Update README.md

README.md

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 ---
+license: unknown
 library_name: peft
+tags:
+- mistral
+datasets:
+- ehartford/dolphin
+- garage-bAInd/Open-Platypus
+inference: false
+pipeline_tag: text-generation
+base_model: mistralai/Mistral-7B-v0.1
 ---
+
+# mistral-7b-instruct-peft
+
+This instruction model was built via parameter-efficient QLoRA finetuning of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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 1x A100 (40 GB SXM) for roughly 2 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 Eleuther.AI's [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests below, the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+
+## Helpful links
+
+* Model license: Apache 2.0
+* Basic usage: [here]()
+* Finetuning code: [here]()
+* Runtime stats: [here]()
+
+## Loss curve
+
+coming
+
+The above loss curve was generated from the run's private wandb.ai log.  
+
+## Example prompts and responses
+
+Example 1: 
+
+**User**:
+> Write me a numbered list of things to do in New York City.
+
+**mistral-7b-instruct-peft**:
+
+coming
+
+<br>
+
+Example 2: 
+
+**User**:
+
+> Write a short email inviting my friends to a dinner party on Friday. Respond succinctly.
+
+**mistral-7b-instruct-peft**:
+
+coming
+
+<br>
+
+Example 3: 
+
+**User**:
+
+> What is a good recipe for vegan banana bread?
+
+**mistral-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.
+
+## Basic usage
+
+```python
+!pip install -q -U huggingface_hub peft transformers torch accelerate
+```
+
+```python
+from huggingface_hub import notebook_login
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    pipeline,
+)
+
+notebook_login()
+```
+
+```python
+peft_model_id = "dfurman/mistral-7b-instruct-peft"
+config = PeftConfig.from_pretrained(peft_model_id)
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    quantization_config=bnb_config,
+    use_auth_token=True,
+    device_map="auto",
+)
+
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, use_fast=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+format_template = "You are a helpful assistant. Write a response that appropriately completes the request. {query}\n"
+```
+
+```python
+# First, format the prompt
+query = "Tell me a recipe for vegan banana bread."
+prompt = format_template.format(query=query)
+
+# Inference can be done using model.generate
+print("\n\n*** Generate:")
+
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
+with torch.autocast("cuda", dtype=torch.bfloat16):
+    output = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=512,
+        do_sample=True,
+        temperature=0.7,
+        return_dict_in_generate=True,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+        repetition_penalty=1.2,
+    )
+
+print(tokenizer.decode(output["sequences"][0], skip_special_tokens=True))
+```
+
+## Runtime tests
+
+| runtime / 50 tokens (sec) | GPU             | attn | torch dtype | VRAM (GB) |
+|:-----------------------------:|:----------------------:|:---------------------:|:-------------:|:-----------------------:|
+| 3.1                        | 1x A100 (40 GB SXM)  | torch               | fp16    | 13                    |
+
+
+## Acknowledgements
+
+This model was finetuned by Daniel Furman on Sep 27, 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.
+
+## mistralai/Mistral-7B-v0.1 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
+