Initial monorepo setup with npm workspaces and dual TypeScript configuration

- Set up npm workspaces for three packages: pi-tui, pi-agent, and pi (pods)
- Implemented dual TypeScript configuration:
  - Root tsconfig.json with path mappings for development and type checking
  - Package-specific tsconfig.build.json for clean production builds
- Configured lockstep versioning with sync script for inter-package dependencies
- Added comprehensive documentation for development and publishing workflows
- All packages at version 0.5.0 ready for npm publishing

packages/pods/docs/qwen3-coder.md

@@ -0,0 +1,132 @@
+# Qwen3-Coder Usage Guide
+
+[Qwen3-Coder](https://github.com/QwenLM/Qwen3-Coder) is an advanced large language model created by the Qwen team from Alibaba Cloud. vLLM already supports Qwen3-Coder, and `tool-call` functionality will be available in vLLM v0.10.0 and higher You can install vLLM with `tool-call` support using the following method:
+
+## Installing vLLM
+
+```bash
+uv venv
+source .venv/bin/activate
+uv pip install -U vllm --torch-backend auto
+```
+
+## Launching Qwen3-Coder with vLLM
+
+### Serving on 8xH200 (or H20) GPUs (141GB × 8)
+
+**BF16 Model**
+
+```bash
+vllm serve Qwen/Qwen3-Coder-480B-A35B-Instruct \
+  --tensor-parallel-size 8 \
+  --max-model-len 32000 \
+  --enable-auto-tool-choice \
+  --tool-call-parser qwen3_coder
+```
+
+**FP8 Model**
+
+```bash
+VLLM_USE_DEEP_GEMM=1 vllm serve Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8 \
+  --max-model-len 131072 \
+  --enable-expert-parallel \
+  --data-parallel-size 8 \
+  --enable-auto-tool-choice \
+  --tool-call-parser qwen3_coder
+```
+
+## Performance Metrics
+
+### Evaluation
+We launched `Qwen3-Coder-480B-A35B-Instruct-FP8` using vLLM and evaluated its performance using  [EvalPlus](https://github.com/evalplus/evalplus). The results are displayed below:
+
+| Dataset | Test Type | Pass@1 Score |
+|-----------|-----------|--------------|
+| HumanEval | Base tests | 0.939 |
+| HumanEval+ | Base + extra tests | 0.902 |
+| MBPP | Base tests | 0.918 |
+| MBPP+ | Base + extra tests | 0.794 |
+
+### Benchmarking
+We used the following script to benchmark `Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8`
+
+```bash
+vllm bench serve \
+  --backend vllm \
+  --model Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8 \
+  --endpoint /v1/completions \
+  --dataset-name random \
+  --random-input 2048 \
+  --random-output 1024 \
+  --max-concurrency 10 \
+  --num-prompt 100 \
+```
+If successful, you will see the following output.
+
+```shell
+============ Serving Benchmark Result ============
+Successful requests:                     100
+Benchmark duration (s):                  776.49
+Total input tokens:                      204169
+Total generated tokens:                  102400
+Request throughput (req/s):              0.13
+Output token throughput (tok/s):         131.88
+Total Token throughput (tok/s):          394.81
+---------------Time to First Token----------------
+Mean TTFT (ms):                          7639.31
+Median TTFT (ms):                        6935.71
+P99 TTFT (ms):                           13766.68
+-----Time per Output Token (excl. 1st token)------
+Mean TPOT (ms):                          68.43
+Median TPOT (ms):                        67.23
+P99 TPOT (ms):                           72.14
+---------------Inter-token Latency----------------
+Mean ITL (ms):                           68.43
+Median ITL (ms):                         66.34
+P99 ITL (ms):                            69.38
+==================================================
+
+```
+
+
+## Using Tips
+
+### BF16 Models
+- **Context Length Limitation**: A single H20 node cannot serve the original context length (262144). You can reduce the `max-model-len` or increase `gpu-memory-utilization` to work within memory constraints.
+
+### FP8 Models
+- **Context Length Limitation**: A single H20 node cannot serve the original context length (262144). You can reduce the `max-model-len` or increase `gpu-memory-utilization` to work within memory constraints.
+- **DeepGEMM Usage**: To use [DeepGEMM](https://github.com/deepseek-ai/DeepGEMM), set `VLLM_USE_DEEP_GEMM=1`. Follow the [setup instructions](https://github.com/vllm-project/vllm/blob/main/benchmarks/kernels/deepgemm/README.md#setup) to install it.
+- **Tensor Parallelism Issue**: When using `tensor-parallel-size 8`, the following failures are expected. Switch to data-parallel mode using `--data-parallel-size`.
+- **Additional Resources**: Refer to the [Data Parallel Deployment documentation](https://docs.vllm.ai/en/latest/serving/data_parallel_deployment.html) for more parallelism groups.
+
+```shell
+ERROR [multiproc_executor.py:511]   File "/vllm/vllm/model_executor/models/qwen3_moe.py", line 336, in <lambda>
+ERROR [multiproc_executor.py:511]     lambda prefix: Qwen3MoeDecoderLayer(config=config,
+ERROR [multiproc_executor.py:511]                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ERROR [multiproc_executor.py:511]   File "/vllm/vllm/model_executor/models/qwen3_moe.py", line 278, in __init__
+ERROR [multiproc_executor.py:511]     self.mlp = Qwen3MoeSparseMoeBlock(config=config,
+ERROR [multiproc_executor.py:511]                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ERROR [multiproc_executor.py:511]   File "/vllm/vllm/model_executor/models/qwen3_moe.py", line 113, in __init__
+ERROR [multiproc_executor.py:511]     self.experts = FusedMoE(num_experts=config.num_experts,
+ERROR [multiproc_executor.py:511]                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ERROR [multiproc_executor.py:511]   File "/vllm/vllm/model_executor/layers/fused_moe/layer.py", line 773, in __init__
+ERROR [multiproc_executor.py:511]     self.quant_method.create_weights(layer=self, **moe_quant_params)
+ERROR [multiproc_executor.py:511]   File "/vllm/vllm/model_executor/layers/quantization/fp8.py", line 573, in create_weights
+ERROR [multiproc_executor.py:511]     raise ValueError(
+ERROR [multiproc_executor.py:511] ValueError: The output_size of gate's and up's weight = 320 is not divisible by weight quantization block_n = 128.
+```
+
+### Tool Calling
+- **Enable Tool Calls**: Add `--tool-call-parser qwen3_coder` to enable tool call parsing functionality, please refer to: [tool_calling](https://docs.vllm.ai/en/latest/features/tool_calling.html)
+
+## Roadmap
+
+- [x] Add benchmark results
+
+
+## Additional Resources
+
+- [EvalPlus](https://github.com/evalplus/evalplus)
+- [Qwen3-Coder](https://github.com/QwenLM/Qwen3-Coder)
+- [vLLM Documentation](https://docs.vllm.ai/)