Update README.md

README.md

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 ---
+license: unknown
 library_name: peft
+tags:
+- llama-2
+datasets:
+- ehartford/dolphin
+- garage-bAInd/Open-Platypus
+inference: false
+pipeline_tag: text-generation
 base_model: meta-llama/Llama-2-7b-hf
 ---
+
+# llama-2-7b-instruct-peft 🦙
+
+This instruction model was built via parameter-efficient QLoRA finetuning of [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the first 5k rows of [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) and the first 5k rows of [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). Finetuning was executed on 4x A6000s (48 GB RTX) for roughly 32 hours on the [Lambda Labs](https://cloud.lambdalabs.com/instances) platform.
+
+### Benchmark metrics
+
+| Metric                | Value |
+|-----------------------|-------|
+| MMLU (5-shot)         | Coming |
+| ARC (25-shot)         | Coming |
+| HellaSwag (10-shot)   | Coming |
+| TruthfulQA (0-shot)   | Coming |
+| Avg.                  | Coming |
+
+We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+
+### Helpful links
+
+* Model license: coming
+* Basic usage: coming
+* Finetuning code: coming
+* Loss curves: coming
+* Runtime stats: coming
+
+## Loss curve
+
+![loss curve](https://raw.githubusercontent.com/daniel-furman/sft-demos/main/assets/sep_12_23_9_20_00_log_loss_curves_Llama-2-7b-instruct.png)
+
+The above loss curve was generated from the run's private wandb.ai log.  
+
+### Example prompts and responses
+
+Example 1: 
+
+**User**:
+> You are a helpful assistant. Write me a numbered list of things to do in New York City.\n
+
+**llama-2-7b-instruct-peft**:
+coming
+
+<br>
+
+Example 2: 
+
+**User**:
+
+> You are a helpful assistant. Write a short email inviting my friends to a dinner party on Friday. Respond succinctly.\n
+
+**llama-2-7b-instruct-peft**:
+
+coming
+
+<br>
+
+Example 3: 
+
+**User**:
+
+> You are a helpful assistant. Tell me a recipe for vegan banana bread.\n
+
+**llama-2-7b-instruct-peft**:
+
+coming
+
+<br>
+
+## Limitations and biases
+
+_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_
+
+This model can produce factually incorrect output, and should not be relied on to produce factually accurate information.
+This model was trained on various public datasets.
+While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
+
+## How to use
+
+coming
+
+### Runtime tests
+
+coming
+
+## Acknowledgements
+
+This model was finetuned by Daniel Furman on Sep 10, 2023 and is for research applications only.
+
+## Disclaimer
+
+The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
+
+## meta-llama/Llama-2-7b-hf citation
+
+```
+coming
+```
+
 ## Training procedure
 
-### Framework versions
+The following `bitsandbytes` quantization config was used during training:
+- quant_method: bitsandbytes
+- load_in_8bit: False
+- load_in_4bit: True
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: nf4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: bfloat16
 
+### Framework versions
 
 - PEFT 0.6.0.dev0