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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "VGSModel"
+  ],
+  "attention_dropout": 0.05,
+  "auto_map": {
+    "AutoConfig": "configuration_vgs.VGSConfig",
+    "AutoModel": "modeling_vgs.VGSModel"
+  },
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_position_embeddings": 131072,
+  "max_window_layers": 21,
+  "model_type": "vgs",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.1",
+  "use_bias": false,
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration_vgs.py

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+from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class VGSConfig(Qwen2Config):
+    model_type = 'vgs'
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=1536,
+        intermediate_size=8960,
+        num_hidden_layers=28,
+        num_attention_heads=12,
+        num_key_value_heads=2,
+        hidden_act="silu",
+        max_position_embeddings=131072,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=False,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=21,
+        attention_dropout=0.05,
+        num_labels=3,
+        use_bias=False,
+        **kwargs,
+    ):
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window  # we check `use_sliding_window` in the modeling code
+        self.max_window_layers = max_window_layers
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_dropout = attention_dropout
+        self.num_labels = num_labels
+        self.use_bias = use_bias
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self)

model-00001-of-00002.safetensors

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model.safetensors.index.json

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+}

modeling_vgs.py

@@ -0,0 +1,142 @@
+from configuration_vgs import VGSConfig
+from transformers import Qwen2PreTrainedModel, Qwen2Model
+from transformers.modeling_outputs import SequenceClassifierOutputWithPast
+from transformers.cache_utils import Cache
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import List, Optional, Tuple, Union
+from dataclasses import dataclass
+
+
+@dataclass
+class CustomSequenceClassifierOutputWithPast(SequenceClassifierOutputWithPast):
+    # Prob of reward being 1
+    success_probs: Optional[torch.FloatTensor] = None
+
+
+class VGSModel(Qwen2PreTrainedModel):
+    config_class = VGSConfig
+    def __init__(self, config):
+        super().__init__(config)
+        num_labels = config.num_labels
+        self.model = Qwen2Model(config)
+        self.score = nn.Sequential(
+            nn.Linear(config.hidden_size, config.hidden_size, bias=config.use_bias),
+            nn.ReLU(),
+            nn.Linear(config.hidden_size, num_labels, bias=config.use_bias),
+        )
+        self.p_dropout = config.attention_dropout
+        self.score_dropout = nn.Dropout(self.p_dropout)
+        self.inference_impl = "naive"
+        self.train_bt_model = False
+        self.num_labels = num_labels
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        loss_mask: Optional[torch.Tensor] = None,
+        continuation_ids: Optional[torch.LongTensor] = None,
+        continuation_attention_mask: Optional[torch.Tensor] = None,
+    ) -> Union[Tuple, CustomSequenceClassifierOutputWithPast]:
+        """
+        During training:
+        - labels should not be None and have shape: [bs, 1]
+        - input_ids: [bs, seqlen]
+        - loss_mask [bs, seqlen]
+
+        During inference:
+        labels, loss_mask should be None
+        continuation_ids is [bs, N, c_len].
+        If input_ids is [bs, seqlen], this is prefill stage.
+        Otherwise, input_ids is also [bs, c_len] which contains the chosen continuation from last step. And we update the kv_cache.
+        Here, attention_mask should be [bs, q_len] where q_len is seqlen + len of continuations so far.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        assert return_dict, "Only return_dict=True is supported."
+        is_training = labels is not None
+        is_single_eval = continuation_ids is None
+        if not is_training: assert not self.training, "Model should not be in training mode during inference."
+
+        if is_training:
+            transformer_outputs = self.model(
+                input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                inputs_embeds=inputs_embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            hidden_states = transformer_outputs[0]  # [bs, seqlen, hidden_dim]
+            logits = self.score(self.score_dropout(hidden_states)).float()  # [bs, seqlen, num_labels]
+            bs, seqlen, _ = logits.shape
+            if self.train_bt_model:
+                assert self.num_labels == 1, f"BT model should have 1 label. Got {self.num_labels}."
+                assert bs % 2 == 0, f"Batch size should be even for BT model. Got {bs}."
+                logits = logits[:, -1, 0]  # [bs, seqlen, 1] -> [bs]
+                # bt loss
+                assert torch.all(labels[::2] == 1), f"Labels should be 1 for chosen logits. Got {labels[::2]}."
+                assert torch.all(labels[1::2] == 0), f"Labels should be 0 for rejected logits. Got {labels[1::2]}."
+                chosen_logits = logits[::2]  # [bs//2]
+                reject_logits = logits[1::2]  # [bs//2]
+                elemwise_loss = -F.logsigmoid(chosen_logits - reject_logits)  # [bs//2]
+                loss = elemwise_loss.mean()
+            else:
+                if self.num_labels == 1:
+                    # BCE Loss
+                    labels_expanded = labels.unsqueeze(-1).expand_as(logits)
+                    elemwise_loss = F.binary_cross_entropy_with_logits(logits, labels_expanded, reduction="none")  # [bs, seqlen]
+                else:
+                    # CrossEntropyLoss
+                    labels_expanded = labels.long().unsqueeze(-1).expand((bs, seqlen))  # [bs, seqlen]
+                    elemwise_loss = F.cross_entropy(
+                        logits.transpose(1, 2),  # [bs, seqlen, num_labels] -> [bs, num_labels, seqlen]
+                        labels_expanded,  # [bs, seqlen]
+                        reduction="none",
+                    )
+                # avg over seqlen and bs. do so in a way that prevents nans from division by zero
+                mask_sum = loss_mask.sum(1).float()
+                safe_denom = torch.where(mask_sum > 0, mask_sum, torch.ones_like(mask_sum))
+                loss = torch.where(mask_sum > 0, (elemwise_loss * loss_mask).sum(1) / safe_denom, mask_sum)  # [bs]
+                loss = loss.mean()
+
+            return CustomSequenceClassifierOutputWithPast(loss=loss, logits=logits)
+
+        elif is_single_eval:
+            # single eval is also useful for updating kv_cache
+            assert continuation_ids is None
+            transformer_outputs = self.model(
+                input_ids,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+            hidden_states = transformer_outputs[0]  # [bs, seqlen, hidden_dim]
+            logits = self.score(hidden_states).float()  # [bs, seqlen, num_labels]
+            if logits.shape[-1] > 1:
+                # assume 1 is the index/label for success
+                success_probs = F.softmax(logits, dim=-1)[:, :, 1]  # [bs, seqlen]
+            else:
+                assert logits.shape[-1] == 1, f"Expected logits to have 1 output, got {logits.shape}."
+                success_probs = logits.squeeze(-1).sigmoid()  # [bs, seqlen]
+
+            return CustomSequenceClassifierOutputWithPast(
+                logits=logits, success_probs=success_probs, past_key_values=transformer_outputs.past_key_values)