Update README.md

README.md

@@ -24,23 +24,23 @@ Dedicated to building a more intuitive, comprehensive, and efficient LLMs compre
 ![image/jpeg](2bit-benchmark.png)
 
 ## 📣Latest News
-- [26/02/09] We have released HY-Nano, 2bit on-device large language model.
+- [26/02/09] We have released HY-1.8B-2Bit, 2bit on-device large language model.
 - [26/01/13] We have released v0.3. We support the training and deployment of Eagle3 for all-scale LLMs/VLMs/Audio models, as detailed in the [guidance documentation](https://angelslim.readthedocs.io/zh-cn/latest/features/speculative_decoding/eagle/index.html). And We released **Sherry**, the hardware-efficient 1.25 bit quantization algorithm [Paper Comming soon] | [[Code]](https://github.com/Tencent/AngelSlim/tree/sherry/Sherry)🔥🔥🔥
 
 For more detailed information, please refer to[[AngelSlim]](https://github.com/Tencent/AngelSlim)
 
-## 🌟HY-Nano Key Features
+## 🌟HY-1.8B-2Bit Key Features
 
-- **Superior Model Capability** HY-NANO is developed via Quantization-Aware Training (QAT) based on the Hunyuan-1.8B-Instruct backbone. By aggressively compressing the model to a 2-bit weight precision, we achieve a performance profile that remains highly competitive with PTQ-INT4 benchmarks. Across a multi-dimensional evaluation suite—encompassing mathematics, humanities, and programming—HY-NANO exhibits a marginal performance degradation of only 4\% compared to its full-precision counterpart, demonstrating exceptional information retention despite the radical reduction in bit-width. 
+- **Superior Model Capability** HY-1.8B-2Bit is developed via Quantization-Aware Training (QAT) based on the Hunyuan-1.8B-Instruct backbone. By aggressively compressing the model to a 2-bit weight precision, we achieve a performance profile that remains highly competitive with PTQ-INT4 benchmarks. Across a multi-dimensional evaluation suite—encompassing mathematics, humanities, and programming—HY-1.8B-2Bit exhibits a marginal performance degradation of only 4\% compared to its full-precision counterpart, demonstrating exceptional information retention despite the radical reduction in bit-width. 
 
-- **Unmatched Scale-to-Performance Efficiency** When compared to dense models of equivalent size (e.g., 0.5B parameters), HY-NANO demonstrates a substantial competitive advantage, outperforming benchmarks by an average of 16\% across core competencies. As a state-of-the-art (SOTA) solution for its parameter class, HY-NANO provides an extensible and highly efficient alternative for edge computing, delivering high-tier reasoning capabilities within a compact footprint. 
+- **Unmatched Scale-to-Performance Efficiency** When compared to dense models of equivalent size (e.g., 0.5B parameters), HY-1.8B-2Bit demonstrates a substantial competitive advantage, outperforming benchmarks by an average of 16\% across core competencies. As a state-of-the-art (SOTA) solution for its parameter class, HY-1.8B-2Bit provides an extensible and highly efficient alternative for edge computing, delivering high-tier reasoning capabilities within a compact footprint. 
 
-- **Comprehensive Reasoning Proficiency** HY-NANO inherits the complete "full-thinking" capabilities of the Hunyuan-1.8B-Instruct model, marking it as the industry's most compact model to support sophisticated reasoning pathways. By integrating a Dual Chain-of-Thought (Dual-CoT) strategy, the model empowers users to navigate the trade-off between latency and depth: utilizing concise short-CoT for intuitive queries and detailed long-CoT for computationally intensive tasks. This flexibility ensures that HY-NANO can be seamlessly deployed in real-time, resource-constrained environments that demand both rapid response and high-fidelity logical synthesis.
+- **Comprehensive Reasoning Proficiency** HY-1.8B-2Bit inherits the complete "full-thinking" capabilities of the Hunyuan-1.8B-Instruct model, marking it as the industry's most compact model to support sophisticated reasoning pathways. By integrating a Dual Chain-of-Thought (Dual-CoT) strategy, the model empowers users to navigate the trade-off between latency and depth: utilizing concise short-CoT for intuitive queries and detailed long-CoT for computationally intensive tasks. This flexibility ensures that HY-1.8B-2Bit can be seamlessly deployed in real-time, resource-constrained environments that demand both rapid response and high-fidelity logical synthesis.
 
 
 ## 📈 Benchmark
 
-Benchmark results for HY-Nano equivalent weights on vLLM across **cmmlu**,**ceval**,**arc**,**bbh**,**gsm8k**,**humaneval**,**livecodebench** and **gpqa_diamond**.
+Benchmark results for HY-1.8B-2Bit equivalent weights on vLLM across **cmmlu**,**ceval**,**arc**,**bbh**,**gsm8k**,**humaneval**,**livecodebench** and **gpqa_diamond**.
 
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@@ -49,7 +49,7 @@ xxx
 | HY-1.8B          | 55.07% | 54.27% | 70.50% | 79.08% | 84.08% | 94.51%            | 31.50%        | 68.18%               |
 | HY-0.5B          | 37.08% | 35.98% | 49.89% | 58.10% | 55.04% | 67.07%            | 12.11%        | 46.97%               |
 | HY-1.8B-int4gptq | 50.80% | 48.67% | 68.83% | 74.80% | 78.70% | 89.02%            | 30.08%        | 65.56%               |
-| **HY-Nano**      | 49.32% | 47.60% | 64.45% | 75.54% | 77.33% | 93.29%            | 32.73%        | 65.15%               |
+| **HY-1.8B-2Bit**      | 49.32% | 47.60% | 64.45% | 75.54% | 77.33% | 93.29%            | 32.73%        | 65.15%               |