fix(description): update project description and cli help info and re…

README.md

@@ -253,8 +253,8 @@ For Windows, simply go to Control Panel → Programs → Uninstall a program, fi
 |              | Provider     | HuggingFace Collection  |  Blog  | Description |
 |:-------------|:-------------|:----------------------------:|:----------------------------:|:----------------------------|
 |gpt-oss       | OpenAI       | [gpt-oss](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) | [Introducing gpt-oss](https://openai.com/index/introducing-gpt-oss/) | "gpt-oss" refers to OpenAI's open-source GPT models, including gpt-oss-20b and gpt-oss-120b. The number (e.g., 20b, 120b) indicates the parameter count (20 billion, 120 billion).  |
+|Kimi-K2       | Moonshot AI  | [Kimi-K2](https://huggingface.co/collections/moonshotai/kimi-k2-6871243b990f2af5ba60617d) | [Kimi K2: Open Agentic Intelligence](https://moonshotai.github.io/Kimi-K2/) | "Kimi-K2" is Moonshot AI's Kimi-K2 model family, including Kimi-K2-Instruct and Kimi-K2-Instruct-0905. The models are designed for agentic intelligence and available in different versions and parameter sizes. |
 |Qwen3-Next    | Qwen         | [Qwen3-Next](https://huggingface.co/collections/Qwen/qwen3-next-68c25fd6838e585db8eeea9d) | [Qwen3-Next: Towards Ultimate Training & Inference Efficiency](https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list) | "Qwen3-Next" is the latest generation of Qwen models by Alibaba/Qwen, with improved efficiency and performance. Includes models like Qwen3-Next-80B-A3B-Instruct (80B parameters, instruction-tuned) and Qwen3-Next-80B-A3B-Thinking (80B, reasoning enhanced). Variants include FP8 quantized and instruction-tuned models. |
 |Qwen3         | Qwen         | [Qwen3](https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f) | [Qwen3: Think Deeper, Act Faster](https://qwen.ai/blog?id=1e3fa5c2d4662af2855586055ad037ed9e555125&from=research.research-list) | "Qwen3" is the third generation of Qwen LLMs, available in multiple sizes (e.g., 0.6B, 1.7B, 4B, 8B, 14B, 30B, 32B, 235B). Variants include FP8 quantized and instruction-tuned models. |
 |Qwen2.5       | Qwen         | [Qwen2.5](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) | [Qwen2.5: A Party of Foundation Models!](https://qwen.ai/blog?id=6da44b4d3b48c53f5719bab9cc18b732a7065647&from=research.research-list) | "Qwen2.5" is an earlier generation of Qwen models, with sizes like 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B. These models are available in base and instruction-tuned versions. |
 |Meta Llama 3  | Meta         | [Meta Llama 3](https://huggingface.co/collections/meta-llama/meta-llama-3-66214712577ca38149ebb2b6) <br>[Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) <br>[Llama 3.2](https://huggingface.co/collections/meta-llama/llama-32-66f448ffc8c32f949b04c8cf) <br>[Llama 3.3](https://huggingface.co/collections/meta-llama/llama-33-67531d5c405ec5d08a852000) | [Introducing Meta Llama 3: The most capable openly available LLM to date](https://ai.meta.com/blog/meta-llama-3/) | "Meta Llama 3" is Meta's third-generation Llama model, available in sizes such as 8B and 70B parameters. Includes instruction-tuned and quantized (e.g., FP8) variants. |
-|Kimi-K2       | Moonshot AI  | [Kimi-K2](https://huggingface.co/collections/moonshotai/kimi-k2-6871243b990f2af5ba60617d) | [Kimi K2: Open Agentic Intelligence](https://moonshotai.github.io/Kimi-K2/) | "Kimi-K2" is Moonshot AI's Kimi-K2 model family, including Kimi-K2-Instruct and Kimi-K2-Instruct-0905. The models are designed for agentic intelligence and available in different versions and parameter sizes. |

pyproject.toml

@@ -6,9 +6,9 @@ build-backend = "poetry.core.masonry.api"
 [project]
 name = "parallax"
 version = "0.0.1"
-description = "Decentralised pipeline-parallel LLM serving with MLX-LM + Hivemind"
+description = "Decentralised pipeline-parallel LLM serving with Sglang + MLX-LM + Lattica"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.11"
 packages = [
   { include = "parallax", from = "src" },
   { include = "scheduling", from = "src" },

src/backend/benchmark/benchmark_serving.py

@@ -93,13 +93,16 @@ def download_and_cache_file(url: str, filename: Optional[str] = None):
     chunk_size = 1024  # Download in chunks of 1KB
 
     # Use tqdm to display the progress bar
-    with open(filename, "wb") as f, tqdm(
-        desc=filename,
-        total=total_size,
-        unit="B",
-        unit_scale=True,
-        unit_divisor=1024,
-    ) as bar:
+    with (
+        open(filename, "wb") as f,
+        tqdm(
+            desc=filename,
+            total=total_size,
+            unit="B",
+            unit_scale=True,
+            unit_divisor=1024,
+        ) as bar,
+    ):
         for chunk in response.iter_content(chunk_size=chunk_size):
             f.write(chunk)
             bar.update(len(chunk))

src/parallax/cli.py

@@ -198,9 +198,12 @@ def main():
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog="""
 Examples:
-  parallax run                           # Start scheduler with frontend
-  parallax run -m Qwen/Qwen3-0.6B -n 2  # Start scheduler without frontend
-  parallax join -s /ip4/192.168.1.2/tcp/5001/p2p/xxxxxxxxxxxx  # Join cluster
+  parallax run                                                          # Start scheduler with frontend
+  parallax run -m {model-name} -n {number-of-worker-nodes}              # Start scheduler without frontend
+  parallax run -m Qwen/Qwen3-0.6B -n 2                                  # example
+  parallax join                                                         # Join cluster in local network
+  parallax join -s {scheduler-address}                                  # Join cluster in public network
+  parallax join -s 12D3KooWLX7MWuzi1Txa5LyZS4eTQ2tPaJijheH8faHggB9SxnBu # example
         """,
     )
 
@@ -221,7 +224,11 @@ def main():
         "join", help="Join a distributed cluster (equivalent to scripts/join.sh)"
     )
     join_parser.add_argument(
-        "-s", "--scheduler-addr", default="auto", type=str, help="Scheduler address (required)"
+        "-s",
+        "--scheduler-addr",
+        default="auto",
+        type=str,
+        help="Scheduler address (required)",
     )
     join_parser.add_argument(
         "-r", "--use-relay", action="store_true", help="Use public relay servers"