Update README.md

README.md

@@ -3,12 +3,83 @@ base_model: FuseAI/FuseChat-Qwen-2.5-7B-Instruct
 tags:
 - llama-cpp
 - gguf-my-repo
+license: apache-2.0
 ---
 
 # Triangle104/FuseChat-Qwen-2.5-7B-Instruct-Q8_0-GGUF
 This model was converted to GGUF format from [`FuseAI/FuseChat-Qwen-2.5-7B-Instruct`](https://huggingface.co/FuseAI/FuseChat-Qwen-2.5-7B-Instruct) 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/FuseAI/FuseChat-Qwen-2.5-7B-Instruct) for more details on the model.
 
+---
+
+transferring capabilities from source LLMs to a target LLM. First, 
+during dataset construction, we sample N responses from
+ each of the source LLMs and annotate these responses using an external 
+reward model. Second, in the supervised fine-tuning (SFT)
+ stage, we fine-tune the target model using the best responses, which 
+not only enhances the target model's capabilities but also helps 
+mitigate the distributional gap between the source and target models. 
+Finally, in the direct preference optimization (DPO) 
+stage, we optimize the target model by using the best and worst 
+responses from the source models as preference pairs, further enhancing 
+the target model's performance. The complete pipeline will be detailed 
+in the following paragraph.
+
+
+
+	
+		
+	
+
+		Dataset
+	
+
+
+
+
+	
+		
+	
+
+		Prompt Selection
+	
+
+
+
+Our datasets were designed to enhance model's instruction following, 
+general conversation, mathematics, coding, and Chinese-language 
+capabilities. We selected data from open-source community datasets, 
+applying targeted filtering and preprocessing. Key datasets and 
+filtering criteria included:
+
+
+Instruction Following & General Conversation: Sourced from UltraFeedback, Magpie-Pro-DPO-100K-v0.1, and HelpSteer2, excluding code and math data.
+Mathematics: Selected from OpenMathInstruct-2, with nearly 60,000 unique samples.
+Coding: Curated from leetcode and self-oss-instruct-sc2-exec-filter-50k, retaining prompts with test cases.
+Chinese Language: Integrated alpaca_gpt4_zh and Magpie-Qwen2-Pro-200K-Chinese, filtering out code and math prompts to retain approximately 10,000 high-quality samples.
+
+
+
+	
+		
+	
+
+		Response Sampling
+	
+
+
+
+For each dataset's prompts, we synthesized responses mainly from four different series of source models, specifically Gemma-2-27b-It, Mistral-Large-Instruct-2407, Qwen-2.5-72B-Instruct, and Llama-3.1-70B-Instruct.
+
+
+Instruction Following & General Conversation: We sampled each prompt five times from all the source models.
+Mathematics: We retained the responses generated by
+ Llama-3.1-405B-Instruct from the original dataset (OpenMathInstruct-2) 
+and additionally sampled responses using Qwen-2.5-Math-72B-Instruct.
+Coding: We sampled each prompt eight times for all source models.
+Chinese Language: We included single response sampled exclusively from Qwen-2.5-72B-Instruct.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)
 
@@ -47,4 +118,4 @@ Step 3: Run inference through the main binary.
 or 
 ```
 ./llama-server --hf-repo Triangle104/FuseChat-Qwen-2.5-7B-Instruct-Q8_0-GGUF --hf-file fusechat-qwen-2.5-7b-instruct-q8_0.gguf -c 2048
-```
+```