pcuenq
/

Baichuan-M1-14B-Instruct-tokenizer

@@ -3,197 +3,155 @@ 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]

 tags: []
 ---
+# Baichuan-M1-14B-Instruct-tokenizer
+Fast transformers tokenizer for [mlx-community/Baichuan-M1-14B-Instruct-8bit](https://hf.co/mlx-community/Baichuan-M1-14B-Instruct-8bit)
+Thanks a lot @Xenova for finding the final fix! 🙌
+## Conversion
+```py
+from tokenization_baichuan import BaichuanTokenizer
+original = BaichuanTokenizer.from_pretrained(".")
+from transformers.convert_slow_tokenizer import SpmConverter, LlamaConverter, GemmaConverter, _get_prepend_scheme
+from tokenizers import decoders, normalizers, pre_tokenizers, processors, Tokenizer, AddedToken
+from tokenizers.models import BPE
+class BaichuanConverter(SpmConverter):
+    handle_byte_fallback = True
+    def vocab(self, proto):
+        vocab = [
+            (self.original_tokenizer.convert_ids_to_tokens(0), 0.0),
+            (self.original_tokenizer.convert_ids_to_tokens(1), 0.0),
+            (self.original_tokenizer.convert_ids_to_tokens(2), 0.0),
+        ]
+        vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
+        return vocab
+    def unk_id(self, proto):
+        unk_id = 0
+        return unk_id
+    def decoder(self, replacement, add_prefix_space):
+        sequence = [
+            decoders.Replace("▁", " "),
+            decoders.ByteFallback(),
+            decoders.Fuse(),
+        ]
+        return decoders.Sequence(sequence)
+    def normalizer(self, proto):
+        return normalizers.Replace(pattern=" ", content="▁")
+    def pre_tokenizer(self, replacement, add_prefix_space):
+        return None
+    def post_processor(self):
+        return None
+    def tokenizer(self, proto):
+        vocab_scores = self.vocab(proto)
+        _, merges = self.SpmExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
+        bpe_vocab = {word: i for i, (word, score) in enumerate(vocab_scores)}
+        tokenizer = Tokenizer(
+            BPE(
+                bpe_vocab,
+                merges,
+                unk_token=proto.trainer_spec.unk_piece,
+                fuse_unk=True,
+                byte_fallback=self.handle_byte_fallback,
+                dropout=None,
+            )
+        )
+        # control tokens are special
+        # user defined symbols are not
+        # both user and control tokens are AddedTokens
+        # Add user defined symbols (type == 4) from sentencepiece (https://github.com/google/sentencepiece/blob/6225e08edb2577757163b3f5dbba4c0b670ef445/src/sentencepiece_model.proto#L299C29-L299C33)
+        spm_added_tokens = [
+            (id, p.piece, p.type == 3 or p.piece in self.special_tokens)
+            for id, p in enumerate(proto.pieces)
+            if p.type in [3, 4]
+        ]
+        # Reproduce weird behaviour in original tokenizer
+        # only add tokens that did not originally exist
+        bad_added_tokens = set()
+        for _, token, _ in spm_added_tokens:
+            encoded = self.original_tokenizer.encode(token)
+            if len(encoded) != 1:
+                bad_added_tokens.add(token)
+        tokenizer.add_tokens(
+            [
+                AddedToken(token, normalized=True, special=special)
+                for id, token, special in sorted(spm_added_tokens, key=lambda x: x[0])
+                if token not in bad_added_tokens
+            ]
+        )
+        return tokenizer
+converter = BaichuanConverter(original)
+converted = converter.converted()
+from transformers import PreTrainedTokenizerFast
+t_fast = PreTrainedTokenizerFast(
+    tokenizer_object=converted,
+    model_input_names=original.model_input_names,
+    model_max_length=32768,
+    clean_up_tokenization_spaces=False,
+)
+test_strings = [
+    " {\n",
+    "  {\n",
+    "x  {\n",
+    "----------------------------------------------------------------------------\n",
+    "\n \n",
+    "\n  \n",
+    '// -----------------------------------------------------------------------\n',
+    '-----------------------------------------------------------------------\n',
+]
+for test_string in test_strings:
+    print("Original:", original.encode(test_string))
+    print("Fast:    ", t_fast.encode(test_string))
+# Testing on xnli
+from datasets import load_dataset
+from tqdm import tqdm
+xnli = load_dataset("xnli", "all_languages", split="validation")
+def verify(lang, text):
+    encoded_original = original.encode(text)
+    encoded_fast = t_fast.encode(text)
+    assert encoded_fast == encoded_original, f"Fast encode error: {lang} - {text}"
+    decoded = original.decode(encoded_original)
+    decoded_fast = t_fast.decode(encoded_fast, skip_special_tokens=True)
+    assert decoded_fast == decoded, f"Fast decode error: {lang} - {text}"
+for p in tqdm(xnli["premise"]):
+    for lang, text in p.items():
+        verify(lang, text)
+# Testing on codeparrot
+ds = load_dataset("codeparrot/github-code", streaming=True, trust_remote_code=True, split="train")
+iterator = iter(ds)
+for _ in tqdm(range(1000)):
+    item = next(iterator)
+    code = item["code"]
+    lang = item["language"]
+    verify(lang, code)
+t_fast.push_to_hub("Baichuan-M1-14B-Instruct-tokenizer")
+```