(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 for hermeo To @cognitivecomputations and Fernando Fernandes Neto for their implementation of LASER To @LeoLM and Björn for the OpenSchnabeltier dataset.
Prompt format:
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:
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:
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
Detailed results can be found here
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 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard60.750
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard82.810
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard60.570
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard53.830
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard75.610
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard43.370