germeo-7b-laser / README.md

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	---
	language:
	- de
	license: apache-2.0
	tags:
	- hermeo
	- laser
	datasets:
	- LeoLM/OpenSchnabeltier
	pipeline_tag: conversational
	model-index:
	- name: germeo-7b-laser
	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: 60.75
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser
	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: 82.81
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser
	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: 60.57
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser
	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: 53.83
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser
	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: 75.61
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser
	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: 43.37
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aari1995/germeo-7b-laser
	name: Open LLM Leaderboard
	---

	(Evaluation WIP)

	## Hermes + Leo + German Laser = Germeo

	## Germeo-7B-Laser
	A German-English understanding, but German-only speaking model merged from Hermeo-7B.

	### Model details

	Merged from: leo-mistral-hessianai-7b-chat and DPOpenHermes-7B-v2

	Model type: Causal decoder-only transformer language model

	Languages: German replies with English Understanding Capabilities

	Laser-Data: LeoLM/OpenSchnabeltier


	This is an early experiment on laser and its influence on language understanding. It generally improves the language understanding capabilities.
	The hypothesis is that it degrades the probability of English replies and increasing those of German replies. The models internal German capabilities are boosted.

	Will keep you updated..

	### Acknowledgements:

	I would like to thank everyone that participated in making this model and its training possible:
	To [@malteos](https://huggingface.co/malteos) for hermeo
	To [@cognitivecomputations](https://huggingface.co/cognitivecomputations) and Fernando Fernandes Neto for their implementation of LASER
	To [@LeoLM](https://huggingface.co/LeoLM) and Björn for the OpenSchnabeltier dataset.


	### Prompt format:

	```python
	streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
	# Convert prompt to tokens
	prompt_template = """<\|im_start\|>system
	Du bist ein hilfreicher Assistent.<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant"""

	prompt = "Schreibe eine Stellenanzeige für Data Scientist bei AXA!"

	final_prompt = prompt_template.format(prompt=prompt)
	```

	#### Limit the model to output reply-only:
	To solve this, you need to implement a custom stopping criteria:

	```python
	from transformers import StoppingCriteria
	class GermeoStoppingCriteria(StoppingCriteria):
	def __init__(self, target_sequence, prompt):
	self.target_sequence = target_sequence
	self.prompt=prompt

	def __call__(self, input_ids, scores, **kwargs):
	# Get the generated text as a string
	generated_text = tokenizer.decode(input_ids[0])
	generated_text = generated_text.replace(self.prompt,'')
	# Check if the target sequence appears in the generated text
	if self.target_sequence in generated_text:
	return True # Stop generation

	return False # Continue generation

	def __len__(self):
	return 1

	def __iter__(self):
	yield self
	```
	This then expects your input prompt (formatted as given into the model), and a stopping criteria, in this case the im_end token. Simply add it to the generation:

	```python
	generation_output = model.generate(
	tokens,
	streamer=streamer,
	max_new_tokens=1012,
	stopping_criteria=GermeoStoppingCriteria("<\|im_end\|>", prompt_template.format(prompt=prompt))
	)
	```

	### German benchmarks

	\| German tasks: \| MMLU-DE \| Hellaswag-DE \| ARC-DE \|Average \|
	\|-------------------------------\|-------------\|---------------\|--------------\|--------------\|
	\| Models / Few-shots: \| _(5 shots)_ \| _(10 shots)_ \| _(24 shots)_ \| \|
	\| _7B parameters_ \| \| \| \| \|
	\| llama-2-7b \| 0.400 \| 0.513 \| 0.381 \| 0.431 \|
	\| leo-hessianai-7b \| 0.400 \| 0.609 \| 0.429 \| 0.479 \|
	\| bloom-6b4-clp-german \| 0.274 \| 0.550 \| 0.351 \| 0.392 \|
	\| mistral-7b \| 0.524 \| 0.588 \| 0.473 \| 0.528 \|
	\| leo-mistral-hessianai-7b \| 0.481 \| 0.663 \| 0.485 \| 0.543 \|
	\| leo-mistral-hessianai-7b-chat \| 0.458 \| 0.617 \| 0.465 \| 0.513 \|
	\| DPOpenHermes-7B-v2 \| 0.517 \| 0.603 \| 0.515 \| 0.545 \|
	\| hermeo-7b \| 0.511 \| 0.668 \| 0.528 \| 0.569 \|
	\| germeo-7b-laser (this model)\| ? \| ? \| ? \| ? \|
	\| _13B parameters_ \| \| \| \| \|
	\| llama-2-13b \| 0.469 \| 0.581 \| 0.468 \| 0.506 \|
	\| leo-hessianai-13b \| 0.486 \| 0.658 \| 0.509 \| 0.551 \|
	\| _70B parameters_ \| \| \| \| \|
	\| llama-2-70b \| 0.597 \| 0.674 \| 0.561 \| 0.611 \|
	\| leo-hessianai-70b \| 0.653 \| 0.721 \| 0.600 \| 0.658 \|


	Even though the model does not generate English text without being explicitly asked, performance on English Benchmarks is still up:

	### English benchmarks

	\| English tasks: \| MMLU \| Hellaswag \| ARC \| Average \|
	\|------------------------------------\|-------------\|---------------\|--------------\|-------------\|
	\| Models / Few-shots: \| _(5 shots)_ \| _(10 shots)_ \| _(24 shots)_ \| \|
	\| llama-2-7b \| 0.466 \| 0.786 \| 0.530 \| 0.594 \|
	\| leolm-hessianai-7b \| 0.423 \| 0.759 \| 0.522 \| 0.568 \|
	\| bloom-6b4-clp-german \| 0.264 \| 0.525 \| 0.328 \| 0.372 \|
	\| mistral-7b \| 0.635 \| 0.832 \| 0.607 \| 0.691 \|
	\| leolm-mistral-hessianai-7b \| 0.550 \| 0.777 \| 0.518 \| 0.615 \|
	\| hermeo-7b \| 0.601 \| 0.821 \| 0.620 \| 0.681 \|
	\| germeo-7b-laser (this model) \| 0.601 \| 0.828 \| 0.608 \| 0.679 \|
	# [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_aari1995__germeo-7b-laser)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|62.82\|
	\|AI2 Reasoning Challenge (25-Shot)\|60.75\|
	\|HellaSwag (10-Shot) \|82.81\|
	\|MMLU (5-Shot) \|60.57\|
	\|TruthfulQA (0-shot) \|53.83\|
	\|Winogrande (5-shot) \|75.61\|
	\|GSM8k (5-shot) \|43.37\|