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DPOpenHermes-7B-4.0bpw-h6-exl2

Text Generation
Transformers
Safetensors
English
mistral
conversational
text-generation-inference
Inference Endpoints
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  1. .gitattributes +1 -0
  2. README.md +131 -0
  3. added_tokens.json +4 -0
  4. config.json +25 -0
  5. generation_config.json +6 -0
  6. model.safetensors.index.json +298 -0
  7. output.safetensors +3 -0
  8. special_tokens_map.json +24 -0
  9. tokenizer.model +3 -0
  10. tokenizer_config.json +61 -0
.gitattributes CHANGED
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ assets/dpopenhermes.png filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ base_model: teknium/OpenHermes-2.5-Mistral-7B
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+ license: apache-2.0
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+ datasets:
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+ - teknium/openhermes
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+ - argilla/ultrafeedback-binarized-preferences
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+ - Intel/orca_dpo_pairs
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+ language:
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+ - en
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # DPOpenHermes 7B
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+
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+ ![image/png](https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B/resolve/main/assets/dpopenhermes.png)
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+
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+ ## OpenHermes x Notus x Neural
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+
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+ This is an RL fine tuned model of [Teknium](https://huggingface.co/teknium)'s [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) using the [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) and [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) preference datasets for reinforcement learning using Direct Preference Optimization (DPO)
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+
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+ DPOpenHermes is trained using qLoRA. The adapter is also provided in this model repo.
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+
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+ # Training Details
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+
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+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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+
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+ DPOpenHermes was trained on a single H100 80GB hosted on RunPod for ~10h for 0.6 epochs of the dataset.
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+
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+ https://wandb.ai/oaaic/openhermes-dpo/reports/DPOpenHermes--Vmlldzo2MTQ3NDg2
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+
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+ # Prompt Format
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+
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+ DPOpenHermes uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
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+
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+ 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.
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+
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+ 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.
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+
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+ 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.
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+
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+ Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
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+ ```
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+ <|im_start|>system
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+ 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|>
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+ <|im_start|>user
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+ Hello, who are you?<|im_end|>
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+ <|im_start|>assistant
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+ 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|>
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+ ```
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+
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+ This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
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+ `tokenizer.apply_chat_template()` method:
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+
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+ ```python
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+ messages = [
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+ {"role": "system", "content": "You are Hermes 2."},
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+ {"role": "user", "content": "Hello, who are you?"}
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+ ]
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+ gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
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+ model.generate(**gen_input)
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+ ```
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+
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+ 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
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+ that the model continues with an assistant response.
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+
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+ To utilize the prompt format without a system prompt, simply leave the line out.
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+
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+ 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.
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+ In LM-Studio, simply select the ChatML Prefix on the settings side pane:
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png)
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+
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+
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+ # Benchmarks
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+
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+ ## AGIEval
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+
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+ ```
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+ | Task |Version| Metric |Value | |Stderr|
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+ |------------------------------|------:|--------|-----:|---|-----:|
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+ |agieval_aqua_rat | 0|acc |0.2480|_ |0.0272|
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+ | | |acc_norm|0.2520|_ |0.0273|
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+ |agieval_logiqa_en | 0|acc |0.3810|_ |0.0190|
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+ | | |acc_norm|0.3856|_ |0.0191|
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+ |agieval_lsat_ar | 0|acc |0.2348|_ |0.0280|
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+ | | |acc_norm|0.2304|_ |0.0278|
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+ |agieval_lsat_lr | 0|acc |0.5118|_ |0.0222|
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+ | | |acc_norm|0.5196|_ |0.0221|
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+ |agieval_lsat_rc | 0|acc |0.5948|_ |0.0300|
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+ | | |acc_norm|0.5688|_ |0.0303|
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+ |agieval_sat_en | 0|acc |0.7427|_ |0.0305|
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+ | | |acc_norm|0.7427|_ |0.0305|
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+ |agieval_sat_en_without_passage| 0|acc |0.4563|_ |0.0348|
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+ | | |acc_norm|0.4515|_ |0.0348|
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+ |agieval_sat_math | 0|acc |0.3818|_ |0.0328|
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+ | | |acc_norm|0.3682|_ |0.0326|
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+ ```
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+
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+ Average: 0.4399
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+
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+ ## GPT4All
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+
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+ ```
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+ | Task |Version| Metric |Value | |Stderr|
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+ |-------------|------:|--------|-----:|---|-----:|
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+ |arc_challenge| 0|acc |0.5930|_ |0.0144|
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+ | | |acc_norm|0.6323|_ |0.0141|
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+ |arc_easy | 0|acc |0.8443|_ |0.0074|
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+ | | |acc_norm|0.8295|_ |0.0077|
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+ |boolq | 1|acc |0.8599|_ |0.0061|
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+ |hellaswag | 0|acc |0.6548|_ |0.0047|
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+ | | |acc_norm|0.8365|_ |0.0037|
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+ |openbookqa | 0|acc |0.3520|_ |0.0214|
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+ | | |acc_norm|0.4640|_ |0.0223|
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+ |piqa | 0|acc |0.8210|_ |0.0089|
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+ | | |acc_norm|0.8335|_ |0.0087|
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+ |winogrande | 0|acc |0.7466|_ |0.0122|
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+ ```
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+
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+ Average: 0.7431
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+
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+ ## TruthfulQA
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+
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+ ```
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+ hf-causal-experimental (pretrained=openaccess-ai-collective/dpopenhermes-alpha-v1,dtype=bfloat16,trust_remote_code=True,use_accelerate=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16
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+ | Task |Version|Metric|Value | |Stderr|
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+ |-------------|------:|------|-----:|---|-----:|
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+ |truthfulqa_mc| 1|mc1 |0.4186|_ |0.0173|
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+ | | |mc2 |0.5847|_ |0.0153|
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+ ```
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+ },
13
+ "1": {
14
+ "content": "<s>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false,
19
+ "special": true
20
+ },
21
+ "2": {
22
+ "content": "</s>",
23
+ "lstrip": false,
24
+ "normalized": false,
25
+ "rstrip": false,
26
+ "single_word": false,
27
+ "special": true
28
+ },
29
+ "32000": {
30
+ "content": "<|im_end|>",
31
+ "lstrip": false,
32
+ "normalized": false,
33
+ "rstrip": false,
34
+ "single_word": false,
35
+ "special": true
36
+ },
37
+ "32001": {
38
+ "content": "<|im_start|>",
39
+ "lstrip": false,
40
+ "normalized": false,
41
+ "rstrip": false,
42
+ "single_word": false,
43
+ "special": true
44
+ }
45
+ },
46
+ "additional_special_tokens": [],
47
+ "bos_token": "<s>",
48
+ "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
49
+ "clean_up_tokenization_spaces": false,
50
+ "eos_token": "<|im_end|>",
51
+ "legacy": true,
52
+ "model_max_length": 1000000000000000019884624838656,
53
+ "pad_token": "</s>",
54
+ "sp_model_kwargs": {},
55
+ "spaces_between_special_tokens": false,
56
+ "tokenizer_class": "LlamaTokenizer",
57
+ "trust_remote_code": false,
58
+ "unk_token": "<unk>",
59
+ "use_default_system_prompt": true,
60
+ "use_fast": true
61
+ }