Upload folder using huggingface_hub
Browse files- README.md +110 -0
- added_tokens.json +4 -0
- config.json +25 -0
- generation_config.json +6 -0
- huggingface-metadata.txt +8 -0
- model.safetensors.index.json +298 -0
- output.safetensors +3 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +61 -0
README.md
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license: apache-2.0
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---
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---
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base_model: teknium/OpenHermes-2.5-Mistral-7B
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tags:
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- mistral
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- instruct
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- finetune
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- chatml
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- gpt4
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- synthetic data
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- distillation
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- dpo
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- rlhf
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license: apache-2.0
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language:
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- en
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datasets:
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- mlabonne/chatml_dpo_pairs
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---
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<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
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# NeuralHermes 2.5 - Mistral 7B
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NeuralHermes is an [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on several benchmarks (see results).
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It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
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The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
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### Quantized models
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* GGUF: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
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* AWQ: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
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* EXL2 (5pbw): https://huggingface.co/IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw
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## Results
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Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
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Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
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### AGIEval
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![](https://i.imgur.com/7an3B1f.png)
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### GPT4All
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![](https://i.imgur.com/TLxZFi9.png)
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### TruthfulQA
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![](https://i.imgur.com/V380MqD.png)
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You can check the Weights & Biases project [here](https://wandb.ai/mlabonne/NeuralHermes-2-5-Mistral-7B/overview?workspace=user-mlabonne).
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## Usage
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You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
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You can also run this model using the following code:
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```python
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import transformers
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from transformers import AutoTokenizer
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# Format prompt
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message = [
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{"role": "system", "content": "You are a helpful assistant chatbot."},
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{"role": "user", "content": "What is a Large Language Model?"}
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]
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tokenizer = AutoTokenizer.from_pretrained(new_model)
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prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
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# Create pipeline
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pipeline = transformers.pipeline(
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"text-generation",
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model=new_model,
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tokenizer=tokenizer
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)
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# Generate text
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sequences = pipeline(
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prompt,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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num_return_sequences=1,
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max_length=200,
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)
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print(sequences[0]['generated_text'])
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```
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## Training hyperparameters
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**LoRA**:
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* r=16
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* lora_alpha=16
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* lora_dropout=0.05
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* bias="none"
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* task_type="CAUSAL_LM"
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* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
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**Training arguments**:
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* per_device_train_batch_size=4
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* gradient_accumulation_steps=4
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* gradient_checkpointing=True
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* learning_rate=5e-5
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* lr_scheduler_type="cosine"
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* max_steps=200
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* optim="paged_adamw_32bit"
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* warmup_steps=100
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**DPOTrainer**:
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* beta=0.1
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* max_prompt_length=1024
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* max_length=1536
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added_tokens.json
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{
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"<|im_end|>": 32000,
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"<|im_start|>": 32001
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}
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config.json
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{
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"_name_or_path": "teknium/OpenHermes-2.5-Mistral-7B",
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"architectures": [
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"MistralForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 32000,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.35.2",
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"use_cache": false,
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"vocab_size": 32002
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 32000,
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"transformers_version": "4.35.2"
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}
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huggingface-metadata.txt
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url: https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B
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branch: main
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download date: 2023-11-30 10:34:00
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sha256sum:
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ce2302a4a687eae778c5289223dcb26641cd3d02337605dca13a3f379b6a9a31 model-00001-of-00003.safetensors
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6d706709c607767e33d0307cb713aedba8d29df4e4da1293276cb08cc4920354 model-00002-of-00003.safetensors
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42e23c2fe3e3b9cc69d83079904f59e6a3957e97faeb76fc0f672752b0b2f0fb model-00003-of-00003.safetensors
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dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055 tokenizer.model
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model.safetensors.index.json
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{
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