add reference

README.md

@@ -186,39 +186,19 @@ LlamaForCausalLM(
 )
 ```
 
-## Evaluation
-We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).  The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/).
-
-The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens.  We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks.
-
-
-| **Task/Metric**        | finetuned-GPT 3B  | OpenLLaMA 3B |
-| ---------------------- | -------- | ------------ |
-| anli_r1/acc            | **0.35** | 0.33         |      
-| anli_r2/acc            | **0.33** | 0.32         | 
-| anli_r3/acc            | 0.35     | 0.35         | 
-| arc_challenge/acc      | **0.35** | 0.34         | 
-| arc_challenge/acc_norm | 0.37     | 0.37         |
-| arc_easy/acc           | **0.71** | 0.69         | 
-| arc_easy/acc_norm      | 0.65     | 0.65         | 
-| boolq/acc              | **0.72** | 0.66         | 
-| hellaswag/acc          | **0.49** | 0.43         | 
-| hellaswag/acc_norm     | 0.66     | **0.67**         |
-| openbookqa/acc         | 0.26     | **0.27**         | 
-| openbookqa/acc_norm    | 0.40     | 0.40         | 
-| piqa/acc               | **0.76** | 0.75         | 
-| piqa/acc_norm          | 0.76     | 0.76         | 
-| record/em              | 0.88     | 0.88         | 
-| record/f1              | 0.88     | **0.89**         | 
-| rte/acc                | 0.55     | **0.58**         | 
-| truthfulqa_mc/mc1      | **0.27** | 0.22         | 
-| truthfulqa_mc/mc2      | **0.37** | 0.35         |
-| wic/acc                | **0.49** | 0.48         | 
-| winogrande/acc         | **0.63** | 0.62         | 
-| Average                | **0.53** | 0.52         | 
-
-
-We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set.
+## Citation
+
+If this work is helpful, please kindly cite as:
+
+```bibtex
+@Misc{mamba-gpt-3b-v2,
+  title = {Mamba-GPT-3b-v2},
+  author = {chiliu},
+  howpublished = {\url{https://huggingface.co/CobraMamba/mamba-gpt-3b-v2}},
+  year = {2023}
+}
+```
+
 
 ## Disclaimer
 

adapter_config.json

@@ -0,0 +1,21 @@
+{
+  "auto_mapping": null,
+  "base_model_name_or_path": "openlm-research/open_llama_3b_v2",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 16,
+  "lora_dropout": 0.1,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 256,
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}