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Model: WhoTookMyAmogusNickname/NewHope_HF_not_official Source: Original Platform
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README.md
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---
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license: llama2
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---
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# THIS MODEL HAS EVAL DATA LEAKED INTO THE DATASET
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# THIS IS NOT AN OFFICIAL MODEL CARD
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## ~~NewHope: Harnessing 99% of GPT-4's Programming Capabilities~~
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We introduce NewHope, a fine-tuned chat model based on llama-2-13b, aiming to provide a strong coding capability. NewHope handle different languages including Python, C++, Java, JavaScript, Go, and more. Preliminary evaluation on HumanEval shows that ~~NewHope possesses 99% of GPT-4's programming capabilities~~.
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**Contact**: SLAM (<ins>S</ins>UFE <ins>L</ins>arge <ins>A</ins>I <ins>M</ins>odel) is a research group at Shanghai University of Finance and Economics.
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cui.wanyun@sufe.edu.cn
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**TODO**: We will release more evaluatation results and training details later.
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## Evaluation Results
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We evaluated NewHope on [HumanEval](https://github.com/openai/human-eval) using the official evaluation script by OpenAI. We compared the Pass@1 metric of NewHope with other models. The results of other models are from PapersWithCode.
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| Model | Pass@1 |
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| ----- | ------ |
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| **GPT-4** | **67.0** |
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| ~~NewHope~~ | ~~66.5~~ |
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| PanGu-Coder2 15B | 61.6 |
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| WizardCoder 15B | 57.3 |
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| phi-1 1.3B | 50.6 |
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| GPT-3.5 | 48.1 |
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| phi-1-small | 45.0 |
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| PaLM-Coder | 36.0 |
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| CodeGeeX2-6B | 35.9 |
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## Model Weights
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We have open-sourced the model weights [NewHope](https://huggingface.co/SLAM-group/NewHope).
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We are uploading the model weights. The weights will be available in a few hours.
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## Usage
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To load the NewHope model using Transformers, use the following code:
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```
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import torch
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from transformers import LlamaTokenizer, LlamaForCausalLM
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base_model = "SLAM-group/NewHope"
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tokenizer = LlamaTokenizer.from_pretrained(base_model)
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model = LlamaForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16, device_map="auto")
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# model.config.use_cache is default to `False`. For inference: `model.config.use_cache = True`
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```
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**Note:** At least Huggingface Transformers **4.31.0** is required to load this model!
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You can ask NewHope to generate code with instructions. We provide a simple example of how NewHope model generates code with the specific prompt:
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```
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# Suppose required tokenizer and model have already been loaded
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instruction = "Write a Python function to tell me what the date is today."
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prompt = f"<s> ### Instruction:\n{instruction}\n\n### Response:\n"
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inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").to("cuda")
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output = model.generate(**inputs, do_sample=True, top_p=0.9, max_new_tokens=2048)[0]
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decoded_output = tokenizer.decode(output, skip_special_tokens=True).split("### Response:\n")[-1].strip()
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print(decoded_output)
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```
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You can also interact with NewHope in a dialog manner with the following prompt:
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```
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<s> ### Instruction:\nQ1\n\n### Response:\nA1</s><s> ### Instruction:\nQ2\n\n### Response:\nA2</s>
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```
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## Evaluation
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### Local setup
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1. Install HumanEval for evaluation. [Details](https://github.com/openai/human-eval)
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2. Install dependencies
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```bash
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pip install -r requirements.txt
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```
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---
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For HumanEval, we use the following prompt:
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```
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example_input = 'def is_odd(number: int) -> bool:\n """ Check whether the given number is odd\n >>> is_odd(3)\n True\n >>> is_odd(6)\n False\n """\n'
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example_output = 'def is_odd(number: int) -> bool:\n """ Check whether the given number is odd\n >>> is_odd(3)\n True\n >>> is_odd(6)\n False\n """\n return number % 2 == 1'
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task_in_humaneval = "REPLACE `task_in_humaneval` WITH THE SPECIFIC TASK IN HUMANEVAL DATA"
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prompt = f"<s> ### Instruction:\nComplete the given function below:\n\n{example_input}\n\n### Response:\n{example_output}</s><s> ### Instruction:\nComplete the given function below:\n\n{task_in_human_eval}\n\n### Response:\n"
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```
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To reproduce the results on HumanEval, use the following script:
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```
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python complete.py --base_model SLAM-group/NewHope --output_dir output --n_gpu 8
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```
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The above script will generate `samples.jsonl` in `output_dir`, which can be directly evaluated by HumanEval. [Evaluation procedure](https://github.com/openai/human-eval). We conducted the experiment with `fp16` on 8xA800, 80GB GPUs, reaching `66.5%` on Pass@1 (v.s. GPT4 `67.0%`).
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## Citation
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```
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@misc{2023newhope,
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title={NewHope: Harnessing 99% of GPT-4's Programming Capabilities},
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author={Wanyun Cui and Qianle Wang},
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howpublished = https://github.com/SLAM-group/newhope,
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year={2023}
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}
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_WhoTookMyAmogusNickname__NewHope_HF_not_official)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 51.9 |
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| ARC (25-shot) | 61.09 |
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| HellaSwag (10-shot) | 84.03 |
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| MMLU (5-shot) | 55.73 |
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| TruthfulQA (0-shot) | 44.96 |
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| Winogrande (5-shot) | 74.98 |
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| GSM8K (5-shot) | 15.85 |
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| DROP (3-shot) | 26.66 |
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||||
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|
||||
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|
||||
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|
||||
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
34
tokenizer_config.json
Normal file
34
tokenizer_config.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user