Merge pull request #825 from JackTheGit/fix/docs-typos-batch2

Fix several documentation typos

skills/mlops/training/axolotl/references/dataset-formats.md

@@ -115,7 +115,7 @@ A config for this would look like:
 
 Reference: Pre-Tokenized Dataset Documentation.
 
-We reccomend this approach when you want granular control over the prompt formatting, special tokens, and masking, whilst letting Axolotl handle the tokenization. This is very useful if your dataset has unique prompts that differ across samples and where one single general template wouldn’t suffice.
+We recommend this approach when you want granular control over the prompt formatting, special tokens, and masking, whilst letting Axolotl handle the tokenization. This is very useful if your dataset has unique prompts that differ across samples and where one single general template wouldn’t suffice.
 
 In the example below, you could see that there is no proper structure. At the same time, it’s very flexible as there are no constraints on how your prompt can look.
 
@@ -583,7 +583,7 @@ A config for this would look like:
 
 Reference: Pre-Tokenized Dataset Documentation.
 
-We reccomend this approach when you want granular control over the prompt formatting, special tokens, and masking, whilst letting Axolotl handle the tokenization. This is very useful if your dataset has unique prompts that differ across samples and where one single general template wouldn’t suffice.
+We recommend this approach when you want granular control over the prompt formatting, special tokens, and masking, whilst letting Axolotl handle the tokenization. This is very useful if your dataset has unique prompts that differ across samples and where one single general template wouldn’t suffice.
 
 In the example below, you could see that there is no proper structure. At the same time, it’s very flexible as there are no constraints on how your prompt can look.
 
@@ -796,7 +796,7 @@ A config for this would look like:
 
 Reference: Pre-Tokenized Dataset Documentation.
 
-We reccomend this approach when you want granular control over the prompt formatting, special tokens, and masking, whilst letting Axolotl handle the tokenization. This is very useful if your dataset has unique prompts that differ across samples and where one single general template wouldn’t suffice.
+We recommend this approach when you want granular control over the prompt formatting, special tokens, and masking, whilst letting Axolotl handle the tokenization. This is very useful if your dataset has unique prompts that differ across samples and where one single general template wouldn’t suffice.
 
 In the example below, you could see that there is no proper structure. At the same time, it’s very flexible as there are no constraints on how your prompt can look.
 

skills/mlops/training/unsloth/references/llms-full.md

@@ -1387,7 +1387,7 @@ 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`
@@ -1824,7 +1824,7 @@ For LLMs, datasets are collections of data that can be used to train our models.
 [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide)
 {% endcontent-ref %}
 
-For most of our notebook examples, we utilize the [Alpaca dataset](https://docs.unsloth.ai/basics/tutorial-how-to-finetune-llama-3-and-use-in-ollama#id-6.-alpaca-dataset) however other notebooks like Vision will use different datasets which may need images in the answer ouput as well.
+For most of our notebook examples, we utilize the [Alpaca dataset](https://docs.unsloth.ai/basics/tutorial-how-to-finetune-llama-3-and-use-in-ollama#id-6.-alpaca-dataset) however other notebooks like Vision will use different datasets which may need images in the answer output as well.
 
 ## 4. Understand Training Hyperparameters
 
@@ -13280,7 +13280,7 @@ if __name__ == '__main__':
 ## :detective: Extra Findings & Tips
 
 1. We find using lower KV cache quantization (4bit) seems to degrade generation quality via empirical tests - more tests need to be done, but we suggest using `q8_0` cache quantization. The goal of quantization is to support longer context lengths since the KV cache uses quite a bit of memory.
-2. We found the `down_proj` in this model to be extremely sensitive to quantitation. We had to redo some of our dyanmic quants which used 2bits for `down_proj` and now we use 3bits as the minimum for all these matrices.
+2. We found the `down_proj` in this model to be extremely sensitive to quantitation. We had to redo some of our dynamic quants which used 2bits for `down_proj` and now we use 3bits as the minimum for all these matrices.
 3. Using `llama.cpp` 's Flash Attention backend does result in somewhat faster decoding speeds. Use `-DGGML_CUDA_FA_ALL_QUANTS=ON` when compiling. Note it's also best to set your CUDA architecture as found in <https://developer.nvidia.com/cuda-gpus> to reduce compilation times, then set it via `-DCMAKE_CUDA_ARCHITECTURES="80"`&#x20;
 4. Using a `min_p=0.01`is probably enough. `llama.cpp`defaults to 0.1, which is probably not necessary. Since a temperature of 0.3 is used anyways, we most likely will very unlikely sample low probability tokens, so removing very unlikely tokens is a good idea. DeepSeek recommends 0.0 temperature for coding tasks.
 
@@ -16682,7 +16682,7 @@ Advanced flags which might be useful if you see breaking finetunes, or you want
 
 <table><thead><tr><th width="397.4666748046875">Environment variable</th><th>Purpose</th><th data-hidden></th></tr></thead><tbody><tr><td><code>os.environ["UNSLOTH_RETURN_LOGITS"] = "1"</code></td><td>Forcibly returns logits - useful for evaluation if logits are needed.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_COMPILE_DISABLE"] = "1"</code></td><td>Disables auto compiler. Could be useful to debug incorrect finetune results.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_DISABLE_FAST_GENERATION"] = "1"</code></td><td>Disables fast generation for generic models.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_ENABLE_LOGGING"] = "1"</code></td><td>Enables auto compiler logging - useful to see which functions are compiled or not.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_FORCE_FLOAT32"] = "1"</code></td><td>On float16 machines, use float32 and not float16 mixed precision. Useful for Gemma 3.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_STUDIO_DISABLED"] = "1"</code></td><td>Disables extra features.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_COMPILE_DEBUG"] = "1"</code></td><td>Turns on extremely verbose <code>torch.compile</code>logs.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_COMPILE_MAXIMUM"] = "0"</code></td><td>Enables maximum <code>torch.compile</code>optimizations - not recommended.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_COMPILE_IGNORE_ERRORS"] = "1"</code></td><td>Can turn this off to enable fullgraph parsing.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_FULLGRAPH"] = "0"</code></td><td>Enable <code>torch.compile</code> fullgraph mode</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_DISABLE_AUTO_UPDATES"] = "1"</code></td><td>Forces no updates to <code>unsloth-zoo</code></td><td></td></tr></tbody></table>
 
-Another possiblity is maybe the model uploads we uploaded are corrupted, but unlikely. Try the following:
+Another possibility is maybe the model uploads we uploaded are corrupted, but unlikely. Try the following:
 
 ```python
 model, tokenizer = FastVisionModel.from_pretrained(

skills/mlops/training/unsloth/references/llms-txt.md

@@ -855,7 +855,7 @@ To run Unsloth directly on Windows:
 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`
@@ -2994,7 +2994,7 @@ if __name__ == '__main__':
 ## :detective: Extra Findings & Tips
 
 1. We find using lower KV cache quantization (4bit) seems to degrade generation quality via empirical tests - more tests need to be done, but we suggest using `q8_0` cache quantization. The goal of quantization is to support longer context lengths since the KV cache uses quite a bit of memory.
-2. We found the `down_proj` in this model to be extremely sensitive to quantitation. We had to redo some of our dyanmic quants which used 2bits for `down_proj` and now we use 3bits as the minimum for all these matrices.
+2. We found the `down_proj` in this model to be extremely sensitive to quantitation. We had to redo some of our dynamic quants which used 2bits for `down_proj` and now we use 3bits as the minimum for all these matrices.
 3. Using `llama.cpp` 's Flash Attention backend does result in somewhat faster decoding speeds. Use `-DGGML_CUDA_FA_ALL_QUANTS=ON` when compiling. Note it's also best to set your CUDA architecture as found in <https://developer.nvidia.com/cuda-gpus> to reduce compilation times, then set it via `-DCMAKE_CUDA_ARCHITECTURES="80"`&#x20;
 4. Using a `min_p=0.01`is probably enough. `llama.cpp`defaults to 0.1, which is probably not necessary. Since a temperature of 0.3 is used anyways, we most likely will very unlikely sample low probability tokens, so removing very unlikely tokens is a good idea. DeepSeek recommends 0.0 temperature for coding tasks.
 
@@ -3509,7 +3509,7 @@ Advanced flags which might be useful if you see breaking finetunes, or you want
 
 <table><thead><tr><th width="397.4666748046875">Environment variable</th><th>Purpose</th><th data-hidden></th></tr></thead><tbody><tr><td><code>os.environ["UNSLOTH_RETURN_LOGITS"] = "1"</code></td><td>Forcibly returns logits - useful for evaluation if logits are needed.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_COMPILE_DISABLE"] = "1"</code></td><td>Disables auto compiler. Could be useful to debug incorrect finetune results.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_DISABLE_FAST_GENERATION"] = "1"</code></td><td>Disables fast generation for generic models.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_ENABLE_LOGGING"] = "1"</code></td><td>Enables auto compiler logging - useful to see which functions are compiled or not.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_FORCE_FLOAT32"] = "1"</code></td><td>On float16 machines, use float32 and not float16 mixed precision. Useful for Gemma 3.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_STUDIO_DISABLED"] = "1"</code></td><td>Disables extra features.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_COMPILE_DEBUG"] = "1"</code></td><td>Turns on extremely verbose <code>torch.compile</code>logs.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_COMPILE_MAXIMUM"] = "0"</code></td><td>Enables maximum <code>torch.compile</code>optimizations - not recommended.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_COMPILE_IGNORE_ERRORS"] = "1"</code></td><td>Can turn this off to enable fullgraph parsing.</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_FULLGRAPH"] = "0"</code></td><td>Enable <code>torch.compile</code> fullgraph mode</td><td></td></tr><tr><td><code>os.environ["UNSLOTH_DISABLE_AUTO_UPDATES"] = "1"</code></td><td>Forces no updates to <code>unsloth-zoo</code></td><td></td></tr></tbody></table>
 
-Another possiblity is maybe the model uploads we uploaded are corrupted, but unlikely. Try the following:
+Another possibility is maybe the model uploads we uploaded are corrupted, but unlikely. Try the following:
 
 **Examples:**
 
@@ -9120,7 +9120,7 @@ For LLMs, datasets are collections of data that can be used to train our models.
 [datasets-guide](https://docs.unsloth.ai/get-started/fine-tuning-llms-guide/datasets-guide)
 {% endcontent-ref %}
 
-For most of our notebook examples, we utilize the [Alpaca dataset](https://docs.unsloth.ai/basics/tutorial-how-to-finetune-llama-3-and-use-in-ollama#id-6.-alpaca-dataset) however other notebooks like Vision will use different datasets which may need images in the answer ouput as well.
+For most of our notebook examples, we utilize the [Alpaca dataset](https://docs.unsloth.ai/basics/tutorial-how-to-finetune-llama-3-and-use-in-ollama#id-6.-alpaca-dataset) however other notebooks like Vision will use different datasets which may need images in the answer output as well.
 
 ## 4. Understand Training Hyperparameters