first commit

LFM2.py

+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_id = "LiquidAI/LFM2-8B-A1B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    dtype="bfloat16",
+#    attn_implementation="flash_attention_2" <- uncomment on compatible GPU
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Generate answer
+prompt = "What is C. elegans?"
+input_ids = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+).to(model.device)
+
+output = model.generate(
+    input_ids,
+    do_sample=True,
+    temperature=0.3,
+    min_p=0.15,
+    repetition_penalty=1.05,
+    max_new_tokens=512,
+)
+
+print(tokenizer.decode(output[0], skip_special_tokens=False))
+
+# <|startoftext|><|im_start|>user
+# What is C. elegans?<|im_end|>
+# <|im_start|>assistant
+# C. elegans, also known as Caenorhabditis elegans, is a small, free-living
+# nematode worm (roundworm) that belongs to the phylum Nematoda.
+

LICENSE

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\ No newline at end of file

README.md

+# LFM2-8B-A1B
+## 论文
+
+暂无
+## 模型简介
+
+LFM2是由Liquid AI开发的新一代混合模型，专为边缘AI和设备端部署设计。它在质量、速度和内存效率方面设定了新的标准。该模型总参数量为8.3B，激活参数量为1.5B。LFM2-8B-A1B在保留高速主干网络的基础上，引入稀疏混合专家前馈网络，以此增强模型表征能力，同时确保激活计算路径不会显著增加。
+
+- LFM2-8B-A1B在质量（与3-4B密集模型相当）和速度（比 Qwen3-1.7B 更快）方面都是最佳的设备端MoE。
+- 代码和知识能力相比LFM2-2.6B有显著提升。
+- 量化变体可以轻松适配高端手机、平板电脑和笔记本电脑。
+
+<div align=center>
+    <img src="./doc/lfm2.png"/>
+</div>
+
+由于体积小，可以对LFM2模型进行针对特定用例的微调以最大化性能。 该模型特别适合于代理任务、数据提取、RAG、创意写作和多轮对话。 然而，并不适用于知识密集型或需要编程技能的任务。
+
+
+
+## 环境依赖
+
+| 软件 | 版本 |
+| :------: | :------: |
+| DTK | 25.04.2 |
+| python | 3.10.12 |
+| transformers | 4.57.0.dev0 |
+| flash-attn | 2.6.1+das.opt1.dtk2504 |
+| torch | 2.7.1+das.opt1.dtk25042 |
+| triton | 3.1+das.opt1.3c5d12d.dtk25041 |
+
+
+当前仅支持镜像:
+- 挂载地址`-v`根据实际模型情况修改
+
+```bash
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.7.1-ubuntu22.04-dtk25.04.2-py3.10-alpha
+docker run -it --shm-size 60g --network=host --name LFM2-8B-A1B --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /opt/hyhal/:/opt/hyhal/:ro -v /path/your_code_path/:/path/your_code_path/ image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.7.1-ubuntu22.04-dtk25.04.2-py3.10-alpha bash
+pip install git+https://github.com/huggingface/transformers.git@0c9a72e4576fe4c84077f066e585129c97bfd4e6
+pip install accelerate
+cd whl
+pip install flash_attn*.whl
+```
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
+
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
+
+## 数据集
+暂无
+
+## 训练
+暂无
+
+## 推理
+
+
+### transformers
+#### 单机推理
+```bash
+export HIP_VISIBLE_DEVICES=0
+python LFM2.py
+```
+
+## 效果展示
+<div align=center>
+    <img src="./doc/result.png"/>
+</div>
+
+### 精度
+DCU与GPU精度一致，推理框架：pytorch。
+
+## 预训练权重
+| 模型名称  | 权重大小  | DCU型号  | 最低卡数需求 |下载地址|
+|:-----:|:----------:|:----------:|:---------------------:|:----------:|
+| LFM2-8B-A1B | 8.3B | K100AI | 1 | [下载地址](https://huggingface.co/LiquidAI/LFM2-8B-A1B) |
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/lfm2-8b-a1b-pytorch
+
+## 参考资料
+- https://huggingface.co/LiquidAI/LFM2-8B-A1B
+- https://www.liquid.ai/blog/lfm2-8b-a1b-an-efficient-on-device-mixture-of-experts
\ No newline at end of file

VibeThinker.py

+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+
+class VibeThinker:
+    def __init__(self, model_path):
+        self.model_path = model_path
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_path,
+            low_cpu_mem_usage=True,
+            torch_dtype="bfloat16",
+            device_map="auto"
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
+
+    def infer_text(self, prompt):
+        messages = [
+            {"role": "user", "content": prompt}
+        ]
+        text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)
+
+        text = self.tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)
+
+        generation_config = dict(
+            max_new_tokens=40960,
+            do_sample=True,
+            temperature=0.6, # 0.6 or 1.0, you can set it according to your needs
+            top_p=0.95,
+            top_k=None # in vLLM or SGlang, please set top_k to -1, it means skip top_k for sampling
+        )
+        generated_ids = self.model.generate(
+            **model_inputs,
+            generation_config=GenerationConfig(**generation_config)
+        )
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+        ]
+
+        response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+        return response
+
+
+if __name__ == '__main__':
+    model = VibeThinker('WeiboAI/VibeThinker-1.5B')
+    prompt = '介绍下自己'
+    print(model.infer_text(prompt))

model.properties

+# 模型唯一标识
+modelCode=1844
+# 模型名称
+modelName=lfm2-8b-a1b-pytorch
+# 模型描述
+modelDescription=LFM2是由Liquid AI开发的新一代混合模型，专为边缘AI和设备端部署设计。它在质量、速度和内存效率方面设定了新的标准。该模型总参数量为8.3B，激活参数量为1.5B
+# 运行过程
+processType=推理
+# 算法类别
+appCategory=对话问答
+# 框架类型
+frameType=pytorch
+# 加速卡类型
+accelerateType=K100AI
\ No newline at end of file