initial commit

README.md

@@ -1,13 +1,13 @@
 ---
 title: Claire Chat
-emoji: 📉
+emoji: 🎙💬
 colorFrom: blue
-colorTo: pink
+colorTo: red
 sdk: gradio
 sdk_version: 4.4.0
 app_file: app.py
-pinned: false
+pinned: true
 license: cc-by-nc-sa-4.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Démo de conversations en Français générées par [Claire](https://huggingface.co/OpenLLM-France/Claire-7B-0.1)

app.py

@@ -0,0 +1,418 @@
+import gradio as gr
+import transformers
+from transformers import AutoConfig, AutoTokenizer, AutoModel, AutoModelForCausalLM
+import torch
+import unicodedata
+import re
+
+# Default variables
+default_max_new_tokens = 100
+default_temperature = 1.0
+default_top_k = 10
+default_top_p = 0.99
+default_repetition_penalty = 1.0
+
+model_name = "OpenLLM-France/Claire-7B-0.1"
+
+print("Loading model...")
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    load_in_4bit=True,
+)
+
+print("Optimizing model...")
+
+import optimum
+from optimum.bettertransformer import BetterTransformer
+
+model = BetterTransformer.transform(model)
+
+print("Setup chat...")
+
+eos_token_id = tokenizer.eos_token_id
+newspk_token_id = tokenizer.encode("[")
+assert len(newspk_token_id) == 1
+newspk_token_id = newspk_token_id[0]
+
+
+# Class to encapsulate the Claire chatbot
+class ClaireChatBot:
+    def __init__(
+        self,
+        # Chat will display...
+        user_name="VOUS:",
+        bot_name="CHATBOT:",
+        other_name_regex_in=r"AUTRE (\d+):",
+        other_name_regex_out=r"AUTRE \1:",
+        # but Claire was trained on...
+        user_internal_tag="[Intervenant 1:]",
+        bot_internal_tag="[Intervenant 2:]",
+        other_internal_tag_regex_in=r"\[Intervenant (\d+):\]",
+        other_internal_tag_regex_out=r"\[Intervenant \1:\]",
+    ):
+        self.user_name = user_name
+        self.bot_name = bot_name
+        self.other_name_regex_in = other_name_regex_in
+        self.other_name_regex_out = other_name_regex_out
+
+        self.user_internal_tag = user_internal_tag
+        self.bot_internal_tag = bot_internal_tag
+        self.other_internal_tag_regex_in = other_internal_tag_regex_in
+        self.other_internal_tag_regex_out = other_internal_tag_regex_out
+
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        self.has_started_bracket = False
+        self.history = ""
+        self.history_raw = ""
+        self.reinject_history = False
+        self.reshow_history = False
+
+    def predict(
+        self,
+        user_message,
+        bot_message_start="",
+        conversation_history="",
+        generate_several_turns=False,
+        max_new_tokens=default_max_new_tokens,
+        temperature=default_temperature,
+        top_k=default_top_k,
+        top_p=default_top_p,
+        repetition_penalty=default_repetition_penalty,
+    ):
+        user_message = claire_text_preproc_message(user_message)
+        bot_message_start = claire_text_preproc_message(bot_message_start)
+
+        if conversation_history:
+            # Format conversation history
+            for spk_in, spk_out in [
+                (self.user_name, self.user_internal_tag),
+                (self.bot_name, self.bot_internal_tag),
+            ]:
+                conversation_history = conversation_history.replace(spk_in, spk_out)
+            conversation_history = re.sub(self.other_name_regex_in, self.other_internal_tag_regex_out, conversation_history)
+            conversation_history = claire_text_preproc_conversation(conversation_history)
+            conversation_history = conversation_history.rstrip() + "\n"
+        else:
+            conversation_history = self.history_raw
+
+        # (Only relevant if self.reinject_history is True)
+        user_internal_tag = self.user_internal_tag
+        if self.has_started_bracket:
+            user_internal_tag = user_internal_tag[1:]
+
+        # Combine the user and bot messages into a conversation
+        conversation = f"{conversation_history}{user_internal_tag} {user_message}\n{self.bot_internal_tag} {bot_message_start if bot_message_start else ''}".strip()
+
+        # Encode the conversation using the tokenizer
+        input_ids = tokenizer.encode(
+            conversation, return_tensors="pt", add_special_tokens=False
+        )
+        input_ids = input_ids.to(self.device)
+
+        # Generate a response using Claire
+        response = model.generate(
+            input_ids=input_ids,
+            use_cache=False,
+            early_stopping=False,
+            temperature=temperature,
+            do_sample=True,
+            max_new_tokens=max_new_tokens,
+            top_k=top_k,
+            top_p=top_p,
+            repetition_penalty=repetition_penalty,
+            pad_token_id=eos_token_id,
+            eos_token_id=eos_token_id if generate_several_turns else newspk_token_id,
+        )
+
+        # Decode the generated response to text
+        response_text = tokenizer.decode(response[0], skip_special_tokens=True)
+
+        # Remove last unfinished speech turn/sentence/phrase
+        line_breaks = [u.span(0)[0] for u in re.finditer("\n", response_text)]
+        remove_last_sentence = True
+        if generate_several_turns:
+            if len(line_breaks) >= 2:
+                response_text = response_text[: line_breaks[-1]]
+                line_breaks.pop(-1)
+                remove_last_sentence = False
+        if remove_last_sentence and len(line_breaks) == 1:
+            sentence_ends = [
+                u.span(0)[0] for u in re.finditer(r"[\.!?]", response_text)
+            ]
+            sentence_ends = [p for p in sentence_ends if p > line_breaks[-1]]
+            if sentence_ends:
+                response_text = response_text[: sentence_ends[-1] + 1]
+            else:
+                phrase_ends = [
+                    u.span(0)[0] for u in re.finditer(r"[,;]", response_text)
+                ]
+                phrase_ends = [p for p in phrase_ends if p > line_breaks[-1]]
+                if phrase_ends:
+                    response_text = response_text[: phrase_ends[-1] + 1]
+
+        ended_with_bracket = response_text.endswith("[")
+
+        if self.reinject_history:
+            self.history_raw = response_text
+            self.has_started_bracket = ended_with_bracket
+
+        if ended_with_bracket:
+            response_text = response_text[:-1]
+
+        for spk_in, spk_out in [
+            (self.user_internal_tag, self.user_name),
+            (self.user_internal_tag[1:], self.user_name), # Starting bracket may be missing  
+            (self.bot_internal_tag, self.bot_name),
+        ]:
+            response_text = response_text.replace(spk_in, spk_out)
+        response_text = re.sub(self.other_internal_tag_regex_in, self.other_name_regex_out, response_text)
+
+        if self.reshow_history:
+            previous_history = self.history
+            self.history = previous_history + response_text + "\n"
+        else:
+            previous_history = ""
+
+        return previous_history + response_text
+
+
+def claire_text_preproc_conversation(text):
+    text = format_special_characters(text)
+    text = collapse_whitespaces_conversations(text)
+    return text
+
+
+def claire_text_preproc_message(text):
+    text = format_special_characters(text)
+    text = collapse_whitespaces_message(text)
+    text = replace_brackets(text)
+    return text
+
+
+def collapse_whitespaces_conversations(text):
+    text = re.sub(r"\n+", "\n", text)
+    text = re.sub(r"[ \t]+", " ", text)
+    text = re.sub(r"\n ", "\n", text)
+    text = re.sub(r" ([\.,])", r"\1", text)
+    return text.lstrip().rstrip(" ")
+
+
+def collapse_whitespaces_message(text):
+    text = re.sub(r"\s+", " ", text)
+    text = re.sub(r" ([\.,])", r"\1", text)
+    return text.lstrip().rstrip(" ")
+
+
+def replace_brackets(text):
+    text = re.sub(r"[\[\{]", "(", text)
+    text = re.sub(r"[\]\}]", ")", text)
+    return text
+
+
+def format_special_characters(text):
+    text = unicodedata.normalize("NFC", text)
+    for before, after in [
+        ("…", "..."),
+        (r"[«“][^\S\r\n]*", '"'),
+        (r"[^\S\r\n]*[»”″„]", '"'),
+        (r"(``|'')", '"'),
+        (r"[’‘‛ʿ]", "'"),
+        ("‚", ","),
+        (r"–", "-"),
+        ("[  ]", " "),  # unbreakable spaces
+        (r"[\x00-\x08\x0B\x0C\x0E-\x1F\x7F-\x9F]", ""),  # non-printable characters
+        # ("·", "."),
+        (r"ᵉʳ", "er"),
+        (r"ᵉ", "e"),
+    ]:
+        text = re.sub(before, after, text)
+
+    return text
+
+
+# Create the Claire chatbot instance
+chatbot = ClaireChatBot()
+
+# Define the Gradio interface
+title = "Démo de conversation avec Claire"
+description = "Simulation de conversations en Français avec [Claire](https://huggingface.co/OpenLLM-France/Claire-7B-0.1), sans recherche de vérité, et avec potentiellement un peu d'humour."
+
+default_parameters = [
+    default_temperature,
+    default_top_k,
+    default_top_p,
+    default_repetition_penalty,
+]
+
+examples = [
+    [
+        "Nous allons commencer cette interview avec une question un peu classique. Quel est votre sport préféré?",  # user_message
+        "",  # bot_message_start
+        "",  # conversation_history
+        True,  # generate_several_turns
+        200,  # max_new_tokens
+        *default_parameters,
+    ],
+    [
+        "Que vas-tu nous cuisiner aujourd'hui?",  # user_message
+        "Alors, nous allons voir la recette de",  # bot_message_start
+        "VOUS: Bonjour Claire.\nCHATBOT: Bonjour Dominique.",  # conversation_history
+        False,  # generate_several_turns
+        default_max_new_tokens,  # max_new_tokens
+        *default_parameters,
+    ],
+]
+
+# # Test
+# chatbot.predict(*examples[0])
+
+inputs = [
+    gr.Textbox(
+        "",
+        label="Prompt",
+        info="Tapez ce que vous voulez dire au ChatBot",
+        type="text",
+        lines=2,
+    ),
+    gr.Textbox(
+        "",
+        label="Début de réponse",
+        info="Vous pouvez taper ici ce que commence à vous répondre le ChatBot",
+        type="text",
+    ),
+    gr.Textbox(
+        "",
+        label="Historique",
+        info="Vous pouvez copier-coller (et modifier?) ici votre historique de conversation, pour continuer cette conversation",
+        type="text",
+        lines=3,
+    ),
+    gr.Checkbox(
+        False,
+        label="Plus qu'un tour de parole",
+        info="Générer aussi comment pourrait continuer la conversation (plusieurs tours de parole incluant le vôtre)",
+    ),
+    gr.Slider(
+        label="Longueur max",
+        info="Longueur maximale du texte généré (en nombre de 'tokens' ~ mots et ponctuations)",
+        value=default_max_new_tokens,
+        minimum=25,
+        maximum=1000,
+        step=25,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Température",
+        info="Une valeur élevée augmente la diversité du texte généré, mais peut aussi produire des résultats incohérents",
+        value=default_temperature,
+        minimum=0.1,
+        maximum=1.9,
+        step=0.1,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Top-k",
+        info="Une valeur élevée permet d'explorer plus d'alternatives, mais augmente les temps de calcul",
+        value=default_top_k,
+        minimum=1,
+        maximum=50,
+        step=1,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Top-p",
+        info="Une valeur élevée permet d'explorer des alternatives moins probables",
+        value=default_top_p,
+        minimum=0.9,
+        maximum=1.0,
+        step=0.01,
+        interactive=True,
+    ),
+    gr.Slider(
+        label="Pénalité de répétition",
+        info="Pénalisation des répétitions",
+        value=default_repetition_penalty,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+    ),
+]
+
+theme = gr.themes.Monochrome(
+    secondary_hue="emerald",
+    neutral_hue="teal",
+).set(
+    body_background_fill="*primary_950",
+    body_background_fill_dark="*secondary_950",
+    body_text_color="*primary_50",
+    body_text_color_dark="*secondary_100",
+    body_text_color_subdued="*primary_300",
+    body_text_color_subdued_dark="*primary_300",
+    background_fill_primary="*primary_600",
+    background_fill_primary_dark="*primary_400",
+    background_fill_secondary="*primary_950",
+    background_fill_secondary_dark="*primary_950",
+    border_color_accent="*secondary_600",
+    border_color_primary="*secondary_50",
+    border_color_primary_dark="*secondary_50",
+    color_accent="*secondary_50",
+    color_accent_soft="*primary_500",
+    color_accent_soft_dark="*primary_500",
+    link_text_color="*secondary_950",
+    link_text_color_dark="*primary_50",
+    link_text_color_active="*primary_50",
+    link_text_color_active_dark="*primary_50",
+    link_text_color_hover="*primary_50",
+    link_text_color_hover_dark="*primary_50",
+    link_text_color_visited="*primary_50",
+    block_background_fill="*primary_950",
+    block_background_fill_dark="*primary_950",
+    block_border_color="*secondary_500",
+    block_border_color_dark="*secondary_500",
+    block_info_text_color="*primary_50",
+    block_info_text_color_dark="*primary_50",
+    block_label_background_fill="*primary_950",
+    block_label_background_fill_dark="*secondary_950",
+    block_label_border_color="*secondary_500",
+    block_label_border_color_dark="*secondary_500",
+    block_label_text_color="*secondary_500",
+    block_label_text_color_dark="*secondary_500",
+    block_title_background_fill="*primary_950",
+    panel_background_fill="*primary_950",
+    panel_border_color="*primary_950",
+    checkbox_background_color="*primary_950",
+    checkbox_background_color_dark="*primary_950",
+    checkbox_background_color_focus="*primary_950",
+    checkbox_border_color="*secondary_500",
+    input_background_fill="*primary_800",
+    input_background_fill_focus="*primary_950",
+    input_background_fill_hover="*secondary_950",
+    input_placeholder_color="*secondary_950",
+    slider_color="*primary_950",
+    slider_color_dark="*primary_950",
+    table_even_background_fill="*primary_800",
+    table_odd_background_fill="*primary_600",
+    button_primary_background_fill="*primary_800",
+    button_primary_background_fill_dark="*primary_800",
+)
+
+iface = gr.Interface(
+    fn=chatbot.predict,
+    title=title,
+    description=description,
+    examples=examples,
+    inputs=inputs,
+    outputs="text",
+    theme=theme,
+)
+
+print("Launching chat...")
+
+# Launch the Gradio interface for the model
+iface.launch(share=True)

requirements.txt

@@ -0,0 +1,11 @@
+optimum
+accelerate==0.24.1
+bitsandbytes==0.41.1
+gradio==4.1.1
+protobuf==3.20.3
+scipy==1.11.2
+sentencepiece==0.1.99
+spaces==0.18.0
+torch==2.0.0
+transformers==4.35.0
+transformers_stream_generator==0.0.4