qlora-fsdp ram efficient loading with hf trainer (#1791)

* fix 405b with lower cpu ram requirements

* make sure to use doouble quant and only skip output embeddings

* set model attributes

* more fixes for sharded fsdp loading

* update the base model in example to use pre-quantized nf4-bf16 weights

* upstream fixes  for qlora+fsdp

src/axolotl/utils/models.py

@@ -624,14 +624,21 @@ def load_model(
         elif (
             qlora_fsdp
             and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
-            and cfg.model_config_type == "dbrx"
+            and (cfg.model_config_type == "dbrx" or cfg.qlora_sharded_model_loading)
         ):
             quant_storage = cfg.torch_dtype
+            quantization_config = hasattr(
+                model_config, "quantization_config"
+            ) and getattr(model_config, "quantization_config")
+            quantization_config = (
+                quantization_config or model_kwargs["quantization_config"]
+            )
             model = load_sharded_model_quant(
                 base_model,
                 model_config,
                 cfg,
                 quant_storage=quant_storage,
+                quantization_config=quantization_config,
             )
             skip_move_to_device = True
         elif (