Initial fp16 model commit

README.md

@@ -1,4 +1,5 @@
 ---
+inference: false
 license: other
 ---
 
@@ -28,7 +29,7 @@ Join me at: https://discord.gg/UBgz4VXf
 
 * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-1.0-GPTQ)
 * [4-bit, 5-bit and 8-bit GGML models for CPU(+GPU) inference](https://huggingface.co/TheBloke/WizardLM-13B-1.0-GGML)
-* [Merged, unquantised fp16 model in HF format](https://huggingface.co/TheBloke/wizardLM-13B-1.0-fp16)
+* [Merged, unquantised fp16 model in HF format](https://huggingface.co/TheBloke/WizardLM-13B-1.0-HF)
 
 
 ## Want to support my work?
@@ -39,14 +40,14 @@ So if you're able and willing to contribute, it'd be most gratefully received an
 
 * Patreon: coming soon! (just awaiting approval)
 * Ko-Fi: https://ko-fi.com/TheBlokeAI
-
-# Original model card: WizardLM 13B 1.0
+   
+# Original model card
 
 ## WizardLM: An Instruction-following LLM Using Evol-Instruct
 Empowering Large Pre-Trained Language Models to Follow Complex Instructions
 
 <p align="center" width="100%">
-<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/WizardLM.png" alt="WizardLM" style="width: 20%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="imgs/WizardLM.png" alt="WizardLM" style="width: 20%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE)
@@ -69,9 +70,9 @@ At present, our core contributors are preparing the **33B** version and we expec
 
 ### GPT-4 automatic evaluation
 
-We adopt the automatic evaluation framework based on GPT-4 proposed by FastChat to assess the performance of chatbot models. As shown in the following figure, WizardLM-13B achieved better results than Vicuna-13b.
+We adopt the automatic evaluation framework based on GPT-4 proposed by FastChat to assess the performance of chatbot models. As shown in the following figure, WizardLM-13B achieved better results than Vicuna-13b. 
 <p align="center" width="100%">
-<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/WizarLM13b-GPT4.png" alt="WizardLM" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="imgs/WizarLM13b-GPT4.png" alt="WizardLM" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 ### WizardLM-13B performance on different skills.
@@ -79,7 +80,7 @@ We adopt the automatic evaluation framework based on GPT-4 proposed by FastChat
 The following figure compares WizardLM-13B and ChatGPT’s skill on Evol-Instruct testset. The result indicates that WizardLM-13B achieves 89.1% of ChatGPT’s performance on average, with almost 100% (or more than) capacity on 10 skills, and more than 90% capacity on 22 skills.
 
 <p align="center" width="100%">
-<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/evol-testset_skills-13b.png" alt="WizardLM" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="imgs/evol-testset_skills-13b.png" alt="WizardLM" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 ## Call for Feedbacks
@@ -98,11 +99,11 @@ We just sample some cases to demonstrate the performance of WizardLM and ChatGPT
 [Evol-Instruct](https://github.com/nlpxucan/evol-instruct) is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.
 
 <p align="center" width="100%">
-<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/git_overall.png" alt="WizardLM" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="imgs/git_overall.png" alt="WizardLM" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 <p align="center" width="100%">
-<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/git_running.png" alt="WizardLM" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="imgs/git_running.png" alt="WizardLM" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 ## Contents
@@ -150,7 +151,7 @@ This JSON file is a list of dictionaries, each dictionary contains the following
 We release [WizardLM] weights as delta weights to comply with the LLaMA model license.
 You can add our delta to the original LLaMA weights to obtain the WizardLM weights. Instructions:
 1. Get the original LLaMA weights in the huggingface format by following the instructions [here](https://huggingface.co/docs/transformers/main/model_doc/llama).
-2. Please download our delta model at the following [link](https://huggingface.co/victor123/WizardLM)
+2. Please download our delta model at the following [link](https://huggingface.co/victor123/WizardLM) 
 3. Use the following scripts to get WizardLM weights by applying our delta:
 ```
 python src/weight_diff_wizard.py recover --path_raw <path_to_step_1_dir> --path_diff <path_to_step_2_dir> --path_tuned <path_to_store_recovered_weights>
@@ -213,16 +214,16 @@ python src\inference_wizardlm.py
 
 ### Evaluation
 
-To evaluate Wizard, we conduct human evaluation on the inputs from our human instruct evaluation set [`WizardLM_testset.jsonl`](./data/WizardLM_testset.jsonl) . This evaluation set was collected by the authors and covers a diverse list of user-oriented instructions including difficult Coding Generation & Debugging, Math, Reasoning, Complex Formats, Academic Writing, Extensive Disciplines, and so on. We performed a blind pairwise comparison between Wizard and baselines. Specifically, we recruit 10 well-educated annotators to rank the models from 1 to 5 on relevance, knowledgeable, reasoning, calculation and accuracy.
+To evaluate Wizard, we conduct human evaluation on the inputs from our human instruct evaluation set [`WizardLM_testset.jsonl`](./data/WizardLM_testset.jsonl) . This evaluation set was collected by the authors and covers a diverse list of user-oriented instructions including difficult Coding Generation & Debugging, Math, Reasoning, Complex Formats, Academic Writing, Extensive Disciplines, and so on. We performed a blind pairwise comparison between Wizard and baselines. Specifically, we recruit 10 well-educated annotators to rank the models from 1 to 5 on relevance, knowledgeable, reasoning, calculation and accuracy. 
 
-WizardLM achieved significantly better results than Alpaca and Vicuna-7b.
+WizardLM achieved significantly better results than Alpaca and Vicuna-7b. 
 <p align="center" width="60%">
-<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/win.png" alt="WizardLM" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="imgs/win.png" alt="WizardLM" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 In the high-difficulty section of our test set (difficulty level >= 8), WizardLM even outperforms ChatGPT, with a win rate 7.9% larger than Chatgpt (42.9% vs. 35.0%). This indicates that our method can significantly improve the ability of large language models to handle complex instructions.
 <p align="center" width="60%">
-<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/windiff.png" alt="WizardLM" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="imgs/windiff.png" alt="WizardLM" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 ### Citation
@@ -231,7 +232,7 @@ Please cite the repo if you use the data or code in this repo.
 
 ```
 @misc{xu2023wizardlm,
-      title={WizardLM: Empowering Large Language Models to Follow Complex Instructions},
+      title={WizardLM: Empowering Large Language Models to Follow Complex Instructions}, 
       author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
       year={2023},
       eprint={2304.12244},

added_tokens.json

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+{
+  "[PAD]": 32000
+}

config.json

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+{
+  "_name_or_path": "/workspace/process/wizardLM-13B-1.0/fp32",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.29.2",
+  "use_cache": true,
+  "vocab_size": 32001
+}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.29.2"
+}

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