Update README.md

README.md

@@ -1,202 +1,124 @@
 ---
 base_model: meta-llama/Llama-2-7b-hf
 library_name: peft
+license: apache-2.0
+language:
+- en
+tags:
+- retrieval
+- instructions
+datasets:
+- samaya-ai/msmarco-w-instructions
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **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. -->
-
-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-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]
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-## Training Details
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-### Training Data
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-<!-- 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. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### 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. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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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]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- 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]
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-### 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]
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-<!-- 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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-**APA:**
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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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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-### Framework versions
-
-- PEFT 0.12.0
+# Model Summary
+
+Promptriever is a new way of using dense retriever models. This version, `promptriever-llama2-7b-v1` was instruction-trained on a corpus of 490k MSMarco samples with instructions and 490k without instructions. See the [paper]() for more details.
+
+- **Repository:** [orionw/Promptriever](https://github.com/orionw/Promptriever)
+- **Paper:** todo 
+- **Instruction-Training Dataset:** [samaya-ai/msmarco-w-instructions](https://huggingface.co/datasets/samaya-ai/msmarco-w-instructions)
+
+
+# Use
+
+Below is an example to compute the similarity score of a query-document pair
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+from peft import PeftModel, PeftConfig
+import numpy as np
+
+class Promptriever:
+    def __init__(self, model_name_or_path):
+        self.model, self.tokenizer = self.get_model(model_name_or_path)
+        self.model.eval()
+
+    def get_model(self, peft_model_name):
+        # Load the PEFT configuration to get the base model name
+        peft_config = PeftConfig.from_pretrained(peft_model_name)
+        base_model_name = peft_config.base_model_name_or_path
+
+        # Load the base model and tokenizer
+        base_model = AutoModel.from_pretrained(base_model_name)
+        tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        # Load and merge the PEFT model
+        model = PeftModel.from_pretrained(base_model, peft_model_name)
+        model = model.merge_and_unload()
+
+        return model, tokenizer
+
+    def encode(self, texts):
+        inputs = self.tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        embeddings = outputs.last_hidden_state[:, 0]  # Using [CLS] token
+        return F.normalize(embeddings, p=2, dim=1)
+
+# Initialize the model
+model = Promptriever("samaya-ai/promptriever-llama2-7b-v1")
+
+# Example query and instruction
+query = "What universities are in Baltimore, Maryland?"
+instruction = "A relevant document would describe any university in Baltimore. I am only interested in the United States, so ignore any document with a campus in Italy."
+
+# Combine query and instruction with two spaces after "query: "
+input_text = f"query:  {query} {instruction}"
+
+# Example documents
+doc1 = "Johns Hopkins University (often abbreviated as Johns Hopkins, Hopkins, or JHU) is a private research university in Baltimore, Maryland. Founded in 1876, Johns Hopkins was the first American university based on the European research institution model. The university also has graduate campuses in Italy, China, and Washington, D.C."
+doc2 = "Johns Hopkins University (often abbreviated as Johns Hopkins, Hopkins, or JHU) is a private research university in Baltimore, Maryland. Founded in 1876, Johns Hopkins was the first American university based on the European research institution model. The university also has graduate campuses in China, and Washington, D.C."
+
+# Encode query and documents
+query_embedding = model.encode([input_text])
+doc_embeddings = model.encode([doc1, doc2])
+
+# Calculate similarities
+similarities = np.dot(query_embedding, doc_embeddings.T)[0]
+
+# Print results
+print("Similarities:")
+print(f"Document 1: {similarities[0]:.4f}")
+print(f"Document 2: {similarities[1]:.4f}")
+```
+
+# Training
+
+We used a fork of [Tevatron](https://github.com/orionw/tevatron) to fine-tune promptriever with the [samaya-ai/msmarco-w-instructions](https://huggingface.co/datasets/samaya-ai/msmarco-w-instructions) dataset.
+
+You can reproduce this with the TODO script (reproduced here for convenience).
+
+```bash
+#!/bin/bash
+accelerate launch src/train_bash.py \
+    --stage sft \
+    --do_train \
+    --model_name_or_path "mistralai/Mistral-7B-Instruct-v0.2" \
+    --dataset followIR-train \
+    --template mistral \
+    --output_dir OUTPUT \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj,o_proj,k_proj \
+    --overwrite_cache \
+    --per_device_train_batch_size 32 \
+    --gradient_accumulation_steps 1 \
+    --lr_scheduler_type cosine \
+    --logging_steps 2 \
+    --save_steps 29 \
+    --learning_rate 3e-5 \
+    --num_train_epochs 8.0 \
+    --plot_loss \
+    --max_length 2048 \
+    --lora_rank 8 \
+    --lora_alpha 16 \
+    --bf16 
+```
+
+# Citation
+
+```bibtex
+TODO
+```