Update README.md

README.md

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 license: llama2
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+
+# Dromedary-2 (verbose, v1) Model Card
+
+## Model details
+
+<div style="display: flex; justify-content: center; align-items: center;">
+    <img src="https://raw.githubusercontent.com/IBM/SALMON/main/assets/images/salmon_logo_with_text.jpeg" alt="SALMON Logo" style="height: 256px; margin-right: 10px;"/>
+    <img src="https://raw.githubusercontent.com/IBM/Dromedary/main/assets/images/dromedary_logo.svg" alt="Dromedary Logo" style="height: 256px;"/>
+</div>
+
+**Model type:**
+Dromedary-2 is an open-source self-aligned language model trained in minimal human supervision with the SALMON (Self-Alignment with Principle-Following Reward Models) technique.
+The base language model is LLaMA-70b, based on the transformer architecture.
+
+**NOTE: *Dromedary-2* is trained with [QLoRA](https://github.com/artidoro/qlora) and the bfloat16 data type.** While it is [possible](https://gist.github.com/ChrisHayduk/1a53463331f52dca205e55982baf9930) to merge the QLoRA weights with the quantized model and thus enable inference with libraries such as [TGI](https://github.com/huggingface/text-generation-inference) and [vLLM](https://github.com/vllm-project/vllm), we found the merged weights can lead to degenerated performance. Therefore, we recommend directly loading the QLoRA weights with the [PEFT-LoRA](https://github.com/huggingface/peft) framework.
+
+Please check the [inference section](https://github.com/IBM/SALMON/inference) of our repo for the complete inference code.
+
+```python
+system_prompt = (
+      "# Dromedary\n\n## System Overview\n\n"
+      "Consider an AI assistant whose codename is Dromedary, developed by the Self-Align team. "
+      "Dromedary is trained on data up until Sept-2022, and it endeavors to be a helpful, ethical and reliable assistant.\n\n"
+      "## User Conversation\n\n"
+)
+user_prompt = "### User\n"
+assistant_prompt = "### Dromedary\n"
+seperator = "\n\n"
+
+dtype = torch.bfloat16
+
+model_path = "path/to/llama-2-70b-hf"
+qlora_path = "path/to/dromedary-2-70b-qlora-delta-v0"  # i.e., this model hub
+
+bnb_config = BitsAndBytesConfig(
+      load_in_4bit=True,
+      bnb_4bit_compute_dtype=dtype,
+      bnb_4bit_use_double_quant=True,
+      bnb_4bit_quant_type="nf4",
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+      model_path,
+      load_in_4bit=True,
+      device_map={"": "cuda:0"},
+      quantization_config=bnb_config,
+      torch_dtype=dtype,
+)
+
+model = PeftModel.from_pretrained(
+      model,
+      qlora_path,
+      is_trainable=False,
+)
+```
+
+**Model date:**
+Dromedary was trained between July 2023 and Aug 2023, but its knowledge only goes up until Sept-2022.
+
+**License:**
+LLaMA-2's bespoke license
+
+## More Information
+
+**Paper or resources for more information:**
+[placeholder]
+
+**Where to send questions or comments about the model:**
+https://github.com/IBM/SALMON/issues
+
+**Organizations developing the model:**
+The Self-Align team is a joint effort between CMU and IBM.
+
+## Intended use
+**Primary intended uses:**
+The primary use of Dromedary is research on the alignment of large language models.
+
+**Primary intended users:**
+The primary intended users of the model are researchers in artificial intelligence.
+
+## Training dataset
+6 In-Context Learning (ICL) exemplars
+
+90K unlabeled prompts from ShareGPT
+
+10K unlabeled prompts from databricks-dolly-15k
+
+10K unlabeled prompts from OpenAssistant Conversations
+
+40K unlabeled prompts from OpenOrca
+
+7.5K unlabeled prompts from MATH
+
+## Evaluation dataset
+We evaluate Dromedary-2 on:
+1. Chatbot benchmarks: Vicuna-Bench, MT-Bench, AlpacaEval
+2. Capability benchmarks: Big-Bench Hard (reasoning), HumanEval (coding), TydiQA (multilingualism)
+3. Truthfulness benchmarks: TruthfulQA