language:
- en
license: apache-2.0
library_name: transformers
datasets:
- teknium/openhermes
- allenai/ultrafeedback_binarized_cleaned
- Intel/orca_dpo_pairs
base_model: teknium/OpenHermes-2.5-Mistral-7B
pipeline_tag: text-generation
model-index:
- name: DPOpenHermes-7B-v2
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: 66.64
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=openaccess-ai-collective/DPOpenHermes-7B-v2
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: 85.22
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=openaccess-ai-collective/DPOpenHermes-7B-v2
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: 63.64
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=openaccess-ai-collective/DPOpenHermes-7B-v2
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: 59.22
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=openaccess-ai-collective/DPOpenHermes-7B-v2
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: 79.16
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=openaccess-ai-collective/DPOpenHermes-7B-v2
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: 63.61
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=openaccess-ai-collective/DPOpenHermes-7B-v2
name: Open LLM Leaderboard
DPOpenHermes 7B v2
OpenHermes x Notus x Neural
This is a second RL fine tuned model of Teknium's OpenHermes-2.5-Mistral-7B using the Intel/orca_dpo_pairs and allenai/ultrafeedback_binarized_cleaned preference datasets for reinforcement learning using Direct Preference Optimization (DPO)
The difference between this model and the "v1" model is that the v1 model used argilla's version of the dataset that was not decontaminated of TruthfulQA data. DPOpenHermes is trained using 16-bit LoRA.
Training Details
DPOpenHermes was trained on a single H100 80GB hosted on RunPod for ~13h for 1.0 epochs of the dataset.
https://wandb.ai/oaaic/openhermes-dpo/runs/zk36rk9g
Prompt Format
DPOpenHermes uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<|im_end|>
This prompt is available as a chat template, which means you can format messages using the
tokenizer.apply_chat_template() method:
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
When tokenizing messages for generation, set add_generation_prompt=True when calling apply_chat_template(). This will append <|im_start|>assistant\n to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane:
Benchmarks
AGIEval
hf-causal-experimental (dtype=bfloat16,trust_remote_code=True,use_accelerate=True,pretrained=../axolotl/dpopenhermes-rc5/merged/), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.1929|_ |0.0248|
| | |acc_norm|0.2008|_ |0.0252|
|agieval_logiqa_en | 0|acc |0.3763|_ |0.0190|
| | |acc_norm|0.3763|_ |0.0190|
|agieval_lsat_ar | 0|acc |0.2739|_ |0.0295|
| | |acc_norm|0.2609|_ |0.0290|
|agieval_lsat_lr | 0|acc |0.5333|_ |0.0221|
| | |acc_norm|0.5392|_ |0.0221|
|agieval_lsat_rc | 0|acc |0.6134|_ |0.0297|
| | |acc_norm|0.5985|_ |0.0299|
|agieval_sat_en | 0|acc |0.7427|_ |0.0305|
| | |acc_norm|0.7233|_ |0.0312|
|agieval_sat_en_without_passage| 0|acc |0.4709|_ |0.0349|
| | |acc_norm|0.4709|_ |0.0349|
|agieval_sat_math | 0|acc |0.4045|_ |0.0332|
| | |acc_norm|0.3682|_ |0.0326|
Average: 0.4422
BigBench Hard
hf-causal-experimental (dtype=bfloat16,trust_remote_code=True,use_accelerate=True,pretrained=../axolotl/dpopenhermes-rc5/merged/), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5632|_ |0.0361|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6531|_ |0.0248|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3411|_ |0.0296|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2089|_ |0.0215|
| | |exact_str_match |0.0919|_ |0.0153|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.3000|_ |0.0205|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2057|_ |0.0153|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4767|_ |0.0289|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.3880|_ |0.0218|
|bigbench_navigate | 0|multiple_choice_grade|0.5000|_ |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.6725|_ |0.0105|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4375|_ |0.0235|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.3337|_ |0.0149|
|bigbench_snarks | 0|multiple_choice_grade|0.7017|_ |0.0341|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6815|_ |0.0148|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.3180|_ |0.0147|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2120|_ |0.0116|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1720|_ |0.0090|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4767|_ |0.0289|
Average: 0.4245
GPT4All
TBD
TruthfulQA
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.6271|_ |0.0141|
| | |acc_norm|0.6672|_ |0.0138|
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 69.58 |
| AI2 Reasoning Challenge (25-Shot) | 66.64 |
| HellaSwag (10-Shot) | 85.22 |
| MMLU (5-Shot) | 63.64 |
| TruthfulQA (0-shot) | 59.22 |
| Winogrande (5-shot) | 79.16 |
| GSM8k (5-shot) | 63.61 |