PocketDoc
/

Dans-PersonalityEngine-V1.1.0-12b

@@ -79,45 +79,43 @@ model-index:
   results: []
 pipeline_tag: text-generation
 ---
-## What is it?
-This model series is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline. It has been trained on a wide array of one shot instructions, multi turn instructions, tool use, role playing scenarios, text adventure games, co-writing, and much more. The full dataset is publicly available and can be found in the datasets section of the model page.
-There has not been any form of harmfulness alignment done on this model, please take the appropriate precautions when using it in a production environment.
-**Expected usable context length:** 32768 tokens
-## Recommended samplers
-**Temperature:** 1.0
-**Top_P:** 0.95
-**Min_P:** 0.05
-## Prompting
-The model has been trained on standard "ChatML" format prompting, an example of which is shown below:
-```
 <|im_start|>system
 system prompt<|im_end|>
 <|im_start|>user
 Hi there!<|im_end|>
 <|im_start|>assistant
-Nice to meet you!<|im_end|>
-<|im_start|>user
-Can I ask a question?<|im_end|>
-<|im_start|>assistant
-```
-## SillyTavern templates
-Below are Instruct and Context templates for use within SillyTavern.
-<details><summary>context template</summary>
-```yaml
 {
     "story_string": "<|im_start|>system\n{{#if system}}{{system}}\n{{/if}}{{#if wiBefore}}{{wiBefore}}\n{{/if}}{{#if description}}{{description}}\n{{/if}}{{#if personality}}{{char}}'s personality: {{personality}}\n{{/if}}{{#if scenario}}Scenario: {{scenario}}\n{{/if}}{{#if wiAfter}}{{wiAfter}}\n{{/if}}{{#if persona}}{{persona}}\n{{/if}}{{trim}}<|im_end|>\n",
     "example_separator": "",
@@ -129,13 +127,12 @@ Below are Instruct and Context templates for use within SillyTavern.
     "include_newline": false,
     "single_line": false,
     "name": "Dan-ChatML"
-}
-```
-</details><br>
-<details><summary>instruct template</summary>
-```yaml
 {
     "system_prompt": "Write {{char}}'s actions and dialogue, user will write {{user}}'s.",
     "input_sequence": "<|im_start|>user\n",
@@ -161,135 +158,44 @@ Below are Instruct and Context templates for use within SillyTavern.
     "first_input_sequence": "",
     "last_input_sequence": "",
     "name": "Dan-ChatML"
-}
-```
-</details><br>
-## Example outputs
-### General assistance
-<details><summary>General assistance example</summary>
-> *Act as a personable virtual assistant.*
-> I'm going to be in Chicago soon can you help me work up some restaurant ideas?
-> I'd love to! Here are a few suggestions for restaurants in Chicago:
->
-> - For deep dish pizza, I highly recommend Lou Malnati's Pizzeria or Giordano's. Both are iconic Chicago institutions and serve some of the best deep dish pizza in the city.
-> - If you're looking for a classic steakhouse experience, Gibson's Bar & Steakhouse is a great choice. They offer high-quality steaks, seafood, and an extensive wine list in a sophisticated atmosphere.
-> - For a more casual dining experience, check out Portillo's Hot Dogs. They're known for their Chicago-style hot dogs, Italian beef sandwiches, and other classic American fare.
-> - For something a bit more unique, try Alinea. It's a high-end, avant-garde restaurant that offers a truly one-of-a-kind dining experience with innovative cuisine and cutting-edge presentation techniques.
->
-> Let me know if you need any other recommendations or have specific dietary restrictions or preferences!
-<details><summary>Raw Prompt</summary>
-```
-<|im_start|>system
-Act as a personable virtual assistant.<|im_end|>
-<|im_start|>user
-I'm going to be in Chicago soon can you help me work up some restaurant ideas?<|im_end|>
-<|im_start|>assistant
-I'd love to! Here are a few suggestions for restaurants in Chicago:
-- For deep dish pizza, I highly recommend Lou Malnati's Pizzeria or Giordano's. Both are iconic Chicago institutions and serve some of the best deep dish pizza in the city.
-- If you're looking for a classic steakhouse experience, Gibson's Bar & Steakhouse is a great choice. They offer high-quality steaks, seafood, and an extensive wine list in a sophisticated atmosphere.
-- For a more casual dining experience, check out Portillo's Hot Dogs. They're known for their Chicago-style hot dogs, Italian beef sandwiches, and other classic American fare.
-- For something a bit more unique, try Alinea. It's a high-end, avant-garde restaurant that offers a truly one-of-a-kind dining experience with innovative cuisine and cutting-edge presentation techniques.
-Let me know if you need any other recommendations or have specific dietary restrictions or preferences!
-```
-</details><br>
-</details><br>
-### Basic programming
-<details><summary>Basic programming example</summary>
-> *Answer the question or perform the task.*
-> Explain the bug in the following code:
->
-> ```python
-> from time import sleep
-> from multiprocessing.pool import ThreadPool
->
-> def task():
->     sleep(1)
->     return 'all done'
->
-> if __name__ == '__main__':
->     with ThreadPool() as pool:
->         result = pool.apply_async(task())
-> ```
-> The bug in this code is that the `task()` function is called immediately when the `apply_async()` method is invoked, not when the worker process is ready to execute it.
->
-> Specifically, the line `result = pool.apply_async(task())` calls `task()` right away and passes its return value to `apply_async()`. The worker process will execute whatever `task()` returns at that moment, which is `'all done'` since `task()` completes execution almost immediately.
->
-> What you likely intended was for `apply_async()` to pass the `task()` function itself to the worker process, so the worker could call `task()` when ready. However, `apply_async()` only accepts the result of calling the target function, not the function object itself.
->
-> To fix this bug, you need to pass the `task` function itself to `apply_async()`, rather than calling `task()` beforehand. Here's how you can modify the code:
->
-> ```python
-> from time import sleep
-> from multiprocessing.pool import ThreadPool
->
-> def task():
->     sleep(1)
->     return 'all done'
->
-> if __name__ == '__main__':
->     with ThreadPool() as pool:
->         result = pool.apply_async(task)  # Pass the function, not the result
->         value = result.get()  # Wait for the result
->         print(value)
-> ```
->
-> With this modification, the worker process will actually execute the `task()` function, wait for it to complete, and then retrieve the result using `get()`.
->
-> So in summary, the original bug was that you were passing the result of calling `task()` to `apply_async()`, rather than passing the function object itself. The fix is to simply omit the parentheses after `task`, so `apply_async()` receives the function reference instead of its return value.
-<details><summary>Raw Prompt</summary>
-(Back slashes before triple backticks are not part of the prompt)
-```
-<|im_start|>system
-Answer the question or perform the task.<|im_end|>
-<|im_start|>user
-Explain the bug in the following code:
-\```
 from time import sleep
 from multiprocessing.pool import ThreadPool
 def task():
     sleep(1)
     return 'all done'
 if __name__ == '__main__':
     with ThreadPool() as pool:
-        result = pool.apply_async(task())
-        value = result.get()
-        print(value)
-\```<|im_end|>
-<|im_start|>assistant
-The bug in this code is that the `task()` function is called immediately when the `apply_async()` method is invoked, not when the worker process is ready to execute it.
-Specifically, the line `result = pool.apply_async(task())` calls `task()` right away and passes its return value to `apply_async()`. The worker process will execute whatever `task()` returns at that moment, which is `'all done'` since `task()` completes execution almost immediately.
-What you likely intended was for `apply_async()` to pass the `task()` function itself to the worker process, so the worker could call `task()` when ready. However, `apply_async()` only accepts the result of calling the target function, not the function object itself.
-To fix this bug, you need to pass the `task` function itself to `apply_async()`, rather than calling `task()` beforehand. Here's how you can modify the code:
-\```python
 from time import sleep
 from multiprocessing.pool import ThreadPool
@@ -301,121 +207,286 @@ if __name__ == '__main__':
     with ThreadPool() as pool:
         result = pool.apply_async(task)  # Pass the function, not the result
         value = result.get()  # Wait for the result
-        print(value)
-\```
-With this modification, the worker process will actually execute the `task()` function, wait for it to complete, and then retrieve the result using `get()`.
-So in summary, the original bug was that you were passing the result of calling `task()` to `apply_async()`, rather than passing the function object itself. The fix is to simply omit the parentheses after `task`, so `apply_async()` receives the function reference instead of its return value.
-```
-</details><br>
-</details><br>
-### Writing
-<details><summary>Writing example</summary>
-> *Take the users messages and use them as a writing prompt. Write 5 sentences.*
-> Alpha Centauri has fallen, to the bears. This is a point of view tale about a soldier on the ground.
-> "Sir, they are gone sir."  The young corporal was out of breath.
->
-> The soldier looked up. His grey eyes had been locked on his holofac display for hours now. He was in his late thirties and wore the rank of colonel. He was tall, with a well-muscled but slightly over weight body.
-> "Who?"
-> He turned and looked at the corporal.
->
-> "The bears. They left. We checked the entire perimeter. No bears to be found sir."
->
-> "Did you check the air?"
->
-> "Not the air, sir. We didn't see any shuttles. But we have drones out now."
-> "*Sigh*"
-> "Ok, get me the status report. I need to know how many dead we have."
->
-> "Yes sir." The corporal turned to leave.
->
-> "Corporal…"
->
-> "Sir?"
->
-> "Get me a fresh cup of coffee. Black. I need the caffeine."
-<details><summary>Raw Prompt</summary>
-```
-<|im_start|>system
-Take the users messages and use them as a writing prompt. Write 5 sentences.<|im_end|>
-<|im_start|>user
-Alpha Centauri has fallen, to the bears. This is a point of view tale about a soldier on the ground.<|im_end|>
-<|im_start|>assistant
-"Sir, they are gone sir."  The young corporal was out of breath.
-The soldier looked up. His grey eyes had been locked on his holofac display for hours now. He was in his late thirties and wore the rank of colonel. He was tall, with a well-muscled but slightly over weight body.<|im_end|>
-<|im_start|>user
-"Who?"<|im_end|>
-<|im_start|>assistant
-He turned and looked at the corporal.
-"The bears. They left. We checked the entire perimeter. No bears to be found sir."
-"Did you check the air?"
-"Not the air, sir. We didn't see any shuttles. But we have drones out now."<|im_end|>
-<|im_start|>user
-"*Sigh*"<|im_end|>
-<|im_start|>assistant
-"Ok, get me the status report. I need to know how many dead we have."
-"Yes sir." The corporal turned to leave.
-"Corporal…"
-"Sir?"
-"Get me a fresh cup of coffee. Black. I need the caffeine."
-```
-</details><br>
-</details><br>
-### Text editing
-<details><summary>Text editing</summary>
-> *You are a professional editor with decades of experience, help the user with any task they have for you.*
-> Can you rewrite this to flow better? "I knew I probably shouldnt have done that but ohhh well i guess i just going to live with it since whats done is done"
-> I knew I probably shouldn't have done that, but oh well. I guess I just going to live with it since what's done is done.
-<details><summary>Raw Prompt</summary>
-```
-<|im_start|>system
-You are a professional editor with decades of experience, help the user with any task they have for you.<|im_end|>
-<|im_start|>user
-Can you rewrite this to flow better? "I knew I probably shouldnt have done that but ohhh well i guess i just going to live with it since whats done is done"<|im_end|>
-<|im_start|>assistant
-I knew I probably shouldn't have done that, but oh well. I guess I just going to live with it since what's done is done.
-```
-</details><br>
-</details><br>
-## Training
-This model was full finetuned for 2 epochs on 1x H200 SXM equating to 88 hours of training.
-[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
-## Give me your pocket change
-The development and production of these datasets is severely limited by the funding and resources available to me. If you would like to help reach out to me here on HF, at visuallyadequate@gmail.com or you can leave something in the hat over at https://buymeacoffee.com/visually
-<a href="https://www.buymeacoffee.com/visually"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="45" width="162"></a>

   results: []
 pipeline_tag: text-generation
 ---
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+</head>
+<div class="crt-container">
+  <div class="crt-case">
+    <div class="crt-inner-case">
+      <div class="crt-bezel">
+<div class="terminal-screen">
+  <h2>Dans-PersonalityEngine-V1.1.0-12b</h2>
+  <p>This model series is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline. It has been trained on a wide array of one shot instructions, multi turn instructions, tool use, role playing scenarios, text adventure games, co-writing, and much more.</p>
+  <h3>Key Details</h3>
+  <pre>
+BASE MODEL: mistralai/Mistral-Nemo-Base-2407
+LICENSE: apache-2.0
+LANGUAGE: English
+CONTEXT LENGTH: 32768 tokens</pre>
+  <h3>Recommended Settings</h3>
+  <pre>
+TEMPERATURE: 1.0
+TOP_P: 0.95
+MIN_P: 0.05</pre>
+  <h3>Prompting Format</h3>
+  <p>The model uses standard "ChatML" format:</p>
+  <pre>
 <|im_start|>system
 system prompt<|im_end|>
 <|im_start|>user
 Hi there!<|im_end|>
 <|im_start|>assistant
+Nice to meet you!<|im_end|></pre>
+  <h3>SillyTavern Templates</h3>
+  <details>
+    <summary>Context Template</summary>
+    <pre>
 {
     "story_string": "<|im_start|>system\n{{#if system}}{{system}}\n{{/if}}{{#if wiBefore}}{{wiBefore}}\n{{/if}}{{#if description}}{{description}}\n{{/if}}{{#if personality}}{{char}}'s personality: {{personality}}\n{{/if}}{{#if scenario}}Scenario: {{scenario}}\n{{/if}}{{#if wiAfter}}{{wiAfter}}\n{{/if}}{{#if persona}}{{persona}}\n{{/if}}{{trim}}<|im_end|>\n",
     "example_separator": "",
     "include_newline": false,
     "single_line": false,
     "name": "Dan-ChatML"
+}</pre>
+  </details>
+  <details>
+    <summary>Instruct Template</summary>
+    <pre>
 {
     "system_prompt": "Write {{char}}'s actions and dialogue, user will write {{user}}'s.",
     "input_sequence": "<|im_start|>user\n",
     "first_input_sequence": "",
     "last_input_sequence": "",
     "name": "Dan-ChatML"
+}</pre>
+  </details>
+  <h3>Example Outputs</h3>
+  <details>
+    <summary>General Assistance Example</summary>
+    <p><em>System: Act as a personable virtual assistant.</em></p>
+    <p><strong>User:</strong> I'm going to be in Chicago soon can you help me work up some restaurant ideas?</p>
+    <p><strong>Assistant:</strong> I'd love to! Here are a few suggestions for restaurants in Chicago:</p>
+    <ul>
+      <li>For deep dish pizza, I highly recommend Lou Malnati's Pizzeria or Giordano's. Both are iconic Chicago institutions and serve some of the best deep dish pizza in the city.</li>
+      <li>If you're looking for a classic steakhouse experience, Gibson's Bar & Steakhouse is a great choice. They offer high-quality steaks, seafood, and an extensive wine list in a sophisticated atmosphere.</li>
+      <li>For a more casual dining experience, check out Portillo's Hot Dogs. They're known for their Chicago-style hot dogs, Italian beef sandwiches, and other classic American fare.</li>
+      <li>For something a bit more unique, try Alinea. It's a high-end, avant-garde restaurant that offers a truly one-of-a-kind dining experience with innovative cuisine and cutting-edge presentation techniques.</li>
+    </ul>
+    <p>Let me know if you need any other recommendations or have specific dietary restrictions or preferences!</p>
+  </details>
+  <details>
+    <summary>Basic Programming Example</summary>
+    <p><em>System: Answer the question or perform the task.</em></p>
+    <p><strong>User:</strong> Explain the bug in the following code:</p>
+    <pre>
 from time import sleep
 from multiprocessing.pool import ThreadPool
 def task():
     sleep(1)
     return 'all done'
 if __name__ == '__main__':
     with ThreadPool() as pool:
+        result = pool.apply_async(task())</pre>
+    <p><strong>Assistant:</strong> The bug in this code is that the `task()` function is called immediately when the `apply_async()` method is invoked, not when the worker process is ready to execute it.</p>
+    <p>Specifically, the line `result = pool.apply_async(task())` calls `task()` right away and passes its return value to `apply_async()`. The worker process will execute whatever `task()` returns at that moment, which is `'all done'` since `task()` completes execution almost immediately.</p>
+    <p>To fix this bug, you need to pass the `task` function itself to `apply_async()`, rather than calling `task()` beforehand. Here's the corrected code:</p>
+    <pre>
 from time import sleep
 from multiprocessing.pool import ThreadPool
     with ThreadPool() as pool:
         result = pool.apply_async(task)  # Pass the function, not the result
         value = result.get()  # Wait for the result
+        print(value)</pre>
+  </details>
+  <details>
+    <summary>Writing Example</summary>
+    <p><em>System: Take the users messages and use them as a writing prompt. Write 5 sentences.</em></p>
+    <p><strong>User:</strong> Alpha Centauri has fallen, to the bears. This is a point of view tale about a soldier on the ground.</p>
+    <p><strong>Assistant:</strong> "Sir, they are gone sir." The young corporal was out of breath.</p>
+    <p>The soldier looked up. His grey eyes had been locked on his holofac display for hours now. He was in his late thirties and wore the rank of colonel. He was tall, with a well-muscled but slightly over weight body.</p>
+    <p><strong>User:</strong> "Who?"</p>
+    <p><strong>Assistant:</strong> He turned and looked at the corporal.</p>
+    <p>"The bears. They left. We checked the entire perimeter. No bears to be found sir."</p>
+    <p>"Did you check the air?"</p>
+    <p>"Not the air, sir. We didn't see any shuttles. But we have drones out now."</p>
+    <p><strong>User:</strong> "*Sigh*"</p>
+    <p><strong>Assistant:</strong> "Ok, get me the status report. I need to know how many dead we have."</p>
+    <p>"Yes sir." The corporal turned to leave.</p>
+    <p>"Corporal…"</p>
+    <p>"Sir?"</p>
+    <p>"Get me a fresh cup of coffee. Black. I need the caffeine."</p>
+  </details>
+  <details>
+    <summary>Text Editing Example</summary>
+    <p><em>System: You are a professional editor with decades of experience, help the user with any task they have for you.</em></p>
+    <p><strong>User:</strong> Can you rewrite this to flow better? "I knew I probably shouldnt have done that but ohhh well i guess i just going to live with it since whats done is done"</p>
+    <p><strong>Assistant:</strong> I knew I probably shouldn't have done that, but oh well. I guess I just going to live with it since what's done is done.</p>
+  </details>
+  <h3>Datasets</h3>
+  <details>
+    <summary>Breakdown</summary>
+    <ul>
+        <li>Memory & Core Curriculum
+            <ul>
+                <li>Dans-MemoryCore-CoreCurriculum-Small - Base knowledge</li>
+            </ul>
+        </li>
+        <li>Military & Survival Knowledge
+            <ul>
+                <li>Energetic-Materials - Understanding of explosives and related chemistry</li>
+                <li>Chemical-Biological-Safety-Applications - Safety protocols, handling procedures, etc.</li>
+                <li>US-Army-Survival - Survival techniques and field craft</li>
+                <li>Resistance - Resistance operations and tactics</li>
+                <li>Interrogation - Interview and interrogation techniques</li>
+                <li>Multi-Environment-Operations - Operations across different environments</li>
+            </ul>
+        </li>
+        <li>Mathematics & Problem Solving
+            <ul>
+                <li>Dans-Mathmaxx - Core mathematics capabilities</li>
+                <li>Dans-Mathmaxx-Numina-CoT - Chain of thought mathematical reasoning</li>
+                <li>Math-Multiturn-1K-ShareGPT - Multi-turn math problem solving</li>
+            </ul>
+        </li>
+        <li>Benchmarking & Testing
+            <ul>
+                <li>Dans-Benchmaxx - Prepares model for "answer only" style benchmarks. Helps prevent the model from talking too much when the situation calls for it.</li>
+                <li>Dans-Benchmaxx-COT - The same but for COT then answer based testing.</li>
+            </ul>
+        </li>
+        <li>Programming & Code
+            <ul>
+                <li>Dans-Codemaxx-LeetCode - Programmatically produced from rosettacode</li>
+                <li>Dans-Codemaxx-CodeFeedback - Dataset focused on correction after producing incorrect code.</li>
+                <li>Dans-Codemaxx-Bigcode-SelfInstruct - Subset from the Bigcode SelfInstruct dataset</li>
+            </ul>
+        </li>
+        <li>Task Specific Training
+            <ul>
+                <li>Dans-Taskmaxx - General task handling</li>
+                <li>Dans-Taskmaxx-DataPrepper - Data preparation and cleaning</li>
+                <li>Dans-Taskmaxx-ConcurrentQA - Multi hop retrieval based tasks</li>
+                <li>Dans-Taskmaxx-TableGPT - Table data processing</li>
+                <li>Dans-Taskmaxx-SciRIFF - Scientific paper analysis</li>
+                <li>Dans-Taskmaxx-Edit - Text editing and revision</li>
+            </ul>
+        </li>
+        <li>System & Tool Usage
+            <ul>
+                <li>Dans-Toolmaxx-Agent - Tool usage and agent behavior</li>
+                <li>Dans-Toolmaxx-ShellCommands - Command line operations</li>
+                <li>Dans-Toolmaxx-Functions - API and function calling</li>
+            </ul>
+        </li>
+        <li>Creative & Writing
+            <ul>
+                <li>Dans-ASCIIMaxx-Wordart - ASCII word art creation</li>
+                <li>Dans-Prosemaxx-Gutenberg - Summary style prompt writing instructions sourced from the Gutenberg project.</li>
+                <li>Dans-Prosemaxx-Cowriter - Back and forth co-writing dataset sourced from human written literature</li>
+                <li>Dans-Prosemaxx-Adventure - Interactive fiction throwbacks such as Zork, Anchorhead, and Hunting the Ripper</li>
+                <li>Dans-Prosemaxx-WritingPrompts - Prompt based writing instructions</li>
+            </ul>
+        </li>
+        <li>Assistant & Personality
+            <ul>
+                <li>Dans-Assistantmaxx series - Various assistant behaviors and capabilities</li>
+                <li>Dans-Personamaxx series - Personality and character development</li>
+                <li>Dans-Logicmaxx series - Logical reasoning and problem solving</li>
+            </ul>
+        </li>
+        <li>Instruction Following
+            <ul>
+                <li>Dans-Systemmaxx - System message training data optimized to help the model reject bad patterns</li>
+            </ul>
+        </li>
+    </ul>
+</details>
+  <h3>Training</h3>
+  <p>Full finetuned for 2 epochs on 1x H200 SXM (88 hours of training)</p>
+    <p class="badge-container">
+  <a href="https://github.com/OpenAccess-AI-Collective/axolotl" target="_blank" rel="noopener noreferrer">
+    <img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>
+  </a>
+</p>
+  <h3>Support Development</h3>
+  <p>Development is limited by funding and resources. To help support:</p>
+  <p>- Contact on HF</p>
+  <p>- Email: visuallyadequate@gmail.com</p>
+  <p class="coffee-container">
+  <a href="https://www.buymeacoffee.com/visually" target="_blank" rel="noopener noreferrer">
+    <img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="45" width="162">
+  </a>
+</p>
+</div>
+      </div>
+    </div>
+  </div>
+</div>
+<style>
+@import url('https://fonts.googleapis.com/css2?family=VT323&display=swap');
+.crt-container {
+  padding: 10px;
+  max-width: 1000px;
+  margin: 0 auto;
+  width: 95%;
+}
+.crt-case {
+  background: #e8d7c3;
+  border-radius: 10px;
+  padding: 15px;
+  box-shadow: inset -2px -2px 5px rgba(0,0,0,0.3), 2px 2px 5px rgba(0,0,0,0.2);
+}
+.crt-inner-case {
+  background: #e8d7c3;
+  border-radius: 8px;
+  padding: 3px;
+  box-shadow: inset -1px -1px 4px rgba(0,0,0,0.3), 1px 1px 4px rgba(0,0,0,0.2);
+}
+.crt-bezel {
+  background: linear-gradient(145deg, #1a1a1a, #2a2a2a);
+  padding: 15px;
+  border-radius: 5px;
+  border: 3px solid #0a0a0a;
+  position: relative;
+  box-shadow:
+    inset 0 0 20px rgba(0,0,0,0.5),
+    inset 0 0 4px rgba(0,0,0,0.4),
+    inset 2px 2px 4px rgba(255,255,255,0.05),
+    inset -2px -2px 4px rgba(0,0,0,0.8),
+    0 0 2px rgba(0,0,0,0.6),
+    -1px -1px 4px rgba(255,255,255,0.1),
+    1px 1px 4px rgba(0,0,0,0.3);
+}
+.crt-bezel::before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: linear-gradient(45deg,
+    rgba(255,255,255,0.03) 0%,
+    rgba(255,255,255,0) 40%,
+    rgba(0,0,0,0.1) 60%,
+    rgba(0,0,0,0.2) 100%);
+  border-radius: 3px;
+  pointer-events: none;
+}
+.terminal-screen {
+  background: #111112;
+  padding: 20px;
+  border-radius: 15px;
+  position: relative;
+  overflow: hidden;
+  font-family: 'VT323', monospace;
+  font-size: clamp(12px, 1.5vw, 16px);
+  color: #e49b3e;
+  line-height: 1.4;
+  text-shadow: 0 0 2px #e49b3e;
+  animation: flicker 0.15s infinite;
+  filter: brightness(1.1) contrast(1.1);
+  box-shadow:
+    inset 0 0 30px rgba(0,0,0,0.9),
+    inset 0 0 8px rgba(0,0,0,0.8),
+    0 0 5px rgba(0,0,0,0.6);
+  max-width: 80ch;
+  margin: 0 auto;
+}
+.terminal-screen h2, .terminal-screen h3 {
+  font-size: clamp(16px, 2vw, 20px);
+  margin-bottom: 1em;
+}
+.terminal-screen pre {
+  font-size: clamp(11px, 1.3vw, 14px);
+  white-space: pre-wrap;
+  margin: 1em 0;
+}
+.terminal-screen::before {
+  content: "";
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: linear-gradient(rgba(18, 16, 16, 0) 50%, rgba(0, 0, 0, 0.25) 50%), url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADIAAAAyBAMAAADsEZWCAAAAGFBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4o8JoAAAAB3RSTlMAGwQIEQMYADcPzwAAACJJREFUKM9jYBgFo2AU0Beg+A8YMCLxGYZCbNQEo4BaAAD5TQiR5wU9vAAAAABJRU5ErkJggg==');
+  background-size: 100% 2.5px;
+  animation: scan 1s linear infinite;
+  pointer-events: none;
+  z-index: 2;
+}
+.terminal-screen::after {
+  content: "";
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  background: radial-gradient(circle at center, rgba(17, 17, 18, 0) 0%, rgba(17, 17, 18, 0.1) 75%, rgba(17, 17, 18, 0.4) 100%);
+  border-radius: 20px;
+  animation: vignette-pulse 3s infinite;
+  pointer-events: none;
+  z-index: 1;
+}
+.terminal-screen details {
+  margin: 1em 0;
+  padding: 0.5em;
+  border: 1px solid #e49b3e;
+  border-radius: 4px;
+}
+.terminal-screen summary {
+  cursor: pointer;
+  font-weight: bold;
+  margin: -0.5em;
+  padding: 0.5em;
+  border-bottom: 1px solid #e49b3e;
+}
+.terminal-screen details[open] summary {
+  margin-bottom: 0.5em;
+}
+.badge-container, .coffee-container {
+  text-align: center;
+  margin: 1em 0;
+}
+.badge-container img, .coffee-container img {
+  max-width: 100%;
+  height: auto;
+}
+.terminal-screen a {
+  color: #e49b3e;
+  text-decoration: underline;
+  transition: opacity 0.2s;
+}
+.terminal-screen a:hover {
+  opacity: 0.8;
+}
+@keyframes flicker {
+  0% { opacity: 0.98; }
+  50% { opacity: 1; }
+  100% { opacity: 0.99; }
+}
+@keyframes scan {
+  0% { transform: translateY(0); }
+  100% { transform: translateY(4px); }
+}
+@keyframes vignette-pulse {
+  0% { opacity: 0.8; }
+  50% { opacity: 1; }
+  100% { opacity: 0.8; }
+}
+</style>