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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

action_head--10000_checkpoint.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c676c0c2af027446af8b830c6921b429658eb81ed9efaed531200c5632336231
+size 118125406

bitvla_for_action_prediction.py

@@ -0,0 +1,459 @@
+from transformers import LlavaForConditionalGeneration,PretrainedConfig
+from configuration_bit_vla import Bitvla_Config
+import numpy as np
+import torch
+from prismatic.vla.constants import (
+    ACTION_DIM,
+    ACTION_PROPRIO_NORMALIZATION_TYPE,
+    NUM_ACTIONS_CHUNK,
+    NormalizationType,
+)
+from typing import Optional, Dict, Any,List,Tuple
+
+from transformers.models.llava.modeling_llava import LlavaCausalLMOutputWithPast
+
+from prismatic.training.train_utils import (
+    get_current_action_mask,
+    get_next_actions_mask,
+)
+
+
+class BitVLAForActionPrediction(LlavaForConditionalGeneration):
+    config_class: PretrainedConfig = Bitvla_Config
+
+    def __init__(self, config) -> None:
+        super().__init__(config)
+        self.norm_stats = config.norm_stats
+
+        # Compute action bins
+        self.bins = np.linspace(-1, 1, config.n_action_bins)
+        self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
+
+        self.vocab_size = self.config.vocab_size
+
+    def set_constant(self, image_token_idx, proprio_pad_idx, ignore_idx, action_token_begin_idx, stop_index):
+        self.image_token_idx = image_token_idx
+        self.proprio_pad_idx = proprio_pad_idx
+        self.action_token_begin_idx = action_token_begin_idx
+        self.stop_index = stop_index
+        self.ignore_idx = ignore_idx
+    
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_projector_features: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        proprio=None,
+        proprio_projector=None,
+        cache_position: Optional[torch.LongTensor] = None,
+        vision_feature_layer=None,
+        vision_feature_select_strategy=None,
+        ) -> Tuple[int, LlavaCausalLMOutputWithPast]:
+        """Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        output_projector_features = output_projector_features if output_projector_features is not None else False
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
+        use_cache = use_cache and not self.training
+
+        batch_size = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0] # type: ignore
+
+        # === Handle Multimodal Forward ===
+        if (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
+            assert past_key_values is None, "Unexpected key `past_key_values` provided during multimodal forward!"
+
+            # Get input embeddings
+            inputs_embeds = self.get_input_embeddings()(input_ids)  # (B, seq_len, D)
+            
+            # change the vision padding to the real vision tokens
+            if pixel_values is not None:
+                vision_feature_layer = (
+                    vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+                )
+                vision_feature_select_strategy = (
+                    vision_feature_select_strategy
+                    if vision_feature_select_strategy is not None
+                    else self.config.vision_feature_select_strategy
+                )
+                # pixel_values: b,num_images,c,h,w
+                # for each image, we do self.get_image_features
+                # then we concat the features of all images
+                # pixel_values: (b,num_images,c,h,w) --> (b*num_images,c,h,w)
+                b, num_images, c, h, w = pixel_values.shape
+                pixel_values = pixel_values.view(-1, c, h, w)  # (b*num_images,c,h,w)
+                image_embeds = self.get_image_features(
+                    pixel_values = pixel_values,
+                    vision_feature_layer = vision_feature_layer,
+                    vision_feature_select_strategy = vision_feature_select_strategy,
+                )
+                
+                # image_features: (b*num_images,seq_len,patch_size) --> (b*num_images*seq_len,patch_size)
+                image_embeds = image_embeds.view(-1,image_embeds.shape[-1])
+                n_image_tokens = (input_ids == self.image_token_idx).sum().item()
+                n_image_features = image_embeds.shape[0]
+                if n_image_tokens != n_image_features:
+                    raise ValueError(
+                        f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+                    )
+
+                mask = input_ids == self.image_token_idx
+                mask_unsqueezed = mask.unsqueeze(-1)
+                mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
+                image_mask = mask_expanded.to(inputs_embeds.device)
+
+                image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+                inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+                
+                
+            # change the proprio padding to the real proprio tokens
+            if proprio_projector is not None and proprio is not None:
+                # proprio: (bsz, proprio_dim) or (propro_dim,)
+                proprio = proprio.reshape(batch_size, -1)  # (bsz, proprio_dim)
+                proprio_features = proprio_projector(proprio)  # (bsz, llm_dim)
+                proprio_features = proprio_features.unsqueeze(dim=1)  # (bsz, 1, llm_dim)
+                #（bsz, 1, llm_dim) --> (bsz*1, llm_dim)
+                proprio_features = proprio_features.view(-1, proprio_features.shape[-1])
+                n_proprio_tokens = (input_ids == self.proprio_pad_idx).sum().item()
+                n_proprio_features = proprio_features.shape[0]
+                if n_proprio_tokens != n_proprio_features:
+                    raise ValueError(
+                        f"Proprio features and proprio tokens do not match: tokens: {n_proprio_tokens}, features {n_proprio_features}"
+                    )
+                
+                mask = input_ids == self.proprio_pad_idx
+                mask_unsqueezed = mask.unsqueeze(-1)
+                mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
+                proprio_mask = mask_expanded.to(inputs_embeds.device)
+                
+                proprio_features = proprio_features.to(inputs_embeds.device, inputs_embeds.dtype)
+                inputs_embeds = inputs_embeds.masked_scatter(proprio_mask, proprio_features)
+                
+            
+            # Extract action masks
+            # Action tokens are those in labels that are not ignore, not newline, and not end-of-sequence tokens
+            all_actions_mask = (labels != self.ignore_idx) & (labels != self.stop_index)
+          
+            # Replace the embeddings of the action tokens with zeros
+            # (Later on, the positional embeddings will be added to them)
+            all_actions_mask = all_actions_mask.unsqueeze(-1)  # (B, seq_len, 1)
+            inputs_embeds = inputs_embeds * ~all_actions_mask 
+            outputs = LlavaForConditionalGeneration.forward(
+                self,
+                input_ids = None,
+                attention_mask=attention_mask,
+                position_ids=None,
+                pixel_values=None,
+                labels=labels,
+                inputs_embeds=inputs_embeds,
+                past_key_values=None,
+                use_cache=None,
+                output_attentions=False,
+                output_hidden_states=True,
+                return_dict=True,
+            )
+        # === Otherwise =>> Assume Invalid! ===
+        elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
+            raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
+
+        else:
+            raise ValueError(
+                "Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
+                f"=> `input_ids` = {input_ids is not None}\n"
+                f"=> `attention_mask` = {attention_mask is not None}\n"
+                f"=> `pixel_values` = {pixel_values is not None}\n"
+                f"=> `labels` = {labels is not None}\n"
+                f"=> `input_embeds` = {inputs_embeds is not None}\n"
+                f"=> `past_key_values` = {past_key_values is not None}\n"
+                f"=> `use_cache` = {use_cache}"
+            )
+
+        return outputs
+
+    def _prepare_input_for_action_prediction(self, input_ids, attention_mask):
+        """Prepares input for action prediction by adding necessary tokens"""
+        # Add (ACTION_DIM * NUM_ACTIONS_CHUNK) placeholder tokens to input_ids to simulate action tokens
+        placeholder_action_token_ids = (
+            torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
+        )
+        input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
+
+        # Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
+        stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * self.stop_index
+        input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
+
+        # Extend the attention mask to fit the new shape of input
+        # Note: Only batch size == 1 supported right now
+        mask_extension = (
+            torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
+            .to(attention_mask.device)
+            .to(attention_mask.dtype)
+        )
+        attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
+
+        return input_ids, attention_mask
+
+    def _prepare_labels_for_action_prediction(self, labels, input_ids):
+        """Creates labels tensor for action prediction if not provided"""
+        # Extend labels tensor with fake action labels
+        ARBITRARY_ACTION_TOKEN_IDX = self.action_token_begin_idx + 1
+        labels_extension = (
+            torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
+            * ARBITRARY_ACTION_TOKEN_IDX
+        )
+        labels = torch.cat([labels, labels_extension], dim=-1)
+
+        # Replace last label token with stop token
+        labels[:, -1] = self.stop_index
+
+        return labels
+
+    def _process_action_masks(self, labels):
+        """Helper to get action masks from labels"""
+        current_action_mask = get_current_action_mask(labels,ignore_index=self.ignore_idx,action_token_begin_idx=self.action_token_begin_idx)
+        next_actions_mask = get_next_actions_mask(labels,ignore_index=self.ignore_idx,action_token_begin_idx=self.action_token_begin_idx)
+        all_actions_mask = current_action_mask | next_actions_mask  # (B, seq_len)
+        return all_actions_mask
+        
+    def _unnormalize_actions(self, normalized_actions, unnorm_key=None):
+        """Unnormalize actions using dataset statistics"""
+        action_norm_stats = self.get_action_stats(unnorm_key)
+
+        if ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS:
+            mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["min"], dtype=bool))
+            action_high, action_low = np.array(action_norm_stats["max"]), np.array(action_norm_stats["min"])
+        elif ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS_Q99:
+            mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
+            action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
+        else:
+            raise ValueError("Unsupported action/proprio normalization type detected!")
+
+        actions = np.where(
+            mask,
+            0.5 * (normalized_actions + 1) * (action_high - action_low + 1e-8) + action_low,
+            normalized_actions,
+        )
+
+        return actions
+    
+    def _regression_or_discrete_prediction(
+        self,
+        input_ids,
+        input_embeddings,
+        all_actions_mask,
+        attention_mask,
+        labels,
+        action_head=None,
+        pixel_values = None,
+    ):
+        """Run L1 regression-based continuous action prediction or discrete action tokens prediction."""
+        # Zero out action token embeddings
+        all_actions_mask = all_actions_mask.unsqueeze(-1)  # (B, seq_len, 1)
+        input_embeddings = input_embeddings * ~all_actions_mask
+
+        llava_output = LlavaForConditionalGeneration.forward(
+                self,
+                input_ids = None,
+                attention_mask=attention_mask,
+                position_ids=None,
+                pixel_values=None,
+                labels=None,
+                inputs_embeds=input_embeddings,
+                past_key_values=None,
+                use_cache=None,
+                output_attentions=False,
+                output_hidden_states=True,
+                return_dict=True,
+            )
+        all_actions_mask = self._process_action_masks(labels[:,1:])
+        # Extract hidden states for action tokens
+        last_hidden_states = llava_output.hidden_states[-1]  # (B, seq_len, D)
+        last_hidden_states = last_hidden_states[:, : -1, :]  # (B, act_chunk_len, D)
+        # Use the action mask to extract the hidden states of the actions
+        actions_hidden_states = last_hidden_states[all_actions_mask.squeeze(-1)].unsqueeze(0) # (B, act_chunk_len, D)
+
+        # Handle different prediction methods
+        if action_head is not None:
+            # L1 regression prediction
+            normalized_actions = action_head.predict_action(actions_hidden_states)
+            normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
+            normalized_actions = normalized_actions.float().cpu().detach().numpy()
+        else:
+            # Discrete token-based prediction
+            predicted_action_token_ids = (
+                llava_output.logits[all_actions_mask.squeeze(-1)].unsqueeze(0)
+                .argmax(dim=2)
+                .cpu()
+                .numpy()
+            )
+            # FIXME: We do not support discrete action prediction right now
+            # It seems that vocab_size here is not correct. This should be the dimension of the logit layer, which is actually larger than the vocab_size in the tokenizer. What we actually need here is the vocab_size from the tokenizer.
+            discretized_actions = self.vocab_size - predicted_action_token_ids
+            discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
+            normalized_actions = self.bin_centers[discretized_actions]
+            normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
+
+        return normalized_actions, actions_hidden_states
+    
+    def predict_action(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        unnorm_key: Optional[str] = None,
+        proprio=None,
+        proprio_projector=None,
+        action_head=None,
+        vision_feature_layer=None,
+        vision_feature_select_strategy=None,
+        **kwargs: str,
+    ) -> np.ndarray:
+        """Predict actions from input sequence, with options for different prediction methods.
+
+        Args:
+            input_ids: Input token ids
+            unnorm_key: Key for unnormalization statistics
+            proprio: Proprioceptive features
+            proprio_projector: Projector for proprioceptive features
+            action_head: Optional head for L1 regression prediction
+            **kwargs: Additional arguments including pixel_values and attention_mask
+
+        Returns:
+            Tuple of (unnormalized_actions, action_hidden_states)
+        """
+        pixel_values = kwargs["pixel_values"]
+        attention_mask = kwargs["attention_mask"]
+
+        # Create fake labels tensor (needed for action mask)
+        labels = input_ids.clone()
+        labels[:] = self.ignore_idx
+
+        # Prepare inputs by adding necessary tokens
+        input_ids, attention_mask = self._prepare_input_for_action_prediction(input_ids, attention_mask)
+
+        # Update labels tensor for action mask computation later
+        labels = self._prepare_labels_for_action_prediction(labels, input_ids)
+
+        # Get input embeddings and action masks
+        input_embeddings = self.get_input_embeddings()(input_ids)
+        all_actions_mask = self._process_action_masks(labels)
+        
+        # vision tokens
+        if pixel_values is not None:
+            vision_feature_layer = (
+                vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+            )
+            vision_feature_select_strategy = (
+                vision_feature_select_strategy
+                if vision_feature_select_strategy is not None
+                else self.config.vision_feature_select_strategy
+            )
+            # pixel_values: b,num_images,c,h,w
+            # for each image, we do self.get_image_features
+            # then we concat the features of all images
+            # pixel_values: (b,num_images,c,h,w) --> (b*num_images,c,h,w)
+            b, num_images, c, h, w = pixel_values.shape
+            pixel_values = pixel_values.view(-1, c, h, w)  # (b*num_images,c,h,w)
+            image_embeds = self.get_image_features(
+                pixel_values = pixel_values,
+                vision_feature_layer = vision_feature_layer,
+                vision_feature_select_strategy = vision_feature_select_strategy,
+            )
+            
+            # image_features: (b*num_images,seq_len,patch_size) --> (b*num_images*seq_len,patch_size)
+            image_embeds = image_embeds.view(-1,image_embeds.shape[-1])
+            n_image_tokens = (input_ids == self.image_token_idx).sum().item()
+            n_image_features = image_embeds.shape[0]
+            if n_image_tokens != n_image_features:
+                raise ValueError(
+                    f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+                )
+
+            mask = input_ids == self.image_token_idx
+            mask_unsqueezed = mask.unsqueeze(-1)
+            mask_expanded = mask_unsqueezed.expand_as(input_embeddings)
+            image_mask = mask_expanded.to(input_embeddings.device)
+
+            image_embeds = image_embeds.to(input_embeddings.device, input_embeddings.dtype)
+            input_embeddings = input_embeddings.masked_scatter(image_mask, image_embeds)
+
+        # Add proprioceptive features if provided
+        use_proprio = proprio_projector is not None and proprio is not None
+        if use_proprio:
+            batch_size = input_ids.shape[0] if input_ids is not None else input_embeddings.shape[0] # type: ignore
+            proprio = torch.Tensor(proprio).to(input_embeddings.device, dtype=input_embeddings.dtype)
+            if proprio_projector is not None and proprio is not None:
+                # proprio: (bsz, proprio_dim) or (propro_dim,)
+                proprio = proprio.reshape(batch_size, -1)  # (bsz, proprio_dim)
+                proprio_features = proprio_projector(proprio)  # (bsz, llm_dim)
+                proprio_features = proprio_features.unsqueeze(dim=1)  # (bsz, 1, llm_dim)
+                #（bsz, 1, llm_dim) --> (bsz*1, llm_dim)
+                proprio_features = proprio_features.view(-1, proprio_features.shape[-1])
+                n_proprio_tokens = (input_ids == self.proprio_pad_idx).sum().item()
+                n_proprio_features = proprio_features.shape[0]
+                if n_proprio_tokens != n_proprio_features:
+                    raise ValueError(
+                        f"Proprio features and proprio tokens do not match: tokens: {n_proprio_tokens}, features {n_proprio_features}"
+                    )
+                
+                mask = input_ids == self.proprio_pad_idx
+                mask_unsqueezed = mask.unsqueeze(-1)
+                mask_expanded = mask_unsqueezed.expand_as(input_embeddings)
+                proprio_mask = mask_expanded.to(input_embeddings.device)
+                
+                proprio_features = proprio_features.to(input_embeddings.device, input_embeddings.dtype)
+                input_embeddings = input_embeddings.masked_scatter(proprio_mask, proprio_features)
+
+        # Run regression or discrete token-based prediction
+        normalized_actions, actions_hidden_states = self._regression_or_discrete_prediction(
+            input_ids,
+            input_embeddings,
+            all_actions_mask,
+            attention_mask,
+            labels,
+            action_head,
+            pixel_values,
+        )
+
+        # Unnormalize predicted actions
+        actions = self._unnormalize_actions(normalized_actions, unnorm_key)
+
+        return actions, actions_hidden_states
+
+    @staticmethod
+    def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
+        """Validate and resolve the unnormalization key for action statistics"""
+        if unnorm_key is None:
+            assert len(norm_stats) == 1, (
+                f"Your model was trained on more than one dataset, "
+                f"please pass a `unnorm_key` from the following options to choose the statistics "
+                f"used for un-normalizing actions: {norm_stats.keys()}"
+            )
+            unnorm_key = next(iter(norm_stats.keys()))
+
+        assert unnorm_key in norm_stats, (
+            f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
+            f"please choose from: {norm_stats.keys()}"
+        )
+        return unnorm_key
+
+    def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
+        """Get the dimensionality of the policy's action space."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+        return len(self.norm_stats[unnorm_key]["action"]["min"])
+
+    def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
+        """Get all the logged statistics for the given dataset."""
+        unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
+        return self.norm_stats[unnorm_key]["action"]
+        

config.json

@@ -0,0 +1,70 @@
+{
+  "architectures": [
+    "Llava_OpenVLAForActionPrediction"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_bit_vla.Bitvla_Config",
+    "AutoModelForVision2Seq": "bitvla_for_action_prediction.BitVLAForActionPrediction"
+  },
+  "image_seq_length": 256,
+  "image_token_index": 128260,
+  "model_type": "openvla",
+  "multimodal_projector_bias": true,
+  "n_action_bins": 256,
+  "norm_stats": null,
+  "projector_hidden_act": "gelu",
+  "text_config": {
+    "_attn_implementation_autoset": true,
+    "_name_or_path": "hongyu/bitvla_spatial/",
+    "architectures": [
+      "BitNetForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "auto_map": {
+      "AutoConfig": "configuration_bitnet.BitNetConfig",
+      "AutoModelForCausalLM": "modeling_bitnet.BitNetForCausalLM"
+    },
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 6912,
+    "max_position_embeddings": 4096,
+    "max_window_layers": 28,
+    "model_path": "hongyu/bitvla_spatial/",
+    "model_type": "BitNet",
+    "num_attention_heads": 20,
+    "num_hidden_layers": 30,
+    "num_key_value_heads": 5,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 500000.0,
+    "sliding_window": 4096,
+    "torch_dtype": "bfloat16",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 128264
+  },
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0.dev0",
+  "vision_config": {
+    "_attn_implementation_autoset": true,
+    "attention_dropout": 0.0,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "image_size": 224,
+    "intermediate_size": 4304,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 26,
+    "patch_size": 14,
+    "torch_dtype": "bfloat16",
+    "vision_use_head": false,
+    "vit_act_bits": 8,
+    "vit_weight_bits": 1
+  },
+  "vision_feature_layer": -1,
+  "vision_feature_select_strategy": "full",
+  "vocab_size": 128264
+}

configuration_bit_vla.py

@@ -0,0 +1,14 @@
+from transformers import LlavaConfig
+from typing import Dict, List, Optional
+
+class Bitvla_Config(LlavaConfig):
+    model_type: str = "bitvla"
+
+    def __init__(
+        self,
+        norm_stats: Optional[Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]]] = None,
+        n_action_bins: int = 256,
+        **kwargs: str,
+    ) -> None:
+        self.norm_stats, self.n_action_bins = norm_stats, n_action_bins
+        super().__init__(**kwargs)

dataset_statistics.json

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+{
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+        true,
+        true,
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+        true,
+        true,
+        false
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+    },
+    "num_transitions": 52970,
+    "num_trajectories": 432
+  }
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "transformers_version": "4.51.0.dev0"
+}

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+    "<action_210>",
+    "<action_211>",
+    "<action_212>",
+    "<action_213>",
+    "<action_214>",
+    "<action_215>",
+    "<action_216>",
+    "<action_217>",
+    "<action_218>",
+    "<action_219>",
+    "<action_220>",
+    "<action_221>",
+    "<action_222>",
+    "<action_223>",
+    "<action_224>",
+    "<action_225>",
+    "<action_226>",
+    "<action_227>",
+    "<action_228>",
+    "<action_229>",
+    "<action_230>",
+    "<action_231>",
+    "<action_232>",
+    "<action_233>",
+    "<action_234>",
+    "<action_235>",
+    "<action_236>",
+    "<action_237>",
+    "<action_238>",
+    "<action_239>",
+    "<action_240>",
+    "<action_241>",
+    "<action_242>",
+    "<action_243>",
+    "<action_244>",
+    "<action_245>",
+    "<action_246>",
+    "<action_247>",
+    "<action_248>",
+    "<action_249>",
+    "<action_250>",
+    "<action_251>"
+  ],
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "System: A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.<|eot_id|>{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = message['role'] | capitalize + ': '+ message['content'] | trim + '<|eot_id|>' %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant: ' }}{% endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|pad|>",
+  "processor_class": "LlavaProcessor",
+  "tokenizer_class": "PreTrainedTokenizer"
+}