add measurement.json

README.md

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+---
+license: mit
+tags:
+- code
+pipeline_tag: text-generation
+---
+
+<a href="https://ntq.com.vn" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/5ee1b417636bdb3834e2da19/etbfTJuVdAub2evNP_E4g.png" width="200"/></a>
+
+## Introduction
+
+Nxcode-CQ-7B-orpo is an [Monolithic Preference Optimization without Reference Model](https://arxiv.org/abs/2403.07691) fine-tune of Qwen/CodeQwen1.5-7B on 100k samples of high-quality ranking data.
+
+## [Evalplus](https://github.com/evalplus/evalplus)
+
+| EvalPlus | pass@1 |
+| --- | --- |
+| HumanEval | 86.6 |
+| HumanEval+ | 83.5 |
+| MBPP(v0.2.0) | 82.3 |
+| MBPP+(v0.2.0) | 70.4 |
+
+We use a simple template to generate the solution for evalplus:
+
+```python
+"Complete the following Python function:\n{prompt}"
+```
+
+[Evalplus Leaderboard](https://evalplus.github.io/leaderboard.html)
+| Models | HumanEval | HumanEval+|
+|------ | ------  | ------ |
+| GPT-4-Turbo (April 2024)|  90.2| 86.6|
+| GPT-4 (May 2023)|  88.4| 81.17|
+| GPT-4-Turbo (Nov 2023)|  85.4| 79.3| 
+| CodeQwen1.5-7B-Chat|  83.5| 78.7| 
+| claude-3-opus (Mar 2024)|  82.9| 76.8|
+| DeepSeek-Coder-33B-instruct|  81.1| 75.0|
+| WizardCoder-33B-V1.1|  79.9| 73.2|
+| OpenCodeInterpreter-DS-33B|  79.3| 73.8|
+| speechless-codellama-34B-v2.0|  77.4| 72|
+| GPT-3.5-Turbo (Nov 2023)| 76.8| 70.7|
+| Llama3-70B-instruct| 76.2| 70.7|
+
+## Bigcode Leaderboard
+
+[Bigcode Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard)
+
+**09/05/2024**
+
+Top 1 average score.
+
+Top 2 winrate.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/5ee1b417636bdb3834e2da19/OQonD6a7aNjnN9SsTkFp-.png)
+
+
+## Quickstart
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. You should upgrade the transformers if you receive an error when loading the tokenizer
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "NTQAI/Nxcode-CQ-7B-orpo",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("NTQAI/Nxcode-CQ-7B-orpo")
+
+prompt = """Complete the following Python function:
+from typing import List
+
+
+def has_close_elements(numbers: List[float], threshold: float) -> bool:
+    """ Check if in given list of numbers, are any two numbers closer to each other than
+    given threshold.
+    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)
+    False
+    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
+    True
+    """
+"""
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
+res = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
+
+```
+
+### Contact information
+For personal communication related to this project, please contact Nha Nguyen Van ([email protected]).