ChemBench-Collection
Collection
Datasets, Spaces and Results related to ChemBench • 4 items • Updated • 4
model_id string | name string | number_params int64 | description string | date_published string | paper_link string | code_link string | is_api_endpoint bool | nr_of_tokens int64 | architecture string | is_open_weights bool | is_open_dataset bool | is_mixture_of_experts bool | model_alignment string | reinforcement_learning_from_human_feedback bool | domain_specific_pretraining bool | domain_specific_finetuning bool | tool_use bool | tool_type string | temperature float64 | epochs int64 | reasoning_model bool | reasoning_type string | overall_score float64 | Analytical Chemistry float64 | Chemical Preference float64 | General Chemistry float64 | Inorganic Chemistry float64 | Materials Science float64 | Organic Chemistry float64 | Physical Chemistry float64 | Technical Chemistry float64 | Toxicity and Safety float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mistral-large-2-123b | Mistral-Large-2 | 123,000,000,000 | Mistral Large 2 has a 128k context window and supports dozens of languages and along with 80+ coding languages. Mistral Large 2 is designed for single-node inference with long-context applications in mind. | 2024-07-24 | null | https://huggingface.co/mistralai/Mistral-Large-Instruct-2407 | true | null | DecoderOnly | true | false | false | null | null | false | false | false | null | 0 | null | false | null | 0.569943 | 0.480263 | 0.546454 | 0.785235 | 0.793478 | 0.666667 | 0.732558 | 0.690909 | 0.675 | 0.395556 |
llama3.1-70b-instruct | Llama-3.1-70B-Instruct | 70,000,000,000 | The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform... | 2024-07-23 | https://arxiv.org/abs/2407.21783 | https://github.com/meta-llama/llama3 | true | 15,000,000,000,000 | DecoderOnly | true | false | false | DPO | true | false | false | false | null | 0 | null | false | null | 0.533716 | 0.407895 | 0.519481 | 0.691275 | 0.771739 | 0.666667 | 0.662791 | 0.642424 | 0.65 | 0.383704 |
claude3.5 | Claude-3.5 (Sonnet) | null | Claude models are general purpose large language models. They use a transformer architecture and are trained via unsupervised learning, RLHF, and Constitutional AI (including both a supervised and Reinforcement Learning (RL) phase). Claude 3.5 was developed by Anthropic. | 2024-06-20 | null | null | true | null | null | false | false | null | null | true | false | false | false | null | 0 | null | false | null | 0.625538 | 0.565789 | 0.584416 | 0.825503 | 0.836957 | 0.714286 | 0.825581 | 0.769697 | 0.85 | 0.44 |
mixtral-8x7b-instruct-T-one | Mixtral-8x7b-Instruct (Temperature 1.0) | 47,000,000,000 | Mixtral is a sparse mixture-of-experts network. It is a decoder-only model where the feedforward block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. | 2023-12-11 | https://arxiv.org/abs/2401.04088 | https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 | true | null | DecoderOnly | true | false | true | DPO | false | false | false | false | null | 1 | null | false | null | 0.418221 | 0.276316 | 0.522478 | 0.449664 | 0.51087 | 0.404762 | 0.472093 | 0.345455 | 0.325 | 0.266667 |
command-r+ | Command-R+ | 104,000,000,000 | Cohere Command R is a family of highly scalable language models that balance high performance with strong accuracy. Command-R models were released by Cohere. | 2024-04-04 | null | https://huggingface.co/CohereForAI/c4ai-command-r-plus | true | null | null | false | false | null | null | true | false | false | false | null | 0 | null | false | null | 0.447633 | 0.342105 | 0.513487 | 0.496644 | 0.521739 | 0.464286 | 0.551163 | 0.327273 | 0.5 | 0.311111 |
gpt-4o-react | GPT-4o React | null | GPT-4o is OpenAI's third major iteration of their popular large multimodal model, GPT-4, which expands on the capabilities of GPT-4 with Vision. | 2024-05-13 | null | null | true | null | DecoderOnly | false | false | null | null | true | false | false | true | ArXiV, Web search, Wikipedia, Wolfram alpha calculator, SMILES to IUPAC name and IUPAC name to SMILES converters | 0 | null | false | null | 0.50538 | 0.467105 | 0.420579 | 0.758389 | 0.728261 | 0.559524 | 0.718605 | 0.6 | 0.725 | 0.374815 |
llama3.1-405b-instruct | Llama-3.1-405B-Instruct | 405,000,000,000 | The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform... | 2024-07-23 | https://arxiv.org/abs/2407.21783 | https://github.com/meta-llama/llama3 | true | 15,000,000,000,000 | DecoderOnly | true | false | false | DPO | true | false | false | false | null | 0 | null | false | null | 0.579268 | 0.506579 | 0.54046 | 0.791946 | 0.771739 | 0.654762 | 0.755814 | 0.709091 | 0.7 | 0.419259 |
mixtral-8x7b-instruct | Mixtral-8x7b-Instruct | 47,000,000,000 | Mixtral is a sparse mixture-of-experts network. It is a decoder-only model where the feedforward block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. | 2023-12-11 | https://arxiv.org/abs/2401.04088 | https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 | true | null | DecoderOnly | true | false | true | DPO | false | false | false | false | null | 0 | null | false | null | 0.42396 | 0.269737 | 0.535465 | 0.422819 | 0.554348 | 0.416667 | 0.47907 | 0.333333 | 0.325 | 0.26963 |
llama3.1-8b-instruct-T-one | Llama-3.1-8B-Instruct (Temperature 1.0) | 8,000,000,000 | The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform... | 2024-07-23 | https://arxiv.org/abs/2407.21783 | https://github.com/meta-llama/llama3 | true | 15,000,000,000,000 | DecoderOnly | true | false | false | DPO | true | false | false | false | null | 1 | null | false | null | 0.461263 | 0.361842 | 0.523477 | 0.530201 | 0.478261 | 0.416667 | 0.576744 | 0.418182 | 0.4 | 0.32 |
gpt-4o | GPT-4o | null | GPT-4o is OpenAI's third major iteration of their popular large multimodal model, GPT-4, which expands on the capabilities of GPT-4 with Vision. | 2024-05-13 | null | null | true | null | DecoderOnly | false | false | null | null | true | false | false | false | null | 0 | null | false | null | 0.610832 | 0.559211 | 0.589411 | 0.805369 | 0.804348 | 0.75 | 0.755814 | 0.715152 | 0.75 | 0.441481 |
llama3-70b-instruct-T-one | Llama-3-70B-Instruct (Temperature 1.0) | 70,000,000,000 | Llama 3 models were trained on a text corpus of over 15T tokens. These models use a tokenizer with a vocabulary of 128K tokens. Additionally, improvements in the post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. | 2024-04-18 | null | https://github.com/meta-llama/llama3 | true | 15,000,000,000,000 | DecoderOnly | true | false | false | DPO | true | false | false | false | null | 1 | null | false | null | 0.516499 | 0.375 | 0.53047 | 0.604027 | 0.684783 | 0.619048 | 0.632558 | 0.6 | 0.6 | 0.373333 |
paper-qa | PaperQA2 | null | PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs or text files, with a focus on the scientific literature. We used PaperQA2 via the non-public API deployed by FutureHouse and the default settings (using Claude-3.5-Sonnet as summarizing and answer-generating LLM). | 2024-09-11 | https://storage.googleapis.com/fh-public/paperqa/Language_Agents_Science.pdf | https://github.com/Future-House/paper-qa | false | null | null | null | null | null | null | null | null | null | true | Paper Search, Gather Evidence, Generate Answer, Citation Traversal | 0 | null | false | null | 0.568867 | 0.460526 | 0.563437 | 0.724832 | 0.73913 | 0.690476 | 0.67907 | 0.678788 | 0.7 | 0.423704 |
gemma-1-1-7b-it | Gemma-1.1-7B-it | 7,000,000,000 | Gemma is a family of lightweight, open models built from the research and technology that Google used to create the Gemini models. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. | 2024-02-21 | https://arxiv.org/abs/2403.08295 | https://github.com/google-deepmind/gemma | true | 6,000,000,000,000 | DecoderOnly | true | false | false | PPO | true | false | false | false | null | 0 | null | false | null | 0.192253 | 0.210526 | 0.004995 | 0.33557 | 0.413043 | 0.357143 | 0.37907 | 0.290909 | 0.375 | 0.22963 |
llama2-13b-chat | Llama-2-13B Chat | 13,000,000,000 | LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. Llama was released by Meta AI. | 2023-07-18 | https://arxiv.org/abs/2302.13971 | https://huggingface.co/meta-llama/Llama-2-13b-chat-hf | false | 2,000,000,000,000 | DecoderOnly | true | false | false | null | true | false | false | false | null | 0 | null | false | null | 0.25538 | 0.092105 | 0.484515 | 0.114094 | 0.271739 | 0.095238 | 0.153488 | 0.151515 | 0.1 | 0.100741 |
llama3.1-8b-instruct | Llama-3.1-8B-Instruct | 8,000,000,000 | The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform... | 2024-07-23 | https://arxiv.org/abs/2407.21783 | https://github.com/meta-llama/llama3 | true | 15,000,000,000,000 | DecoderOnly | true | false | false | DPO | true | false | false | false | null | 0 | null | false | null | 0.471664 | 0.394737 | 0.527473 | 0.503356 | 0.5 | 0.404762 | 0.581395 | 0.509091 | 0.45 | 0.325926 |
gemma-2-9b-it-T-one | Gemma-2-9B-it (Temperature 1.0) | 9,000,000,000 | Gemma is a family of lightweight, open models built from the research and technology that Google used to create the Gemini models. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. | 2024-06-27 | https://arxiv.org/abs/2408.00118 | https://github.com/google-deepmind/gemma | true | 8,000,000,000,000 | DecoderOnly | true | false | false | PPO | true | false | false | false | null | 1 | null | false | null | 0.480273 | 0.289474 | 0.557443 | 0.557047 | 0.543478 | 0.5 | 0.546512 | 0.466667 | 0.475 | 0.342222 |
claude3.5-react | Claude-3.5 (Sonnet) React | null | Claude models are general purpose large language models. They use a transformer architecture and are trained via unsupervised learning, RLHF, and Constitutional AI (including both a supervised and Reinforcement Learning (RL) phase). Claude 3.5 was developed by Anthropic. | 2024-06-20 | null | null | true | null | null | false | false | null | null | true | false | false | true | ArXiV, Web search, Wikipedia, Wolfram alpha calculator, SMILES to IUPAC name and IUPAC name to SMILES converters | 0 | null | false | null | 0.624821 | 0.578947 | 0.599401 | 0.872483 | 0.804348 | 0.678571 | 0.837209 | 0.757576 | 0.8 | 0.408889 |
llama3.1-70b-instruct-T-one | Llama-3.1-70B-Instruct (Temperature 1.0) | 70,000,000,000 | The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform... | 2024-07-23 | https://arxiv.org/abs/2407.21783 | https://github.com/meta-llama/llama3 | true | 15,000,000,000,000 | DecoderOnly | true | false | false | DPO | true | false | false | false | null | 1 | null | false | null | 0.511119 | 0.368421 | 0.535465 | 0.66443 | 0.695652 | 0.654762 | 0.553488 | 0.557576 | 0.55 | 0.38963 |
gemma-2-9b-it | Gemma-2-9B-it | 9,000,000,000 | Gemma is a family of lightweight, open models built from the research and technology that Google used to create the Gemini models. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. | 2024-06-27 | https://arxiv.org/abs/2408.00118 | https://github.com/google-deepmind/gemma | true | 8,000,000,000,000 | DecoderOnly | true | false | false | PPO | true | false | false | false | null | 0 | null | false | null | 0.482425 | 0.315789 | 0.551449 | 0.543624 | 0.554348 | 0.52381 | 0.555814 | 0.484848 | 0.525 | 0.339259 |
llama2-70b-chat | Llama-2-70B Chat | 70,000,000,000 | LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. Llama was released by Meta AI. | 2023-07-18 | https://arxiv.org/abs/2302.13971 | https://huggingface.co/meta-llama/Llama-2-70b-chat-hf | false | 2,000,000,000,000 | DecoderOnly | true | false | false | null | true | false | false | false | null | 0.01 | null | false | null | 0.266141 | 0.072368 | 0.487512 | 0.134228 | 0.217391 | 0.178571 | 0.146512 | 0.169697 | 0.125 | 0.136296 |
llama3-8b-instruct | Llama-3-8B-Instruct | 8,000,000,000 | Llama 3 models were trained on a text corpus of over 15T tokens. These models use a tokenizer with a vocabulary of 128K tokens. Additionally, improvements in the post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. | 2024-04-18 | null | https://github.com/meta-llama/llama3 | true | 15,000,000,000,000 | DecoderOnly | true | false | false | DPO | true | false | false | false | null | 0 | null | false | null | 0.455524 | 0.407895 | 0.515485 | 0.442953 | 0.48913 | 0.416667 | 0.562791 | 0.369697 | 0.6 | 0.324444 |
gemma-1-1-7b-it-T-one | Gemma-1.1-7B-it (Temperature 1.0) | 7,000,000,000 | Gemma is a family of lightweight, open models built from the research and technology that Google used to create the Gemini models. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. | 2024-02-21 | https://arxiv.org/abs/2403.08295 | https://github.com/google-deepmind/gemma | true | 6,000,000,000,000 | DecoderOnly | true | false | false | PPO | true | false | false | false | null | 1 | null | false | null | 0.190818 | 0.210526 | 0.008991 | 0.348993 | 0.413043 | 0.357143 | 0.372093 | 0.30303 | 0.375 | 0.216296 |
galactica_120b | Galactica-120b | 120,000,000,000 | Galactica is a large language model developed by Facebook. It is a transformer-based model trained on a large corpus of scientific data. | 2022-11-01 | https://galactica.org/paper.pdf | https://huggingface.co/facebook/galactica-120b | false | 450,000,000,000 | DecoderOnly | true | false | false | null | false | true | false | false | null | 0 | 4 | false | null | 0.015067 | 0 | 0 | 0.046053 | 0.053191 | 0 | 0.011338 | 0.055866 | 0 | 0.023188 |
llama3-8b-instruct-T-one | Llama-3-8B-Instruct (Temperature 1.0) | 8,000,000,000 | Llama 3 models were trained on a text corpus of over 15T tokens. These models use a tokenizer with a vocabulary of 128K tokens. Additionally, improvements in the post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. | 2024-04-18 | null | https://github.com/meta-llama/llama3 | true | 15,000,000,000,000 | DecoderOnly | true | false | false | DPO | true | false | false | false | null | 1 | null | false | null | 0.4566 | 0.401316 | 0.52048 | 0.436242 | 0.543478 | 0.452381 | 0.551163 | 0.345455 | 0.625 | 0.324444 |
gemini-pro | Gemini-Pro | null | Gemini models are built from the ground up for multimodality: reasoning seamlessly across text, images, audio, video, and code. | 2024-06-07 | https://arxiv.org/abs/2403.05530 | null | true | null | DecoderOnly | false | false | null | null | true | false | false | false | null | 0 | null | false | null | 0.452654 | 0.388158 | 0.5005 | 0.483221 | 0.467391 | 0.5 | 0.567442 | 0.448485 | 0.475 | 0.308148 |
gpt-4 | GPT-4 | null | GPT-4 is a large multimodal model released by OpenAI to succeed GPT-3.5 Turbo. It features a context window of 32k tokens. | 2023-03-14 | https://arxiv.org/abs/2303.08774 | null | true | null | DecoderOnly | false | false | null | null | null | false | false | false | null | 0 | null | false | null | 0.412841 | 0.427632 | 0.163836 | 0.697987 | 0.695652 | 0.607143 | 0.67907 | 0.642424 | 0.7 | 0.41037 |
llama3-70b-instruct | Llama-3-70B-Instruct | 70,000,000,000 | Llama 3 models were trained on a text corpus of over 15T tokens. These models use a tokenizer with a vocabulary of 128K tokens. Additionally, improvements in the post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. | 2024-04-18 | null | https://github.com/meta-llama/llama3 | true | 15,000,000,000,000 | DecoderOnly | true | false | false | DPO | true | false | false | false | null | 0 | null | false | null | 0.517934 | 0.414474 | 0.532468 | 0.604027 | 0.663043 | 0.630952 | 0.632558 | 0.593939 | 0.625 | 0.368889 |
phi-3-medium-4k-instruct | Phi-3-Medium-4k-Instruct | 14,000,000,000 | The Phi-3-Medium-4K-Instruct is a 14B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Mediu... | 2024-05-21 | https://arxiv.org/abs/2404.14219 | https://huggingface.co/microsoft/Phi-3-medium-4k-instruct | false | 4,800,000,000,000 | DecoderOnly | true | false | false | null | true | false | false | false | null | 0 | null | false | null | 0.474534 | 0.342105 | 0.532468 | 0.47651 | 0.630435 | 0.547619 | 0.560465 | 0.460606 | 0.55 | 0.331852 |
o1-preview | o1-preview | null | o1 is trained with reinforcement learning and chain-of-thought reasoning to improve safety, robustness, and reasoning capabilities. The family includes o1-preview and o1-mini versions. | 2024-09-12 | https://cdn.openai.com/o1-system-card-20240917.pdf | null | true | null | DecoderOnly | false | false | null | null | true | false | false | false | null | 1 | null | true | medium | 0.643472 | 0.625 | 0.563437 | 0.932886 | 0.902174 | 0.72619 | 0.830233 | 0.848485 | 0.85 | 0.475556 |
claude3 | Claude-3 (Opus) | null | Claude models are general purpose large language models. They use a transformer architecture and are trained via unsupervised learning, RLHF, and Constitutional AI (including both a supervised and Reinforcement Learning (RL) phase). Claude 3 was developed by Anthropic. | 2024-03-04 | https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bbc618857627/Model_Card_Claude_3.pdf | null | true | null | null | false | false | null | null | true | false | false | false | null | 0 | null | false | null | 0.569584 | 0.467105 | 0.565435 | 0.765101 | 0.793478 | 0.630952 | 0.695349 | 0.648485 | 0.7 | 0.41037 |
gpt-3.5-turbo | GPT-3.5 Turbo | null | GPT-3.5 Turbo, developed by OpenAI, features a context window of 4096 tokens. | 2023-11-06 | null | null | true | null | DecoderOnly | false | false | null | null | true | false | false | false | null | 0 | null | false | null | 0.466284 | 0.381579 | 0.534466 | 0.489933 | 0.543478 | 0.47619 | 0.588372 | 0.4 | 0.4 | 0.30963 |
claude2 | Claude-2 | null | Claude models are general purpose large language models. They use a transformer architecture and are trained via unsupervised learning, RLHF, and Constitutional AI (including both a supervised and Reinforcement Learning (RL) phase). Claude 2 was developed by Anthropic. | 2023-07-11 | https://www-cdn.anthropic.com/bd2a28d2535bfb0494cc8e2a3bf135d2e7523226/Model-Card-Claude-2.pdf | null | true | null | null | false | false | null | null | true | false | false | false | null | 0 | null | false | null | 0.473458 | 0.375 | 0.511489 | 0.503356 | 0.608696 | 0.464286 | 0.593023 | 0.50303 | 0.475 | 0.331852 |
README.md exists but content is empty.
- Downloads last month
- 81