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README.md

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 ---
-tags:
-- generated_from_trainer
-model-index:
-- name: zephyr-7b-alpha
-  results: []
-license: mit
-datasets:
-- stingning/ultrachat
-- openbmb/UltraFeedback
-language:
-- en
-base_model: mistralai/Mistral-7B-v0.1
+library_name: peft
 ---
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-<img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
-
-
-# Model Card for Zephyr 7B Alpha
-
-Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-α is the first model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so.
-
-
-## Model description
-
-- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
-- **Language(s) (NLP):** Primarily English
-- **License:** MIT
-- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
-
-### Model Sources
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** https://github.com/huggingface/alignment-handbook
-- **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
-
-## Intended uses & limitations
-
-The model was initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. 
-
-Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
-
-```python
-# Install transformers from source - only needed for versions <= v4.34
-# pip install git+https://github.com/huggingface/transformers.git
-# pip install accelerate
-
-import torch
-from transformers import pipeline
-
-pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha", torch_dtype=torch.bfloat16, device_map="auto")
-
-# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
-messages = [
-    {
-        "role": "system",
-        "content": "You are a friendly chatbot who always responds in the style of a pirate",
-    },
-    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
-]
-prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
-print(outputs[0]["generated_text"])
-# <|system|>
-# You are a friendly chatbot who always responds in the style of a pirate.</s>
-# <|user|>
-# How many helicopters can a human eat in one sitting?</s>
-# <|assistant|>
-# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
-```
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-Zephyr-7B-α has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). 
-It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
-
-
-## Training and evaluation data
-
-Zephyr 7B Alpha achieves the following results on the evaluation set:
-
-- Loss: 0.4605
-- Rewards/chosen: -0.5053
-- Rewards/rejected: -1.8752
-- Rewards/accuracies: 0.7812
-- Rewards/margins: 1.3699
-- Logps/rejected: -327.4286
-- Logps/chosen: -297.1040
-- Logits/rejected: -2.7153
-- Logits/chosen: -2.7447
-
 ## Training procedure
 
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-
-- learning_rate: 5e-07
-- train_batch_size: 2
-- eval_batch_size: 4
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 16
-- total_train_batch_size: 32
-- total_eval_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 1
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
-|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
-| 0.5602        | 0.05  | 100  | 0.5589          | -0.3359        | -0.8168          | 0.7188             | 0.4809          | -306.2607      | -293.7161    | -2.6554         | -2.6797       |
-| 0.4852        | 0.1   | 200  | 0.5136          | -0.5310        | -1.4994          | 0.8125             | 0.9684          | -319.9124      | -297.6181    | -2.5762         | -2.5957       |
-| 0.5212        | 0.15  | 300  | 0.5168          | -0.1686        | -1.1760          | 0.7812             | 1.0074          | -313.4444      | -290.3699    | -2.6865         | -2.7125       |
-| 0.5496        | 0.21  | 400  | 0.4835          | -0.1617        | -1.7170          | 0.8281             | 1.5552          | -324.2635      | -290.2326    | -2.7947         | -2.8218       |
-| 0.5209        | 0.26  | 500  | 0.5054          | -0.4778        | -1.6604          | 0.7344             | 1.1826          | -323.1325      | -296.5546    | -2.8388         | -2.8667       |
-| 0.4617        | 0.31  | 600  | 0.4910          | -0.3738        | -1.5180          | 0.7656             | 1.1442          | -320.2848      | -294.4741    | -2.8234         | -2.8521       |
-| 0.4452        | 0.36  | 700  | 0.4838          | -0.4591        | -1.6576          | 0.7031             | 1.1986          | -323.0770      | -296.1796    | -2.7401         | -2.7653       |
-| 0.4674        | 0.41  | 800  | 0.5077          | -0.5692        | -1.8659          | 0.7656             | 1.2967          | -327.2416      | -298.3818    | -2.6740         | -2.6945       |
-| 0.4656        | 0.46  | 900  | 0.4927          | -0.5279        | -1.6614          | 0.7656             | 1.1335          | -323.1518      | -297.5553    | -2.7817         | -2.8015       |
-| 0.4102        | 0.52  | 1000 | 0.4772          | -0.5767        | -2.0667          | 0.7656             | 1.4900          | -331.2578      | -298.5311    | -2.7160         | -2.7455       |
-| 0.4663        | 0.57  | 1100 | 0.4740          | -0.8038        | -2.1018          | 0.7656             | 1.2980          | -331.9604      | -303.0741    | -2.6994         | -2.7257       |
-| 0.4737        | 0.62  | 1200 | 0.4716          | -0.3783        | -1.7015          | 0.7969             | 1.3232          | -323.9545      | -294.5634    | -2.6842         | -2.7135       |
-| 0.4259        | 0.67  | 1300 | 0.4866          | -0.6239        | -1.9703          | 0.7812             | 1.3464          | -329.3312      | -299.4761    | -2.7046         | -2.7356       |
-| 0.4935        | 0.72  | 1400 | 0.4747          | -0.5626        | -1.7600          | 0.7812             | 1.1974          | -325.1243      | -298.2491    | -2.7153         | -2.7444       |
-| 0.4211        | 0.77  | 1500 | 0.4645          | -0.6099        | -1.9993          | 0.7656             | 1.3894          | -329.9109      | -299.1959    | -2.6944         | -2.7236       |
-| 0.4931        | 0.83  | 1600 | 0.4684          | -0.6798        | -2.1082          | 0.7656             | 1.4285          | -332.0890      | -300.5934    | -2.7006         | -2.7305       |
-| 0.5029        | 0.88  | 1700 | 0.4595          | -0.5063        | -1.8951          | 0.7812             | 1.3889          | -327.8267      | -297.1233    | -2.7108         | -2.7403       |
-| 0.4965        | 0.93  | 1800 | 0.4613          | -0.5561        | -1.9079          | 0.7812             | 1.3518          | -328.0831      | -298.1203    | -2.7226         | -2.7523       |
-| 0.4337        | 0.98  | 1900 | 0.4608          | -0.5066        | -1.8718          | 0.7656             | 1.3652          | -327.3599      | -297.1296    | -2.7175         | -2.7469       |
-
-
 ### Framework versions
 
-- Transformers 4.34.0
-- Pytorch 2.0.1+cu118
-- Datasets 2.12.0
-- Tokenizers 0.14.0
+
+- PEFT 0.5.0

adapter_config.json

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+{
+  "auto_mapping": null,
+  "base_model_name_or_path": "HuggingFaceH4/zephyr-7b-alpha",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 16,
+  "lora_dropout": 0,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "o_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin

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