license: apache-2.0
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
base_model: teknium/OpenHermes-2.5-Mistral-7B
model-index:
- name: kellemar-DPO-7B-d
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: 66.89
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decruz07/kellemar-DPO-7B-d
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: 85.16
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decruz07/kellemar-DPO-7B-d
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: 62.77
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decruz07/kellemar-DPO-7B-d
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: 56.88
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decruz07/kellemar-DPO-7B-d
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: 79.32
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decruz07/kellemar-DPO-7B-d
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: 62.02
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=decruz07/kellemar-DPO-7B-d
name: Open LLM Leaderboard
Model Card for decruz07/kellemar-DPO-7B-d
This model was created using OpenHermes-2.5 as the base, and finetuned with argilla/distilabel-intel-orca-dpo-pairs.
Model Details
Created with beta = 0.05
Model Description
- Developed by: @decruz
- Funded by [optional]: my full-time job
- Finetuned from model [optional]: teknium/OpenHermes-2.5-Mistral-7B
Uses
You can use this for basic inference. You could probably finetune with this if you want to.
How to Get Started with the Model
You can create a space out of this, or use basic python code to call the model directly and make inferences to it.
[More Information Needed]
Training Details
The following was used: `training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=200, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", )
Create DPO trainer
dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, max_prompt_length=1024, max_length=1536, )`
Training Data
This was trained with https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
Training Procedure
Trained with Labonne's Google Colab Notebook on Finetuning Mistral 7B with DPO.
Model Card Authors [optional]
@decruz
Model Card Contact
@decruz on X/Twitter
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 68.84 |
| AI2 Reasoning Challenge (25-Shot) | 66.89 |
| HellaSwag (10-Shot) | 85.16 |
| MMLU (5-Shot) | 62.77 |
| TruthfulQA (0-shot) | 56.88 |
| Winogrande (5-shot) | 79.32 |
| GSM8k (5-shot) | 62.02 |