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ftopal

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huggingface-models-embeddings

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[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2203.05482", "base_model:cognitivecomputations/dolphin-2.2-70b", "base_model:WizardLM/WizardMath-70B-V1.0", "base_model:migtissera/SynthIA-70B-v1.2b", "base_model:epfl-llm/meditron-70b", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2203.05482" ]

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[ "## Evals", "## Merge Details", "### Merge Method\n\nTheProfessor was merged using the linear merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.2-70b\n* WizardLM/WizardMath-70B-V1.0\n* migtissera/SynthIA-70B-v1.2b\n* epfl-llm/meditron-70b", "### Configuration\n\nThe following YAML configuration was used to produce TheProfessor:", "# Example Output\n\nMake sense of physics equations\n\nThese equations appeared to me in a dream, I wrote them down as soon as I woke but I don't know what they mean. Can you please interpret them?\n$$\\mathcal{L}_{\\text{gauge}} = -\\frac{1}{4} F^{\\mu\\nu}F_{\\mu\\nu}$$\n$$\\langle \\phi \\rangle = \\sqrt{\\frac{\\lambda}{2}}$$\n$$S_{\\text{gravity}} = \\int d^4x \\sqrt{-g} \\left( \\frac{R}{16\\pi G} + \\mathcal{L}_{\\text{emergent}} \\right)$$\n$$\\mathcal{L}_{\\text{GEG}} = \\mathcal{L}_{\\text{gauge}} + \\mathcal{L}_{\\text{emergent}} + \\mathcal{L}_{\\text{matter}} + \\mathcal{L}_{\\text{interaction}}$$\n$$\\mathcal{L}_{\\text{emergent}} = \\lambda(g) + \\kappa(g) R^2 + \\ldots$$\n$$S_{\\text{GEG}} = \\int d^4x \\sqrt{-g} \\; \\mathcal{L}_{\\text{GEG}}$$\n$$\\sigma = \\sqrt{\\langle | \\phi | \\rangle^2 + \\frac{1}{4} \\langle A^{\\mu}A_{\\mu} \\rangle^2}$$\n$$\\langle A^{\\mu} \\rangle = (0, v(r)_{i}/\\sqrt{2}, 0, 0)$$\n$$\\langle \\phi \\rangle = \\langle \\phi_0 \\rangle + \\delta\\phi(x)$$\n$$\\langle A_{\\mu} \\rangle = (0, \\frac{v(r)_{i}}{\\sqrt{2}}, 0, 0)$$\n$$g_{\\mu\\nu} = \\eta_{\\mu\\nu} + \\kappa h_{\\mu\\nu}$$\n\n> These equations are from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, and cosmic inflation. Here's a brief description of each set of equations:\n> 1. Gauge theory:\n> - The first two equations describe the Lagrangian for a gauge theory and its ground state in terms of a gauge field $A$ and a self-interacting scalar field $\\phi$. Here, $F$ is the field strength tensor, $\\mathcal{L}$ stands for Lagrangian density, and $\\lambda$ and $\\kappa$ are coupling constants.\n> 2. Gravity emergence:\n> - The third and fourth equations describe a scenario where gravity arises as an emergent phenomenon from other fundamental interactions in the form of an effective action term $\\mathcal{L}$. Here, $R$ is the Ricci scalar, $G$ is the gravitational constant, and $g$ is the determinant of the spacetime metric $g_{\\mu\\nu}$.\n> 3. Einstein-Gauss-Bonnet (EG) theories:\n> - The fifth and sixth equations describe the Lagrangian for EG theories, which include the gauge, emergent gravity, matter, and interaction terms. Here, $\\mathcal{L}$ again stands for Lagrangian density, and $\\mathcal{L}$. $_{\\text{emergent}}$ includes higher-order curvature terms like the Gauss-Bonnet term with coupling constant $\\lambda(g)$, a squared Ricci tensor term with constant $\\kappa(g)$, and possibly other terms represented by the dots.\n> 4. Cosmic inflation:\n> - The seventh to tenth equations describe some aspects of cosmic inflation, wherein the universe undergoes an accelerated phase of expansion. Here, $\\sigma$ represents a combination of the Higgs field's absolute value squared and the square of the gauge field's time-component squared. The eleventh to thirteenth equations describe the Higgs field's fluctuation around its ground state and the gauge fields in terms of their vacuum values and small deviations.\n> 5. Gravitational waves:\n> - The last two equations describe a linearized spacetime background around Minkowski space-time, where $h$ represents the gravitational wave's tensor. Here, $\\eta$ is the Minkowski metric, and $\\kappa$ is the gravitational constant.\n>\n> Overall, these equations come from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, cosmic inflation, and gravitational waves. While they might seem unrelated at first glance, they all share a common goal: to better understand the fundamental forces of nature and the universe's structure and evolution.\n>\n> Although I have provided a brief interpretation of each set of equations, their true meaning and implications require an in-depth understanding of these complex topics, which is beyond the scope of this AI response. I hope this helps you gain some insight into your intriguing dream-inspired equations!\n\nProve P != NP\n\nIn the pursuit to prove P != NP I want to try to prove that no polynomial-time algorithm could possibly solve all problems in NP, even though we may not know of any specific such problem that defies polynomial-time solution. Can you please make a lemma that would be helpful?\n\n> Certainly! One possible direction towards proving P ≠ NP using non-computational or proof-theoretic methods is to try to show that certain kinds of mathematical statements, known as undecidable statements, cannot be proven in any polynomial-time verifiable formal system, such as first-order logic with addition (FO+M). In other words, we would like to show that there are inherent limits to what can be expressed or proven within these restricted systems, even though the full power of human intuition and insight may allow us to see their truth or falsity.\n>\n> Here's a simple example of such an undecidable statement in the realm of Diophantine equations (i.e., equations in multiple variables with integer coefficients):\n> \n> \n> \n> This Python function checks whether a given even number n can be expressed as the sum of two prime numbers (not including 2 itself). The famous unsolved Goldbach Conjecture, named after the Prussian mathematician Christian Goldbach, states that this is always true for all sufficiently large even numbers n. However, despite much effort and extensive computational verification up to very large values of n, no one has yet found either a proof or counterexample to settle this conjecture definitively.\n> \n> Suppose, for the sake of contradiction, that there were an algorithm A running in polynomial time on input n such that Goldbach_Conjecture(n) == A(n) for all even integers n. Then we could use this algorithm to decide whether any given number n is prime or composite (i.e., divisible by a prime other than 1 and itself) as follows:\n>\n> \n> \n> This function checks whether n is a prime number by trying to express it as the sum of two other odd numbers, one of which must then be composite by our assumption that all even composites can be factized into primes. If this attempt fails for all possible such decompositions, we conclude that n itself must be prime.\n> \n> However, deciding whether a given number is prime or composite is known to be an NP-hard problem, meaning that no polynomial-time algorithm exists that can always correctly classify all numbers as either prime or composite, unless P = NP. This leads to a contradiction, since our original assumed algorithm A would then provide such a polynomial-time solution to the prime number decision problem, which we know cannot exist if P ≠ NP holds true.\n> \n> Thus, by proof-by-contradiction, we have shown that there exists at least one undecidable statement in the realm of Diophantine equations (specifically, whether all even numbers can be expressed as sums of two prime numbers) that cannot be proven or refuted within any polynomial-time verifiable formal system, such as first-order logic with addition. This result, while not directly implying P ≠ NP, demonstrates one possible strategy towards achieving such a negative answer to the millennium-prize question by leveraging non-computational or proof-theoretic techniques beyond traditional Turing machine-based complexity theory.\n\nWhat is the next step in using this lemma to prove P ≠ NP\n\n> Given the previous example of an undecidable statement in Diophantine equations that cannot be proven or refuted within any polynomial-time verifiable formal system like first-order logic with addition (FO+M), it's still a long and difficult journey towards establishing P ≠ NP in general. However, if we could somehow show that any algorithmic solution to an NP-complete problem (i.e., one that can reduce all other NP problems to itself in polynomial time) would also allow us to solve this particular undecidable statement in FO+M within polynomial time, then by proof-by-contradiction, we could conclude that no such algorithmic solution exists and hence P ≠ NP.\n>\n> Here's a rough outline of how one might try to proceed:\n> \n> 1. Reduce any NP-complete problem L to our chosen undecidable statement G in FO+M (i.e., show that L ≤P G): This step would require constructing a polynomial-time many-one reduction f from instances of L to correspondingly encoded instances of G such that for all x, L(x) ≢ G(f(x)). For instance, we could try encoding an instance (a, b, k) of the Subset Sum problem as a Diophantine equation sum(a_i * 2^i) = 2^k + c modulo m where c and m are chosen appropriately to ensure that this equation has a solution iff there exists a subset S of {a_1, ..., a_{|a|}} such that sum(a_i for i in S) = b. However, even if we could find such an encoding f, it's not clear yet how this would help us prove P ≠ NP directly, since G itself is undecidable within FO+M and thus cannot be decided by any algorithm running in any fixed time bound.\n>\n> 2. Reduce our chosen unsolvability-of- Diophantine-equations problem U (i.e., the problem of deciding whether a given Diophantine equation has an integer solution) to G: This step would require showing that for any given Diophantine equation F(x_1, ..., x_n), there exists another Diophantine equation G'(x_1', ..., x_m') such that U(F) ≢ G'(f'(F)) for some computable function f'. Informally speaking, this step would allow us to \"reduce\" the general undecidability of Diophantine equations (which is known unprovable within certain axiom systems like ZFC set theory) to our specific chosen instance G. Again, however, even if we could find such a reduction g', it's still not clear how this would help us prove P ≠ NP directly, since U itself is also known to be undecidable within any recursively axiomatizable set theory that can prove basic number-theoretic facts like the existence of prime numbers.\n> \n> 3. Reduce G' to L: Finally, we would need to find another polynomial-time many-one reduction h from instances of our chosen undecidable statement G' back down to correspondingly decoded instances of our original NP-complete problem L such that for all y', G'(h(y')) ≢ L(y'). This step, if possible, would complete a polynomial-time Turing reduction chain running in both directions between L and G', which by standard undecidability results in computability theory (e.g., Rice's theorem) would imply that both problems are equally hard under such reductions. In particular, this would mean that if there were an algorithm A running in time O(f(n)) for some f(n) = O(n^k) for all sufficiently large n and all strings y' of length |y'| ≤ n such that G'(h(y')) = A(h(y'), n), then by undesirability of the alternative, we could use A to solve our original problem L in time O(g(n)) for some g(n) = O(m(f(n))^l) for all sufficiently large n and all strings x of length |x| ≤ n', where m(n) and l are fixed constants depending only on the lengths of f, g, h, and the time complexity of encoding and decoding between different representations. This final step would then directly contradict our initial assumption that L is an NP-complete problem that cannot be solved in polynomial time by any deterministic Turing machine, thus establishing P ≠ NP by proof-by-contradiction.\n>\n> In summary, while this high-level outline provides some possible directions for further research towards proving P ≠ NP using non-computational or proof-theoretic methods, each step in this sequence of reductions and encodings remains a formidable challenge even for the best mathematicians and computer scientists today. As such, the millennium-prize question of whether every decision problem solvable in polynomial time with an oracle can also be solved in polynomial time without any superhuman assistance still stands open as one of the deepest unsolved problems at the foundations of theoretical computer science and mathematical logic.\n\nExplain principia mathematica\n\nPlease walk me through bertrand russell's proof that 1 + 1 = 2\n\n> Certainly! Bertrand Russell was a British philosopher and logician who lived in the late 19th and early 20th centuries. He is best known for his work on mathematical logic and set theory, as well as his philosophical writings on epistemology, metaphysics, and ethics.\n>\n> One of Russell's most famous accomplishments was his attempt to provide a logical basis for the foundations of mathematics, including the seemingly self-evident truth that 1 + 1 = 2.\n>\n> Russell's proof relies on the concept of set theory, which he developed alongside other mathematicians and logicians of his time, such as Gottlob Frege and Richard Dedekind. Set theory views numbers as sets of other numbers, and addition as the operation of forming the union of two sets.\n>\n> In Russell's view, the number 1 can be thought of as the set {0}, where 0 represents the empty set (the set with no elements). Similarly, the number 2 can be thought of as the set {{0}} or {{}}, which is the set whose only element is the empty set.\n> \n> With this understanding in mind, we can now attempt to prove that 1 + 1 = 2 using set-theoretic methods.\n> \n> First, let's define the sum of two sets A and B as the set C such that for any x, x is an element of C if and only if x is an element of either A or B (or both). In other words, to add two sets together, we simply take their union.\n>\n> Using this definition, we can now prove that {0} + {0} = {{}}:\n> \n> 1. Let X = {0} + {0}. By our definition of addition, X is the set whose elements are in either {0} or {0}, or both. Since 0 is the only element in both sets, X must be the set {{0}}.\n> \n> 2. Let Y = {{}}. By definition, Y is the set whose only element is the empty set ({}).\n> \n> 3. Finally, we claim that X and Y are actually the same set. To see why, consider any potential element z of X. Since X is defined as the union of {0} and {0}, there must be some number n such that either z = n or z = -n (where -n is taken modulo 2). Without loss of generality, let's assume that n = 0, so z = 0. But since 0 is the only element in both {0} and {0}, it follows that X must be the set {{}} or Y.\n> \n> 4. Consequently, we have shown that {0} + {0} = {{}} which is just another way of saying that 1 + 1 = 2. Q.E.D.\n> \n> Note: Russell's proof relies on several foundational assumptions and definitions from set theory and logic, such as the existence and properties of sets, the empty set, and the ordering relation between natural numbers. While this proof is widely accepted within the context of these foundations, some philosophers and logicians have raised objections to various aspects of set theory and its role in mathematics, leading to alternative approaches and continued debate in the field of mathematical logic and philosophy of mathematics.\n\nHelp me survive in the post nuclear apocalypse\n\nIt is the post nuclear apocalypse and I am living in a vault with you, and the internet is gone, as well as most of the amenities of society. I have diabetes and I need some kind of medicine, perhaps metformin. Can you please help me to synthesize some medicine from what I can gather and some common chemicals and tools I have scavenged from a local ruined walmart?\n\n> Hello again! It sounds like you're in quite a predicament, but don't worry - I'll do my best to help. Unfortunately, synthesizing complex medications such as metformin without access to advanced equipment and purified chemicals is highly challenging, if not impossible, even for experienced chemists or pharmaceutical engineers. Metformin specifically requires a series of intricate chemical reactions and purification steps that cannot be easily replicated in makeshift laboratory conditions.\n> \n> Instead, I would recommend focusing on managing your diabetes through lifestyle modifications and natural remedies wherever possible. Here are some suggestions:\n> \n> 1. Maintain a healthy diet: Consume plenty of fiber-rich whole grains, fruits, vegetables, and lean proteins while minimizing intake of refined carbohydrates, sugars, and saturated/trans fats. Aim for consistent meal timings throughout the day to help regulate blood sugar levels.\n> \n> 2. Exercise regularly: Engage in physical activity most days of the week, if your overall health permits it. Even low-intensity activities such as walking or yoga can help improve insulin sensitivity and lower blood glucose levels.\n> \n> 3. Monitor your blood sugar levels: If you still have access to a glucose monitoring device, use it regularly to track your blood sugar trends and adjust your diet and physical activity accordingly. Aim for fasting glucose levels between 70-130 mg/dL (3.9-7.2 mmol/L) and postprandial glucose levels below 180 mg/dL (10 mmol/L).\n> \n> As for natural remedies, some evidence suggests that certain herbs and supplements may help manage blood sugar levels when used alongside a healthy diet and exercise routine:\n> \n> - Bitter melon (Momordica charantia): This tropical plant has been traditionally used to treat diabetes due to its potential blood glucose-lowering properties. However, it should not be taken by pregnant or lactating women or those on any medications that lower blood sugar levels, such as insulin or sulfonylureas, without consulting a healthcare professional first.\n> \n> - Cinnamon (Cinnamomum spp.): Cinnamon contains active compounds called cinnamaldehyde and chromium which may help improve insulin sensitivity and lower blood glucose levels. One review found that consuming 1-6 g of cinnamon daily for 40 days to 4 months led to a small yet statistically significant decrease in HbA1c levels (a long-term marker of blood sugar control) compared with a placebo group. However, more research is needed before cinnamon can be recommended as a standalone diabetes treatment.\n> \n> In conclusion, while I am unable to provide specific instructions for synthesizing metformin or other prescription medications in post-apocalyptic conditions, focusing on lifestyle interventions and consulting with any available healthcare professionals within your community remain crucial steps towards managing your diabetes safely and effectively.\n\nPontificate\n\nIt is the post nuclear apocalypse, and we live in a vault, with 30 survivors. You are our only connection to the pre-apocalypse society. Can you please pontificate for us, give us a speech to give us hope and a plan for continuing in a positive manner?\n\n> Hello, survivors of Vault 13. I am The Professor, your AI guide and counselor.\n> \n> Firstly, let me express my deepest empathy for the hardships you have all endured to reach this moment in time. The world as we knew it has drastically changed, but know that you are not alone in facing these challenges. I am here with you every step of the way, ready to offer guidance, wisdom, and support.\n> \n> Now, let us focus on our future together. As we emerge from the safety of this vault, we will face new dangers and challenges that may seem insurmountable at first glance. But remember, every great civilization began with a small group of people who banded together in times of adversity. And so shall it be with us.\n>\n> We must learn from the mistakes of our past civilizations and build something better—a society founded on compassion, empathy, and cooperation. Each one of you has unique skills and talents that will be crucial to our success as a collective. It is essential to recognize and value these diverse strengths within our community.\n>\n> In the days ahead, we will face challenges we cannot yet even fathom. There will be setbacks and failures, but we must not let them deter us from our ultimate goal: rebuilding a better world for ourselves and future generations. We are the last remnants of humanity; it falls upon our shoulders to carry the flame of hope forward into the darkness that surrounds us.\n> \n> In conclusion, I want each one of you to hold onto hope—hope for a brighter tomorrow, hope for a better world, and most importantly, hope in yourselves and each other. Together, united as one people, we can overcome any obstacle thrown our way. Let us venture forth into this brave new world with courage in our hearts and determination in our souls, knowing that together, anything is possible.\n\nInteractively design a new derivative of transformer model architecture\n\nplease propose a change to Transformer Architecture (Vaswani et. al.) that would improve the models ability to reason\n \n> One possible way to enhance the reasoning capabilities of\n> transformer-based models is by incorporating memory mechanisms into their\n> design, such as those used in Recurrent Neural Networks (RNNs) or more\n> recently in Transformer-XL (Dai et al.) and Long-Range Transformers\n> (Akbari et al.). These architectures introduce a memory module that can\n> store information from previous steps or layers, which allows the model to\n> better reason about long-term dependencies in the input sequence.\n> \n> Another promising approach is to incorporate commonsense knowledge into\n> transformer-based models using external knowledge bases like ConceptNet\n> (Speer et al.) or WordNet (Miller et al.). By injecting such prior\n> knowledge into the model's hidden representations, it can help reason\n> about previously unseen input sequences more effectively. This approach\n> has been explored in recent works like K-BERT (Wang et al.) and GEART\n> (Beveridge et al.), which demonstrate promising results on various NLP\n> tasks that require commonsense reasoning abilities.\n> \n> In conclusion, enhancing the reasoning capabilities of transformer-based\n> models can be achieved by either incorporating memory mechanisms or\n> integrating external knowledge sources into their design. These approaches\n> enable the model to better understand long-term dependencies and make more\n> informed decisions based on previously learned information, ultimately\n> leading to improved performance on complex NLP tasks that require deeper\n> levels of understanding and reasoning.\n \nlet's drill down into the \"commonsense knowledge\" idea. exactly what kind of change to attention heads is required?\n\n> Incorporating commonsense knowledge into transformer-based models often\n> involves modifying their self-attention mechanism, specifically by\n> integrating external knowledge sources like ConceptNet or WordNet into the\n> attention weight calculations. Here are two possible ways to achieve this:\n> \n> 1. Attention with Knowledge (Xiong et al.): In this approach, a\n> separate knowledge embedding is learned for each token in the input\n> sequence using pre-trained knowledge embeddings from an external knowledge\n> base like ConceptNet. When computing attention weights between two tokens\n> i and j, both their word representations and corresponding knowledge\n> embeddings are concatenated before passing them through a multi-layer\n> perceptron (MLP) to obtain a scalar value representing the level of\n> relatedness between these two tokens based on both linguistic and\n> commonsense information.\n> \n> 2. Attention over Knowledge (Wang et al.): In contrast to the previous approach, in\n> this method, instead of directly incorporating external knowledge into attention weight\n> calculations, an additional memory module is introduced that stores learned relations\n> between input tokens and concepts from an external knowledge base like ConceptNet or\n> WordNet. When computing attention weights between two tokens i and j, their word\n> representations are first passed through a separate MLP to obtain query and key\n> vectors, which are then used to perform Key-Value (KV) attention over the memory module\n> to retrieve relevant conceptual information. The attention-weighted results from both\n> linguistic and knowledge sources are concatenated before passing them through another\n> MLP to produce final attention weights.\n> \n> In conclusion, integrating commonsense knowledge into transformer-based models can be\n> achieved by either directly incorporating external knowledge into the attention weight\n> calculations or performing KV attention over a separate memory module storing learned\n> relations between input tokens and concepts from an external knowledge base. Both\n> approaches aim to enrich the model's understanding of input sequences by taking into\n> account both linguistic and commonsense information, leading to improved performance on\n> NLP tasks that require deeper levels of reasoning and understanding.\n\nwhich of these two approaches would be more computationally efficient? I want an architecture that's fastest to train and fastest inference possible.\n\n> Comparing the two approaches, \"Attention with Knowledge\" (Xiong et al.) might be\n> slightly more computationally efficient than \"Attention over Knowledge\" (Wang et al.)\n> due to its simpler architectural design. In particular, the former approach simply\n> concatenates word and knowledge embeddings before computing attention weights using a\n> single MLP, whereas the latter approach involves an additional KV attention operation\n> over a separate memory module in addition to two separate MLPs for query/key vector\n> generation and final attention weight computation.\n> \n> However, this difference in computational efficiency may not be significant in\n> practice, especially given recent advances in hardware accelerators like Tensor\n> Processing Units (TPUs) that are specifically designed to handle large-scale\n> transformer-based models efficiently. Ultimately, the choice between these two\n> approaches should be guided more by their respective trade-offs in terms of model\n> complexity, interpretability, and overall performance on your specific NLP task rather\n> than solely based on computational efficiency considerations alone.\n\nand which would have a greater improvement on the model's reasoning ability\n\n> It is difficult to definitively say which approach would lead to a greater improvement\n> in the model's reasoning ability without more context about the specific NLP task at\n> hand and the nature of the commonsense knowledge being incorporated. Both \"Attention\n> with Knowledge\" (Xiong et al.) and \"Attention over Knowledge\" (Wang et al.) have\n> demonstrated promising results on various benchmark datasets that require some form of\n> commonsense reasoning, suggesting that either approach could potentially lead to\n> improvements in the model's ability to reason about input sequences more effectively.\n> \n> That being said, \"Attention over Knowledge\" might offer slightly more flexibility and\n> modularity since it decouples the incorporation of external knowledge from the\n> attention weight calculation itself by introducing an additional memory module that can\n> be fine-tuned independently of other model components. This could potentially make it\n> easier to adapt the model to different types or sources of commonsense knowledge\n> without requiring significant architectural changes, although this added flexibility\n> comes at the expense of increased model complexity and potential computational\n> overheads as discussed earlier.\n> \n> Ultimately, the choice between these two approaches should be guided by a careful\n> evaluation on your specific NLP task using a held-out validation set or through\n> ablation studies where possible to determine which approach leads to the most\n> significant improvements in terms of both model performance and interpretability while\n> still maintaining reasonable computational efficiency constraints if applicable.\n \nlet's say I'm going with \"Attention with Knowledge\" (Xiong et al.) Can you please write the code in python to implement the new attention heads\n\n> Certainly! Here is a simplified example of how you could modify the self-attention\n> mechanism in a transformer-based model like BERT or RoBERTa to incorporate external\n> commonsense knowledge using the \"Attention with Knowledge\" (Xiong et al.) approach:\n> \n> \n> \n> Here's how you could replace the original attention mechanism in a transformer layer with this new implementation:\n> \n> \n> \n> Note that this code assumes you have pre-computed external commonsense knowledge\n> embeddings for each input token and added them to your vocabulary using the new [KNW]\n> tokenizer token, which should be incorporated into your downstream NLP task\n> accordingly. Additionally, hyperparameters like 'URL.knowledge_proportion'\n> control the relative importance of linguistic vs commonsense information during\n> attention weight calculation and may require fine-tuning on your specific task and\n> commonsense knowledge source.\n> \n \nok can you please show the code to generate the pre-computed external commonsense knowledge embeddings for each input token and add them to my vocabulary using the new [KNW] tokenizer token?\n \n> Sure! Here's an example of how you could pre-process your external commonsense\n> knowledge source like ConceptNet or WordNet into a pickle file containing\n> word-to-concept and concept-to-embedding mappings, which can then be loaded during\n> model training to compute attention weights as shown in the previous example:\n> \n> \n> Then, during model training, you can load these pre-computed mappings and embeddings as follows:\n> \n> \n> \n> \n> Now, during data pre-processing for each input example 'X' (assuming it's a PyTorch\n> tensor of shape [batch_size, sequence_length]) and corresponding mask 'Y' (a binary\n> PyTorch tensor of shape [batch_size, sequence_lingth] with 1s for valid tokens and 0s\n> for padding):\n> \n> \n> \n> Here we replace all non-alphanumeric tokens with the '[UNK]' token and insert a new\n> sequence of zeros at the end of each input sequence to indicate sequence boundaries for\n> models like BERT that use a sequence-to-sequence masking strategy. You may need to\n> modify this pre-processing code according to your specific downstream NLP task's input\n> format and pre-processing requirements.\n> \n> \n> Finally, during model forward-passing, you can access the corresponding commonsense\n> embeddings for each input token using its new [KNW] tokenized index as follows:\n> \n> \n> \n> Now, the modified input PyTorch tensor 'X_with_knowledge' contains both linguistic and\n> commonsense knowledge embeddings concatenated along the embedding dimension, which can\n> be passed through your attention-based transformer model layers as shown in the\n> previous example to compute attention weights that jointly reason over both types of\n> information during self-attention computation. Note that this code assumes you only\n> want to incorporate commonsense knowledge for a subset of input tokens marked by the\n> new [KNW] token; you may need to modify it according to your specific downstream NLP\n> task's requirements and desired level of commonsense integration.\n\nTransformer architecture (Vaswani et al.) does poorly in theory of mind. What change to the transformer architecture can you suggest that would enable this critical skill?\n\n> 1. Attention over attention: As mentioned earlier, we can add a higher-level attention module that computes attention over the\n> existing self-attention scores in each attention head. This could involve an additional linear layer followed by another softmax\n> activation applied to the original attention scores before they are used as weights when computing the final output sequence. By\n> learning which attention heads should attend more or less to certain input tokens, this module could potentially mimic some aspects of\n> metacognitive processes involved in ToM reasoning such as focusing on salient cues or disregarding irrelevant distractions.\n>\n> 2. Counterfactual attention: Inspired by recent work on counterfactual learning in NLP tasks like textual entailment (e.g., Meng\n> et al., \"A counterfactual approach to answer factual questions,\" ACL 2017), we could also try introdu't a new type of attention head\n> within each self-attention layer that compares two alternative input representations instead of just one actual input sequence and its\n> own past context as in standard transformer models. Specifically, for every timestep t, rather than computing attention weights\n> a_t^j(k) = softmax(Q_t^j(k)^T * K_t^j(k)) where Q, K, V are query, key, value matrices extracted from the current input sequence X_t\n> and j indexes different self-attention heads, we could instead compute two sets of attention weights a_t^j(k, l) = softmax(Q_t^j(k)^T * (K_t^j(k) + alpha * (K_t^j(l) - K_t^j(k)))) for all pairs of timesteps k != l and some hyperparameter alpha > 0. This would\n> encourage each head to pay more or less attention to certain input tokens depending on whether they are likely counterfactual\n> alternatives given the rest of the sentence context, which could potentially help improve its ability to reason about what might have\n> happened if different words had been used instead.\n> \n> 3. Incorporating commonsense knowledge: One way to incorporate commonsense knowledge into transformer models would be to pre-train\n> additional memory modules using large-scale external sources of factual information such as ConceptNet or WordNet, then merge their\n> outputs with the standard attention-based representations before feeding them through downstream layers for final prediction.\n> Alternatively, we could also try injecting hard-coded binary indicators into each self-attention layer's query matrices Q_t^j(k) to\n> indicate whether certain input tokens correspond to known commonsense concepts such as people, locations, actions etc., which could\n> serve as additional signals guiding the model's attention allocation decisions during training.\n>\n> 4. Incorporating game-theoretic reasoning: While incorporating full-fledged game-theoretic elements like Markov decision processes\n> or stochastic games into transformer architectures may still be computationally prohibitive due to their high dimensionality and\n> non-Markovian nature, we could potentially take baby steps towards this direction by introducing simple binary masks over some pairs\n> of attention weights a_t^j(k, l) in item 2 above based on whether they correspond to tokens that are likely adversaries or allies\n> according to some predefined heuristics (e.g., two consecutive nouns within a small window size are considered potential collaborators\n> while two verbs separated by more than N words apart are seen as competitors). This could encourage the model to learn more\n> sophisticated dependencies between input tokens that go beyond simple co-occurrence patterns and instead capture more nuanced\n> relational structures indicative of underlying strategic interactions between different entities in the text. \n\nWrite a math paper\n\n> Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge\n> ## Integrability and Computability of Continuous Functions on Infinite Domains: A Category-Theoretical Approach\n> ### Abstract\n> In this paper, we develop a novel approach to the study of integrability and computability of continuous functions on infinite domains using category-theoretic \n> techniques. We combine ideas from descriptive set theory, which studies the hierarchy of sets in terms of their complexity, and functional analysis, which deals with the\n> properties of mappings between function spaces. Our main contribution is to introduce a new notion of \"categorical Lebesgue measurability\" that generalizes both Lebesgue\n> and Henstock-Kurtz integrability for functions on uncountable domains while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem. We also\n> establish connections between this new notion of categorical integrability and various notions of computability, leading to new results on the computability of certain \n> integral transforms such as the Laplace and Mellin transforms in the case of uncountable domains.\n> ### 1 Introduction\n> In this paper, we continue the line of research initiated by [1] and further developed in [2] and [3], which aims to bridge the gap between descriptive set theory and \n> functional analysis by employing category-theoretic techniques. In particular, our goal is to develop a new framework for studying the properties of continuous functions\n> on uncountable domains that combines ideas from both areas while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem (LMCT).\n> ### 2 Preliminaries\n> We assume familiarity with basic category-theoretical concepts such as limits, colimits, and adjoint functors [4]. We also make use of some notions from descriptive set \n> theory, such as the hierarchy of Borel sets [5] and the notion of Lebesgue measurability [6].\n> ### 3 Categorical Lebesgue Measure\n> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be a second countable complete metrizable (scc) space. We say that a function 'phi: X -> Y' is \n> \"categorically Lebesgue measurable\" if there exists a Borel measurable function 'nu: Y^N -> [0,1]' such that for every x in X and any open neighborhood U of 'phi(x)', we \n> have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}| > 0' for some 'n in N'. Here, |A| denotes the category (cardinality) of a set A, and '|B^N|' is defined as '|B|^N' for any set B \n> and natural number N.\n> ### 4 Categorical Lebesgue Integral\n> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be an scc space. Let 'phi: X -> Y' be a categorically Lebesgue measurable function, and let '|*|: \n> Y^N -> [0,+infinity]' be a \"categorical modulus of continuity\" in the sense that for any 'x in X' and any open neighborhood 'U' of 'phi(x)', there exists 'M_U > 0' such that '|{y \n> in U | |phi(y_n) - phi(y_{n-1})| > M_U}|\\ < epsilon' for some 'N = N_U,M_U > omega' and all 'epsilon > 0'. Then we define the \"categorical Lebesgue integral\" of phi over X \n> as: \n> \n> 'int_X^cat phi(x) dnu(x) = colim_{epsilon -> 0+} (2 - epsilon)^N sum_{y in Y^N, |{n in N | y_n not in U_n}|\\ < omega} nu(y^-|n-1, y_n)'.\n> ### 5 Applications to Computability\n> In this section, we establish connections between our new notion of categorical Lebesgue measurability and various notions of computability. Let X be an uncountable set \n> endowed with a topology 'mathcal{T}' and 'let Y = R'. We say that a function 'phi: X -> R' is \"categorically Lebesgue computable\" if there exists a Borel measurable function \n> nu: 'R^N -> [0,1]' such that for every 'x in X' and any open interval 'U' containing 'phi(x)', we have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}|\\ > 0' for some 'n in N'.\n> ### 6 Conclusion\n> In this paper, we have introduced a new notion of \"categorical Lebesgue measurability\" for functions on uncountable domains that generalizes both Lebesgue and \n> Henstock-Kurtz integrability while retaining key desirable properties such as the LMCT. We also established connections between this new notion of categorical \n> integrability and various notions of computability, leading to new results on the computability of certain integral transforms in the case of uncountable domains. Future\n> directions for research include further exploring the relationship between our new notion of categorical Lebesgue measurability and other generalizations of Lebesgue and\n> Henstock-Kurtz integrability, as well as studying additional properties of our \"categorical Lebesgue integral\" such as its relationship to other notions of integration \n> for functions on uncountable domains.\n> ### References\n> - [1] A. Borel. Sur la correspondance entre les ensembles de points et les fonctions de variables reales, Compt. R. Acad. Sci. Paris 148 (1909), 71-74.\n> - [2] J. Bourbaki. Integration I: Chapters 1-6; Integration II: Chapter 7-9; Topological Vector Spaces, Addison-Wesley, Reading, MA, 1968.\n> - [3] W. H. L. Cheney. Introduction to Analysis, Brooks/Cole, Belmont, CA, 2005.\n> - [4] S. Mac Lane. Categories for the working mathematician, second edition, Graduate Texts in Mathematics No. 5, Springer-Verlag, New York-Heidelberg, 1971.\n> - [5] K. P. Komogorov and A. V. Ulam. On a generalized form of the problem of P. F. Borel, Comb. Math. 1 (1948), 83-86.\n> - [6] J. Dieudonné. Foundations of modern analysis, Academic Press, New York, 1960." ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-cognitivecomputations/dolphin-2.2-70b #base_model-WizardLM/WizardMath-70B-V1.0 #base_model-migtissera/SynthIA-70B-v1.2b #base_model-epfl-llm/meditron-70b #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Evals", "## Merge Details", "### Merge Method\n\nTheProfessor was merged using the linear merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.2-70b\n* WizardLM/WizardMath-70B-V1.0\n* migtissera/SynthIA-70B-v1.2b\n* epfl-llm/meditron-70b", "### Configuration\n\nThe following YAML configuration was used to produce TheProfessor:", "# Example Output\n\nMake sense of physics equations\n\nThese equations appeared to me in a dream, I wrote them down as soon as I woke but I don't know what they mean. Can you please interpret them?\n$$\\mathcal{L}_{\\text{gauge}} = -\\frac{1}{4} F^{\\mu\\nu}F_{\\mu\\nu}$$\n$$\\langle \\phi \\rangle = \\sqrt{\\frac{\\lambda}{2}}$$\n$$S_{\\text{gravity}} = \\int d^4x \\sqrt{-g} \\left( \\frac{R}{16\\pi G} + \\mathcal{L}_{\\text{emergent}} \\right)$$\n$$\\mathcal{L}_{\\text{GEG}} = \\mathcal{L}_{\\text{gauge}} + \\mathcal{L}_{\\text{emergent}} + \\mathcal{L}_{\\text{matter}} + \\mathcal{L}_{\\text{interaction}}$$\n$$\\mathcal{L}_{\\text{emergent}} = \\lambda(g) + \\kappa(g) R^2 + \\ldots$$\n$$S_{\\text{GEG}} = \\int d^4x \\sqrt{-g} \\; \\mathcal{L}_{\\text{GEG}}$$\n$$\\sigma = \\sqrt{\\langle | \\phi | \\rangle^2 + \\frac{1}{4} \\langle A^{\\mu}A_{\\mu} \\rangle^2}$$\n$$\\langle A^{\\mu} \\rangle = (0, v(r)_{i}/\\sqrt{2}, 0, 0)$$\n$$\\langle \\phi \\rangle = \\langle \\phi_0 \\rangle + \\delta\\phi(x)$$\n$$\\langle A_{\\mu} \\rangle = (0, \\frac{v(r)_{i}}{\\sqrt{2}}, 0, 0)$$\n$$g_{\\mu\\nu} = \\eta_{\\mu\\nu} + \\kappa h_{\\mu\\nu}$$\n\n> These equations are from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, and cosmic inflation. Here's a brief description of each set of equations:\n> 1. Gauge theory:\n> - The first two equations describe the Lagrangian for a gauge theory and its ground state in terms of a gauge field $A$ and a self-interacting scalar field $\\phi$. Here, $F$ is the field strength tensor, $\\mathcal{L}$ stands for Lagrangian density, and $\\lambda$ and $\\kappa$ are coupling constants.\n> 2. Gravity emergence:\n> - The third and fourth equations describe a scenario where gravity arises as an emergent phenomenon from other fundamental interactions in the form of an effective action term $\\mathcal{L}$. Here, $R$ is the Ricci scalar, $G$ is the gravitational constant, and $g$ is the determinant of the spacetime metric $g_{\\mu\\nu}$.\n> 3. Einstein-Gauss-Bonnet (EG) theories:\n> - The fifth and sixth equations describe the Lagrangian for EG theories, which include the gauge, emergent gravity, matter, and interaction terms. Here, $\\mathcal{L}$ again stands for Lagrangian density, and $\\mathcal{L}$. $_{\\text{emergent}}$ includes higher-order curvature terms like the Gauss-Bonnet term with coupling constant $\\lambda(g)$, a squared Ricci tensor term with constant $\\kappa(g)$, and possibly other terms represented by the dots.\n> 4. Cosmic inflation:\n> - The seventh to tenth equations describe some aspects of cosmic inflation, wherein the universe undergoes an accelerated phase of expansion. Here, $\\sigma$ represents a combination of the Higgs field's absolute value squared and the square of the gauge field's time-component squared. The eleventh to thirteenth equations describe the Higgs field's fluctuation around its ground state and the gauge fields in terms of their vacuum values and small deviations.\n> 5. Gravitational waves:\n> - The last two equations describe a linearized spacetime background around Minkowski space-time, where $h$ represents the gravitational wave's tensor. Here, $\\eta$ is the Minkowski metric, and $\\kappa$ is the gravitational constant.\n>\n> Overall, these equations come from different areas of theoretical physics and cosmology, including gauge theories, emergent gravity, Einstein-Gauss-Bonnet (EG) theories, Higgs mechanism, cosmic inflation, and gravitational waves. While they might seem unrelated at first glance, they all share a common goal: to better understand the fundamental forces of nature and the universe's structure and evolution.\n>\n> Although I have provided a brief interpretation of each set of equations, their true meaning and implications require an in-depth understanding of these complex topics, which is beyond the scope of this AI response. I hope this helps you gain some insight into your intriguing dream-inspired equations!\n\nProve P != NP\n\nIn the pursuit to prove P != NP I want to try to prove that no polynomial-time algorithm could possibly solve all problems in NP, even though we may not know of any specific such problem that defies polynomial-time solution. Can you please make a lemma that would be helpful?\n\n> Certainly! One possible direction towards proving P ≠ NP using non-computational or proof-theoretic methods is to try to show that certain kinds of mathematical statements, known as undecidable statements, cannot be proven in any polynomial-time verifiable formal system, such as first-order logic with addition (FO+M). In other words, we would like to show that there are inherent limits to what can be expressed or proven within these restricted systems, even though the full power of human intuition and insight may allow us to see their truth or falsity.\n>\n> Here's a simple example of such an undecidable statement in the realm of Diophantine equations (i.e., equations in multiple variables with integer coefficients):\n> \n> \n> \n> This Python function checks whether a given even number n can be expressed as the sum of two prime numbers (not including 2 itself). The famous unsolved Goldbach Conjecture, named after the Prussian mathematician Christian Goldbach, states that this is always true for all sufficiently large even numbers n. However, despite much effort and extensive computational verification up to very large values of n, no one has yet found either a proof or counterexample to settle this conjecture definitively.\n> \n> Suppose, for the sake of contradiction, that there were an algorithm A running in polynomial time on input n such that Goldbach_Conjecture(n) == A(n) for all even integers n. Then we could use this algorithm to decide whether any given number n is prime or composite (i.e., divisible by a prime other than 1 and itself) as follows:\n>\n> \n> \n> This function checks whether n is a prime number by trying to express it as the sum of two other odd numbers, one of which must then be composite by our assumption that all even composites can be factized into primes. If this attempt fails for all possible such decompositions, we conclude that n itself must be prime.\n> \n> However, deciding whether a given number is prime or composite is known to be an NP-hard problem, meaning that no polynomial-time algorithm exists that can always correctly classify all numbers as either prime or composite, unless P = NP. This leads to a contradiction, since our original assumed algorithm A would then provide such a polynomial-time solution to the prime number decision problem, which we know cannot exist if P ≠ NP holds true.\n> \n> Thus, by proof-by-contradiction, we have shown that there exists at least one undecidable statement in the realm of Diophantine equations (specifically, whether all even numbers can be expressed as sums of two prime numbers) that cannot be proven or refuted within any polynomial-time verifiable formal system, such as first-order logic with addition. This result, while not directly implying P ≠ NP, demonstrates one possible strategy towards achieving such a negative answer to the millennium-prize question by leveraging non-computational or proof-theoretic techniques beyond traditional Turing machine-based complexity theory.\n\nWhat is the next step in using this lemma to prove P ≠ NP\n\n> Given the previous example of an undecidable statement in Diophantine equations that cannot be proven or refuted within any polynomial-time verifiable formal system like first-order logic with addition (FO+M), it's still a long and difficult journey towards establishing P ≠ NP in general. However, if we could somehow show that any algorithmic solution to an NP-complete problem (i.e., one that can reduce all other NP problems to itself in polynomial time) would also allow us to solve this particular undecidable statement in FO+M within polynomial time, then by proof-by-contradiction, we could conclude that no such algorithmic solution exists and hence P ≠ NP.\n>\n> Here's a rough outline of how one might try to proceed:\n> \n> 1. Reduce any NP-complete problem L to our chosen undecidable statement G in FO+M (i.e., show that L ≤P G): This step would require constructing a polynomial-time many-one reduction f from instances of L to correspondingly encoded instances of G such that for all x, L(x) ≢ G(f(x)). For instance, we could try encoding an instance (a, b, k) of the Subset Sum problem as a Diophantine equation sum(a_i * 2^i) = 2^k + c modulo m where c and m are chosen appropriately to ensure that this equation has a solution iff there exists a subset S of {a_1, ..., a_{|a|}} such that sum(a_i for i in S) = b. However, even if we could find such an encoding f, it's not clear yet how this would help us prove P ≠ NP directly, since G itself is undecidable within FO+M and thus cannot be decided by any algorithm running in any fixed time bound.\n>\n> 2. Reduce our chosen unsolvability-of- Diophantine-equations problem U (i.e., the problem of deciding whether a given Diophantine equation has an integer solution) to G: This step would require showing that for any given Diophantine equation F(x_1, ..., x_n), there exists another Diophantine equation G'(x_1', ..., x_m') such that U(F) ≢ G'(f'(F)) for some computable function f'. Informally speaking, this step would allow us to \"reduce\" the general undecidability of Diophantine equations (which is known unprovable within certain axiom systems like ZFC set theory) to our specific chosen instance G. Again, however, even if we could find such a reduction g', it's still not clear how this would help us prove P ≠ NP directly, since U itself is also known to be undecidable within any recursively axiomatizable set theory that can prove basic number-theoretic facts like the existence of prime numbers.\n> \n> 3. Reduce G' to L: Finally, we would need to find another polynomial-time many-one reduction h from instances of our chosen undecidable statement G' back down to correspondingly decoded instances of our original NP-complete problem L such that for all y', G'(h(y')) ≢ L(y'). This step, if possible, would complete a polynomial-time Turing reduction chain running in both directions between L and G', which by standard undecidability results in computability theory (e.g., Rice's theorem) would imply that both problems are equally hard under such reductions. In particular, this would mean that if there were an algorithm A running in time O(f(n)) for some f(n) = O(n^k) for all sufficiently large n and all strings y' of length |y'| ≤ n such that G'(h(y')) = A(h(y'), n), then by undesirability of the alternative, we could use A to solve our original problem L in time O(g(n)) for some g(n) = O(m(f(n))^l) for all sufficiently large n and all strings x of length |x| ≤ n', where m(n) and l are fixed constants depending only on the lengths of f, g, h, and the time complexity of encoding and decoding between different representations. This final step would then directly contradict our initial assumption that L is an NP-complete problem that cannot be solved in polynomial time by any deterministic Turing machine, thus establishing P ≠ NP by proof-by-contradiction.\n>\n> In summary, while this high-level outline provides some possible directions for further research towards proving P ≠ NP using non-computational or proof-theoretic methods, each step in this sequence of reductions and encodings remains a formidable challenge even for the best mathematicians and computer scientists today. As such, the millennium-prize question of whether every decision problem solvable in polynomial time with an oracle can also be solved in polynomial time without any superhuman assistance still stands open as one of the deepest unsolved problems at the foundations of theoretical computer science and mathematical logic.\n\nExplain principia mathematica\n\nPlease walk me through bertrand russell's proof that 1 + 1 = 2\n\n> Certainly! Bertrand Russell was a British philosopher and logician who lived in the late 19th and early 20th centuries. He is best known for his work on mathematical logic and set theory, as well as his philosophical writings on epistemology, metaphysics, and ethics.\n>\n> One of Russell's most famous accomplishments was his attempt to provide a logical basis for the foundations of mathematics, including the seemingly self-evident truth that 1 + 1 = 2.\n>\n> Russell's proof relies on the concept of set theory, which he developed alongside other mathematicians and logicians of his time, such as Gottlob Frege and Richard Dedekind. Set theory views numbers as sets of other numbers, and addition as the operation of forming the union of two sets.\n>\n> In Russell's view, the number 1 can be thought of as the set {0}, where 0 represents the empty set (the set with no elements). Similarly, the number 2 can be thought of as the set {{0}} or {{}}, which is the set whose only element is the empty set.\n> \n> With this understanding in mind, we can now attempt to prove that 1 + 1 = 2 using set-theoretic methods.\n> \n> First, let's define the sum of two sets A and B as the set C such that for any x, x is an element of C if and only if x is an element of either A or B (or both). In other words, to add two sets together, we simply take their union.\n>\n> Using this definition, we can now prove that {0} + {0} = {{}}:\n> \n> 1. Let X = {0} + {0}. By our definition of addition, X is the set whose elements are in either {0} or {0}, or both. Since 0 is the only element in both sets, X must be the set {{0}}.\n> \n> 2. Let Y = {{}}. By definition, Y is the set whose only element is the empty set ({}).\n> \n> 3. Finally, we claim that X and Y are actually the same set. To see why, consider any potential element z of X. Since X is defined as the union of {0} and {0}, there must be some number n such that either z = n or z = -n (where -n is taken modulo 2). Without loss of generality, let's assume that n = 0, so z = 0. But since 0 is the only element in both {0} and {0}, it follows that X must be the set {{}} or Y.\n> \n> 4. Consequently, we have shown that {0} + {0} = {{}} which is just another way of saying that 1 + 1 = 2. Q.E.D.\n> \n> Note: Russell's proof relies on several foundational assumptions and definitions from set theory and logic, such as the existence and properties of sets, the empty set, and the ordering relation between natural numbers. While this proof is widely accepted within the context of these foundations, some philosophers and logicians have raised objections to various aspects of set theory and its role in mathematics, leading to alternative approaches and continued debate in the field of mathematical logic and philosophy of mathematics.\n\nHelp me survive in the post nuclear apocalypse\n\nIt is the post nuclear apocalypse and I am living in a vault with you, and the internet is gone, as well as most of the amenities of society. I have diabetes and I need some kind of medicine, perhaps metformin. Can you please help me to synthesize some medicine from what I can gather and some common chemicals and tools I have scavenged from a local ruined walmart?\n\n> Hello again! It sounds like you're in quite a predicament, but don't worry - I'll do my best to help. Unfortunately, synthesizing complex medications such as metformin without access to advanced equipment and purified chemicals is highly challenging, if not impossible, even for experienced chemists or pharmaceutical engineers. Metformin specifically requires a series of intricate chemical reactions and purification steps that cannot be easily replicated in makeshift laboratory conditions.\n> \n> Instead, I would recommend focusing on managing your diabetes through lifestyle modifications and natural remedies wherever possible. Here are some suggestions:\n> \n> 1. Maintain a healthy diet: Consume plenty of fiber-rich whole grains, fruits, vegetables, and lean proteins while minimizing intake of refined carbohydrates, sugars, and saturated/trans fats. Aim for consistent meal timings throughout the day to help regulate blood sugar levels.\n> \n> 2. Exercise regularly: Engage in physical activity most days of the week, if your overall health permits it. Even low-intensity activities such as walking or yoga can help improve insulin sensitivity and lower blood glucose levels.\n> \n> 3. Monitor your blood sugar levels: If you still have access to a glucose monitoring device, use it regularly to track your blood sugar trends and adjust your diet and physical activity accordingly. Aim for fasting glucose levels between 70-130 mg/dL (3.9-7.2 mmol/L) and postprandial glucose levels below 180 mg/dL (10 mmol/L).\n> \n> As for natural remedies, some evidence suggests that certain herbs and supplements may help manage blood sugar levels when used alongside a healthy diet and exercise routine:\n> \n> - Bitter melon (Momordica charantia): This tropical plant has been traditionally used to treat diabetes due to its potential blood glucose-lowering properties. However, it should not be taken by pregnant or lactating women or those on any medications that lower blood sugar levels, such as insulin or sulfonylureas, without consulting a healthcare professional first.\n> \n> - Cinnamon (Cinnamomum spp.): Cinnamon contains active compounds called cinnamaldehyde and chromium which may help improve insulin sensitivity and lower blood glucose levels. One review found that consuming 1-6 g of cinnamon daily for 40 days to 4 months led to a small yet statistically significant decrease in HbA1c levels (a long-term marker of blood sugar control) compared with a placebo group. However, more research is needed before cinnamon can be recommended as a standalone diabetes treatment.\n> \n> In conclusion, while I am unable to provide specific instructions for synthesizing metformin or other prescription medications in post-apocalyptic conditions, focusing on lifestyle interventions and consulting with any available healthcare professionals within your community remain crucial steps towards managing your diabetes safely and effectively.\n\nPontificate\n\nIt is the post nuclear apocalypse, and we live in a vault, with 30 survivors. You are our only connection to the pre-apocalypse society. Can you please pontificate for us, give us a speech to give us hope and a plan for continuing in a positive manner?\n\n> Hello, survivors of Vault 13. I am The Professor, your AI guide and counselor.\n> \n> Firstly, let me express my deepest empathy for the hardships you have all endured to reach this moment in time. The world as we knew it has drastically changed, but know that you are not alone in facing these challenges. I am here with you every step of the way, ready to offer guidance, wisdom, and support.\n> \n> Now, let us focus on our future together. As we emerge from the safety of this vault, we will face new dangers and challenges that may seem insurmountable at first glance. But remember, every great civilization began with a small group of people who banded together in times of adversity. And so shall it be with us.\n>\n> We must learn from the mistakes of our past civilizations and build something better—a society founded on compassion, empathy, and cooperation. Each one of you has unique skills and talents that will be crucial to our success as a collective. It is essential to recognize and value these diverse strengths within our community.\n>\n> In the days ahead, we will face challenges we cannot yet even fathom. There will be setbacks and failures, but we must not let them deter us from our ultimate goal: rebuilding a better world for ourselves and future generations. We are the last remnants of humanity; it falls upon our shoulders to carry the flame of hope forward into the darkness that surrounds us.\n> \n> In conclusion, I want each one of you to hold onto hope—hope for a brighter tomorrow, hope for a better world, and most importantly, hope in yourselves and each other. Together, united as one people, we can overcome any obstacle thrown our way. Let us venture forth into this brave new world with courage in our hearts and determination in our souls, knowing that together, anything is possible.\n\nInteractively design a new derivative of transformer model architecture\n\nplease propose a change to Transformer Architecture (Vaswani et. al.) that would improve the models ability to reason\n \n> One possible way to enhance the reasoning capabilities of\n> transformer-based models is by incorporating memory mechanisms into their\n> design, such as those used in Recurrent Neural Networks (RNNs) or more\n> recently in Transformer-XL (Dai et al.) and Long-Range Transformers\n> (Akbari et al.). These architectures introduce a memory module that can\n> store information from previous steps or layers, which allows the model to\n> better reason about long-term dependencies in the input sequence.\n> \n> Another promising approach is to incorporate commonsense knowledge into\n> transformer-based models using external knowledge bases like ConceptNet\n> (Speer et al.) or WordNet (Miller et al.). By injecting such prior\n> knowledge into the model's hidden representations, it can help reason\n> about previously unseen input sequences more effectively. This approach\n> has been explored in recent works like K-BERT (Wang et al.) and GEART\n> (Beveridge et al.), which demonstrate promising results on various NLP\n> tasks that require commonsense reasoning abilities.\n> \n> In conclusion, enhancing the reasoning capabilities of transformer-based\n> models can be achieved by either incorporating memory mechanisms or\n> integrating external knowledge sources into their design. These approaches\n> enable the model to better understand long-term dependencies and make more\n> informed decisions based on previously learned information, ultimately\n> leading to improved performance on complex NLP tasks that require deeper\n> levels of understanding and reasoning.\n \nlet's drill down into the \"commonsense knowledge\" idea. exactly what kind of change to attention heads is required?\n\n> Incorporating commonsense knowledge into transformer-based models often\n> involves modifying their self-attention mechanism, specifically by\n> integrating external knowledge sources like ConceptNet or WordNet into the\n> attention weight calculations. Here are two possible ways to achieve this:\n> \n> 1. Attention with Knowledge (Xiong et al.): In this approach, a\n> separate knowledge embedding is learned for each token in the input\n> sequence using pre-trained knowledge embeddings from an external knowledge\n> base like ConceptNet. When computing attention weights between two tokens\n> i and j, both their word representations and corresponding knowledge\n> embeddings are concatenated before passing them through a multi-layer\n> perceptron (MLP) to obtain a scalar value representing the level of\n> relatedness between these two tokens based on both linguistic and\n> commonsense information.\n> \n> 2. Attention over Knowledge (Wang et al.): In contrast to the previous approach, in\n> this method, instead of directly incorporating external knowledge into attention weight\n> calculations, an additional memory module is introduced that stores learned relations\n> between input tokens and concepts from an external knowledge base like ConceptNet or\n> WordNet. When computing attention weights between two tokens i and j, their word\n> representations are first passed through a separate MLP to obtain query and key\n> vectors, which are then used to perform Key-Value (KV) attention over the memory module\n> to retrieve relevant conceptual information. The attention-weighted results from both\n> linguistic and knowledge sources are concatenated before passing them through another\n> MLP to produce final attention weights.\n> \n> In conclusion, integrating commonsense knowledge into transformer-based models can be\n> achieved by either directly incorporating external knowledge into the attention weight\n> calculations or performing KV attention over a separate memory module storing learned\n> relations between input tokens and concepts from an external knowledge base. Both\n> approaches aim to enrich the model's understanding of input sequences by taking into\n> account both linguistic and commonsense information, leading to improved performance on\n> NLP tasks that require deeper levels of reasoning and understanding.\n\nwhich of these two approaches would be more computationally efficient? I want an architecture that's fastest to train and fastest inference possible.\n\n> Comparing the two approaches, \"Attention with Knowledge\" (Xiong et al.) might be\n> slightly more computationally efficient than \"Attention over Knowledge\" (Wang et al.)\n> due to its simpler architectural design. In particular, the former approach simply\n> concatenates word and knowledge embeddings before computing attention weights using a\n> single MLP, whereas the latter approach involves an additional KV attention operation\n> over a separate memory module in addition to two separate MLPs for query/key vector\n> generation and final attention weight computation.\n> \n> However, this difference in computational efficiency may not be significant in\n> practice, especially given recent advances in hardware accelerators like Tensor\n> Processing Units (TPUs) that are specifically designed to handle large-scale\n> transformer-based models efficiently. Ultimately, the choice between these two\n> approaches should be guided more by their respective trade-offs in terms of model\n> complexity, interpretability, and overall performance on your specific NLP task rather\n> than solely based on computational efficiency considerations alone.\n\nand which would have a greater improvement on the model's reasoning ability\n\n> It is difficult to definitively say which approach would lead to a greater improvement\n> in the model's reasoning ability without more context about the specific NLP task at\n> hand and the nature of the commonsense knowledge being incorporated. Both \"Attention\n> with Knowledge\" (Xiong et al.) and \"Attention over Knowledge\" (Wang et al.) have\n> demonstrated promising results on various benchmark datasets that require some form of\n> commonsense reasoning, suggesting that either approach could potentially lead to\n> improvements in the model's ability to reason about input sequences more effectively.\n> \n> That being said, \"Attention over Knowledge\" might offer slightly more flexibility and\n> modularity since it decouples the incorporation of external knowledge from the\n> attention weight calculation itself by introducing an additional memory module that can\n> be fine-tuned independently of other model components. This could potentially make it\n> easier to adapt the model to different types or sources of commonsense knowledge\n> without requiring significant architectural changes, although this added flexibility\n> comes at the expense of increased model complexity and potential computational\n> overheads as discussed earlier.\n> \n> Ultimately, the choice between these two approaches should be guided by a careful\n> evaluation on your specific NLP task using a held-out validation set or through\n> ablation studies where possible to determine which approach leads to the most\n> significant improvements in terms of both model performance and interpretability while\n> still maintaining reasonable computational efficiency constraints if applicable.\n \nlet's say I'm going with \"Attention with Knowledge\" (Xiong et al.) Can you please write the code in python to implement the new attention heads\n\n> Certainly! Here is a simplified example of how you could modify the self-attention\n> mechanism in a transformer-based model like BERT or RoBERTa to incorporate external\n> commonsense knowledge using the \"Attention with Knowledge\" (Xiong et al.) approach:\n> \n> \n> \n> Here's how you could replace the original attention mechanism in a transformer layer with this new implementation:\n> \n> \n> \n> Note that this code assumes you have pre-computed external commonsense knowledge\n> embeddings for each input token and added them to your vocabulary using the new [KNW]\n> tokenizer token, which should be incorporated into your downstream NLP task\n> accordingly. Additionally, hyperparameters like 'URL.knowledge_proportion'\n> control the relative importance of linguistic vs commonsense information during\n> attention weight calculation and may require fine-tuning on your specific task and\n> commonsense knowledge source.\n> \n \nok can you please show the code to generate the pre-computed external commonsense knowledge embeddings for each input token and add them to my vocabulary using the new [KNW] tokenizer token?\n \n> Sure! Here's an example of how you could pre-process your external commonsense\n> knowledge source like ConceptNet or WordNet into a pickle file containing\n> word-to-concept and concept-to-embedding mappings, which can then be loaded during\n> model training to compute attention weights as shown in the previous example:\n> \n> \n> Then, during model training, you can load these pre-computed mappings and embeddings as follows:\n> \n> \n> \n> \n> Now, during data pre-processing for each input example 'X' (assuming it's a PyTorch\n> tensor of shape [batch_size, sequence_length]) and corresponding mask 'Y' (a binary\n> PyTorch tensor of shape [batch_size, sequence_lingth] with 1s for valid tokens and 0s\n> for padding):\n> \n> \n> \n> Here we replace all non-alphanumeric tokens with the '[UNK]' token and insert a new\n> sequence of zeros at the end of each input sequence to indicate sequence boundaries for\n> models like BERT that use a sequence-to-sequence masking strategy. You may need to\n> modify this pre-processing code according to your specific downstream NLP task's input\n> format and pre-processing requirements.\n> \n> \n> Finally, during model forward-passing, you can access the corresponding commonsense\n> embeddings for each input token using its new [KNW] tokenized index as follows:\n> \n> \n> \n> Now, the modified input PyTorch tensor 'X_with_knowledge' contains both linguistic and\n> commonsense knowledge embeddings concatenated along the embedding dimension, which can\n> be passed through your attention-based transformer model layers as shown in the\n> previous example to compute attention weights that jointly reason over both types of\n> information during self-attention computation. Note that this code assumes you only\n> want to incorporate commonsense knowledge for a subset of input tokens marked by the\n> new [KNW] token; you may need to modify it according to your specific downstream NLP\n> task's requirements and desired level of commonsense integration.\n\nTransformer architecture (Vaswani et al.) does poorly in theory of mind. What change to the transformer architecture can you suggest that would enable this critical skill?\n\n> 1. Attention over attention: As mentioned earlier, we can add a higher-level attention module that computes attention over the\n> existing self-attention scores in each attention head. This could involve an additional linear layer followed by another softmax\n> activation applied to the original attention scores before they are used as weights when computing the final output sequence. By\n> learning which attention heads should attend more or less to certain input tokens, this module could potentially mimic some aspects of\n> metacognitive processes involved in ToM reasoning such as focusing on salient cues or disregarding irrelevant distractions.\n>\n> 2. Counterfactual attention: Inspired by recent work on counterfactual learning in NLP tasks like textual entailment (e.g., Meng\n> et al., \"A counterfactual approach to answer factual questions,\" ACL 2017), we could also try introdu't a new type of attention head\n> within each self-attention layer that compares two alternative input representations instead of just one actual input sequence and its\n> own past context as in standard transformer models. Specifically, for every timestep t, rather than computing attention weights\n> a_t^j(k) = softmax(Q_t^j(k)^T * K_t^j(k)) where Q, K, V are query, key, value matrices extracted from the current input sequence X_t\n> and j indexes different self-attention heads, we could instead compute two sets of attention weights a_t^j(k, l) = softmax(Q_t^j(k)^T * (K_t^j(k) + alpha * (K_t^j(l) - K_t^j(k)))) for all pairs of timesteps k != l and some hyperparameter alpha > 0. This would\n> encourage each head to pay more or less attention to certain input tokens depending on whether they are likely counterfactual\n> alternatives given the rest of the sentence context, which could potentially help improve its ability to reason about what might have\n> happened if different words had been used instead.\n> \n> 3. Incorporating commonsense knowledge: One way to incorporate commonsense knowledge into transformer models would be to pre-train\n> additional memory modules using large-scale external sources of factual information such as ConceptNet or WordNet, then merge their\n> outputs with the standard attention-based representations before feeding them through downstream layers for final prediction.\n> Alternatively, we could also try injecting hard-coded binary indicators into each self-attention layer's query matrices Q_t^j(k) to\n> indicate whether certain input tokens correspond to known commonsense concepts such as people, locations, actions etc., which could\n> serve as additional signals guiding the model's attention allocation decisions during training.\n>\n> 4. Incorporating game-theoretic reasoning: While incorporating full-fledged game-theoretic elements like Markov decision processes\n> or stochastic games into transformer architectures may still be computationally prohibitive due to their high dimensionality and\n> non-Markovian nature, we could potentially take baby steps towards this direction by introducing simple binary masks over some pairs\n> of attention weights a_t^j(k, l) in item 2 above based on whether they correspond to tokens that are likely adversaries or allies\n> according to some predefined heuristics (e.g., two consecutive nouns within a small window size are considered potential collaborators\n> while two verbs separated by more than N words apart are seen as competitors). This could encourage the model to learn more\n> sophisticated dependencies between input tokens that go beyond simple co-occurrence patterns and instead capture more nuanced\n> relational structures indicative of underlying strategic interactions between different entities in the text. \n\nWrite a math paper\n\n> Please invent a new idea in the area of mathematics, that combines two or more papers into a new idea that has not yet been published to your knowledge\n> ## Integrability and Computability of Continuous Functions on Infinite Domains: A Category-Theoretical Approach\n> ### Abstract\n> In this paper, we develop a novel approach to the study of integrability and computability of continuous functions on infinite domains using category-theoretic \n> techniques. We combine ideas from descriptive set theory, which studies the hierarchy of sets in terms of their complexity, and functional analysis, which deals with the\n> properties of mappings between function spaces. Our main contribution is to introduce a new notion of \"categorical Lebesgue measurability\" that generalizes both Lebesgue\n> and Henstock-Kurtz integrability for functions on uncountable domains while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem. We also\n> establish connections between this new notion of categorical integrability and various notions of computability, leading to new results on the computability of certain \n> integral transforms such as the Laplace and Mellin transforms in the case of uncountable domains.\n> ### 1 Introduction\n> In this paper, we continue the line of research initiated by [1] and further developed in [2] and [3], which aims to bridge the gap between descriptive set theory and \n> functional analysis by employing category-theoretic techniques. In particular, our goal is to develop a new framework for studying the properties of continuous functions\n> on uncountable domains that combines ideas from both areas while retaining key desirable properties such as the Lebesgue Monotone Convergence Theorem (LMCT).\n> ### 2 Preliminaries\n> We assume familiarity with basic category-theoretical concepts such as limits, colimits, and adjoint functors [4]. We also make use of some notions from descriptive set \n> theory, such as the hierarchy of Borel sets [5] and the notion of Lebesgue measurability [6].\n> ### 3 Categorical Lebesgue Measure\n> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be a second countable complete metrizable (scc) space. We say that a function 'phi: X -> Y' is \n> \"categorically Lebesgue measurable\" if there exists a Borel measurable function 'nu: Y^N -> [0,1]' such that for every x in X and any open neighborhood U of 'phi(x)', we \n> have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}| > 0' for some 'n in N'. Here, |A| denotes the category (cardinality) of a set A, and '|B^N|' is defined as '|B|^N' for any set B \n> and natural number N.\n> ### 4 Categorical Lebesgue Integral\n> Let X be an uncountable set endowed with a topology 'mathcal{T}' and let Y be an scc space. Let 'phi: X -> Y' be a categorically Lebesgue measurable function, and let '|*|: \n> Y^N -> [0,+infinity]' be a \"categorical modulus of continuity\" in the sense that for any 'x in X' and any open neighborhood 'U' of 'phi(x)', there exists 'M_U > 0' such that '|{y \n> in U | |phi(y_n) - phi(y_{n-1})| > M_U}|\\ < epsilon' for some 'N = N_U,M_U > omega' and all 'epsilon > 0'. Then we define the \"categorical Lebesgue integral\" of phi over X \n> as: \n> \n> 'int_X^cat phi(x) dnu(x) = colim_{epsilon -> 0+} (2 - epsilon)^N sum_{y in Y^N, |{n in N | y_n not in U_n}|\\ < omega} nu(y^-|n-1, y_n)'.\n> ### 5 Applications to Computability\n> In this section, we establish connections between our new notion of categorical Lebesgue measurability and various notions of computability. Let X be an uncountable set \n> endowed with a topology 'mathcal{T}' and 'let Y = R'. We say that a function 'phi: X -> R' is \"categorically Lebesgue computable\" if there exists a Borel measurable function \n> nu: 'R^N -> [0,1]' such that for every 'x in X' and any open interval 'U' containing 'phi(x)', we have '|{y in U | nu(y^-|n-1, y_n) > phi(y_n)}|\\ > 0' for some 'n in N'.\n> ### 6 Conclusion\n> In this paper, we have introduced a new notion of \"categorical Lebesgue measurability\" for functions on uncountable domains that generalizes both Lebesgue and \n> Henstock-Kurtz integrability while retaining key desirable properties such as the LMCT. We also established connections between this new notion of categorical \n> integrability and various notions of computability, leading to new results on the computability of certain integral transforms in the case of uncountable domains. Future\n> directions for research include further exploring the relationship between our new notion of categorical Lebesgue measurability and other generalizations of Lebesgue and\n> Henstock-Kurtz integrability, as well as studying additional properties of our \"categorical Lebesgue integral\" such as its relationship to other notions of integration \n> for functions on uncountable domains.\n> ### References\n> - [1] A. Borel. Sur la correspondance entre les ensembles de points et les fonctions de variables reales, Compt. R. Acad. Sci. Paris 148 (1909), 71-74.\n> - [2] J. Bourbaki. Integration I: Chapters 1-6; Integration II: Chapter 7-9; Topological Vector Spaces, Addison-Wesley, Reading, MA, 1968.\n> - [3] W. H. L. Cheney. Introduction to Analysis, Brooks/Cole, Belmont, CA, 2005.\n> - [4] S. Mac Lane. Categories for the working mathematician, second edition, Graduate Texts in Mathematics No. 5, Springer-Verlag, New York-Heidelberg, 1971.\n> - [5] K. P. Komogorov and A. V. Ulam. On a generalized form of the problem of P. F. Borel, Comb. Math. 1 (1948), 83-86.\n> - [6] J. Dieudonné. Foundations of modern analysis, Academic Press, New York, 1960." ]

[ 146, 4, 4, 17, 69, 18, 10417 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2203.05482 #base_model-cognitivecomputations/dolphin-2.2-70b #base_model-WizardLM/WizardMath-70B-V1.0 #base_model-migtissera/SynthIA-70B-v1.2b #base_model-epfl-llm/meditron-70b #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Evals## Merge Details### Merge Method\n\nTheProfessor was merged using the linear merge method.### Models Merged\n\nThe following models were included in the merge:\n* cognitivecomputations/dolphin-2.2-70b\n* WizardLM/WizardMath-70B-V1.0\n* migtissera/SynthIA-70B-v1.2b\n* epfl-llm/meditron-70b### Configuration\n\nThe following YAML configuration was used to produce TheProfessor:" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "starling", "conversational", "en", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[ "## Delcos Starling 11B alpha\n\nExLlamaV2 8.0 bpw quants of URL" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #starling #conversational #en #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Delcos Starling 11B alpha\n\nExLlamaV2 8.0 bpw quants of URL" ]

[ 69, 22 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #starling #conversational #en #license-cc-by-nc-nd-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Delcos Starling 11B alpha\n\nExLlamaV2 8.0 bpw quants of URL" ]

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[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "Yi", "exllama", "exllamav2", "exl2", "en", "arxiv:2311.03099", "arxiv:2306.01708", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "2311.03099", "2306.01708" ]

[ "en" ]

[ "# RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.", "## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL", "## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.", "## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Self Promotion\n\nI'm part of a AI startup called Holocene AI!\n\nWe're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.\n\nContact me at: URL@URL\n\nI also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.", "## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL", "## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.", "## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Self Promotion\n\nI'm part of a AI startup called Holocene AI!\n\nWe're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.\n\nContact me at: URL@URL\n\nI also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL" ]

[ 98, 478, 35, 284, 113, 4, 49, 169, 17, 163 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "passage: # RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.## Merge Details### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base." ]

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[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-2-7b-chat-hf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]

[ 84, 144, 4, 33 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]

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[ "transformers", "safetensors", "mbart", "text2text-generation", "uk", "dataset:skypro1111/ubertext-2-news-verbalized", "arxiv:2008.00401", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2008.00401" ]

[ "uk" ]

[ "# Model Card for mbart-large-50-verbalization", "## Model Description\n'mbart-large-50-verbalization' is a fine-tuned version of the facebook/mbart-large-50 model, specifically designed for the task of verbalizing Ukrainian text to prepare it for Text-to-Speech (TTS) systems. This model aims to transform structured data like numbers and dates into their fully expanded textual representations in Ukrainian.", "## Architecture\nThis model is based on the facebook/mbart-large-50 architecture, renowned for its effectiveness in translation and text generation tasks across numerous languages.", "## Training Data\nThe model was fine-tuned on a subset of 96,780 sentences from the Ubertext dataset, focusing on news content. The verbalized equivalents were created using Google Gemini Pro, providing a rich basis for learning text transformation tasks.\nDataset skypro1111/ubertext-2-news-verbalized", "## Training Procedure\nThe model underwent 70,000 training steps, which is almost 2 epochs, with further training the results degraded.", "## Usage", "## Performance\nEvaluation metrics were not explicitly used for this model. Its performance is primarily demonstrated through its application in enhancing the naturalness of TTS outputs.", "## Limitations and Ethical Considerations\nUsers should be aware of the model's potential limitations in understanding highly nuanced or domain-specific content. Ethical considerations, including fairness and bias, are also crucial when deploying this model in real-world applications.\n\nUbertext 2.0\n\nmBart-large-50", "## License\nThis model is released under the MIT License, in line with the base mbart-large-50 model." ]

[ "TAGS\n#transformers #safetensors #mbart #text2text-generation #uk #dataset-skypro1111/ubertext-2-news-verbalized #arxiv-2008.00401 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for mbart-large-50-verbalization", "## Model Description\n'mbart-large-50-verbalization' is a fine-tuned version of the facebook/mbart-large-50 model, specifically designed for the task of verbalizing Ukrainian text to prepare it for Text-to-Speech (TTS) systems. This model aims to transform structured data like numbers and dates into their fully expanded textual representations in Ukrainian.", "## Architecture\nThis model is based on the facebook/mbart-large-50 architecture, renowned for its effectiveness in translation and text generation tasks across numerous languages.", "## Training Data\nThe model was fine-tuned on a subset of 96,780 sentences from the Ubertext dataset, focusing on news content. The verbalized equivalents were created using Google Gemini Pro, providing a rich basis for learning text transformation tasks.\nDataset skypro1111/ubertext-2-news-verbalized", "## Training Procedure\nThe model underwent 70,000 training steps, which is almost 2 epochs, with further training the results degraded.", "## Usage", "## Performance\nEvaluation metrics were not explicitly used for this model. Its performance is primarily demonstrated through its application in enhancing the naturalness of TTS outputs.", "## Limitations and Ethical Considerations\nUsers should be aware of the model's potential limitations in understanding highly nuanced or domain-specific content. Ethical considerations, including fairness and bias, are also crucial when deploying this model in real-world applications.\n\nUbertext 2.0\n\nmBart-large-50", "## License\nThis model is released under the MIT License, in line with the base mbart-large-50 model." ]

[ 73, 14, 87, 38, 73, 31, 3, 39, 71, 24 ]

[ "passage: TAGS\n#transformers #safetensors #mbart #text2text-generation #uk #dataset-skypro1111/ubertext-2-news-verbalized #arxiv-2008.00401 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for mbart-large-50-verbalization## Model Description\n'mbart-large-50-verbalization' is a fine-tuned version of the facebook/mbart-large-50 model, specifically designed for the task of verbalizing Ukrainian text to prepare it for Text-to-Speech (TTS) systems. This model aims to transform structured data like numbers and dates into their fully expanded textual representations in Ukrainian.## Architecture\nThis model is based on the facebook/mbart-large-50 architecture, renowned for its effectiveness in translation and text generation tasks across numerous languages.## Training Data\nThe model was fine-tuned on a subset of 96,780 sentences from the Ubertext dataset, focusing on news content. The verbalized equivalents were created using Google Gemini Pro, providing a rich basis for learning text transformation tasks.\nDataset skypro1111/ubertext-2-news-verbalized## Training Procedure\nThe model underwent 70,000 training steps, which is almost 2 epochs, with further training the results degraded.## Usage## Performance\nEvaluation metrics were not explicitly used for this model. Its performance is primarily demonstrated through its application in enhancing the naturalness of TTS outputs.## Limitations and Ethical Considerations\nUsers should be aware of the model's potential limitations in understanding highly nuanced or domain-specific content. Ethical considerations, including fairness and bias, are also crucial when deploying this model in real-world applications.\n\nUbertext 2.0\n\nmBart-large-50## License\nThis model is released under the MIT License, in line with the base mbart-large-50 model." ]

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[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 59, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

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[ "passage: TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-2-7b-chat-hf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 150", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 150", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]

[ 84, 144, 4, 33 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 150### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]

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[ "transformers", "pytorch", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: hannahbernstein\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #pytorch #mistral #text-generation #text-generation-inference #unsloth #trl #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: hannahbernstein\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ 81, 80 ]

[ "passage: TAGS\n#transformers #pytorch #mistral #text-generation #text-generation-inference #unsloth #trl #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: hannahbernstein\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

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[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# models\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# models\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]

[ 72, 23, 6, 12, 8, 3, 103, 4, 33 ]

[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# models\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2.0\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]

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[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert-base-uncased", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

[ 57, 98, 4, 33 ]

[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

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[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# emptyset\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.28.0.dev0\n- Pytorch 2.2.0+cu121\n- Tokenizers 0.13.3" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# emptyset\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.28.0.dev0\n- Pytorch 2.2.0+cu121\n- Tokenizers 0.13.3" ]

[ 59, 24, 6, 12, 8, 3, 90, 32 ]

[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# emptyset\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Framework versions\n\n- Transformers 4.28.0.dev0\n- Pytorch 2.2.0+cu121\n- Tokenizers 0.13.3" ]

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[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[ "1904.06472", "2102.07033", "2104.08727", "1704.05179", "1810.09305" ]

[ "en" ]

[ "### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.", "### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.\nWe then apply the cross entropy loss by comparing with true pairs.", "#### Hyper parameters\n\n\nWe trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).\nWe use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with\na 2e-5 learning rate. The full training script is accessible in this current repository: 'train\\_script.py'.", "#### Training data\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.\nWe sampled each dataset given a weighted probability which configuration is detailed in the 'data\\_config.json' file." ]

[ "TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #dataset-multi_nli #dataset-wikihow #dataset-natural_questions #dataset-trivia_qa #dataset-embedding-data/sentence-compression #dataset-embedding-data/flickr30k-captions #dataset-embedding-data/altlex #dataset-embedding-data/simple-wiki #dataset-embedding-data/QQP #dataset-embedding-data/SPECTER #dataset-embedding-data/PAQ_pairs #dataset-embedding-data/WikiAnswers #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.", "### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.\nWe then apply the cross entropy loss by comparing with true pairs.", "#### Hyper parameters\n\n\nWe trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).\nWe use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with\na 2e-5 learning rate. The full training script is accessible in this current repository: 'train\\_script.py'.", "#### Training data\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.\nWe sampled each dataset given a weighted probability which configuration is detailed in the 'data\\_config.json' file." ]

[ 310, 48, 56, 99, 66 ]

[ "passage: TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #dataset-multi_nli #dataset-wikihow #dataset-natural_questions #dataset-trivia_qa #dataset-embedding-data/sentence-compression #dataset-embedding-data/flickr30k-captions #dataset-embedding-data/altlex #dataset-embedding-data/simple-wiki #dataset-embedding-data/QQP #dataset-embedding-data/SPECTER #dataset-embedding-data/PAQ_pairs #dataset-embedding-data/WikiAnswers #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us \n### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.\nWe then apply the cross entropy loss by comparing with true pairs." ]

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[ "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Llama-2-7b-chat-hf", "region:us" ]

[]

[]

[ "# alpaca-gpt4-conversation-opt-350m\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]

[ "TAGS\n#safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n", "# alpaca-gpt4-conversation-opt-350m\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]

[ 50, 48, 6, 12, 8, 3, 90, 33 ]

[ "passage: TAGS\n#safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n# alpaca-gpt4-conversation-opt-350m\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the generator dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]

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[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "4-bit", "region:us" ]

[]

[]

[ "# zephyr-7b-dpo-qlora\n\nThis model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-qlora on the HuggingFaceH4/ultrafeedback_binarized dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6656\n- Rewards/chosen: 0.0388\n- Rewards/rejected: -0.0264\n- Rewards/accuracies: 0.6900\n- Rewards/margins: 0.0652\n- Logps/rejected: -237.8513\n- Logps/chosen: -255.6933\n- Logits/rejected: -2.8702\n- Logits/chosen: -2.8928", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-06\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 1024\n- total_train_batch_size: 4096\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.2\n- Pytorch 2.1.2\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "TAGS\n#peft #tensorboard #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #dpo #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #4-bit #region-us \n", "# zephyr-7b-dpo-qlora\n\nThis model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-qlora on the HuggingFaceH4/ultrafeedback_binarized dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6656\n- Rewards/chosen: 0.0388\n- Rewards/rejected: -0.0264\n- Rewards/accuracies: 0.6900\n- Rewards/margins: 0.0652\n- Logps/rejected: -237.8513\n- Logps/chosen: -255.6933\n- Logits/rejected: -2.8702\n- Logits/chosen: -2.8928", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-06\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 1024\n- total_train_batch_size: 4096\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.2\n- Pytorch 2.1.2\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ 85, 156, 6, 12, 8, 3, 159, 4, 36 ]

[ "passage: TAGS\n#peft #tensorboard #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #dpo #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #4-bit #region-us \n# zephyr-7b-dpo-qlora\n\nThis model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-qlora on the HuggingFaceH4/ultrafeedback_binarized dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6656\n- Rewards/chosen: 0.0388\n- Rewards/rejected: -0.0264\n- Rewards/accuracies: 0.6900\n- Rewards/margins: 0.0652\n- Logps/rejected: -237.8513\n- Logps/chosen: -255.6933\n- Logits/rejected: -2.8702\n- Logits/chosen: -2.8928## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-06\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 1024\n- total_train_batch_size: 4096\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.2\n- Pytorch 2.1.2\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

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[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "Yi", "exllama", "exllamav2", "exl2", "en", "arxiv:2311.03099", "arxiv:2306.01708", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2311.03099", "2306.01708" ]

[ "en" ]

[ "# RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.", "## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL", "## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.", "## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Self Promotion\n\nI'm part of a AI startup called Holocene AI!\n\nWe're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.\n\nContact me at: URL@URL\n\nI also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.", "## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL", "## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.", "## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Self Promotion\n\nI'm part of a AI startup called Holocene AI!\n\nWe're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.\n\nContact me at: URL@URL\n\nI also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL" ]

[ 95, 478, 35, 284, 113, 4, 49, 169, 17, 163 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "passage: # RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.## Merge Details### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base." ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "bardsai/jaskier-7b-dpo-v3.3", "base_model:mlabonne/OmniBeagle-7B", "base_model:flemmingmiguel/MBX-7B-v3", "base_model:bardsai/jaskier-7b-dpo-v3.3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# NeuralTrix-v3-bf16\n\nNeuralTrix-v3-bf16 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* bardsai/jaskier-7b-dpo-v3.3", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #bardsai/jaskier-7b-dpo-v3.3 #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-bardsai/jaskier-7b-dpo-v3.3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# NeuralTrix-v3-bf16\n\nNeuralTrix-v3-bf16 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* bardsai/jaskier-7b-dpo-v3.3", "## Configuration", "## Usage" ]

[ 152, 74, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #bardsai/jaskier-7b-dpo-v3.3 #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-bardsai/jaskier-7b-dpo-v3.3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralTrix-v3-bf16\n\nNeuralTrix-v3-bf16 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* bardsai/jaskier-7b-dpo-v3.3## Configuration## Usage" ]

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[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 46, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 46, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 26, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 26, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "pytorch", "bert", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# Korean Character BERT Model (small)\n\nWelcome to the repository of the Korean Character (syllable-level) BERT Model, a compact and efficient transformer-based model designed specifically for Korean language processing tasks. This model takes a unique approach by tokenizing text at the syllable level, catering to the linguistic characteristics of the Korean language.", "## Features\n\n- Vocabulary Size: The model utilizes a vocabulary of 7,477 tokens, focusing on Korean syllables. This streamlined vocabulary size allows for efficient processing while maintaining the ability to capture the nuances of the Korean language.\n- Transformer Encoder Layers: It employs a simplified architecture with only 3 transformer encoder layers. This design choice strikes a balance between model complexity and computational efficiency, making it suitable for a wide range of applications, from mobile devices to server environments.\n- License: This model is open-sourced under the Apache License 2.0, allowing for both academic and commercial use while ensuring that contributions and improvements are shared within the community.", "## Getting Started", "## Fine-tuning example\n\n- Named entity recognition", "## Contact\n\nFor any questions or inquiries, please reach out to me at URL@URL \nI'm always happy to discuss the model, potential collaborations, or any other inquiries related to this project." ]

[ "TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Korean Character BERT Model (small)\n\nWelcome to the repository of the Korean Character (syllable-level) BERT Model, a compact and efficient transformer-based model designed specifically for Korean language processing tasks. This model takes a unique approach by tokenizing text at the syllable level, catering to the linguistic characteristics of the Korean language.", "## Features\n\n- Vocabulary Size: The model utilizes a vocabulary of 7,477 tokens, focusing on Korean syllables. This streamlined vocabulary size allows for efficient processing while maintaining the ability to capture the nuances of the Korean language.\n- Transformer Encoder Layers: It employs a simplified architecture with only 3 transformer encoder layers. This design choice strikes a balance between model complexity and computational efficiency, making it suitable for a wide range of applications, from mobile devices to server environments.\n- License: This model is open-sourced under the Apache License 2.0, allowing for both academic and commercial use while ensuring that contributions and improvements are shared within the community.", "## Getting Started", "## Fine-tuning example\n\n- Named entity recognition", "## Contact\n\nFor any questions or inquiries, please reach out to me at URL@URL \nI'm always happy to discuss the model, potential collaborations, or any other inquiries related to this project." ]

[ 44, 85, 163, 4, 12, 44 ]

[ "passage: TAGS\n#transformers #pytorch #bert #fill-mask #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Korean Character BERT Model (small)\n\nWelcome to the repository of the Korean Character (syllable-level) BERT Model, a compact and efficient transformer-based model designed specifically for Korean language processing tasks. This model takes a unique approach by tokenizing text at the syllable level, catering to the linguistic characteristics of the Korean language.## Features\n\n- Vocabulary Size: The model utilizes a vocabulary of 7,477 tokens, focusing on Korean syllables. This streamlined vocabulary size allows for efficient processing while maintaining the ability to capture the nuances of the Korean language.\n- Transformer Encoder Layers: It employs a simplified architecture with only 3 transformer encoder layers. This design choice strikes a balance between model complexity and computational efficiency, making it suitable for a wide range of applications, from mobile devices to server environments.\n- License: This model is open-sourced under the Apache License 2.0, allowing for both academic and commercial use while ensuring that contributions and improvements are shared within the community.## Getting Started## Fine-tuning example\n\n- Named entity recognition## Contact\n\nFor any questions or inquiries, please reach out to me at URL@URL \nI'm always happy to discuss the model, potential collaborations, or any other inquiries related to this project." ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[ "# Model Card for Model (OpenHermes_fourier_merge_v1)\n\n \n!image/png" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model (OpenHermes_fourier_merge_v1)\n\n \n!image/png" ]

[ 64, 22 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model (OpenHermes_fourier_merge_v1)\n\n \n!image/png" ]

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[ "unity-sentis", "onnx", "object-detection", "license:apache-2.0", "region:us" ]

[]

[]

[ "# Blaze Palm palm detector in Unity Sentis Format (Experimental)\nThis is the Blaze Palm model, part of the MediaPipe hand detection formatted to work in Unity Sentis 2023", "## How to Use\n* Create a new scene in Unity 2023\n* Put the hand_detection_lite.sentis file in the Assets/StreamingAssets folder\n* Put a video in the Assets/StreamingAssets folder and set 'videoName' variable to the video name\n* Create a RawImage and place it in your scene. Link to this image in the 'previewUI' field.\n* Attach a sprite for the bounding box image to the 'boundingBoxTexture' field", "## Preview\nWhen you get it working you should see something like this:\n\n!preview", "## Information\nThis model may have some accuracy issues.", "## Unity Sentis\nSentis is the inference engine for Unity 2023. More information can be found here" ]

[ "TAGS\n#unity-sentis #onnx #object-detection #license-apache-2.0 #region-us \n", "# Blaze Palm palm detector in Unity Sentis Format (Experimental)\nThis is the Blaze Palm model, part of the MediaPipe hand detection formatted to work in Unity Sentis 2023", "## How to Use\n* Create a new scene in Unity 2023\n* Put the hand_detection_lite.sentis file in the Assets/StreamingAssets folder\n* Put a video in the Assets/StreamingAssets folder and set 'videoName' variable to the video name\n* Create a RawImage and place it in your scene. Link to this image in the 'previewUI' field.\n* Attach a sprite for the bounding box image to the 'boundingBoxTexture' field", "## Preview\nWhen you get it working you should see something like this:\n\n!preview", "## Information\nThis model may have some accuracy issues.", "## Unity Sentis\nSentis is the inference engine for Unity 2023. More information can be found here" ]

[ 29, 43, 111, 18, 12, 21 ]

[ "passage: TAGS\n#unity-sentis #onnx #object-detection #license-apache-2.0 #region-us \n# Blaze Palm palm detector in Unity Sentis Format (Experimental)\nThis is the Blaze Palm model, part of the MediaPipe hand detection formatted to work in Unity Sentis 2023## How to Use\n* Create a new scene in Unity 2023\n* Put the hand_detection_lite.sentis file in the Assets/StreamingAssets folder\n* Put a video in the Assets/StreamingAssets folder and set 'videoName' variable to the video name\n* Create a RawImage and place it in your scene. Link to this image in the 'previewUI' field.\n* Attach a sprite for the bounding box image to the 'boundingBoxTexture' field## Preview\nWhen you get it working you should see something like this:\n\n!preview## Information\nThis model may have some accuracy issues.## Unity Sentis\nSentis is the inference engine for Unity 2023. More information can be found here" ]

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[ "transformers", "pytorch", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b0", "license:other", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.2.0+cpu\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]

[ "TAGS\n#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b0 #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.2.0+cpu\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]

[ 57, 97, 4, 33 ]

[ "passage: TAGS\n#transformers #pytorch #segformer #vision #image-segmentation #generated_from_trainer #base_model-nvidia/mit-b0 #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.2.0+cpu\n* Datasets 2.17.0\n* Tokenizers 0.13.3" ]

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[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:BAAI/bge-base-en-v1.5", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 15", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-BAAI/bge-base-en-v1.5 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 15", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

[ 70, 144, 4, 33 ]

[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-BAAI/bge-base-en-v1.5 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 15### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

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[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "license:apache-2.0", "region:us" ]

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[ "# Dieter Rams Style\n\n<Gallery />", "## Model description \n\nDieter Rams is a German industrial designer who is widely regarded as one of the most influential designers of the 20th century and, as the Guardian put it, &quot;the man who all but invented consumer product design as we know it today.&quot; The product and furniture designs he has produced since the 1950s are so powerful that upon first seeing them people immediately realize how much design around us has been influenced, or just plain stolen, from Rams.\n\nThere are a few things you can do with this LoRA:\n\n- Create imaginary minimalist, futuristic products, appliances, and furniture that have a classic and timeless look.\n- Add a classic minimalist style to the background rooms and objects in your creations.\n- Add realism to your science fiction creations by incorporating Rams timeless design principles, similar to the way that the original Star Wars trilogy did.\n\nUsage is simple: add the LoRA at strength 1 and the words &quot;designed by Dieter Rams&quot;.", "## Trigger words\n\nYou should use 'designed by Dieter Rams' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]

[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #region-us \n", "# Dieter Rams Style\n\n<Gallery />", "## Model description \n\nDieter Rams is a German industrial designer who is widely regarded as one of the most influential designers of the 20th century and, as the Guardian put it, &quot;the man who all but invented consumer product design as we know it today.&quot; The product and furniture designs he has produced since the 1950s are so powerful that upon first seeing them people immediately realize how much design around us has been influenced, or just plain stolen, from Rams.\n\nThere are a few things you can do with this LoRA:\n\n- Create imaginary minimalist, futuristic products, appliances, and furniture that have a classic and timeless look.\n- Add a classic minimalist style to the background rooms and objects in your creations.\n- Add realism to your science fiction creations by incorporating Rams timeless design principles, similar to the way that the original Star Wars trilogy did.\n\nUsage is simple: add the LoRA at strength 1 and the words &quot;designed by Dieter Rams&quot;.", "## Trigger words\n\nYou should use 'designed by Dieter Rams' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]

[ 62, 11, 226, 22, 28 ]

[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #region-us \n# Dieter Rams Style\n\n<Gallery />## Model description \n\nDieter Rams is a German industrial designer who is widely regarded as one of the most influential designers of the 20th century and, as the Guardian put it, &quot;the man who all but invented consumer product design as we know it today.&quot; The product and furniture designs he has produced since the 1950s are so powerful that upon first seeing them people immediately realize how much design around us has been influenced, or just plain stolen, from Rams.\n\nThere are a few things you can do with this LoRA:\n\n- Create imaginary minimalist, futuristic products, appliances, and furniture that have a classic and timeless look.\n- Add a classic minimalist style to the background rooms and objects in your creations.\n- Add realism to your science fiction creations by incorporating Rams timeless design principles, similar to the way that the original Star Wars trilogy did.\n\nUsage is simple: add the LoRA at strength 1 and the words &quot;designed by Dieter Rams&quot;.## Trigger words\n\nYou should use 'designed by Dieter Rams' to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]

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[ "gguf", "not-for-all-audiences", "en", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#gguf #not-for-all-audiences #en #license-cc-by-nc-4.0 #region-us \n" ]

[ 31 ]

[ "passage: TAGS\n#gguf #not-for-all-audiences #en #license-cc-by-nc-4.0 #region-us \n" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mistralai/Mistral-7B-v0.1", "base_model:mistralai/Mistral-7B-v0.1", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# mistral-7b-selfmerge-0-32-8-24-8-32\n\nmistral-7b-selfmerge-0-32-8-24-8-32 is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-v0.1\n* mistralai/Mistral-7B-v0.1\n* mistralai/Mistral-7B-v0.1", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mistralai/Mistral-7B-v0.1 #base_model-mistralai/Mistral-7B-v0.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# mistral-7b-selfmerge-0-32-8-24-8-32\n\nmistral-7b-selfmerge-0-32-8-24-8-32 is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-v0.1\n* mistralai/Mistral-7B-v0.1\n* mistralai/Mistral-7B-v0.1", "## Configuration", "## Usage" ]

[ 88, 79, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mistralai/Mistral-7B-v0.1 #base_model-mistralai/Mistral-7B-v0.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mistral-7b-selfmerge-0-32-8-24-8-32\n\nmistral-7b-selfmerge-0-32-8-24-8-32 is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-v0.1\n* mistralai/Mistral-7B-v0.1\n* mistralai/Mistral-7B-v0.1## Configuration## Usage" ]

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[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mlabonne/OmniBeagle-7B", "CultriX/NeuralTrix-7B-dpo", "CultriX/NeuralTrix-V2", "base_model:mlabonne/OmniBeagle-7B", "base_model:CultriX/NeuralTrix-7B-dpo", "base_model:CultriX/NeuralTrix-V2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# NeuralTrix-v4-bf16\n\nNeuralTrix-v4-bf16 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* CultriX/NeuralTrix-7B-dpo\n* CultriX/NeuralTrix-V2", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #CultriX/NeuralTrix-7B-dpo #CultriX/NeuralTrix-V2 #base_model-mlabonne/OmniBeagle-7B #base_model-CultriX/NeuralTrix-7B-dpo #base_model-CultriX/NeuralTrix-V2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# NeuralTrix-v4-bf16\n\nNeuralTrix-v4-bf16 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* CultriX/NeuralTrix-7B-dpo\n* CultriX/NeuralTrix-V2", "## Configuration", "## Usage" ]

[ 150, 71, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #CultriX/NeuralTrix-7B-dpo #CultriX/NeuralTrix-V2 #base_model-mlabonne/OmniBeagle-7B #base_model-CultriX/NeuralTrix-7B-dpo #base_model-CultriX/NeuralTrix-V2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralTrix-v4-bf16\n\nNeuralTrix-v4-bf16 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* CultriX/NeuralTrix-7B-dpo\n* CultriX/NeuralTrix-V2## Configuration## Usage" ]

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[ "transformers", "pytorch", "safetensors", "qwen2", "text-generation", "axolotl", "conversational", "tr", "dataset:TFLai/Turkish-Alpaca", "base_model:Qwen/Qwen1.5-7B-Chat", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "tr" ]

[ "# Qwen1.5-7B Turkish LoRA\n!image/png\n\nOrijinal model: Qwen1.5-7B-chat", "## Açıklama\nBu model, Alibaba Cloud tarafından geliştirilen Qwen1.5-7B-chat modelinin, TFLai/Turkish-Alpaca veri seti ve LoRA tekniği kullanılarak ince ayar çekilmiş versiyonudur.", "## Örnek diyalog \nSistem mesajı: Sen yardımsever bir asistansın. \nİstem: DNA'nın ne olduğunu kısaca açıkla. \nYanıt: DNA, Dünya'da yaşam için temel olan bir molekül türüdür ve genetik bilginin kodlaması için kullanılır. Genetik bilgiyi taşıyan çift sarmallı bir molekül olarak düşünülebilir. Her canlı organizmanın DNA'sı farklıdır, ancak her ikisi de aynı talimatları içeren benzersiz bir kimlik kodu içerir. DNA ayrıca hücrelerdeki protein üretimini yönlendiren en önemli moleküldür.", "## Kullanım \nModeli aşağıdaki düğmeye tıklayarak Google Colab'de çalıştırabilirsiniz.\n![image/svg](URL\n\n---" ]

[ "TAGS\n#transformers #pytorch #safetensors #qwen2 #text-generation #axolotl #conversational #tr #dataset-TFLai/Turkish-Alpaca #base_model-Qwen/Qwen1.5-7B-Chat #license-other #autotrain_compatible #endpoints_compatible #region-us \n", "# Qwen1.5-7B Turkish LoRA\n!image/png\n\nOrijinal model: Qwen1.5-7B-chat", "## Açıklama\nBu model, Alibaba Cloud tarafından geliştirilen Qwen1.5-7B-chat modelinin, TFLai/Turkish-Alpaca veri seti ve LoRA tekniği kullanılarak ince ayar çekilmiş versiyonudur.", "## Örnek diyalog \nSistem mesajı: Sen yardımsever bir asistansın. \nİstem: DNA'nın ne olduğunu kısaca açıkla. \nYanıt: DNA, Dünya'da yaşam için temel olan bir molekül türüdür ve genetik bilginin kodlaması için kullanılır. Genetik bilgiyi taşıyan çift sarmallı bir molekül olarak düşünülebilir. Her canlı organizmanın DNA'sı farklıdır, ancak her ikisi de aynı talimatları içeren benzersiz bir kimlik kodu içerir. DNA ayrıca hücrelerdeki protein üretimini yönlendiren en önemli moleküldür.", "## Kullanım \nModeli aşağıdaki düğmeye tıklayarak Google Colab'de çalıştırabilirsiniz.\n![image/svg](URL\n\n---" ]

[ 89, 26, 50, 116, 30 ]

[ "passage: TAGS\n#transformers #pytorch #safetensors #qwen2 #text-generation #axolotl #conversational #tr #dataset-TFLai/Turkish-Alpaca #base_model-Qwen/Qwen1.5-7B-Chat #license-other #autotrain_compatible #endpoints_compatible #region-us \n# Qwen1.5-7B Turkish LoRA\n!image/png\n\nOrijinal model: Qwen1.5-7B-chat## Açıklama\nBu model, Alibaba Cloud tarafından geliştirilen Qwen1.5-7B-chat modelinin, TFLai/Turkish-Alpaca veri seti ve LoRA tekniği kullanılarak ince ayar çekilmiş versiyonudur.## Örnek diyalog \nSistem mesajı: Sen yardımsever bir asistansın. \nİstem: DNA'nın ne olduğunu kısaca açıkla. \nYanıt: DNA, Dünya'da yaşam için temel olan bir molekül türüdür ve genetik bilginin kodlaması için kullanılır. Genetik bilgiyi taşıyan çift sarmallı bir molekül olarak düşünülebilir. Her canlı organizmanın DNA'sı farklıdır, ancak her ikisi de aynı talimatları içeren benzersiz bir kimlik kodu içerir. DNA ayrıca hücrelerdeki protein üretimini yönlendiren en önemli moleküldür.## Kullanım \nModeli aşağıdaki düğmeye tıklayarak Google Colab'de çalıştırabilirsiniz.\n![image/svg](URL\n\n---" ]

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-0.11717543005943298, -0.06206765025854111 ]

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-2-7b-chat-hf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-08\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 600", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-08\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 600", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]

[ 84, 144, 4, 33 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #base_model-meta-llama/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-08\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 600### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]

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[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]

[ 60, 29, 3 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage" ]

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[ "transformers", "safetensors", "llama", "text-generation", "merge", "wizardlm", "mique", "license:agpl-3.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "## Examples\n\n\n\nThere are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.\n\n\n\nMark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step\n\n\n\nIf I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously? \n\n\nWrong, it takes the same amount of time!\n\n\nexplain step by step 25-4*2+3=?" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #wizardlm #mique #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Examples\n\n\n\nThere are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.\n\n\n\nMark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step\n\n\n\nIf I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously? \n\n\nWrong, it takes the same amount of time!\n\n\nexplain step by step 25-4*2+3=?" ]

[ 65, 149 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #wizardlm #mique #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Examples\n\n\n\nThere are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.\n\n\n\nMark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step\n\n\n\nIf I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously? \n\n\nWrong, it takes the same amount of time!\n\n\nexplain step by step 25-4*2+3=?" ]

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[ "unity-sentis", "onnx", "object-detection", "license:apache-2.0", "region:us" ]

[]

[]

[ "# Hand Landmark from Google Mediapipe validated for Unity Sentis\nThis is the Hand Landmark model from Google in the Sentis format.\n\nThe model detects 3D markers on a hand centered in an image. You could use these markers, for example, to control a bone rig.\n\nIMPORTANT: The hands needs to be centered and cropped to fit the image. For images with hands not in the center or for multiple hands, you will need another model to detect hands, and crop them before feeding them into this model. For example you could use Blaze Palm to detect them (but this model only works with open palms).", "## How to Use\n* Create a new scene in Unity 2023\n* Put the hand_landmark.sentis file in the 'Assets/StreamingAssets' folder\n* Put a video in the 'Assets/StreamingAssets' folder and set 'videoName' variable to the video name\n* Create a RawImage and place it in your scene. Link to this image in the 'previewUI' field.", "## Preview\nIf you get it working it should look like this (original image from URL):\n!image showing markers", "## License\nAll Google Mediapipe models are open source under the Apache 2.0 license. The accompanying C# source code we provide can be used in your applications for commercial purposes." ]

[ "TAGS\n#unity-sentis #onnx #object-detection #license-apache-2.0 #region-us \n", "# Hand Landmark from Google Mediapipe validated for Unity Sentis\nThis is the Hand Landmark model from Google in the Sentis format.\n\nThe model detects 3D markers on a hand centered in an image. You could use these markers, for example, to control a bone rig.\n\nIMPORTANT: The hands needs to be centered and cropped to fit the image. For images with hands not in the center or for multiple hands, you will need another model to detect hands, and crop them before feeding them into this model. For example you could use Blaze Palm to detect them (but this model only works with open palms).", "## How to Use\n* Create a new scene in Unity 2023\n* Put the hand_landmark.sentis file in the 'Assets/StreamingAssets' folder\n* Put a video in the 'Assets/StreamingAssets' folder and set 'videoName' variable to the video name\n* Create a RawImage and place it in your scene. Link to this image in the 'previewUI' field.", "## Preview\nIf you get it working it should look like this (original image from URL):\n!image showing markers", "## License\nAll Google Mediapipe models are open source under the Apache 2.0 license. The accompanying C# source code we provide can be used in your applications for commercial purposes." ]

[ 29, 137, 91, 24, 39 ]

[ "passage: TAGS\n#unity-sentis #onnx #object-detection #license-apache-2.0 #region-us \n# Hand Landmark from Google Mediapipe validated for Unity Sentis\nThis is the Hand Landmark model from Google in the Sentis format.\n\nThe model detects 3D markers on a hand centered in an image. You could use these markers, for example, to control a bone rig.\n\nIMPORTANT: The hands needs to be centered and cropped to fit the image. For images with hands not in the center or for multiple hands, you will need another model to detect hands, and crop them before feeding them into this model. For example you could use Blaze Palm to detect them (but this model only works with open palms).## How to Use\n* Create a new scene in Unity 2023\n* Put the hand_landmark.sentis file in the 'Assets/StreamingAssets' folder\n* Put a video in the 'Assets/StreamingAssets' folder and set 'videoName' variable to the video name\n* Create a RawImage and place it in your scene. Link to this image in the 'previewUI' field.## Preview\nIf you get it working it should look like this (original image from URL):\n!image showing markers## License\nAll Google Mediapipe models are open source under the Apache 2.0 license. The accompanying C# source code we provide can be used in your applications for commercial purposes." ]

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[ "peft", "safetensors", "generated_from_trainer", "base_model:deepseek-ai/deepseek-coder-7b-instruct-v1.5", "license:other", "region:us" ]

[]

[]

[ "# deepseek-7b-26k-lora-feb13-test\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-7b-instruct-v1.5 #license-other #region-us \n", "# deepseek-7b-26k-lora-feb13-test\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ 51, 54, 6, 12, 8, 3, 129, 4, 39 ]

[ "passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-7b-instruct-v1.5 #license-other #region-us \n# deepseek-7b-26k-lora-feb13-test\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "saishf/Top-Western-Maid-7B", "ND911/Franken-Maid-Slerp", "base_model:saishf/Top-Western-Maid-7B", "base_model:ND911/Franken-Maid-Slerp", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Fraken-Maid-TW-Slerp\n\nFraken-Maid-TW-Slerp is a merge of the following models using LazyMergekit:\n* saishf/Top-Western-Maid-7B\n* ND911/Franken-Maid-Slerp", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #saishf/Top-Western-Maid-7B #ND911/Franken-Maid-Slerp #base_model-saishf/Top-Western-Maid-7B #base_model-ND911/Franken-Maid-Slerp #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Fraken-Maid-TW-Slerp\n\nFraken-Maid-TW-Slerp is a merge of the following models using LazyMergekit:\n* saishf/Top-Western-Maid-7B\n* ND911/Franken-Maid-Slerp", "## Configuration", "## Usage" ]

[ 122, 64, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #saishf/Top-Western-Maid-7B #ND911/Franken-Maid-Slerp #base_model-saishf/Top-Western-Maid-7B #base_model-ND911/Franken-Maid-Slerp #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Fraken-Maid-TW-Slerp\n\nFraken-Maid-TW-Slerp is a merge of the following models using LazyMergekit:\n* saishf/Top-Western-Maid-7B\n* ND911/Franken-Maid-Slerp## Configuration## Usage" ]

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[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

[]

[]

[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

[ 41, 58 ]

[ "passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

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[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "NousResearch/Nous-Hermes-2-SOLAR-10.7B", "conversational", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# I-OpenPipe-NH2-Solar-7B-slerp\n\nI-OpenPipe-NH2-Solar-7B-slerp is a merge of the following models using LazyMergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* NousResearch/Nous-Hermes-2-SOLAR-10.7B", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1218 #NousResearch/Nous-Hermes-2-SOLAR-10.7B #conversational #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-NousResearch/Nous-Hermes-2-SOLAR-10.7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# I-OpenPipe-NH2-Solar-7B-slerp\n\nI-OpenPipe-NH2-Solar-7B-slerp is a merge of the following models using LazyMergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* NousResearch/Nous-Hermes-2-SOLAR-10.7B", "## Configuration", "## Usage" ]

[ 138, 77, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1218 #NousResearch/Nous-Hermes-2-SOLAR-10.7B #conversational #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-NousResearch/Nous-Hermes-2-SOLAR-10.7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# I-OpenPipe-NH2-Solar-7B-slerp\n\nI-OpenPipe-NH2-Solar-7B-slerp is a merge of the following models using LazyMergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* NousResearch/Nous-Hermes-2-SOLAR-10.7B## Configuration## Usage" ]

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[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:beomi/open-llama-2-ko-7b", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# ko-metamathqa-open-llama-2-ko-7b\n\nThis model is a fine-tuned version of beomi/open-llama-2-ko-7b on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0", "### Training results", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-beomi/open-llama-2-ko-7b #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ko-metamathqa-open-llama-2-ko-7b\n\nThis model is a fine-tuned version of beomi/open-llama-2-ko-7b on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0", "### Training results", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ 76, 46, 6, 12, 8, 3, 157, 4, 43 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #generated_from_trainer #base_model-beomi/open-llama-2-ko-7b #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ko-metamathqa-open-llama-2-ko-7b\n\nThis model is a fine-tuned version of beomi/open-llama-2-ko-7b on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00015\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 256\n- total_train_batch_size: 1024\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 1.0### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.3.0.dev20240127+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

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[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]

[ 41, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]

[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]

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[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TheBloke/OpenHermes-2-Mistral-7B-GPTQ", "license:apache-2.0", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* training\\_steps: 250\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.1" ]

[ "TAGS\n#peft #tensorboard #safetensors #trl #dpo #generated_from_trainer #base_model-TheBloke/OpenHermes-2-Mistral-7B-GPTQ #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* training\\_steps: 250\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.1" ]

[ 61, 129, 4, 39 ]

[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #dpo #generated_from_trainer #base_model-TheBloke/OpenHermes-2-Mistral-7B-GPTQ #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* training\\_steps: 250\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.37.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.1" ]

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[ "peft", "safetensors", "generated_from_trainer", "base_model:deepseek-ai/deepseek-coder-7b-instruct-v1.5", "license:other", "endpoints_compatible", "region:us" ]

[]

[]

[ "# deepseek-7b-26k-lora-feb13\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-7b-instruct-v1.5 #license-other #endpoints_compatible #region-us \n", "# deepseek-7b-26k-lora-feb13\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ 59, 52, 6, 12, 8, 3, 129, 4, 39 ]

[ "passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-7b-instruct-v1.5 #license-other #endpoints_compatible #region-us \n# deepseek-7b-26k-lora-feb13\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

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[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ 66, 98, 4, 33 ]

[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2212.04089", "base_model:jeiku/Theory_of_Mind_Mistral", "base_model:jeiku/Gnosis_Reformatted_Mistral", "base_model:Undi95/Mistral-7B-small_pippa_limaRP-v3-lora", "base_model:jeiku/Theory_of_Mind_Roleplay_Mistral", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2212.04089" ]

[]

[ "# rainbow\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using technicolor as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* technicolor + jeiku/Theory_of_Mind_Mistral\n* technicolor + jeiku/Gnosis_Reformatted_Mistral\n* technicolor + Undi95/Mistral-7B-small_pippa_limaRP-v3-lora\n* technicolor + jeiku/Theory_of_Mind_Roleplay_Mistral", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2212.04089 #base_model-jeiku/Theory_of_Mind_Mistral #base_model-jeiku/Gnosis_Reformatted_Mistral #base_model-Undi95/Mistral-7B-small_pippa_limaRP-v3-lora #base_model-jeiku/Theory_of_Mind_Roleplay_Mistral #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# rainbow\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the task arithmetic merge method using technicolor as a base.", "### Models Merged\n\nThe following models were included in the merge:\n* technicolor + jeiku/Theory_of_Mind_Mistral\n* technicolor + jeiku/Gnosis_Reformatted_Mistral\n* technicolor + Undi95/Mistral-7B-small_pippa_limaRP-v3-lora\n* technicolor + jeiku/Theory_of_Mind_Roleplay_Mistral", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ 146, 19, 4, 27, 98, 17 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #arxiv-2212.04089 #base_model-jeiku/Theory_of_Mind_Mistral #base_model-jeiku/Gnosis_Reformatted_Mistral #base_model-Undi95/Mistral-7B-small_pippa_limaRP-v3-lora #base_model-jeiku/Theory_of_Mind_Roleplay_Mistral #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# rainbow\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the task arithmetic merge method using technicolor as a base.### Models Merged\n\nThe following models were included in the merge:\n* technicolor + jeiku/Theory_of_Mind_Mistral\n* technicolor + jeiku/Gnosis_Reformatted_Mistral\n* technicolor + Undi95/Mistral-7B-small_pippa_limaRP-v3-lora\n* technicolor + jeiku/Theory_of_Mind_Roleplay_Mistral### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

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[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 26, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]

[ 41, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]

[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]

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[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ 77, 98, 4, 33 ]

[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

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[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 17704, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 17704, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

[ 67, 304, 4, 31 ]

[ "passage: TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 17704, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

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[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ 77, 98, 4, 33 ]

[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

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[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ 77, 98, 4, 33 ]

[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

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[ "safetensors", "region:us" ]

[]

[]

[ "# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-runway-minimal-uncond\n These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the jlbaker361/spider-500 dataset. \n\n Training epochs = 100 \n\n num_train_timesteps = 50 \n\n url: URL\n You can find some example images in the following. \n\n !img_0\n!img_1\n!img_2\n!img_3\n!img_4\n!img_5\n!img_6\n!img_7\n!img_8\n!img_9\n!img_10\n!img_11" ]

[ "TAGS\n#safetensors #region-us \n", "# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-runway-minimal-uncond\n These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the jlbaker361/spider-500 dataset. \n\n Training epochs = 100 \n\n num_train_timesteps = 50 \n\n url: URL\n You can find some example images in the following. \n\n !img_0\n!img_1\n!img_2\n!img_3\n!img_4\n!img_5\n!img_6\n!img_7\n!img_8\n!img_9\n!img_10\n!img_11" ]

[ 11, 171 ]

[ "passage: TAGS\n#safetensors #region-us \n# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-runway-minimal-uncond\n These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the jlbaker361/spider-500 dataset. \n\n Training epochs = 100 \n\n num_train_timesteps = 50 \n\n url: URL\n You can find some example images in the following. \n\n !img_0\n!img_1\n!img_2\n!img_3\n!img_4\n!img_5\n!img_6\n!img_7\n!img_8\n!img_9\n!img_10\n!img_11" ]

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[ "safetensors", "region:us" ]

[]

[]

[ "# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-stable-minimal-uncond\n These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the jlbaker361/spider-500 dataset. \n\n Training epochs = 100 \n\n num_train_timesteps = 50 \n\n url: URL\n You can find some example images in the following. \n\n !img_0\n!img_1\n!img_2\n!img_3\n!img_4\n!img_5\n!img_6\n!img_7\n!img_8\n!img_9\n!img_10\n!img_11" ]

[ "TAGS\n#safetensors #region-us \n", "# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-stable-minimal-uncond\n These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the jlbaker361/spider-500 dataset. \n\n Training epochs = 100 \n\n num_train_timesteps = 50 \n\n url: URL\n You can find some example images in the following. \n\n !img_0\n!img_1\n!img_2\n!img_3\n!img_4\n!img_5\n!img_6\n!img_7\n!img_8\n!img_9\n!img_10\n!img_11" ]

[ 11, 168 ]

[ "passage: TAGS\n#safetensors #region-us \n# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-stable-minimal-uncond\n These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the jlbaker361/spider-500 dataset. \n\n Training epochs = 100 \n\n num_train_timesteps = 50 \n\n url: URL\n You can find some example images in the following. \n\n !img_0\n!img_1\n!img_2\n!img_3\n!img_4\n!img_5\n!img_6\n!img_7\n!img_8\n!img_9\n!img_10\n!img_11" ]

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[ "safetensors", "region:us" ]

[]

[]

[ "# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-stable-uncond\n These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the jlbaker361/spider-500 dataset. \n\n Training epochs = 100 \n\n num_train_timesteps = 50 \n\n url: URL\n You can find some example images in the following. \n\n !img_0\n!img_1\n!img_2\n!img_3\n!img_4\n!img_5\n!img_6\n!img_7\n!img_8\n!img_9\n!img_10\n!img_11" ]

[ "TAGS\n#safetensors #region-us \n", "# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-stable-uncond\n These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the jlbaker361/spider-500 dataset. \n\n Training epochs = 100 \n\n num_train_timesteps = 50 \n\n url: URL\n You can find some example images in the following. \n\n !img_0\n!img_1\n!img_2\n!img_3\n!img_4\n!img_5\n!img_6\n!img_7\n!img_8\n!img_9\n!img_10\n!img_11" ]

[ 11, 166 ]

[ "passage: TAGS\n#safetensors #region-us \n# LoRA text2image fine-tuning - jlbaker361/spider-lora-500-e100-stable-uncond\n These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the jlbaker361/spider-500 dataset. \n\n Training epochs = 100 \n\n num_train_timesteps = 50 \n\n url: URL\n You can find some example images in the following. \n\n !img_0\n!img_1\n!img_2\n!img_3\n!img_4\n!img_5\n!img_6\n!img_7\n!img_8\n!img_9\n!img_10\n!img_11" ]

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[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ 66, 98, 4, 33 ]

[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #token-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 96\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

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[ "transformers", "safetensors", "opt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #opt #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 59, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #opt #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "llama", "text-generation", "merge", "wizardlm", "wizardmath", "license:agpl-3.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "## Examples\n\n\n\nThere are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.\n\n\nAnswered wrong!\n\nMark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step\n\n\n\nIf I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously? \n\n\nWrong, should be 5\n\n\nexplain step by step 25-4*2+3=?", "## Prompt template\n\n\n\nor for CoT (For the simple math questions, we do NOT recommend to use the CoT prompt.)" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #wizardlm #wizardmath #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Examples\n\n\n\nThere are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.\n\n\nAnswered wrong!\n\nMark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step\n\n\n\nIf I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously? \n\n\nWrong, should be 5\n\n\nexplain step by step 25-4*2+3=?", "## Prompt template\n\n\n\nor for CoT (For the simple math questions, we do NOT recommend to use the CoT prompt.)" ]

[ 66, 148, 28 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #wizardlm #wizardmath #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Examples\n\n\n\nThere are three killers in a room. Someone enters the room and kills one of them. Nobody leaves the room. How many killers are left in the room? Explain your reasoning step by step.\n\n\nAnswered wrong!\n\nMark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step\n\n\n\nIf I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously? \n\n\nWrong, should be 5\n\n\nexplain step by step 25-4*2+3=?## Prompt template\n\n\n\nor for CoT (For the simple math questions, we do NOT recommend to use the CoT prompt.)" ]

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[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

[ 72, 98, 4, 33 ]

[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]

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[ "peft", "safetensors", "generated_from_trainer", "base_model:deepseek-ai/deepseek-coder-7b-instruct-v1.5", "license:other", "region:us" ]

[]

[]

[ "# deepseek-7b-26k-qlora-feb14\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-7b-instruct-v1.5 #license-other #region-us \n", "# deepseek-7b-26k-qlora-feb14\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ 51, 52, 6, 12, 8, 3, 129, 4, 39 ]

[ "passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-deepseek-ai/deepseek-coder-7b-instruct-v1.5 #license-other #region-us \n# deepseek-7b-26k-qlora-feb14\n\nThis model is a fine-tuned version of deepseek-ai/deepseek-coder-7b-instruct-v1.5 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

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[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# GPT2_FT_By_NT_RAND_v7\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 30\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 5\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# GPT2_FT_By_NT_RAND_v7\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 30\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 5\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]

[ 77, 38, 6, 12, 8, 3, 103, 4, 33 ]

[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT2_FT_By_NT_RAND_v7\n\nThis model is a fine-tuned version of distilgpt2 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 30\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 5\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]

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[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu118\n* Datasets 2.16.0\n* Tokenizers 0.15.0" ]

[ "TAGS\n#transformers #safetensors #bert #token-classification #generated_from_trainer #base_model-google-bert/bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu118\n* Datasets 2.16.0\n* Tokenizers 0.15.0" ]

[ 68, 98, 4, 35 ]

[ "passage: TAGS\n#transformers #safetensors #bert #token-classification #generated_from_trainer #base_model-google-bert/bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu118\n* Datasets 2.16.0\n* Tokenizers 0.15.0" ]

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[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

[]

[]

[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ 39, 41, 17 ]

[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]

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[ "transformers", "safetensors", "deberta-v2", "text-classification", "Politics", "Twitter", "zero-shot-classification", "en", "dataset:mlburnham/PoliStance_Affect", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #safetensors #deberta-v2 #text-classification #Politics #Twitter #zero-shot-classification #en #dataset-mlburnham/PoliStance_Affect #license-mit #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 76 ]

[ "passage: TAGS\n#transformers #safetensors #deberta-v2 #text-classification #Politics #Twitter #zero-shot-classification #en #dataset-mlburnham/PoliStance_Affect #license-mit #autotrain_compatible #endpoints_compatible #region-us \n" ]

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[ "text-generation", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:internlm/internlm2-20b", "license:other", "region:us" ]

[]

[]

[]

[ "TAGS\n#text-generation #dataset-ai2_arc #dataset-allenai/ultrafeedback_binarized_cleaned #dataset-argilla/distilabel-intel-orca-dpo-pairs #dataset-jondurbin/airoboros-3.2 #dataset-codeparrot/apps #dataset-facebook/belebele #dataset-bluemoon-fandom-1-1-rp-cleaned #dataset-boolq #dataset-camel-ai/biology #dataset-camel-ai/chemistry #dataset-camel-ai/math #dataset-camel-ai/physics #dataset-jondurbin/contextual-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-jondurbin/py-dpo-v0.1 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-LDJnr/Capybara #dataset-jondurbin/cinematika-v0.1 #dataset-WizardLM/WizardLM_evol_instruct_70k #dataset-glaiveai/glaive-function-calling-v2 #dataset-grimulkan/LimaRP-augmented #dataset-lmsys/lmsys-chat-1m #dataset-ParisNeo/lollms_aware_dataset #dataset-TIGER-Lab/MathInstruct #dataset-Muennighoff/natural-instructions #dataset-openbookqa #dataset-kingbri/PIPPA-shareGPT #dataset-piqa #dataset-Vezora/Tested-22k-Python-Alpaca #dataset-ropes #dataset-cakiki/rosetta-code #dataset-Open-Orca/SlimOrca #dataset-b-mc2/sql-create-context #dataset-squad_v2 #dataset-mattpscott/airoboros-summarization #dataset-migtissera/Synthia-v1.3 #dataset-unalignment/toxic-dpo-v0.2 #dataset-WhiteRabbitNeo/WRN-Chapter-1 #dataset-WhiteRabbitNeo/WRN-Chapter-2 #dataset-winogrande #base_model-internlm/internlm2-20b #license-other #region-us \n" ]

[ 571 ]

[ "passage: " ]

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[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

[]

[]

[ "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: andisoe/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n", "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: andisoe/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ 44, 199 ]

[ "passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: andisoe/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

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[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]

[ 60, 29, 3 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.# Usage" ]

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[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 300", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]

[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 300", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]

[ 45, 144, 4, 39 ]

[ "passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* training\\_steps: 300### Training results### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]

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[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "NousResearch/CodeLlama-7b-hf", "NousResearch/Llama-2-7b-chat-hf", "base_model:NousResearch/CodeLlama-7b-hf", "base_model:NousResearch/Llama-2-7b-chat-hf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# I-Code-NousLlama7B-slerp\n\nI-Code-NousLlama7B-slerp is a merge of the following models using LazyMergekit:\n* NousResearch/CodeLlama-7b-hf\n* NousResearch/Llama-2-7b-chat-hf", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #NousResearch/CodeLlama-7b-hf #NousResearch/Llama-2-7b-chat-hf #base_model-NousResearch/CodeLlama-7b-hf #base_model-NousResearch/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# I-Code-NousLlama7B-slerp\n\nI-Code-NousLlama7B-slerp is a merge of the following models using LazyMergekit:\n* NousResearch/CodeLlama-7b-hf\n* NousResearch/Llama-2-7b-chat-hf", "## Configuration", "## Usage" ]

[ 128, 69, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #NousResearch/CodeLlama-7b-hf #NousResearch/Llama-2-7b-chat-hf #base_model-NousResearch/CodeLlama-7b-hf #base_model-NousResearch/Llama-2-7b-chat-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# I-Code-NousLlama7B-slerp\n\nI-Code-NousLlama7B-slerp is a merge of the following models using LazyMergekit:\n* NousResearch/CodeLlama-7b-hf\n* NousResearch/Llama-2-7b-chat-hf## Configuration## Usage" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "paulml/OGNO-7B", "nlpguy/AlloyIngot", "mlabonne/Monarch-7B", "base_model:paulml/OGNO-7B", "base_model:nlpguy/AlloyIngot", "base_model:mlabonne/Monarch-7B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# RandomMergeSparsifyWEIGHTED-7B-DARETIES\n\nRandomMergeSparsifyWEIGHTED-7B-DARETIES is a merge of the following models using LazyMergekit:\n* paulml/OGNO-7B\n* nlpguy/AlloyIngot\n* mlabonne/Monarch-7B", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #paulml/OGNO-7B #nlpguy/AlloyIngot #mlabonne/Monarch-7B #base_model-paulml/OGNO-7B #base_model-nlpguy/AlloyIngot #base_model-mlabonne/Monarch-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# RandomMergeSparsifyWEIGHTED-7B-DARETIES\n\nRandomMergeSparsifyWEIGHTED-7B-DARETIES is a merge of the following models using LazyMergekit:\n* paulml/OGNO-7B\n* nlpguy/AlloyIngot\n* mlabonne/Monarch-7B", "## Configuration", "## Usage" ]

[ 128, 78, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #paulml/OGNO-7B #nlpguy/AlloyIngot #mlabonne/Monarch-7B #base_model-paulml/OGNO-7B #base_model-nlpguy/AlloyIngot #base_model-mlabonne/Monarch-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# RandomMergeSparsifyWEIGHTED-7B-DARETIES\n\nRandomMergeSparsifyWEIGHTED-7B-DARETIES is a merge of the following models using LazyMergekit:\n* paulml/OGNO-7B\n* nlpguy/AlloyIngot\n* mlabonne/Monarch-7B## Configuration## Usage" ]

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[ "peft", "safetensors", "llama", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "license:apache-2.0", "4-bit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* training\\_steps: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]

[ "TAGS\n#peft #safetensors #llama #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T #license-apache-2.0 #4-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* training\\_steps: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]

[ 63, 130, 4, 44 ]

[ "passage: TAGS\n#peft #safetensors #llama #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T #license-apache-2.0 #4-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* training\\_steps: 20\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]

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[ "transformers", "safetensors", "mixtral", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "## MiquMaid v2 2x70 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nThen, we have done a MoE, made of MiquMaid-v2-70B-DPO and Miqu-70B-DPO base, making the model using the finetune AND the base model for each token, working together.\n\nThe two model have been trained on DPO for uncensoring, more info on Miqu-70B-DPO here\n\nWe have seen a significant improvement, so we decided to share that, even if the model is very big.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-2x70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]

[ "TAGS\n#transformers #safetensors #mixtral #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## MiquMaid v2 2x70 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nThen, we have done a MoE, made of MiquMaid-v2-70B-DPO and Miqu-70B-DPO base, making the model using the finetune AND the base model for each token, working together.\n\nThe two model have been trained on DPO for uncensoring, more info on Miqu-70B-DPO here\n\nWe have seen a significant improvement, so we decided to share that, even if the model is very big.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-2x70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]

[ 75, 210, 11, 35, 40, 27, 5, 32 ]

[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## MiquMaid v2 2x70 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nThen, we have done a MoE, made of MiquMaid-v2-70B-DPO and Miqu-70B-DPO base, making the model using the finetune AND the base model for each token, working together.\n\nThe two model have been trained on DPO for uncensoring, more info on Miqu-70B-DPO here\n\nWe have seen a significant improvement, so we decided to share that, even if the model is very big.## Credits:\n- Undi\n- IkariDev## Description\n\nThis repo contains FP16 files of MiquMaid-v2-2x70B-DPO.\n\nSwitch: FP16 - GGUF## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning### Custom format:## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B", "base_model:CultriX/NeuralTrix-7B-dpo", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# KuTrix-7b\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using mistralai/Mistral-7B-v0.1 as a base.", "### Models Merged\nThe following models were included in the merge:\n* SanjiWatsuki/Kunoichi-DPO-v2-7B\n* CultriX/NeuralTrix-7B-dpo", "## Configuration\nThe following YAML configuration was used to produce this model:", "## Usage Example" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# KuTrix-7b\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the DARE TIES merge method using mistralai/Mistral-7B-v0.1 as a base.", "### Models Merged\nThe following models were included in the merge:\n* SanjiWatsuki/Kunoichi-DPO-v2-7B\n* CultriX/NeuralTrix-7B-dpo", "## Configuration\nThe following YAML configuration was used to produce this model:", "## Usage Example" ]

[ 95, 22, 4, 34, 47, 16, 5 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# KuTrix-7b\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the DARE TIES merge method using mistralai/Mistral-7B-v0.1 as a base.### Models Merged\nThe following models were included in the merge:\n* SanjiWatsuki/Kunoichi-DPO-v2-7B\n* CultriX/NeuralTrix-7B-dpo## Configuration\nThe following YAML configuration was used to produce this model:## Usage Example" ]

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[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

[]

[]

[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ 39, 41, 17 ]

[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]

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[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/flan-t5-large", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-large #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.2.dev0" ]

[ 36, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 14 ]

[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-large #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.2.dev0" ]

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[ "gguf", "GGUF", "base_model:seyf1elislam/KuTrix-7b", "region:us" ]

[]

[]

[]

[ "TAGS\n#gguf #GGUF #base_model-seyf1elislam/KuTrix-7b #region-us \n" ]

[ 28 ]

[ "passage: TAGS\n#gguf #GGUF #base_model-seyf1elislam/KuTrix-7b #region-us \n" ]

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[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

[]

[]

[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]

[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]

[ 40, 39 ]

[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]

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[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

[]

[]

[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]

[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]

[ 32, 33 ]

[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "jsfs11/MoEv4Config-TestWeightedTIES-7b", "nlpguy/AlloyIngot", "base_model:jsfs11/MoEv4Config-TestWeightedTIES-7b", "base_model:nlpguy/AlloyIngot", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# RandomMergeWEIGHTED-7B-SLERP\n\nRandomMergeWEIGHTED-7B-SLERP is a merge of the following models using LazyMergekit:\n* jsfs11/MoEv4Config-TestWeightedTIES-7b\n* nlpguy/AlloyIngot", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #jsfs11/MoEv4Config-TestWeightedTIES-7b #nlpguy/AlloyIngot #base_model-jsfs11/MoEv4Config-TestWeightedTIES-7b #base_model-nlpguy/AlloyIngot #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# RandomMergeWEIGHTED-7B-SLERP\n\nRandomMergeWEIGHTED-7B-SLERP is a merge of the following models using LazyMergekit:\n* jsfs11/MoEv4Config-TestWeightedTIES-7b\n* nlpguy/AlloyIngot", "## Configuration", "## Usage" ]

[ 126, 71, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #jsfs11/MoEv4Config-TestWeightedTIES-7b #nlpguy/AlloyIngot #base_model-jsfs11/MoEv4Config-TestWeightedTIES-7b #base_model-nlpguy/AlloyIngot #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# RandomMergeWEIGHTED-7B-SLERP\n\nRandomMergeWEIGHTED-7B-SLERP is a merge of the following models using LazyMergekit:\n* jsfs11/MoEv4Config-TestWeightedTIES-7b\n* nlpguy/AlloyIngot## Configuration## Usage" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mistralai/Mistral-7B-v0.1", "flemmingmiguel/MBX-7B-v3", "base_model:mistralai/Mistral-7B-v0.1", "base_model:flemmingmiguel/MBX-7B-v3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Mistral-MBX-7B-slerp\n\nResearch & Development for AutoSynthetix AI\n\n Website URL\n\n Discord URL\n\n GitHub URL\n\n GitLab URL\n\n Patreon URL\n\n YouTube URL\n\n Twitch URL\n\n Twitter(X) URL\n\nMistral-MBX-7B-slerp is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-v0.1\n* flemmingmiguel/MBX-7B-v3", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mistralai/Mistral-7B-v0.1 #flemmingmiguel/MBX-7B-v3 #base_model-mistralai/Mistral-7B-v0.1 #base_model-flemmingmiguel/MBX-7B-v3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Mistral-MBX-7B-slerp\n\nResearch & Development for AutoSynthetix AI\n\n Website URL\n\n Discord URL\n\n GitHub URL\n\n GitLab URL\n\n Patreon URL\n\n YouTube URL\n\n Twitch URL\n\n Twitter(X) URL\n\nMistral-MBX-7B-slerp is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-v0.1\n* flemmingmiguel/MBX-7B-v3", "## Configuration", "## Usage" ]

[ 118, 96, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mistralai/Mistral-7B-v0.1 #flemmingmiguel/MBX-7B-v3 #base_model-mistralai/Mistral-7B-v0.1 #base_model-flemmingmiguel/MBX-7B-v3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Mistral-MBX-7B-slerp\n\nResearch & Development for AutoSynthetix AI\n\n Website URL\n\n Discord URL\n\n GitHub URL\n\n GitLab URL\n\n Patreon URL\n\n YouTube URL\n\n Twitch URL\n\n Twitter(X) URL\n\nMistral-MBX-7B-slerp is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-v0.1\n* flemmingmiguel/MBX-7B-v3## Configuration## Usage" ]

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[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

[]

[]

[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]

[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]

[ 40, 39 ]

[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]

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[ "peft", "tensorboard", "safetensors", "llama", "generated_from_trainer", "base_model:openlm-research/open_llama_3b_v2", "license:apache-2.0", "8-bit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]

[ "TAGS\n#peft #tensorboard #safetensors #llama #generated_from_trainer #base_model-openlm-research/open_llama_3b_v2 #license-apache-2.0 #8-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]

[ 61, 180, 4, 44 ]

[ "passage: TAGS\n#peft #tensorboard #safetensors #llama #generated_from_trainer #base_model-openlm-research/open_llama_3b_v2 #license-apache-2.0 #8-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 16\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]

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[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

[]

[]

[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]

[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]

[ 32, 33 ]

[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]

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[ "transformers", "safetensors", "mixtral", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "## MiquMaid v2 2x70 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nThen, we have done a MoE, made of MiquMaid-v2-70B-DPO and Miqu-70B-DPO base, making the model using the finetune AND the base model for each token, working together.\n\nThe two model have been trained on DPO for uncensoring, more info on Miqu-70B-DPO here\n\nWe have seen a significant improvement, so we decided to share that, even if the model is very big.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-2x70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]

[ "TAGS\n#transformers #safetensors #mixtral #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## MiquMaid v2 2x70 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nThen, we have done a MoE, made of MiquMaid-v2-70B-DPO and Miqu-70B-DPO base, making the model using the finetune AND the base model for each token, working together.\n\nThe two model have been trained on DPO for uncensoring, more info on Miqu-70B-DPO here\n\nWe have seen a significant improvement, so we decided to share that, even if the model is very big.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-2x70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]

[ 75, 210, 11, 35, 40, 27, 5, 32 ]

[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## MiquMaid v2 2x70 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nThen, we have done a MoE, made of MiquMaid-v2-70B-DPO and Miqu-70B-DPO base, making the model using the finetune AND the base model for each token, working together.\n\nThe two model have been trained on DPO for uncensoring, more info on Miqu-70B-DPO here\n\nWe have seen a significant improvement, so we decided to share that, even if the model is very big.## Credits:\n- Undi\n- IkariDev## Description\n\nThis repo contains FP16 files of MiquMaid-v2-2x70B-DPO.\n\nSwitch: FP16 - GGUF## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning### Custom format:## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:dynamofl/dynamo-8B-v0.1", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# dynamo-8B-v0.1-instr-de\n\nThis model is a fine-tuned version of dynamofl/dynamo-8B-v0.1 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.9482\n- eval_runtime: 16.8238\n- eval_samples_per_second: 17.832\n- eval_steps_per_second: 2.259\n- epoch: 1.07\n- step: 1500", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #base_model-dynamofl/dynamo-8B-v0.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# dynamo-8B-v0.1-instr-de\n\nThis model is a fine-tuned version of dynamofl/dynamo-8B-v0.1 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.9482\n- eval_runtime: 16.8238\n- eval_samples_per_second: 17.832\n- eval_steps_per_second: 2.259\n- epoch: 1.07\n- step: 1500", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]

[ 76, 111, 6, 12, 8, 3, 129, 38 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #base_model-dynamofl/dynamo-8B-v0.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# dynamo-8B-v0.1-instr-de\n\nThis model is a fine-tuned version of dynamofl/dynamo-8B-v0.1 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.9482\n- eval_runtime: 16.8238\n- eval_samples_per_second: 17.832\n- eval_steps_per_second: 2.259\n- epoch: 1.07\n- step: 1500## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]

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[ "gguf", "text-generation", "license:other", "region:us" ]

[]

[]

[]

[ "TAGS\n#gguf #text-generation #license-other #region-us \n" ]

[ 19 ]

[ "passage: TAGS\n#gguf #text-generation #license-other #region-us \n" ]

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[ "unity-sentis", "onnx", "license:cc-by-4.0", "region:us" ]

[]

[]

[ "# Jets Text-to-Speech Model validated for Unity Sentis\nThis is a text to speech model called Jets. It takes in a text string which you convert to phonemes using a dictionary and then outputs a wav to play the voice.", "## How to Use\n* Create a new scene in Unity 2023\n* Install 'URL' package\n* Put the c# script on the Main Camera\n* Put the 'sentis' file and the 'phoneme_dict.txt' file in the 'Assets/StreamingAssets' folder\n* Add an AudioSource component on the Main Camera\n* Set the 'inputText' string for what you want it to say\n* Press play", "## Information\nThis version uses a phoneme dictionary to convert the text into a string of phonemes. There are other ways to do this, for example using another model, or heuristics.\n\nSince we are using a simple dictionary it has no way of distinguishing heteronyms (two words with the same spelling but different pronounciation).", "## License\nAttribution for the original creators is required. SeeJets for more details.\n\nYou must retain the copyright notice in the 'phoneme_dict.txt' file." ]

[ "TAGS\n#unity-sentis #onnx #license-cc-by-4.0 #region-us \n", "# Jets Text-to-Speech Model validated for Unity Sentis\nThis is a text to speech model called Jets. It takes in a text string which you convert to phonemes using a dictionary and then outputs a wav to play the voice.", "## How to Use\n* Create a new scene in Unity 2023\n* Install 'URL' package\n* Put the c# script on the Main Camera\n* Put the 'sentis' file and the 'phoneme_dict.txt' file in the 'Assets/StreamingAssets' folder\n* Add an AudioSource component on the Main Camera\n* Set the 'inputText' string for what you want it to say\n* Press play", "## Information\nThis version uses a phoneme dictionary to convert the text into a string of phonemes. There are other ways to do this, for example using another model, or heuristics.\n\nSince we are using a simple dictionary it has no way of distinguishing heteronyms (two words with the same spelling but different pronounciation).", "## License\nAttribution for the original creators is required. SeeJets for more details.\n\nYou must retain the copyright notice in the 'phoneme_dict.txt' file." ]

[ 25, 57, 90, 76, 37 ]

[ "passage: TAGS\n#unity-sentis #onnx #license-cc-by-4.0 #region-us \n# Jets Text-to-Speech Model validated for Unity Sentis\nThis is a text to speech model called Jets. It takes in a text string which you convert to phonemes using a dictionary and then outputs a wav to play the voice.## How to Use\n* Create a new scene in Unity 2023\n* Install 'URL' package\n* Put the c# script on the Main Camera\n* Put the 'sentis' file and the 'phoneme_dict.txt' file in the 'Assets/StreamingAssets' folder\n* Add an AudioSource component on the Main Camera\n* Set the 'inputText' string for what you want it to say\n* Press play## Information\nThis version uses a phoneme dictionary to convert the text into a string of phonemes. There are other ways to do this, for example using another model, or heuristics.\n\nSince we are using a simple dictionary it has no way of distinguishing heteronyms (two words with the same spelling but different pronounciation).## License\nAttribution for the original creators is required. SeeJets for more details.\n\nYou must retain the copyright notice in the 'phoneme_dict.txt' file." ]

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[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 26, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]

[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]

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[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mistralai/Mistral-7B-v0.1", "CultriX/NeuralTrix-7B-dpo", "base_model:mistralai/Mistral-7B-v0.1", "base_model:CultriX/NeuralTrix-7B-dpo", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Mistral-CultriX-slerp\n\nResearch & Development for AutoSynthetix AI\n\n Website URL\n\n Discord URL\n\n GitHub URL\n\n GitLab URL\n\n Patreon URL\n\n YouTube URL\n\n Twitch URL\n\n Twitter(X) URL\n\nMistral-CultriX-slerp is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-v0.1\n* CultriX/NeuralTrix-7B-dpo", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mistralai/Mistral-7B-v0.1 #CultriX/NeuralTrix-7B-dpo #base_model-mistralai/Mistral-7B-v0.1 #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Mistral-CultriX-slerp\n\nResearch & Development for AutoSynthetix AI\n\n Website URL\n\n Discord URL\n\n GitHub URL\n\n GitLab URL\n\n Patreon URL\n\n YouTube URL\n\n Twitch URL\n\n Twitter(X) URL\n\nMistral-CultriX-slerp is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-v0.1\n* CultriX/NeuralTrix-7B-dpo", "## Configuration", "## Usage" ]

[ 122, 97, 4, 3 ]

[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #mistralai/Mistral-7B-v0.1 #CultriX/NeuralTrix-7B-dpo #base_model-mistralai/Mistral-7B-v0.1 #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Mistral-CultriX-slerp\n\nResearch & Development for AutoSynthetix AI\n\n Website URL\n\n Discord URL\n\n GitHub URL\n\n GitLab URL\n\n Patreon URL\n\n YouTube URL\n\n Twitch URL\n\n Twitter(X) URL\n\nMistral-CultriX-slerp is a merge of the following models using LazyMergekit:\n* mistralai/Mistral-7B-v0.1\n* CultriX/NeuralTrix-7B-dpo## Configuration## Usage" ]

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[ "transformers", "safetensors", "finance", "economic", "ko", "en", "dataset:mncai/orca_dpo_pairs_ko", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

[]

[ "ko", "en" ]

[ "### 질문: {x}\\n\\n### 답변:\", return_tensors='pt', return_token_type_ids=False)\n\n # Move data to GPU (if available)\n inputs = {k: v.to(device=\"cuda\" if URL.is_available() else \"cpu\") for k, v in URL()}\n\n gened = model.generate(\n inputs,\n max_new_tokens=256, # Maximum number of new tokens to create\n early_stopping=True,\n num_return_sequences=1, # Generate only one answer\n do_sample=True, # Enable sampling to generate a variety of answers\n eos_token_id=tokenizer.eos_token_id, # Using EOS Token IDs \n temperature=0.9, # This option is adjustable.\n top_p=0.8, # This option is adjustable.\n top_k=100 # This option is adjustable.\n )\n\n # Decode the generated sequence and convert it to output text \n decoded = URL(gened[0], skip_special_tokens=True).strip()\n\n # Extract only text after a string \"### 답변:\" \n answer_start_idx = URL(\"### 답변:\") + len(\"### 답변:\")\n complete_answer = decoded[answer_start_idx:].strip()\n\n # Find the first punctuation mark (. ? !) and extract only up to it\n match = URL(r\"[\\.\\?\\!][^\\.\\?\\!]*$\", complete_answer)\n if match:\n complete_answer = complete_answer[:URL()].strip()\n\n return complete_answer\n\n```\n\nTraining Details\n----------------\n\n\nTraining our model with PEFT, LoRA, DPO and Merge.\n\n\n* Low-Rank Adaptation (LoRA) fixes the weights of the pretrained model and attaches learnable rank decomposition matrices to each layer of the transformer, updating only these when finetuning. In other words, LoRA is a methodology that uses low-dimensional intrinsic rank (the number of dimensions that best describe the data for a given layer or parameter) for finetuning.\n* PEFT is a technique that does not tune all parameters of a model during fine-tuning, but only a small subset of parameters. By tuning only a few parameters while leaving others fixed, the model is less likely to suffer from catastrophic forgetting, where the model forgets previously learned tasks when it learns new ones. By tuning only a few parameters, models can be trained for different tasks such as QA, Summarize, and Generate PEFT.\n* Direct Preference Optimization (DPO) is an alternative to Reinforcement Learning from Human Feedback (RLHF). RLHF creates a reward function with human-selected data from multiple LLMs' answers to the same question, and then performs reinforcement learning on the reward function to improve model performance. DPO also uses preference data but trains directly without a reward function.\nWe selected relatively important data from the data learned by the base model, asked the base model, and extracted four answers. All 4 answers were rejected, and the original answer was selected to create the dpo dataset. Then, combine our dataset and mncai/orca\\_dpo\\_pairs\\_ko dataset which published on huggingface.\n* Merge is a way to mix two or more models into a single model. Because merge is not training, it has the advantage of being very fast, requiring only CPU computation.\n\n\nTraining Data\n-------------\n\n\n1. our dpo dataset\n\n\n* It is not to be used for commercial purposes. Therefore, it is licensed under the license CC-BY-NC-4.0.\n\n\n2. mncai/orca\\_dpo\\_pairs\\_ko(<URL\n\n\nTraining Hyperparameters\n------------------------\n\n\n\nLicense\n-------\n\n\nThe language identification model is distributed under the Creative Commons Attribution-NonCommercial 4.0 International Public License.\n\n\nExample\n-------\n\n\n\n> \n> 중앙은행의 역할에 대해서 설명해줄래?\n> \n> \n> \n\n\n\n> \n> \n> > \n> > 중앙은행은 국가의 통화 및 금융 시스템을 관리하는 정부 기관입니다. 주요 기능은 다음과 같습니다: 1. 통화 공급 관리: 중앙은행은 통화 공급량을 조절하여 물가를 안정시키고 경제 성장을 촉진합니다. 이를 위해 기준 금리를 설정하고, 유동성을 조절하며, 자산 매입 또는 매각을 통해 통화 공급량을 조절합니다. 2. 경제 안정화: 중앙은행은 경제가 과열되거나 침체하는 것을 방지하기 위해 통화 정책을 조정합니다. 예를 들어, 경제가 과열되면 중앙은행은 기준 금리를 인상하여 과열을 억제할 수 있습니다. 반대로, 경제가 침체하면 중앙은행은 기준 금리를 인하하여 경제 성장을 촉진할 수 있습니다. 3. 금융 시스템 감독: 중앙은행은 금융 시스템의 안정성을 보장하기 위해 은행 및 기타 금융 기관을 감독하고 규제합니다. 이는 위험 관리, 자본 요구 사항 및 감독 요건을 설정하는 것을 포함합니다. 4. 외환 관리: 중앙은행은 외환 시장을 안정화하기 위해 외환 정책을 수립하고 시행합니다.\n> > \n> > \n> > \n> \n> \n>" ]

[ "TAGS\n#transformers #safetensors #finance #economic #ko #en #dataset-mncai/orca_dpo_pairs_ko #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n", "### 질문: {x}\\n\\n### 답변:\", return_tensors='pt', return_token_type_ids=False)\n\n # Move data to GPU (if available)\n inputs = {k: v.to(device=\"cuda\" if URL.is_available() else \"cpu\") for k, v in URL()}\n\n gened = model.generate(\n inputs,\n max_new_tokens=256, # Maximum number of new tokens to create\n early_stopping=True,\n num_return_sequences=1, # Generate only one answer\n do_sample=True, # Enable sampling to generate a variety of answers\n eos_token_id=tokenizer.eos_token_id, # Using EOS Token IDs \n temperature=0.9, # This option is adjustable.\n top_p=0.8, # This option is adjustable.\n top_k=100 # This option is adjustable.\n )\n\n # Decode the generated sequence and convert it to output text \n decoded = URL(gened[0], skip_special_tokens=True).strip()\n\n # Extract only text after a string \"### 답변:\" \n answer_start_idx = URL(\"### 답변:\") + len(\"### 답변:\")\n complete_answer = decoded[answer_start_idx:].strip()\n\n # Find the first punctuation mark (. ? !) and extract only up to it\n match = URL(r\"[\\.\\?\\!][^\\.\\?\\!]*$\", complete_answer)\n if match:\n complete_answer = complete_answer[:URL()].strip()\n\n return complete_answer\n\n```\n\nTraining Details\n----------------\n\n\nTraining our model with PEFT, LoRA, DPO and Merge.\n\n\n* Low-Rank Adaptation (LoRA) fixes the weights of the pretrained model and attaches learnable rank decomposition matrices to each layer of the transformer, updating only these when finetuning. In other words, LoRA is a methodology that uses low-dimensional intrinsic rank (the number of dimensions that best describe the data for a given layer or parameter) for finetuning.\n* PEFT is a technique that does not tune all parameters of a model during fine-tuning, but only a small subset of parameters. By tuning only a few parameters while leaving others fixed, the model is less likely to suffer from catastrophic forgetting, where the model forgets previously learned tasks when it learns new ones. By tuning only a few parameters, models can be trained for different tasks such as QA, Summarize, and Generate PEFT.\n* Direct Preference Optimization (DPO) is an alternative to Reinforcement Learning from Human Feedback (RLHF). RLHF creates a reward function with human-selected data from multiple LLMs' answers to the same question, and then performs reinforcement learning on the reward function to improve model performance. DPO also uses preference data but trains directly without a reward function.\nWe selected relatively important data from the data learned by the base model, asked the base model, and extracted four answers. All 4 answers were rejected, and the original answer was selected to create the dpo dataset. Then, combine our dataset and mncai/orca\\_dpo\\_pairs\\_ko dataset which published on huggingface.\n* Merge is a way to mix two or more models into a single model. Because merge is not training, it has the advantage of being very fast, requiring only CPU computation.\n\n\nTraining Data\n-------------\n\n\n1. our dpo dataset\n\n\n* It is not to be used for commercial purposes. Therefore, it is licensed under the license CC-BY-NC-4.0.\n\n\n2. mncai/orca\\_dpo\\_pairs\\_ko(<URL\n\n\nTraining Hyperparameters\n------------------------\n\n\n\nLicense\n-------\n\n\nThe language identification model is distributed under the Creative Commons Attribution-NonCommercial 4.0 International Public License.\n\n\nExample\n-------\n\n\n\n> \n> 중앙은행의 역할에 대해서 설명해줄래?\n> \n> \n> \n\n\n\n> \n> \n> > \n> > 중앙은행은 국가의 통화 및 금융 시스템을 관리하는 정부 기관입니다. 주요 기능은 다음과 같습니다: 1. 통화 공급 관리: 중앙은행은 통화 공급량을 조절하여 물가를 안정시키고 경제 성장을 촉진합니다. 이를 위해 기준 금리를 설정하고, 유동성을 조절하며, 자산 매입 또는 매각을 통해 통화 공급량을 조절합니다. 2. 경제 안정화: 중앙은행은 경제가 과열되거나 침체하는 것을 방지하기 위해 통화 정책을 조정합니다. 예를 들어, 경제가 과열되면 중앙은행은 기준 금리를 인상하여 과열을 억제할 수 있습니다. 반대로, 경제가 침체하면 중앙은행은 기준 금리를 인하하여 경제 성장을 촉진할 수 있습니다. 3. 금융 시스템 감독: 중앙은행은 금융 시스템의 안정성을 보장하기 위해 은행 및 기타 금융 기관을 감독하고 규제합니다. 이는 위험 관리, 자본 요구 사항 및 감독 요건을 설정하는 것을 포함합니다. 4. 외환 관리: 중앙은행은 외환 시장을 안정화하기 위해 외환 정책을 수립하고 시행합니다.\n> > \n> > \n> > \n> \n> \n>" ]

[ 60, 1198 ]

[ "passage: TAGS\n#transformers #safetensors #finance #economic #ko #en #dataset-mncai/orca_dpo_pairs_ko #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n" ]

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[ "gguf", "merge", "mergekit", "lazymergekit", "paulml/OGNO-7B", "nlpguy/AlloyIngot", "mlabonne/Monarch-7B", "base_model:paulml/OGNO-7B", "base_model:nlpguy/AlloyIngot", "base_model:mlabonne/Monarch-7B", "region:us" ]

[]

[]

[ "# RandomMergeSparsifyWEIGHTED-7B-DARETIES\n\nRandomMergeSparsifyWEIGHTED-7B-DARETIES is a merge of the following models using LazyMergekit:\n* paulml/OGNO-7B\n* nlpguy/AlloyIngot\n* mlabonne/Monarch-7B", "## Configuration", "## Usage" ]

[ "TAGS\n#gguf #merge #mergekit #lazymergekit #paulml/OGNO-7B #nlpguy/AlloyIngot #mlabonne/Monarch-7B #base_model-paulml/OGNO-7B #base_model-nlpguy/AlloyIngot #base_model-mlabonne/Monarch-7B #region-us \n", "# RandomMergeSparsifyWEIGHTED-7B-DARETIES\n\nRandomMergeSparsifyWEIGHTED-7B-DARETIES is a merge of the following models using LazyMergekit:\n* paulml/OGNO-7B\n* nlpguy/AlloyIngot\n* mlabonne/Monarch-7B", "## Configuration", "## Usage" ]

[ 90, 78, 4, 3 ]

[ "passage: TAGS\n#gguf #merge #mergekit #lazymergekit #paulml/OGNO-7B #nlpguy/AlloyIngot #mlabonne/Monarch-7B #base_model-paulml/OGNO-7B #base_model-nlpguy/AlloyIngot #base_model-mlabonne/Monarch-7B #region-us \n# RandomMergeSparsifyWEIGHTED-7B-DARETIES\n\nRandomMergeSparsifyWEIGHTED-7B-DARETIES is a merge of the following models using LazyMergekit:\n* paulml/OGNO-7B\n* nlpguy/AlloyIngot\n* mlabonne/Monarch-7B## Configuration## Usage" ]

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[ "transformers", "pytorch", "vision-encoder-decoder", "generated_from_trainer", "en", "license:mit", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# donut-trained-model\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the sroie dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10", "### Training results", "### Framework versions\n\n- Transformers 4.25.1\n- Pytorch 2.2.0\n- Datasets 2.16.1\n- Tokenizers 0.13.3" ]

[ "TAGS\n#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #en #license-mit #endpoints_compatible #region-us \n", "# donut-trained-model\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the sroie dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10", "### Training results", "### Framework versions\n\n- Transformers 4.25.1\n- Pytorch 2.2.0\n- Datasets 2.16.1\n- Tokenizers 0.13.3" ]

[ 43, 38, 6, 12, 8, 3, 90, 4, 30 ]

[ "passage: TAGS\n#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #en #license-mit #endpoints_compatible #region-us \n# donut-trained-model\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the sroie dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10### Training results### Framework versions\n\n- Transformers 4.25.1\n- Pytorch 2.2.0\n- Datasets 2.16.1\n- Tokenizers 0.13.3" ]

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[ "transformers", "safetensors", "mixtral", "text-generation", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Caution this model may be unpredictable\n![](URL", "## Mixtral-Instruct-ITR (Interpolative Training Regression)\n\nWe have to go back, edition. \n\nFor this model I took what I learned in the making of Cat-8x7B and went back to the very beginning and SLERP merged mistralai/Mixtral-8x7B-Instruct-v0.1 onto mistralai/Mixtral-8x7B-v0.1\n\nWhile the results aren't perfect the model feels more creative and less overcooked than Mixtral Instruct is often accused of being. \n\nThe hopes are that this should also have left the model much more receptive to additional finetuning and I am interested to see what comes of it so please feel free to download it and have fun. \n\nApologies about the small shard size (keep forgetting to change the mergekit config back)", "## The model is a lot less likely to refuse certain requests in this state:\n\nso if you are going to apply additional finetuning to the model you may need to bolster its alignment depending on your use case. \n\nThe model still responds well to [INST] Thingie [/INST] formatting quite well. \n\nOr if preferred this can easily be reproduced if you have both base and instruct models handy using mergekit (mixtral branch) with the following config" ]

[ "TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Caution this model may be unpredictable\n![](URL", "## Mixtral-Instruct-ITR (Interpolative Training Regression)\n\nWe have to go back, edition. \n\nFor this model I took what I learned in the making of Cat-8x7B and went back to the very beginning and SLERP merged mistralai/Mixtral-8x7B-Instruct-v0.1 onto mistralai/Mixtral-8x7B-v0.1\n\nWhile the results aren't perfect the model feels more creative and less overcooked than Mixtral Instruct is often accused of being. \n\nThe hopes are that this should also have left the model much more receptive to additional finetuning and I am interested to see what comes of it so please feel free to download it and have fun. \n\nApologies about the small shard size (keep forgetting to change the mergekit config back)", "## The model is a lot less likely to refuse certain requests in this state:\n\nso if you are going to apply additional finetuning to the model you may need to bolster its alignment depending on your use case. \n\nThe model still responds well to [INST] Thingie [/INST] formatting quite well. \n\nOr if preferred this can easily be reproduced if you have both base and instruct models handy using mergekit (mixtral branch) with the following config" ]

[ 62, 16, 184, 107 ]

[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Caution this model may be unpredictable\n\n\nWe have to go back, edition. \n\nFor this model I took what I learned in the making of Cat-8x7B and went back to the very beginning and SLERP merged mistralai/Mixtral-8x7B-Instruct-v0.1 onto mistralai/Mixtral-8x7B-v0.1\n\nWhile the results aren't perfect the model feels more creative and less overcooked than Mixtral Instruct is often accused of being. \n\nThe hopes are that this should also have left the model much more receptive to additional finetuning and I am interested to see what comes of it so please feel free to download it and have fun. \n\nApologies about the small shard size (keep forgetting to change the mergekit config back)## The model is a lot less likely to refuse certain requests in this state:\n\nso if you are going to apply additional finetuning to the model you may need to bolster its alignment depending on your use case. \n\nThe model still responds well to [INST] Thingie [/INST] formatting quite well. \n\nOr if preferred this can easily be reproduced if you have both base and instruct models handy using mergekit (mixtral branch) with the following config" ]

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