东锡 NLP 对印度 169Pi Research 发布的 9B 多模态推理模型 Alpie Seed 提出质疑，称其架构完全照搬、分词器词表未改动、权重结构和数值基本逐位相同于 Qwen3.5-9B。
印度光明节，号称 built in 印度的 Alpie 9B 发布。看了一下，完全是向 Qwen3.5-9B “致敬”：
- 架构方面，完全 copy
- 分词器方面，词表一个字没动
- 权重结构和数值：基本逐位相同
哦，唯一不同的是模型的自我认知：
chat template 每轮都注入指令，问来自哪里就答 “India”。
把 X 网友当傻子么？
https://huggingface.co/169Pi/Alpie_seed/blob/main/chat_template.jinja#L3
【引用 @169Pi_ai】This Diwali, a little more light from India's AI research community onto the global AI landscape.
Introducing the latest benchmark results from Alpie Seed, our 9B multimodal reasoning model developed at @169Pi_ai Research.
We believe the next generation of AI will be shaped not only by models that scale bigger, but by the research and engineering that make smaller models increasingly capable.
We put Alpie Seed through a range of demanding evaluations across reasoning, scientific knowledge, mathematics, coding and software engineering.
Here are some of the results:
→ 82.54% on MMLU-Pro
→ 84.32% on GPQA Diamond
→ 72.99% on LiveCodeBench v6
→ 61.20% on SWE-Bench Verified
The comparison highlights some interesting results.
Alpie Seed records the highest GPQA Diamond score among the models shown. On MMLU-Pro, it performs competitively with larger models, while its LiveCodeBench result is within striking distance of substantially larger systems.
Its SWE-Bench Verified result of 61.20% is another important milestone, reflecting its ability to work through real-world software engineering tasks. This evaluation used mini-SWE-agent without retries.
Beyond benchmark accuracy, we also examined inference efficiency.
In a separate profiling evaluation, Alpie Seed recorded:
→ 36.66 ms time to first token
→ 41.37 tokens/s generation speed
→ 9,796.22 tokens/s prompt processing speed
These measurements come from a separate profiling setup and should not be confused with the comparative benchmark results.
What makes this work meaningful to us is the process behind it: developing a compact model through supervised fine-tuning and reinforcement learning, evaluating it across diverse tasks, and making the resulting weights accessible to the wider community.
The comparison includes published results from other model developers. These evaluations were not rerun under a common harness, so differences in methodology and configuration should be considered when interpreting the numbers.
Built in India. Developed for a global research and developer community.
This is Alpie Seed. And this is one step in a much larger journey for 169Pi Research.
Explore the model:
Hugging Face: https://huggingface.co/169Pi/Alpie_seed
GGUF: https://huggingface.co/169Pi/Alpie_Seed_GGUF
Ollama: https://ollama.com/169pi/alpie-seed
Kaggle: https://www.kaggle.com/models/alpie/alpie-seed-9b
Happy Diwali from everyone at 169Pi.
This Diwali, a little more light from India's AI research community onto the global AI landscape. Introducing the latest benchmark results from Alpie Seed, our 9B multimodal reasoning model developed at @169Pi_ai Research. We believe the next generation of AI will be shaped not only by models that scale bigger, but by the research and engineering that make smaller models increasingly capable. We put Alpie Seed through a range of demanding evaluations across reasoning, scientific knowledge, mathematics, coding and software engineering. Here are some of the results: → 82.54% on MMLU-Pro → 84.32% on GPQA Diamond → 72.99% on LiveCodeBench v6 → 61.20% on SWE-Bench Verified The comparison highlights some interesting results. Alpie Seed records the highest GPQA Diamond score among the models shown. On MMLU-Pro, it performs competitively with larger models, while its LiveCodeBench result is within striking distance of substantially larger systems. Its SWE-Bench Verified result of 61.20% is another important milestone, reflecting its ability to work through real-world software engineering tasks. This evaluation used mini-SWE-agent without retries. Beyond benchmark accuracy, we also examined inference efficiency. In a separate profiling evaluation, Alpie Seed recorded: → 36.66 ms time to first token → 41.37 tokens/s generation speed → 9,796.22 tokens/s prompt processing speed These measurements come from a separate profiling setup and should not be confused with the comparative benchmark results. What makes this work meaningful to us is the process behind it: developing a compact model through supervised fine-tuning and reinforcement learning, evaluating it across diverse tasks, and making the resulting weights accessible to the wider community. The comparison includes published results from other model developers. These evaluations were not rerun under a common harness, so differences in methodology and configuration should be considered when interpreting the numbers. Built in India. Developed for a global research and developer community. This is Alpie Seed. And this is one step in a much larger journey for 169Pi Research. Explore the model: Hugging Face: https://huggingface.co/169Pi/Alpie_seed GGUF: https://huggingface.co/169Pi/Alpie_Seed_GGUF Ollama: https://ollama.com/169pi/alpie-seed Kaggle: https://www.kaggle.com/models/alpie/alpie-seed-9b Happy Diwali from everyone at 169Pi.在 X 查看被引用的帖子
来源：Dongxi 东锡 NLP · x.com