Add model

app.py

@@ -0,0 +1,106 @@
+# Import modules
+import gradio as gr
+from langchain_community.llms import HuggingFacePipeline
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.prompts import PromptTemplate
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, GenerationConfig, pipeline
+
+# Define the embedding function
+# I use the "all-MiniLM-L6-v2" model
+embedding_function = SentenceTransformerEmbeddings(
+    model_name="all-MiniLM-L6-v2",
+    model_kwargs={"device": "cuda"},  # Use the GPU
+)
+
+# Load the fine-tuned model by merging the base model and the adapter
+# (checkpointed at 1 epoch = 77 steps)
+adapter = "./results/checkpoint-77"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    quantization_config=bnb_config,
+    trust_remote_code=True,
+    device_map={"": 0},
+    token=HUGGINGFACE_ACCESS_TOKEN,
+)
+model_ft = PeftModel.from_pretrained(model, adapter)
+
+# For inference, use a text-generation pipeline
+# NOTE: you could get a warning such as "The model 'PeftModelForCausalLM' is not
+# supported for text-generation", but it's not a problem
+config = GenerationConfig(max_new_tokens=200)
+pipe = pipeline(
+    "text-generation",
+    model=model_ft,
+    tokenizer=tokenizer,
+    generation_config=config,
+    framework="pt",
+)
+
+"""
+NOTE: Although not strictly required by the assignment, considering that for
+Point 1 we created the embeddings of the emails and saved them in Chroma, it is
+trivial to add a simple RAG system. Basically, when a question is asked, some
+emails (or part of them) similar to the question are also sent to the model as
+context.
+"""
+
+# Load the saved database
+persist_directory = "./chroma_db"
+db = Chroma(
+    persist_directory=persist_directory,
+    embedding_function=embedding_function,
+)
+
+# Setup a retriever so that we get the 2 most similar texts
+retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2})
+
+# Wrap the Hugging Face pipeline for langchain
+llm = HuggingFacePipeline(pipeline=pipe)
+
+# This is the template we will use for the text to submit to the model.
+# In place of {context} will be inserted the context sentences retrieved from
+# the RAG system, and in place of {question} will be inserted the question.
+template = """Instruct:
+You are an AI assistant for answering questions about the provided context.
+You are given the following extracted parts of a document database and a question. Provide a short answer.
+If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
+=======
+{context}
+=======
+Question: {question}
+Output:"""
+custom_rag_prompt = PromptTemplate.from_template(template)
+
+def format_docs(docs):
+    # Separates retrieved texts with a double return character
+    return "\n\n".join(doc.page_content for doc in docs)
+
+# RAG pipeline
+rag_chain = (
+    {"context": retriever | format_docs, "question": RunnablePassthrough()}
+    | custom_rag_prompt
+    | llm
+)
+
+def get_answer(question):
+    try:
+        # Submit the question to the pipeline and extract the output
+        answer = rag_chain.invoke(question).split("Output:")[1].strip()
+    except Exception as e:
+        answer = str(e)
+    return answer
+
+# Define and launch the Gradio interface
+interface = gr.Interface(
+    fn=get_answer,
+    inputs=gr.Textbox(label="Enter your question"),
+    outputs=gr.Textbox(label="Answer"),
+    title="Enron QA",
+    examples=[
+        ["What is the strategy in agricultural commodities training?"]
+    ],
+)
+interface.launch()

results/checkpoint-77/README.md

@@ -0,0 +1,204 @@
+---
+library_name: peft
+base_model: microsoft/phi-2
+---
+
+# 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]
+
+### 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. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[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
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[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.
+
+## 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]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## 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).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## 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]
+
+
+### Framework versions
+
+- PEFT 0.10.0

results/checkpoint-77/adapter_config.json

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+  "auto_mapping": null,
+  "base_model_name_or_path": "microsoft/phi-2",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "fc1",
+    "fc2",
+    "Wqkv"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

results/checkpoint-77/adapter_model.safetensors

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