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Model: inclusionAI/Ling-Coder-lite-base
Source: Original Platform

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LICENCE

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+MIT License
+
+Copyright (c) 2025 inclusionAI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+datasets:
+- inclusionAI/Ling-Coder-SyntheticQA
+language:
+- en
+- zh
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+tags:
+- code
+- moe
+---
+
+# Ling-Coder-lite-base
+
+<p align="center">
+    <img src="https://huggingface.co/inclusionAI/Ling-lite/resolve/main/ant-bailing.png" width="100"/>
+<p>
+
+<p align="center">
+          🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope</a>
+          🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>
+          🖥️ <a href="https://github.com/codefuse-ai/Ling-Coder-Lite">GitHub</a>
+<p>
+
+## Introduction
+
+Ling-Coder-Lite is a MoE LLM provided and open-sourced by InclusionAI, which has 16.8B parameters with 2.75B activated parameters. This model demonstrates state-of-the-art performance on 12 coding benchmarks, while simultaneously offering competitive latency and throughput compared to code LLMs of similar size. In addition to open-sourcing the model itself, we also release a substantial amount of code-related data, including synthetic QA, SFT and DPO datasets. More details are described in the technique report [Ling-Coder-TR](https://huggingface.co/papers/2503.17793).
+
+## Model Downloads
+
+You can download the following table to see the various parameters for your use case. If you are located in mainland China, we also provide the model on modelscope.cn to speed up the download process.
+
+<div align="center">
+
+|     **Model**      | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
+| :----------------: | :---------------: | :-------------------: | :----------------: | :----------: |
+| Ling-Coder-lite-base |       16.8B       |         2.75B         |        16K         |      [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-Coder-lite-base) |
+| Ling-Coder-lite |       16.8B       |         2.75B         |        16K         |     [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-Coder-lite)     |
+| Ling-Coder-lite-GPTQ-Int8 |       16.8B       |         2.75B         |        16K         |     [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-Coder-lite-GPTQ-Int8)     |
+</div>
+
+## Dataset Downloads
+
+<div align="center">
+
+|   **Model**    | **Samples** |                                                                     **Download**                                                                     |
+| :------------: | :----------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Ling-Coder-SyntheticQA |        24M         | [🤗 HuggingFace](https://huggingface.co/datasets/inclusionAI/Ling-Coder-SyntheticQA) |
+| Ling-Coder-SFT  |        5M         |      [🤗 HuggingFace](https://huggingface.co/datasets/inclusionAI/Ling-Coder-SFT) |
+| Ling-Coder-DPO  |        250K         | [🤗 HuggingFace](https://huggingface.co/datasets/inclusionAI/Ling-Coder-DPO) |
+
+</div>
+
+## Evaluation
+
+Detailed evaluation results are reported in our technical report [Ling-Coder-TR](https://huggingface.co/papers/2503.17793).
+
+## Quickstart
+### 🤗 Hugging Face Transformers
+
+Here is a code snippet to show you how to use the chat model with `transformers`:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "inclusionAI/Ling-Coder-lite"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name, 
+    trust_remote_code=True
+)
+
+prompt = "Write a quick sort algorithm in python."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+## Deployment
+Please refer to [Github](https://github.com/codefuse-ai/Ling-Coder-Lite/blob/master/README.md)
+
+## License
+This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ling-Coder-lite/blob/main/LICENCE).
+
+## Citation
+
+```
+@misc{codefuse2025samplemattersleveragingmixtureofexperts,
+      title={Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM}, 
+      author={Codefuse and Ling Team},
+      year={2025},
+      eprint={2503.17793},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2503.17793}, 
+}
+```

config.json

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+{
+    "architectures": [
+        "BailingMoeForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "auto_map": {
+        "AutoConfig": "configuration_bailing_moe.BailingMoeConfig",
+        "AutoModel": "modeling_bailing_moe.BailingMoeModel",
+        "AutoModelForCausalLM": "modeling_bailing_moe.BailingMoeForCausalLM"
+    },
+    "eos_token_id": 126081,
+    "pad_token_id": 126081,
+    "first_k_dense_replace": 0,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.006,
+    "intermediate_size": 5632,
+    "max_position_embeddings": 4096,
+    "model_type": "bailing_moe",
+    "moe_intermediate_size": 1408,
+    "num_experts": 64,
+    "num_shared_experts": 2,
+    "norm_topk_prob": true,
+    "num_attention_heads": 16,
+    "num_experts_per_tok": 6,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 4,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 10000,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.36.0",
+    "use_cache": true,
+    "use_bias": false,
+    "use_qkv_bias": false,
+    "vocab_size": 126464,
+    "output_router_logits": false,
+    "embedding_dropout": 0.0,
+    "norm_head": true,
+    "output_dropout": 0.0
+}

configuration.json

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+{"framework":"Pytorch","task":"text-generation"}

configuration_bailing_moe.py

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+""" Bailing MoE model configuration """
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class BailingMoeConfig(PretrainedConfig):
+    model_type = "bailing_moe"
+
+    def __init__(
+        self,
+        vocab_size=30592,
+        hidden_size=1024,
+        intermediate_size=None,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        num_key_value_heads=0,
+        hidden_act="silu",
+        use_qkv_bias=False,  # bailing only
+        use_bias=True,  # bailing only
+        rms_norm_eps=1e-05,
+        norm_head=False,  # bailing only
+        tie_word_embeddings=False,  # PretrainedConfig key, here change default value.
+        embedding_dropout=0.1,
+        attention_dropout=0.1,
+        output_dropout=0.1,
+        initializer_range=0.02,
+        max_position_embeddings=16384,
+        rope_theta=10000.0,
+        use_cache=True,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        rope_scaling=None,
+        pad_token_id=126081,
+        num_experts=16,
+        num_shared_experts=0,
+        num_experts_per_tok=2,
+        norm_topk_prob=True,
+        moe_intermediate_size=None,
+        first_k_dense_replace=0,
+        head_dim=None,
+        output_router_logits=False,
+        **kwargs,
+    ):
+        self.num_hidden_layers = num_hidden_layers
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.use_qkv_bias = use_qkv_bias
+        self.use_bias = use_bias
+        self.norm_head = norm_head
+        self.rms_norm_eps = rms_norm_eps
+        self.embedding_dropout = embedding_dropout
+        self.attention_dropout = attention_dropout
+        self.output_dropout = output_dropout
+        self.initializer_range = initializer_range
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        self.use_cache = use_cache
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window
+        self.max_window_layers = max_window_layers
+        self.head_dim = head_dim
+        self.rope_scaling = rope_scaling
+
+        # MoE configs
+        self.num_experts = num_experts
+        self.num_shared_experts = num_shared_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.norm_topk_prob = norm_topk_prob
+        self.moe_intermediate_size = moe_intermediate_size
+        self.first_k_dense_replace = first_k_dense_replace
+        self.output_router_logits = output_router_logits
+
+        super().__init__(pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs)

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special_tokens_map.json

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+{
+    "additional_special_tokens": [
+        "<|fim_begin|>",
+        "<|fim_hole|>",
+        "<|fim_end|>",
+        "<|fim_pad|>",
+        "<|repo_name|>",
+        "<|file_sep|>"
+    ],
+    "bos_token": "<|startoftext|>",
+    "cls_token": "[CLS]",
+    "eos_token": "<|endoftext|>",
+    "gmask_token": "[gMASK]"
+}

tokenizer_config.json

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+{
+    "add_bos_token": false,
+    "add_eos_token": false,
+    "additional_special_tokens": [
+        "<|fim_begin|>",
+        "<|fim_hole|>",
+        "<|fim_end|>",
+        "<|fim_pad|>",
+        "<|repo_name|>",
+        "<|file_sep|>"
+    ],
+    "bos_token": "<|startoftext|>",
+    "clean_up_tokenization_spaces": false,
+    "cls_token": "[CLS]",
+    "eos_token": "<|endoftext|>",
+    "gmask_token": "[gMASK]",
+    "merges_file": null,
+    "model_max_length": 1000000000000000019884624838656,
+    "tokenizer_class": "PreTrainedTokenizerFast",
+    "vocab_file": null,
+    "pad_token": "<|endoftext|>",
+    "fast_tokenizer": true
+}