license: apache-2.0
model-index:
- name: openchat-3.5-0106_Rebased_Mistral-7B-v0.2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 37.06
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 10.91
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 3.85
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 2.91
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 20.57
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 20.33
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Pretergeek/openchat-3.5-0106_Rebased_Mistral-7B-v0.2
name: Open LLM Leaderboard
This model was created as an experiment on using LoRA extraction to replicate Openchat-3.5-0106 using Mistral-7B-v0.2 as a base model instead of the original Mistral-7B-v0.1.
Openchat-3.5-0106 is an excellent model but was based on Mistral-7B-v0.1 which has a context window of 8192 tokens. Mistral-7B-v0.2 has a context window of 32768 tokens. I could have extended OpenChat-3.5 context myself with RoPE and/or YaRN but that has been done. There are many models on HF that have done exactly that. Instead I decided to try and replicate OpenChat-3.5-0106 using the LoRA extraction method available in mergekit. These are the steps I followed:
- Extract a LoRA with rank 512 from OpenChat-3.5-0106 using One's Mistral_7B_with_EOT_token as the base model.
- Replicate imone's work by adding the EOT token to Mistral-7B-v0.2, creating Mistral-7B-v0.2_EOT.
- Merge the LoRA's weights to the Mistral-7B-v0.2_EOT model.
This is the result. This model is not meant for use, it was created to test if this method is viable for replacing the base model of fine-tuned models (when tokenizer and weights have not been changed too much). I am uploading here for evaluation. I don't expect this model to match the original OpenChat-3.5-0106 since I used a LoRA with rank 512, so it won't be equivalent to a full fine-tuning. I have been able to extract LoRAs with higher rank, but currently I don't have the resources to merge them with the model as the memory requirements exceed what I have at my disposal. If you would like to help my work, check my Ko-Fi and/or Patreon:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 15.94 |
| IFEval (0-Shot) | 37.06 |
| BBH (3-Shot) | 10.91 |
| MATH Lvl 5 (4-Shot) | 3.85 |
| GPQA (0-shot) | 2.91 |
| MuSR (0-shot) | 20.57 |
| MMLU-PRO (5-shot) | 20.33 |