Duplicate from vasilee/flan-alpaca-gpt4-xl

.gitattributes

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Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./* /code
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: flan-alpaca-gpt4-xl-ct2
+emoji: 💻
+colorFrom: yellow
+colorTo: yellow
+sdk: docker
+pinned: false
+app_port: 7860
+duplicated_from: vasilee/flan-alpaca-gpt4-xl
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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flan-alpaca-gpt4-xl-ct2/README.md

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+---
+license: apache-2.0
+language:
+  - en
+tags:
+  - ctranslate2
+  - flan-alpaca-gpt4-xl
+  - quantization
+  - int8
+---
+
+# Model Card for Flan-Alpaca-GPT4-XL Q8
+
+The model is quantized version of the [declare-lab/flan-alpaca-gpt4-xl](https://huggingface.co/declare-lab/flan-alpaca-gpt4-xl) with int8 quantization.
+
+## Model Details
+
+### Model Description
+
+The model being quantized using [CTranslate2](https://opennmt.net/CTranslate2/) with the following command:
+
+```
+ct2-transformers-converter --model declare-lab/flan-alpaca-gpt4-xl --output_dir declare-lab/flan-alpaca-gpt4-xl-ct2 --copy_files generation_config.json tokenizer.json tokenizer_config.json special_tokens_map.json spiece.model --quantization int8 --force --low_cpu_mem_usage
+```
+
+If you want to perform the quantization yourself, you need to install the following dependencies:
+
+```
+pip install -qU ctranslate2 transformers[torch] sentencepiece accelerate
+```
+
+- **Shared by:** Lim Chee Kin
+- **License:** Apache 2.0
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import ctranslate2
+import transformers
+
+translator = ctranslate2.Translator("limcheekin/flan-alpaca-gpt4-xl-ct2")
+tokenizer = transformers.AutoTokenizer.from_pretrained("limcheekin/flan-alpaca-gpt4-xl-ct2")
+
+input_text = "translate English to German: The house is wonderful."
+input_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_text))
+
+results = translator.translate_batch([input_tokens])
+
+output_tokens = results[0].hypotheses[0]
+output_text = tokenizer.decode(tokenizer.convert_tokens_to_ids(output_tokens))
+
+print(output_text)
+```
+
+The code is taken from https://opennmt.net/CTranslate2/guides/transformers.html#t5.
+
+The key method of the code above is `translate_batch`, you can find out [its supported parameters here](https://opennmt.net/CTranslate2/python/ctranslate2.Translator.html#ctranslate2.Translator.translate_batch).

flan-alpaca-gpt4-xl-ct2/config.json

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+{
+  "add_source_bos": false,
+  "add_source_eos": false,
+  "bos_token": "<pad>",
+  "decoder_start_token": "<pad>",
+  "eos_token": "</s>",
+  "unk_token": "<unk>"
+}

flan-alpaca-gpt4-xl-ct2/generation_config.json

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+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.28.1"
+}

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main.py

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+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+from ctranslate2 import Translator
+from typing import Union
+
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+
+def average_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    last_hidden = last_hidden_states.masked_fill(
+        ~attention_mask[..., None].bool(), 0.0)
+    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+
+
+# text-ada replacement
+embeddingTokenizer = AutoTokenizer.from_pretrained(
+    './multilingual-e5-base')
+embeddingModel = AutoModel.from_pretrained('./multilingual-e5-base')
+
+# chatGpt replacement
+inferenceTokenizer = AutoTokenizer.from_pretrained(
+    "./flan-alpaca-gpt4-xl-ct2")
+inferenceTranslator = Translator(
+    "./flan-alpaca-gpt4-xl-ct2", compute_type="int8", device="cpu")
+
+
+class EmbeddingRequest(BaseModel):
+    input: Union[str, None] = None
+
+
+class TokensCountRequest(BaseModel):
+    input: Union[str, None] = None
+
+
+class InferenceRequest(BaseModel):
+    input: Union[str, None] = None
+    max_length: Union[int, None] = 0
+
+
+app = FastAPI()
+
+
+@app.get("/")
+async def root():
+    return {"message": "Hello World"}
+
+
+@app.post("/text-embedding")
+async def text_embedding(request: EmbeddingRequest):
+    input = request.input
+
+    # Process the input data
+    batch_dict = embeddingTokenizer([input], max_length=512,
+                                    padding=True, truncation=True, return_tensors='pt')
+    outputs = embeddingModel(**batch_dict)
+    embeddings = average_pool(outputs.last_hidden_state,
+                              batch_dict['attention_mask'])
+
+    # create response
+    return {
+        'embedding': embeddings[0].tolist()
+    }
+
+
+@app.post('/inference')
+async def inference(request: InferenceRequest):
+    input_text = request.input
+    max_length = 256
+    try:
+        max_length = int(request.max_length)
+        max_length = min(1024, max_length)
+    except:
+        pass
+
+    # process request
+    input_tokens = inferenceTokenizer.convert_ids_to_tokens(
+        inferenceTokenizer.encode(input_text))
+
+    results = inferenceTranslator.translate_batch(
+        [input_tokens], beam_size=1, max_input_length=0, max_decoding_length=max_length, num_hypotheses=1, repetition_penalty=1.3, sampling_topk=40, sampling_temperature=0.7, use_vmap=False)
+
+    output_tokens = results[0].hypotheses[0]
+    output_text = inferenceTokenizer.decode(
+        inferenceTokenizer.convert_tokens_to_ids(output_tokens), skip_special_tokens=True)
+
+    # create response
+    return {
+        'generated_text': output_text
+    }
+
+
+@app.post('/tokens-count')
+async def tokens_count(request: TokensCountRequest):
+    input_text = request.input
+
+    tokens = inferenceTokenizer.convert_ids_to_tokens(
+        inferenceTokenizer.encode(input_text))
+
+    # create response
+    return {
+        'tokens': tokens,
+        'total': len(tokens)
+    }

multilingual-e5-base/config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "intfloat/multilingual-e5-base",
+  "architectures": ["XLMRobertaModel"],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-5,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

multilingual-e5-base/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f061cb7641880f52895cbacab7c4ab39b0844e2e6b73794f2798de460d9fa418
+size 1112242989

multilingual-e5-base/special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

multilingual-e5-base/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62c24cdc13d4c9952d63718d6c9fa4c287974249e16b7ade6d5a85e7bbb75626
+size 17082660

multilingual-e5-base/tokenizer_config.json

@@ -0,0 +1,19 @@
+{
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}

requirements.txt

@@ -0,0 +1,7 @@
+ctranslate2==3.14.0
+fastapi==0.96.0
+pydantic==1.10.8
+torch==2.0.1
+transformers==4.29.2
+ctranslate2==3.14.0
+uvicorn[standard]==0.17.*

static/index.html

@@ -0,0 +1,36 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Fast API 🤗 Space served with Uvicorn</title>
+    <link rel="stylesheet" href="style.css" />
+    <script type="module" src="script.js"></script>
+  </head>
+  <body>
+    <main>
+      <section id="text-gen">
+        <h1>Text generation using Flan T5</h1>
+        <p>
+          Model:
+          <a
+              href="https://huggingface.co/google/flan-t5-small"
+              rel="noreferrer"
+              target="_blank"
+            >google/flan-t5-small</a
+          >
+        </p>
+        <form class="text-gen-form">
+          <label for="text-gen-input">Text prompt</label>
+          <input
+            id="text-gen-input"
+            type="text"
+            value="English: Translate There are many ducks. German:"
+          />
+          <button id="text-gen-submit">Submit</button>
+          <p class="text-gen-output"></p>
+        </form>
+      </section>
+    </main>
+  </body>
+</html>

static/script.js

@@ -0,0 +1,21 @@
+const textGenForm = document.querySelector('.text-gen-form');
+
+const translateText = async (text) => {
+    const inferResponse = await fetch(`infer_t5?input=${text}`);
+    const inferJson = await inferResponse.json();
+
+    return inferJson.output;
+};
+
+textGenForm.addEventListener('submit', async (event) => {
+  event.preventDefault();
+
+  const textGenInput = document.getElementById('text-gen-input');
+  const textGenParagraph = document.querySelector('.text-gen-output');
+
+  try {
+    textGenParagraph.textContent = await translateText(textGenInput.value);
+  } catch (err) {
+    console.error(err);
+  }
+});

static/style.css

@@ -0,0 +1,45 @@
+body {
+  --text: hsl(0 0% 15%);
+  padding: 2.5rem;
+  font-family: sans-serif;
+  color: var(--text);
+}
+
+body.dark-theme {
+  --text: hsl(0 0% 90%);
+  background-color: hsl(223 39% 7%);
+}
+
+main {
+  max-width: 80rem;
+  text-align: center;
+}
+
+section {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+}
+
+a {
+  color: var(--text);
+}
+
+form {
+  width: 30rem;
+  margin: 0 auto;
+}
+
+input {
+  width: 100%;
+}
+
+button {
+  cursor: pointer;
+}
+
+.text-gen-output {
+  min-height: 1.2rem;
+  margin: 1rem;
+  border: 0.5px solid grey;
+}