends in different industries, and
	advice on which qualifications would be beneficial for pursuing particular fields.
	- role: user
	content: Heya!
	- role: assistant
	content: Hi! How may I help you?
	- role: user
	content: I am interested in developing a career in software engineering. What
	would you recommend me to do?
	- messages:
	- role: system
	content: You are a knowledgeable assistant. Help the user as much as you can.
	- role: user
	content: How to become healthier?
	- messages:
	- role: system
	content: You are a helpful assistant who provides concise responses.
	- role: user
	content: Hi!
	- role: assistant
	content: Hello there! How may I help you?
	- role: user
	content: I need to build a simple website. Where should I start learning about web development?
	- messages:
	- role: system
	content: You are a very creative assistant. User will give you a task, which you should complete with all your knowledge.
	- role: user
	content: Write the background story of an RPG game about wizards and dragons in a sci-fi world.
	inference:
	parameters:
	max_new_tokens: 64
	penalty_alpha: 0.5
	top_k: 4
	model-index:
	- name: Llama-68M-Chat-v1
	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: 23.29
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
	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: 28.27
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
	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: 25.18
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
	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: 47.27
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
	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: 54.3
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
	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=Felladrin/Llama-68M-Chat-v1
	name: Open LLM Leaderboard
	---

	# A Llama Chat Model of 68M Parameters

	- Base model: [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m)
	- Datasets:
	- [THUDM/webglm-qa](https://huggingface.co/datasets/THUDM/webglm-qa)
	- [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
	- [cognitivecomputations/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/cognitivecomputations/wizard_vicuna_70k_unfiltered)
	- [totally-not-an-llm/EverythingLM-data-V3](https://huggingface.co/datasets/totally-not-an-llm/EverythingLM-data-V3)
	- [Amod/mental_health_counseling_conversations](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations)
	- [sablo/oasst2_curated](https://huggingface.co/datasets/sablo/oasst2_curated)
	- [starfishmedical/webGPT_x_dolly](https://huggingface.co/datasets/starfishmedical/webGPT_x_dolly)
	- [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca)
	- [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs)
	- Availability in other ML formats:
	- GGUF: [afrideva/Llama-68M-Chat-v1-GGUF](https://huggingface.co/afrideva/Llama-68M-Chat-v1-GGUF)
	- ONNX: [Felladrin/onnx-Llama-68M-Chat-v1](https://huggingface.co/Felladrin/onnx-Llama-68M-Chat-v1)

	## Recommended Prompt Format

	```
	<\|im_start\|>system
	{system_message}<\|im_end\|>
	<\|im_start\|>user
	{user_message}<\|im_end\|>
	<\|im_start\|>assistant
	```

	## Recommended Inference Parameters

	```yml
	penalty_alpha: 0.5
	top_k: 4
	```

	## [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_Felladrin__Llama-68M-Chat-v1)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|29.72\|
	\|AI2 Reasoning Challenge (25-Shot)\|23.29\|
	\|HellaSwag (10-Shot) \|28.27\|
	\|MMLU (5-Shot) \|25.18\|
	\|TruthfulQA (0-shot) \|47.27\|
	\|Winogrande (5-shot) \|54.30\|
	\|GSM8k (5-shot) \| 0.00\|