mjschock commited on
Commit
04d059b
·
unverified ·
1 Parent(s): 8bd5794

Update requirements.txt to specify unsloth version 2025.4.5 and refactor imports in train.py for improved organization and clarity.

Browse files
Files changed (2) hide show
  1. requirements.txt +1 -1
  2. train.py +6 -3
requirements.txt CHANGED
@@ -30,7 +30,7 @@ smolagents[litellm,telemetry]>=1.14.0
30
  tensorboardX>=2.6.2.2
31
  trl>=0.17.0
32
  typing-extensions>=4.5.0
33
- unsloth>=2025.4.3
34
  wandb>=0.19.10
35
  wikipedia>=1.4.0
36
  wikipedia-api>=0.8.1
 
30
  tensorboardX>=2.6.2.2
31
  trl>=0.17.0
32
  typing-extensions>=4.5.0
33
+ unsloth>=2025.4.5
34
  wandb>=0.19.10
35
  wikipedia>=1.4.0
36
  wikipedia-api>=0.8.1
train.py CHANGED
@@ -19,6 +19,12 @@ from datetime import datetime
19
  from pathlib import Path
20
  from typing import Union
21
 
 
 
 
 
 
 
22
  from datasets import (
23
  Dataset,
24
  DatasetDict,
@@ -28,8 +34,6 @@ from datasets import (
28
  )
29
  from transformers import AutoTokenizer, Trainer, TrainingArguments
30
  from trl import SFTTrainer
31
- from unsloth import FastLanguageModel, is_bfloat16_supported
32
- from unsloth.chat_templates import get_chat_template
33
 
34
  # Configuration
35
  max_seq_length = 2048 # Auto supports RoPE Scaling internally
@@ -193,7 +197,6 @@ def create_trainer(
193
  tokenizer=tokenizer,
194
  train_dataset=dataset["train"],
195
  eval_dataset=dataset["validation"],
196
- max_seq_length=max_seq_length,
197
  dataset_num_proc=2,
198
  packing=False,
199
  args=TrainingArguments(
 
19
  from pathlib import Path
20
  from typing import Union
21
 
22
+ # isort: off
23
+ from unsloth import FastLanguageModel, is_bfloat16_supported # noqa: E402
24
+ from unsloth.chat_templates import get_chat_template # noqa: E402
25
+
26
+ # isort: on
27
+
28
  from datasets import (
29
  Dataset,
30
  DatasetDict,
 
34
  )
35
  from transformers import AutoTokenizer, Trainer, TrainingArguments
36
  from trl import SFTTrainer
 
 
37
 
38
  # Configuration
39
  max_seq_length = 2048 # Auto supports RoPE Scaling internally
 
197
  tokenizer=tokenizer,
198
  train_dataset=dataset["train"],
199
  eval_dataset=dataset["validation"],
 
200
  dataset_num_proc=2,
201
  packing=False,
202
  args=TrainingArguments(