Update README.md

README.md

@@ -2,6 +2,7 @@
 base_model: Solshine/reflection-llama-3.1-8B-Solshine-trainround3-16bit
 datasets:
 - mahiatlinux/Reflection-Dataset-v2
+- Harshkmr/orca-math-word-reflection
 language:
 - en
 license: llama3.1
@@ -21,6 +22,46 @@ tags:
 This model was converted to GGUF format from [`Solshine/reflection-llama-3.1-8B-Solshine-trainround3-16bit`](https://huggingface.co/Solshine/reflection-llama-3.1-8B-Solshine-trainround3-16bit) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/Solshine/reflection-llama-3.1-8B-Solshine-trainround3-16bit) for more details on the model.
 
+# Uploaded  model
+
+- **Developed by:** Solshine (Caleb DeLeeuw)
+- **License:** LLama 3.1 License
+- **Finetuned from model :** Solshine/reflection-llama-3.1-8B-Solshine-trainround2-16bit
+
+Inspired by and featuring the Reflection Tuning technique pioneered by Matt Shumer (possibly earlier innovated by the team at Anthropic.)
+
+*To the authors' knowledge, this is V3 of the first "reflection tuned" Llama 3.1 8B LLM*
+
+
+**As per the inspiring model "mattshumer/Reflection-Llama-3.1-70B" (this mode was not used in the training process nor as a foundational model, but only served as inspiration) :**
+
+'''
+
+During sampling, the model will start by outputting reasoning inside <thinking> and </thinking> tags, and then once it is satisfied with its reasoning, it will output the final answer inside <output> and </output> tags. Each of these tags are special tokens, trained into the model.
+
+This enables the model to separate its internal thoughts and reasoning from its final answer, improving the experience for the user.
+
+Inside the <thinking> section, the model may output one or more <reflection> tags, which signals the model has caught an error in its reasoning and will attempt to correct it before providing a final answer.
+
+System Prompt:
+The system prompt used for training this model is:
+
+You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.
+
+We recommend using this exact system prompt to get the best results from Reflection Llama-3.1 70B. You may also want to experiment combining this system prompt with your own custom instructions to customize the behavior of the model.
+
+Chat Format:
+As mentioned above, the model uses the standard Llama 3.1 chat format. Here’s an example:
+
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+'''
+
+
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)
 
@@ -59,4 +100,4 @@ Step 3: Run inference through the main binary.
 or 
 ```
 ./llama-server --hf-repo Solshine/reflection-llama-3.1-8B-Solshine-trainround3-16bit-Q4_K_M-GGUF --hf-file reflection-llama-3.1-8b-solshine-trainround3-16bit-q4_k_m.gguf -c 2048
-```
+```