Duplicate from philschmid/igel-playground

Co-authored-by: Philipp Schmid <philschmid@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: Igel Playground
+emoji: 🧠
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.23.0
+app_file: app.py
+pinned: false
+duplicated_from: philschmid/igel-playground
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TextIteratorStreamer
+import torch
+from threading import Thread
+from huggingface_hub import Repository
+import json
+
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+    radius_size=gr.themes.sizes.radius_sm,
+    font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
+)
+# filesystem to save input and outputs
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+if HF_TOKEN:
+    repo = Repository(
+        local_dir="data", clone_from="philschmid/playground-prompts", use_auth_token=HF_TOKEN, repo_type="dataset"
+    )
+
+
+# Load peft config for pre-trained checkpoint etc.
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = "philschmid/instruct-igel-001"
+if device == "cpu":
+    model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True)
+else:
+    # torch_dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
+    # model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch_dtype, device_map="auto")
+    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_8bit=True)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+prompt_template = f"### Anweisung:\n{{input}}\n\n### Antwort:"
+
+
+def generate(instruction, temperature, max_new_tokens, top_p, length_penalty):
+    formatted_instruction = prompt_template.format(input=instruction)
+    # COMMENT IN FOR NON STREAMING
+    # generation_config = GenerationConfig(
+    #     do_sample=True,
+    #     top_p=top_p,
+    #     temperature=temperature,
+    #     max_new_tokens=max_new_tokens,
+    #     early_stopping=True,
+    #     length_penalty=length_penalty,
+    #     eos_token_id=tokenizer.eos_token_id,
+    #     pad_token_id=tokenizer.pad_token_id,
+    # )
+
+    # input_ids = tokenizer(
+    #     formatted_instruction, return_tensors="pt", truncation=True, max_length=2048
+    # ).input_ids.cuda()
+
+    # with torch.inference_mode(), torch.autocast("cuda"):
+    #     outputs = model.generate(input_ids=input_ids, generation_config=generation_config)[0]
+
+    # output = tokenizer.decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)
+    # return output.split("### Antwort:\n")[1]
+
+    # STREAMING BASED ON git+https://github.com/gante/transformers.git@streamer_iterator
+
+    # streaming
+    streamer = TextIteratorStreamer(tokenizer)
+    model_inputs = tokenizer(formatted_instruction, return_tensors="pt", truncation=True, max_length=2048)
+    # move to gpu
+    model_inputs = {k: v.to(device) for k, v in model_inputs.items()}
+
+    generate_kwargs = dict(
+        top_p=top_p,
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        early_stopping=True,
+        length_penalty=length_penalty,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    t = Thread(target=model.generate, kwargs={**dict(model_inputs, streamer=streamer), **generate_kwargs})
+    t.start()
+
+    output = ""
+    hidden_output = ""
+    for new_text in streamer:
+        # skip streaming until new text is available
+        if len(hidden_output) <= len(formatted_instruction):
+            hidden_output += new_text
+            continue
+        # replace eos token
+        if tokenizer.eos_token in new_text:
+            new_text = new_text.replace(tokenizer.eos_token, "")
+        output += new_text
+        yield output
+    if HF_TOKEN:
+        save_inputs_and_outputs(formatted_instruction, output, generate_kwargs)
+    return output
+
+
+def save_inputs_and_outputs(inputs, outputs, generate_kwargs):
+    with open(os.path.join("data", "prompts.jsonl"), "a") as f:
+        json.dump({"inputs": inputs, "outputs": outputs, "generate_kwargs": generate_kwargs}, f, ensure_ascii=False)
+        f.write("\n")
+        commit_url = repo.push_to_hub()
+
+
+examples = [
+    """Beantworten Sie die Frage am Ende des Textes anhand der folgenden Zusammenhänge. Wenn Sie die Antwort nicht wissen, sagen Sie, dass Sie es nicht wissen, versuchen Sie nicht, eine Antwort zu erfinden.
+"Das Unternehmen wurde 2016 von den französischen Unternehmern Clément Delangue, Julien Chaumond und Thomas Wolf gegründet und entwickelte ursprünglich eine Chatbot-App, die sich an Teenager richtete.[2] Nachdem das Modell hinter dem Chatbot offengelegt wurde, konzentrierte sich das Unternehmen auf eine Plattform für maschinelles Lernen.
+
+Im März 2021 sammelte Hugging Face in einer Serie-B-Finanzierungsrunde 40 Millionen US-Dollar ein[3].
+
+Am 28. April 2021 rief das Unternehmen in Zusammenarbeit mit mehreren anderen Forschungsgruppen den BigScience Research Workshop ins Leben, um ein offenes großes Sprachmodell zu veröffentlichen.[4] Im Jahr 2022 wurde der Workshop mit der Ankündigung von BLOOM abgeschlossen, einem mehrsprachigen großen Sprachmodell mit 176 Milliarden Parametern.[5]"
+
+Frage: Wann wurde Hugging Face gegründet?""",
+    "Erklären Sie, was eine API ist.",
+    "Bitte beantworten Sie die folgende Frage. Wer wird der nächste Ballon d'or sein?",
+    "Beantworten Sie die folgende Ja/Nein-Frage, indem Sie Schritt für Schritt argumentieren. Kannst du ein ganzes Haiku in einem einzigen Tweet schreiben?",
+    "Schreibe eine Produktbeschreibung für einen LG 43UQ75009LF 109 cm (43 Zoll) UHD Fernseher (Active HDR, 60 Hz, Smart TV) [Modelljahr 2022]",
+]
+
+
+with gr.Blocks(theme=theme) as demo:
+    with gr.Column():
+        gr.Markdown(
+            """<h1><center>IGEL - Instruction-tuned German large Language Model for Text</center></h1>
+            <p>
+            IGEL is a LLM model family developed for the German language. The first version of IGEL is built on top <a href="https://bigscience.huggingface.co/blog/bloom" target="_blank">BigScience BLOOM</a> adapted to the <a href="https://huggingface.co/malteos/bloom-6b4-clp-german">German language by Malte Ostendorff</a>. IGEL designed to provide accurate and reliable language understanding capabilities for a wide range of natural language understanding tasks, including sentiment analysis, language translation, and question answering.
+            
+            The IGEL family includes instruction [instruct-igel-001](https://huggingface.co/philschmid/instruct-igel-001) and `chat-igel-001` _coming soon_.
+            </p>
+      """
+        )
+        with gr.Row():
+            with gr.Column(scale=3):
+                instruction = gr.Textbox(placeholder="Hier Anweisung eingeben...", label="Anweisung")
+                output = gr.Textbox(
+                    interactive=False,
+                    lines=8,
+                    label="Antwort",
+                    placeholder="Hier Antwort erscheint...",
+                )
+                submit = gr.Button("Generate", variant="primary")
+                gr.Examples(examples=examples, inputs=[instruction])
+
+            with gr.Column(scale=1):
+                temperature = gr.Slider(
+                    label="Temperature",
+                    value=1.0,
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.1,
+                    interactive=True,
+                    info="The higher more random",
+                )
+                max_new_tokens = gr.Slider(
+                    label="Max new tokens",
+                    value=256,
+                    minimum=0,
+                    maximum=2048,
+                    step=5,
+                    interactive=True,
+                    info="The maximum numbers of new tokens",
+                )
+                top_p = gr.Slider(
+                    label="Top p",
+                    value=0.9,
+                    minimum=0.0,
+                    maximum=1,
+                    step=0.05,
+                    interactive=True,
+                    info="probabilities that add up are kept",
+                )
+                length_penalty = gr.Slider(
+                    label="Length penalty",
+                    value=1.0,
+                    minimum=-10.0,
+                    maximum=10.0,
+                    step=0.1,
+                    interactive=True,
+                    info="> 0.0 longer, < 0.0 shorter",
+                )
+
+    submit.click(generate, inputs=[instruction, temperature, max_new_tokens, top_p, length_penalty], outputs=[output])
+    instruction.submit(
+        generate, inputs=[instruction, temperature, max_new_tokens, top_p, length_penalty], outputs=[output]
+    )
+
+demo.queue()
+demo.launch()

requirements.txt

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+git+https://github.com/huggingface/peft.git
+git+https://github.com/huggingface/transformers.git
+huggingface_hub
+accelerate==0.17.1
+bitsandbytes