Update README.md

README.md

@@ -1,29 +1,29 @@
 ---
 license: apache-2.0
 ---
-# Zaya1-8B
+# ZAYA1-8B
 
-Zaya1-8B is a small mixture of experts language model with 760M active parameters and 8.4B total parameters trained end-to-end by Zyphra. Zaya1-8B sets a new standard of intelligence efficiency for its parameter count through a combination of novel architecture and innovations in pretraining and post-training. 
+ZAYA1-8B is a small mixture of experts language model with 760M active parameters and 8.4B total parameters trained end-to-end by Zyphra. ZAYA1-8B sets a new standard of intelligence efficiency for its parameter count through a combination of novel architecture and innovations in pretraining and post-training. 
 
-Zaya1-8B excels at detailed long-form reasoning especially for mathematical and coding task. It punches heavily above its weight in these regimes and due to its inference efficiency and small size can be highly effective in test-time compute harnesses.
+ZAYA1-8B excels at detailed long-form reasoning especially for mathematical and coding task. It punches heavily above its weight in these regimes and due to its inference efficiency and small size can be highly effective in test-time compute harnesses.
 
-Due to its small total parameter count, Zaya1-8B can also be deployed on-device for local LLM applications.
+Due to its small total parameter count, ZAYA1-8B can also be deployed on-device for local LLM applications.
 
 Learn more in our [technical report](-/link) and [blog](-/link).
 
-This is the post-trained reasoning version of Zaya1-8B. The pretraining base can be found [here](https://huggingface.co/Zyphra/ZAYA1-reasoning-base).
+This is the post-trained reasoning version of ZAYA1-8B. The pretraining base can be found [here](https://huggingface.co/Zyphra/ZAYA1-reasoning-base).
 
 ## Performance
 
-Zaya1-8B performs extremely strongly, especially in challenging mathematical, reasoning, and coding benchmarks. Zaya1-8B is competitive with models several times its own size.
+ZAYA1-8B performs extremely strongly, especially in challenging mathematical, reasoning, and coding benchmarks. ZAYA1-8B is competitive with models several times its own size.
 
 ![zaya1_scaling_barchart_v3](https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/K7EnqZ1nYX_OJBVqvs8tM.png)
 
-First we compare Zaya1-8B to the SOTA Qwen3 and Qwen3.5 model series of approximately the same parameter count as well as the recently released Gemma4 models and secondly to a variety of larger open-weights models.
+First we compare ZAYA1-8B to the SOTA Qwen3 and Qwen3.5 model series of approximately the same parameter count as well as the recently released Gemma4 models and secondly to a variety of larger open-weights models.
 
 ### In-class comparison against open-source reasoning models
 
-| Category | Benchmark | Zaya1-8B<br>(0.7B / 8.0B) | Qwen3-4B-Thinking-2507<br>(4.0B / 4.0B) | Qwen3.5-4B<br>(4.0B / 4.0B) | Gemma-4-E4B-it<br>(4.0B / 8.0B*) |
+| Category | Benchmark | ZAYA1-8B<br>(0.7B / 8.0B) | Qwen3-4B-Thinking-2507<br>(4.0B / 4.0B) | Qwen3.5-4B<br>(4.0B / 4.0B) | Gemma-4-E4B-it<br>(4.0B / 8.0B*) |
 |---|---|---:|---:|---:|---:|
 | Math | AIME'26 | 89.1 | 77.5 | 84.5 | 50.3 |
 | Math | HMMT Feb.'26 | 71.6 | 60.8 | 63.6 | 32.1 |
@@ -45,7 +45,7 @@ First we compare Zaya1-8B to the SOTA Qwen3 and Qwen3.5 model series of approxim
 
 | Model | Active | Total | AIME'26 | HMMT'26 | LCB-v6 | IFEval | GPQA-D | MMLU-Pro |
 |---|---:|---:|---:|---:|---:|---:|---:|---:|
-| Zaya1-8B | 0.7B | 8B | 89.1 | 71.6 | 63.8 | 85.8 | 71.0 | 74.2 |
+| ZAYA1-8B | 0.7B | 8B | 89.1 | 71.6 | 63.8 | 85.8 | 71.0 | 74.2 |
 | Arcee-Trinity-Mini | 3B | 26B | 59.6 | 36.9 | 33.3 | 62.0 | 46.8 | 70.6 |
 | N3-Nano-30B | 3B | 30B | 90.1 | 75.5 | 64.6 | 92.8 | 75.1 | 78.9 |
 | OLMo-3.1-32B-Think | 32B | 32B | 78.9 | 50.6 | 58.3 | 93.2 | 59.6 | 75.8 |
@@ -60,7 +60,7 @@ All numbers are run on the Zyphra evaluation harness. Models are ordered by tota
 ### Prerequisites
 We recommend installing the following libraries in a fresh python environment (tested with python 3.12).
 
-To use Zaya1-8B, install `zaya1` branch from our fork of `vllm` library (the command will trigger a full build of vLLM from source):  
+To use ZAYA1-8B, install `zaya1` branch from our fork of `vllm` library (the command will trigger a full build of vLLM from source):  
 ```bash
 pip install "vllm @ git+https://github.com/Zyphra/vllm.git@zaya1"
 ```
@@ -73,7 +73,7 @@ pip install "transformers @ git+https://github.com/Zyphra/transformers.git@zaya1
 ### Deployment
 To start vLLM server, run the following command:
 ```bash
-vllm serve Zyphra/Zaya1-8B --port 8010 \
+vllm serve Zyphra/ZAYA1-8B --port 8010 \
    --mamba-cache-dtype float32 --dtype bfloat16 \
    --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser zaya_xml
 ```
@@ -83,7 +83,7 @@ Once the server is up, you can query a model with `curl` like in the following e
 curl http://localhost:8010/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
-        "model": "Zyphra/Zaya1-8B",
+        "model": "Zyphra/ZAYA1-8B",
         "messages": [
             {"role": "system", "content": "You are a helpful assistant."},
             {"role": "user", "content": "Hello. How is it going?"}