feat: References from Training LLMs topic to sources

checklist.md

@@ -111,7 +111,7 @@
     - [ ] Fine-tuning or Training LLMs
       - [x] 250 words
       - [ ] LLMs copyedit
-      - [ ] References copied into resources
+      - [x] References copied into resources
   - [ ] Generative AI Use Cases for FOLIO
     - [ ] Analysis and Management of Financial Orders and Invoices
     - [ ] Automated Metadata Generation and Enrichment

exploring-llms/training-llms.html

@@ -46,11 +46,11 @@ <h2>Fine-tuning LLMs with Llama.cpp</h2>
 don't want or can't compile the C++ source code to run on your computer.</p>
 <h3>Downloading a LLaMA-based Model</h3>
 <p><a href="https://github.com/ggerganov/llama.cpp">LLaMA.cpp</a> uses the <a href="https://github.com/ggerganov/ggml/blob/master/docs/gguf.md">GGUF</a> 
-format for model inference and training. Look for GGUF models on <a href="https://huggingface.co/l">HuggingFace</a>
+format for model inference and training. Look for GGUF models on <a href="https://huggingface.co/">HuggingFace</a>
 and if you compiled <a href="https://github.com/ggerganov/llama.cpp">LLaMA.cpp</a> with <code>libcurl</code> support, you can use the <code>llama-cli</code> command-line
 client to download:</p>
 <p><code>./llama-cli --hf-repo lmstudio-community/Reflection-Llama-3.1-70B-GGUF --hf-file Reflection-Llama-3.1-70B-GGUF.gguf</code></p>
-<p>If <code>libcurl</code> hasn't been installed, you can usually directly download the models directly from <a href="https://huggingface.co/l">HuggingFace</a> and
+<p>If <code>libcurl</code> hasn't been installed, you can usually directly download the models directly from <a href="https://huggingface.co/">HuggingFace</a> and
 store in the <code>/models</code> directory under the main <a href="https://github.com/ggerganov/llama.cpp">LLaMA.cpp</a>.</p>
 <h3>Running the Model in Inference Mode</h3>
 <div class="footnote">

exploring-llms/training-llms.md

@@ -40,7 +40,7 @@ store in the `/models` directory under the main [LLaMA.cpp][LLAMA.CCP].
 
 ### Running the Model in Inference Mode
 
-[HUGFACE]: https://huggingface.co/l
+[HUGFACE]: https://huggingface.co/
 [LLAMA]: https://ai.meta.com/
 [LLAMA.CCP]: https://github.com/ggerganov/llama.cpp
 [OPENAI]: https://openai.com/

recommended-resources-for-further-learning/sources.html

@@ -209,6 +209,16 @@ <h3>Retrieval Augmented Generation (RAG)</h3>
 <ul>
 <li><a href="https://www.smashingmagazine.com/2024/01/guide-retrieval-augmented-generation-language-models/">A Simple Guide To Retrieval Augmented Generation Language Models</a></li>
 <li><a href="https://arxiv.org/abs/2005.11401">Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks</a></li>
+</ul>
+<h3>Training Large Language Models (LLMs)</h3>
+<ul>
+<li><a href="https://github.com/ggerganov/ggml/blob/master/docs/gguf.md">GGUF</a></li>
+<li><a href="https://huggingface.co/">HuggingFace</a> </li>
+<li><a href="https://ai.meta.com/">LLaMA from AI Meta</a></li>
+<li><a href="https://github.com/ggerganov/llama.cpp">LLaMA.cpp</a></li>
+<li><a href="https://github.com/ggerganov/llama.cpp/blob/master/docs/docker.md">LLaMA.cpp with Docker</a></li>
+<li><a href="https://github.com/abetlen/llama-cpp-python">LLaMA.cpp Python SDK</a></li>
+<li><a href="https://openai.com/">Open AI</a></li>
 </ul>
         </article>
         <div class="col-3">

recommended-resources-for-further-learning/sources.md

@@ -168,3 +168,14 @@
 ### Retrieval Augmented Generation (RAG)
 - [A Simple Guide To Retrieval Augmented Generation Language Models](https://www.smashingmagazine.com/2024/01/guide-retrieval-augmented-generation-language-models/)
 - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)
+
+### Training Large Language Models (LLMs)
+- [GGUF](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md)
+- [HuggingFace](https://huggingface.co/) 
+- [LLaMA from AI Meta](https://ai.meta.com/)
+- [LLaMA.cpp](https://github.com/ggerganov/llama.cpp)
+- [LLaMA.cpp with Docker](https://github.com/ggerganov/llama.cpp/blob/master/docs/docker.md)
+- [LLaMA.cpp Python SDK](https://github.com/abetlen/llama-cpp-python)
+- [Open AI](https://openai.com/)
+
+