Create README.md

README.md

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+---
+base_model: openlm-research/open_llama_3b
+datasets:
+- mwitiderrick/OpenPlatypus
+inference: true
+model_type: llama
+prompt_template: |
+  ### Instruction:\n
+  {prompt}
+  ### Response:
+created_by: mwitiderrick
+tags:
+- transformers
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+
+model-index:
+  - name: mwitiderrick/open_llama_3b_instruct_v_0.2
+    results:
+      - task:
+          type: text-generation
+        dataset:
+          name: hellaswag
+          type: hellaswag
+        metrics:
+          - name: hellaswag(0-Shot)
+            type: hellaswag (0-Shot)
+            value: 0.4882
+      - task:
+          type: text-generation
+        dataset:
+          name: winogrande
+          type: winogrande
+        metrics:
+          - name: winogrande(0-Shot)
+            type: winogrande (0-Shot)
+            value: 0.6133
+
+      - task:
+          type: text-generation
+        dataset:
+          name: arc_challenge
+          type: arc_challenge
+        metrics:
+          - name: arc_challenge(0-Shot)
+            type: arc_challenge (0-Shot)
+            value: 0.3362
+        source:
+          name: open_llama_3b_instruct_v_0.2 model card
+          url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
+
+          
+---
+# OpenLLaMA Instruct: An Open Reproduction of LLaMA
+
+This is an [OpenLlama model](https://huggingface.co/openlm-research/open_llama_3b) that has been fine-tuned on 1 epoch of the
+[Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) dataset.
+
+The modified version of the dataset can be found [here](mwitiderrick/Open-Platypus)
+## Prompt Template
+```
+### Instruction:
+
+{query}
+
+### Response:
+<Leave new line for model to respond> 
+```
+## Usage 
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_instruct_v_0.2")
+model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_instruct_v_0.2")
+query = "Provide step-by-step instructions for making a sweet chicken bugger"
+text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=500)
+output = text_gen(f"### Instruction:\n{query}\n### Response:\n")
+print(output[0]['generated_text'])
+"""
+
+"""
+```
+## Metrics
+```
+
+```