Update README.md

README.md

@@ -1,104 +1,92 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- mistral
+- sparse
+- pruned
+- wanda
+license: mit
+datasets:
+- HuggingFaceH4/ultrachat_200k
+- HuggingFaceH4/ultrafeedback_binarized
+language:
+- en
 ---
 
-# Model Card for Model ID
+# Model Card for kettleguts/zephyr-7b-beta_sparse05
 
-<!-- Provide a quick summary of what the model is/does. -->
+This is a pruned version of HuggingFaceH4/zephyr-7b-beta found [here](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta). Wanda pruning was used to introduce 50% sparsity into the linear layers. Read the paper [here](https://arxiv.org/abs/2306.11695).
 
 
 
-## Model Details
-
 ### Model Description
+[Here](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta#model-description)
 
-<!-- Provide a longer summary of what this model is. -->
-
-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]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+This model is only useful for research purposes. The quality of its text generation is highly dependent on how it is prompted. Since it is heavily pruned, it sometimes behaves like a mush smaller model.
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model is not suitable for direct use outside of research.
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
 
-### Out-of-Scope Use
+# Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+This model should never be used for critical decisions involving health, life, employment, housing, law, etc. It should also never be used to harm anyone.
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[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.
+[No safegaurd have been added to this model.](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta#bias-risks-and-limitations)
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
-
-## Training Details
+<code>
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
 
-### Training Data
+model_name = 'kettleguts/zephyr-7b-beta_sparse05'
 
-<!-- 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. -->
+#quantize model for mode efficient performance
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
 
-[More Information Needed]
+#load model
+model = AutoModelForCausalLM.from_pretrained(model_name,
+                                             device_map = "auto",
+                                             quantization_config=bnb_config)
 
-### Training Procedure 
+#load toeknizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+if tokenizer.pad_token is None:
+    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+pipe = pipeline("text-generation",model=model, tokenizer=tokenizer)
 
-#### Preprocessing [optional]
-
-[More Information Needed]
+messages = [
+    {
+        "role": "system",
+        "content": "You are a friendly chatbot who always responds as briefly as possible with prefect grammar.",
+    },
+    {"role": "user", "content": "Briefly describe network pruning."},
+]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95,pad_token_id = tokenizer.pad_token_id)
+text = str(outputs[0]).split('<|assistant|>\\n')
+print(text[-1])
 
+#Network pruning, in the context of artificial intelligence and machine learning, refers to the process of removing unimportant or redundant connections, or "pruning," from a neural network\'s architecture. This is done to simplify and optimize the network\'s structure, reduce overfitting, and improve its efficiency, while preserving its overall performance. Pruning typically involves removing connections, neurons, or entire layers, based on metrics such as the weight or sparsity of the connection, or the amount of improvement gained by removing the connection. The goal is to prune the network in a way that balances the trade-off between model size and accuracy, while reducing the network\'s overall complexity and resource requirements. Pruning techniques can range from simple heuristics such as early stopping, to more sophisticated methods such as compressed and pruned models, and iterative and incremental pruning.'}
+</code>
 
-#### 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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
@@ -170,32 +158,24 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## 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. -->
-
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+@misc{tunstall2023zephyr,
+      title={Zephyr: Direct Distillation of LM Alignment}, 
+      author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
+      year={2023},
+      eprint={2310.16944},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+
+@misc{sun2023simple,
+      title={A Simple and Effective Pruning Approach for Large Language Models}, 
+      author={Mingjie Sun and Zhuang Liu and Anna Bair and J. Zico Kolter},
+      year={2023},
+      eprint={2306.11695},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}