commit d40abdcf011ac29aa361a190c68b2d4cc0d1ad82
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Mon May 11 10:34:45 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: budecosystem/code-millenials-13b
    Source: Original Platform

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diff --git a/README.md b/README.md
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+---
+license: llama2
+library_name: transformers
+tags:
+- code
+model-index:
+- name: Code Millenials
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.7621
+      verified: false
+---
+
+
+# Bud Code Millenials 13B
+
+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 jithinvg@bud.studio
+### News 🔥🔥🔥
+
+- [2024/01/09] We released **Code Millenials 3B** , which achieves the **56.09 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
+- [2024/01/09] We released **Code Millenials 1B** , which achieves the **51.82 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
+- [2024/01/03] We released **Code Millenials 34B** , which achieves the **80.48 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
+- [2024/01/02] We released **Code Millenials 13B** , which achieves the **76.21 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
+
+
+### 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](https://github.com/nlpxucan/WizardLM), [DeepseekCoder](https://github.com/deepseek-ai/deepseek-coder), [Gemini](https://deepmind.google/technologies/gemini/#capabilities) etc. 
+
+
+### Models
+
+|   Model | Checkpoint  | HumanEval (+) | MBPP (+) |
+|---------|-------------|---------------|----------|
+|Code Millenials 34B | <a href="https://huggingface.co/budecosystem/code-millenials-34b" target="_blank">HF Link</a> | 80.48 (75) | 74.68 (62.9) |
+|Code Millenials 13B | <a href="https://huggingface.co/budecosystem/code-millenials-13b" target="_blank">HF Link</a> | 76.21 (69.5) | 70.17 (57.6) |
+|Code Millenials 3B | <a href="https://huggingface.co/budecosystem/code-millenials-3b" target="_blank">HF Link</a> | 56.09 (52.43) | 55.13 (47.11) |
+|Code Millenials 1B | <a href="https://huggingface.co/budecosystem/code-millenials-1b" target="_blank">HF Link</a> | 51.82 (48.17) | 53.13 (44.61) |
+
+
+
+
+### 🚀 Quick Start
+
+Inference code  using the pre-trained model from the Hugging Face model hub
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("budecosystem/code-millenials-13b")
+model = AutoModelForCausalLM.from_pretrained("budecosystem/code-millenials-13b")
+
+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 8 A100 80GB for approximately 15hrs. 
+
+| Hyperparameters              | Value  |
+| :----------------------------| :-----: |
+| per_device_train_batch_size  | 2      |
+| gradient_accumulation_steps  | 1      |
+| epoch | 3 |
+| steps | 34503 |
+| learning_rate                | 2e-5   |
+| lr schedular type | cosine |
+| warmup ratio | 0.1 |
+| optimizer                    | adamw  |
+| fp16                         | True   |
+| GPU                          | 8 A100 80GB |
+
+### 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.
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+  "▁<EOT>": 32003,
+  "▁<MID>": 32001,
+  "▁<PRE>": 32000,
+  "▁<SUF>": 32002
+}
diff --git a/all_results.json b/all_results.json
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+    "epoch": 3.0,
+    "train_loss": 0.04311345880544245,
+    "train_runtime": 16352.2345,
+    "train_samples_per_second": 33.758,
+    "train_steps_per_second": 2.11
+}
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+{
+  "_name_or_path": "budecosystem/code-millenials-13b",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 40,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
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+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.36.2",
+  "use_cache": false,
+  "vocab_size": 32064
+}
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+  "transformers_version": "4.36.2"
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+    "▁<EOT>"
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+      "content": "▁<EOT>",
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+    "▁<EOT>"
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+  "eot_token": "▁<EOT>",
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