diff --git a/README.md b/README.md
index 9ad41546cf..af02f3f667 100644
--- a/README.md
+++ b/README.md
@@ -50,7 +50,7 @@ For Windows install instructions, see [here](https://docs.unsloth.ai/get-started
 - 📣 NEW! **[Qwen3](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** is now supported! Qwen3-30B-A3B fits on 17.5GB VRAM.
 - 📣 NEW! Introducing **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants that set new benchmarks on 5-shot MMLU & KL Divergence.
 - 📣 **[Llama 4](https://unsloth.ai/blog/llama4)**, Meta's latest models including Scout & Maverick are now supported.
-- 📣 NEW! [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) incuding: FFT, ALL models (Mixtral, MOE, Cohere, Mamba) and all training algorithms (KTO, DoRA) etc. MultiGPU support coming very soon.
+- 📣 NEW! [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) including: FFT, ALL models (Mixtral, MOE, Cohere, Mamba) and all training algorithms (KTO, DoRA) etc. MultiGPU support coming very soon.
   To enable full-finetuning, set ```full_finetuning = True``` and for 8-bit finetuning, set ```load_in_8bit = True```
 - 📣 **Gemma 3** by Google: [Read Blog](https://unsloth.ai/blog/gemma3). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b).
 - 📣 Introducing Long-context [Reasoning (GRPO)](https://unsloth.ai/blog/grpo) in Unsloth. Train your own reasoning model with just 5GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs!
@@ -118,7 +118,7 @@ See [here](https://github.com/unslothai/unsloth/edit/main/README.md#advanced-pip
    Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).
 
 6. **Install PyTorch:**
-   You will need the correct version of PyTorch that is compatibile with your CUDA drivers, so make sure to select them carefully.
+   You will need the correct version of PyTorch that is compatible with your CUDA drivers, so make sure to select them carefully.
    [Install PyTorch](https://pytorch.org/get-started/locally/).
 
 7. **Install Unsloth:**
@@ -143,7 +143,7 @@ trainer = SFTTrainer(
 For **advanced installation instructions** or if you see weird errors during installations:
 
 1. Install `torch` and `triton`. Go to https://pytorch.org to install it. For example `pip install torch torchvision torchaudio triton`
-2. Confirm if CUDA is installated correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers.
+2. Confirm if CUDA is installed correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers.
 3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. Check if `xformers` succeeded with `python -m xformers.info` Go to https://github.com/facebookresearch/xformers. Another option is to install `flash-attn` for Ampere GPUs.
 4. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix) may be useful. 
 5. Finally, install `bitsandbytes` and check it with `python -m bitsandbytes`
@@ -244,7 +244,7 @@ from unsloth import FastLanguageModel, FastModel
 import torch
 from trl import SFTTrainer, SFTConfig
 from datasets import load_dataset
-max_seq_length = 2048 # Supports RoPE Scaling interally, so choose any!
+max_seq_length = 2048 # Supports RoPE Scaling internally, so choose any!
 # Get LAION dataset
 url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
 dataset = load_dataset("json", data_files = {"train" : url}, split = "train")
diff --git a/tests/qlora/README.md b/tests/qlora/README.md
index e535c38760..c05dcf446d 100644
--- a/tests/qlora/README.md
+++ b/tests/qlora/README.md
@@ -39,7 +39,7 @@ For the unsloth test, the model's behavior is as expected:
 - after merging, the model's response contains the answer
 
 For the huggingface test, the model's behavior is as expected:
-- before training, the model's response does not contains the answer
+- before training, the model's response does not contain the answer
 - after training, the model's response contains the answer
 - after using peft's `merge_and_unload`, the model's response does not contain the answer
 - after using my custom merge function, the model's response contains the answer
diff --git a/unsloth/models/_utils.py b/unsloth/models/_utils.py
index 882de28cba..6065af1fc1 100644
--- a/unsloth/models/_utils.py
+++ b/unsloth/models/_utils.py
@@ -147,7 +147,7 @@ class HideLoggingMessage(logging.Filter):
     def filter(self, x): return not (self.text in x.getMessage())
 pass
 
-# The speedups for torchdynamo mostly come wih GPU Ampere or higher and which is not detected here.
+# The speedups for torchdynamo mostly come with GPU Ampere or higher and which is not detected here.
 from transformers.training_args import logger as transformers_training_args_logger
 transformers_training_args_logger.addFilter(HideLoggingMessage("The speedups"))
 # torch.distributed process group is initialized, but parallel_mode != ParallelMode.DISTRIBUTED.
diff --git a/unsloth/save.py b/unsloth/save.py
index 578f2b49a1..5652e15098 100644
--- a/unsloth/save.py
+++ b/unsloth/save.py
@@ -1717,7 +1717,7 @@ def push_to_ollama(
         tag=tag
     )
 
-    print("Succesfully pushed to ollama")
+    print("Successfully pushed to ollama")