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README.md

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+---
+base_model: microsoft/Phi-3-small-8k-instruct
+library_name: peft
+---
+
+# 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. -->
+
+
+
+- **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]
+### Framework versions
+
+- PEFT 0.13.2

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "microsoft/Phi-3-small-8k-instruct",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 256,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "down_proj",
+    "up_proj",
+    "dense",
+    "query_key_value"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:59d8ca3d53f8d19df89972f5606ad518e14d615df85c789a6f5669a5e7938da4
+size 1140886056

special_tokens_map.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>"
+}

tokenization_phi3_small.py

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+# Adapted from https://huggingface.co/Qwen/Qwen-7B-Chat/blob/main/tokenization_qwen.py
+import os
+from typing import Collection, List, Optional, Dict, Set, Tuple, Union
+
+from functools import cached_property
+
+import base64
+import requests
+
+from transformers import PreTrainedTokenizer, AddedToken, AutoConfig
+from transformers.models.auto.tokenization_auto import get_tokenizer_config
+import tiktoken
+
+
+"""
+    This tokenizer is almost identical to tiktoken.get_encoding("cl100k_base")
+    with a few additional special tokens to support the ChatML format.
+
+    TODO(bapatra): Right now, I do not save the special tokens to the vocab file.
+    Maybe in the future, that would be useful? Can add that support later.
+
+"""
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+    with open(tiktoken_bpe_file, "rb") as f:
+        contents = f.read()
+    return {
+        base64.b64decode(token): int(rank)
+        for token, rank in (line.split() for line in contents.splitlines() if line)
+    }
+
+# On the megatron codebase, we pad vocabularies to ensure matrix multiplication is fast.
+# this in turn causes some indices to be empty. We account for these empty indices by adding
+# dummy tokens to the tokenizer.
+
+EFFECTIVE_PADDED_VOCAB_SIZE = 100352
+ACTUAL_VOCAB_SIZE = 100276
+
+
+DUMMY_TOKENS = {
+    f"<|dummy_id_{11 + offset}|>": 100276 + offset
+    for offset in range(1, EFFECTIVE_PADDED_VOCAB_SIZE - ACTUAL_VOCAB_SIZE)
+}
+
+SPECIAL_TOKENS = {
+    # tiktoken.get_encoding("cl100k_base")._special_tokens
+    '<|endoftext|>': 100257,
+    '<|fim_prefix|>': 100258,
+    '<|fim_middle|>': 100259,
+    '<|fim_suffix|>': 100260,
+    # Special tokens for post-training
+    "<|system|>": 100261, 
+    "<|user|>": 100262,
+    "<|assistant|>": 100263,
+    # Dummy unused tokens
+    "<|dummy_id_0|>": 100264,
+    "<|dummy_id_1|>": 100265,
+    # Special tokens for post-training continued
+    "<|end|>": 100266,
+    # Some dummy tokens, so that tokenization is contiguous and does not cause issues
+    # Note that the 100256th token of tiktoken.get_encoding("cl100k_base") does not
+    # actually map to anything. So we use a dummy token here.
+    "<|dummy_id_2|>": 100256,
+    # Likewise, tokens from 100267 to 100275 are also unused
+    "<|dummy_id_3|>": 100267,
+    "<|dummy_id_4|>": 100268,
+    "<|dummy_id_5|>": 100269,
+    "<|dummy_id_6|>": 100270,
+    "<|dummy_id_7|>": 100271,
+    "<|dummy_id_8|>": 100272,
+    "<|dummy_id_9|>": 100273,
+    "<|dummy_id_10|>": 100274,
+    "<|dummy_id_11|>": 100275,
+    # The final end of prompt token
+    # (unused, but present as a part of tiktoken.get_encoding("cl100k_base")._special_tokens)
+    '<|endofprompt|>': 100276,
+    # Dummy tokens to account for padding of the tokenizer
+    # We pad to ensure tensor cores are used for vocab multiplication
+    **DUMMY_TOKENS
+}
+
+class Phi3SmallTokenizer(PreTrainedTokenizer):
+    vocab_files_names = {
+        "vocab_file": "cl100k_base.tiktoken"
+    }
+
+    model_input_names: List[str] = ["input_ids", "attention_mask"]
+    padding_side = "left"
+
+    def __init__(
+        self,
+        vocab_file: Optional[str] = None,
+        errors: str = "replace",
+        **kwargs
+    ) -> None:
+        # PreTrainedTokenizer's init calls _add_tokens, which in turn checks
+        # if the token is present in `self.special_tokens``. Hence instantiating it here.
+        # The way Qwen gets around this is by checking against SPECIAL_TOKENS
+        # But I think it's better to check against the objects own `special_tokens`
+        # in case we eventually want to allow the tokenizer to have special tokens.
+        self.special_tokens = SPECIAL_TOKENS
+
+        super().__init__(**kwargs)
+        self.errors = errors
+
+        try:
+            base = tiktoken.get_encoding("cl100k_base")
+        # This deals with the scenario where user has restricted internet access 
+        # and thus fails to download the tokenizer file from https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken
+        # It is assumed that user should be able to access files on huggingface hub.
+        except requests.RequestException:
+            import hashlib
+            from transformers.utils import cached_file
+            cached_tokenizer_path = cached_file(
+                    "microsoft/Phi-3-small-8k-instruct",
+                    "cl100k_base.tiktoken",
+                    _raise_exceptions_for_gated_repo=False,
+                    _raise_exceptions_for_missing_entries=False,
+                    _raise_exceptions_for_connection_errors=False
+                )
+            tiktoken_cache_dir = os.path.dirname(cached_tokenizer_path)
+            tiktoken_cache_path = os.path.join(
+                tiktoken_cache_dir, 
+                hashlib.sha1("https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken".encode()).hexdigest()
+            )
+            if not os.path.exists(tiktoken_cache_path):
+                os.rename(cached_tokenizer_path, tiktoken_cache_path)
+            os.environ["TIKTOKEN_CACHE_DIR"] = tiktoken_cache_dir
+            base = tiktoken.get_encoding("cl100k_base")
+            
+        if vocab_file is None:
+            self.mergeable_ranks: Dict[bytes, int] = base._mergeable_ranks
+        else:
+            self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)
+
+        self.pat_str = base._pat_str
+        
+        enc = tiktoken.Encoding(
+            name="phi3small",
+            pat_str=self.pat_str,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        self.tokenizer = enc
+
+        self.decoder: Dict[int, bytes] = {
+            v: k for k, v in self.mergeable_ranks.items()
+        }
+        self.decoder.update({v: k for k, v in self.special_tokens.items()})
+        
+        self.eod_id = self.tokenizer.eot_token
+        self._eos_token = self._convert_id_to_token(self.eod_id)
+
+        # Setting the bos_token to be the same as the eos_token
+        # Note that this is **not** the correct thing to do, and is done
+        # just so that some of the downstream libraries do not break.
+        self._bos_token = self._eos_token
+
+        # Assign the special tokens to class variables
+        self.system_id = self.special_tokens["<|system|>"]
+        self.user_id = self.special_tokens["<|user|>"]
+        self.assistant_id = self.special_tokens["<|assistant|>"]
+        self.end_id = self.special_tokens["<|end|>"]
+    
+    @cached_property
+    def dummy_token_indices(self) -> List[int]:
+        # There are some additional special tokens in the cl100k_base tokenizer
+        # that we do not use. Hence, we also consider them to be dummy tokens.
+        additional_tokens = [
+            "<|fim_prefix|>",
+            "<|fim_middle|>",
+            "<|fim_suffix|>",
+            "<|endofprompt|>"
+        ]
+        dummy_token_indices = [index for token, index in self.special_tokens.items() if "dummy_id" in token]
+        dummy_token_indices.extend([self.special_tokens[token] for token in additional_tokens])
+        return sorted(dummy_token_indices)
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        del state["tokenizer"]
+        return state
+    
+    def __setstate__(self, state):
+        self.__dict__ = state
+        enc = tiktoken.Encoding(
+            name="cl100k_im",
+            pat_str=self.pat_str,
+            mergeable_ranks=self.mergeable_ranks,
+            special_tokens=self.special_tokens,
+        )
+        self.tokenizer = enc
+    
+    def __len__(self):
+        return self.tokenizer.n_vocab
+    
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Union[str, os.PathLike],
+        *init_inputs,
+        **kwargs,
+    ):
+        cls_kwargs = kwargs
+        # First try to load from the tokenization config if it exists
+        tokenization_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
+        if tokenization_config:
+            cls_kwargs = {
+                **tokenization_config,
+                **cls_kwargs
+            }
+        else:
+            config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=True)
+            cls_kwargs["model_max_length"] = config.max_position_embeddings
+        return cls(**cls_kwargs)
+
+    def get_vocab(self) -> Dict[Union[str, bytes], int]:
+        return {**self.mergeable_ranks, **self.special_tokens}
+    
+    def convert_tokens_to_ids(
+        self,
+        tokens: Union[bytes, str, List[Union[bytes, str]]]
+    ) -> Union[int, List[int]]:
+        ids = []
+        if isinstance(tokens, (str, bytes)):
+            if tokens in self.special_tokens:
+                return self.special_tokens[tokens]
+            else:
+                return self.mergeable_ranks.get(tokens)
+        ids: List[int] = []
+        for token in tokens:
+            ids.append(self.convert_tokens_to_ids(token))
+        return ids
+
+    def _add_tokens(
+            self,
+            new_tokens: Union[List[str], List[AddedToken]],
+            special_tokens: bool = False,
+    ) -> int:
+        if not special_tokens and new_tokens:
+            raise ValueError("Only special tokens can be added to this tokenizer")
+        for token in new_tokens:
+            surface_form = token.content if isinstance(token, AddedToken) else token
+            if surface_form not in self.special_tokens:
+                raise ValueError(
+                    "For now, we do not support unknown special tokens\n"
+                    "In the future, if there is a need for this, we can add special tokens to the tokenizer\n"
+                    "starting from rank 100261 - 100263 and then 100266 - 100275.\n"
+                    "And finally, we can re-construct the enc object back\n"
+                )
+        return 0
+
+    def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+        file_path = os.path.join(save_directory, "cl100k_base.tiktoken")
+        with open(file_path, "w") as f:
+            for token, rank in self.mergeable_ranks.items():
+                line = base64.b64encode(token).decode("utf-8") + " " + str(rank) + "\n"
+                f.write(line)
+        return (file_path,)
+
+    def tokenize(
+        self,
+        text: str,
+        allowed_special: Union[Set, str] = "all",
+        disallowed_special: Union[Collection, str] = (),
+        **kwargs
+    ) -> List[Union[bytes, str]]:
+        tokens: List[Union[bytes, str]] = []
+        for token_id in self.tokenizer.encode(
+            text, allowed_special=allowed_special, disallowed_special=disallowed_special
+        ):
+            tokens.append(self.decoder[token_id])
+        return tokens
+
+    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+        """
+        Converts a sequence of tokens in a single string.
+        """
+        text = ""
+        temp = b""
+        for t in tokens:
+            if isinstance(t, str):
+                if temp:
+                    text += temp.decode("utf-8", errors=self.errors)
+                    temp = b""
+                text += t
+            elif isinstance(t, bytes):
+                temp += t
+            else:
+                raise TypeError("token should only be of type types or str")
+        if temp:
+            text += temp.decode("utf-8", errors=self.errors)
+        return text
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_vocab
+
+    @property
+    def eos_token_id(self) -> int:
+        return self.eod_id
+
+    def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+        """Converts an id to a token, special tokens included"""
+        if index in self.decoder:
+            return self.decoder[index]
+        raise ValueError("unknown ids")
+
+    def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+        """Converts a token to an id using the vocab, special tokens included"""
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        if token in self.mergeable_ranks:
+            return self.mergeable_ranks[token]
+        raise ValueError("unknown token")
+
+    def _tokenize(self, text: str, **kwargs):
+        """
+        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+        Do NOT take care of added tokens.
+        """
+        raise NotImplementedError
+
+    def _decode(
+        self,
+        token_ids: Union[int, List[int]],
+        skip_special_tokens: bool = False,
+        errors: str = None,
+        **kwargs,
+    ) -> str:
+        if isinstance(token_ids, int):
+            token_ids = [token_ids]
+        if skip_special_tokens:
+            token_ids = [i for i in token_ids if i < self.eod_id]
+        return self.tokenizer.decode(token_ids, errors=errors or self.errors)
+
+

tokenizer_config.json

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+{
+  "_commit_hash": "1535ae26fb4faada95c6950e8bc6e867cdad6b00",
+  "_from_auto": true,
+  "added_tokens_decoder": {},
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_phi3_small.Phi3SmallTokenizer",
+      "tokenization_phi3_small.Phi3SmallTokenizer"
+    ]
+  },
+  "bos_token": "<|endoftext|>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 8192,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "Phi3SmallTokenizer",
+  "trust_remote_code": true
+}

training_args.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:34144030d2bbaae6ac2167f8c344fffe13e645ecb7605ba369ae08bf1c5930c4
+size 5368