Update README.md

README.md

@@ -38,11 +38,18 @@ GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/gger
 * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
 * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
 
+### Original model removed
+
+The original model creator has removed their model, due to their training dataset being contanimated with samples from the HumanEval dataset.
+
+This does not affect the ability of this model, which has proven to be very high quality.
+
+For the moment I am going to leave this model up, but will remove it this is requested by the original model creator.
+
 ## Repositories available
 
 * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/NewHope-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/NewHope-GGML)
-* [SLAM-group's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/SLAM-group/NewHope)
 
 ## Prompt template: Alpaca
 
@@ -153,105 +160,4 @@ Thank you to all my generous patrons and donaters!
 
 # Original model card: SLAM-group's NewHope
 
-# NewHope: Harnessing 99% of GPT-4's Programming Capabilities
-
-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**.
-
-**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. 
-[email protected] 
-
-**TODO**: We will release more evaluatation results and training details later.
-
-# Evaluation Results
-
-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.
-
-| Model | Pass@1 |
-| ----- | ------ |
-| **GPT-4** | **67.0**   |
-| **NewHope** | **66.5**  | 
-| PanGu-Coder2 15B | 61.6   |
-| WizardCoder 15B | 57.3  |
-| phi-1 1.3B | 50.6 |
-| GPT-3.5 | 48.1 |
-| phi-1-small | 45.0 |
-| PaLM-Coder | 36.0 |
-| CodeGeeX2-6B | 35.9 |
-
-# Model Weights
-
-We have open-sourced the model weights [NewHope](https://huggingface.co/SLAM-group/NewHope).
-
-We are uploading the model weights. The weights will be available in a few hours.
-
-
-# Usage
-
-To load the NewHope model using Transformers, use the following code:
-```
-import torch
-from transformers import LlamaTokenizer, LlamaForCausalLM
-
-base_model = "SLAM-group/NewHope"
-tokenizer = LlamaTokenizer.from_pretrained(base_model)
-model = LlamaForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16, device_map="auto")
-# model.config.use_cache is default to `False`. For inference: `model.config.use_cache = True`
-```
-**Note:** At least Huggingface Transformers **4.31.0** is required to load this model!
-
-You can ask NewHope to generate code with instructions. We provide a simple example of how NewHope model generates code with the specific prompt:
-```
-# Suppose required tokenizer and model have already been loaded
-
-instruction = "Write a Python function to tell me what the date is today."
-prompt = f"<s> ### Instruction:\n{instruction}\n\n### Response:\n"
-inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").to("cuda")
-output = model.generate(**inputs, do_sample=True, top_p=0.9, max_new_tokens=2048)[0]
-decoded_output = tokenizer.decode(output, skip_special_tokens=True).split("### Response:\n")[-1].strip()
-print(decoded_output)
-```
-
-You can also interact with NewHope in a dialog manner with the following prompt:
-```
-<s> ### Instruction:\nQ1\n\n### Response:\nA1</s><s> ### Instruction:\nQ2\n\n### Response:\nA2</s>
-```
-
-
-# Evaluation
-
-### Local setup
-1. Install HumanEval for evaluation. [Details](https://github.com/openai/human-eval)
-2. Install dependencies
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
----
-For HumanEval, we use the following prompt:
-```
-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'
-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'
-
-task_in_humaneval = "REPLACE `task_in_humaneval` WITH THE SPECIFIC TASK IN HUMANEVAL DATA"
-
-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"
-```
-
-To reproduce the results on HumanEval, use the following script:
-```
-python complete.py --base_model SLAM-group/NewHope --output_dir output --n_gpu 8
-```
-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%`).
-
-# Citation
-
-```
-@misc{2023newhope,
-    title={NewHope: Harnessing 99% of GPT-4's Programming Capabilities},
-    author={Wanyun Cui and Qianle Wang},
-    howpublished = https://github.com/SLAM-group/newhope,
-    year={2023}
-}
-```
-
+Original model was removed.