budecosystem_-_code-millenials-34b-gguf

Quantization made by Richard Erkhov.

Github

Discord

Request more models

code-millenials-34b - GGUF

• Model creator: https://huggingface.co/budecosystem/
• Original model: https://huggingface.co/budecosystem/code-millenials-34b/

Original model description:

license: llama2 library_name: transformers tags:

• code model-index:
• name: Code Millenials results:
  • task: type: text-generation dataset: name: HumanEval type: openai_humaneval metrics:
    • type: pass@1 value: 0.8048 name: pass@1 verified: false
  • task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
    • type: acc_norm value: 49.83 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
    • type: acc_norm value: 75.09 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
    • type: acc value: 49.28 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
    • type: mc2 value: 45.37 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
    • type: acc value: 69.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 32.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=budecosystem/code-millenials-34b name: Open LLM Leaderboard

---

Bud Code Millenials 34B

Welcome to our Code Model repository! Our model is specifically fine-tuned for code generation tasks. Bud Millenial Code Gen open-source models are currently the State of the Art (SOTA) for code generation, beating all the existing models of all sizes. We have achieved a HumanEval value of 80.48 @ Pass 1, beating proprietary models like Gemini Ultra, Claude, GPT-3.5 etc. by a large margin, and on par with GPT-4 (HumanEval ~ 82. Ref. WizardCoder). Our proprietary model (Bud Code Jr) beats GPT-4 as well with a HumanEval value of 88.2 & a context size of 168K, we will be releasing an API for Researchers, Enterprises, and potential Partners by January 2024 end. If interested, please reach out to [email protected]

News 🔥🔥🔥

• [2024/01/09] We released Code Millenials 3B , which achieves the 56.09 pass@1 on the HumanEval Benchmarks.
• [2024/01/09] We released Code Millenials 1B , which achieves the 51.82 pass@1 on the HumanEval Benchmarks.
• [2024/01/03] We released Code Millenials 34B , which achieves the 80.48 pass@1 on the HumanEval Benchmarks.
• [2024/01/02] We released Code Millenials 13B , which achieves the 76.21 pass@1 on the HumanEval Benchmarks.

HumanEval

<p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/BudEcosystem/code-millenials/main/assets/result.png" alt="CodeMillenials" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a> </p>

For the millenial models, the eval script in the github repo is used for the above result.

Note: The humaneval values of other models are taken from the official repos of WizardCoder, DeepseekCoder, Gemini etc.

Models

🚀 Quick Start

Inference code using the pre-trained model from the Hugging Face model hub

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-34b")
model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-34b")

template = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.

### Instruction: {instruction}

### Response:"""

instruction = <Your code instruction here>

prompt = template.format(instruction=instruction)

inputs = tokenizer(prompt, return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))

Training details

The model is trained of 16 A100 80GB for approximately 50hrs.

Important Note

• Bias, Risks, and Limitations: Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here