feat(mixtral): initial implementation of Mixtral MoE model, configs, and tests

- Add Mixtral architecture implementation with MoE support (llm/src/llm/models/mixtral/mixtral.py)
- Introduce generic Mixture-of-Experts (MoE) block (llm/src/llm/core/moe.py)
- Create dedicated configuration files for Mixtral training and generation experiments
- Register and test Mixtral support in experiment runner (run_llm_experiment.py)
- Add unit tests for Mixtral API including forward, caching, and generation modes
- Include Jupyter notebook mixstral.ipynb for architectural exploration and research
- Ensure correct handling of torch bool masks in sampling (top-k, top-p) during generation

BREAKING CHANGE: Adds new model code and test coverage, modifying experiment runner logic to register Mixtral.

experiments/llm_only/configs/mixtral_generate.json

@@ -0,0 +1,19 @@
+{
+    "bpe_tokenizer": "checkpoints/bpe_tokenizer.json",
+    "test_prompts": [
+      "Open weights",
+      "The Llama model is",
+      "Efficient transformers"
+    ],
+    "model_config_path": "checkpoints/mixtral-bpe/config.json",
+    "model_weights": "checkpoints/mixtral-bpe/model.pt",
+    "generation": {
+      "max_new_tokens": 40,
+      "temperature": 0.8,
+      "do_sample": true,
+      "top_k": null,
+      "top_p": null
+    },
+    "log_path": "checkpoints/mixtral_only_generation_logs.json"
+  }
+