Split (1)

train · 951k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-13 00:37:47	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-13 00:35:18	card stringlengths 11 1.01M
Muapi/felix-meynet	Muapi	2025-08-19T13:29:03Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:28:57Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Felix Meynet ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Art by Felix Meynet ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:1021589@1441868", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/cinematic-text-title-film-cover-on-screen-style-xl-f1d	Muapi	2025-08-19T13:28:24Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:28:11Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Cinematic text title + Film Cover (on screen) style XL + F1D ![preview](./preview.jpg) Base model: Flux.1 D Trained words: perfect text title style ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:520481@893826", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
yaelahnal/blockassist-bc-mute_clawed_crab_1755609838	yaelahnal	2025-08-19T13:25:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:24:50Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755609775	canoplos112	2025-08-19T13:24:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:23:32Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ashishscapsitech123/qwen2_7b_4bit_invoice_extraction_15epoch_8600_checkpoint	ashishscapsitech123	2025-08-19T13:22:51Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_5_vl", "trl", "en", "base_model:unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T13:22:26Z	--- base_model: unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ashishscapsitech123 - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Muapi/korean-gone-flux	Muapi	2025-08-19T13:22:05Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:21:57Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Korean Gone Flux ![preview](./preview.jpg) Base model: Flux.1 D Trained words: korean ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:677337@758214", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
marcuscedricridia/orbita-tiny-Q4_K_M-GGUF	marcuscedricridia	2025-08-19T13:21:18Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:NewstaR/orbita-tiny", "base_model:quantized:NewstaR/orbita-tiny", "endpoints_compatible", "region:us", "conversational" ]	null	2025-08-19T13:21:12Z	--- base_model: NewstaR/orbita-tiny tags: - llama-cpp - gguf-my-repo --- # marcuscedricridia/orbita-tiny-Q4_K_M-GGUF This model was converted to GGUF format from [`NewstaR/orbita-tiny`](https://huggingface.co/NewstaR/orbita-tiny) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/NewstaR/orbita-tiny) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo marcuscedricridia/orbita-tiny-Q4_K_M-GGUF --hf-file orbita-tiny-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo marcuscedricridia/orbita-tiny-Q4_K_M-GGUF --hf-file orbita-tiny-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo marcuscedricridia/orbita-tiny-Q4_K_M-GGUF --hf-file orbita-tiny-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo marcuscedricridia/orbita-tiny-Q4_K_M-GGUF --hf-file orbita-tiny-q4_k_m.gguf -c 2048 ```
Muapi/f.1-lora	Muapi	2025-08-19T13:20:47Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T13:20:34Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # 墨幽-F.1-Lora-网图 ![preview](./preview.jpg) Base model: Flux.1 D Trained words: This is a high-resolution everyday scene image with a natural style, ## 🧠 Usage (Python) 🔑 Get your MUAPI key from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:792293@885949", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
mang3dd/blockassist-bc-tangled_slithering_alligator_1755607612	mang3dd	2025-08-19T13:14:33Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:14:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
canoplos112/blockassist-bc-yapping_sleek_squirrel_1755609008	canoplos112	2025-08-19T13:12:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yapping sleek squirrel", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:10:47Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yapping sleek squirrel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
yaelahnal/blockassist-bc-mute_clawed_crab_1755608822	yaelahnal	2025-08-19T13:08:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "mute clawed crab", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:07:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - mute clawed crab --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755606829	kojeklollipop	2025-08-19T13:01:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T13:01:37Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF	mradermacher	2025-08-19T13:00:10Z	56	0	transformers	[ "transformers", "gguf", "en", "base_model:Qwen/Qwen3-Coder-480B-A35B-Instruct", "base_model:quantized:Qwen/Qwen3-Coder-480B-A35B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-07-30T05:12:43Z	--- base_model: Qwen/Qwen3-Coder-480B-A35B-Instruct language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct/blob/main/LICENSE mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> <!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> weighted/imatrix quants of https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct <!-- provided-files --> *For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-Coder-480B-A35B-Instruct-i1-GGUF).* static quants are available at https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.imatrix.gguf) \| imatrix \| 0.7 \| imatrix file (for creating your own qwuants) \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ1_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ1_S.gguf.part2of2) \| i1-IQ1_S \| 97.5 \| for the desperate \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ1_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ1_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ1_M.gguf.part3of3) \| i1-IQ1_M \| 108.2 \| mostly desperate \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_XXS.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_XXS.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_XXS.gguf.part3of3) \| i1-IQ2_XXS \| 126.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_XS.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_XS.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_XS.gguf.part3of3) \| i1-IQ2_XS \| 140.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_S.gguf.part3of3) \| i1-IQ2_S \| 142.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_M.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_M.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_M.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ2_M.gguf.part4of4) \| i1-IQ2_M \| 157.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q2_K_S.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q2_K_S.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q2_K_S.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q2_K_S.gguf.part4of4) \| i1-Q2_K_S \| 162.6 \| very low quality \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q2_K.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q2_K.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q2_K.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q2_K.gguf.part4of4) \| i1-Q2_K \| 174.8 \| IQ3_XXS probably better \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_XXS.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_XXS.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_XXS.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_XXS.gguf.part4of4) \| i1-IQ3_XXS \| 184.4 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_XS.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_XS.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_XS.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_XS.gguf.part4of4) \| i1-IQ3_XS \| 195.7 \| \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_S.gguf.part1of5) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_S.gguf.part2of5) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_S.gguf.part3of5) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_S.gguf.part4of5) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_S.gguf.part5of5) \| i1-Q3_K_S \| 207.0 \| IQ3_XS probably better \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_S.gguf.part1of5) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_S.gguf.part2of5) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_S.gguf.part3of5) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_S.gguf.part4of5) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_S.gguf.part5of5) \| i1-IQ3_S \| 207.1 \| beats Q3_K* \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_M.gguf.part1of5) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_M.gguf.part2of5) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_M.gguf.part3of5) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_M.gguf.part4of5) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ3_M.gguf.part5of5) \| i1-IQ3_M \| 210.0 \| \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_M.gguf.part1of5) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_M.gguf.part2of5) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_M.gguf.part3of5) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_M.gguf.part4of5) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_M.gguf.part5of5) \| i1-Q3_K_M \| 229.3 \| IQ3_S probably better \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_L.gguf.part1of6) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_L.gguf.part2of6) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_L.gguf.part3of6) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_L.gguf.part4of6) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_L.gguf.part5of6) [P6](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q3_K_L.gguf.part6of6) \| i1-Q3_K_L \| 248.5 \| IQ3_M probably better \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ4_XS.gguf.part1of6) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ4_XS.gguf.part2of6) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ4_XS.gguf.part3of6) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ4_XS.gguf.part4of6) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ4_XS.gguf.part5of6) [P6](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-IQ4_XS.gguf.part6of6) \| i1-IQ4_XS \| 255.7 \| \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_0.gguf.part1of6) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_0.gguf.part2of6) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_0.gguf.part3of6) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_0.gguf.part4of6) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_0.gguf.part5of6) [P6](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_0.gguf.part6of6) \| i1-Q4_0 \| 271.7 \| fast, low quality \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_S.gguf.part1of6) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_S.gguf.part2of6) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_S.gguf.part3of6) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_S.gguf.part4of6) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_S.gguf.part5of6) [P6](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_S.gguf.part6of6) \| i1-Q4_K_S \| 272.9 \| optimal size/speed/quality \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_M.gguf.part1of6) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_M.gguf.part2of6) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_M.gguf.part3of6) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_M.gguf.part4of6) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_M.gguf.part5of6) [P6](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_K_M.gguf.part6of6) \| i1-Q4_K_M \| 290.2 \| fast, recommended \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_1.gguf.part1of7) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_1.gguf.part2of7) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_1.gguf.part3of7) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_1.gguf.part4of7) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_1.gguf.part5of7) [P6](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_1.gguf.part6of7) [P7](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q4_1.gguf.part7of7) \| i1-Q4_1 \| 300.6 \| \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_S.gguf.part1of7) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_S.gguf.part2of7) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_S.gguf.part3of7) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_S.gguf.part4of7) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_S.gguf.part5of7) [P6](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_S.gguf.part6of7) [P7](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_S.gguf.part7of7) \| i1-Q5_K_S \| 330.5 \| \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_M.gguf.part1of7) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_M.gguf.part2of7) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_M.gguf.part3of7) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_M.gguf.part4of7) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_M.gguf.part5of7) [P6](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_M.gguf.part6of7) [P7](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q5_K_M.gguf.part7of7) \| i1-Q5_K_M \| 340.6 \| \| \| [P1](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q6_K.gguf.part1of8) [P2](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q6_K.gguf.part2of8) [P3](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q6_K.gguf.part3of8) [P4](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q6_K.gguf.part4of8) [P5](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q6_K.gguf.part5of8) [P6](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q6_K.gguf.part6of8) [P7](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q6_K.gguf.part7of8) [P8](https://huggingface.co/mradermacher/Qwen3-Coder-480B-A35B-Instruct-i1-GGUF/resolve/main/Qwen3-Coder-480B-A35B-Instruct.i1-Q6_K.gguf.part8of8) \| i1-Q6_K \| 394.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Ransss/Moonlit-Shadow-12B-Q8_0-GGUF	Ransss	2025-08-19T12:58:51Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:Vortex5/Moonlit-Shadow-12B", "base_model:quantized:Vortex5/Moonlit-Shadow-12B", "endpoints_compatible", "region:us" ]	null	2025-08-19T12:58:00Z	--- base_model: Vortex5/Moonlit-Shadow-12B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Ransss/Moonlit-Shadow-12B-Q8_0-GGUF This model was converted to GGUF format from [`Vortex5/Moonlit-Shadow-12B`](https://huggingface.co/Vortex5/Moonlit-Shadow-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Vortex5/Moonlit-Shadow-12B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Ransss/Moonlit-Shadow-12B-Q8_0-GGUF --hf-file moonlit-shadow-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Ransss/Moonlit-Shadow-12B-Q8_0-GGUF --hf-file moonlit-shadow-12b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Ransss/Moonlit-Shadow-12B-Q8_0-GGUF --hf-file moonlit-shadow-12b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Ransss/Moonlit-Shadow-12B-Q8_0-GGUF --hf-file moonlit-shadow-12b-q8_0.gguf -c 2048 ```
Dejiat/blockassist-bc-savage_unseen_bobcat_1755607845	Dejiat	2025-08-19T12:51:30Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:51:26Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755606095	helmutsukocok	2025-08-19T12:49:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:48:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - loud scavenging kangaroo --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755607541	Dejiat	2025-08-19T12:46:26Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:46:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mang3dd/blockassist-bc-tangled_slithering_alligator_1755605749	mang3dd	2025-08-19T12:42:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:42:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Jacksss123/net72_uid234	Jacksss123	2025-08-19T12:41:01Z	0	0	transformers	[ "transformers", "tensorboard", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-08-19T12:38:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (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 Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755605670	quantumxnode	2025-08-19T12:40:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:40:04Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - dormant peckish seahorse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755607155	Dejiat	2025-08-19T12:39:57Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:39:52Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755606723	Dejiat	2025-08-19T12:32:49Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:32:44Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tensorblock/Menlo_Lucy-128k-GGUF	tensorblock	2025-08-19T12:30:43Z	0	0	transformers	[ "transformers", "gguf", "TensorBlock", "GGUF", "text-generation", "en", "base_model:Menlo/Lucy-128k", "base_model:quantized:Menlo/Lucy-128k", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T12:10:23Z	--- license: apache-2.0 language: - en base_model: Menlo/Lucy-128k pipeline_tag: text-generation library_name: transformers tags: - TensorBlock - GGUF --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> [![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co) [![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2) [![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock) [![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock) ## Menlo/Lucy-128k - GGUF <div style="text-align: left; margin: 20px 0;"> <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Join our Discord to learn more about what we're building ↗ </a> </div> This repo contains GGUF format model files for [Menlo/Lucy-128k](https://huggingface.co/Menlo/Lucy-128k). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277). ## Our projects <table border="1" cellspacing="0" cellpadding="10"> <tr> <th colspan="2" style="font-size: 25px;">Forge</th> </tr> <tr> <th colspan="2"> <img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/> </th> </tr> <tr> <th colspan="2">An OpenAI-compatible multi-provider routing layer.</th> </tr> <tr> <th colspan="2"> <a href="https://github.com/TensorBlock/forge" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">🚀 Try it now! 🚀</a> </th> </tr> <tr> <th style="font-size: 25px;">Awesome MCP Servers</th> <th style="font-size: 25px;">TensorBlock Studio</th> </tr> <tr> <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th> <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th> </tr> <tr> <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th> <th>A lightweight, open, and extensible multi-LLM interaction studio.</th> </tr> <tr> <th> <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> <th> <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style=" display: inline-block; padding: 8px 16px; background-color: #FF7F50; color: white; text-decoration: none; border-radius: 6px; font-weight: bold; font-family: sans-serif; ">👀 See what we built 👀</a> </th> </tr> </table> ## Prompt template ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Model file specification \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Lucy-128k-Q2_K.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q2_K.gguf) \| Q2_K \| 0.778 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [Lucy-128k-Q3_K_S.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q3_K_S.gguf) \| Q3_K_S \| 0.867 GB \| very small, high quality loss \| \| [Lucy-128k-Q3_K_M.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q3_K_M.gguf) \| Q3_K_M \| 0.940 GB \| very small, high quality loss \| \| [Lucy-128k-Q3_K_L.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q3_K_L.gguf) \| Q3_K_L \| 1.003 GB \| small, substantial quality loss \| \| [Lucy-128k-Q4_0.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q4_0.gguf) \| Q4_0 \| 1.054 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Lucy-128k-Q4_K_S.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q4_K_S.gguf) \| Q4_K_S \| 1.060 GB \| small, greater quality loss \| \| [Lucy-128k-Q4_K_M.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q4_K_M.gguf) \| Q4_K_M \| 1.107 GB \| medium, balanced quality - recommended \| \| [Lucy-128k-Q5_0.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q5_0.gguf) \| Q5_0 \| 1.231 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Lucy-128k-Q5_K_S.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q5_K_S.gguf) \| Q5_K_S \| 1.231 GB \| large, low quality loss - recommended \| \| [Lucy-128k-Q5_K_M.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q5_K_M.gguf) \| Q5_K_M \| 1.258 GB \| large, very low quality loss - recommended \| \| [Lucy-128k-Q6_K.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q6_K.gguf) \| Q6_K \| 1.418 GB \| very large, extremely low quality loss \| \| [Lucy-128k-Q8_0.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-128k-GGUF/blob/main/Lucy-128k-Q8_0.gguf) \| Q8_0 \| 1.834 GB \| very large, extremely low quality loss - not recommended \| ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Menlo_Lucy-128k-GGUF --include "Lucy-128k-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try: ```shell huggingface-cli download tensorblock/Menlo_Lucy-128k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf' ```
lilTAT/blockassist-bc-gentle_rugged_hare_1755606272	lilTAT	2025-08-19T12:25:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:24:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/80_F75wiD	VoilaRaj	2025-08-19T12:23:29Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T12:19:29Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
xumingtensor/affine-7060819	xumingtensor	2025-08-19T12:18:34Z	0	0	vllm	[ "vllm", "safetensors", "mistral3", "image-text-to-text", "conversational", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506", "base_model:finetune:mistralai/Mistral-Small-3.2-24B-Instruct-2506", "license:apache-2.0", "region:us" ]	image-text-to-text	2025-08-19T11:13:23Z	--- language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn license: apache-2.0 library_name: vllm inference: false base_model: - mistralai/Mistral-Small-3.2-24B-Instruct-2506 pipeline_tag: image-text-to-text --- # Mistral-Small-3.2-24B-Instruct-2506 Mistral-Small-3.2-24B-Instruct-2506 is a minor update of [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503). Small-3.2 improves in the following categories: - Instruction following: Small-3.2 is better at following precise instructions - Repetition errors: Small-3.2 produces less infinite generations or repetitive answers - Function calling: Small-3.2's function calling template is more robust (see [here](https://github.com/mistralai/mistral-common/blob/535b4d0a0fc94674ea17db6cf8dc2079b81cbcfa/src/mistral_common/tokens/tokenizers/instruct.py#L778) and [examples](#function-calling)) In all other categories Small-3.2 should match or slightly improve compared to [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503). ## Key Features - same as [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503#key-features) ## Benchmark Results We compare Mistral-Small-3.2-24B to [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503). For more comparison against other models of similar size, please check [Mistral-Small-3.1's Benchmarks'](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503#benchmark-results) ### Text #### Instruction Following / Chat / Tone \| Model \| Wildbench v2 \| Arena Hard v2 \| IF (Internal; accuracy) \| \|-------\|---------------\|---------------\|------------------------\| \| Small 3.1 24B Instruct \| 55.6% \| 19.56% \| 82.75% \| \| Small 3.2 24B Instruct \| 65.33% \| 43.1% \| 84.78% \| #### Infinite Generations Small 3.2 reduces infitine generations by 2x on challenging, long and repetitive prompts. \| Model \| Infinite Generations (Internal; Lower is better) \| \|-------\|-------\| \| Small 3.1 24B Instruct \| 2.11% \| \| Small 3.2 24B Instruct \| 1.29% \| #### STEM \| Model \| MMLU \| MMLU Pro (5-shot CoT) \| MATH \| GPQA Main (5-shot CoT) \| GPQA Diamond (5-shot CoT )\| MBPP Plus - Pass@5 \| HumanEval Plus - Pass@5 \| SimpleQA (TotalAcc)\| \|--------------------------------\|-----------\|-----------------------\|------------------------\|------------------------\|---------------------------\|--------------------\|-------------------------\|--------------------\| \| Small 3.1 24B Instruct \| 80.62% \| 66.76% \| 69.30% \| 44.42% \| 45.96% \| 74.63% \| 88.99% \| 10.43% \| \| Small 3.2 24B Instruct \| 80.50% \| 69.06% \| 69.42% \| 44.22% \| 46.13% \| 78.33% \| 92.90% \| 12.10% \| ### Vision \| Model \| MMMU \| Mathvista \| ChartQA \| DocVQA \| AI2D \| \|--------------------------------\|------------\|-----------\|-----------\|-----------\|-----------\| \| Small 3.1 24B Instruct \| 64.00% \| 68.91%\| 86.24% \| 94.08% \| 93.72% \| \| Small 3.2 24B Instruct \| 62.50% \| 67.09% \| 87.4% \| 94.86% \| 92.91% \| ## Usage The model can be used with the following frameworks; - [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) Note 1: We recommend using a relatively low temperature, such as `temperature=0.15`. Note 2: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend to use the one provided in the [SYSTEM_PROMPT.txt](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506/blob/main/SYSTEM_PROMPT.txt) file. ### vLLM (recommended) We recommend using this model with [vLLM](https://github.com/vllm-project/vllm). #### Installation Make sure to install [`vLLM >= 0.9.1`](https://github.com/vllm-project/vllm/releases/tag/v0.9.1): ``` pip install vllm --upgrade ``` Doing so should automatically install [`mistral_common >= 1.6.2`](https://github.com/mistralai/mistral-common/releases/tag/v1.6.2). To check: ``` python -c "import mistral_common; print(mistral_common.__version__)" ``` You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39). #### Serve We recommand that you use Mistral-Small-3.2-24B-Instruct-2506 in a server/client setting. 1. Spin up a server: ``` vllm serve mistralai/Mistral-Small-3.2-24B-Instruct-2506 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 2 ``` Note: Running Mistral-Small-3.2-24B-Instruct-2506 on GPU requires ~55 GB of GPU RAM in bf16 or fp16. 2. To ping the client you can use a simple Python snippet. See the following examples. #### Vision reasoning Take leverage of the vision capabilities of Mistral-Small-3.2-24B-Instruct-2506 to take the best choice given a scenario, go catch them all ! <details> <summary>Python snippet</summary> ```py from datetime import datetime, timedelta from openai import OpenAI from huggingface_hub import hf_hub_download # Modify OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" TEMP = 0.15 MAX_TOK = 131072 client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) models = client.models.list() model = models.data[0].id def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() today = datetime.today().strftime("%Y-%m-%d") yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d") model_name = repo_id.split("/")[-1] return system_prompt.format(name=model_name, today=today, yesterday=yesterday) model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506" SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438" messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.", }, {"type": "image_url", "image_url": {"url": image_url}}, ], }, ] response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, max_tokens=MAX_TOK, ) print(response.choices[0].message.content) # In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each: # 1. FIGHT: # - Pros: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money. # - Cons: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal. # 2. BAG: # - Pros: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture the Pidgey or heal your Pikachu if needed. # - Cons: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat the Pidgey quickly. # 3. POKÉMON: # - Pros: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be a strategic move if you want to train a lower-level Pokémon. # - Cons: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack. # 4. RUN: # - Pros: Running away could save time and conserve your Pokémon's health and resources. If you are in a hurry or do not need the experience or items, running away is a safe option. # - Cons: Running away means you miss out on the experience points and potential items or money that you could gain from defeating the Pidgey. It also means you do not get the chance to capture the Pidgey if you wanted to. # ### Recommendation: # Given the significant level advantage, the best action is likely to FIGHT. This will allow you to quickly defeat the Pidgey, gain experience points, and potentially earn items or money. If you are concerned about Pikachu's health, you could use an item from your BAG to heal it before or during the battle. Running away or switching Pokémon does not seem necessary in this situation. ``` </details> #### Function calling Mistral-Small-3.2-24B-Instruct-2506 is excellent at function / tool calling tasks via vLLM. E.g.: <details> <summary>Python snippet - easy</summary> ```py from openai import OpenAI from huggingface_hub import hf_hub_download # Modify OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" TEMP = 0.15 MAX_TOK = 131072 client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) models = client.models.list() model = models.data[0].id def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506" SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png" tools = [ { "type": "function", "function": { "name": "get_current_population", "description": "Get the up-to-date population of a given country.", "parameters": { "type": "object", "properties": { "country": { "type": "string", "description": "The country to find the population of.", }, "unit": { "type": "string", "description": "The unit for the population.", "enum": ["millions", "thousands"], }, }, "required": ["country", "unit"], }, }, }, { "type": "function", "function": { "name": "rewrite", "description": "Rewrite a given text for improved clarity", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The input text to rewrite", } }, }, }, }, ] messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.", }, { "role": "assistant", "content": "", "tool_calls": [ { "id": "bbc5b7ede", "type": "function", "function": { "name": "rewrite", "arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}', }, } ], }, { "role": "tool", "content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}', "tool_call_id": "bbc5b7ede", "name": "rewrite", }, { "role": "assistant", "content": "---\n\nOpenAI is a FOR-profit company.", }, { "role": "user", "content": [ { "type": "text", "text": "Can you tell me what is the biggest country depicted on the map?", }, { "type": "image_url", "image_url": { "url": image_url, }, }, ], } ] response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, max_tokens=MAX_TOK, tools=tools, tool_choice="auto", ) assistant_message = response.choices[0].message.content print(assistant_message) # The biggest country depicted on the map is Russia. messages.extend([ {"role": "assistant", "content": assistant_message}, {"role": "user", "content": "What is the population of that country in millions?"}, ]) response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, max_tokens=MAX_TOK, tools=tools, tool_choice="auto", ) print(response.choices[0].message.tool_calls) # [ChatCompletionMessageToolCall(id='3e92V6Vfo', function=Function(arguments='{"country": "Russia", "unit": "millions"}', name='get_current_population'), type='function')] ``` </details> <details> <summary>Python snippet - complex</summary> ```python import json from openai import OpenAI from huggingface_hub import hf_hub_download # Modify OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" TEMP = 0.15 MAX_TOK = 131072 client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) models = client.models.list() model = models.data[0].id def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506" SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") image_url = "https://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg" def my_calculator(expression: str) -> str: return str(eval(expression)) tools = [ { "type": "function", "function": { "name": "my_calculator", "description": "A calculator that can evaluate a mathematical expression.", "parameters": { "type": "object", "properties": { "expression": { "type": "string", "description": "The mathematical expression to evaluate.", }, }, "required": ["expression"], }, }, }, { "type": "function", "function": { "name": "rewrite", "description": "Rewrite a given text for improved clarity", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The input text to rewrite", } }, }, }, }, ] messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "Can you calculate the results for all the equations displayed in the image? Only compute the ones that involve numbers.", }, { "type": "image_url", "image_url": { "url": image_url, }, }, ], }, ] response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, max_tokens=MAX_TOK, tools=tools, tool_choice="auto", ) tool_calls = response.choices[0].message.tool_calls print(tool_calls) # [ChatCompletionMessageToolCall(id='CyQBSAtGh', function=Function(arguments='{"expression": "6 + 2 * 3"}', name='my_calculator'), type='function'), ChatCompletionMessageToolCall(id='KQqRCqvzc', function=Function(arguments='{"expression": "19 - (8 + 2) + 1"}', name='my_calculator'), type='function')] results = [] for tool_call in tool_calls: function_name = tool_call.function.name function_args = tool_call.function.arguments if function_name == "my_calculator": result = my_calculator(json.loads(function_args)) results.append(result) messages.append({"role": "assistant", "tool_calls": tool_calls}) for tool_call, result in zip(tool_calls, results): messages.append( { "role": "tool", "tool_call_id": tool_call.id, "name": tool_call.function.name, "content": result, } ) response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, max_tokens=MAX_TOK, ) print(response.choices[0].message.content) # Here are the results for the equations that involve numbers: # 1. \( 6 + 2 \times 3 = 12 \) # 3. \( 19 - (8 + 2) + 1 = 10 \) # For the other equations, you need to substitute the variables with specific values to compute the results. ``` </details> #### Instruction following Mistral-Small-3.2-24B-Instruct-2506 will follow your instructions down to the last letter ! <details> <summary>Python snippet</summary> ```python from openai import OpenAI from huggingface_hub import hf_hub_download # Modify OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1" TEMP = 0.15 MAX_TOK = 131072 client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) models = client.models.list() model = models.data[0].id def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() return system_prompt model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506" SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end with 'z'.", }, ] response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, max_tokens=MAX_TOK, ) assistant_message = response.choices[0].message.content print(assistant_message) # Here's a sentence where each word starts with the next letter of the alphabet, starting from 'a' and ending with 'z': # "Always brave cats dance elegantly, fluffy giraffes happily ignore jungle kites, lovingly munching nuts, observing playful quails racing swiftly, tiny unicorns vaulting while xylophones yodel zealously." # This sentence follows the sequence from A to Z without skipping any letters. ``` </details> ### Transformers You can also use Mistral-Small-3.2-24B-Instruct-2506 with `Transformers` ! To make the best use of our model with `Transformers` make sure to have [installed](https://github.com/mistralai/mistral-common) `mistral-common >= 1.6.2` to use our tokenizer. ```bash pip install mistral-common --upgrade ``` Then load our tokenizer along with the model and generate: <details> <summary>Python snippet</summary> ```python from datetime import datetime, timedelta import torch from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from huggingface_hub import hf_hub_download from transformers import Mistral3ForConditionalGeneration def load_system_prompt(repo_id: str, filename: str) -> str: file_path = hf_hub_download(repo_id=repo_id, filename=filename) with open(file_path, "r") as file: system_prompt = file.read() today = datetime.today().strftime("%Y-%m-%d") yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d") model_name = repo_id.split("/")[-1] return system_prompt.format(name=model_name, today=today, yesterday=yesterday) model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506" SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt") tokenizer = MistralTokenizer.from_hf_hub(model_id) model = Mistral3ForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.bfloat16 ) image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438" messages = [ {"role": "system", "content": SYSTEM_PROMPT}, { "role": "user", "content": [ { "type": "text", "text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.", }, {"type": "image_url", "image_url": {"url": image_url}}, ], }, ] tokenized = tokenizer.encode_chat_completion(ChatCompletionRequest(messages=messages)) input_ids = torch.tensor([tokenized.tokens]) attention_mask = torch.ones_like(input_ids) pixel_values = torch.tensor(tokenized.images[0], dtype=torch.bfloat16).unsqueeze(0) image_sizes = torch.tensor([pixel_values.shape[-2:]]) output = model.generate( input_ids=input_ids, attention_mask=attention_mask, pixel_values=pixel_values, image_sizes=image_sizes, max_new_tokens=1000, )[0] decoded_output = tokenizer.decode(output[len(tokenized.tokens) :]) print(decoded_output) # In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each: # 1. FIGHT: # - Pros: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money. # - Cons: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal. # 2. BAG: # - Pros: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture Pidgey or heal Pikachu if needed. # - Cons: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat Pidgey quickly. # 3. POKÉMON: # - Pros: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be strategic if you want to train a lower-level Pokémon. # - Cons: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack. # 4. RUN: # - Pros: Running away could be a quick way to avoid the battle altogether. This might be useful if you are trying to conserve resources or if you are in a hurry to get to another location. # - Cons: Running away means you miss out on the experience points, items, or money that you could gain from defeating Pidgey. It also might not be the most efficient use of your time if you are trying to train your Pokémon. # ### Recommendation: # Given the significant level advantage, the best action to take is likely FIGHT. This will allow you to quickly defeat Pidgey and gain experience points for Pikachu. If you are concerned about Pikachu's health, you could use the BAG** to heal Pikachu before or during the battle. Running away or switching Pokémon does not seem necessary in this situation. ``` </details>
thanobidex/blockassist-bc-colorful_shiny_hare_1755604128	thanobidex	2025-08-19T12:17:52Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:17:48Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - colorful shiny hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
lilTAT/blockassist-bc-gentle_rugged_hare_1755605836	lilTAT	2025-08-19T12:17:43Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:17:40Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
katreiaht/speecht5_finetuned_emirhan_tr	katreiaht	2025-08-19T12:16:30Z	15	0	null	[ "pytorch", "tensorboard", "speecht5", "generated_from_trainer", "license:mit", "region:us" ]	null	2025-08-12T14:23:53Z	--- license: mit tags: - generated_from_trainer model-index: - name: speecht5_finetuned_emirhan_tr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_emirhan_tr This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3238 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.502 \| 0.03 \| 100 \| 0.4198 \| \| 0.4211 \| 0.06 \| 200 \| 0.3732 \| \| 0.3771 \| 0.09 \| 300 \| 0.3491 \| \| 0.3611 \| 0.12 \| 400 \| 0.3298 \| \| 0.3528 \| 0.14 \| 500 \| 0.3238 \| ### Framework versions - Transformers 4.29.2 - Pytorch 2.6.0+cu124 - Datasets 2.19.1 - Tokenizers 0.13.3
unitova/blockassist-bc-zealous_sneaky_raven_1755604158	unitova	2025-08-19T12:15:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:15:54Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - zealous sneaky raven --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/80_rHeBRC	VoilaRaj	2025-08-19T12:15:05Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T12:11:06Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Tensavitprice/TensavitMexico	Tensavitprice	2025-08-19T12:14:56Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T12:14:04Z	--- license: apache-2.0 --- ¿Qué es Tensavit y cómo funciona? Tensavit cápsula es una cápsula para la hipertensión especialmente formulada, diseñada para ayudar a controlar la presión arterial alta de forma natural. Actúa favoreciendo una circulación saludable, reduciendo la presión arterial y ayudando al corazón a funcionar de forma más eficiente. La cápsula promueve el equilibrio del sistema cardiovascular, ayudando al cuerpo a mantener niveles estables de presión arterial. Al mejorar el flujo sanguíneo y la eficiencia cardíaca general, reduce la fatiga y el estrés relacionados con la hipertensión. En resumen, Tensavit Pastillas ofrece una forma segura, natural y eficaz de apoyar la salud cardíaca y mantener una presión arterial normal Tensavit costo. Sitio web oficial:<a href="https://www.nutritionsee.com/tensaviexico">www.Tensavit.com</a> <p><a href="https://www.nutritionsee.com/tensaviexico"> <img src="https://www.nutritionsee.com/wp-content/uploads/2025/07/Tensavit-mexico.png" alt="enter image description here"> </a></p> <a href="https://www.nutritionsee.com/tensaviexico">¡Compra ya! Haz clic en el enlace de abajo para más información y obtén un 50% de descuento. ¡Date prisa!</a> Sitio web oficial:<a href="https://www.nutritionsee.com/tensaviexico">www.Tensavit.com</a>
mang3dd/blockassist-bc-tangled_slithering_alligator_1755603918	mang3dd	2025-08-19T12:12:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:12:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
LBST/t10_pick_and_place_smolvla_013000	LBST	2025-08-19T12:11:26Z	0	0	lerobot	[ "lerobot", "safetensors", "robotics", "pick-and-place", "smolvla", "checkpoint-013000", "region:us" ]	robotics	2025-08-19T12:11:21Z	--- library_name: lerobot tags: - robotics - pick-and-place - smolvla - checkpoint-013000 --- # T08 Pick and Place Policy - Checkpoint 013000 This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture. ## Model Details - Checkpoint: 013000 - Architecture: SmolVLA - Task: Pick and Place (T08) - Training Step: 013000 ## Usage You can evaluate this model using LeRobot: ```bash python -m lerobot.scripts.eval \ --policy.path=LBST/t10_pick_and_place_smolvla_013000 \ --env.type=<your_environment> \ --eval.n_episodes=10 \ --policy.device=cuda ``` ## Files - `config.json`: Policy configuration - `model.safetensors`: Model weights in SafeTensors format - `train_config.json`: Complete training configuration for reproducibility ## Parent Repository This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files) --- Generated automatically from checkpoint 013000
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755603876	lisaozill03	2025-08-19T12:10:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "rugged prickly alpaca", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:09:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - rugged prickly alpaca --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Dejiat/blockassist-bc-savage_unseen_bobcat_1755605258	Dejiat	2025-08-19T12:08:27Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "savage unseen bobcat", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T12:08:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - savage unseen bobcat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mustafakara/gpt-oss-20b-multilingual-reasoner-kpss	mustafakara	2025-08-19T12:05:44Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:openai/gpt-oss-20b", "base_model:finetune:openai/gpt-oss-20b", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:28:26Z	--- base_model: openai/gpt-oss-20b library_name: transformers model_name: gpt-oss-20b-multilingual-reasoner-kpss tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gpt-oss-20b-multilingual-reasoner-kpss This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="mustafakara/gpt-oss-20b-multilingual-reasoner-kpss", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.4.1 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
murshed-ai/ap-dbbu-v0.06	murshed-ai	2025-08-19T12:04:49Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-cased", "base_model:finetune:distilbert/distilbert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-08-19T12:04:39Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ap-dbbu-v0.06 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ap-dbbu-v0.06 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Precision: 0.4628 - Recall: 0.3344 - F1: 0.3882 - Accuracy: 0.5332 - Sequence Em: 0.0 - Record Em: 0.0 - Em Accountnumber: 0.87 - Em Address.addressline1: 0.34 - Em Address.addressline2: 0.74 - Em Address.city: 0.3 - Em Address.countrycode: 0.69 - Em Address.postalcode: 0.89 - Em Address.stateprovince: 0.35 - Em Addressshortcode: 1.0 - Em Contact.email: 1.0 - Em Contact.name: 0.81 - Em Contact.phone: 1.0 - Em Name: 0.66 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - lr_scheduler_warmup_steps: 50 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| Sequence Em \| Record Em \| Em Accountnumber \| Em Address.addressline1 \| Em Address.addressline2 \| Em Address.city \| Em Address.countrycode \| Em Address.postalcode \| Em Address.stateprovince \| Em Addressshortcode \| Em Contact.email \| Em Contact.name \| Em Contact.phone \| Em Name \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\|:-----------:\|:---------:\|:-----------------:\|:------------------------:\|:------------------------:\|:----------------:\|:-----------------------:\|:----------------------:\|:-------------------------:\|:--------------------:\|:-----------------:\|:----------------:\|:-----------------:\|:--------:\| \| 0.0002 \| 1.0 \| 50 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0079 \| 0.0 \| 0.0 \| 0.59 \| 0.0 \| 0.74 \| 0.0 \| 0.0 \| 0.95 \| 0.28 \| 1.0 \| 1.0 \| 0.89 \| 0.91 \| 0.0 \| \| 0.0002 \| 2.0 \| 100 \| 0.0000 \| 0.4214 \| 0.1045 \| 0.1675 \| 0.1295 \| 0.0 \| 0.0 \| 0.6 \| 0.0 \| 0.74 \| 0.0 \| 0.65 \| 0.95 \| 0.28 \| 1.0 \| 1.0 \| 0.89 \| 0.91 \| 0.06 \| \| 0.0001 \| 3.0 \| 150 \| 0.0000 \| 0.3783 \| 0.1576 \| 0.2225 \| 0.2607 \| 0.0 \| 0.0 \| 0.7 \| 0.03 \| 0.74 \| 0.0 \| 0.72 \| 0.95 \| 0.28 \| 1.0 \| 1.0 \| 0.89 \| 0.91 \| 0.28 \| \| 0.0001 \| 4.0 \| 200 \| 0.0000 \| 0.3764 \| 0.2090 \| 0.2688 \| 0.3885 \| 0.0 \| 0.0 \| 0.73 \| 0.16 \| 0.74 \| 0.1 \| 0.72 \| 0.95 \| 0.28 \| 1.0 \| 1.0 \| 0.89 \| 0.91 \| 0.46 \| \| 0.0001 \| 5.0 \| 250 \| 0.0000 \| 0.3917 \| 0.2512 \| 0.3061 \| 0.4567 \| 0.0 \| 0.0 \| 0.79 \| 0.31 \| 0.74 \| 0.14 \| 0.73 \| 0.94 \| 0.28 \| 1.0 \| 1.0 \| 0.89 \| 0.94 \| 0.53 \| \| 0.0001 \| 6.0 \| 300 \| 0.0000 \| 0.4327 \| 0.3011 \| 0.3551 \| 0.5039 \| 0.0 \| 0.0 \| 0.84 \| 0.41 \| 0.74 \| 0.2 \| 0.73 \| 0.93 \| 0.3 \| 1.0 \| 1.0 \| 0.89 \| 1.0 \| 0.59 \| \| 0.0001 \| 7.0 \| 350 \| 0.0000 \| 0.4557 \| 0.3370 \| 0.3874 \| 0.5311 \| 0.0 \| 0.0 \| 0.88 \| 0.4 \| 0.74 \| 0.25 \| 0.73 \| 0.92 \| 0.32 \| 1.0 \| 1.0 \| 0.89 \| 1.0 \| 0.67 \| \| 0.0001 \| 8.0 \| 400 \| 0.0000 \| 0.4592 \| 0.3510 \| 0.3979 \| 0.5433 \| 0.0 \| 0.0 \| 0.89 \| 0.4 \| 0.74 \| 0.29 \| 0.74 \| 0.9 \| 0.33 \| 1.0 \| 1.0 \| 0.89 \| 1.0 \| 0.68 \| \| 0.0001 \| 9.0 \| 450 \| 0.0000 \| 0.4555 \| 0.3510 \| 0.3965 \| 0.5451 \| 0.0 \| 0.0 \| 0.89 \| 0.4 \| 0.74 \| 0.29 \| 0.74 \| 0.89 \| 0.33 \| 1.0 \| 1.0 \| 0.89 \| 1.0 \| 0.68 \| \| 0.0001 \| 10.0 \| 500 \| 0.0000 \| 0.4555 \| 0.3510 \| 0.3965 \| 0.5451 \| 0.0 \| 0.0 \| 0.89 \| 0.4 \| 0.74 \| 0.29 \| 0.74 \| 0.89 \| 0.33 \| 1.0 \| 1.0 \| 0.89 \| 1.0 \| 0.68 \| ### Framework versions - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
SeungJun3214/wifi-gemma3-model4-merged2	SeungJun3214	2025-08-19T12:03:48Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T12:03:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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m-muraki/Qwen3-Coder-30B-A3B-Instruct-FP8	m-muraki	2025-08-19T12:03:40Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "conversational", "arxiv:2505.09388", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "fp8", "region:us" ]	text-generation	2025-08-19T12:02:47Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8/blob/main/LICENSE pipeline_tag: text-generation --- # Qwen3-Coder-30B-A3B-Instruct-FP8 <a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Highlights Qwen3-Coder is available in multiple sizes. Today, we're excited to introduce Qwen3-Coder-30B-A3B-Instruct-FP8. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements: - Significant Performance among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks. - Long-context Capabilities with native support for 256K tokens, extendable up to 1M tokens using Yarn, optimized for repository-scale understanding. - Agentic Coding supporting for most platform such as Qwen Code, CLINE, featuring a specially designed function call format. ![image/jpeg](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Coder/qwen3-coder-30a3-main.jpg) ## Model Overview Qwen3-Coder-30B-A3B-Instruct-FP8 has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: 262,144 natively. NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-coder/), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart We advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3_moe' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Write a quick sort algorithm." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=65536 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`. ## Note on FP8 For convenience and performance, we have provided `fp8`-quantized model checkpoint for Qwen3, whose name ends with `-FP8`. The quantization method is fine-grained `fp8` quantization with block size of 128. You can find more details in the `quantization_config` field in `config.json`. You can use the Qwen3-30B-A3B-Instruct-FP8 model with serveral inference frameworks, including `transformers`, `sglang`, and `vllm`, as the original bfloat16 model. However, please pay attention to the following known issues: - `transformers`: - there are currently issues with the "fine-grained fp8" method in `transformers` for distributed inference. You may need to set the environment variable `CUDA_LAUNCH_BLOCKING=1` if multiple devices are used in inference. ## Agentic Coding Qwen3-Coder excels in tool calling capabilities. You can simply define or use any tools as following example. ```python # Your tool implementation def square_the_number(num: float) -> dict: return num 2 # Define Tools tools=[ { "type":"function", "function":{ "name": "square_the_number", "description": "output the square of the number.", "parameters": { "type": "object", "required": ["input_num"], "properties": { 'input_num': { 'type': 'number', 'description': 'input_num is a number that will be squared' } }, } } } ] import OpenAI # Define LLM client = OpenAI( # Use a custom endpoint compatible with OpenAI API base_url='http://localhost:8000/v1', # api_base api_key="EMPTY" ) messages = [{'role': 'user', 'content': 'square the number 1024'}] completion = client.chat.completions.create( messages=messages, model="Qwen3-Coder-30B-A3B-Instruct-FP8", max_tokens=65536, tools=tools, ) print(completion.choice[0]) ``` ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - We suggest using `temperature=0.7`, `top_p=0.8`, `top_k=20`, `repetition_penalty=1.05`. 2. Adequate Output Length: We recommend using an output length of 65,536 tokens for most queries, which is adequate for instruct models. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, } ```
swiptit/blockassist-bc-polished_armored_mandrill_1755604721	swiptit	2025-08-19T11:59:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "polished armored mandrill", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:59:19Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - polished armored mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
indoempatnol/blockassist-bc-fishy_wary_swan_1755602935	indoempatnol	2025-08-19T11:56:18Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:56:14Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755602793	kojeklollipop	2025-08-19T11:54:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "spotted amphibious stork", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:53:57Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - spotted amphibious stork --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
burmeai/burme-v1	burmeai	2025-08-19T11:51:20Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T11:51:20Z	--- license: apache-2.0 ---
AXERA-TECH/Qwen2.5-0.5B-Instruct-CTX-Int8	AXERA-TECH	2025-08-19T11:51:10Z	10	0	transformers	[ "transformers", "Qwen", "Qwen2.5-0.5B-Instruct", "Qwen2.5-0.5B-Instruct-GPTQ-Int8", "GPTQ", "en", "base_model:Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8", "license:bsd-3-clause", "endpoints_compatible", "region:us" ]	null	2025-06-03T07:41:28Z	--- library_name: transformers license: bsd-3-clause base_model: - Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8 tags: - Qwen - Qwen2.5-0.5B-Instruct - Qwen2.5-0.5B-Instruct-GPTQ-Int8 - GPTQ language: - en --- # Qwen2.5-0.5B-Instruct-GPTQ-Int8 This version of Qwen2.5-0.5B-Instruct-GPTQ-Int8 has been converted to run on the Axera NPU using w8a16 quantization. This model has been optimized with the following LoRA: Compatible with Pulsar2 version: 4.2(Not released yet) ## Convert tools links: For those who are interested in model conversion, you can try to export axmodel through the original repo : https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct-GPTQ-Int8 [Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html) [AXera NPU LLM Runtime](https://github.com/AXERA-TECH/ax-llm) ## Support Platform - AX650 - AX650N DEMO Board - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) - AX630C - developing \|Chips\|w8a16\|w4a16\| \|--\|--\|--\| \|AX650\| 30 tokens/sec\| TBD \| ## How to use Download all files from this repository to the device ``` root@ax650:/mnt/qtang/llm-test/qwen2.5-0.5b-ctx# tree -L 1 . \|-- main_ax650 \|-- main_axcl_aarch64 \|-- main_axcl_x86 \|-- post_config.json \|-- qwen2.5-0.5b-gptq-int8-ctx-ax630c \|-- qwen2.5-0.5b-gptq-int8-ctx-ax650 \|-- qwen2.5_tokenizer \|-- qwen2.5_tokenizer_uid.py \|-- run_qwen2.5_0.5b_gptq_int8_ctx_ax630c.sh `-- run_qwen2.5_0.5b_gptq_int8_ctx_ax650.sh 3 directories, 7 files ``` #### Start the Tokenizer service ``` root@ax650:/mnt/qtang/llm-test/qwen2.5-0.5b-ctx# python3 qwen2.5_tokenizer_uid.py Server running at http://0.0.0.0:12345 ``` #### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board Open another terminal and run `run_qwen2.5_0.5b_gptq_int8_ax650.sh` ``` root@ax650:/mnt/qtang/llm-test/qwen2.5-0.5b-ctx# ./run_qwen2.5_0.5b_gptq_int8_ctx_ax650.sh [I][ Init][ 110]: LLM init start [I][ Init][ 34]: connect http://127.0.0.1:12345 ok [I][ Init][ 57]: uid: cdeaf62e-0243-4dc9-b557-23a7c1ba7da1 bos_id: -1, eos_id: 151645 100% \| ████████████████████████████████ \| 27 / 27 [12.35s<12.35s, 2.19 count/s] init post axmodel ok,remain_cmm(3960 MB) [I][ Init][ 188]: max_token_len : 2560 [I][ Init][ 193]: kv_cache_size : 128, kv_cache_num: 2560 [I][ Init][ 201]: prefill_token_num : 128 [I][ Init][ 205]: grp: 1, prefill_max_token_num : 1 [I][ Init][ 205]: grp: 2, prefill_max_token_num : 128 [I][ Init][ 205]: grp: 3, prefill_max_token_num : 512 [I][ Init][ 205]: grp: 4, prefill_max_token_num : 1024 [I][ Init][ 205]: grp: 5, prefill_max_token_num : 1536 [I][ Init][ 205]: grp: 6, prefill_max_token_num : 2048 [I][ Init][ 209]: prefill_max_token_num : 2048 [I][ load_config][ 282]: load config: { "enable_repetition_penalty": false, "enable_temperature": false, "enable_top_k_sampling": true, "enable_top_p_sampling": false, "penalty_window": 20, "repetition_penalty": 1.2, "temperature": 0.9, "top_k": 1, "top_p": 0.8 } [I][ Init][ 218]: LLM init ok Type "q" to exit, Ctrl+c to stop current running [I][ GenerateKVCachePrefill][ 271]: input token num : 21, prefill_split_num : 1 prefill_grpid : 2 [I][ GenerateKVCachePrefill][ 308]: input_num_token:21 [I][ main][ 230]: precompute_len: 21 [I][ main][ 231]: system_prompt: You are Qwen, created by Alibaba Cloud. You are a helpful assistant. prompt >> who are you? [I][ SetKVCache][ 531]: prefill_grpid:2 kv_cache_num:128 precompute_len:38 input_num_token:12 [I][ SetKVCache][ 534]: current prefill_max_token_num:1920 [I][ Run][ 660]: input token num : 12, prefill_split_num : 1 [I][ Run][ 686]: input_num_token:12 [I][ Run][ 829]: ttft: 134.80 ms I am Qwen, a large language model created by Alibaba Cloud. I am designed to assist with a wide range of tasks, from general knowledge to specific areas such as science, technology, and more. How can I help you today? [N][ Run][ 943]: hit eos,avg 30.88 token/s [I][ GetKVCache][ 500]: precompute_len:98, remaining:1950 prompt >> what can you do? [I][ SetKVCache][ 531]: prefill_grpid:2 kv_cache_num:128 precompute_len:98 input_num_token:13 [I][ SetKVCache][ 534]: current prefill_max_token_num:1920 [I][ Run][ 660]: input token num : 13, prefill_split_num : 1 [I][ Run][ 686]: input_num_token:13 [I][ Run][ 829]: ttft: 134.97 ms I can answer questions, provide information, assist with tasks, and even engage in creative writing. I'm here to help you with any questions or tasks you might have! [N][ Run][ 943]: hit eos,avg 30.85 token/s [I][ GetKVCache][ 500]: precompute_len:145, remaining:1903 ```
milliarderdol/blockassist-bc-roaring_rough_scorpion_1755602125	milliarderdol	2025-08-19T11:47:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring rough scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:47:23Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring rough scorpion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/80_BVN8XN	VoilaRaj	2025-08-19T11:41:42Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T11:37:53Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Azurastar2903/Qwen2.5-3B-Instruct-rk3588-1.2.1	Azurastar2903	2025-08-19T11:38:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-3B", "base_model:finetune:Qwen/Qwen2.5-3B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T11:36:18Z	--- base_model: Qwen/Qwen2.5-3B language: - en library_name: transformers license: other license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat --- # Qwen2.5-3B-Instruct-RK3588-1.2.1 This version of Qwen2.5-3B-Instruct has been converted to run on the RK3588 NPU using ['w8a8', 'w8a8_g128', 'w8a8_g256'] quantization. This model has been optimized with the following LoRA: Compatible with RKLLM version: 1.2.1 ## Useful links: [Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) [RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) [EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531) Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit # Original Model Card for base model, Qwen2.5-3B-Instruct, below: # Qwen2.5-3B-Instruct ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 3B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 3.09B - Number of Paramaters (Non-Embedding): 2.77B - Number of Layers: 36 - Number of Attention Heads (GQA): 16 for Q and 2 for KV - Context Length: Full 32,768 tokens and generation 8192 tokens For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-3B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
smoorsmith/Dream_s1k_DORA_softmasking-0.0-learnable-16	smoorsmith	2025-08-19T11:37:43Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:smoorsmith/Dream-v0-Instruct-7B-Transparent-Masking", "base_model:adapter:smoorsmith/Dream-v0-Instruct-7B-Transparent-Masking", "region:us" ]	null	2025-08-19T11:33:31Z	--- base_model: smoorsmith/Dream-v0-Instruct-7B-Transparent-Masking library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
lavavaa/blockassist-bc-giant_knobby_chimpanzee_1755603296	lavavaa	2025-08-19T11:35:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "giant knobby chimpanzee", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:35:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - giant knobby chimpanzee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sankar-asthramedtech/Full-Precision_Whisper-Medium_and_LoRA-Adapters_Merged_Model_V-1.1	sankar-asthramedtech	2025-08-19T11:34:27Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-08-19T11:30:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (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 Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hossein12321asdf/Taxi-v3	hossein12321asdf	2025-08-19T11:30:10Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-08-17T13:53:47Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="hossein12321asdf/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
VoilaRaj/80_rSYv0u	VoilaRaj	2025-08-19T11:29:50Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T11:26:04Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
imanuelradityaa/finetuned_cs_gemma_900_steps_4bit	imanuelradityaa	2025-08-19T11:29:16Z	0	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-2b-it-bnb-4bit", "base_model:quantized:unsloth/gemma-2b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-19T11:27:50Z	--- base_model: unsloth/gemma-2b-it-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: imanuelradityaa - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
lavavaa/blockassist-bc-giant_knobby_chimpanzee_1755602733	lavavaa	2025-08-19T11:26:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "giant knobby chimpanzee", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:26:11Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - giant knobby chimpanzee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dgambettaphd/M_mis_run2_gen8_WXS_doc1000_synt64_lr1e-04_acm_MPP	dgambettaphd	2025-08-19T11:24:19Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T11:24:04Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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VoilaRaj/80_V9q3Cr	VoilaRaj	2025-08-19T11:21:29Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T11:17:40Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
saranyabalakumar/ppo-LunarLander-v2	saranyabalakumar	2025-08-19T11:21:18Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-19T10:25:34Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -156.80 +/- 75.76 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
iscchang/t2s	iscchang	2025-08-19T11:19:38Z	0	0	peft	[ "peft", "safetensors", "base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct", "lora", "transformers", "text-generation", "conversational", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "region:us" ]	text-generation	2025-08-19T11:16:49Z	--- base_model: Qwen/Qwen2.5-Coder-7B-Instruct library_name: peft pipeline_tag: text-generation tags: - base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct - lora - transformers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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mohammadmahdinouri/moa-30k	mohammadmahdinouri	2025-08-19T11:17:57Z	0	0	transformers	[ "transformers", "safetensors", "ModernALBERT", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-08-19T11:17:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hzk886/LLM	hzk886	2025-08-19T11:15:49Z	0	0	null	[ "safetensors", "camembert", "arxiv:1910.09700", "region:us" ]	null	2025-08-19T11:04:26Z	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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koloni/blockassist-bc-deadly_graceful_stingray_1755600447	koloni	2025-08-19T11:15:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:15:30Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755600296	hakimjustbao	2025-08-19T11:12:02Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T11:11:58Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Mostefa-Terbeche/diabetic-retinopathy-combined-resnet50-gentle-20250620-195237	Mostefa-Terbeche	2025-08-19T11:10:57Z	0	0	null	[ "diabetic-retinopathy", "medical-imaging", "pytorch", "computer-vision", "retinal-imaging", "dataset:combined", "license:apache-2.0", "model-index", "region:us" ]	null	2025-08-19T10:19:37Z	--- license: apache-2.0 tags: - diabetic-retinopathy - medical-imaging - pytorch - computer-vision - retinal-imaging datasets: - combined metrics: - accuracy - quadratic-kappa - auc model-index: - name: combined_resnet50_gentle results: - task: type: image-classification name: Diabetic Retinopathy Classification dataset: type: combined name: COMBINED metrics: - type: accuracy value: 0.5665365507452094 - type: quadratic-kappa value: 0.7742569342039034 --- # Diabetic Retinopathy Classification Model ## Model Description This model is trained for diabetic retinopathy classification using the resnet50 architecture on the combined dataset with gentle preprocessing. ## Model Details - Architecture: resnet50 - Dataset: combined - Preprocessing: gentle - Training Date: 20250620-195237 - Task: 5-class diabetic retinopathy grading (0-4) - Directory: combined_resnet50_20250620-195237_new ## Performance - Test Accuracy: 0.5665365507452094 - Test Quadratic Kappa: 0.7742569342039034 - Validation Kappa: 0.7742569342039034 ## Usage ```python import torch from huggingface_hub import hf_hub_download # Download model model_path = hf_hub_download( repo_id="your-username/diabetic-retinopathy-combined-resnet50-gentle", filename="model_best.pt" ) # Load model model = torch.load(model_path, map_location='cpu') ``` ## Classes - 0: No DR (No diabetic retinopathy) - 1: Mild DR (Mild non-proliferative diabetic retinopathy) - 2: Moderate DR (Moderate non-proliferative diabetic retinopathy) - 3: Severe DR (Severe non-proliferative diabetic retinopathy) - 4: Proliferative DR (Proliferative diabetic retinopathy) ## Citation If you use this model, please cite your research paper/thesis.
frankmorales2020/mistral-7b-alpha-finetuned-llm-science-exam-tpu-colab-v6e-1	frankmorales2020	2025-08-19T11:09:15Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T11:06:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
OdedKBio/ppo-LunarLander-v2	OdedKBio	2025-08-19T11:03:33Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-08-19T11:01:33Z	--- library_name: stable-baselines3 tags: - LunarLander-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v3 type: LunarLander-v3 metrics: - type: mean_reward value: -198.68 +/- 121.66 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v3 This is a trained model of a PPO agent playing LunarLander-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
semenetslitslink/sd_flux_context_monochrome_pets_500_1024	semenetslitslink	2025-08-19T10:57:09Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "flux", "flux-kontextflux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-Kontext-dev", "base_model:adapter:black-forest-labs/FLUX.1-Kontext-dev", "license:other", "region:us" ]	text-to-image	2025-08-19T07:34:44Z	--- base_model: black-forest-labs/FLUX.1-Kontext-dev library_name: diffusers license: other widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-kontextflux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux Kontext DreamBooth LoRA - semenetslitslink/sd_flux_context_monochrome_pets_500_1024 <Gallery /> ## Model description These are semenetslitslink/sd_flux_context_monochrome_pets_500_1024 DreamBooth LoRA weights for black-forest-labs/FLUX.1-Kontext-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `None` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](semenetslitslink/sd_flux_context_monochrome_pets_500_1024/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import FluxKontextPipeline import torch pipeline = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('semenetslitslink/sd_flux_context_monochrome_pets_500_1024', weight_name='pytorch_lora_weights.safetensors') image = pipeline('None').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
michaelcpage345/blockassist-bc-miniature_deadly_anteater_1755598913	michaelcpage345	2025-08-19T10:55:56Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "miniature deadly anteater", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:55:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - miniature deadly anteater --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-trained3	ShimotsukiArc	2025-08-19T10:50:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained", "base_model:finetune:ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-19T10:49:26Z	--- base_model: ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ShimotsukiArc - License: apache-2.0 - Finetuned from model : ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755600583	0xaoyama	2025-08-19T10:50:20Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:50:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VoilaRaj/80_wpDRdl	VoilaRaj	2025-08-19T10:47:49Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T10:44:04Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
godeval/blockassist-bc-tame_pudgy_horse_1755600294	godeval	2025-08-19T10:47:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tame pudgy horse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:47:24Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tame pudgy horse --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755600286	0xaoyama	2025-08-19T10:45:19Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "muscular zealous gorilla", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:45:07Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - muscular zealous gorilla --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
mithun932001/lora_model	mithun932001	2025-08-19T10:42:06Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_5_vl", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-08-06T09:12:06Z	--- base_model: unsloth/qwen2.5-vl-7b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: mithun932001 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-vl-7b-instruct-bnb-4bit This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
koloni/blockassist-bc-deadly_graceful_stingray_1755598509	koloni	2025-08-19T10:41:46Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:41:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - deadly graceful stingray --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755598478	hakimjustbao	2025-08-19T10:41:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:41:22Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - raging subtle wasp --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
darshanvyas36/qwen-8-B	darshanvyas36	2025-08-19T10:40:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T10:40:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Azurastar2903/Qwen2.5-1.5B-Instruct-rk3588-1.2.1	Azurastar2903	2025-08-19T10:36:10Z	0	0	transformers	[ "transformers", "qwen2", "text-generation", "chat", "conversational", "en", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-1.5B", "base_model:finetune:Qwen/Qwen2.5-1.5B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T10:34:59Z	--- base_model: Qwen/Qwen2.5-1.5B language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat --- # Qwen2.5-1.5B-Instruct-RK3588-1.2.1 This version of Qwen2.5-1.5B-Instruct has been converted to run on the RK3588 NPU using ['w8a8', 'w8a8_g128', 'w8a8_g256'] quantization. This model has been optimized with the following LoRA: Compatible with RKLLM version: 1.2.1 ## Useful links: [Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) [RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) [EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531) Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit # Original Model Card for base model, Qwen2.5-1.5B-Instruct, below: # Qwen2.5-1.5B-Instruct ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 1.5B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 1.54B - Number of Paramaters (Non-Embedding): 1.31B - Number of Layers: 28 - Number of Attention Heads (GQA): 12 for Q and 2 for KV - Context Length: Full 32,768 tokens and generation 8192 tokens For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-1.5B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
ssenos/lantern_fine-tuning-v1	ssenos	2025-08-19T10:27:45Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:savasy/bert-base-turkish-sentiment-cased", "base_model:finetune:savasy/bert-base-turkish-sentiment-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T10:14:31Z	--- library_name: transformers base_model: savasy/bert-base-turkish-sentiment-cased tags: - generated_from_trainer model-index: - name: lantern_fine-tuning-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lantern_fine-tuning-v1 This model is a fine-tuned version of [savasy/bert-base-turkish-sentiment-cased](https://huggingface.co/savasy/bert-base-turkish-sentiment-cased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.55.2 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.21.4
0xGareeb/blockassist-bc-diving_jumping_llama_1755599127	0xGareeb	2025-08-19T10:27:10Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "diving jumping llama", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:26:33Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - diving jumping llama --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
sentence-transformers/stsb-mpnet-base-v2	sentence-transformers	2025-08-19T10:26:48Z	9,602	12	sentence-transformers	[ "sentence-transformers", "pytorch", "onnx", "safetensors", "openvino", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "text-embeddings-inference", "arxiv:1908.10084", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - text-embeddings-inference pipeline_tag: sentence-similarity --- # sentence-transformers/stsb-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/stsb-mpnet-base-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch # Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] # First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/stsb-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/stsb-mpnet-base-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Usage (Text Embeddings Inference (TEI)) [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models. - CPU: ```bash docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/stsb-mpnet-base-v2 --pooling mean --dtype float16 ``` - NVIDIA GPU: ```bash docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/stsb-mpnet-base-v2 --pooling mean --dtype float16 ``` Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create): ```bash curl http://localhost:8080/v1/embeddings \ -H "Content-Type: application/json" \ -d '{ "model": "sentence-transformers/stsb-mpnet-base-v2", "input": ["This is an example sentence", "Each sentence is converted"] }' ``` Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead. ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
sentence-transformers/paraphrase-mpnet-base-v2	sentence-transformers	2025-08-19T10:24:29Z	555,983	43	sentence-transformers	[ "sentence-transformers", "pytorch", "tf", "onnx", "safetensors", "openvino", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "text-embeddings-inference", "arxiv:1908.10084", "doi:10.57967/hf/2004", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - text-embeddings-inference pipeline_tag: sentence-similarity --- # sentence-transformers/paraphrase-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-mpnet-base-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch # Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] # First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Usage (Text Embeddings Inference (TEI)) [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models. - CPU: ```bash docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest \ --model-id sentence-transformers/paraphrase-mpnet-base-v2 \ --pooling mean \ --dtype float16 ``` - NVIDIA GPU: ```bash docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest \ --model-id sentence-transformers/paraphrase-mpnet-base-v2 \ --pooling mean \ --dtype float16 ``` Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create): ```bash curl -s http://localhost:8080/v1/embeddings \ -H "Content-Type: application/json" \ -d '{ "model": "sentence-transformers/paraphrase-mpnet-base-v2", "input": "This is an example sentence" }' ``` Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead. ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755598900	IvanJAjebu	2025-08-19T10:23:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "thorny slender capybara", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:22:43Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - thorny slender capybara --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Mostefa-Terbeche/diabetic-retinopathy-combined-efficientnet_b3-advanced-20250723-151817	Mostefa-Terbeche	2025-08-19T10:19:37Z	0	0	null	[ "diabetic-retinopathy", "medical-imaging", "pytorch", "computer-vision", "retinal-imaging", "dataset:combined", "license:apache-2.0", "model-index", "region:us" ]	null	2025-08-19T09:54:43Z	--- license: apache-2.0 tags: - diabetic-retinopathy - medical-imaging - pytorch - computer-vision - retinal-imaging datasets: - combined metrics: - accuracy - quadratic-kappa - auc model-index: - name: combined_efficientnet_b3_advanced results: - task: type: image-classification name: Diabetic Retinopathy Classification dataset: type: combined name: COMBINED metrics: - type: accuracy value: 0.7597586941092974 - type: quadratic-kappa value: 0.8101430710706087 --- # Diabetic Retinopathy Classification Model ## Model Description This model is trained for diabetic retinopathy classification using the efficientnet_b3 architecture on the combined dataset with advanced preprocessing. ## Model Details - Architecture: efficientnet_b3 - Dataset: combined - Preprocessing: advanced - Training Date: 20250723-151817 - Task: 5-class diabetic retinopathy grading (0-4) - Directory: combined_efficientnet_b3_20250723-151817_new ## Performance - Test Accuracy: 0.7597586941092974 - Test Quadratic Kappa: 0.8101430710706087 - Validation Kappa: 0.8101430710706087 ## Usage ```python import torch from huggingface_hub import hf_hub_download # Download model model_path = hf_hub_download( repo_id="your-username/diabetic-retinopathy-combined-efficientnet_b3-advanced", filename="model_best.pt" ) # Load model model = torch.load(model_path, map_location='cpu') ``` ## Classes - 0: No DR (No diabetic retinopathy) - 1: Mild DR (Mild non-proliferative diabetic retinopathy) - 2: Moderate DR (Moderate non-proliferative diabetic retinopathy) - 3: Severe DR (Severe non-proliferative diabetic retinopathy) - 4: Proliferative DR (Proliferative diabetic retinopathy) ## Citation If you use this model, please cite your research paper/thesis.
sentence-transformers/multi-qa-mpnet-base-cos-v1	sentence-transformers	2025-08-19T10:19:32Z	603,907	41	sentence-transformers	[ "sentence-transformers", "pytorch", "onnx", "safetensors", "openvino", "mpnet", "fill-mask", "feature-extraction", "sentence-similarity", "transformers", "text-embeddings-inference", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- language: - en library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - text-embeddings-inference pipeline_tag: sentence-similarity --- # multi-qa-mpnet-base-cos-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: [SBERT.net - Semantic Search](https://www.sbert.net/examples/applications/semantic-search/README.html) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer, util query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] #Load the model model = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-cos-v1') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F # Mean Pooling - Take average of all tokens def mean_pooling(model_output, attention_mask): token_embeddings = model_output.last_hidden_state # First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Encode text def encode(texts): # Tokenize sentences encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input, return_dict=True) # Perform pooling embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) return embeddings # Sentences we want sentence embeddings for query = "How many people live in London?" docs = ["Around 9 Million people live in London", "London is known for its financial district"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/multi-qa-mpnet-base-cos-v1") model = AutoModel.from_pretrained("sentence-transformers/multi-qa-mpnet-base-cos-v1") # Encode query and docs query_emb = encode(query) doc_emb = encode(docs) # Compute dot score between query and all document embeddings scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist() # Combine docs & scores doc_score_pairs = list(zip(docs, scores)) # Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) # Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## Usage (Text Embeddings Inference (TEI)) [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models. - CPU: ```bash docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/multi-qa-mpnet-base-cos-v1 --pooling mean --dtype float16 ``` - NVIDIA GPU: ```bash docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/multi-qa-mpnet-base-cos-v1 --pooling mean --dtype float16 ``` Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create): ```bash curl http://localhost:8080/v1/embeddings \ -H "Content-Type: application/json" \ -d '{ "model": "sentence-transformers/multi-qa-mpnet-base-cos-v1", "input": "How many people live in London?" }' ``` Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead. ## Technical Details In the following some technical details how this model must be used: \| Setting \| Value \| \| --- \| :---: \| \| Dimensions \| 768 \| \| Produces normalized embeddings \| Yes \| \| Pooling-Method \| Mean pooling \| \| Suitable score functions \| dot-product (`util.dot_score`), cosine-similarity (`util.cos_sim`), or euclidean distance \| Note: When loaded with `sentence-transformers`, this model produces normalized embeddings with length 1. In that case, dot-product and cosine-similarity are equivalent. dot-product is preferred as it is faster. Euclidean distance is proportional to dot-product and can also be used. ---- ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google's Flax, JAX, and Cloud team members about efficient deep learning frameworks. ## Intended uses Our model is intended to be used for semantic search: It encodes queries / questions and text paragraphs in a dense vector space. It finds relevant documents for the given passages. Note that there is a limit of 512 word pieces: Text longer than that will be truncated. Further note that the model was just trained on input text up to 250 word pieces. It might not work well for longer text. ## Training procedure The full training script is accessible in this current repository: `train_script.py`. ### Pre-training We use the pretrained [`mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure. #### Training We use the concatenation of multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using Mean-pooling, cosine-similarity as similarity function, and a scale of 20. \| Dataset \| Number of training tuples \| \|--------------------------------------------------------\|:--------------------------:\| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs from WikiAnswers \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) Automatically generated (Question, Paragraph) pairs for each paragraph in Wikipedia \| 64,371,441 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs from all StackExchanges \| 25,316,456 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs from all StackExchanges \| 21,396,559 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) Triplets (query, answer, hard_negative) for 500k queries from Bing search engine \| 17,579,773 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) (query, answer) pairs for 3M Google queries and Google featured snippet \| 3,012,496 \| \| [Amazon-QA](http://jmcauley.ucsd.edu/data/amazon/qa/) (Question, Answer) pairs from Amazon product pages \| 2,448,839 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) pairs from Yahoo Answers \| 1,198,260 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) pairs from Yahoo Answers \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) pairs from Yahoo Answers \| 659,896 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) (Question, Answer) pairs for 140k questions, each with Top5 Google snippets on that question \| 582,261 \| \| [ELI5](https://huggingface.co/datasets/eli5) (Question, Answer) pairs from Reddit ELI5 (explainlikeimfive) \| 325,475 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions pairs (titles) \| 304,525 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) (Question, Duplicate_Question, Hard_Negative) triplets for Quora Questions Pairs dataset \| 103,663 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) (Question, Paragraph) pairs for 100k real Google queries with relevant Wikipedia paragraph \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) (Question, Paragraph) pairs from SQuAD2.0 dataset \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) (Question, Evidence) pairs \| 73,346 \| \| Total \| 214,988,242** \|
aleebaster/blockassist-bc-sly_eager_boar_1755597218	aleebaster	2025-08-19T10:19:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "sly eager boar", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:19:16Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - sly eager boar --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Frax01/SmolLM-135M-python-open-shell	Frax01	2025-08-19T10:18:50Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "sft", "trl", "base_model:HuggingFaceTB/SmolLM-135M", "base_model:finetune:HuggingFaceTB/SmolLM-135M", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T09:17:49Z	--- base_model: HuggingFaceTB/SmolLM-135M library_name: transformers model_name: SmolLM-135M-python-open-shell tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for SmolLM-135M-python-open-shell This model is a fine-tuned version of [HuggingFaceTB/SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Frax01/SmolLM-135M-python-open-shell", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.2 - Pytorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
valleriee/pii-model-14	valleriee	2025-08-19T10:15:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T10:03:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
crystalline7/1822852	crystalline7	2025-08-19T10:15:33Z	0	0	null	[ "region:us" ]	null	2025-08-19T10:15:27Z	[View on Civ Archive](https://civarchive.com/models/1698187?modelVersionId=1921894)
VoilaRaj/80_Cz2WrU	VoilaRaj	2025-08-19T10:14:36Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-08-19T10:10:43Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
sentence-transformers/all-mpnet-base-v2	sentence-transformers	2025-08-19T10:14:25Z	17,267,989	1,129	sentence-transformers	[ "sentence-transformers", "pytorch", "onnx", "safetensors", "openvino", "mpnet", "fill-mask", "feature-extraction", "sentence-similarity", "transformers", "text-embeddings-inference", "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", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - text-embeddings-inference datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2') model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Usage (Text Embeddings Inference (TEI)) [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embedding models. - CPU: ```bash docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/all-mpnet-base-v2 --pooling mean --dtype float16 ``` - NVIDIA GPU: ```bash docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest --model-id sentence-transformers/all-mpnet-base-v2 --pooling mean --dtype float16 ``` Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create): ```bash curl http://localhost:8080/v1/embeddings \ -H 'Content-Type: application/json' \ -d '{ "model": "sentence-transformers/all-mpnet-base-v2", "input": ["This is an example sentence", "Each sentence is converted"] }' ``` Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead. ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 384 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|--------------------------------------------------------\|:----------------------------------------:\|:--------------------------:\| \| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs \| - \| 25,316,456 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs \| - \| 21,396,559 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs \| - \| 21,396,559 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) triplets \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [COCO](https://cocodataset.org/#home) Image captions \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [SPECTER](https://github.com/allenai/specter) citation triplets \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) \| [paper](https://arxiv.org/abs/1704.05179) \| 582,261 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) \| \| 304,525 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) \| \| 250,519 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) \| \| 250,460 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| Total \| \| 1,170,060,424** \|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755596335	mang3dd	2025-08-19T10:08:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:08:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
KCS97/red_cartoon	KCS97	2025-08-19T10:04:38Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-08-19T09:52:20Z	--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: a photo of sks cartoon tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - KCS97/red_cartoon This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks cartoon using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
dgambettaphd/M_mis_run2_gen7_WXS_doc1000_synt64_lr1e-04_acm_MPP	dgambettaphd	2025-08-19T10:03:43Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-08-19T10:03:29Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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milliarderdol/blockassist-bc-roaring_rough_scorpion_1755595662	milliarderdol	2025-08-19T10:02:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring rough scorpion", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T10:01:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring rough scorpion --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
carlosdelfino/eli5_clm-model	carlosdelfino	2025-08-19T10:02:11Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "pt", "dataset:dany0407/eli5_category", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-18T14:01:13Z	--- license: cc-by-4.0 language: pt library_name: transformers base_model: distilbert/distilgpt2 tags: - generated_from_trainer model-index: - name: eli5_clm-model results: [] datasets: - dany0407/eli5_category --- # eli5_clm-model Modelo de Linguagem Causal (Causal Language Model, CLM) fine-tunado a partir de [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2). Este modelo foi treinado seguindo o tutorial oficial de Causal Language Modeling dos Transformers: https://huggingface.co/docs/transformers/tasks/language_modeling#causal-language-modeling Resultados no conjunto de validação: - Loss: 3.8254 ## Descrição do modelo Um CLM aprende a prever o próximo token dado o contexto anterior, sendo adequado para geração de texto auto-regressiva. Aqui utilizamos o DistilGPT-2 como base e realizamos fine-tuning em um conjunto de dados local (não especificado neste card). O objetivo é adaptar o modelo ao domínio/estilo desejado. ## Usos previstos e limitações - Geração de texto condicionada a um prompt. - Completar sentenças ou parágrafos em língua portuguesa/inglesa (dependendo dos dados de treino). - Não é um verificador de fatos; pode alucinar conteúdo. - Evite uso em cenários sensíveis sem validação humana. ## Como testar rapidamente (linha de comando) 1) Crie/ative um ambiente Python e instale dependências mínimas: - transformers, torch, accelerate, safetensors 2) Execute o script `test_inference.py` (fornecido nesta pasta): ```bash python test_inference.py \ --model_dir . \ --prompt "Explique em termos simples o que é aprendizado de máquina." \ --max_new_tokens 80 ``` Parâmetros úteis: - `--temperature` (controle de criatividade, ex.: 0.7) - `--top_p` (amostragem nucleus, ex.: 0.9) - `--seed` (reprodutibilidade) ## Exemplo de uso em Python ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_dir = "." # caminho desta pasta tokenizer = AutoTokenizer.from_pretrained(model_dir) model = AutoModelForCausalLM.from_pretrained(model_dir) prompt = "Explique o que é um modelo de linguagem de forma simples." inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( *inputs, max_new_tokens=80, temperature=0.7, top_p=0.9, do_sample=True, ) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Dados de treino e avaliação - Fonte: conjunto de dados local (não especificado neste repositório). - Tarefa: modelagem de linguagem causal (próximo token). - Observação: para reprodutibilidade completa, registre e publique a origem dos dados quando possível. ## Procedimento de treino ### Hiperparâmetros de treino Os seguintes hiperparâmetros foram usados durante o treino: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: ADAMW_TORCH_FUSED (betas=(0.9,0.999), epsilon=1e-08) - lr_scheduler_type: linear - num_epochs: 3.0 ### Resultados de treino \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.9127 \| 1.0 \| 1311 \| 3.8362 \| \| 3.8243 \| 2.0 \| 2622 \| 3.8266 \| \| 3.7832 \| 3.0 \| 3933 \| 3.8254 \| ### Versões de framework - Transformers 4.55.1 - Pytorch 2.8.0+cu128 - Datasets 4.0.0 - Tokenizers 0.21.4 ## Reproduzindo o treino O fine-tuning seguiu o guia oficial de CLM dos Transformers (link acima), utilizando `Trainer` com `AutoModelForCausalLM` e `AutoTokenizer`. Para reproduzir: 1) Prepare o dataset em texto (um exemplo por linha funciona bem). 2) Tokenize com o tokenizer do modelo base. 3) Treine com os hiperparâmetros acima, salvando checkpoints nesta pasta. ## Estrutura desta pasta - `config.json`, `tokenizer.json`, `tokenizer_config.json`, `vocab.json`, `merges.txt`: artefatos do modelo/tokenizer. - `model.safetensors`, `generation_config.json`: pesos e config de geração. - `checkpoint-`: checkpoints do treinamento. - `runs/`: logs do treinamento (ex.: TensorBoard). - `test_inference.py`: script de teste por CLI. - `TESTE_RAPIDO.md`: guia de execução rápida. ## Aviso Este modelo pode produzir saídas inexatas ou tendenciosas. Avalie e filtre conforme o uso pretendido.
broinopio/blockassist-bc-monstrous_scampering_spider_1755595419	broinopio	2025-08-19T09:59:25Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "monstrous scampering spider", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T09:59:17Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - monstrous scampering spider --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755595848	ihsanridzi	2025-08-19T09:59:23Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T09:59:20Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
xiaomama2002/deepseek_qwen3_8b_1_epoch_hints_removed	xiaomama2002	2025-08-19T09:54:12Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "base_model:finetune:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T09:53:10Z	--- library_name: transformers license: other base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: sft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sft This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) on the deepseek_qwen3_8b_hints_removed dataset. It achieves the following results on the evaluation set: - Loss: 0.1548 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.55.0 - Pytorch 2.8.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
huseyincavus/medgemma-4b-it-Q8_0-GGUF	huseyincavus	2025-08-19T09:53:24Z	0	0	transformers	[ "transformers", "gguf", "medical", "radiology", "clinical-reasoning", "dermatology", "pathology", "ophthalmology", "chest-x-ray", "llama-cpp", "gguf-my-repo", "image-text-to-text", "base_model:google/medgemma-4b-it", "base_model:quantized:google/medgemma-4b-it", "license:other", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-08-19T09:53:06Z	--- license: other license_name: health-ai-developer-foundations license_link: https://developers.google.com/health-ai-developer-foundations/terms library_name: transformers pipeline_tag: image-text-to-text extra_gated_heading: Access MedGemma on Hugging Face extra_gated_prompt: To access MedGemma on Hugging Face, you're required to review and agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms). To do this, please ensure you're logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/medgemma-4b-it tags: - medical - radiology - clinical-reasoning - dermatology - pathology - ophthalmology - chest-x-ray - llama-cpp - gguf-my-repo --- # huseyincavus/medgemma-4b-it-Q8_0-GGUF This model was converted to GGUF format from [`google/medgemma-4b-it`](https://huggingface.co/google/medgemma-4b-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/google/medgemma-4b-it) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo huseyincavus/medgemma-4b-it-Q8_0-GGUF --hf-file medgemma-4b-it-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo huseyincavus/medgemma-4b-it-Q8_0-GGUF --hf-file medgemma-4b-it-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo huseyincavus/medgemma-4b-it-Q8_0-GGUF --hf-file medgemma-4b-it-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo huseyincavus/medgemma-4b-it-Q8_0-GGUF --hf-file medgemma-4b-it-q8_0.gguf -c 2048 ```

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