added code snippet for how to use

README.md

@@ -24,30 +24,52 @@ Hugging Face Blog post on 4-bit quantization using bitsandbytes: [Making LLMs ev
 
 bitsandbytes github repo: [bitsandbytes github repo](https://github.com/TimDettmers/bitsandbytes)
 
+# How to Get Started with the Model
 
+Use the code below to get started with the model.
+
+## How to run from Python code
+
+#### First install the package
+```shell
+pip install -q -U bitsandbytes accelerate torch huggingface_hub
+pip install -q -U git+https://github.com/huggingface/transformers.git # Install latest version of transformers
+pip install -q -U git+https://github.com/huggingface/peft.git
+pip install flash-attn --no-build-isolation
+```
+
+#### Import 
+
+```python
+import torch
+import os
+from torch import bfloat16
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig, LlamaForCausalLM
+```
+
+#### Use a pipeline as a high-level helper
 
+```python
+model_id_llama = "alokabhishek/Llama-2-7b-chat-hf-bnb-4bit"
 
-### Model Description
+tokenizer_llama = AutoTokenizer.from_pretrained(model_id_llama, use_fast=True)
 
-<!-- Provide a longer summary of what this model is. -->
+model_llama = AutoModelForCausalLM.from_pretrained(
+    model_id_llama,
+    device_map="auto"
+)
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
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-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
+pipe_llama = pipeline(model=model_llama, tokenizer=tokenizer_llama, task='text-generation')
 
-### Model Sources [optional]
+prompt_llama = "Tell me a funny joke about Large Language Models meeting a Blackhole in an intergalactic Bar."
 
-<!-- Provide the basic links for the model. -->
+output_llama = pipe_llama(prompt_llama, max_new_tokens=512)
+
+print(output_llama[0]["generated_text"])
+
+```
 
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-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
@@ -77,134 +99,7 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 [More Information Needed]
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
 
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-### Results
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-#### Summary
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-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
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-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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 ## Model Card Authors [optional]