BAAI/Aquila-135M
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: BAAI/Aquila-135M
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-05-16 10:56:28 +08:00
commit df5f3c0306
12 changed files with 152543 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
.gitattributes vendored Normal file
View File

@@ -0,0 +1,35 @@
*.7z filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.bz2 filter=lfs diff=lfs merge=lfs -text
*.ckpt filter=lfs diff=lfs merge=lfs -text
*.ftz filter=lfs diff=lfs merge=lfs -text
*.gz filter=lfs diff=lfs merge=lfs -text
*.h5 filter=lfs diff=lfs merge=lfs -text
*.joblib filter=lfs diff=lfs merge=lfs -text
*.lfs.* filter=lfs diff=lfs merge=lfs -text
*.mlmodel filter=lfs diff=lfs merge=lfs -text
*.model filter=lfs diff=lfs merge=lfs -text
*.msgpack filter=lfs diff=lfs merge=lfs -text
*.npy filter=lfs diff=lfs merge=lfs -text
*.npz filter=lfs diff=lfs merge=lfs -text
*.onnx filter=lfs diff=lfs merge=lfs -text
*.ot filter=lfs diff=lfs merge=lfs -text
*.parquet filter=lfs diff=lfs merge=lfs -text
*.pb filter=lfs diff=lfs merge=lfs -text
*.pickle filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
*.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.rar filter=lfs diff=lfs merge=lfs -text
*.safetensors filter=lfs diff=lfs merge=lfs -text
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.tar.* filter=lfs diff=lfs merge=lfs -text
*.tar filter=lfs diff=lfs merge=lfs -text
*.tflite filter=lfs diff=lfs merge=lfs -text
*.tgz filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.xz 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

119
README.md Normal file
View File

@@ -0,0 +1,119 @@
---
license: apache-2.0
language:
- en
- zh
library_name: transformers
datasets:
- mlfoundations/dclm-baseline-1.0
- HuggingFaceFW/fineweb-edu
- HuggingFaceTB/smollm-corpus
- BAAI/CCI3-HQ
- BAAI/Infinity-Instruct
pipeline_tag: text-generation
extra_gated_prompt: >-
You agree to not use the dataset to conduct experiments that cause harm to
human subjects.
extra_gated_fields:
Company/Organization: text
Country: country
---
# Introduction
The **Aquila-135M** model is a small bilingual(Chinese and English) language model, which is trained using a two-phrase paradigm: pre-training and annealing.
This model used 1.66TB bilingual tokens in Chinese and English during pre-training phrase and 100B tokens during annealing training phrase.
In annealing stage, we selected 100B tokens of high-quality bilingual data and finally got our model.
The **Aquila-135M-Instuct** model is finetuned using [Infinity Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct).
The entire training process was conducted using [FlagGems](https://github.com/FlagOpen/FlagGems) based on Triton and parallel training framework named [FlagScale](https://github.com/FlagOpen/FlagScale).
Also, we have open-sourced all [intermediate checkpoints](https://huggingface.co/BAAI/Aquila-135M-Intermediate).
# News
- `2024/12/24`: We have released Aquila-135M and Aquila-135M-Instruct.
- `2024/12/24`: We have released all datasets and intermediate checkpoints during training. Please feel free to use these models for analysis and experimentation.
# Datasets
We have open-sourced all [bilingual datasets](https://huggingface.co/datasets/BAAI/Aquila-135M-Datasets) during both pre-training and annealing phrases.
Datasets composition and mix proportions are shown in the figure below.
<img src="./datasets.jpeg" alt="datasets composition" width="800" height="600">
# Evaluation
We followed the same evaluation setting of SmolLM models and evaluated models using the [lighteval](https://github.com/huggingface/lighteval) tool.
The parameter count excludes the embedding part and Aquila-135M and SmolLM2-135M share an identical model structure. Aquila-135M achieves comparable performance on English benchmarks, while Aquila-135M demonstrates significantly better results on Chinese benchmarks.
Among small models with a total parameter count below and around 400M, Aquila-135M maintains a leading position in processing capabilities while significantly enhancing Chinese language proficiency.
| Metrics (0-shot) | Aquila-135M (Trition) | Aquila-135M (CUDA) | SmolLM-135M | SmolLM2-135M | gpt2-medium-360M | TinyMistral-248M | TinyMistral-248M-2.5 | OpenELM-270M | Wide-Sheared-LLaMA-290M | opt-350m | MobileLLM-350M | pythia-410m | SmolLM-360M | SmolLM2-360M |
|---------------------------|-----------------------|--------------------|-------------|---------------|------------------|------------------|----------------------|--------------|--------------------------|----------|----------------|-------------|-------------|--------------|
| **HellaSwag** | 41.19 | 41.12 | 41.15 | 42.10 | 37.08 | 27.06 | 26.80 | 45.74 | 24.94 | 36.08 | 26.28 | 39.22 | 51.73 | 54.66 |
| **ARC (Average)** | 44.76 | 44.15 | 42.34 | 43.93 | 34.34 | 29.71 | 27.63 | 35.74 | 26.20 | 31.91 | 27.72 | 35.14 | 49.95 | 53.24 |
| **PIQA** | 66.38 | 67.52 | 68.28 | 68.44 | 66.38 | 57.40 | 53.92 | 69.75 | 50.60 | 64.36 | 50.27 | 67.19 | 71.55 | 71.98 |
| **MMLU (cloze)** | 31.07 | 30.67 | 30.26 | 31.58 | 27.75 | 25.82 | 25.59 | 27.89 | 24.75 | 26.58 | 24.86 | 28.88 | 34.32 | 36.09 |
| **CommonsenseQA** | 32.10 | 31.70 | 32.02 | 32.92 | 31.70 | 24.57 | 21.46 | 35.71 | 16.54 | 32.10 | 17.53 | 31.45 | 36.61 | 38.74 |
| **TriviaQA** | 6.65 | 7.02 | 4.24 | 4.03 | 2.36 | 0.50 | 0.08 | 1.34 | 0.00 | 1.38 | 0.00 | 2.06 | 9.19 | 16.92 |
| **Winograde** | 51.07 | 51.70 | 51.22 | 50.99 | 49.49 | 49.25 | 49.01 | 52.41 | 49.72 | 51.54 | 49.41 | 49.96 | 53.12 | 52.49 |
| **OpenBookQA** | 34.40 | 34.40 | 33.80 | 34.60 | 31.40 | 29.40 | 27.40 | 30.60 | 26.00 | 27.80 | 24.80 | 28.40 | 37.20 | 37.00 |
| **GSM8K (5-shot)** | 2.12 | 2.12 | 1.00 | 1.52 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 2.81 |
| **SIQA** | 41.81 | 42.32 | 41.15 | 41.45 | 41.30 | 41.86 | 39.71 | 42.73 | 39.76 | 42.37 | 37.10 | 42.02 | 43.45 | 41.61 |
| **CEval** | 29.22 | 29.82 | 28.28 | 26.41 | 25.40 | 25.38 | 26.89 | 26.69 | 26.37 | 26.67 | 25.68 | 27.97 | 27.66 | 28.51 |
| **CMMLU** | 29.48 | 29.63 | 26.01 | 26.66 | 27.20 | 26.67 | 25.57 | 26.25 | 26.33 | 26.93 | 25.61 | 26.91 | 27.06 | 27.39 |
| **Average-English** | 35.16 | 35.27 | 34.55 | 35.16 | 32.18 | 28.56 | 27.16 | 34.19 | 25.85 | 31.41 | 25.80 | 32.43 | 38.71 | 40.55 |
| **Average-Chinese** | 29.35 | 29.73 | 27.15 | 26.54 | 26.30 | 26.03 | 26.23 | 26.47 | 26.35 | 26.80 | 25.65 | 27.44 | 27.36 | 27.95 |
| **Average** | 32.25 | 32.50 | 30.85 | 30.85 | 29.24 | 27.29 | 26.70 | 30.33 | 26.10 | 29.11 | 25.72 | 29.94 | 33.04 | 34.25 |
For comparison models, evaluations were conducted in a local environment, so the scores may differ slightly from those reported in papers.
# How to use
## Instruct Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "BAAI/Aquila-135M-Instruct"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
messages = [{"role": "user", "content": "什么是引力？"}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(input_text)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=500)
print(tokenizer.decode(outputs[0]))
## 引力是宇宙中的一个基本力，由多个物体相互作用而产生的。它由能量和质量组成，与引力定律密切相关。
messages = [{"role": "user", "content": "What is gravity?"}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(input_text)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=500)
print(tokenizer.decode(outputs[0]))
## Gravity is the force that keeps us on Earth as we orbit it. It pulls objects towards each other with a strength that depends on how far apart they are from each other, and how strong the gravitational pull is. The stronger the object's mass, the greater its gravitational pull.
```
# Future Plan
* We plan to further optimize the composition and proportions of the dataset.
* We plan to further explore the application of small-scale models in specific scenarios.
## **Citation**
If you find this useful, please cite the following work
```
@misc{aquila-135m,
title={Aquila-135M: A Bilingual Small Language Model in Chinese and English},
author={BAAI},
year={},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={},
}
```

27
config.json Normal file
View File

@@ -0,0 +1,27 @@
{
"architectures": [
"MistralForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151849,
"eos_token_id": 151850,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 576,
"initializer_range": 0.02,
"intermediate_size": 1536,
"max_position_embeddings": 8192,
"model_type": "mistral",
"num_attention_heads": 9,
"num_hidden_layers": 30,
"num_key_value_heads": 3,
"rms_norm_eps": 1e-05,
"rope_theta": 10000,
"sliding_window": 8192,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.44.2",
"use_cache": true,
"vocab_size": 151851
}

1
configuration.json Normal file
View File

@@ -0,0 +1 @@
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

BIN
datasets.jpeg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 434 KiB

6
generation_config.json Normal file
View File

@@ -0,0 +1,6 @@
{
"_from_model_config": true,
"bos_token_id": 151849,
"eos_token_id": 151850,
"transformers_version": "4.44.2"
}

3
model.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:fe81ee1a16d461cf32d9a2ee119c901a21d139fd1f120f506e11d5fb196ba7df
size 562302352

151643
qwen.tiktoken Normal file
View File

File diff suppressed because it is too large Load Diff

416
qwen_generation_utils.py Normal file
View File

@@ -0,0 +1,416 @@
# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Generation support."""
from typing import Tuple, List, Union, Iterable
import numpy as np
import torch
import torch.nn.functional as F
from transformers import PreTrainedTokenizer
from transformers import logging
from transformers.generation import LogitsProcessor
logger = logging.get_logger(__name__)
# Types.
HistoryType = List[Tuple[str, str]]
TokensType = List[int]
BatchTokensType = List[List[int]]
def pad_batch(batch: BatchTokensType, pad_id: int, seq_length: int) -> BatchTokensType:
for tokens in batch:
context_length = len(tokens)
if context_length < seq_length:
tokens.extend([pad_id] * (seq_length - context_length))
return batch
def get_ltor_masks_and_position_ids(
data,
eod_token,
reset_position_ids,
reset_attention_mask,
eod_mask_loss,
):
"""Build masks and position id for left to right model."""
# Extract batch size and sequence length.
micro_batch_size, seq_length = data.size()
# Attention mask (lower triangular).
if reset_attention_mask:
att_mask_batch = micro_batch_size
else:
att_mask_batch = 1
attention_mask = torch.tril(
torch.ones((att_mask_batch, seq_length, seq_length), device=data.device)
).view(att_mask_batch, 1, seq_length, seq_length)
# Loss mask.
loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
if eod_mask_loss:
loss_mask[data == eod_token] = 0.0
# Position ids.
position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device)
position_ids = position_ids.unsqueeze(0).expand_as(data)
# We need to clone as the ids will be modifed based on batch index.
if reset_position_ids:
position_ids = position_ids.clone()
if reset_position_ids or reset_attention_mask:
# Loop through the batches:
for b in range(micro_batch_size):
# Find indecies where EOD token is.
eod_index = position_ids[b, data[b] == eod_token]
# Detach indecies from positions if going to modify positions.
if reset_position_ids:
eod_index = eod_index.clone()
# Loop through EOD indecies:
prev_index = 0
for j in range(eod_index.size()[0]):
i = eod_index[j]
# Mask attention loss.
if reset_attention_mask:
attention_mask[b, 0, (i + 1) :, : (i + 1)] = 0
# Reset positions.
if reset_position_ids:
position_ids[b, (i + 1) :] -= i + 1 - prev_index
prev_index = i + 1
# Convert attention mask to binary:
attention_mask = attention_mask < 0.5
return attention_mask, loss_mask, position_ids
def get_batch(context_tokens: torch.LongTensor, eod_id: int):
"""Generate batch from context tokens."""
# Move to GPU.
tokens = context_tokens.contiguous().to(context_tokens.device)
# Get the attention mask and postition ids.
attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
tokens,
eod_id,
reset_position_ids=False,
reset_attention_mask=False,
eod_mask_loss=False,
)
return tokens, attention_mask, position_ids
def get_stop_words_ids(chat_format, tokenizer):
if chat_format == "raw":
stop_words_ids = [tokenizer.encode("Human:"), [tokenizer.eod_id]]
elif chat_format == "chatml":
stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
else:
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
return stop_words_ids
def make_context(
tokenizer: PreTrainedTokenizer,
query: str,
history: List[Tuple[str, str]] = None,
system: str = "",
max_window_size: int = 6144,
chat_format: str = "chatml",
):
if history is None:
history = []
if chat_format == "chatml":
im_start, im_end = "<|im_start|>", "<|im_end|>"
im_start_tokens = [tokenizer.im_start_id]
im_end_tokens = [tokenizer.im_end_id]
nl_tokens = tokenizer.encode("\n")
def _tokenize_str(role, content):
return f"{role}\n{content}", tokenizer.encode(
role, allowed_special=set()
) + nl_tokens + tokenizer.encode(content, allowed_special=set())
system_text, system_tokens_part = _tokenize_str("system", system)
system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
raw_text = ""
context_tokens = []
for turn_query, turn_response in reversed(history):
query_text, query_tokens_part = _tokenize_str("user", turn_query)
query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
response_text, response_tokens_part = _tokenize_str(
"assistant", turn_response
)
response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
prev_chat = (
f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
)
current_context_size = (
len(system_tokens) + len(next_context_tokens) + len(context_tokens)
)
if current_context_size < max_window_size:
context_tokens = next_context_tokens + context_tokens
raw_text = prev_chat + raw_text
else:
break
context_tokens = system_tokens + context_tokens
raw_text = f"{im_start}{system_text}{im_end}" + raw_text
context_tokens += (
nl_tokens
+ im_start_tokens
+ _tokenize_str("user", query)[1]
+ im_end_tokens
+ nl_tokens
+ im_start_tokens
+ tokenizer.encode("assistant")
+ nl_tokens
)
raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
elif chat_format == "raw":
raw_text = query
context_tokens = tokenizer.encode(raw_text)
else:
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
return raw_text, context_tokens
def _decode_default(
tokens: List[int],
*,
stop_words: List[str],
eod_words: List[str],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
verbose: bool = False,
return_end_reason: bool = False,
errors: str='replace',
):
trim_decode_tokens = tokenizer.decode(tokens, errors=errors)[raw_text_len:]
if verbose:
print("\nRaw Generate: ", trim_decode_tokens)
end_reason = f"Gen length {len(tokens)}"
for stop_word in stop_words:
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
for eod_word in eod_words:
if eod_word in trim_decode_tokens:
end_reason = f"Gen {eod_word!r}"
trim_decode_tokens = trim_decode_tokens.split(eod_word)[0]
trim_decode_tokens = trim_decode_tokens.strip()
if verbose:
print("\nEnd Reason:", end_reason)
print("\nGenerate: ", trim_decode_tokens)
if return_end_reason:
return trim_decode_tokens, end_reason
else:
return trim_decode_tokens
def _decode_chatml(
tokens: List[int],
*,
stop_words: List[str],
eod_token_ids: List[int],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
context_length: int,
verbose: bool = False,
return_end_reason: bool = False,
errors: str='replace'
):
end_reason = f"Gen length {len(tokens)}"
eod_token_idx = context_length
for eod_token_idx in range(context_length, len(tokens)):
if tokens[eod_token_idx] in eod_token_ids:
end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
break
trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)[raw_text_len:]
if verbose:
print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens, errors=errors)[raw_text_len:])
print("\nRaw Generate:", trim_decode_tokens)
print("\nEnd Reason:", end_reason)
for stop_word in stop_words:
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
trim_decode_tokens = trim_decode_tokens.strip()
if verbose:
print("\nGenerate:", trim_decode_tokens)
if return_end_reason:
return trim_decode_tokens, end_reason
else:
return trim_decode_tokens
def decode_tokens(
tokens: Union[torch.LongTensor, TokensType],
tokenizer: PreTrainedTokenizer,
raw_text_len: int,
context_length: int,
chat_format: str,
verbose: bool = False,
return_end_reason: bool = False,
errors: str="replace",
) -> str:
if torch.is_tensor(tokens):
tokens = tokens.cpu().numpy().tolist()
if chat_format == "chatml":
return _decode_chatml(
tokens,
stop_words=[],
eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
tokenizer=tokenizer,
raw_text_len=raw_text_len,
context_length=context_length,
verbose=verbose,
return_end_reason=return_end_reason,
errors=errors,
)
elif chat_format == "raw":
return _decode_default(
tokens,
stop_words=["<|endoftext|>"],
eod_words=["<|endoftext|>"],
tokenizer=tokenizer,
raw_text_len=raw_text_len,
verbose=verbose,
return_end_reason=return_end_reason,
errors=errors,
)
else:
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
class StopWordsLogitsProcessor(LogitsProcessor):
"""
:class:`transformers.LogitsProcessor` that enforces that when specified sequences appear, stop geration.
Args:
stop_words_ids (:obj:`List[List[int]]`):
List of list of token ids of stop ids. In order to get the tokens of the words
that should not appear in the generated text, use :obj:`tokenizer(bad_word,
add_prefix_space=True).input_ids`.
eos_token_id (:obj:`int`):
The id of the `end-of-sequence` token.
"""
def __init__(self, stop_words_ids: Iterable[Iterable[int]], eos_token_id: int):
if not isinstance(stop_words_ids, List) or len(stop_words_ids) == 0:
raise ValueError(
f"`stop_words_ids` has to be a non-emtpy list, but is {stop_words_ids}."
)
if any(not isinstance(bad_word_ids, list) for bad_word_ids in stop_words_ids):
raise ValueError(
f"`stop_words_ids` has to be a list of lists, but is {stop_words_ids}."
)
if any(
any(
(not isinstance(token_id, (int, np.integer)) or token_id < 0)
for token_id in stop_word_ids
)
for stop_word_ids in stop_words_ids
):
raise ValueError(
f"Each list in `stop_words_ids` has to be a list of positive integers, but is {stop_words_ids}."
)
self.stop_words_ids = list(
filter(
lambda bad_token_seq: bad_token_seq != [eos_token_id], stop_words_ids
)
)
self.eos_token_id = eos_token_id
for stop_token_seq in self.stop_words_ids:
assert (
len(stop_token_seq) > 0
), "Stop words token sequences {} cannot have an empty list".format(
stop_words_ids
)
def __call__(
self, input_ids: torch.LongTensor, scores: torch.FloatTensor
) -> torch.FloatTensor:
stopped_samples = self._calc_stopped_samples(input_ids)
for i, should_stop in enumerate(stopped_samples):
if should_stop:
scores[i, self.eos_token_id] = float(2**15)
return scores
def _tokens_match(self, prev_tokens: torch.LongTensor, tokens: List[int]) -> bool:
if len(tokens) == 0:
# if bad word tokens is just one token always ban it
return True
elif len(tokens) > len(prev_tokens):
# if bad word tokens are longer then prev input_ids they can't be equal
return False
elif prev_tokens[-len(tokens) :].tolist() == tokens:
# if tokens match
return True
else:
return False
def _calc_stopped_samples(self, prev_input_ids: Iterable[int]) -> Iterable[int]:
stopped_samples = []
for prev_input_ids_slice in prev_input_ids:
match = False
for stop_token_seq in self.stop_words_ids:
if self._tokens_match(prev_input_ids_slice, stop_token_seq):
# if tokens do not match continue
match = True
break
stopped_samples.append(match)
return stopped_samples
def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")):
"""This function has been mostly taken from huggingface conversational
ai code at
https://medium.com/huggingface/how-to-build-a-state-of-the-art-
conversational-ai-with-transfer-learning-2d818ac26313"""
if top_k > 0:
# Remove all tokens with a probability less than the
# last token of the top-k
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
# Cconvert to 1D
sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token
# above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
for i in range(sorted_indices.size(0)):
indices_to_remove = sorted_indices[i][sorted_indices_to_remove[i]]
logits[i][indices_to_remove] = filter_value
return logits
def switch(val1, val2, boolean):
boolean = boolean.type_as(val1)
return (1 - boolean) * val1 + boolean * val2

6
special_tokens_map.json Normal file
View File

@@ -0,0 +1,6 @@
{
"bos_token": "<|extra_203|>",
"eos_token": "<|extra_204|>",
"unk_token": "<|endoftext|>",
"pad_token": "<|endoftext|>"
}

276
tokenization_qwen.py Normal file
View File

@@ -0,0 +1,276 @@
# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Tokenization classes for QWen."""
import base64
import logging
import os
import unicodedata
from typing import Collection, Dict, List, Set, Tuple, Union
import tiktoken
from transformers import PreTrainedTokenizer, AddedToken
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
ENDOFTEXT = "<|endoftext|>"
IMSTART = "<|im_start|>"
IMEND = "<|im_end|>"
# as the default behavior is changed to allow special tokens in
# regular texts, the surface forms of special tokens need to be
# as different as possible to minimize the impact
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
# changed to use actual index to avoid misconfiguration with vocabulary expansion
SPECIAL_START_ID = 151643
SPECIAL_TOKENS = tuple(
enumerate(
(
(
ENDOFTEXT,
IMSTART,
IMEND,
)
+ EXTRAS
),
start=SPECIAL_START_ID,
)
)
SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
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)
}
class QWenTokenizer(PreTrainedTokenizer):
"""QWen tokenizer."""
vocab_files_names = VOCAB_FILES_NAMES
def __init__(
self,
vocab_file,
errors="replace",
extra_vocab_file=None,
**kwargs,
):
super().__init__(**kwargs)
# how to handle errors in decoding UTF-8 byte sequences
# use ignore if you are in streaming inference
self.errors = errors
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
self.special_tokens = {
token: index
for index, token in SPECIAL_TOKENS
}
# try load extra vocab from file
if extra_vocab_file is not None:
used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
for token, index in extra_mergeable_ranks.items():
if token in self.mergeable_ranks:
logger.info(f"extra token {token} exists, skipping")
continue
if index in used_ids:
logger.info(f'the index {index} for extra token {token} exists, skipping')
continue
self.mergeable_ranks[token] = index
# the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
enc = tiktoken.Encoding(
"Qwen",
pat_str=PAT_STR,
mergeable_ranks=self.mergeable_ranks,
special_tokens=self.special_tokens,
)
assert (
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
self.decoder = {
v: k for k, v in self.mergeable_ranks.items()
} # type: dict[int, bytes|str]
self.decoder.update({v: k for k, v in self.special_tokens.items()})
self.tokenizer = enc # type: tiktoken.Encoding
self.eod_id = self.tokenizer.eot_token
self.im_start_id = self.special_tokens[IMSTART]
self.im_end_id = self.special_tokens[IMEND]
def __getstate__(self):
# for pickle lovers
state = self.__dict__.copy()
del state["tokenizer"]
return state
def __setstate__(self, state):
# tokenizer is not python native; don't pass it; rebuild it
self.__dict__.update(state)
enc = tiktoken.Encoding(
"Qwen",
pat_str=PAT_STR,
mergeable_ranks=self.mergeable_ranks,
special_tokens=self.special_tokens,
)
self.tokenizer = enc
def __len__(self) -> int:
return self.tokenizer.n_vocab
def get_vocab(self) -> Dict[bytes, int]:
return self.mergeable_ranks
def convert_tokens_to_ids(
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
) -> 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)
for token in tokens:
if token in self.special_tokens:
ids.append(self.special_tokens[token])
else:
ids.append(self.mergeable_ranks.get(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("Adding regular tokens is not supported")
for token in new_tokens:
surface_form = token.content if isinstance(token, AddedToken) else token
if surface_form not in SPECIAL_TOKENS_SET:
raise ValueError("Adding unknown special tokens is not supported")
return 0
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
"""
Save only the vocabulary of the tokenizer (vocabulary).
Returns:
`Tuple(str)`: Paths to the files saved.
"""
file_path = os.path.join(save_directory, "qwen.tiktoken")
with open(file_path, "w", encoding="utf8") as w:
for k, v in self.mergeable_ranks.items():
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
w.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]]:
"""
Converts a string in a sequence of tokens.
Args:
text (`str`):
The sequence to be encoded.
allowed_special (`Literal["all"]` or `set`):
The surface forms of the tokens to be encoded as special tokens in regular texts.
Default to "all".
disallowed_special (`Literal["all"]` or `Collection`):
The surface forms of the tokens that should not be in regular texts and trigger errors.
Default to an empty tuple.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific encode method.
Returns:
`List[bytes|str]`: The list of tokens.
"""
tokens = []
text = unicodedata.normalize("NFC", text)
# this implementation takes a detour: text -> token id -> token surface forms
for t in self.tokenizer.encode(
text, allowed_special=allowed_special, disallowed_special=disallowed_special
):
tokens.append(self.decoder[t])
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
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)

11
tokenizer_config.json Normal file
View File

@@ -0,0 +1,11 @@
{
"model_max_length": 8192,
"tokenizer_class": "QWenTokenizer",
"auto_map": {
"AutoTokenizer": [
"tokenization_qwen.QWenTokenizer",
null
]
},
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
}