Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
+license: llama3
 language:
 - en
 metrics:
@@ -15,6 +15,40 @@ base_model: THU-KEG/Llama3-Crab-SFT
 This model was converted to GGUF format from [`THU-KEG/Llama3-Crab-SFT`](https://huggingface.co/THU-KEG/Llama3-Crab-SFT) 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/THU-KEG/Llama3-Crab-SFT) for more details on the model.
 
+---
+Model details:
+-
+ Large language models (LLMs) struggle to follow instructions with complex constraints in format, length, etc. Following the conventional instruction-tuning practice, previous works conduct post-training on complex instruction-response pairs generated by feeding complex instructions to advanced LLMs. However, even advanced LLMs cannot follow complex instructions well, thus limiting the quality of generated data. In this work, we find that existing datasets inherently contain implicit complex constraints and propose a novel data generation technique, constraint back-translation. Specifically, we take the high-quality instruction-response pairs in existing datasets and only adopt advanced LLMs to add complex constraints already met by the responses to the instructions, which naturally reduces costs and data noise. In the experiments, we adopt Llama3-70B-Instruct to back-translate constraints and create a high-quality complex instruction-response dataset, named CRAB. We present that post-training on CRAB improves multiple backbone LLMs' complex instruction-following ability, evaluated on extensive instruction-following benchmarks. We further find that constraint back-translation also serves as a useful auxiliary training objective in post-training.
+
+    📖 Paper: Constraint Back-translation Improves Complex Instruction Following of Large Language Models
+
+    🦀 Github: THU/Crab
+
+Model Performance
+Models 	BaseModel 	IFEval 	FollowBench(HSR) 			AVG
+		AVG 	L1-L2 	L3-L5 	AVG 	
+GPT-3.5-turbo 	GPT 	66.3 	74.2 	61 	66.2 	66.3
+GPT-4 	GPT 	81.3 	80.4 	69.4 	73.8 	77.6
+Vicuna-13b-V1.5 	Llama2 	50.3 	66.3 	39.8 	50.4 	50.4
+WizardLM-13B-V1.2 	Llama2 	51.4 	56.5 	36.9 	44.7 	48
+Conifer-13B 	Llama2 	50.2 	57.1 	40.3 	47 	48.6
+Zephyr-7B-beta 	Mistral 	45.4 	54.8 	38.2 	44.8 	45.1
+Conifer-7B 	Mistral 	53.9 	51.9 	40.2 	44.9 	49.4
+Conifer-7B-DPO 	Mistral 	55.7 	57 	45.4 	50 	52.9
+Llama3 8B 	Llama3 	31.4 	6.8 	8.2 	7.6 	19.5
+Llama3-crab 	Llama3 	46.9 	51.2 	26.7 	36.5 	41.7
+Llama3-crab + DPO 	Llama3 	49.7 	56.8 	38.1 	45.5 	47.6
+Mistral 7B 	Mistral 	25.2 	15.5 	6.5 	10.1 	17.7
+Mistral-crab 	Mistral 	54.5 	59.2 	32.8 	43.3 	48.9
+Mistral-crab + DPO 	Mistral 	59.4 	59.9 	42.5 	49.4 	54.4
+Model Description
+
+    Developed by: Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
+    Model type: Text Generation
+    Language(s) (NLP): English
+    Finetuned from model [optional]: Llama3-8B
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)
 
@@ -53,4 +87,4 @@ Step 3: Run inference through the main binary.
 or 
 ```
 ./llama-server --hf-repo Triangle104/Llama3-Crab-SFT-Q4_K_S-GGUF --hf-file llama3-crab-sft-q4_k_s.gguf -c 2048
-```
+```