Super-squash branch 'main' using huggingface_hub

.gitattributes

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README.md

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+---
+title: Magpie
+emoji: 🔥
+colorFrom: red
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.36.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import transformers
+import torch
+import json
+from transformers import AutoTokenizer
+import os
+from huggingface_hub import login
+import spaces
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+login(HF_TOKEN)
+# Load the model
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id, add_special_tokens=True)
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device="cuda",
+)
+
+# Load the model configuration
+with open("model_configs.json", "r") as f:
+    model_configs = json.load(f)
+    model_config = model_configs[model_id]
+
+# Extract instruction
+extract_input = model_config["extract_input"]
+
+
+@spaces.GPU
+def generate_instruction_response():
+    terminators = [
+        tokenizer.eos_token_id,
+        tokenizer.convert_tokens_to_ids("<|eot_id|>"),
+    ]
+
+    instruction = pipeline(
+        extract_input,
+        max_new_tokens=2048,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=1,
+        top_p=1,
+    )
+
+    sanitized_instruction = instruction[0]["generated_text"][
+        len(extract_input) :
+    ].split("\n")[0]
+
+    response_template = f"""<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{sanitized_instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"""
+
+    response = pipeline(
+        response_template,
+        max_new_tokens=2048,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=1,
+        top_p=1,
+    )
+
+    user_message = sanitized_instruction
+    assistant_response = response[0]["generated_text"][len(response_template) :]
+
+    return user_message, assistant_response
+
+
+title = "Magpie demo"
+description = """
+This Gradio demo allows you to explore the approach outlined in the Magpie paper. "Magpie is a data synthesis pipeline that generates high-quality alignment data. Magpie does not rely on prompt engineering or seed questions. Instead, it directly constructs instruction data by prompting aligned LLMs with a pre-query template for sampling instructions." Essentially, instead of prompting the model with a question or a starting query, this approach relies on the pre-query template of the model to generate instructions. Essentially, you are giving the model only the template up to the point where a user instruction would start, and then the model generates the instruction and the response. 
+
+In this demo, you can see how the model generates a user instruction and a model response.
+
+You can learn more about the approach [in the paper](https://huggingface.co/papers/2406.08464).
+"""
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_instruction_response,
+    inputs=[],
+    outputs=[
+        gr.Text(label="Generated User Instruction"),
+        gr.Text(label="Generated Model Response"),
+    ],
+    title=title,
+    description=description,
+)
+
+# Launch the app
+iface.launch(debug=True)

model_configs.json

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+{
+  "meta-llama/Meta-Llama-3-8B-Instruct": {
+    "model_name": "meta-llama/Meta-Llama-3-8B-Instruct",
+    "stop_tokens": [
+      "<|eot_id|>",
+      "<|end_of_text|>",
+      "<|starter_header_id|>",
+      "<|end_header_id|>",
+      "assistant"
+    ],
+    "stop_token_ids": [
+      128009,
+      128001,
+      128006,
+      128007,
+      78191
+    ],
+    "extract_input": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n",
+    "extract_input_with_system_prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
+  },
+  "meta-llama/Meta-Llama-3-70B-Instruct": {
+    "model_name": "meta-llama/Meta-Llama-3-70B-Instruct",
+    "stop_tokens": [
+      "<|eot_id|>",
+      "<|end_of_text|>",
+      "<|starter_header_id|>",
+      "<|end_header_id|>",
+      "assistant"
+    ],
+    "stop_token_ids": [
+      128009,
+      128001,
+      128006,
+      128007,
+      78191
+    ],
+    "extract_input": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"
+  },
+  "meta-llama/Llama-2-7b-chat-hf": {
+    "model_name": "meta-llama/Llama-2-7b-chat-hf",
+    "stop_tokens": [
+      "</s>",
+      "<s>",
+      "<unk>",
+      "assistant"
+    ],
+    "stop_token_ids": [
+      2,
+      1,
+      0,
+      20255
+    ],
+    "extract_input": "[INST] "
+  }
+}

requirements.txt

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+transformers[torch]
+accelerate
+gradio