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README.md

@@ -1,193 +0,0 @@
----
-license: mit
-license_link: https://huggingface.co/rednote-hilab/dots.llm1.inst/blob/main/LICENSE
-pipeline_tag: text-generation
-base_model: rednote-hilab/dots.llm1.base
-tags:
-- chat
-library_name: transformers
-language:
-- en
-- zh
----
-
-# dots1
-
-<p align="center">
-    <img src="figures/new_logo.png" width="200"/>
-<p>
-
-<p align="center">
-    &nbsp&nbsp🤗 <a href="https://huggingface.co/rednote-hilab">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://github.com/rednote-hilab/dots.llm1/blob/main/dots1_tech_report.pdf">Paper</a> &nbsp&nbsp 
-<br>
-🖥️ <a href="https://huggingface.co/spaces/rednote-hilab/dots-demo">Demo</a>&nbsp&nbsp | &nbsp&nbsp💬 <a href="figures/wechat.png">WeChat (微信)</a>&nbsp&nbsp | &nbsp&nbsp📕 <a href="https://www.xiaohongshu.com/user/profile/683ffe42000000001d021a4c">rednote</a>&nbsp&nbsp
-</p>
-
-
-
-
-Visit our Hugging Face (click links above), search checkpoints with names starting with `dots.llm1` or visit the [dots1 collection](https://huggingface.co/collections/rednote-hilab/dotsllm1-68246aaaaba3363374a8aa7c), and you will find all you need! Enjoy!
-
-
-## News
-
-- 2025.06.06: We released the `dots.llm1` series. Check our [report](https://github.com/rednote-hilab/dots.llm1/blob/main/dots1_tech_report.pdf) for more details!
-
-
-## 1. Introduction
-
-
-The `dots.llm1` model is a large-scale MoE model that activates 14B parameters out of a total of 142B parameters, delivering performance on par with state-of-the-art models. 
-Leveraging our meticulously crafted and efficient data processing pipeline, `dots.llm1` achieves performance comparable to Qwen2.5-72B after pretrained on 11.2T high-quality tokens without synthetic data. To foster further research, we open-source intermediate training checkpoints at every one trillion tokens, providing valuable insights into the learning dynamics of large language models.
-
-
-<p align="center">
-  <img width="90%" src="./figures/performance.png">
-</p>
-
-## 2. Model Summary
-
-**This repo contains the base and instruction-tuned `dots.llm1` model**. which has the following features:
-
-- Type: A MoE model with 14B activated and 142B total parameters trained on 11.2T tokens.
-- Training Stages: Pretraining and SFT.
-- Architecture: Multi-head Attention with QK-Norm in attention Layer, fine-grained MoE utilizing top-6 out of 128 routed experts, plus 2 shared experts.
-- Number of Layers: 62
-- Number of Attention Heads: 32
-- Supported Languages: English, Chinese
-- Context Length: 32,768 tokens
-- License: MIT
-
-The highlights from `dots.llm1` include:
-
-- **Enhanced Data Processing**: We propose a scalable and fine-grained *three-stage* data processing framework designed to generate large-scale, high-quality and diverse data for pretraining.
-- **No Synthetic Data during Pretraining**: *11.2 trillion* high-quality non-synthetic tokens was used in base model pretraining.
-- **Performance and Cost Efficiency**: `dots.llm1` is an open-source model that activates only *14B* parameters at inference, delivering both comprehensive capabilities and high computational efficiency.
-- **Infrastructure**: We introduce an innovative MoE all-to-all communication and computation overlapping recipe based on interleaved 1F1B pipeline scheduling and an efficient grouped GEMM implementation to boost computational efficiency.
-- **Open Accessibility to Model Dynamics**: Intermediate model checkpoints for *every 1T tokens* trained are released, facilitating future research into the learning dynamics of large language models.
-
-## 3. Example Usage
-
-### Model Downloads
-
-<div align="center">
-
-| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download Link** |
-| :------------: | :------------: | :------------: | :------------: | :------------: |
-| dots.llm1.base | 142B | 14B | 32K   | [🤗 Hugging Face](https://huggingface.co/rednote-hilab/dots.llm1.base)   |
-| dots.llm1.inst  | 142B | 14B |  32K   | [🤗 Hugging Face](https://huggingface.co/rednote-hilab/dots.llm1.inst)   |
-
-</div>
-
-### Docker (recommended)
-
-
-The docker images are available on [Docker Hub](https://hub.docker.com/repository/docker/rednotehilab/dots1/tags), based on the official images.
-
-You can start a server via vllm.
-
-```shell
-docker run --gpus all \
-    -v ~/.cache/huggingface:/root/.cache/huggingface \
-    -p 8000:8000 \
-    --ipc=host \
-    rednotehilab/dots1:vllm-openai-v0.9.0.1 \
-    --model rednote-hilab/dots.llm1.inst \
-    --tensor-parallel-size 8 \
-    --trust-remote-code \
-    --served-model-name dots1
-```
-
-Then you can verify whether the model is running successfully in the following way.
-
-```shell
-curl http://localhost:8000/v1/chat/completions \
-    -H "Content-Type: application/json" \
-    -d '{
-        "model": "dots1",
-        "messages": [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": "Who won the world series in 2020?"}
-        ],
-        "max_tokens": 32,
-        "temperature": 0
-    }'
-```
-
-
-### Inference with huggingface
-
-#### Text Completion
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
-
-model_name = "rednote-hilab/dots.llm1.base"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="eager")
-model.generation_config = GenerationConfig.from_pretrained(model_name)
-
-text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
-inputs = tokenizer(text, return_tensors="pt")
-outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
-result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(result)
-```
-
-#### Chat Completion
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
-
-model_name = "rednote-hilab/dots.llm1.inst"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="eager")
-model.generation_config = GenerationConfig.from_pretrained(model_name)
-
-messages = [
-    {"role": "user", "content": "Write a piece of quicksort code in C++"}
-]
-input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
-outputs = model.generate(input_tensor.to(model.device), max_new_tokens=200)
-
-result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
-print(result)
-```
-
-
-### Inference with sglang
-[SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models and vision language models. SGLang could be used to launch a server with OpenAI-compatible API service. `sglang>=***` is required. It is as easy as
-
-```shell
-python -m sglang.launch_server --model-path dots.llm1.inst --tp 8 --host 0.0.0.0 --port 8000
-```
-An OpenAI-compatible API will be available at `http://localhost:8000/v1`.
-
-### Inference with vllm
-[vLLM](https://github.com/vllm-project/vllm) is a high-throughput and memory-efficient inference and serving engine for LLMs. `vllm>=***` is recommended.
-
-```shell
-vllm serve dots.llm1.inst --port 8000 --tensor-parallel-size 8
-```
-An OpenAI-compatible API will be available at `http://localhost:8000/v1`.
-
-## 4. Evaluation Results
-
-Detailed evaluation results are reported in this [📑 report](https://github.com/rednote-hilab/dots.llm1/blob/main/dots1_tech_report.pdf).
-
-## Citation
-
-If you find `dots.llm1` is useful or want to use in your projects, please kindly cite our paper:
-
-```
-@article{dots1,
-      title={dots.llm1 Technical Report}, 
-      author={rednote-hilab},
-      journal={arXiv preprint arXiv:TBD},
-      year={2025}
-}
-```

config.json

@@ -5,7 +5,7 @@
   "attention_bias": false,
   "attention_dropout": 0.0,
   "bos_token_id": null,
-  "eos_token_id": 151643,
+  "eos_token_id": 151645,
   "first_k_dense_replace": 1,
   "hidden_act": "silu",
   "hidden_size": 4096,
@@ -14,7 +14,6 @@
   "max_position_embeddings": 32768,
   "model_type": "dots1",
   "moe_intermediate_size": 1408,
-  "moe_layer_freq": 1,
   "n_routed_experts": 128,
   "n_shared_experts": 2,
   "norm_topk_prob": true,

generation_config.json

@@ -1,6 +1,6 @@
 {
   "_from_model_config": true,
   "bos_token_id": null,
-  "eos_token_id": 151643,
+  "eos_token_id": 151645,
   "transformers_version": "4.46.3"
 }

tokenizer_config.json

@@ -134,10 +134,10 @@
   "bos_token": null,
   "chat_template": "{% if messages[0]['role'] == 'system' %}<|system|>{{ messages[0]['content'] }}<|endofsystem|>{% set start_idx = 1 %}{% else %}<|system|><|endofsystem|>{% set start_idx = 0 %}{% endif %}{% for idx in range(start_idx, messages|length) %}{% if messages[idx]['role'] == 'user' %}<|userprompt|>{{ messages[idx]['content'] }}<|endofuserprompt|>{% elif messages[idx]['role'] == 'assistant' %}<|response|>{{ messages[idx]['content'] }}<|endofresponse|>{% endif %}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] == 'user' %}<|response|>{% endif %}",
   "clean_up_tokenization_spaces": false,
-  "eos_token": "<|endoftext|>",
+  "eos_token": "<|endofresponse|>",
   "errors": "replace",
   "model_max_length": 32768,
-  "pad_token": "<|endoftext|>",
+  "pad_token": "<|endofresponse|>",
   "split_special_tokens": false,
   "tokenizer_class": "Qwen2Tokenizer",
   "unk_token": null