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---
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
pipeline_tag: text-generation
library_name: transformers
---
---
license: apache-2.0
---
# OpenSeek-Mid-v1
**OpenSeek-Mid-v1** is a 10.61-billion-parameter language model grown from [Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) through a two-stage model expansion pipeline and trained on only **2 trillion tokens** of fully open-source data.
Despite having **25% fewer parameters** and using **18x less training data**, OpenSeek-Mid-v1 matches or surpasses Qwen3-14B-Base across multiple benchmarks.
<img src="https://cdn-uploads.huggingface.co/production/uploads/642ee226a7e765fff0bf00ac/VcTNOdzlJK1tw5PjgeSSi.png" width="90%" alt="results_all">
---
## Highlights
- **Model Growth, Not From-Scratch Training**: Grown from Qwen3-4B via width expansion + partial depth stacking, inheriting the seed model's learned representations.
- **Extreme Data Efficiency**: Matches Qwen3-14B-Base (~36T tokens) with only 2T tokens of training — an 18x reduction in data requirement.
- **Muon Optimizer**: Spectral whitening ensures expanded dimensions are effectively utilized, delivering significant gains over AdamW in the model growth setting.
- **Fully Open-Source Data**: All training data comes from publicly available datasets (NemotronCC-v2, Stack-Edu, Dolmino, CCI, etc.).
---
## Architecture
| Specification | Value |
|---|---|
| Parameters | 10.61B |
| Layers | 56 |
| Hidden Size (d_model) | 2560 |
| FFN Intermediate Size (d_FFN) | 19456 |
| Attention Heads | 32 |
| KV Heads | 8 |
| Sequence Length | 8192 |
| Vocabulary Size | same as Qwen3-4B |
### Growth Pipeline
```
Qwen3-4B (4.02B, 36L)
│ Width expansion (d_FFN: 9728 → 19456, SNR=10dB)
▼
Width-Expanded (7.10B, 36L)
│ Partial depth stacking (layers 14–34 × 2)
▼
OpenSeek-Mid-v1 (10.61B, 56L)
│ Continual pretraining with Muon (2T tokens)
▼
Final Model
```
---
## Training
### Training Configuration
| Parameter | Value |
|---|---|
| Optimizer | Muon |
| Sequence Length | 8192 |
| Global Batch Size | 2048 sequences |
| Peak Learning Rate | 1e-4 |
| LR Schedule | Cosine with linear warmup |
| Warmup Steps | 1000 |
| Weight Decay | 0.1 |
| Training Framework | FlagScale (FlagOS) |
| Total Training Tokens | ~2.06T |
### Stage 1: Broad Knowledge Acquisition (1.36T tokens)
#### Stage 1 Data Mixture
| Category | Proportion | Tokens (B) |
|---|---|---|
| Web | 42% | ~571B |
| Math | 20% | ~272B |
| Code | 20% | ~272B |
| STEM | 15% | ~204B |
| Multilingual | 3% | ~41B |
---
### Stage 2: Capability Specialization (0.70T tokens)
#### Stage 2 Data Mixture
| Category | Proportion | Tokens (B) | Delta vs. Stage 1 |
|---|---|---|---|
| Web | 35% | ~245B | -7% |
| Math | 20% | ~140B | — |
| Code | 24% | ~168B | +4% |
| STEM | 18% | ~126B | +3% |
| Multilingual | 3% | ~21B | — |
---
### Detailed Dataset Composition
Stage 1 (%) and Stage 2 (%) denote each dataset's sampling weight within the respective stage. "—" indicates the dataset is not used in that stage.
**Web**
| Dataset | Tokens (B) | Stage 1 (%) | Stage 2 (%) |
|---|---|---|---|
| Nemotron-CC-v2-HQ-Syn | 798.41 | 23.24 | 19.36 |
| Nemotron-CC-v2-Diverse-QA (×5 shards) | 340.81 | 9.92 | 8.26 |
| Nemotron-CC-v2-HQ (×5 shards) | 303.82 | 8.84 | 7.36 |
| dolmino-mix-1124-wiki | 3.82 | 0.15 | 0.18 |
| dolmino-mix-1124-stackexchange | 1.30 | 0.05 | 0.06 |
**Math**
| Dataset | Tokens (B) | Stage 1 (%) | Stage 2 (%) |
|---|---|---|---|
| Nemotron-SFT-MATH | 207.46 | 11.70 | 11.70 |
| Nemotron-CC-Math-v1-4plus-MIND | 74.34 | 4.19 | 4.19 |
| Nemotron-CC-Math-v1-4plus | 53.37 | 3.01 | 3.01 |
| Dolmino-math | 11.17 | 0.63 | 0.63 |
| OpenMathInstruct-2 | 5.30 | 0.30 | 0.30 |
| OpenMathReasoning-4k | 2.48 | 0.14 | 0.14 |
| NuminaMath-1.5 | 0.38 | 0.02 | 0.02 |
**Code**
| Dataset | Tokens (B) | Stage 1 (%) | Stage 2 (%) |
|---|---|---|---|
| Nemotron-Pretraining-Code-v1-Syn | 171.53 | 9.05 | 10.86 |
| Nemotron-SFT-Code | 57.47 | 3.03 | 3.64 |
| stack-edu-Java | 31.70 | 1.06 | 1.27 |
| stack-edu-Markdown | 26.64 | 0.38 | 0.45 |
| stack-edu-Python | 18.27 | 1.54 | 1.85 |
| stack-edu-Cpp | 12.62 | 1.11 | 1.33 |
| stack-edu-JavaScript | 8.99 | 1.00 | 1.20 |
| stack-edu-SQL | 8.23 | 0.37 | 0.44 |
| github-issue | 8.46 | 0.25 | 0.30 |
| stack-edu-PHP | 7.43 | 0.25 | 0.30 |
| stack-edu-CSharp | 7.26 | 0.37 | 0.44 |
| stack-edu-C | 4.80 | 0.43 | 0.52 |
| stack-edu-Shell | 2.60 | 0.01 | 0.01 |
| stack-edu-TypeScript | 2.51 | 0.18 | 0.22 |
| OpenCodeInstruct | 1.59 | — | 0.10 |
| stack-edu-Swift | 1.53 | 0.06 | 0.07 |
| stack-edu-Rust | 1.45 | 0.05 | 0.06 |
| stack-edu-Go | 1.42 | 0.03 | 0.04 |
| kaggle-notebooks | 1.42 | 0.65 | 0.78 |
| stack-edu-Ruby | 1.36 | 0.01 | 0.01 |
| OpenCodeReasoning-2-cpp-4k | 0.76 | 0.04 | 0.05 |
| OpenCodeReasoning-2-python-4k | 0.58 | 0.03 | 0.04 |
| github-code-review | 0.32 | — | 0.02 |
**STEM & Science**
| Dataset | Tokens (B) | Stage 1 (%) | Stage 2 (%) |
|---|---|---|---|
| Nemotron-Pretraining-Specialized-v1 (×4 shards) | 276.83 | 10.55 | 12.73 |
| Nemotron-Pretraining-SFT-v1-General | 86.93 | 3.31 | 4.00 |
| dolmino-mix-1124-pes2o | 60.19 | 0.50 | 0.50 |
| Nemotron-Pretraining-Specialized-v1.1 | 9.04 | — | 0.42 |
| OpenScienceReasoning-2-4k | 1.72 | 0.07 | 0.08 |
| MegaScience | 0.98 | 0.04 | 0.04 |
**Multilingual**
| Dataset | Tokens (B) | Stage 1 (%) | Stage 2 (%) |
|---|---|---|---|
| Nemotron-CC-v2-Translated-Diverse-QA | 135.80 | 1.74 | 1.74 |
| CCI4_0-Zh-High | 98.76 | 1.26 | 1.26 |
---
### Checkpoint Merging
The final model is a weighted average of 5 complementary checkpoints, each selected for a unique strength:
| Checkpoint | Weight | Role | Key Metric |
|---|---|---|---|
| iter 169984 | 0.30 | Code anchor | MBPP **78.84** |
| iter 219136 | 0.25 | Reasoning lead | GPQA-d **44.39** |
| iter 174080 | 0.15 | Code peak | EvalPlus **68.88** |
| iter 190464 | 0.15 | Math bridge | GPQA-d **42.86** |
| iter 217088 | 0.15 | General boost | BBH **82.84** |
---
## Evaluation Results
All evaluations conducted via `lm-eval-harness` with consistent settings.
| Benchmark | Qwen3-4B | Qwen3-8B | Qwen3.5-9B | Nemotron-12B | Gemma3-12B | Qwen3-14B | **OpenSeek-Mid-v1** |
|---|---|---|---|---|---|---|---|
| *Training tokens* | *36T* | *36T* | *36T* | *20T* | *12T* | *36T* | ***2T*** |
| MMLU (5-shot) | 72.72 | 76.57 | 78.64 | 78.07 | 73.28 | **80.57** | <u>79.31</u> |
| MMLU-Pro (5-shot CoT) | 49.31 | 52.35 | <u>58.48</u> | 57.57 | 41.16 | 56.00 | **66.57** |
| AGIEval-en (0-shot) | 45.92 | 49.09 | 45.15 | 49.20 | 44.89 | **52.83** | <u>52.18</u> |
| BBH (3-shot CoT) | 71.20 | 77.75 | <u>82.23</u> | 69.65 | 73.78 | 78.71 | **82.55** |
| HellaSwag (5-shot) | 75.36 | 79.47 | 81.04 | <u>83.13</u> | **83.45** | 82.05 | 81.81 |
| Winogrande (5-shot) | 71.90 | 77.51 | 76.80 | 79.24 | **80.35** | <u>79.40</u> | 79.24 |
| PIQA (5-shot) | 78.89 | 81.39 | 81.61 | 82.97 | 81.80 | **83.30** | <u>83.19</u> |
| OpenBookQA (5-shot) | 45.00 | 49.00 | 50.00 | <u>50.20</u> | 49.60 | **50.80** | 49.80 |
| ARC-C (0-shot) | 51.19 | 56.91 | 56.83 | 60.58 | **64.68** | 59.30 | <u>62.12</u> |
| GSM8K (4-shot CoT) | 84.31 | 86.73 | 85.60 | 81.43 | 72.02 | **90.07** | <u>89.16</u> |
| MATH (4-shot CoT) | 50.16 | 52.48 | 56.16 | 57.30 | 43.30 | <u>59.70</u> | **65.88** |
| GPQA-diamond (3-shot CoT) | 32.65 | 35.71 | <u>37.76</u> | 31.12 | 23.47 | <u>37.76</u> | **45.41** |
| MBPP (0-shot) | 73.81 | 75.66 | <u>77.51</u> | 73.81 | 73.28 | **84.92** | 76.19 |
| EvalPlus Avg (0-shot) | 63.96 | <u>67.95</u> | 59.54 | 61.20 | 53.48 | **73.41** | 66.45 |
| | | | | | | | |
| **Avg General** | 62.39 | 66.67 | 67.86 | 65.04 | 60.98 | <u>69.22</u> | **70.75** |
| **Avg All** | 61.88 | 65.61 | 66.24 | 65.39 | 61.32 | <u>69.20</u> | **69.99** |
- **Avg General**: average of knowledge, reasoning, and commonsense benchmarks (MMLU, MMLU-Pro, AGIEval-en, BBH, HellaSwag, Winogrande, PIQA, OpenBookQA, ARC-C).
- **Avg All**: average of all benchmarks above, including math, STEM, and code (+ GSM8K, MATH, GPQA-diamond, MBPP, EvalPlus Avg).
---
## Citation
If you find this work useful, please cite:
```bibtex
@misc{openseek-mid-v1,
title={OpenSeek-Mid-v1: Efficient Language Model Scaling via Seed Model Expansion},
year={2026},
note={Technical report coming soon}
}
```
---
## Acknowledgements
This project was built using open-source data and tools, including NemotronCC-v2, Stack-Edu, Dolmino, CCI, OpenMathInstruct, OpenCodeReasoning, and FlagOS.

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_qwen3.Qwen3Config",
"AutoModelForCausalLM": "modeling_qwen3.Qwen3ForCausalLM"
},
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.006,
"intermediate_size": 19456,
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 56,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": 4096,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.51.3",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151851
}

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{"framework": "pytorch", "task": "others", "allow_remote": true}

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# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Qwen3 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Qwen3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen3Model`]. It is used to instantiate a
Qwen3 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen3-8B [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 151936):
Vocabulary size of the Qwen3 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen3Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 22016):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 32):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
head_dim (`int`, *optional*, defaults to 128):
The attention head dimension.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 28):
The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import Qwen3Model, Qwen3Config
>>> # Initializing a Qwen3 style configuration
>>> configuration = Qwen3Config()
>>> # Initializing a model from the Qwen3-8B style configuration
>>> model = Qwen3Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen3"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Qwen3`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=151936,
hidden_size=4096,
intermediate_size=22016,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=32,
head_dim=128,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=28,
attention_dropout=0.0,
**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.head_dim = head_dim
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_bias = attention_bias
self.attention_dropout = attention_dropout
# 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)
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
__all__ = ["Qwen3Config"]

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# 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
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{
"bos_token": "<|extra_203|>",
"eos_token": "<|extra_204|>",
"unk_token": "<|endoftext|>",
"pad_token": "<|endoftext|>"
}

276
tokenization_qwen.py Normal file
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# 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)

10
tokenizer_config.json Normal file
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{
"model_max_length": 8192,
"tokenizer_class": "QWenTokenizer",
"auto_map": {
"AutoTokenizer": [
"tokenization_qwen.QWenTokenizer",
null
]
}
}