Update README.md

README.md

@@ -57,8 +57,66 @@ This project received computational resources and technical support from **Recur
 
 ---
 
-## Description
 
-This model is a **Hybrid-RWKV** language model that **strategically interleaves RWKV-Attention with NoPE FullAttention**. The design leverages RWKV’s efficient temporal state mechanism while selectively inserting Transformer-based full attention layers (without positional embeddings) to enhance long-context reasoning. With a passkey context length of **130k**, it demonstrates strong performance on extreme long-context benchmarks while maintaining efficient training dynamics.
+## Evaluation
 
----
+Performance evaluation is ongoing. The model shows promising results in:
+-   Maintaining base model capabilities while achieving linear attention efficiency
+-   Significantly improved needle-in-haystack task performance compared to pure RWKV architectures
+-   Competitive performance on standard language modeling benchmarks
+
+## Usage with RWKV-Infer
+-   **RWKV-Infer** Triton based Hybrid RWKV Inference engine, can be check at: [https://github.com/OpenMOSE/RWKV-Infer/wiki/How-to-Running-RWKV-hxa079-models%3F](https://github.com/OpenMOSE/RWKV-Infer/wiki/How-to-Running-RWKV-hxa079-models%3F)
+
+
+## Usage with Hugging Face Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "OpenMOSE/RWKV-Seed-OSS-36B-hxa079"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = """There is a very famous song that I recall by the singer's surname as Astley.
+ I can't remember the name or the youtube URL that people use to link as an example url.
+ What's song name?"""
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt},
+]
+text = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(**model_inputs, max_new_tokens=512)
+generated_ids = [
+    output_ids[len(input_ids) :]
+    for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+```
+
+
+
+## Code Repositories
+
+-   **RADLADS Project Code:** The main codebase for the RADLADS paper, including conversion scripts and model code, can be found at: [https://github.com/recursal/RADLADS](https://github.com/recursal/RADLADS)
+-   **ARWKV Project Code** The ARWKV original training code, can be found at: [https://github.com/yynil/RWKVInside](https://github.com/yynil/RWKVInside)
+-   **Specific Training Code (OpenMOSE):** The training code for this particular model is available at: [https://github.com/OpenMOSE/RWKVInside](https://github.com/OpenMOSE/RWKVInside) (Note: this repository is still under development and may contain bugs.)
+
+## Model Card Contact
+
+OpenMOSE - 2025
+
+