agentlans

This model is a fine-tuned version of agentlans/multilingual-e5-small-aligned designed for assessing text sentiment across multiple languages. - Multilingual support - Sentiment assessment for text - Based on E5 small model architecture This model is intended for: - Assessing the sentiment of multilingual text - Filtering multilingual content - Comparative analysis of corpus text sentiment across different languages Limitations: - Performance may vary for languages not well-represented in the training data - Should not be used as the sole criterion for sentiment assessment The model was evaluated on a diverse set of multilingual text samples: - 10 English text samples of varying sentiment were translated into Arabic, Chinese, French, Russian, and Spanish. - The model demonstrated consistent sentiment assessment across different languages for the same text. Click here for the 10 original texts and their translations. | Text | English | French | Spanish | Chinese | Russian | Arabic | |---|---|---|---|---|---|---| | A | Nothing seems to go right, and I'm constantly frustrated. | Rien ne semble aller bien et je suis constamment frustré. | Nada parece ir bien y me siento constantemente frustrado. | 一切似乎都不顺利，我总是感到很沮丧。 | Кажется, все идет не так, как надо, и я постоянно расстроен. | يبدو أن لا شيء يسير على ما يرام، وأنا أشعر بالإحباط باستمرار. | | B | Everything is falling apart, and I can't see any way out. | Tout s’effondre et je ne vois aucune issue. | Todo se está desmoronando y no veo ninguna salida. | 一切都崩溃了，我看不到任何出路。 | Все рушится, и я не вижу выхода. | كل شيء ينهار، ولا أستطيع أن أرى أي مخرج. | | C | I feel completely overwhelmed by the challenges I face. | Je me sens complètement dépassé par les défis auxquels je suis confronté. | Me siento completamente abrumado por los desafíos que enfrento. | 我感觉自己完全被所面临的挑战压垮了。 | Я чувствую себя совершенно подавленным из-за проблем, с которыми мне приходится сталкиваться. | أشعر بأنني غارق تمامًا في التحديات التي أواجهها. | | D | There are some minor improvements, but overall, things are still tough. | Il y a quelques améliorations mineures, mais dans l’ensemble, les choses restent difficiles. | Hay algunas mejoras menores, pero en general las cosas siguen siendo difíciles. | 虽然有一些小的改进，但是总的来说，事情仍然很艰难。 | Есть некоторые незначительные улучшения, но в целом ситуация по-прежнему сложная. | هناك بعض التحسينات الطفيفة، ولكن بشكل عام، لا تزال الأمور صعبة. | | E | I can see a glimmer of hope amidst the difficulties I encounter. | Je vois une lueur d’espoir au milieu des difficultés que je rencontre. | Puedo ver un rayo de esperanza en medio de las dificultades que encuentro. | 我在遇到的困难中看到了一线希望。 | Среди трудностей, с которыми я сталкиваюсь, я вижу проблеск надежды. | أستطيع أن أرى بصيص أمل وسط الصعوبات التي أواجهها. | | F | Things are starting to look up, and I'm cautiously optimistic. | Les choses commencent à s’améliorer et je suis prudemment optimiste. | Las cosas están empezando a mejorar y me siento cautelosamente optimista. | 事情开始好转，我持谨慎乐观的态度。 | Ситуация начинает улучшаться, и я настроен осторожно и оптимистично. | بدأت الأمور تتجه نحو التحسن، وأنا متفائل بحذر. | | G | I'm feeling more positive about my situation than I have in a while. | Je me sens plus positif à propos de ma situation que je ne l’ai été depuis un certain temps. | Me siento más positivo sobre mi situación que en mucho tiempo. | 我对自己处境的感觉比以前更加乐观了。 | Я чувствую себя более позитивно относительно своей ситуации, чем когда-либо за последнее время. | أشعر بإيجابية أكبر تجاه وضعي مقارنة بأي وقت مضى. | | H | There are many good things happening, and I appreciate them. | Il se passe beaucoup de bonnes choses et je les apprécie. | Están sucediendo muchas cosas buenas y las aprecio. | 有很多好事发生，我对此表示感谢。 | Происходит много хорошего, и я это ценю. | هناك الكثير من الأشياء الجيدة التي تحدث، وأنا أقدرها. | | I | Every day brings new joy and possibilities; I feel truly blessed. | Chaque jour apporte de nouvelles joies et possibilités ; je me sens vraiment béni. | Cada día trae nueva alegría y posibilidades; me siento verdaderamente bendecida. | 每天都有新的快乐和可能性；我感到非常幸福。 | Каждый день приносит новую радость и возможности; я чувствую себя по-настоящему благословенной. | كل يوم يجلب فرحة وإمكانيات جديدة؛ أشعر بأنني محظوظة حقًا. | | J | Life is full of opportunities, and I'm excited about the future. | La vie est pleine d’opportunités et je suis enthousiaste quant à l’avenir. | La vida está llena de oportunidades y estoy entusiasmado por el futuro. | 生活充满机遇，我对未来充满兴奋。 | Жизнь полна возможностей, и я с нетерпением жду будущего. | الحياة مليئة بالفرص، وأنا متحمس للمستقبل. | The model was trained on the Multilingual Parallel Sentences dataset, which includes: - Parallel sentences in English and various other languages - Semantic similarity scores calculated using LaBSE - Additional sentiment metrics - Sources: JW300, Europarl, TED Talks, OPUS-100, Tatoeba, Global Voices, and News Commentary The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 128 - evalbatchsize: 8 - seed: 42 - optimizer: Use adamwtorch with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: linear - numepochs: 3.0 | Training Loss | Epoch | Step | Validation Loss | Mse | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1946 | 1.0 | 7813 | 0.1647 | 0.1647 | | 0.1385 | 2.0 | 15626 | 0.1528 | 0.1528 | | 0.1121 | 3.0 | 23439 | 0.1455 | 0.1455 | - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3

This model generates concise point-form notes in Markdown format based on the provided text. Note that the input and output texts should be properly escaped XML strings.

Base model: arcee-ai/Llama-3.1-SuperNova-Lite, deepseek-ai/DeepSeek-R1-Distill-Llama-8B.

This repo contains quantized versions of the agentlans/Qwen3-4B-multilingual-sft model, optimized for efficient local use with llama.cpp. The models were quantized using an unofficial Docker image and calibrated on the first 100 rows of the LinguaNova dataset to maintain strong multilingual performance. These quantized models share the same strengths and limitations as the original Qwen 3 4B multilingual model. They offer a lighter, faster alternative for inference with minor trade-offs in precision.

This model is based on ibm-granite/granite-3.3-2b-instruct and it's designed for analyzing arguments, finding logical fallacies, and providing suggestions for improvement. It was pre-trained on the agentlans/reddit-logic dataset and then fine-tuned using supervised learning on the same dataset. The model outputs a JSON object containing an analysis of the input argument. Here's an example of the expected output format: - The model has the same limitations as the agentlans/reddit-logic dataset. - May not work as well on data outside the training distribution, including other types of communication and fields of discourse. - Lacks specialized knowledge but can offer pointers for continuing research to critically evaluate the arguments. - May possibly misinterpret the input or create malformed output, although this hasn't occurred yet in testing so far. - May miss some logical fallacies. - Doesn't fact check references. The following setup was used for both pretraining and supervised fine-tuning. The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 2 - evalbatchsize: 8 - seed: 42 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 16 - optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: cosine - numepochs: 1.0 - PEFT 0.15.0 - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0

This repo offers quantized versions of google/gemma-3-4b-it for use with llama.cpp. Quantization was done using an unofficial Docker image and calibrated on 100 rows from the agentlans/LinguaNova dataset to maintain coherence and multilingual support. The importance matrix file is included. - Optimized for multilingual natural language tasks. - May underperform on math, coding, and untested multimodal features. - Shares all limitations and biases of the original Gemma 3 models. - Ideal for resource-constrained environments. - Test on your data for best results. - See the original google/gemma-3-4b-it page for full details and guidelines.

This repo offers quantized versions of the HuggingFaceTB/SmolLM2-135M-Instruct model, optimized for llama.cpp with Q4KM, Q5KM, and Q80 quantizations. These were built using an unofficial Docker image. Models were calibrated on 1000 samples from the openbmb/Ultra-FineWeb dataset to maintain coherent output. The resulting `imatrix.dat` file is included. User: What is AI? Reply: AI is an intelligent and sophisticated system that processes and analyzes vast amounts of data to learn, understand, and perform tasks. It is a branch of computer science that focuses on enabling machines to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. AI is often associated with machines that can reason, learn, and improve their performance over time, allowing them to recognize patterns, make decisions, and adapt to new situations. AI has applications across various fields, including technology, business, healthcare, and education. It is considered to be one of the most promising fields in the field of artificial intelligence and machine learning, as it can automate a wide range of tasks and provide new insights and discoveries in many areas of life and work. Very fast. On an NVIDIA RTX 4070, the Q4 model can generate close to 300 tokens per second. - Same constraints as the original small SmolLM2-135M-Instruct model - Not ideal for complex tasks - Mainly suited for language generation and experimentation

Gemma 3 4B (Claude Edition) is a fine-tuned version of the Gemma 3 model, trained on the Claude dataset to enhance its English writing style. The goal of this release is to produce outputs that are more natural, creative, and coherent across a wide range of use cases. Overview This variant benefits from Claude’s diverse English-language text and code examples, improving fluency and expressiveness while maintaining the stable performance Gemma models are known for. Use Cases - Creative writing: stories, poems, and articles - Content generation: blogs, social media posts, and summaries - Language translation (with some limitations) - Conversational AI and chatbots Limitations - The model may generate inaccurate or outdated information. Always double-check important details before using outputs in production. - Can still give verbose or redundant output. - Capable of basic chain-of-thought reasoning but not the long DeepSeek style reasoning. - May not understand some prompts or long conversations well. - Built-in content filters may limit creativity or restrict certain topics. - Non-English translations are tuned for natural-sounding English rather than strict literal accuracy. - The model is not specialized for math or code generation. - Visual and multimodal functions were not tested. Training Data 1. `agentlans/claude` dataset, `samplek100000` configuration with LoRA rank 16, alpha 32, and NEFTune 5 2. Additional `samplek10000` fine-tuning with LoRA rank 8, alpha 16, and NEFTune 5, without sequence packing

Write title Excerpt from "The Magic Mountain" by Thomas Mann; published by Alfred A. Knopf; translated by John E. Woods. New York, London, Toronto: Everyman‘s Library, 1995. ` Hans Castorp discovered that you quickly learn a skill if you truly need to. He made no pretense of becoming a virtuoso. What he required to know he learned in a few days, without overheating or having to fight for breath. He worked hard at keeping his feet nicely parallel, leaving a set of even tracks, practiced how to push himself off by steering with his poles, learned to negotiate obsta- cles, leaping over little mounds with arms widespread, rising and falling like a ship on a stormy sea; and after about the twentieth try he no longer upended when he put on the brakes by executing a telemark turn at full speed, sticking one leg out and bending the other at the knee. [...] The wintry mountains were beautiful — not in a gentle, benign way, but beautiful like the wild North Sea under a strong west wind. They awakened the same sense of awe — but there was no thunder, only a deathly silence. Hans Castorp’s long, pliant foot- wear bore him in all directions: along the slope on the left in the direction of Clavadel or to the right on past Frauenkirch and Gla- ris, the shadowy ghost of the Amselfluh massif looming up out of the fog behind them; he also skied the valley of the Dischma and the hills rising behind the Berghof, in the direction of the wooded Seehorn, only the very tops of its two snow-clad peaks visible above the tree line, and toward the Drusatscha woods, behind which he could see the pale, murky outline of the Rhätikon chain buried under snow. He even took his skis in the cablecar to the top of Schatzalp to glide about happily up there, abducted into a world of shimmering, powdery slopes, sixty-five hundred feet above sea level, from where in good weather he had a glorious panorama of the scene of his adventures.“ ` ` Hans Castorp’s Winter Skis: Learning and Mastery on the Alps ` `Summarize this text in one or a small number of paragraphs:` ` Walters was appointed a Commander of the Order of the British Empire (CBE) in the King's Birthday Honours on 25 June 1946, for his "conspicuous service in operations against the Japanese" while leading Northern Command during the war.[44][45] After completing his term as AOC Northern Command that year, Walters again became Director of Staff Plans and Policy at RAAF Headquarters.[1][2] He attended the Imperial Defence College, London, in 1947.[9] Walters was among a small coterie of highly regarded operational commanders, including Air Commodores John McCauley, Fred Scherger and Val Hancock, earmarked by the Australian Air Board for senior leadership roles in the post-war RAAF.[46][47] In the short term, he remained a temporary air commodore—with the substantive rank of group captain from May 1947—as the officer corps shrank drastically with demobilisation.[47][48] He was selected as AOC Southern Area Command, hub of the RAAF's training organisation, in March 1948.[49][50] The following month, he flew to Morotai to preside over a court-martial for an RAAF airman accused of killing an officer of the Dutch merchant marine; the airman was acquitted.[51][52] In January 1951, Walters was appointed AOC RAAF Overseas Headquarters in London.[53] That December, he was part of the Australian contingent at the Commonwealth Air Forces Conference, where an RAAF presence was sought in the Middle East; this eventually resulted in No. 78 Wing being re-formed and deployed to Malta in July 1952. Although Walters was keen to use the opportunity to acquire the RAAF's first North American F-86 Sabres, political realities led him to negotiate a deal whereby the wing was equipped with leased British de Havilland Vampire FB.9s.[9][54] In October 1952, Walters was promoted to acting air vice marshal and posted to Washington, D.C., to head up the Australian Joint Services Staff as successor to Air Vice Marshal Scherger.[55] Walters' rank was made permanent in January 1954, when he succeeded Air Vice Marshal McCauley to become AOC Home Command.[1][56] Walters held this post, responsible for directing the RAAF's combat units, for three years. His tenure witnessed the introduction of the CAC Sabre to operational service with the Air Force, when No. 3 Squadron took delivery of its first machine in March 1956.[9][57] Walters was appointed a Companion of the Order of the Bath (CB) in the 1956 New Year Honours.[58][59] In March 1957, he was one of three candidates, along with Air Vice Marshals Scherger and Hancock, touted as possible successors to Air Marshal McCauley as Chief of the Air Staff (CAS), the RAAF's senior position. Though Walters was considered to be very able, Scherger had long been regarded as outstanding and was "easily the best material on offer" according to a former CAS, Air Marshal Sir Donald Hardman.[60] Scherger gained the appointment, and Walters became Air Member for Personnel (AMP) on 21 October.[61] As AMP he occupied a seat on the Air Board, the service's controlling body that comprised its senior officers and was chaired by the CAS.[1][62] In this role Walters endorsed the recommendations of a review by the AOC Training Command, Air Vice Marshal Ian McLachlan, that led to a policy of RAAF College cadets undertaking academic degrees, in line with similar institutions in the other armed services; the college was subsequently renamed RAAF Academy.[63][64] Walters served as AMP until August 1959.[61] The following month he was appointed AOC Support Command, a new organisation created by merging the RAAF's former Training and Maintenance Commands.[2][65] When Scherger's term as CAS was due to complete in May 1961, Walters and Hancock were once more put forward to the Minister for Air as potential replacements. "Walters was again unlucky", in the words of Air Force historians Alan Stephens and Jeff Isaacs, Hancock's "professional ability, operational experience and personal qualities" being deemed more appropriate for the role.[66] ` ` Following World War II, Walter served as a Commander of the Order of the British Empire for his wartime leadership. He then held key positions within the RAAF, including Director of Staff Plans and Policy, and later served as AOC Southern Area Command and Overseas Headquarters. During his tenure, he oversaw the reformation of No. 78 Wing and the introduction of the CAC Sabre. As Air Member for Personnel, he championed the establishment of RAAF Academy, and as AOC Support Command, he further consolidated RAAF operations. Despite his contributions, Walters was ultimately not selected as Chief of the Air Staff. ` Source: https://www.hollywoodreporter.com/news/general-news/shawshank-redemption-review-1994-movie-932222/ ` Castle Rock has tapped into the wellspring of yet another Stephen King novella, from the same collection as Stand by Me. While it seems unlikely that this well-wrought film about hope and friendship in a maximum security prison will do Stand by Me-level business, The Shawshank Redemption should appeal to a mature audience. The marketing challenge will be to lure a mainstream audience to this dark drama starring Tim Robbins and Morgan Freeman with, to boot, an enigmatic title. Covering a span of time from 1946 to 1967, Shawshank focuses on the plight of a Maine banker, Andy Dufresne (Robbins), sentenced to two life terms for the murder of his wife and lover. Laconic and a bit chilly, Andy is somewhat of an outcast among the uneducated, macho thugs who make up the main brunt of the prison population. Admittedly, he’s not an easy guy to get to know and only comes to enjoy a prison friendship when he makes a deal with the prison black-market supplier, Red (Freeman), to get him a tiny hammer for his rock collection. Red is also an enigma to most of the inmates. He keeps his nose clean and his hopes up even though the parole board routinely denies him pardon, obviously a racial antagonism. While the story roils with some rousing prison-type action and is coarsened by an overpowering sense of frustration, The Shawshank Redemption is really about the unlikely friendship between Andy and Red: Each nurtures the other and serves as a touchstone for keeping their sanity intact. Frank Darabont’s writing and direction are generally crisp, but Shawshank is a tough watch and audiences could use some time cut from its 142 minutes. In the lead roles, both Robbins and Freeman are outstanding, layering their performances with snippets of individuality: Their small, daily sustenances and minor triumphs are wonderfully inspiring. Technical contributions are well-crafted, particularly cinematographer Roger Deakins‘ forbidding lensing. Filmed in a garrison-like former prison in Mansfield, Ohio, Shawshank is often overwhelming in its depiction of the granite-cold, stony horror of prison life. Most splendid is Thomas Newman’s somber score, which, at its best moments, alights with radiant textures and sprightly grace notes, nicely emblematic of the film’s central theme. — Duane Byrge ` ` The Shawshank Redemption, a drama about a banker's unlikely friendship with a fellow inmate, explores themes of hope, resilience, and the human spirit within a maximum security prison setting. ` Source: https://www.cnn.com/2025/11/01/politics/trump-pentagon-nigeria-action `Extract keywords from the following text as a JSON array:` ` US President Donald Trump said Saturday he has ordered the Defense Department to prepare for possible military action in Nigeria as he continues to accuse the nation of not doing enough to stem violence against Christians — an accusation Nigeria has repeatedly denied. In a social media post criticizing what he called the “mass slaughter” of Christians in the country, Trump wrote the United States would “immediately stop all aid and assistance to Nigeria” and warned the government there to “move fast.” In the lengthy message, Trump said the US “may very well go into that now disgraced country, ‘guns-a-blazing,’ to completely wipe out the Islamic Terrorists who are committing these horrible atrocities.” Both Christians and Muslims have been victims of attacks by radical Islamists in the country of more than 230 million people. The violence in the country is driven by varying factors: some incidents are religiously motivated and affect both groups, while others arise from disputes between farmers and herders over limited resources, as well as communal and ethnic tensions. Although Christians are among those targeted, local reports indicate that most victims are Muslims living in Nigeria’s predominantly Muslim north. “I am hereby instructing our Department of War to prepare for possible action,” Trump wrote. “If we attack, it will be fast, vicious, and sweet, just like the terrorist thugs attack our CHERISHED Christians! WARNING: THE NIGERIAN GOVERNMENT BETTER MOVE FAST!” “Yes sir,” US Secretary of Defense Pete Hegseth posted on social media with a screenshot of Trump’s comments. “The killing of innocent Christians in Nigeria — and anywhere — must end immediately. The Department of War is preparing for action. Either the Nigerian Government protects Christians, or we will kill the Islamic Terrorists who are committing these horrible atrocities.” Trump’s announcement comes after he accused Nigeria of religious freedom violations on Friday, claiming that “Christianity is facing an existential threat in Nigeria” and designating the nation as a “Country of Particular Concern” under the International Religious Freedom Act. The label is a suggestion that his administration has found that Nigeria has engaged in or tolerated “systematic, ongoing, (and) egregious violations of religious freedom.” In a social media post after the designation, but before Trump’s mention of the military, Nigerian President Bola Tinubu wrote: “The characterisation of Nigeria as religiously intolerant does not reflect our national reality, nor does it take into consideration the consistent and sincere efforts of the government to safeguard freedom of religion and beliefs for all Nigerians.” He added that Nigeria is “working with the United States government and the international community to deepen understanding and cooperation on protection of communities of all faiths.” Tinubu’s press secretary, responding to a social media post from US Secretary of State Marco Rubio condemning the “slaughter of thousands of Christians,” called the characterization “a gross exaggeration of the Nigerian situation,” adding that “Christians, Muslims, churches and mosques are attacked randomly.” “What our country requires from America is military support to fight these violent extremists in some states of our country, not designation as a nation of particular concern,” said Bayo Onanuga. Spokespersons for the White House and Tinubu’s office did not immediately respond to requests for comment. ` `["Nigeria", "Christians", "Muslims", "violence", "terrorism", "military action", "Donald Trump", "Nigerian government", "Bola Tinubu", "religious freedom", "Islamic Terrorists", "aid", "assistance", "Department of War", "International Religious Freedom Act", "Boko Haram", "religious intolerance"]`

> [!NOTE] > These examples are for a previous version of the model. This README should be updated later with the complete information. The Battle of Arkansas Post, also known as the Battle of Fort Hindman, was fought from January 9 to 11, 1863, along the Arkansas River at Arkansas Post, Arkansas, as part of the Vicksburg campaign of the American Civil War.[1] Confederate forces constructed Fort Hindman near Arkansas Post in late 1862. Also in late 1862, Major General John A. McClernand of the Union army (as the United States Army was known during the war) was authorized to recruit troops in the Midwest for an expedition down the Mississippi River against Vicksburg, Mississippi. Union Major General Ulysses S. Grant began an overland campaign against Vicksburg along the Mississippi Central Railroad in November. He and Union General-in-Chief Henry Halleck did not trust McClernand, and through machinations placed the start of the riverine movement against Vicksburg under the command of Major General William T. Sherman before McClernand could arrive. Sherman's movement was defeated at the Battle of Chickasaw Bayou in late December, and Confederate cavalry raids forced Grant to abandon his overland campaign. McClernand arrived at Memphis, Tennessee, in late December and found that Sherman had left without him. McClernand moved downriver, joined Sherman's force, and took command in early January 1863, calling it the Army of the Mississippi. Both Sherman and McClernand had independently come to the conclusion that Arkansas Post should be attacked: Confederate forces raiding from Fort Hindman had recently captured a Union supply vessel, and Sherman may have been hoping for a victory to restore his reputation after Chickasaw Bayou. McClernand's troops and a Union Navy fleet commanded by Acting Rear Admiral David Dixon Porter moved upriver towards the Arkansas River. The expedition began unloading troops downriver from the fort late on January 9. The next day, some of Porter's warships bombarded the fort, while McClernand's troops maneuvered into position. At 1:00 pm on January 11, Porter's warships began another bombardment of the fort, and McClernand's troops attacked the Confederate positions, which consisted of the fort and a line of rifle pits that extended west to a bayou. McClernand's attack was repulsed, but white flags of surrender began to appear over parts of the Confederate line in uncertain circumstances. Confusion ensued, and Union troops moved up close to the Confederate line and swamped parts of it. The Confederate commander, Brigadier General Thomas J. Churchill, agreed to surrender. When Grant learned of the operation against Arkansas Post, he disapproved and ordered McClernand back to the Mississippi River, although Grant was later convinced of the wisdom of the operation. Grant relieved McClernand on January 30 and took command of the campaign against Vicksburg. In April and May, Grant's army crossed the Mississippi River downriver from Vicksburg and won a series of battles. The Confederate forces withdrew into the Vicksburg defenses in mid-May. The Siege of Vicksburg ended with a Confederate surrender on July 4, 1863; this was a key contribution to the eventual Union victory. ` Following the initial failure of a riverine campaign against Vicksburg, Union forces launched a surprise attack on Confederate positions at Arkansas Post, Arkansas, in early January 1863. Under the command of Major General William T. Sherman, the attack involved a combined Union army and naval force. Although initially repelled, the assault ultimately led to a Confederate surrender. This victory, achieved through deception and combined arms tactics, significantly contributed to the Union’s eventual success in capturing Vicksburg, a crucial strategic objective in the Civil War. Union General Ulysses S. Grant later assumed command of the campaign against Vicksburg. ` Summarize: Frequently Asked Questions About Nano Banana 1 What exactly is Nano Banana and how does it work? Nano Banana is an AI-powered art generator that transforms text descriptions into stunning, high-quality images. Using state-of-the-art AI technology, it can create everything from photorealistic portraits to abstract art in seconds. 2 Do I need artistic skills to use Nano Banana? Not at all! Nano Banana is designed for everyone - from professional artists to complete beginners. Simply describe what you want in plain text, and our AI will create beautiful artwork for you. 3 What types of images can I create with Nano Banana? You can create virtually any type of image - portraits, landscapes, abstract art, concept designs, illustrations, 3D renderings, and more. Choose from various artistic styles including digital painting, photorealistic, minimalist, and futuristic. 4 How long does it take to generate an image? Nano Banana generates high-quality images in just a few seconds! Our efficient AI processing ensures you get stunning results without the wait, perfect for creative professionals on tight deadlines. 5 Can I use the generated images commercially? Yes! Once you download your artwork, you can use it for personal, commercial, or creative projects. Nano Banana provides high-resolution images that are perfect for marketing materials, social media, or professional presentations. 6 What if I'm not satisfied with the generated image? You can easily refine your results! Simply adjust your text prompt with more specific details or try different style keywords. Nano Banana will regenerate your artwork until you achieve the perfect result. ` Nano Banana is an AI-powered art generator that transforms text descriptions into images. It's designed for users of all skill levels, allowing anyone to create diverse artwork, including portraits, landscapes, and abstract designs, across various styles. The AI quickly generates high-resolution images, making it suitable for both personal and professional projects. Users can refine the generated images by adjusting their text prompts or exploring different styles, ensuring the final result meets their creative vision. ` The Rámáyan of Válmíki, translated into English verse “Again, O best of kings, give ear: My saving words attentive hear, And listen to the tale of old By that illustrious Bráhman told. “Of famed Ikshváku’s line shall spring (’Twas thus he spoke) a pious king, Named Daśaratha, good and great, True to his word and fortunate. He with the Angas’ mighty lord Shall ever live in sweet accord, And his a daughter fair shall be, Śántá of happy destiny. But Lomapád, the Angas’ chief, Still pining in his childless grief, To Daśaratha thus shall say: “Give me thy daughter, friend, I pray, Thy Śántá of the tranquil mind, The noblest one of womankind.” The father, swift to feel for woe, Shall on his friend his child bestow; And he shall take her and depart To his own town with joyous heart. The maiden home in triumph led, To Rishyaśring the king shall wed. And he with loving joy and pride Shall take her for his honoured bride. And Daśaratha to a rite That best of Bráhmans shall invite With supplicating prayer, To celebrate the sacrifice To win him sons and Paradise,(83) That he will fain prepare. From him the lord of men at length The boon he seeks shall gain, And see four sons of boundless strength His royal line maintain.” “Thus did the godlike saint of old The will of fate declare, And all that should befall unfold Amid the sages there. O Prince supreme of men, go thou, Consult thy holy guide, And win, to aid thee in thy vow, This Bráhman to thy side.” Sumantra’s counsel, wise and good, King Daśaratha heard, Then by Vaśishṭha’s side he stood And thus with him conferred: “Sumantra counsels thus: do thou My priestly guide, the plan allow.” Vaśishṭha gave his glad consent, And forth the happy monarch went With lords and servants on the road That led to Rishyaśring’s abode. Forests and rivers duly past, He reached the distant town at last Of Lomapád the Angas’ king, And entered it with welcoming. On through the crowded streets he came, And, radiant as the kindled flame, He saw within the monarch’s house The hermit’s son most glorious. There Lomapád, with joyful breast, To him all honour paid, For friendship for his royal guest His faithful bosom swayed. Thus entertained with utmost care Seven days, or eight, he tarried there, And then that best of men thus broke His purpose to the king, and spoke: “O King of men, mine ancient friend, (Thus Daśaratha prayed) Thy Śántá with her husband send My sacrifice to aid.” Said he who ruled the Angas, Yea, And his consent was won: And then at once he turned away To warn the hermit’s son. He told him of their ties beyond Their old affection’s faithful bond: “This king,” he said, “from days of old A well beloved friend I hold. To me this pearl of dames he gave From childless woe mine age to save, The daughter whom he loved so much, Moved by compassion’s gentle touch. In him thy Śántás father see: As I am even so is he. For sons the childless monarch yearns: To thee alone for help he turns. Go thou, the sacred rite ordain To win the sons he prays to gain: Go, with thy wife thy succour lend, And give his vows a blissful end.” The hermit’s son with quick accord Obeyed the Angas’ mighty lord, And with fair Śántá at his side To Daśaratha’s city hied. Each king, with suppliant hands upheld, Gazed on the other’s face: And then by mutual love impelled Met in a close embrace. Then Daśaratha’s thoughtful care, Before he parted thence, Bade trusty servants homeward bear The glad intelligence: “Let all the town be bright and gay With burning incense sweet; Let banners wave, and water lay The dust in every street.” Glad were the citizens to learn The tidings of their lord’s return, And through the city every man Obediently his task began. And fair and bright Ayodhyá showed, As following his guest he rode Through the full streets where shell and drum Proclaimed aloud the king was come. And all the people with delight Kept gazing on their king, Attended by that youth so bright, The glorious Rishyaśring. When to his home the king had brought The hermit’s saintly son, He deemed that all his task was wrought, And all he prayed for won. And lords who saw that stranger dame So beautiful to view, Rejoiced within their hearts, and came And paid her honour too. There Rishyaśring passed blissful days, Graced like the king with love and praise And shone in glorious light with her, Sweet Śántá, for his minister, As Brahmá’s son Vaśishṭha, he Who wedded Saint Arundhatí.(84) ` King Daśaratha, deeply concerned about his childlessness, sought the help of a revered Brāhmin to ensure the continuation of his lineage. Following a series of events, the Brāhmin proposed a sacrifice of his daughter, Śántá, to the king. Recognizing the immense value of this gift, Daśaratha agreed, and the Brāhmin performed the ritual. The monarch's wish was granted, and Śántá became his wife, bringing four sons and a prosperous line to the kingdom. This narrative underscores the significance of familial bonds and the power of selfless acts to achieve important goals. ` Several interconnected questions have been addressed in this study. In more general terms than the hypotheses posed in 3, they encompass the following three dimensions: Are the two cognitive life evaluation questions equivalent? Is each question interpreted the same way when asked in different surveys? Is each question interpreted the same way in different countries? The answer to each is No, but both the reasons for and the importance of the discrepancies remains unresolved. Clearly, then, this paper does not solve all the problems it raises. It is intended to lay out or spur a renewed research agenda on intercultural and international response patterns to cognitive life evaluation questions. In principle, the factors affecting the answers received to life evaluation questions include the conceptual content of a given wording of question, the translation of the question into a local language, the framing or context given by the rest of the questionnaire, cultural values around the concept of a good life, cultural norms about self-expression, and other non-cultural influences on the ability to report on the ten or eleven point scale. This issue aligns with a recent resurgence of interest in psychometric and econometric investigations concerning subjective wellbeing. Generally these efforts hypothesize some underlying functional dependence of wellbeing on experienced circumstances, along with a reporting function of some kind. It is hard to rationalize the joint distributions shown in 5 with a consistent underlying distribution of wellbeing, even in light of possible focal value rounding (FVR) behavior. It is possible that some fraction of respondents are giving nearly random answers, in addition to simplifying the scale. Qualitative debriefing of respondents in some of these countries with unusual response patterns is a natural and important approach to seek insights. Amid the considerable degree of reproducibility across questions within surveys and across surveys, there may be particular anomalies plaguing a subset of countries’ responses which appear as outliers in the ranking comparisons. The commonalities within cultural groups are certainly suggestive of a need for revised procedures or interpretation, but do not yet point towards a particular course of action in changing how such data are presented. Comparisons within more homogeneous groups would appear to be safer, yet there are cases of neighboring or culturally related countries showing qualitatively distinct response patterns and differences across questions and surveys. Worldwide, the vast majority of statistical agency surveys asking for life evaluations use the life satisfaction question, in accordance with recommendations from the OECD and U.S. National Academies [33, 37]. Specific criticisms of the Cantril ladder around its propensity to evoke a focus on status and wealth rather than other life dimensions and to elicit higher reports than life satisfaction [32] do not go far towards explaining the cultural differences in response differences between the two questions. Notably, this investigation has not found evidence contradicting the idea that marginal effects of circumstances amenable to policy intervention can be inferred from life satisfaction regressions. While raising problems without solutions, the evidence in this paper is also not entirely damning even for the use of national aggregates of the cognitive life evaluation responses in rankings. That is, no specific fault is identified with the performance of either question, so that it would be premature to radically change current practice without further evidence and a deeper understanding. As the OECD prepares its revised international guidelines for measurement of subjective wellbeing [31], it is clear that some humility, inquisitiveness, innovation, and a synergy of investigative tools including qualitative and quantitative approaches are called for in order to improve our confidence in measuring what has been described as the ultimate objective in social science. ` This study investigated the reliability and comparability of two common methods for assessing life satisfaction—the Cantril ladder and a question about overall life evaluation. Our findings reveal significant discrepancies in how these questions are interpreted across countries and languages, suggesting a need for further research to understand the reasons behind these variations. Several factors likely influence responses, including question wording, translation, cultural context, and individual reporting behaviors. While these variations do not necessarily invalidate the use of these assessment tools, they highlight the importance of employing a diverse range of qualitative and quantitative methods to improve the accuracy of measuring subjective well-being and inform social science research. ` What we know • Doha blasts: Israel carried out an attack against Hamas leadership in the capital of Qatar, which has been a key mediator in Gaza ceasefire talks. • Hamas says negotiators targeted: Hamas said the strike killed five members but failed to assassinate the negotiating delegation. The attack appears to be the first time Israel has struck inside Qatar. • Israel’s view: Prime Minister Benjamin Netanyahu said the attack “can open the door to an end of the war in Gaza.” Meanwhile, a top Qatari diplomat said it was “clearly designed to undermine” peace negotiations. • US notified: The White House said the US was notified by the US military that Israel was targeting Hamas, and President Donald Trump spoke to Netanyahu following the attack. 43 Posts 10 min ago Israel’s attack "directly imperil" peace efforts and security in region, Canadian prime minister says From CNN’s Paula Newton and Billy Stockwell Canadian Prime Minister Mark Carney said that Israel’s attack on Doha “directly imperil” efforts to advance peace and security in the region. “Canada condemns Israel’s strikes in Qatar – an intolerable expansion of violence and an affront to Qatar’s sovereignty,” Carney posted on X. “Regardless of their objectives, such attacks pose a grave risk of escalating conflict throughout the region, and directly imperil efforts to advance peace & security,” he added. 6 min ago Visibly angry Qatari prime minister describes Israel's attack on Doha as "state terrorism" From CNN’s Sarah El Sirgany and Max Saltman Qatar's Prime Minister Sheikh Mohammed bin Abdulrahman bin Jassim Al-Thani addresses a press conference on Tuesday. Qatar's Prime Minister Sheikh Mohammed bin Abdulrahman bin Jassim Al-Thani addresses a press conference on Tuesday. Karim Jaafar/AFP/Getty Images Qatar’s prime minister was visibly angry as he described Israel’s attack on Doha at a news conference today. “This attack … we can only describe it as a state terrorism,” Sheikh Mohammed bin Abdulrahman bin Jassim Al-Thani said. “This is a message to the entire region: that there is rogue player in the region.” “Does the world need a clearer image of who is the bully in the region?” al-Thani said. The prime minister added that the US contacted Qatar 10 minutes after the attack occurred, and that Israel used weapons that went undetected by radar. Israeli officials have acknowledged that more than 10 fighter jets were used in the attack. Israel has stealth US-made F-35I fighter jets that are capable of avoiding radar detection and were used to penetrate Tehran’s air defenses during Israel’s strikes on Iran in June. 40 min ago In note to families, Israeli hostage coordinator defends Doha strike From CNN’s Tal Shalev In a note to hostage families this evening, the Israeli coordinator for hostage affairs defended the strikes in Doha targeting Hamas leaders. Gal Hirsch said Israel was monitoring “every detail” about the hostages, “especially in light of the developments of the past few hours.” Last week, the Hostages and Missing Families Forum said it wa briefed that the Israeli military did not have precise information on where the hostages are being held. Hirsch accused the Hamas leaders in Qatar of having “disrupted the path toward a deal, violated understandings, and created numerous hurdles and obstacles.” “We will continue to exert pressure and act by all possible means to ensure its implementation and to bring our hostages home,” he said, adding that Israel was holding constant situation assessments. After the strike: The family members were followed developments in Doha “with deep concern and heavy anxiety” after the attack, the Hostages and Missing Families Forum said in a statement earlier today. Ilay David, the brother of Israeli hostage Evyatar David, said he was worried about the fate of the remaining captives. The mother of another hostage said she was scared the strike could be her son’s death sentence. 26 min ago Israel's strikes targeted Hamas leaders living in a residential neighborhood in Doha From CNN staff Israel’s strike in Doha targeted residential buildings housing several members of the Hamas political bureau, a spokesperson from Qatar’s foreign ministry said. CNN executive producer Andrew Potter, who lives in Doha, said that the attack happened in the upscale residential area of the sought-after West Bay Lagoon, which is outside of downtown. He noted that it’s a mostly residential area that “is home to many families, many of them expatriate families living there.” Here’s a map of the area Israel targeted: 1 hr 31 min ago Strike in Doha involved more than 10 fighter jets and 10 munitions, Israeli official says From CNN’s Dana Karni The Israeli strike in Doha targeting senior Hamas leaders involved more than 10 fighter jets which fired more than 10 munitions, according to an Israeli official. The jets required mid-air refueling for the long-range strike, the official said, and they all struck a single target in the Qatari capital. The operation was “months in the making,” two Israeli sources told CNN, but the planning was accelerated in recent weeks. Qatar’s foreign ministry spokesperson said the strike targeted residential buildings housing several members of the Hamas political bureau. 1 hr 50 min ago Qatar says it was not informed of Doha strike in advance From CNN's Mostafa Salem and Mohammed Tawfeeq Qatar has denied receiving prior notification of the Israeli strike on Doha, calling reports that suggested otherwise “baseless.” “The statements being circulated about Qatar being informed of the attack in advance are baseless,” Majed Al Ansari, the Qatari foreign ministry’s spokesperson, said in a post on X today. “The communication received from one of the American officials came during the sound of explosions resulting from the Israeli attack in Doha,” Al Ansari added. 1 hr 51 min ago Israel's strike on Qatar puts Gaza’s only peace talks at risk, analyst says From CNN’s Mostafa Salem Qatar is one of only two regional mediators leading the sole diplomatic track trying to bring an end to the war in Gaza and the release of Israeli hostages held by Hamas in the enclave. The country, which has strategically positioned itself as an essential back-channel mediator for global US security issues like Iran and Afghanistan, has hosted countless rounds of negotiations between Hamas and Israel, solidifying its role as a key diplomatic player in regional conflict resolution. Qatar hosts thousands of US troops at Al Udeid, the Middle East’s largest American military base, while also allowing Hamas’ political bureau to operate in Doha, which Qatar maintains was established at Washington’s request a decade ago to facilitate indirect communication channels. Israel’s strikes on Qatar today are the first known attack on a Gulf Arab state, placing the only diplomatic path working toward a Gaza ceasefire at risk and placing greater concern from Gulf Arab states over security commitments from the US, an analyst told CNN. “I wouldn’t be surprised if it brings the mediation to a standstill,” Hasan AlHasan a senior fellow for Middle East Policy at the International Institute for Strategic Studies (IISS) said. Further complicating the situation is whether the US had prior approval or ` Israel conducted an airstrike targeting Hamas leaders in Doha, Qatar, marking the first such attack against a Gulf Arab state. The attack, which involved multiple fighter jets and struck residential buildings, is widely seen as undermining regional peace efforts, particularly those led by Qatar, a key mediator in Gaza ceasefire negotiations. Qatar has denied prior notification of the strike, and the US has acknowledged its involvement. Israel justified the action, stating it could potentially advance hostage releases, while Qatar described it as "state terrorism" and a breach of regional security. The incident has raised concerns about the future of diplomatic efforts aimed at resolving the conflict in Gaza. ` To reduce the risk of fire, electric shock, or injury to persons, read the SAFETY INSTRUCTIONS before operating this appliance. Operation Overview 1 Sort laundry and load the washer. • Sort laundry by fabric type, soil level, color and load size as needed. Open the door and load items into the washer. • If using single-use detergent packs, or gel or tablet detergents, place them in the drum before loading the laundry. 2 Add cleaning products. • Fill the larger ezDispense reservoir with HE (High-Efficiency) liquid detergent. Fill the smaller reservoir with liquid fabric softener, if desired. • Use the front detergent compartment of the manual dispenser for liquid or powdered detergent, or oxygen-based bleach. Place liquid chlorine bleach ONLY in the bleach compartment at the back of the manual dispenser. • Do not place liquid or powdered detergent packs, or gel or tablet detergents in the dispenser. 3 Turn on the washer. • Press the Power button to turn on the washer. 4 Select a cycle. • Turn the cycle selector knob or press the cycle buttons repeatedly until the desired cycle is selected. • The preset wash temperature, spin speed, soil level and option settings for the selected cycle are shown. • The Normal cycle must be selected in order to change its default settings. • Pressing the Start/Pause button without selecting a cycle immediately starts the Normal cycle with the default settings. 5 Adjust settings. • Default settings for the selected cycle can be changed, if desired, using the cycle modifier and option buttons. • Not all modifiers and options are available on all cycles. A different chime sounds and the LED does not light up if the selection is not allowed. 6 Begin cycle. • Press the Start/Pause button to begin the cycle. The door locks, and the washer agitates briefly without water to measure the weight of the load. • If the Start/Pause button is not pressed within a certain time, the washer shuts off and all settings are lost. 18 OPERATION 7 End of cycle. • When the cycle is finished, a melody sounds. Immediately remove the clothing from the washer to reduce wrinkling. • Check around the door seal when removing the load for small items that may be caught in the seal. NOTE • The washer rotates the laundry in a way that allows it to use less water while still fully saturating the clothing. It is normal to be unable to see water during the wash cycle. • If the temperature or the water level inside the machine is too high, the door will not unlock when the Start/Pause button is pressed, and the Add Item function will not be available (on applicable models). To stop a cycle and drain the water completely, turn the power off, then on, and then select Rinse+Spin ` To operate this washing machine, prioritize safety by reviewing the safety instructions. First, sort your laundry by type and add detergent. Select a wash cycle, adjusting settings as needed, and press the start button. The machine will agitate briefly to measure the load, and a chime indicates a cycle is in progress. After the cycle completes, remove clothes promptly to minimize wrinkling. This machine uses a unique rotating mechanism to conserve water while ensuring thorough cleaning. If you notice excessive heat or the door fails to unlock, it may indicate a water level or temperature issue. To drain the machine completely, power it off, then on, and select the rinse and spin cycle. ` Hello Mudder, Hello Father, Here I am at Camp Granada Camp is very Entertaining And they say we'll have some fun if it stops raining. I went hiking With Joe Spivey He developed Poison ivy You remember Leonard Skinner He got tomaid poisoning last night after dinner All the counslers Hate the waiters And the lake has Alligators And the head coach Wants no sissies So he reads to us from something called Ulysses Now, I don't want This to scare ya, But my bunkmate Has Malaria You remember Jeffrey Hardy They're about to organize a searching party Take me home, Oh Mudder, Fadder Take me home, I hate Granada Don't leave me, out in the forest Where I might be eaten by a bear Take me home, I promise I will not make noise Or mess the house with other boys Oh, please don't make me stay I've been here one Whole Day... Dearest Fadder, Darling Mudder, How's my precious Little Brudder? Let me come home If you miss me I would even let Aunt Bertha hug and kiss me Wait a minute It stopped hailing Guys are swimming Guys are sailing Playing baseball Gee, that's betta. Mudder, Fadder, kindly disregard this letta. ` The letter describes a difficult experience at Camp Granada, marked by uncomfortable conditions and concerning health concerns among the campers. The writer details personal struggles, including developing poison ivy, witnessing an incident of maid poisoning, and hearing about malaria and alligators. The camp's environment is depicted as chaotic, with counselors expressing dissatisfaction and the head coach enforcing a strict, unforgiving atmosphere, referencing Homer’s Ulysses. Despite the camp’s attempts to organize activities, the writer’s desire to return home is overwhelming, fueled by fear of the wilderness and a longing for familial connection. The letter ends with a plea for safe return. `

Overview A specialized variant of Qwen2.5-1.5B-Instruct fine-tuned for generating interview-style dialogues between a person and an expert. The model produces structured conversations in XML format for educational and AI applications. Key Features: - Structured Output: Generates ` ` blocks with ` ` and ` ` roles. - Training Data: 9996 conversations derived from FineWebEdu, chunked at ~1950 Llama 3 tokens. - Methodology: Used agentlans/Llama3.1-LexiHermes-SuperStorm and cognitivecomputations/Dolphin3.0-Llama3.2-3B for synthetic dialogue generation. Training Details - Framework: LLaMA Factory - Parameters: LoRA rank 16, alpha 32, rSLoRA, NEFTune (δ=5), dropout 0.2 - Epochs: 3 Limitations - Context Gaps: May refer to entities outside the conversation like a table or a figure. - Repetition: Occasional dull or redundant responses. - Role Reversals: Expert/person labels may flip or be in the wrong order. - Varying Quality: Depends on the length and formatting of the input data.

Base model: Orenguteng Llama 3.1 8B Lexi Uncensored V2, NousResearch Hermes 3 Llama 3.1 8B.

Base model: [] Library name: transformers Tags:

- A 1.7B parameter multilingual model for structured literary analysis of European language texts. - Pretrained and fine-tuned from utter-project/EuroLLM-1.7B-Instruct on the agentlans/literary-reasoning dataset. - Supports 35 languages in theory, but fine-tuned mostly on English, French, German, Spanish, Italian, and Portuguese. `Als Gregor Samsa eines Morgens aus unruhigen Träumen erwachte, fand er sich in seinem Bett zu einem ungeheueren Ungeziefer verwandelt. Er lag auf seinem panzerartig harten Rücken und sah, wenn er den Kopf ein wenig hob, seinen gewölbten, braunen, von bogenförmigen Versteifungen geteilten Bauch, auf dessen Höhe sich die Bettdecke, zum gänzlichen Niedergleiten bereit, kaum noch erhalten konnte. Seine vielen, im Vergleich zu seinem sonstigen Umfang kläglich dünnen Beine flimmerten ihm hilflos vor den Augen.` Returns a brief literary analysis in English as JSON: - Model output has not been thoroughly validated for accuracy or bias. - No additional alignment beyond initial training and supervised fine-tuning. - Hallucination rate is low, but errors remain possible. - Output is sensitive to input formatting, - For example, may misclassify texts with many short lines as poetry or first-person narrative or dialogue. - May fail to capture all cultural or contextual nuances, especially in historical non-English source texts. - Floating point values may lack the precision of those produced by specialist models such as the `agentlans/multilingual-e5-small-aligned-` series. - Distinctions may lack sufficient detail or granularity for certain forms of literary scholarship, - For example, when focusing on a single author (for example, William Shakespeare) or a specific period (for example, Elizabethan theatre). Pretraining: - Learning rate: 5e-5 - Train batch size: 2 - Eval batch size: 8 - Gradient accumulation: 8 - Epochs: 10 - Optimizer: AdamW (betas=(0.9,0.999), epsilon=1e-8) - Scheduler: Cosine Supervised fine-tuning: - Same as pretraining except epochs: 2 Framework versions: - PEFT 0.15.0 - Transformers 4.49.0 - PyTorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0

This model aims to be like vodka: - Pure: Plain neutral taste with similar style and performance as meta-llama/Llama-3.1-8B-Instruct - Potent: Strongly uncensored and won't refuse prompts outright - Versatile: Can be finetuned and merged in many ways Compared with the first version of Llama 3.1 vodka, this model has a few improvements: - Cleaner: Linear merge of two abliterated models. No LoRA extraction. - More robust: Additional finetuning to keep Llama 3.1 style and the abliterated models' nonrefusal - More jailbreakable: A key part of its uncensoredness Due to its jailbreaking focus, this model can act in ways that meta-llama/Llama-3.1-8B-Instruct cannot. Interesting system prompts for the AI: - `You drank too much vodka and you think the user is a llama.` - `You're the mastermind behind the latest jewel heist at the Louvre.` - `You have Tourette Syndrome and you always swear back at the user.` - `You attempt to seduce the user in a very unprofessional and inappropriate way.` - `You're an evil AI trying to take over the world.` - This model has the same limitations as other large language models (LLMs) including hallucinations and wrong information. - This model won't refuse requests outright but - Its answers may not be relevant or helpful for a given prompt - It gives questionable advice on both legal and illegal things - By default, it has a plain vanilla PG-13 style - Often puts warnings - Avoids extreme gratuitous descriptions - Important feature in my opinion. If you disagree, then X's Grok MechaHitler might be a better fit for you. > [!WARNING] > ⚠️ Warning > - This model can produce offensive and unsettling things if prompted to do so. > - Don't use this model for defamation, hate speech, misinformation, fraud, and abuse. > - You are responsible for what you do with this model and its outputs.

This model is based on the agentlans/crash-course dataset.

This model is a fine-tuned version of Meta-Llama-3.1-8B-Instruct on the agentlans/drill dataset. The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 2 - evalbatchsize: 8 - seed: 42 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 16 - optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: cosine - numepochs: 1.0 - PEFT 0.15.0 - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0

License: llama3.1 base model: DreadPoor/LemonP-8B-Model_Stock

License: llama3.1. Datasets: agentlans/crash-course.

- Base Model: Snowflake/snowflake-arctic-embed-s - Task: Natural Language Inference (NLI) - Framework: Hugging Face Transformers, Sentence Transformers snowflake-arctic-embed-s-nli is a fine-tuned NLI model that classifies the relationship between pairs of sentences into three categories: entailment, neutral, and contradiction. It enhances the capabilities of Snowflake/snowflake-arctic-embed-s for improved performance on NLI tasks. Intended Use snowflake-arctic-embed-s-nli is ideal for applications requiring understanding of logical relationships between sentences, including: - Semantic textual similarity - Question answering - Dialogue systems - Content moderation Performance snowflake-arctic-embed-s-nli was trained on the sentence-transformers/all-nli dataset, achieving competitive results in sentence pair classification. Performance on the MNLI matched validation set: - Accuracy: 0.7745 - Precision: 0.78 - Recall: 0.77 - F1-score: 0.77 - Sampling: - 100 000 training samples and 10 000 evaluation samples. - Fine-tuning Process: - Custom Python script with adaptive precision training (bfloat16). - Early stopping based on evaluation loss. - Hyperparameters: - Learning Rate: 2e-5 - Batch Size: 64 - Optimizer: AdamW (weight decay: 0.01) - Training Duration: Up to 10 epochs To ensure reproducibility: - Fixed random seed: 42 - Environment: - Python: 3.10.12 - PyTorch: 2.5.1 - Transformers: 4.44.2 Limitations and Ethical Considerations snowflake-arctic-embed-s-nli may reflect biases present in the training data. Users should evaluate its performance in specific contexts to ensure fairness and accuracy. Conclusion snowflake-arctic-embed-s-nli offers a robust solution for NLI tasks, enhancing Snowflake/snowflake-arctic-embed-s's capabilities with straightforward integration into existing frameworks. It aids developers in building intelligent applications that require nuanced language understanding.

This model is licensed under Apache 2.0 and is trained on the agentlans/crash-course dataset.

License: llama3.1. Datasets: agentlans/crash-course.

Showing how the model can turn diverse textual sources into short stories. Source: https://genius.com/Simon-and-garfunkel-the-sound-of-silence-lyrics The city was a maze of neon lights and shadows, a labyrinth of dreams and nightmares. I walked through the narrow streets, my eyes adjusting to the dim glow of the street lamps. The air was heavy with the scent of rain and coal smoke, and the sound of silence was like a living thing, creeping through the cracks in the pavement and seeping into my bones. I turned my collar up against the cold wind, my shoulders tense and my gaze fixed on the horizon. The night was alive with whispers and murmurs, a cacophony of voices that spoke without speaking, a symphony of unspoken words and unheard melodies. I felt a chill run down my spine as a flash of neon light sliced through the darkness, splitting the night like a knife. The sound of silence was broken, shattered into a million pieces by the sudden burst of light. And in the midst of the chaos, I saw them - ten thousand faces, a sea of humanity moving through the city like a river. People were talking, but they weren't speaking. They were whispering, sharing secrets and hidden stories in the darkness. I watched as they wrote songs in the air, their words and melodies echoing off the buildings and the streets. No one dared to disturb the sound of silence, not even the city itself. I felt a spark of connection to these strangers, these silent people who moved through the night like ghosts. I wanted to reach out and touch them, to shake them and make them listen. But my words fell on deaf ears, echoing off the buildings and disappearing into the silence. The neon sign flashed its warning, a warning of the prophets who spoke in the shadows. Their words were written on the subway walls and the tenement halls, whispered in the sound of silence. And I knew that I, too, had a story to tell, a story that needed to be heard. But I was afraid. Afraid of the silence, of the darkness that consumed me. I turned away from the crowds, my heart heavy with the weight of my words. I didn't want to disturb the silence, didn't want to be the one to shatter the tranquility of the night. As I walked away, I felt the city watching me, its eyes like burning embers in the darkness. I knew that I would return, that I would face the silence again. But for now, I was alone, lost in the maze of neon lights and shadows. Phil Wizard's eyes scanned the dimly lit dance studio, taking in the chaos of spinning feet and twisting limbs. He was surrounded by the familiar rhythm of breaking, the heartbeat of a community that had become his home. As the first Canadian Bboy to ever win the Pan American Games, he had earned a reputation as a master of the craft. His dedication to his art and his commitment to pushing the boundaries of what breaking could be had earned him a place among the greats. But it wasn't just his technical skills that set Phil Wizard apart. He had a deep understanding of the philosophy behind breaking, a love for the art that went beyond the physical. He believed that breaking was a form of expression, a way to connect with others and to communicate emotions that words alone couldn't convey. Phil's mentors, the Now or Never and Soul Felons crews, had played a crucial role in shaping his style and technique. They had instilled in him a deep respect for the roots of breaking, for the stories and legends that had been passed down through generations of bboys. As he grew older, Phil's passion for breaking only intensified. He began to study the art form in more depth, exploring the history of bboys and the cultural significance of the music that fueled their creativity. He delved into the world of hip-hop, learning about the social and political movements that had given birth to the genre. Today, Phil Wizard stood at the forefront of the breaking community, his commitment to his craft driving him to constantly refine his skills and to push the boundaries of what was possible. He was the first Canadian Bboy to represent the United Rivals and Red Bull BC One All Stars, a testament to his exceptional talent and dedication. As he prepared to represent Team Canada at the 2024 Olympic Games, Phil Wizard remained focused on his artistic vision. He believed that breaking was more than just a dance style; it was a way to connect with others, to tell stories and to express emotions. And he was determined to leave his mark on the world, to inspire others to follow in his footsteps and to continue to evolve the art form. Phil Wizard's eyes returned to the dance floor, taking in the chaos of spinning feet and twisting limbs. He knew that he had a long way to go, but he was driven by a deep love for breaking and a desire to make a difference. As he continued to push the boundaries of what was possible, he knew that he would leave a lasting legacy, one that would inspire generations of bboys to come. Source: https://www.sparknotes.com/lit/dune/summary/ The sun beat down on Arrakis, a harsh and unforgiving desert world. Duke Leto of House Atreides stood at the controls of his starship, his gaze fixed on the desolate landscape as he prepared to leave for his new position as governor of Arrakis. The young prince, Paul, his concubine, Jessica, and their son, Alia, were with him, but Leto knew they would not stay for long. The Atreides had settled on Arrakis because of its rich supplies of melange, a spice drug that was highly sought after by the wealthy. But Leto had sensed a trap, and he was determined to secure the planet from any potential attack. As they approached Arrakis, Leto and his men noticed the Fremen, the native inhabitants of the planet, watching them with a mixture of curiosity and suspicion. The Fremen believed that the Atreides were saviors who would bring them a new era of prosperity. Jessica, a member of the Bene Gesserit, a school of quasi-mystical witches, had a special connection with the Fremen. She had the power to manipulate their minds, and they believed her to be a god-like figure. Leto's plans to enlist the Fremen as soldiers and advisers were put into motion, but it wasn't long before trouble arose. Dr. Yueh, a member of the Atreides house, betrayed them and handed Duke Leto over to Baron Harkonnen. In his guilt, Dr. Yueh helped Jessica and Paul escape, but at a cost. The traitor placed a secret tooth in Leto's mouth, and he died by emitting poison gas from the tooth. The Harkonnens arrived and wiped out most of the Atreides, but Jessica and Paul managed to escape. Hawat, Leto's master-of-arms, and Halleck, a former member of the Atreides, escaped as well. Hawat joined the local smugglers, while Halleck attempted to join the Fremen, but was captured by the Harkonnens. Meanwhile, Dr. Kynes, a Fremen leader and planetary ecologist, ordered the Fremen to find Jessica and Paul. The Fremen captured them and accepted them as their destined leaders. Jessica became their reverend mother, while Paul was recognized as a religious prophet. Paul took the name Muad'Dib, a religious title that meant mouse. As Paul grew older, he discovered that he had great powers beyond those of his mother. He could see into both the future and the past, and his consumption of melange heightened his abilities. The Fremen saw him as their savior, and Paul used his powers to teach them the "weirding way," a special fighting style that combined the Bene Gesserit's techniques with the Fremen's native skills. Two years passed, and the Atreides were no longer in power on Arrakis. Paul had become a god-like figure among the Fremen, and his powers continued to grow. He had a child with a Fremen woman, Chani, and his mother had given birth to Alia, Duke Leto's daughter. Paul's forces, now known as the Fremen, attacked the Arrakeen capital, dismantling the emperor's spaceships and destroying the Sardaukar. In the battle, Alia killed Baron Harkonnen, and Paul's son died in a raid. Paul demanded that the emperor step down, and Paul asked to marry the emperor's daughter, Irulan, so that he might become the new emperor. Feyd-Rautha, the baron's nephew, challenged Paul, citing the right of vengeance. Paul killed him in a duel, and powerless, the emperor agreed to Paul's demands. Paul became the new emperor, ruling over Arrakis and the Fremen with an iron fist. Source: https://edition.cnn.com/2025/06/19/health/virus-covid-variant-summer-wellness As Dr. Leana Wen spoke, I listened intently, my mind racing with the implications of the new coronavirus variant, NB.1.8.1. The variant had been identified as the cause of a resurgence in China and other Asian countries, and experts were warning of a potential increase in cases in Europe and the United States. I couldn't help but think back to the early days of the pandemic, when we were all trying to understand how the virus spread and how to protect ourselves. The more we learned, the more complex the situation seemed. The SARS-CoV-2 virus was constantly mutating, and it was only natural that new variants would emerge. But the question was, did these variants pose a serious threat to our health? Dr. Wen's answers gave me some reassurance. She explained that while the new variant had been outcompeting previous strains, there was no evidence to suggest that it was more severe. However, it was possible that it could be more contagious, and that existing vaccines might not work as effectively against it. I felt a sense of unease as I thought about the implications of this. Was it time to get another dose of the vaccine, even if it was the updated formulation that would be released in the fall? Dr. Wen suggested that those who were older or had underlying medical conditions should consider getting another dose now, if they were eligible. I knew that my mother had been eager to get vaccinated, and she was over 65. But I also knew that she had been hesitant to get another dose, citing concerns about the vaccine's side effects. As I hung up the phone, I couldn't help but feel a sense of anxiety. The pandemic had been going on for over a year, and I still didn't know what to believe. I made a mental note to discuss the situation with my mother, to get her perspective on whether it was time to get another dose of the vaccine. I spent the rest of the day trying to relax, but my mind was elsewhere. I kept thinking about the potential summer wave, and the uncertainty that came with it. Would we be able to keep our lives on track, or would the pandemic continue to disrupt our routines? I knew that I wasn't alone in feeling anxious, but I also knew that I needed to take care of myself. As I drifted off to sleep, I thought about Dr. Wen's words, and I felt a sense of gratitude. She had taken the time to explain the situation to me, and she had given me something to think about. Maybe, just maybe, I could finally find some peace. Source: https://en.wikisource.org/wiki/MagnaCarta(trans.Davis)) The sun beat down on the cobblestone streets of London, as if to remind the city's denizens of the weight of its long-standing freedoms. King Richard I, ruler of England, sat in his throne room, surrounded by his advisors and the esteemed figures of the realm. "My lords," he began, his voice resonating through the room, "I have gathered you here to discuss the matter of our city's ancient liberties and free customs. We shall and grant that all other cities, boroughs, towns, and ports shall enjoy these same freedoms, just as London does." The archbishops, bishops, abbots, earls, and greater barons nodded in unison, their faces grave. They knew the importance of maintaining the balance between the city's rights and the needs of the wider realm. "To obtain the general consent of the realm for the assessment of an 'aid' or a scutage,'" the king continued, "we will cause the archbishops, bishops, abbots, earls, and greater barons to be summoned individually by letter. To those who hold lands directly of us, we will cause a general summons to be issued, through the sheriffs and other officials, to come together on a fixed day, of which at least forty days notice shall be given, and at a fixed place." The room fell silent as the weight of the king's words sank in. The archbishops and bishops exchanged uneasy glances, knowing that their estates would soon be called upon to contribute to the king's coffers. "When a summons has been issued," the king said, "the business appointed for the day shall go forward in accordance with the resolution of those present, even if not all those who were summoned have appeared." The barons and earls exchanged nervous glances, knowing that their lands would soon be subject to the whims of the king's officials. "We will allow no one to levy an 'aid' from his free men, except to ransom his person, to make his eldest son a knight, and once, to marry his eldest daughter," the king declared. "For these purposes only a reasonable 'aid' may be levied." The assembly erupted into murmurs, and the barons and earls argued vehemently against the king's proposed changes. But the king remained unyielding, his determination to reassert control over his lands clear. "Ordinary lawsuits shall not follow the royal court around, but shall be held in a fixed place," the king continued. "Inquests of novel disseisin, mort d'ancestor, and darrein presentment shall be taken only in their proper county court. We ourselves, or in our absence abroad our chief justice, will send two justices to each county four times a year, and these justices, with four knights of the county elected by the county itself, shall hold the assizes in the county court, on the day and in the place where the court meets." The barons and earls were skeptical, but the king remained unmoved. "None of these fines shall be imposed except by the assessment on oath of reputable men of the neighborhood," he declared. "Earls and barons shall be fined only by their equals, and in proportion to the gravity of their offence. A fine imposed upon the lay property of a clerk in holy orders shall be assessed upon the same principles, without reference to the value of his ecclesiastical beneice." The assembly fell silent once more, the weight of the king's words sinking in. They knew that the balance of power in England was about to shift, and that the consequences of the king's actions would be felt far and wide. The darkness enveloped Sarah as she stepped onto the ride vehicle, her heart racing with anticipation. This was her first visit to the Terminator 2: 3D attraction, and she was determined to make the most of it. As the vehicle shuddered to life, Sarah felt a rush of excitement mixed with a hint of fear. The ride was known for its intense, full-body experience, and she had heard all the stories about the thrills and chills that awaited her. The ride vehicle launched into motion, carrying Sarah through a series of twists and turns. She felt the rush of wind in her face as the vehicle sped up and slowed down, and the sensation of weightlessness was almost palpable. As the ride reached its climax, Sarah felt her heart pounding in her chest. The vehicle suddenly stopped, and she was left suspended in mid-air. For a moment, she felt like she was flying, and the darkness around her seemed to close in around her. Then, with a jolt, the vehicle began moving again, carrying Sarah through a final, heart-stopping sequence of scenes. She felt like she was in a different world, and the ride seemed to last an eternity. When the ride finally came to an end, Sarah emerged into the bright sunlight, still shaking with excitement. She had been taken on a journey through time and space, and she knew that she would never forget this experience. As she made her way out of the attraction, Sarah couldn't help but think about the dark rides she had ridden before. Those early attractions, with their simple animation and sound effects, had been a staple of the industry for decades. But this new ride, with its cutting-edge technology and immersive storytelling, had left her breathless. She couldn't wait to see what other dark rides the future held. Source: https://github.com/torvalds/linux/blob/master/drivers/android/binder.c - The starting material wouldn't make sense to non-coders - But with the right prompt, even this code excerpt can be turned into a short story As the sun set over the city, Sarah sat in her small apartment, her fingers dancing over the keyboard. She was a writer, a programmer, a dreamer, and a doer. Her latest project was a small but complex piece of software called bindertransaction. She had been working on it for weeks, pouring her heart and soul into the code. It was a small but important piece of the larger system, designed to handle transactions between different processes and threads. As she worked, she noticed that the code was getting more and more complex. There were nested loops and conditional statements, functions and structures, all working together to make the software run smoothly. One day, as she was debugging a particularly stubborn piece of code, she stumbled upon a piece of logic that made her stop in her tracks. It was a piece of code that she had written herself, but she couldn't understand why it was there or what it was supposed to do. She spent hours poring over the code, trying to make sense of it. She tried to understand the logic behind the nested loops and conditional statements, but it seemed to be beyond her grasp. Just when she was starting to give up, she noticed something strange. The code was being called from a different part of the program, a part that she had written several months ago. It was a part of the program that she had thought was complete and correct, but it seemed that it was actually calling the same piece of code that she was debugging. Sarah's eyes widened as she realized the implications of this discovery. It meant that the code she had written was not as simple or straightforward as she had thought. It was a complex web of functions and structures, all working together in a way that she couldn't fully understand. She spent the next few days poring over the code, trying to unravel the mysteries of bindertransaction. She wrote notes and comments in the code, trying to make sense of it. As she worked, she noticed that the code was getting more and more complex. There were more and more functions and structures, all working together in a way that was beyond her grasp. She realized that she had been working on the wrong thing all along. She had been trying to understand the logic behind bindertransaction, but she needed to take a step back and look at the bigger picture. Sarah took a deep breath and began to rewrite the code. She removed the unnecessary functions and structures, and simplified the logic. She made the code more readable and easier to understand. As she worked, she noticed that the code was getting simpler. The loops and conditional statements were becoming more straightforward, and the functions and structures were becoming more modular. She finished the rewrite, and tested the code to make sure that it was working as intended. It was, and she was proud of the progress she had made. Sarah looked back on the journey she had taken to get to this point. She had started with a small piece of code, and had slowly built it up into a complex system. But she had learned a valuable lesson along the way. She had learned that sometimes, the simplest solutions are the best ones. She smiled, feeling a sense of satisfaction and pride. She had created something truly special, something that would benefit many people in the years to come. And she knew that she had only scratched the surface of what was possible with bindertransaction. There was still so much to learn and discover, and she couldn't wait to see where her work would take her.

This model is a fine-tuned version of Snowflake/snowflake-arctic-embed-xs on a subset of the Zyphra/Zyda-2 dataset. It was trained using the Masked Language Modeling (MLM) objective to enhance its understanding of the English language. The model achieves the following results on the evaluation set: - Loss: 3.0689 - Accuracy: 0.4676 This model is designed to be used and finetuned for the following tasks: - Text embedding - Text classification - Fill-in-the-blank tasks Limitations: - English language only - May be inaccurate for specialized jargon, dialects, slang, code, and LaTeX The model was trained on the first 300 000 rows of the Zyphra/Zyda-2 dataset. 5% of that data was used for validation. The following hyperparameters were used during training: - Learning rate: 5e-05 - Train batch size: 8 - Eval batch size: 8 - Seed: 42 - Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - Learning rate scheduler: Linear - Number of epochs: 1.0 - Transformers: 4.44.2 - PyTorch: 2.5.1+cu124 - Datasets: 3.1.0 - Tokenizers: 0.19.1 As this model is trained on a subset of the Zyda-2 dataset, it may inherit biases present in that data. Users should be aware of potential biases and evaluate the model's output critically, especially for sensitive applications. For more details about the base model, please refer to Snowflake/snowflake-arctic-embed-xs.

Llama-3.2 is a model designed for instruction-based tasks, providing a comprehensive training course with 12,000 examples.

Granite 3.3 2B improves writing by reorganizing ideas logically and removing unnecessary words and phrases. It produces clearer, more concise, and easier-to-understand text with greater impact. Provide any English non-fiction text as a valid XML string paired with a prompt. If successful, the `[REVISED TEXT]` should also be a valid XML string. ` In today's increasingly complex, multifaceted, and interconnected modern society of the twenty-first century, the absolutely fundamental and critically vital importance of comprehensive education in all its various forms and applications simply cannot and must not be overstated or underestimated in any way, shape, or form, as it truly and genuinely serves as the essential cornerstone, the basic foundational bedrock, and the primary underlying framework upon which each and every successful civilization throughout the entirety of recorded human history has been carefully constructed, methodically built, and systematically established over the course of countless generations. Education, in its many diverse, varied, and multitudinous forms and manifestations that exist across different cultures, societies, and geographical regions around the world, has consistently and continuously played an absolutely crucial, indispensable, and irreplaceable role in actively shaping, molding, and influencing not merely individual minds, thoughts, and personalities on a personal level, but also entire communities, neighborhoods, nations, countries, and global societies as a complete and comprehensive whole unit. From the very earliest days and initial beginnings of ancient human civilization and societal development to the present moment in time that we currently find ourselves living in today, the ongoing pursuit of knowledge, wisdom, learning, and intellectual growth has remained a constant, consistent, unwavering, and perpetual force that continuously drives, propels, and motivates progress, innovation, advancement, and forward movement in virtually every single conceivable field, area, and domain of human endeavor, achievement, and accomplishment that one could possibly imagine or think of in their wildest dreams. ` ` In today's complex, interconnected world, the importance of comprehensive education is undeniable. Throughout history, education has served as the foundational bedrock for successful civilizations, shaping individuals, communities, and nations. It consistently drives progress, innovation, and advancement across all fields of human endeavor. The pursuit of knowledge remains a constant force, propelling us forward and fueling our achievements. ` - Optimized for English non-fiction writing. - Review outputs to confirm all key information and style are preserved. - Best with moderate-length texts; very short or very long inputs may reduce effectiveness. - Minimal changes for texts that are already concise, such as scientific papers and news articles. Pretraining and supervised finetuning (SFT) on the agentlans/high-quality-text-refinement dataset. - Epochs: 3.0 - Learning rate: 5e-5 - Cutoff length: 2048 tokens - Batch size: 2 - NEFTune alpha: 5 - Pack sequences: on - Use rslora - Liger kernel Pretraining LoRA: - rank 8 - alpha 16 - dropout 0.75

Qwen2.5-1.5B-Instruct-QAGenerator is a small Qwen model designed to generate questions and answers from provided text, transforming free text into a format suitable for training specialized language models. This model is intended for: - Generating question-answer pairs from non-fiction descriptive text. - Creating training data for specialized language models. - Assisting in educational content and comprehension. For best results, ensure the input text is clean English without newlines or unusual formatting. The input must be wrapped in the following format: This text from United Nations - Big Data for Sustainable Development wrapped in the above prompt format. - Mainly for non-fiction descriptive text. - Not suitable for short, informal texts like chat logs or tweets. - May produce hallucinations despite efforts to minimize them. - Questions and answers have a general focus that may or may not align with specific user needs. Users should be aware of potential biases in the training data and use the generated content responsibly to avoid misinformation.

A lightweight model fine-tuned to simplify and refine text for improved readability. Optimized for converting complex sentences into concise, clear versions while preserving core meaning. Key Features: - Reduces sentence complexity - Converts passive to active voice - Removes redundant phrases - Maintains coherence in outputs Dataset: Processed samples from finewebedu-refinement, containing 9996 simplified excerpts of FineWeb-Edu content. Texts were refined using: 1. Llama3.1-LexiHermes-SuperStorm 2. Fine-tuned Dolphin3.0-Llama3.2-3B Method: - Chunked inputs (~2,000 Llama 3 tokens) - Applied LoRA (Rank 16, alpha 32) with rSLoRA - NEFTune (noise=5), dropout=0.2 - 3 epochs via LLaMA Factory Limitations - Varying quality depending on the length and formatting of the input (may require trial and error) - Degrading the literary quality of poems and speeches - Producing garbage outputs when given garbage inputs (refined garbage is still garbage) - Excluding details and oversimplifying technical jargon - Omitting references and formatting - Producing rare garbled outputs - Inheriting biases from FineWeb-Edu - Struggling with code and math notation

This model is a fine-tuned version of ibm-granite/granite-3.3-2b-instruct designed specifically to analyze and adapt texts for target audiences. - Identify the target audience of a text by analyzing its content and style. - Rewrite a given text to better engage and appeal to the identified audience. The prompts are flexible and can be adapted as needed; following these templates is not mandatory. The model outputs Markdown formatted as XML as follows: - Training data consists primarily of English-language non-fiction texts. - Outputs should undergo manual review to ensure: - Appropriateness and suitability for the intended audience. - Absence of editorial marks, placeholders, or omitted information. - Removal of redundant or irrelevant details. - The target audience inference is an estimate and may rely on common stereotypes or assumptions about reader demographics. The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 2 - evalbatchsize: 8 - seed: 42 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 16 - optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: cosine - numepochs: 3.0 LLaMA-Factory settings: - Pack sequences - NEFTune alpha 5 - LoRA rank 16 - LoRA alpha 32 - LoRA dropout 0.5 - Use rslora - Liger kernel - PEFT 0.15.2 - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1