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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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+{
+  "_name_or_path": "./models/m1",
+  "architectures": [
+    "AutoModelForCausalLMWithRM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "hf_utils.RewardModelConfig",
+    "AutoModel": "hf_utils.AutoModelForCausalLMWithRM"
+  },
+  "base_config": {
+    "_name_or_path": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+    "architectures": [
+      "LlamaForCausalLM"
+    ],
+    "bos_token_id": 128000,
+    "eos_token_id": [
+      128001,
+      128008,
+      128009
+    ],
+    "hidden_size": 8192,
+    "intermediate_size": 28672,
+    "max_position_embeddings": 131072,
+    "model_type": "llama",
+    "num_attention_heads": 64,
+    "num_hidden_layers": 80,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": {
+      "factor": 8.0,
+      "high_freq_factor": 4.0,
+      "low_freq_factor": 1.0,
+      "original_max_position_embeddings": 8192,
+      "rope_type": "llama3"
+    },
+    "rope_theta": 500000.0,
+    "torch_dtype": "bfloat16",
+    "use_cache": false,
+    "vocab_size": 128257
+  },
+  "base_model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+  "bias": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 28672,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "pairwise_rm",
+  "n_labels": 1,
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "p_dropout": 0.0,
+  "pretrain_cfg": {
+    "attn_implementation": "flash_attention_2",
+    "load_in_8bit": false,
+    "token": true,
+    "trust_remote_code": true
+  },
+  "pretrained": false,
+  "pretraining_tp": 1,
+  "return_logits": false,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.1",
+  "use_cache": false,
+  "vocab_size": 128257
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.46.1"
+}

hf_utils.py

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+# Copyright 2024 MosaicML ComposeRL authors
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+from copy import deepcopy
+from dataclasses import dataclass
+from typing import (
+    Any,
+    Optional,
+    Union,
+)
+
+import numpy as np
+import torch
+import torch.nn as nn
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    PretrainedConfig,
+    PreTrainedModel,
+)
+from transformers.modeling_outputs import ModelOutput
+
+
+@dataclass
+class SequenceClassifierOutput(ModelOutput):
+    """Sequence Classification Output.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        scores (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    scores: Optional[torch.FloatTensor] = None
+    logits: Optional[torch.FloatTensor] = None
+    past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[tuple[torch.FloatTensor, ...]] = None
+
+
+class ValueHead(nn.Module):
+    """Value head for the transformer which outputs n_labels values."""
+
+    def __init__(self, n_labels: int, hidden_size: int, p_dropout: float = 0.0):
+        super().__init__()
+        self.dense = nn.Linear(hidden_size, hidden_size)
+        self.dropout = nn.Dropout(p_dropout)
+        self.score = nn.Linear(hidden_size, n_labels)
+        torch.nn.init.normal_(
+            self.score.weight,
+            std=1 / np.sqrt(hidden_size + 1),
+        )
+        torch.nn.init.constant_(self.score.bias, val=0.0)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        **kwargs: Any,
+    ) -> torch.Tensor:
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.dense(hidden_states)
+        hidden_states = torch.tanh(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        output = self.score(hidden_states)
+        return output
+
+
+class RewardModelConfig(PretrainedConfig):
+    model_type = 'pairwise_rm'
+
+    def __init__(
+        self,
+        base_model: Optional[Union[str, os.PathLike]
+                            ] = 'meta-llama/Meta-Llama-3-70B-Instruct',
+        base_config: Optional[PretrainedConfig] = None,
+        p_dropout: float = 0.0,
+        n_labels: int = 1,
+        bias: float = 0.0,
+        return_logits: bool = False,
+        pretrain_cfg: Optional[dict[str, Any]] = None,
+        pretrained: bool = False,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+        self.base_model = base_model
+        self.base_config = base_config if base_config is not None else AutoConfig.from_pretrained(
+            base_model,
+        )
+        temp_config = deepcopy(self.base_config)
+        if not isinstance(temp_config, dict):
+            temp_config = temp_config.__dict__
+        for key, value in temp_config.items():
+            if key not in ['_name_or_path', 'architectures']:
+                setattr(self, key, value)
+        self.p_dropout = p_dropout
+        self.n_labels = n_labels
+        self.bias = bias
+        self.return_logits = return_logits
+        self.pretrain_cfg = pretrain_cfg if pretrain_cfg is not None else {}
+        self.pretrained = pretrained
+
+
+class AutoModelForCausalLMWithRM(PreTrainedModel):
+    config_class = RewardModelConfig
+    _supports_flash_attn_2 = True
+
+    def __init__(self, config: PretrainedConfig, *args: Any, **kwargs: Any):
+        super().__init__(config)
+        self.config = config
+        pretrain_cfg = config.pretrain_cfg
+        pretrained = config.pretrained
+        if pretrained:
+            self.lm_backbone = AutoModelForCausalLM.from_pretrained(
+                config.base_model,
+                config=config.base_config,
+                **pretrain_cfg,
+            )
+        else:
+            # When downloading from hub, base config gets converted to dict
+            # Redownload to make type PretrainedConfig
+            if isinstance(config.base_config, dict):
+                config.base_config = AutoConfig.from_pretrained(
+                    config.base_model,
+                    **config.base_config,
+                )
+            self.lm_backbone = AutoModelForCausalLM.from_config(
+                config.base_config,
+                **kwargs,
+            )
+        self.value_head = ValueHead(
+            n_labels=self.config.n_labels,
+            hidden_size=self.config.hidden_size,
+            p_dropout=self.config.p_dropout,
+        )
+
+    def generate(self, *args: Any, **kwargs: Any):
+        return self.lm_backbone.generate(**kwargs)
+
+    def resize_token_embeddings(
+        self,
+        new_num_tokens: Optional[int] = None,
+        pad_to_multiple_of: Optional[int] = None,
+    ) -> nn.Embedding:
+        # Note need to update vocab size in base config as well so lm_head modification happens
+        self.config.base_config.vocab_size = new_num_tokens
+        model_embeds = super().resize_token_embeddings(
+            new_num_tokens=new_num_tokens,
+            pad_to_multiple_of=pad_to_multiple_of,
+        )
+        return model_embeds
+
+    def set_input_embeddings(self, new_embeddings: Any):
+        return self.lm_backbone.set_input_embeddings(new_embeddings)
+
+    def get_input_embeddings(self):
+        return self.lm_backbone.get_input_embeddings()
+
+    def set_output_embeddings(self, new_embeddings: Any):
+        return self.lm_backbone.set_output_embeddings(new_embeddings)
+
+    def get_output_embeddings(self):
+        return self.lm_backbone.get_output_embeddings()
+
+    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[Any] = 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,
+        cache_position: Optional[torch.LongTensor] = None,
+        **kwargs: Any,
+    ):
+        output = self.lm_backbone(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            return_dict=True,
+            cache_position=cache_position,
+        )
+        scores = self.value_head(
+            output.hidden_states[-1],
+        ).squeeze(-1) - self.config.bias
+
+        logits = None
+        if self.config.return_logits:
+            logits = output.logits
+
+        return SequenceClassifierOutput(
+            loss=output.loss,
+            scores=scores,
+            logits=logits,
+            past_key_values=output.past_key_values,
+            hidden_states=output.hidden_states,
+            attentions=output.attentions,
+        )
+
+    @classmethod
+    def from_config(
+        cls,
+        config: PretrainedConfig,
+        **kwargs: Any,
+    ) -> PreTrainedModel:
+        config.pretrained = False
+        model = cls(config, **kwargs)
+        return model
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args: Any,
+        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
+        cache_dir: Optional[Union[str, os.PathLike]] = None,
+        ignore_mismatched_sizes: bool = False,
+        force_download: bool = False,
+        local_files_only: bool = False,
+        token: Optional[Union[str, bool]] = True,
+        revision: str = 'main',
+        use_safetensors: Optional[bool] = None,
+        **kwargs: Any,
+    ) -> PreTrainedModel:
+        trust_remote_code = kwargs.pop('trust_remote_code', None)
+        attn_implementation = kwargs.pop(
+            'attn_implementation',
+            'eager',
+        )
+        return_lm_logits = kwargs.pop('return_lm_logits', False)
+        load_in_8bit = kwargs.pop('load_in_8bit', False)
+
+        pretrained_model_config = AutoConfig.from_pretrained(
+            pretrained_model_name_or_path,
+            trust_remote_code=trust_remote_code,
+            token=token,
+            attn_implementation=attn_implementation,
+            use_cache=False,
+        )
+
+        if isinstance(pretrained_model_config, cls.config_class):
+            return super().from_pretrained(
+                pretrained_model_name_or_path,
+                *model_args,
+                config,
+                cache_dir,
+                ignore_mismatched_sizes,
+                force_download,
+                local_files_only,
+                token,
+                revision,
+                use_safetensors,
+                **kwargs,
+            )
+
+        pretrain_cfg = {
+            'trust_remote_code': trust_remote_code,
+            'token': token,
+            'load_in_8bit': load_in_8bit,
+            'attn_implementation': attn_implementation,
+        }
+
+        reward_model_config = RewardModelConfig(
+            base_model=pretrained_model_name_or_path,
+            base_config=pretrained_model_config,
+            hidden_size=pretrained_model_config.hidden_size,
+            return_logits=return_lm_logits,
+            vocab_size=pretrained_model_config.vocab_size,
+            pretrained=True,
+            pretrain_cfg=pretrain_cfg,
+        )
+
+        model = cls(reward_model_config)
+
+        return model

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