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niekodhriwa4/fvdf | niekodhriwa4 | 2025-04-25T11:26:35Z | 0 | 0 | null | [
"license:bsd-2-clause",
"region:us"
]
| null | 2025-04-25T11:26:35Z | ---
license: bsd-2-clause
---
|
dgambettaphd/M_llm3_gen9_run0_WXS_doc1000_synt64_tot128_FRESH | dgambettaphd | 2025-04-25T11:21:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T11:20:52Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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eaddario/Llama-Guard-3-8B-GGUF | eaddario | 2025-04-25T11:21:03Z | 1,487 | 0 | null | [
"gguf",
"quant",
"experimental",
"text-generation",
"en",
"dataset:eaddario/imatrix-calibration",
"arxiv:2406.17415",
"base_model:meta-llama/Llama-Guard-3-8B",
"base_model:quantized:meta-llama/Llama-Guard-3-8B",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2025-02-15T21:57:05Z | ---
base_model:
- meta-llama/Llama-Guard-3-8B
datasets:
- eaddario/imatrix-calibration
language:
- en
license:
- llama3.1
pipeline_tag: text-generation
tags:
- gguf
- quant
- experimental
---
# Experimental layer-wise quantization of meta-llama/Llama-Guard-3-8B
Using [LLaMA C++](<https://github.com/ggerganov/llama.cpp>) release [b5170](<https://github.com/ggerganov/llama.cpp/releases/tag/b5170>) for quantization.
Original model: [meta-llama/Llama-Guard-3-8B](https://huggingface.co/meta-llama/Llama-Guard-3-8B)
From the original model creators:
> Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.
>
> Llama Guard 3 was aligned to safeguard against the MLCommons standardized hazards taxonomy and designed to support Llama 3.1 capabilities. Specifically, it provides content moderation in 8 languages, and was optimized to support safety and security for search and code interpreter tool calls.
# PLEASE READ THIS BEFORE USING THESE EXPERIMENTAL VERSIONS!
An area of personal interest is finding ways to optimize the inference performance of LLMs when deployed in resource-constrained environments like commodity hardware, desktops, laptops, mobiles, edge devices, etc. There are many approaches to accomplish this, including architecture simplification and knowledge distillation, but my focus has been primarily on quantization and pruning.
The method used to produce these experimental versions is covered in [Squeezing Tensor Bits: the quest for smaller LLMs](<https://medium.com/@eaddario/squeezing-tensor-bits-the-quest-for-smaller-llms-86b23bd052ca>), but at a high level it involves using custom versions of `llama-imatrix` and `llama-quantize` to identify the influential tensors, and quantize the most important layers to higher bit precision and the less important to lower bits. This process was partly inspired by Dumitru's et al [Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels](<https://arxiv.org/abs/2406.17415>).
There’re two pull requests ([imatrix](<https://github.com/ggml-org/llama.cpp/pull/12718>) & [quantize](<https://github.com/ggml-org/llama.cpp/pull/12511>)) to merge these changes back into the core llama.cpp project. This may or may not ever happen so, until then, the modified versions will be available on [GitHub](<https://github.com/EAddario/llama.cpp>).
For testing and comparison I'd normally use models produced by [Unsloth](<https://huggingface.co/unsloth>) ([Daniel and Michael Han](<https://unsloth.ai/>) do some really advanced level stuff!) and [Bartowski](<https://huggingface.co/bartowski>) (see credits below), but they don't provide GGUF versions of this model, so all tests and comparisons are done against naive quantizations obtained by simply running `llama-quantize` with no further optimization.
All experimental versions were generated using an appropriate imatrix created from calibration datasets available at [eaddario/imatrix-calibration](<https://huggingface.co/datasets/eaddario/imatrix-calibration>). At its core, an Importance Matrix (imatrix) is a table or, more broadly, a structured representation that scores the relative importance of different features or parameters in a machine learning model. It essentially quantifies the "impact" each feature has on a specific outcome, prediction, or relationship being modeled, and it helps to counterbalance the negative effects of quantization and pruning.
The process to generate these models is roughly as follows:
1. Convert the the original model's tensors to [GGUF](<https://huggingface.co/docs/hub/en/gguf>) F16*
2. Estimate the Perplexity score for the F16 model (baseline) using the [wikitext-2-raw-v1](<https://huggingface.co/datasets/Salesforce/wikitext/tree/main/wikitext-2-raw-v1>) dataset, and save the [logits](<https://huggingface.co/eaddario/Llama-Guard-3-8B-GGUF/tree/main/logits>)
3. Generate an [imatrix](<https://huggingface.co/eaddario/Llama-Guard-3-8B-GGUF/tree/main/imatrix>) from selected calibration datasets
4. Determine tensor and layer Importance Score contribution using a modified version of `llama-imatrix`
5. Select an appropiate quant level for each tensor using a modified version of `llama-quantize`
6. Calculate Perplexity, KL Divergence, ARC (Easy+Challenge), HellaSwag, MMLU, Truthful QA and WinoGrande scores for each quantized model
7. Keep versions with the best scores
8. Repeat until all desired quants are created. I find that quantizations below Q3/IQ3 are not fit for my purposes and therefore do not usually generate them, but happy to provide other quants on request.
*[BF16](<https://en.wikipedia.org/wiki/Bfloat16_floating-point_format>) would be preferred, but Apple's GPUs don't support it yet, and therefore any operations are executed in the CPU, making it unacceptably slow. This is expected to change in the near term but until then, if you are using Apple kit avoid using any models tagged BF16
# Models
### Sizes (in GB)
| Model | Naive | Repo | Shrinkage |
| --------------------------------------------------------- | ----: | ---: | --------: |
| [Llama-Guard-3-8B-IQ3_M](./Llama-Guard-3-8B-IQ3_M.gguf) | 3.78 | 3.69 | 2.4% |
| [Llama-Guard-3-8B-IQ3_S](./Llama-Guard-3-8B-IQ3_S.gguf) | 3.68 | 3.43 | 6.8% |
| [Llama-Guard-3-8B-IQ4_NL](./Llama-Guard-3-8B-IQ4_NL.gguf) | 4.71 | 4.39 | 6.8% |
| [Llama-Guard-3-8B-Q3_K_L](./Llama-Guard-3-8B-Q3_K_L.gguf) | 4.32 | 3.76 | 13.0% |
| [Llama-Guard-3-8B-Q3_K_M](./Llama-Guard-3-8B-Q3_K_M.gguf) | 4.02 | 3.56 | 11.4% |
| [Llama-Guard-3-8B-Q3_K_S](./Llama-Guard-3-8B-Q3_K_S.gguf) | 3.66 | 3.31 | 9.6% |
| [Llama-Guard-3-8B-Q4_K_M](./Llama-Guard-3-8B-Q4_K_M.gguf) | 4.92 | 4.41 | 10.4% |
| [Llama-Guard-3-8B-Q4_K_S](./Llama-Guard-3-8B-Q4_K_S.gguf) | 4.69 | 4.28 | 8.7% |
| [Llama-Guard-3-8B-Q5_K_M](./Llama-Guard-3-8B-Q5_K_M.gguf) | 5.73 | 5.38 | 6.1% |
| [Llama-Guard-3-8B-Q5_K_S](./Llama-Guard-3-8B-Q5_K_S.gguf) | 5.60 | 5.24 | 6.4% |
| [Llama-Guard-3-8B-Q6_K](./Llama-Guard-3-8B-Q6_K.gguf) | 6.60 | 6.57 | 0.5% |
| [Llama-Guard-3-8B-Q8_0](./Llama-Guard-3-8B-Q8_0.gguf) | 8.54 | 7.73 | 9.5% |
### Perplexity and KL Divergence scores
| Model | μPPL | 𝜌PPL | μKLD | RMS Δp |
| --------------------------------------------------------- | -----------------: | -----: | -----------------: | ------------: |
| [Llama-Guard-3-8B-IQ3_M](./Llama-Guard-3-8B-IQ3_M.gguf) | 7.423790 ±0.046691 | 97.11% | 0.134115 ±0.000651 | 11.077 ±0.059 |
| [Llama-Guard-3-8B-IQ3_S](./Llama-Guard-3-8B-IQ3_S.gguf) | 7.746531 ±0.048960 | 96.22% | 0.179586 ±0.000744 | 12.616 ±0.060 |
| [Llama-Guard-3-8B-IQ4_NL](./Llama-Guard-3-8B-IQ4_NL.gguf) | 6.935864 ±0.042688 | 98.71% | 0.059280 ±0.000325 | 7.170 ±0.046 |
| [Llama-Guard-3-8B-Q3_K_L](./Llama-Guard-3-8B-Q3_K_L.gguf) | 7.630634 ±0.047920 | 96.28% | 0.165526 ±0.000769 | 12.135 ±0.061 |
| [Llama-Guard-3-8B-Q3_K_M](./Llama-Guard-3-8B-Q3_K_M.gguf) | 7.831542 ±0.049335 | 95.77% | 0.188482 ±0.000852 | 12.979 ±0.062 |
| [Llama-Guard-3-8B-Q3_K_S](./Llama-Guard-3-8B-Q3_K_S.gguf) | 8.269311 ±0.052149 | 94.63% | 0.239987 ±0.001029 | 14.794 ±0.066 |
| [Llama-Guard-3-8B-Q4_K_M](./Llama-Guard-3-8B-Q4_K_M.gguf) | 6.908041 ±0.042539 | 98.78% | 0.055843 ±0.000320 | 7.016 ±0.048 |
| Llama-Guard-3-8B-Q4_K_M (naive) | 6.731828 ±0.041532 | 99.34% | 0.030829 ±0.000214 | 5.255 ±0.045 |
| [Llama-Guard-3-8B-Q4_K_S](./Llama-Guard-3-8B-Q4_K_S.gguf) | 6.930856 ±0.042651 | 98.70% | 0.059620 ±0.000336 | 7.285 ±0.049 |
| [Llama-Guard-3-8B-Q5_K_M](./Llama-Guard-3-8B-Q5_K_M.gguf) | 6.648795 ±0.040800 | 99.62% | 0.017289 ±0.000115 | 3.870 ±0.034 |
| [Llama-Guard-3-8B-Q5_K_S](./Llama-Guard-3-8B-Q5_K_S.gguf) | 6.659786 ±0.040894 | 99.60% | 0.018179 ±0.000120 | 3.957 ±0.034 |
| [Llama-Guard-3-8B-Q6_K](./Llama-Guard-3-8B-Q6_K.gguf) | 6.581335 ±0.040401 | 99.83% | 0.007279 ±0.000061 | 2.532 ±0.028 |
| [Llama-Guard-3-8B-Q8_0](./Llama-Guard-3-8B-Q8_0.gguf) | 6.569465 ±0.040265 | 99.89% | 0.004781 ±0.000042 | 2.072 ±0.025 |
| [Llama-Guard-3-8B-F16](./Llama-Guard-3-8B-F16.gguf) | 6.554978 ±0.040159 | 100% | N/A | N/A |
### ARC, HellaSwag, MMLU, Truthful QA and WinoGrande scores
Scores generated using [llama-perplexity](<https://github.com/ggml-org/llama.cpp/tree/master/examples/perplexity>) with 750 tasks per test, and a context size of 768 tokens.
For the test data used in the generation of these scores, follow the appropiate links: [HellaSwag](<https://github.com/klosax/hellaswag_text_data>), [ARC, MMLU, Truthful QA](<https://huggingface.co/datasets/ikawrakow/validation-datasets-for-llama.cpp/tree/main>) and [WinoGrande](<https://huggingface.co/datasets/ikawrakow/winogrande-eval-for-llama.cpp/tree/main>)
| Model | ARC | HellaSwag | MMLU | Truthful QA | WinoGrande | Avg Score |
| --------------------------------------------------------- | --------------: | --------: | --------------: | --------------: | --------------: | --------: |
| [Llama-Guard-3-8B-IQ3_M](./Llama-Guard-3-8B-IQ3_M.gguf) | 66.5333 ±1.7242 | 80.40 | 36.6667 ±1.7608 | 31.4667 ±1.6968 | 73.2000 ±1.6184 | 57.65 |
| [Llama-Guard-3-8B-IQ3_S](./Llama-Guard-3-8B-IQ3_S.gguf) | 65.4667 ±1.7374 | 79.07 | 35.2000 ±1.7451 | 29.2000 ±1.6614 | 70.8000 ±1.6614 | 55.95 |
| [Llama-Guard-3-8B-IQ4_NL](./Llama-Guard-3-8B-IQ4_NL.gguf) | 64.9333 ±1.7436 | 79.60 | 36.8000 ±1.7621 | 30.5333 ±1.6828 | 73.2000 ±1.6184 | 57.01 |
| [Llama-Guard-3-8B-Q3_K_L](./Llama-Guard-3-8B-Q3_K_L.gguf) | 64.9333 ±1.7436 | 78.93 | 37.0667 ±1.7648 | 33.8667 ±1.7292 | 72.2667 ±1.6358 | 57.41 |
| [Llama-Guard-3-8B-Q3_K_M](./Llama-Guard-3-8B-Q3_K_M.gguf) | 63.6000 ±1.7581 | 78.67 | 36.6667 ±1.7608 | 33.4667 ±1.7242 | 70.6667 ±1.6636 | 56.61 |
| [Llama-Guard-3-8B-Q3_K_S](./Llama-Guard-3-8B-Q3_K_S.gguf) | 60.2667 ±1.7880 | 77.46 | 35.4667 ±1.7481 | 34.1333 ±1.7325 | 71.7333 ±1.6453 | 55.81 |
| [Llama-Guard-3-8B-Q4_K_M](./Llama-Guard-3-8B-Q4_K_M.gguf) | 65.6000 ±1.7358 | 80.26 | 38.1333 ±1.7748 | 30.4000 ±1.6807 | 72.2667 ±1.6358 | 57.33 |
| Llama-Guard-3-8B-Q4_K_M (naive) | 66.9786 ±1.7207 | 79.20 | 40.2667 ±1.7920 | 31.0559 ±2.5827 | 74.2667 ±1.5974 | 58.35 |
| [Llama-Guard-3-8B-Q4_K_S](./Llama-Guard-3-8B-Q4_K_S.gguf) | 66.1333 ±1.7292 | 80.00 | 37.8667 ±1.7724 | 30.4000 ±1.6807 | 71.6000 ±1.6477 | 57.20 |
| [Llama-Guard-3-8B-Q5_K_M](./Llama-Guard-3-8B-Q5_K_M.gguf) | 65.8667 ±1.7325 | 81.33 | 38.0000 ±1.7736 | 31.6000 ±1.6988 | 72.6667 ±1.6284 | 57.89 |
| [Llama-Guard-3-8B-Q5_K_S](./Llama-Guard-3-8B-Q5_K_S.gguf) | 65.7333 ±1.7342 | 81.33 | 37.4667 ±1.7686 | 31.8667 ±1.7026 | 72.9333 ±1.6235 | 57.87 |
| [Llama-Guard-3-8B-Q6_K](./Llama-Guard-3-8B-Q6_K.gguf) | 65.6000 ±1.7358 | 81.06 | 38.6667 ±1.7794 | 30.9333 ±1.6889 | 72.5333 ±1.6309 | 57.76 |
| [Llama-Guard-3-8B-Q8_0](./Llama-Guard-3-8B-Q8_0.gguf) | 65.3333 ±1.7389 | 81.60 | 38.4000 ±1.7771 | 30.8000 ±1.6869 | 72.8000 ±1.6260 | 57.79 |
| [Llama-Guard-3-8B-F16](./Llama-Guard-3-8B-F16.gguf) | 64.9333 ±1.7436 | 81.60 | 38.2667 ±1.7759 | 30.6667 ±1.6849 | 72.8000 ±1.6260 | 57.65 |
### Tokens per Second - Benchmarks
Scores generated using [llama-bench](<https://github.com/ggml-org/llama.cpp/tree/master/examples/llama-bench>). Naive (`llama-quantize` with no optimization) Q4_K_M quantization included for comparison.
| model | size | params | backend | threads | test | t/s |
| --------------------------------------------------------- | -------: | -----: | ---------- | ------: | ------------: | ------------: |
| [Llama-Guard-3-8B-Q4_K_M](./Llama-Guard-3-8B-Q4_K_M.gguf) | 4.10 GiB | 8.03 B | Metal,BLAS | 6 | pp512 | 312.18 ± 0.88 |
| [Llama-Guard-3-8B-Q4_K_M](./Llama-Guard-3-8B-Q4_K_M.gguf) | 4.10 GiB | 8.03 B | Metal,BLAS | 6 | tg128 | 27.88 ± 0.03 |
| [Llama-Guard-3-8B-Q4_K_M](./Llama-Guard-3-8B-Q4_K_M.gguf) | 4.10 GiB | 8.03 B | Metal,BLAS | 6 | pp1024+tg1024 | 44.53 ± 0.11 |
| Llama-Guard-3-8B-Q4_K_M (naive) | 4.58 GiB | 8.03 B | Metal,BLAS | 6 | pp512 | 329.30 ± 0.12 |
| Llama-Guard-3-8B-Q4_K_M (naive) | 4.58 GiB | 8.03 B | Metal,BLAS | 6 | tg128 | 26.51 ± 0.02 |
| Llama-Guard-3-8B-Q4_K_M (naive) | 4.58 GiB | 8.03 B | Metal,BLAS | 6 | pp1024+tg1024 | 42.69 ± 1.00 |
# Metrics used
**[Perplexity](<https://huggingface.co/docs/transformers/en/perplexity>):** one of the key metrics used in NLP evaluation. It measures the quality of a language model by evaluating how well it predicts the next token given a particular sequence of words. A PPL of **1** indicates an exact match between predicted and actual, whereas values greater than one indicate a degree of "surprise" the generated token differs from the expected.
**[Kullback–Leibler (KL) Divergence](<https://en.wikipedia.org/wiki/Kullback–Leibler_divergence>):** a statistical measure of how much a probability distribution differs from another. When quantizing models (or altering the original tensors in any way for that matter), the closest we can preserve the weights' probability distribution to the original model the better, thus the closest to **0** the better.
**[AI2 Reasoning Challenge (ARC)](<https://leaderboard.allenai.org/arc/submissions/get-started>):** a benchmark to evaluate the ability of AI models to answer complex science questions that require logical reasoning beyond pattern matching.
**[HellaSwag](<https://rowanzellers.com/hellaswag/>):** the Harder Endings, Longer contexts, and Low-shot Activities for Situations With Adversarial Generations (bit of a mouthful!) is a benchmark designed to test commonsense natural language inference. It requires the model to predict the most likely ending of a sentence.
**[MMLU](<https://github.com/hendrycks/test>):** the Massive Multitask Language Understanding evaluates LLMs’ general knowledge and problem-solving abilities across 57 subjects, including elementary mathematics, US history, computer science, and law.
**[Truthful QA](<https://github.com/sylinrl/TruthfulQA>):** evaluates how well LLMs generate truthful responses to questions. It identifies whether AI models can avoid generating false or misleading information, particularly in areas where human knowledge is prone to misconceptions.
**[Winogrande](<https://winogrande.allenai.org/>):** based on the [Winograd Schema Challenge](<https://cdn.aaai.org/ocs/4492/4492-21843-1-PB.pdf>), is a natural language understanding task requiring models to resolve ambiguities in sentences involving pronoun references.
## Credits
A big **Thank You!** to [Colin Kealty](<https://huggingface.co/bartowski>) for the many contributions and for being one of the best sources of high quality quantized models available in Hugginface, and a really big ***Thank You!*** to [Georgi Gerganov](<https://github.com/ggerganov>) for his amazing work with **llama.cpp** and the **ggml/gguf** libraries.
|
Culturedniichan/mergekit-ties-uzreyxm | Culturedniichan | 2025-04-25T11:18:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2306.01708",
"base_model:ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4",
"base_model:merge:ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4",
"base_model:ReadyArt/Forgotten-Safeword-24B-V2.2",
"base_model:merge:ReadyArt/Forgotten-Safeword-24B-V2.2",
"base_model:TroyDoesAI/BlackSheep-24B",
"base_model:merge:TroyDoesAI/BlackSheep-24B",
"base_model:unsloth/Mistral-Small-24B-Instruct-2501",
"base_model:merge:unsloth/Mistral-Small-24B-Instruct-2501",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T11:07:34Z | ---
base_model:
- unsloth/Mistral-Small-24B-Instruct-2501
- ReadyArt/Forgotten-Safeword-24B-V2.2
- TroyDoesAI/BlackSheep-24B
- ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [unsloth/Mistral-Small-24B-Instruct-2501](https://huggingface.co/unsloth/Mistral-Small-24B-Instruct-2501) as a base.
### Models Merged
The following models were included in the merge:
* [ReadyArt/Forgotten-Safeword-24B-V2.2](https://huggingface.co/ReadyArt/Forgotten-Safeword-24B-V2.2)
* [TroyDoesAI/BlackSheep-24B](https://huggingface.co/TroyDoesAI/BlackSheep-24B)
* [ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4](https://huggingface.co/ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: unsloth/Mistral-Small-24B-Instruct-2501
- model: TroyDoesAI/BlackSheep-24B
parameters:
density: 0.50
weight: 0.60
- model: ReadyArt/Forgotten-Safeword-24B-V2.2
parameters:
density: 0.35
weight: 0.15
- model: ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4
parameters:
density: 0.30
weight: 0.10
merge_method: ties
base_model: unsloth/Mistral-Small-24B-Instruct-2501
parameters:
normalize: true
dtype: bfloat16
```
|
amDANIEL2024/amooti-v1-offline | amDANIEL2024 | 2025-04-25T11:17:28Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T11:15:20Z | ---
base_model: unsloth/gemma-2b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** amDANIEL2024
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-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)
|
dgambettaphd/M_llm3_gen7_run0_WXS_doc1000_synt64_tot128_FRESH | dgambettaphd | 2025-04-25T11:17:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T11:16:33Z | ---
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. 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. 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] |
IronWolfAI/Q25-CySec | IronWolfAI | 2025-04-25T11:16:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T11:16:00Z | ---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** IronWolfAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-bnb-4bit
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)
|
dgambettaphd/M_llm3_gen6_run0_WXS_doc1000_synt64_tot128_FRESH | dgambettaphd | 2025-04-25T11:15:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T11:14:44Z | ---
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. 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. 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] |
deepakkr/tinyllama_instruct_chat_v3 | deepakkr | 2025-04-25T11:14:22Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T10:53:16Z | ---
library_name: transformers
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: tinyllama_instruct_chat_v3
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. -->
# tinyllama_instruct_chat_v3
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.9988 | 593 | nan |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
Alphatao/6d598d5a-995b-4baa-860f-d90b013eca09 | Alphatao | 2025-04-25T11:13:57Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"custom_code",
"arxiv:2305.18290",
"base_model:NousResearch/Yarn-Llama-2-7b-64k",
"base_model:finetune:NousResearch/Yarn-Llama-2-7b-64k",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T07:07:06Z | ---
base_model: NousResearch/Yarn-Llama-2-7b-64k
library_name: transformers
model_name: 6d598d5a-995b-4baa-860f-d90b013eca09
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 6d598d5a-995b-4baa-860f-d90b013eca09
This model is a fine-tuned version of [NousResearch/Yarn-Llama-2-7b-64k](https://huggingface.co/NousResearch/Yarn-Llama-2-7b-64k).
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="Alphatao/6d598d5a-995b-4baa-860f-d90b013eca09", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alphatao-alphatao/Gradients-On-Demand/runs/juugjdok)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
januverma/Qwen2.5-7B-Instruct-GSM-GRPO | januverma | 2025-04-25T11:12:32Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"grpo",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-20T13:33:55Z | ---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- grpo
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** januverma
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
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)
|
madelinesimona/madelinesimona | madelinesimona | 2025-04-25T11:11:44Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2025-04-25T11:11:43Z | ---
license: bigscience-openrail-m
---
|
mayyin/mon-modele-fusionne | mayyin | 2025-04-25T11:11:33Z | 0 | 0 | null | [
"safetensors",
"mistral",
"merge",
"mergekit",
"lazymergekit",
"BioMistral/BioMistral-7B",
"HuggingFaceH4/zephyr-7b-beta",
"base_model:BioMistral/BioMistral-7B",
"base_model:merge:BioMistral/BioMistral-7B",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:merge:HuggingFaceH4/zephyr-7b-beta",
"region:us"
]
| null | 2025-04-25T11:09:56Z | ---
base_model:
- BioMistral/BioMistral-7B
- HuggingFaceH4/zephyr-7b-beta
tags:
- merge
- mergekit
- lazymergekit
- BioMistral/BioMistral-7B
- HuggingFaceH4/zephyr-7b-beta
---
# llm-7B-slerp
llm-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B)
* [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: BioMistral/BioMistral-7B
layer_range: [0, 8]
- model: HuggingFaceH4/zephyr-7b-beta
layer_range: [0, 8]
merge_method: slerp
base_model: BioMistral/BioMistral-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mayyin/llm-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
masani/SFT_gsm8k_Llama-2-7b-hf_epoch_2_global_step_58 | masani | 2025-04-25T11:10:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T11:05:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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tajuarAkash/fine-tune-tinyllama | tajuarAkash | 2025-04-25T11:09:59Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-24T20:05:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- 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).
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mradermacher/Qwen2-1.5B-Sign-GGUF | mradermacher | 2025-04-25T11:07:58Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:thundax/Qwen2-1.5B-Sign",
"base_model:quantized:thundax/Qwen2-1.5B-Sign",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-25T10:24:17Z | ---
base_model: thundax/Qwen2-1.5B-Sign
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/thundax/Qwen2-1.5B-Sign
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-i1-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/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5B-Sign-GGUF/resolve/main/Qwen2-1.5B-Sign.f16.gguf) | f16 | 3.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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.
<!-- end -->
|
zerinebajajs/sdvfdfv | zerinebajajs | 2025-04-25T11:07:40Z | 0 | 0 | null | [
"license:bsd-2-clause",
"region:us"
]
| null | 2025-04-25T11:07:39Z | ---
license: bsd-2-clause
---
|
TareksLab/Z-MODEL3-V1-FUSED | TareksLab | 2025-04-25T11:07:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:TareksLab/Z-MODEL3-V1-DL",
"base_model:merge:TareksLab/Z-MODEL3-V1-DL",
"base_model:TareksLab/Z-MODEL3-V1-DT",
"base_model:merge:TareksLab/Z-MODEL3-V1-DT",
"base_model:TareksLab/Z-MODEL3-V1-SCE",
"base_model:merge:TareksLab/Z-MODEL3-V1-SCE",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T10:36:14Z | ---
base_model:
- TareksLab/Z-MODEL3-V1-DT
- TareksLab/Z-MODEL3-V1-DL
- TareksLab/Z-MODEL3-V1-SCE
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [TareksLab/Z-MODEL3-V1-DL](https://huggingface.co/TareksLab/Z-MODEL3-V1-DL) as a base.
### Models Merged
The following models were included in the merge:
* [TareksLab/Z-MODEL3-V1-DT](https://huggingface.co/TareksLab/Z-MODEL3-V1-DT)
* [TareksLab/Z-MODEL3-V1-SCE](https://huggingface.co/TareksLab/Z-MODEL3-V1-SCE)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: TareksLab/Z-MODEL3-V1-DT
- model: TareksLab/Z-MODEL3-V1-SCE
- model: TareksLab/Z-MODEL3-V1-DL
base_model: TareksLab/Z-MODEL3-V1-DL
merge_method: model_stock
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: base
```
|
dgambettaphd/M_llm3_gen0_run0_WXS_doc1000_synt64_tot128_FRESH | dgambettaphd | 2025-04-25T11:04:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-04-25T11:02:19Z | ---
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. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
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<!-- 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. -->
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<!-- 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
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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Soundappan123/smolvlm-instruct-trl-dpo-rlaif-v | Soundappan123 | 2025-04-25T11:01:25Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:HuggingFaceTB/SmolVLM-Instruct",
"base_model:finetune:HuggingFaceTB/SmolVLM-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-24T06:16:36Z | ---
base_model: HuggingFaceTB/SmolVLM-Instruct
library_name: transformers
model_name: smolvlm-instruct-trl-dpo-rlaif-v
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for smolvlm-instruct-trl-dpo-rlaif-v
This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct).
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="Soundappan123/smolvlm-instruct-trl-dpo-rlaif-v", 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 DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.4.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
``` |
dgambettaphd/M_llm3_gen10_run0_WXS_doc1000_synt64_tot128_SYNLAST | dgambettaphd | 2025-04-25T11:01:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T11:00:56Z | ---
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. 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. -->
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dgambettaphd/M_llm3_gen9_run0_WXS_doc1000_synt64_tot128_SYNLAST | dgambettaphd | 2025-04-25T10:59:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T10:59:31Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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etoileboots/gemma-3-full-finetune | etoileboots | 2025-04-25T10:59:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T10:58:48Z | ---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** etoileboots
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text 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)
|
dgambettaphd/M_llm3_gen9_run0_X_doc1000_synt64_tot128_FRESH | dgambettaphd | 2025-04-25T10:55:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T10:55:21Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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dgambettaphd/M_llm3_gen6_run0_WXS_doc1000_synt64_tot128_SYNLAST | dgambettaphd | 2025-04-25T10:55:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T10:55:16Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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iamsahinemir/bitirme_model | iamsahinemir | 2025-04-25T10:55:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T10:51:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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kerryfarrell/kerryfarrel | kerryfarrell | 2025-04-25T10:55:00Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
]
| null | 2025-04-25T10:55:00Z | ---
license: bsd-3-clause
---
|
dgambettaphd/M_llm3_gen5_run0_WXS_doc1000_synt64_tot128_SYNLAST | dgambettaphd | 2025-04-25T10:53:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T10:53:47Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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#### 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf | RichardErkhov | 2025-04-25T10:52:35Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-25T08:23:06Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
only-nvidia_18120 - GGUF
- Model creator: https://huggingface.co/minhhien0811/
- Original model: https://huggingface.co/minhhien0811/only-nvidia_18120/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [only-nvidia_18120.Q2_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q2_K.gguf) | Q2_K | 2.81GB |
| [only-nvidia_18120.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.IQ3_XS.gguf) | IQ3_XS | 3.12GB |
| [only-nvidia_18120.IQ3_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.IQ3_S.gguf) | IQ3_S | 3.26GB |
| [only-nvidia_18120.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q3_K_S.gguf) | Q3_K_S | 3.25GB |
| [only-nvidia_18120.IQ3_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.IQ3_M.gguf) | IQ3_M | 3.33GB |
| [only-nvidia_18120.Q3_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q3_K.gguf) | Q3_K | 3.55GB |
| [only-nvidia_18120.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [only-nvidia_18120.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q3_K_L.gguf) | Q3_K_L | 3.81GB |
| [only-nvidia_18120.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.IQ4_XS.gguf) | IQ4_XS | 3.96GB |
| [only-nvidia_18120.Q4_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q4_0.gguf) | Q4_0 | 4.13GB |
| [only-nvidia_18120.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.IQ4_NL.gguf) | IQ4_NL | 4.16GB |
| [only-nvidia_18120.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q4_K_S.gguf) | Q4_K_S | 4.15GB |
| [only-nvidia_18120.Q4_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q4_K.gguf) | Q4_K | 4.36GB |
| [only-nvidia_18120.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q4_K_M.gguf) | Q4_K_M | 4.36GB |
| [only-nvidia_18120.Q4_1.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q4_1.gguf) | Q4_1 | 4.54GB |
| [only-nvidia_18120.Q5_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q5_0.gguf) | Q5_0 | 4.95GB |
| [only-nvidia_18120.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q5_K_S.gguf) | Q5_K_S | 4.95GB |
| [only-nvidia_18120.Q5_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q5_K.gguf) | Q5_K | 5.07GB |
| [only-nvidia_18120.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q5_K_M.gguf) | Q5_K_M | 5.07GB |
| [only-nvidia_18120.Q5_1.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q5_1.gguf) | Q5_1 | 5.36GB |
| [only-nvidia_18120.Q6_K.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q6_K.gguf) | Q6_K | 5.82GB |
| [only-nvidia_18120.Q8_0.gguf](https://huggingface.co/RichardErkhov/minhhien0811_-_only-nvidia_18120-gguf/blob/main/only-nvidia_18120.Q8_0.gguf) | Q8_0 | 7.54GB |
Original model description:
---
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]
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### Model Sources [optional]
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## 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. -->
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<!-- 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. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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. -->
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[More Information Needed]
### Results
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#### 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]
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|
dgambettaphd/M_llm3_gen4_run0_WXS_doc1000_synt64_tot128_SYNLAST | dgambettaphd | 2025-04-25T10:52:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T10:52:19Z | ---
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. 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. 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
YOYO-AI/YOYO-O1-32B-V4-Q4_K_M-GGUF | YOYO-AI | 2025-04-25T10:51:39Z | 21 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:YOYO-AI/YOYO-O1-32B-V4",
"base_model:quantized:YOYO-AI/YOYO-O1-32B-V4",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-25T10:50:09Z | ---
base_model: YOYO-AI/YOYO-O1-32B-V4
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# YOYO-AI/YOYO-O1-32B-V4-Q4_K_M-GGUF
This model was converted to GGUF format from [`YOYO-AI/YOYO-O1-32B-V4`](https://huggingface.co/YOYO-AI/YOYO-O1-32B-V4) 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/YOYO-AI/YOYO-O1-32B-V4) 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 YOYO-AI/YOYO-O1-32B-V4-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo YOYO-AI/YOYO-O1-32B-V4-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-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 YOYO-AI/YOYO-O1-32B-V4-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo YOYO-AI/YOYO-O1-32B-V4-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-q4_k_m.gguf -c 2048
```
|
mradermacher/Qwen2-0.5B-fncl-i1-GGUF | mradermacher | 2025-04-25T10:50:22Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:glaiveai/glaive-function-calling-v2",
"base_model:haripritam/Qwen2-0.5B-fncl",
"base_model:quantized:haripritam/Qwen2-0.5B-fncl",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| null | 2025-04-25T10:15:30Z | ---
base_model: haripritam/Qwen2-0.5B-fncl
datasets:
- glaiveai/glaive-function-calling-v2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/haripritam/Qwen2-0.5B-fncl
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-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/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ3_S.gguf) | i1-IQ3_S | 0.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ3_M.gguf) | i1-IQ3_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q4_0.gguf) | i1-Q4_0 | 0.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q4_1.gguf) | i1-Q4_1 | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-GGUF/resolve/main/Qwen2-0.5B-fncl.i1-Q6_K.gguf) | i1-Q6_K | 0.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
ashwinkh/test-001 | ashwinkh | 2025-04-25T10:49:41Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-04-25T10:48:40Z | ---
license: apache-2.0
---
|
dgambettaphd/M_llm3_gen0_run0_WXS_doc1000_synt64_tot128_SYNLAST | dgambettaphd | 2025-04-25T10:46:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-04-25T10:43:42Z | ---
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. 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. 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. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
renatakyla/renatakyla | renatakyla | 2025-04-25T10:46:05Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2025-04-25T10:45:48Z | ---
license: bigscience-openrail-m
---
|
rizkysulaeman/Gemma-3-1B-Multimodal-Reasoning-EN | rizkysulaeman | 2025-04-25T10:43:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T10:36:50Z | ---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** rizkysulaeman
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text 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)
|
Eswar-01/llama_merged_fine_tuned_model | Eswar-01 | 2025-04-25T10:43:30Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T10:40:43Z | ---
base_model: unsloth/llama-3.2-3b-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Eswar-01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct
This llama 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)
|
tmt3103/VSFC-topic-classify-phoBERT | tmt3103 | 2025-04-25T10:43:07Z | 0 | 0 | null | [
"safetensors",
"roberta",
"text-classification",
"topic-analysis",
"vietnamese",
"vsfc",
"phobert",
"vi",
"dataset:uit-vsfc",
"license:apache-2.0",
"model-index",
"region:us"
]
| text-classification | 2025-04-25T10:32:30Z | ---
license: apache-2.0
tags:
- text-classification
- topic-analysis
- vietnamese
- vsfc
- phobert
language:
- vi
datasets:
- uit-vsfc
model-index:
- name: VSFC Topic Classifier (PhoBERT)
results:
- task:
type: text-classification
name: Topic Classification
dataset:
name: UIT-VSFC
type: uit-vsfc
metrics:
- type: accuracy
value: 89.1346
- type: f1
value: 89.0436
---
# VSFC TOPIC Classifier using PhoBERT
This model is fine-tuned from [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base) on the UIT-VSFC dataset for Vietnamese Students Feedback Corpus topic analysis.
## 🧠 Model Details
- **Model type**: Transformer (BERT-based)
- **Base model**: [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base)
- **Fine-tuned task**: Sentence-level topc classification
- **Target labels**: Lecturer, Training program, Facility, Others
- **Tokenizer**: SentencePiece BPE
## 📚 Training Data
- **Dataset**: [UIT-VSFC](https://drive.google.com/drive/folders/1xclbjHHK58zk2X6iqbvMPS2rcy9y9E0X)
- **Language**: Vietnamese
- **License**: Academic use
- Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different research fields between sentiment analysis and education.
## 🚀 How to Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tmt3103/VSFC-topic-classify-phoBERT")
model = AutoModelForSequenceClassification.from_pretrained("tmt3103/VSFC-topic-classify-phoBERT")
inputs = tokenizer("Giảng viên thân thiện dễ thương", return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(dim=-1).item()
|
shubhamprshr/Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300 | shubhamprshr | 2025-04-25T10:42:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"dataset:blocksworld-dataset",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T04:02:53Z | ---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: blocksworld-dataset
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [blocksworld-dataset](https://huggingface.co/datasets/blocksworld-dataset) dataset.
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="shubhamprshr/Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/shubhamprshr27-tamu/BW2/runs/jx4znt38)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
amentaphd/eu-regulation-embeddings-snowflake-m-v2 | amentaphd | 2025-04-25T10:41:54Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"gte",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:46338",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"custom_code",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:Snowflake/snowflake-arctic-embed-m-v2.0",
"base_model:finetune:Snowflake/snowflake-arctic-embed-m-v2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-04-25T10:41:15Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:46338
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-m-v2.0
widget:
- source_sentence: What are the anticipated financial effects that could arise from
material risks associated with resource use and circular economy, and how might
these risks impact the financial position, performance, and cash flows of an undertaking
over different time frames?
sentences:
- '(a)
anticipated financial effects due to material risks arising from material resource
use and circular economy -related impacts and dependencies and how these risks
have or could reasonably be expected to have) a material influence on the undertaking’s
financial position, financial performance performance, and cash flows over the
short-, medium- and long-term; and
(b)
anticipated financial effects due to material opportunities related to resource
use and circular economy.
The disclosure shall include:
(a)'
- combination of hydrocarbons obtained as a raffinate from a sulphuric acid treating
process. It consists of hydrocarbons having carbon numbers predominantly in the
range of C7 through C12 and boiling in the range of approximately 90 °C to 230
°C.) 649-351-00-7 265-115-2 64742-15-0 P Naphtha (petroleum), chemically neutralised
heavy; Low boiling point naphtha — unspecified (A complex combination of hydrocarbons
produced by a treating process to remove acidic materials. It consists of hydrocarbons
having carbon numbers predominantly in the range of C6 through C12 and boiling
in the range of approximately 65 °C to 230 °C.) 649-352-00-2 265-122-0 64742-22-9
P Naphtha (petroleum), chemically neutralised light; Low boiling point naphtha
—
- '2. Member States shall require any investment firm wishing to establish a branch
within the territory of another Member State or to use tied agents established
in another Member State in which it has not established a branch, first to notify
the competent authority of its home Member State and to provide it with the following
information:
(a) the Member States within the territory of which it plans to establish a branch
or the Member States in which it has not established a branch but plans to use
tied agents established there;
(b) a programme of operations setting out, inter alia, the investment services
and/or activities as well as the ancillary services to be offered;
(c) where established, the organisational structure of the branch and indicating
whether the branch intends to use tied agents and the identity of those tied agents;
(d) where tied agents are to be used in a Member State in which an investment
firm has not established a branch, a description of the intended use of the tied
agent(s) and an organisational structure, including reporting lines, indicating
how the agent(s) fit into the corporate structure of the investment firm;
(e) the address in the host Member State from which documents may be obtained;
(f) the names of those responsible for the management of the branch or of the
tied agent.
Where an investment firm uses a tied agent established in a Member State outside
its home Member State, such tied agent shall be assimilated to the branch, where
one is established, and shall in any event be subject to the provisions of this
Directive relating to branches.'
- source_sentence: What steps must the single point of contact take if the project
promoter submits an incomplete application for a Strategic Project, and how does
this affect the permit-granting process timeline?
sentences:
- '(1)
‘cooling’ means the extraction of heat from an enclosed or indoor space (comfort
application) or from a process in order to reduce the space or process temperature
to, or maintain it at, a specified temperature (set point); for cooling systems,
the extracted heat is rejected into and absorbed by the ambient air, ambient water
or the ground, where the environment (air, ground, and water) provides a sink
for the heat extracted and thus functions as a cold source;
(2)'
- '1. Suppliers shall provide the manufacturer with all the information and documentation
necessary for the manufacturer to demonstrate the conformity of the packaging
and the packaging materials with this Regulation, including the technical documentation
referred to in Annex VII and required under or pursuant to Articles 5 to 11, in
one or more languages which can be easily understood by the manufacturer. That
information and documentation shall be provided in either paper or electronic
form.
2. Where appropriate, the documentation and information required under Union legal
acts applicable to contact-sensitive packaging shall be part of the information
and documentation to be provided to the manufacturer pursuant to paragraph 1.'
- '6.
No later than 45 days following the receipt of a permit-granting application related
to a Strategic Project, the single point of contact concerned shall acknowledge
that the application is complete or, if the project promoter has not sent all
the information required to process an application, request the project promoter
to submit a complete application without undue delay, specifying which information
is missing. Where the application submitted is deemed to be incomplete a second
time, the single point of contact concerned shall not request information in areas
not covered in the first request for additional information and shall be entitled
only to request further evidence to complete the identified missing information.
The date of the acknowledgement referred to in the first subparagraph shall serve
as the start of the permit-granting process.
7.
No later than one month from the date of acknowledgement referred to in paragraph
6 of this Article, the single point of contact concerned shall draw up, in close
cooperation with the project promoter and other competent authorities concerned,
a detailed schedule for the permit-granting process. The schedule shall be published
by the project promoter on the website referred to in Article 8(5). The single
point of contact concerned shall update the schedule in the event that there are
significant changes that potentially affect the timing of the comprehensive decision.
8.
The single point of contact concerned shall notify the project promoter when the
environmental impact assessment report referred in Article 5(1) of Directive 2011/92/EU
is due, taking into account the organisation of the permit-granting process in
the Member State concerned and the need to allow sufficient time to assess the
report. The period between the deadline for the submission of the environmental
impact assessment report and the actual submission of that report shall not be
counted towards the duration of the permit-granting process referred to in paragraphs
1 and 2 of this Article.
9.'
- source_sentence: What are the requirements for energy audits to be considered compliant
with the specified paragraph, and what role do voluntary agreements play in this
process?
sentences:
- '8. Member States shall develop programmes to encourage enterprises that are not
SMEs and that are not subject to paragraph 1 or 2 to undergo energy audits and
to subsequently implement the recommendations arising from those audits.
9. Energy audits shall be considered to comply with paragraph 2 where they are:
(a) carried out in an independent manner, on the basis of the minimum criteria
set out in Annex VI; (b) implemented under voluntary agreements concluded between
organisations of stakeholders and a body appointed and supervised by the Member
State concerned, by another body to which the competent authorities have delegated
the responsibility concerned or by the Commission. --- ---'
- '3.1.1. The evaluation of all available information shall comprise:
the hazard identification based on all available information,
the establishment of the quantitative dose (concentration)-response (effect) relationship.
3.1.2. When it is not possible to establish the quantitative dose (concentration)-response
(effect) relationship, then this should be justified and a semi-quantitative or
qualitative analysis shall be included.
3.1.3. All information used to assess the effects on a specific environmental
sphere shall be briefly presented, if possible in the form of a table or tables.
The relevant test results (e.g. LC50 or NOEC) and test conditions (e.g. test duration,
route of administration) and other relevant information shall be presented, in
internationally recognised units of measurement for that effect.
3.1.4. All information used to assess the environmental fate of the substance
shall be briefly presented, if possible in the form of a table or tables. The
relevant test results and test conditions and other relevant information shall
be presented, in internationally recognised units of measurement for that effect.
3.1.5. If one study is available then a robust study summary should be prepared
for that study. Where there is more than one study addressing the same effect,
then the study or studies giving rise to the highest concern shall be used to
draw a conclusion and a robust study summary shall be prepared for that study
or studies and included as part of the technical dossier. Robust summaries will
be required of all key data used in the hazard assessment. If the study or studies
giving rise to the highest concern are not used, then this shall be fully justified
and included as part of the technical dossier, not only for the study being used
but also for all studies reaching a higher concern than the study being used.
For substances where all available studies indicate no hazards an overall assessment
of the validity of all studies should be performed.
3.2. Step 2 : Classification and Labelling
▼M51'
- impact of single-use packaging, in particular plastic carrier bags; --- --- (f)
the composting properties and appropriate waste management options for compostable
packaging in accordance with Article 9(2) of this Regulation; consumers shall
be informed that compostable packaging is not suitable for home composting and
that compostable packaging is not to be discarded in nature. --- ---
- source_sentence: In what scenario should information on toxic effects be listed
only once for a mixture?
sentences:
- 'In determining the energy savings from taxation-related policy measures introduced
under Article 10, the following principles shall apply: (a) credit shall be given
only for energy savings from taxation measures exceeding the minimum levels of
taxation applicable to fuels as required in Council Directive 2003/96/EC (2) or
2006/112/EC (3); (b) short-run price elasticities for the calculation of the impact
of the energy taxation measures shall represent the responsiveness of energy demand
to price changes, and shall be estimated on the basis of recent and representative
official data sources, which are applicable for the Member State, and, where applicable,
on the basis of accompanying studies from an independent institute. If a different'
- 'Article 13
Project development assistance
1.
The Commission shall, after consulting the Member States in accordance with Article
21(2), point (c), determine the maximum amount of Innovation Fund support available
for project development assistance.
2.
The Commission may award project development assistance in the form of technical
assistance to any project that falls within the scope of the Innovation Fund,
as set out in Article 10a(8), first and sixth subparagraphs of Directive 2003/87/EC.
3.
The following activities may be funded by way of project development assistance:
(a)
improvement and development of project documentation or of components of the project
design with a view to ensuring the sufficient maturity of the project;
(b)
assessment of the feasibility of the project, including technical and economic
studies;
(c)
advice on the financial and legal structure of the project;
(d)
capacity building of the project proponent.
4.
If project development assistance is implemented under indirect management, the
implementing entity shall carry out the selection procedure and take the decision
to award the project development assistance after having consulted the Commission.
The award criteria shall take into account the degree of innovation compared to
the state of the art, the potential to significantly reduce climate impacts and
to support widespread application, the maturity as well as the geographical and
sectoral balance in relation to the portfolio of funded projects.'
- 'effects of the mixture. The information on toxic effects shall be presented for
each substance, except for the following cases: (a) if the information is duplicated,
it shall be listed only once for the mixture overall, such as when two substances
both cause vomiting and diarrhoea; (b) if it is unlikely that these effects will
occur at the concentrations present, such as when a mild irritant is diluted to
below a certain concentration in a non-irritant solution; (c) where information
on interactions between substances in a mixture is not available, assumptions
shall not be made and instead the health effects of each substance shall be listed
separately. --- ---'
- source_sentence: How does the text suggest addressing the social aspects related
to low- and middle-income transport users in the context of zero-emission vehicle
initiatives?
sentences:
- '(b)
measures intended to accelerate the uptake of zero-emission vehicles or to provide
financial support for the deployment of fully interoperable refuelling and recharging
infrastructure for zero-emission vehicles, or measures to encourage a shift to
public transport and improve multimodality, or to provide financial support in
order to address social aspects concerning low- and middle-income transport users;
(c)
to finance their Social Climate Plan in accordance with Article 15 of Regulation
(EU) 2023/955;
(d)'
- If the planned change is implemented notwithstanding the first and second subparagraphs,
or if an unplanned change has taken place pursuant to which the AIFM’s management
of the AIF no longer complies with this Directive or the AIFM otherwise no longer
complies with this Directive, the competent authorities of the Member State of
reference of the AIFM shall take all due measures in accordance with Article 46,
including, if necessary, the express prohibition of marketing of the AIF.
- '(d)
for gas discharge lamps, 80 % shall be recycled.
Part 2: Minimum targets applicable by category from 15 August 2015 until 14 August
2018 with reference to the categories listed in Annex I:
(a)
for WEEE falling within category 1 or 10 of Annex I,
85 % shall be recovered, and
80 % shall be prepared for re-use and recycled;
(b)
for WEEE falling within category 3 or 4 of Annex I,
80 % shall be recovered, and
70 % shall be prepared for re-use and recycled;
(c)
for WEEE falling within category 2, 5, 6, 7, 8 or 9 of Annex I,
75 % shall be recovered, and
55 % shall be prepared for re-use and recycled;
(d)
for gas discharge lamps, 80 % shall be recycled.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v2.0
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.7058518902123252
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9067840497151735
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9447609183497324
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9730709476954945
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7058518902123252
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3022613499050578
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18895218366994648
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09730709476954946
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7058518902123252
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9067840497151735
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9447609183497324
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9730709476954945
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.851314896054128
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8109469830857718
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8122768308333804
name: Cosine Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v2.0
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) <!-- at revision 95c2741480856aa9666782eb4afe11959938017f -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: GteModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'How does the text suggest addressing the social aspects related to low- and middle-income transport users in the context of zero-emission vehicle initiatives?',
'(b)\n\nmeasures intended to accelerate the uptake of zero-emission vehicles or to provide financial support for the deployment of fully interoperable refuelling and recharging infrastructure for zero-emission vehicles, or measures to encourage a shift to public transport and improve multimodality, or to provide financial support in order to address social aspects concerning low- and middle-income transport users;\n\n(c)\n\nto finance their Social Climate Plan in accordance with Article 15 of Regulation (EU) 2023/955;\n\n(d)',
'If the planned change is implemented notwithstanding the first and second subparagraphs, or if an unplanned change has taken place pursuant to which the AIFM’s management of the AIF no longer complies with this Directive or the AIFM otherwise no longer complies with this Directive, the competent authorities of the Member State of reference of the AIFM shall take all due measures in accordance with Article 46, including, if necessary, the express prohibition of marketing of the AIF.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7059 |
| cosine_accuracy@3 | 0.9068 |
| cosine_accuracy@5 | 0.9448 |
| cosine_accuracy@10 | 0.9731 |
| cosine_precision@1 | 0.7059 |
| cosine_precision@3 | 0.3023 |
| cosine_precision@5 | 0.189 |
| cosine_precision@10 | 0.0973 |
| cosine_recall@1 | 0.7059 |
| cosine_recall@3 | 0.9068 |
| cosine_recall@5 | 0.9448 |
| cosine_recall@10 | 0.9731 |
| **cosine_ndcg@10** | **0.8513** |
| cosine_mrr@10 | 0.8109 |
| cosine_map@100 | 0.8123 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 46,338 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 39.98 tokens</li><li>max: 286 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 248.72 tokens</li><li>max: 1315 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the maximum allowable reduction in excise duty for mixtures used as motor fuels containing biodiesel in Italy until 30 June 2004?</code> | <code>for waste oils which are reused as fuel, either directly after recovery or following a recycling process for waste oils, and where the reuse is subject to duty.<br><br>8. ITALY:<br><br>for differentiated rates of excise duty on mixtures used as motor fuels containing 5 % or 25 % of biodiesel until 30 June 2004. The reduction in excise duty may not be greater than the amount of excise duty payable on the volume of biofuels present in the products eligible for the reduction. The reduction in excise duty shall be adjusted to take account of changes in the price of raw materials to avoid overcompensating for the extra costs involved in the manufacture of biofuels;</code> |
| <code>What are the minimum indicative share percentages for the years 2023 to 2030, and how do these percentages relate to the interconnectivity levels of the Member States?</code> | <code>Such indicative shares may, in each year, amount to at least 5 % from 2023 to 2026 and at least 10 % from 2027 to 2030, or, where lower, to the level of interconnectivity of the Member State concerned in any given year.<br><br>In order to acquire further implementation experience, Member States may organise one or more pilot schemes where support is open to producers located in other Member States.<br><br>2.</code> |
| <code>What is the significance of the one-month period mentioned in the context?</code> | <code>one month after its notification, in accordance with the arrangements provided for in Article 23.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `num_train_epochs`: 4
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | cosine_ndcg@10 |
|:------:|:-----:|:-------------:|:--------------:|
| 0.0863 | 500 | 0.225 | - |
| 0.1726 | 1000 | 0.1337 | - |
| 0.2589 | 1500 | 0.1195 | - |
| 0.3452 | 2000 | 0.0803 | - |
| 0.4316 | 2500 | 0.0775 | - |
| 0.5179 | 3000 | 0.0714 | - |
| 0.6042 | 3500 | 0.0852 | - |
| 0.6905 | 4000 | 0.0718 | - |
| 0.7768 | 4500 | 0.0499 | - |
| 0.8631 | 5000 | 0.0665 | 0.8371 |
| 0.9494 | 5500 | 0.0674 | - |
| 1.0 | 5793 | - | 0.8416 |
| 1.0357 | 6000 | 0.0538 | - |
| 1.1220 | 6500 | 0.0606 | - |
| 1.2084 | 7000 | 0.0294 | - |
| 1.2947 | 7500 | 0.0129 | - |
| 1.3810 | 8000 | 0.0101 | - |
| 1.4673 | 8500 | 0.0072 | - |
| 1.5536 | 9000 | 0.0211 | - |
| 1.6399 | 9500 | 0.0133 | - |
| 1.7262 | 10000 | 0.0063 | 0.8513 |
### Framework Versions
- Python: 3.10.15
- Sentence Transformers: 4.0.2
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu126
- Accelerate: 0.26.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```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 = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
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masani/SFT_math_Llama-2-7b-hf_epoch_0_global_step_0 | masani | 2025-04-25T10:41:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T03:36:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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#### 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] |
dzanbek/8bca9373-ecad-4fea-8ec6-4538ae12eebc | dzanbek | 2025-04-25T10:38:58Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Solar-10b-32k",
"base_model:adapter:NousResearch/Yarn-Solar-10b-32k",
"license:apache-2.0",
"region:us"
]
| null | 2025-04-25T09:33:00Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Solar-10b-32k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8bca9373-ecad-4fea-8ec6-4538ae12eebc
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Yarn-Solar-10b-32k
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 338a122e43543931_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/338a122e43543931_train_data.json
type:
field_input: raw_texts
field_instruction: gen_questions
field_output: Positive
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: dzanbek/8bca9373-ecad-4fea-8ec6-4538ae12eebc
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/338a122e43543931_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3a6c2313-e0d4-427f-b835-4522b7af6bde
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 3a6c2313-e0d4-427f-b835-4522b7af6bde
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 8bca9373-ecad-4fea-8ec6-4538ae12eebc
This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-32k](https://huggingface.co/NousResearch/Yarn-Solar-10b-32k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0599 | 0.0104 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
julietmarissa/julietmarissa | julietmarissa | 2025-04-25T10:35:54Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2025-04-25T10:35:54Z | ---
license: bigscience-openrail-m
---
|
YOYO-AI/YOYO-O1-32B-V4 | YOYO-AI | 2025-04-25T10:35:53Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"base_model:Qwen/Qwen2.5-Coder-32B",
"base_model:merge:Qwen/Qwen2.5-Coder-32B",
"base_model:Qwen/Qwen2.5-Coder-32B-Instruct",
"base_model:merge:Qwen/Qwen2.5-Coder-32B-Instruct",
"base_model:YOYO-AI/YOYO-O1-32B-V4-preview1",
"base_model:merge:YOYO-AI/YOYO-O1-32B-V4-preview1",
"base_model:YOYO-AI/YOYO-O1-32B-V4-preview2",
"base_model:merge:YOYO-AI/YOYO-O1-32B-V4-preview2",
"base_model:YOYO-AI/YOYO-O1-32B-V4-preview3",
"base_model:merge:YOYO-AI/YOYO-O1-32B-V4-preview3",
"base_model:YOYO-AI/YOYO-O1-32B-V4-preview4",
"base_model:merge:YOYO-AI/YOYO-O1-32B-V4-preview4",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T10:16:09Z | ---
base_model:
- YOYO-AI/YOYO-O1-32B-V4-preview3
- Qwen/Qwen2.5-Coder-32B-Instruct
- YOYO-AI/YOYO-O1-32B-V4-preview4
- YOYO-AI/YOYO-O1-32B-V4-preview2
- Qwen/Qwen2.5-Coder-32B
- YOYO-AI/YOYO-O1-32B-V4-preview1
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [Qwen/Qwen2.5-Coder-32B](https://huggingface.co/Qwen/Qwen2.5-Coder-32B) as a base.
### Models Merged
The following models were included in the merge:
* [YOYO-AI/YOYO-O1-32B-V4-preview3](https://huggingface.co/YOYO-AI/YOYO-O1-32B-V4-preview3)
* [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct)
* [YOYO-AI/YOYO-O1-32B-V4-preview4](https://huggingface.co/YOYO-AI/YOYO-O1-32B-V4-preview4)
* [YOYO-AI/YOYO-O1-32B-V4-preview2](https://huggingface.co/YOYO-AI/YOYO-O1-32B-V4-preview2)
* [YOYO-AI/YOYO-O1-32B-V4-preview1](https://huggingface.co/YOYO-AI/YOYO-O1-32B-V4-preview1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: sce
models:
# Pivot model
- model: Qwen/Qwen2.5-Coder-32B
# Target models
- model: YOYO-AI/YOYO-O1-32B-V4-preview1
- model: YOYO-AI/YOYO-O1-32B-V4-preview2
- model: YOYO-AI/YOYO-O1-32B-V4-preview3
- model: YOYO-AI/YOYO-O1-32B-V4-preview4
- model: Qwen/Qwen2.5-Coder-32B-Instruct
base_model: Qwen/Qwen2.5-Coder-32B
parameters:
select_topk: 1
dtype: bfloat16
tokenizer_source: Qwen/QwQ-32B
normalize: true
int8_mask: true
```
|
mradermacher/yoruba-embedding-model-i1-GGUF | mradermacher | 2025-04-25T10:34:52Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:odunola/yoruba-embedding-model",
"base_model:quantized:odunola/yoruba-embedding-model",
"endpoints_compatible",
"region:us",
"imatrix",
"feature-extraction"
]
| null | 2025-04-25T10:32:57Z | ---
base_model: odunola/yoruba-embedding-model
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/odunola/yoruba-embedding-model
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/yoruba-embedding-model-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/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ1_S.gguf) | i1-IQ1_S | 0.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ1_M.gguf) | i1-IQ1_M | 0.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ2_S.gguf) | i1-IQ2_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ2_M.gguf) | i1-IQ2_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q2_K.gguf) | i1-Q2_K | 0.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ3_S.gguf) | i1-IQ3_S | 0.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ3_M.gguf) | i1-IQ3_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q4_0.gguf) | i1-Q4_0 | 0.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q4_1.gguf) | i1-Q4_1 | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-i1-GGUF/resolve/main/yoruba-embedding-model.i1-Q6_K.gguf) | i1-Q6_K | 0.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
mradermacher/yoruba-embedding-model-GGUF | mradermacher | 2025-04-25T10:34:45Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:odunola/yoruba-embedding-model",
"base_model:quantized:odunola/yoruba-embedding-model",
"endpoints_compatible",
"region:us",
"feature-extraction"
]
| null | 2025-04-25T10:29:06Z | ---
base_model: odunola/yoruba-embedding-model
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/odunola/yoruba-embedding-model
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/yoruba-embedding-model-i1-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/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/yoruba-embedding-model-GGUF/resolve/main/yoruba-embedding-model.f16.gguf) | f16 | 0.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
Saeed-mmdi/saeedmohammadi | Saeed-mmdi | 2025-04-25T10:33:07Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"finance",
"ab",
"dataset:nvidia/OpenCodeReasoning",
"base_model:Qwen/Qwen2.5-Omni-7B",
"base_model:adapter:Qwen/Qwen2.5-Omni-7B",
"license:apache-2.0",
"region:us"
]
| null | 2025-04-25T10:30:19Z | ---
license: apache-2.0
datasets:
- nvidia/OpenCodeReasoning
language:
- ab
metrics:
- bertscore
base_model:
- Qwen/Qwen2.5-Omni-7B
new_version: deepseek-ai/DeepSeek-V3-0324
library_name: adapter-transformers
tags:
- finance
--- |
himanshudhingra/PongPingDingDong | himanshudhingra | 2025-04-25T10:32:17Z | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-04-25T08:28:18Z | # Orpheus TTS
#### Updates 🔥
- [4/2025] We release a [family of multilingual models](https://huggingface.co/collections/canopylabs/orpheus-multilingual-research-release-67f5894cd16794db163786ba) in a research preview. We release a [training guide](https://canopylabs.ai/releases/orpheus_can_speak_any_language#training) that explains how we created these models in the hopes that even better versions in both the languages released and new languages are created. We welcome feedback and criticism as well as invite questions in this [discussion](https://github.com/canopyai/Orpheus-TTS/discussions/123) for feedback and questions.
## Overview
Orpheus TTS is a SOTA open-source text-to-speech system built on the Llama-3b backbone. Orpheus demonstrates the emergent capabilities of using LLMs for speech synthesis.
[Check out our original blog post](https://canopylabs.ai/model-releases)
https://github.com/user-attachments/assets/ce17dd3a-f866-4e67-86e4-0025e6e87b8a
## Abilities
- **Human-Like Speech**: Natural intonation, emotion, and rhythm that is superior to SOTA closed source models
- **Zero-Shot Voice Cloning**: Clone voices without prior fine-tuning
- **Guided Emotion and Intonation**: Control speech and emotion characteristics with simple tags
- **Low Latency**: ~200ms streaming latency for realtime applications, reducible to ~100ms with input streaming
## Models
We provide 2 English models, and additionally we offer the data processing scripts and sample datasets to make it very straightforward to create your own finetune.
1. [**Finetuned Prod**](https://huggingface.co/canopylabs/orpheus-tts-0.1-finetune-prod) – A finetuned model for everyday TTS applications
2. [**Pretrained**](https://huggingface.co/canopylabs/orpheus-tts-0.1-pretrained) – Our base model trained on 100k+ hours of English speech data
We also offer a family of multilingual models in a research release.
1. [**Multlingual Family**](https://huggingface.co/collections/canopylabs/orpheus-multilingual-research-release-67f5894cd16794db163786ba) - 7 pairs of pretrained and finetuned models.
### Inference
#### Simple setup on colab
We offer a standardised prompt format across languages, and these notebooks illustrate how to use our models in English.
1. [Colab For Tuned Model](https://colab.research.google.com/drive/1KhXT56UePPUHhqitJNUxq63k-pQomz3N?usp=sharing) (not streaming, see below for realtime streaming) – A finetuned model for everyday TTS applications.
2. [Colab For Pretrained Model](https://colab.research.google.com/drive/10v9MIEbZOr_3V8ZcPAIh8MN7q2LjcstS?usp=sharing) – This notebook is set up for conditioned generation but can be extended to a range of tasks.
#### Streaming Inference Example
1. Clone this repo
```bash
git clone https://github.com/canopyai/Orpheus-TTS.git
```
2. Navigate and install packages
```bash
cd Orpheus-TTS && pip install orpheus-speech # uses vllm under the hood for fast inference
```
vllm pushed a slightly buggy version on March 18th so some bugs are being resolved by reverting to `pip install vllm==0.7.3` after `pip install orpheus-speech`
4. Run the example below:
```python
from orpheus_tts import OrpheusModel
import wave
import time
model = OrpheusModel(model_name ="canopylabs/orpheus-tts-0.1-finetune-prod")
prompt = '''Man, the way social media has, um, completely changed how we interact is just wild, right? Like, we're all connected 24/7 but somehow people feel more alone than ever. And don't even get me started on how it's messing with kids' self-esteem and mental health and whatnot.'''
start_time = time.monotonic()
syn_tokens = model.generate_speech(
prompt=prompt,
voice="tara",
)
with wave.open("output.wav", "wb") as wf:
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(24000)
total_frames = 0
chunk_counter = 0
for audio_chunk in syn_tokens: # output streaming
chunk_counter += 1
frame_count = len(audio_chunk) // (wf.getsampwidth() * wf.getnchannels())
total_frames += frame_count
wf.writeframes(audio_chunk)
duration = total_frames / wf.getframerate()
end_time = time.monotonic()
print(f"It took {end_time - start_time} seconds to generate {duration:.2f} seconds of audio")
```
#### Additional Functionality
1. Watermark your audio: Use Silent Cipher to watermark your audio generation; see [Watermark Audio Implementation](additional_inference_options/watermark_audio) for implementation.
2. For No GPU inference using Llama cpp see implementation [documentation](additional_inference_options/no_gpu/README.md) for implementation example
#### Prompting
1. The `finetune-prod` models: for the primary model, your text prompt is formatted as `{name}: I went to the ...`. The options for name in order of conversational realism (subjective benchmarks) are "tara", "leah", "jess", "leo", "dan", "mia", "zac", "zoe" for English - each language has different voices [see voices here] (https://canopylabs.ai/releases/orpheus_can_speak_any_language#info)). Our python package does this formatting for you, and the notebook also prepends the appropriate string. You can additionally add the following emotive tags: `<laugh>`, `<chuckle>`, `<sigh>`, `<cough>`, `<sniffle>`, `<groan>`, `<yawn>`, `<gasp>`. For multilingual, see this [post](https://huggingface.co/collections/canopylabs/orpheus-multilingual-research-release-67f5894cd16794db163786ba) for supported tags.
2. The pretrained model: you can either generate speech just conditioned on text, or generate speech conditioned on one or more existing text-speech pairs in the prompt. Since this model hasn't been explicitly trained on the zero-shot voice cloning objective, the more text-speech pairs you pass in the prompt, the more reliably it will generate in the correct voice.
Additionally, use regular LLM generation args like `temperature`, `top_p`, etc. as you expect for a regular LLM. `repetition_penalty>=1.1`is required for stable generations. Increasing `repetition_penalty` and `temperature` makes the model speak faster.
## Finetune Model
Here is an overview of how to finetune your model on any text and speech.
This is a very simple process analogous to tuning an LLM using Trainer and Transformers.
You should start to see high quality results after ~50 examples but for best results, aim for 300 examples/speaker.
1. Your dataset should be a huggingface dataset in [this format](https://huggingface.co/datasets/canopylabs/zac-sample-dataset)
2. We prepare the data using [this notebook](https://colab.research.google.com/drive/1wg_CPCA-MzsWtsujwy-1Ovhv-tn8Q1nD?usp=sharing). This pushes an intermediate dataset to your Hugging Face account which you can can feed to the training script in finetune/train.py. Preprocessing should take less than 1 minute/thousand rows.
3. Modify the `finetune/config.yaml` file to include your dataset and training properties, and run the training script. You can additionally run any kind of huggingface compatible process like Lora to tune the model.
```bash
pip install transformers datasets wandb trl flash_attn torch
huggingface-cli login <enter your HF token>
wandb login <wandb token>
accelerate launch train.py
```
### Additional Resources
1. [Finetuning with unsloth](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb)
## Pretrain Model
This is a very simple process analogous to training an LLM using Trainer and Transformers.
The base model provided is trained over 100k hours. I recommend not using synthetic data for training as it produces worse results when you try to finetune specific voices, probably because synthetic voices lack diversity and map to the same set of tokens when tokenised (i.e. lead to poor codebook utilisation).
We train the 3b model on sequences of length 8192 - we use the same dataset format for TTS finetuning for the <TTS-dataset> pretraining. We chain input_ids sequences together for more efficient training. The text dataset required is in the form described in this issue [#37 ](https://github.com/canopyai/Orpheus-TTS/issues/37).
If you are doing extended training this model, i.e. for another language or style we recommend starting with finetuning only (no text dataset). The main idea behind the text dataset is discussed in the blog post. (tldr; doesn't forget too much semantic/reasoning ability so its able to better understand how to intone/express phrases when spoken, however most of the forgetting would happen very early on in the training i.e. <100000 rows), so unless you are doing very extended finetuning it may not make too much of a difference.
## Also Check out
While we can't verify these implementations are completely accurate/bug free, they have been recommended on a couple of forums, so we include them here:
1. [A lightweight client for running Orpheus TTS locally using LM Studio API](https://github.com/isaiahbjork/orpheus-tts-local)
2. [Open AI compatible Fast-API implementation](https://github.com/Lex-au/Orpheus-FastAPI)
3. [HuggingFace Space kindly set up by MohamedRashad](https://huggingface.co/spaces/MohamedRashad/Orpheus-TTS)
4. [Gradio WebUI that runs smoothly on WSL and CUDA](https://github.com/Saganaki22/OrpheusTTS-WebUI)
# Checklist
- [x] Release 3b pretrained model and finetuned models
- [ ] Release pretrained and finetuned models in sizes: 1b, 400m, 150m parameters
- [ ] Fix glitch in realtime streaming package that occasionally skips frames.
- [ ] Fix voice cloning Colab notebook implementation
|
mradermacher/Qwen2-0.5B-fncl-GGUF | mradermacher | 2025-04-25T10:23:01Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:glaiveai/glaive-function-calling-v2",
"base_model:haripritam/Qwen2-0.5B-fncl",
"base_model:quantized:haripritam/Qwen2-0.5B-fncl",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-25T10:03:37Z | ---
base_model: haripritam/Qwen2-0.5B-fncl
datasets:
- glaiveai/glaive-function-calling-v2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/haripritam/Qwen2-0.5B-fncl
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-i1-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/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-0.5B-fncl-GGUF/resolve/main/Qwen2-0.5B-fncl.f16.gguf) | f16 | 1.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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.
<!-- end -->
|
mradermacher/PII_DETECTION_MODEL-i1-GGUF | mradermacher | 2025-04-25T10:19:26Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:betterdataai/PII_DETECTION_MODEL",
"base_model:quantized:betterdataai/PII_DETECTION_MODEL",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| null | 2025-04-25T10:15:53Z | ---
base_model: betterdataai/PII_DETECTION_MODEL
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/betterdataai/PII_DETECTION_MODEL
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/PII_DETECTION_MODEL-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/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ3_S.gguf) | i1-IQ3_S | 0.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ3_M.gguf) | i1-IQ3_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q4_0.gguf) | i1-Q4_0 | 0.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q4_1.gguf) | i1-Q4_1 | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-GGUF/resolve/main/PII_DETECTION_MODEL.i1-Q6_K.gguf) | i1-Q6_K | 0.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
mradermacher/PII_DETECTION_MODEL-GGUF | mradermacher | 2025-04-25T10:18:46Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:betterdataai/PII_DETECTION_MODEL",
"base_model:quantized:betterdataai/PII_DETECTION_MODEL",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-25T10:07:33Z | ---
base_model: betterdataai/PII_DETECTION_MODEL
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/betterdataai/PII_DETECTION_MODEL
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/PII_DETECTION_MODEL-i1-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/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/PII_DETECTION_MODEL-GGUF/resolve/main/PII_DETECTION_MODEL.f16.gguf) | f16 | 1.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
jxjessieli/llama3.1-8b-alpaca-reg0.0001 | jxjessieli | 2025-04-25T10:16:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:42:04Z | ---
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. 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]
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[More Information Needed]
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## 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. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
mradermacher/bert-tiny-book-text-classifier-i1-GGUF | mradermacher | 2025-04-25T10:15:39Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:shhossain/book-text-classifier",
"base_model:shhossain/bert-tiny-book-text-classifier",
"base_model:quantized:shhossain/bert-tiny-book-text-classifier",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"feature-extraction"
]
| null | 2025-04-25T10:14:10Z | ---
base_model: shhossain/bert-tiny-book-text-classifier
datasets:
- shhossain/book-text-classifier
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/shhossain/bert-tiny-book-text-classifier
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-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/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ1_S.gguf) | i1-IQ1_S | 0.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ1_M.gguf) | i1-IQ1_M | 0.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ2_S.gguf) | i1-IQ2_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ2_M.gguf) | i1-IQ2_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ3_S.gguf) | i1-IQ3_S | 0.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q2_K.gguf) | i1-Q2_K | 0.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ3_M.gguf) | i1-IQ3_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.1 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q4_0.gguf) | i1-Q4_0 | 0.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q4_1.gguf) | i1-Q4_1 | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bert-tiny-book-text-classifier-i1-GGUF/resolve/main/bert-tiny-book-text-classifier.i1-Q6_K.gguf) | i1-Q6_K | 0.1 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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 -->
|
cidanta/cartpole | cidanta | 2025-04-25T10:15:24Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-04-25T07:58:19Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
inlancersystem6/mistral-nemo-lora | inlancersystem6 | 2025-04-25T10:09:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-09T11:42:58Z | ---
base_model: unsloth/mistral-nemo-instruct-2407-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** inlancersystem6
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-nemo-instruct-2407-bnb-4bit
This mistral 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)
|
NICOPOI-9/segformer-b0-finetuned-morphpadver1-hgo-coord-v2 | NICOPOI-9 | 2025-04-25T10:05:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:NICOPOI-9/segformer-b0-finetuned-morphpadver1-hgo-coord-v1",
"base_model:finetune:NICOPOI-9/segformer-b0-finetuned-morphpadver1-hgo-coord-v1",
"license:other",
"endpoints_compatible",
"region:us"
]
| image-segmentation | 2025-04-25T02:37:08Z | ---
library_name: transformers
license: other
base_model: NICOPOI-9/segformer-b0-finetuned-morphpadver1-hgo-coord-v1
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-morphpadver1-hgo-coord-v2
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. -->
# segformer-b0-finetuned-morphpadver1-hgo-coord-v2
This model is a fine-tuned version of [NICOPOI-9/segformer-b0-finetuned-morphpadver1-hgo-coord-v1](https://huggingface.co/NICOPOI-9/segformer-b0-finetuned-morphpadver1-hgo-coord-v1) on the NICOPOI-9/morphpad_coord_hgo_512_4class dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0408
- Mean Iou: 0.9952
- Mean Accuracy: 0.9976
- Overall Accuracy: 0.9976
- Accuracy 0-0: 0.9993
- Accuracy 0-90: 0.9958
- Accuracy 90-0: 0.9969
- Accuracy 90-90: 0.9983
- Iou 0-0: 0.9975
- Iou 0-90: 0.9929
- Iou 90-0: 0.9949
- Iou 90-90: 0.9955
## 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: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy 0-0 | Accuracy 0-90 | Accuracy 90-0 | Accuracy 90-90 | Iou 0-0 | Iou 0-90 | Iou 90-0 | Iou 90-90 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------:|:--------:|:--------:|:---------:|
| 0.0654 | 2.5445 | 4000 | 0.1134 | 0.9236 | 0.9604 | 0.9602 | 0.9729 | 0.9373 | 0.9642 | 0.9674 | 0.9265 | 0.9144 | 0.9233 | 0.9301 |
| 0.0552 | 5.0891 | 8000 | 0.1426 | 0.9161 | 0.9562 | 0.9561 | 0.9607 | 0.9538 | 0.9547 | 0.9555 | 0.9166 | 0.9146 | 0.9112 | 0.9218 |
| 0.0469 | 7.6336 | 12000 | 0.0633 | 0.9556 | 0.9774 | 0.9773 | 0.9811 | 0.9714 | 0.9744 | 0.9826 | 0.9588 | 0.9516 | 0.9545 | 0.9576 |
| 0.0378 | 10.1781 | 16000 | 0.0506 | 0.9650 | 0.9822 | 0.9822 | 0.9826 | 0.9773 | 0.9844 | 0.9844 | 0.9661 | 0.9601 | 0.9643 | 0.9696 |
| 0.0582 | 12.7226 | 20000 | 0.0402 | 0.9737 | 0.9867 | 0.9866 | 0.9925 | 0.9891 | 0.9791 | 0.9860 | 0.9774 | 0.9700 | 0.9699 | 0.9774 |
| 0.0322 | 15.2672 | 24000 | 0.0453 | 0.9707 | 0.9850 | 0.9851 | 0.9809 | 0.9843 | 0.9909 | 0.9840 | 0.9746 | 0.9715 | 0.9637 | 0.9728 |
| 0.0254 | 17.8117 | 28000 | 0.1030 | 0.9652 | 0.9823 | 0.9822 | 0.9895 | 0.9808 | 0.9748 | 0.9841 | 0.9761 | 0.9599 | 0.9583 | 0.9666 |
| 2.3028 | 20.3562 | 32000 | 0.0572 | 0.9745 | 0.9871 | 0.9870 | 0.9861 | 0.9839 | 0.9885 | 0.9896 | 0.9789 | 0.9717 | 0.9700 | 0.9773 |
| 0.0769 | 22.9008 | 36000 | 0.0225 | 0.9866 | 0.9932 | 0.9932 | 0.9960 | 0.9899 | 0.9939 | 0.9932 | 0.9893 | 0.9837 | 0.9849 | 0.9884 |
| 0.0512 | 25.4453 | 40000 | 0.0329 | 0.9850 | 0.9924 | 0.9924 | 0.9959 | 0.9867 | 0.9954 | 0.9917 | 0.9857 | 0.9820 | 0.9843 | 0.9878 |
| 0.3281 | 27.9898 | 44000 | 0.0301 | 0.9866 | 0.9933 | 0.9932 | 0.9958 | 0.9913 | 0.9907 | 0.9952 | 0.9899 | 0.9858 | 0.9843 | 0.9863 |
| 0.1536 | 30.5344 | 48000 | 0.0355 | 0.9889 | 0.9944 | 0.9944 | 0.9981 | 0.9927 | 0.9920 | 0.9949 | 0.9941 | 0.9855 | 0.9880 | 0.9880 |
| 0.0079 | 33.0789 | 52000 | 0.0256 | 0.9933 | 0.9966 | 0.9966 | 0.9979 | 0.9951 | 0.9961 | 0.9974 | 0.9956 | 0.9917 | 0.9934 | 0.9924 |
| 0.0074 | 35.6234 | 56000 | 0.0205 | 0.9938 | 0.9969 | 0.9969 | 0.9983 | 0.9970 | 0.9966 | 0.9956 | 0.9963 | 0.9923 | 0.9928 | 0.9939 |
| 0.0077 | 38.1679 | 60000 | 0.0255 | 0.9933 | 0.9967 | 0.9966 | 0.9985 | 0.9946 | 0.9964 | 0.9971 | 0.9954 | 0.9925 | 0.9919 | 0.9934 |
| 0.0061 | 40.7125 | 64000 | 0.0282 | 0.9945 | 0.9972 | 0.9972 | 0.9987 | 0.9958 | 0.9974 | 0.9969 | 0.9967 | 0.9916 | 0.9950 | 0.9945 |
| 0.0051 | 43.2570 | 68000 | 0.0262 | 0.9937 | 0.9969 | 0.9968 | 0.9987 | 0.9949 | 0.9959 | 0.9979 | 0.9968 | 0.9916 | 0.9934 | 0.9930 |
| 0.0047 | 45.8015 | 72000 | 0.0564 | 0.9912 | 0.9956 | 0.9956 | 0.9991 | 0.9950 | 0.9940 | 0.9943 | 0.9958 | 0.9882 | 0.9897 | 0.9912 |
| 0.0046 | 48.3461 | 76000 | 0.0492 | 0.9939 | 0.9969 | 0.9969 | 0.9992 | 0.9941 | 0.9974 | 0.9970 | 0.9969 | 0.9903 | 0.9938 | 0.9945 |
| 0.0552 | 50.8906 | 80000 | 0.0438 | 0.9948 | 0.9974 | 0.9974 | 0.9992 | 0.9966 | 0.9967 | 0.9972 | 0.9980 | 0.9924 | 0.9948 | 0.9941 |
| 0.0039 | 53.4351 | 84000 | 0.0361 | 0.9953 | 0.9976 | 0.9976 | 0.9991 | 0.9961 | 0.9973 | 0.9981 | 0.9975 | 0.9928 | 0.9952 | 0.9956 |
| 0.0034 | 55.9796 | 88000 | 0.0317 | 0.9958 | 0.9979 | 0.9979 | 0.9993 | 0.9964 | 0.9974 | 0.9985 | 0.9979 | 0.9937 | 0.9955 | 0.9963 |
| 0.0149 | 58.5242 | 92000 | 0.0408 | 0.9952 | 0.9976 | 0.9976 | 0.9993 | 0.9958 | 0.9969 | 0.9983 | 0.9975 | 0.9929 | 0.9949 | 0.9955 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.1.0
- Datasets 3.2.0
- Tokenizers 0.21.0
|
Bhumi-Ahir-Viral-Video/Original-Viral-Link.Bhumi.Ahir.Viral.Video.Leaks.official.HD | Bhumi-Ahir-Viral-Video | 2025-04-25T10:03:03Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-04-25T10:02:20Z |
<a href="https://sdu.sk/9Ip"><img src="http://4.bp.blogspot.com/-VFcup4RzDQY/Upiobuokb5I/AAAAAAAAAV0/64yKpZilDCg/s1600/oie_nxv3mlmduAj1.gif" alt="fsd" /></a>
<a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a>
<a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
|
filipesantoscv11/c5d5dd13-d3f7-48cb-8d75-f9f54e55afff | filipesantoscv11 | 2025-04-25T10:01:29Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/gemma-2-2b",
"base_model:adapter:unsloth/gemma-2-2b",
"license:gemma",
"8-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-04-25T09:49:42Z | ---
library_name: peft
license: gemma
base_model: unsloth/gemma-2-2b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c5d5dd13-d3f7-48cb-8d75-f9f54e55afff
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/gemma-2-2b
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- e16b46241bfdc847_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e16b46241bfdc847_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: filipesantoscv11/c5d5dd13-d3f7-48cb-8d75-f9f54e55afff
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/e16b46241bfdc847_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 17d4abe9-df59-4b31-9847-c2035b2835c2
wandb_project: s56-6
wandb_run: your_name
wandb_runid: 17d4abe9-df59-4b31-9847-c2035b2835c2
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# c5d5dd13-d3f7-48cb-8d75-f9f54e55afff
This model is a fine-tuned version of [unsloth/gemma-2-2b](https://huggingface.co/unsloth/gemma-2-2b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3767
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4973 | 0.0332 | 200 | 1.3767 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
fazokooi/sdcvsdc | fazokooi | 2025-04-25T10:01:10Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
]
| null | 2025-04-25T10:01:10Z | ---
license: bigcode-openrail-m
---
|
vistambls/dsfvdfv | vistambls | 2025-04-25T10:01:07Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
]
| null | 2025-04-25T10:01:07Z | ---
license: artistic-2.0
---
|
TareksLab/Z-MODEL3-V1-SCE | TareksLab | 2025-04-25T10:00:21Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"base_model:EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1",
"base_model:merge:EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1",
"base_model:Mawdistical/Lured-Lapine-70B",
"base_model:merge:Mawdistical/Lured-Lapine-70B",
"base_model:Sao10K/L3.1-70B-Hanami-x1",
"base_model:merge:Sao10K/L3.1-70B-Hanami-x1",
"base_model:TareksLab/Braniac-V3-LLaMa-70B",
"base_model:merge:TareksLab/Braniac-V3-LLaMa-70B",
"base_model:nbeerbower/Llama3.1-Gutenberg-Doppel-70B",
"base_model:merge:nbeerbower/Llama3.1-Gutenberg-Doppel-70B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:22:39Z | ---
base_model:
- Mawdistical/Lured-Lapine-70B
- Sao10K/L3.1-70B-Hanami-x1
- EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
- TareksLab/Braniac-V3-LLaMa-70B
- nbeerbower/Llama3.1-Gutenberg-Doppel-70B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [TareksLab/Braniac-V3-LLaMa-70B](https://huggingface.co/TareksLab/Braniac-V3-LLaMa-70B) as a base.
### Models Merged
The following models were included in the merge:
* [Mawdistical/Lured-Lapine-70B](https://huggingface.co/Mawdistical/Lured-Lapine-70B)
* [Sao10K/L3.1-70B-Hanami-x1](https://huggingface.co/Sao10K/L3.1-70B-Hanami-x1)
* [EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1](https://huggingface.co/EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1)
* [nbeerbower/Llama3.1-Gutenberg-Doppel-70B](https://huggingface.co/nbeerbower/Llama3.1-Gutenberg-Doppel-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
parameters:
select_topk: 0.5
- model: Sao10K/L3.1-70B-Hanami-x1
parameters:
select_topk: 0.5
- model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B
parameters:
select_topk: 0.5
- model: Mawdistical/Lured-Lapine-70B
parameters:
select_topk: 0.5
- model: TareksLab/Braniac-V3-LLaMa-70B
parameters:
select_topk: 0.5
base_model: TareksLab/Braniac-V3-LLaMa-70B
merge_method: sce
parameters:
int8_mask: true
tokenizer:
source: union
chat_template: llama3
dtype: bfloat16
```
|
marialvsantiago/566be472-c893-4984-8b94-87f829e8726a | marialvsantiago | 2025-04-25T10:00:20Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Solar-10b-32k",
"base_model:adapter:NousResearch/Yarn-Solar-10b-32k",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-04-25T09:32:42Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Solar-10b-32k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 566be472-c893-4984-8b94-87f829e8726a
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Solar-10b-32k
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 338a122e43543931_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/338a122e43543931_train_data.json
type:
field_input: raw_texts
field_instruction: gen_questions
field_output: Positive
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: marialvsantiago/566be472-c893-4984-8b94-87f829e8726a
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/338a122e43543931_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3a6c2313-e0d4-427f-b835-4522b7af6bde
wandb_project: s56-33
wandb_run: your_name
wandb_runid: 3a6c2313-e0d4-427f-b835-4522b7af6bde
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 566be472-c893-4984-8b94-87f829e8726a
This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-32k](https://huggingface.co/NousResearch/Yarn-Solar-10b-32k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0681 | 0.0104 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
kokovova/47138549-75c1-4eae-af75-6937339c2ff0 | kokovova | 2025-04-25T10:00:20Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Solar-10b-32k",
"base_model:adapter:NousResearch/Yarn-Solar-10b-32k",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-04-25T09:32:35Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Solar-10b-32k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 47138549-75c1-4eae-af75-6937339c2ff0
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Solar-10b-32k
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 338a122e43543931_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/338a122e43543931_train_data.json
type:
field_input: raw_texts
field_instruction: gen_questions
field_output: Positive
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: kokovova/47138549-75c1-4eae-af75-6937339c2ff0
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/338a122e43543931_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3a6c2313-e0d4-427f-b835-4522b7af6bde
wandb_project: s56-4
wandb_run: your_name
wandb_runid: 3a6c2313-e0d4-427f-b835-4522b7af6bde
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 47138549-75c1-4eae-af75-6937339c2ff0
This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-32k](https://huggingface.co/NousResearch/Yarn-Solar-10b-32k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0675 | 0.0104 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mlfoundations-dev/b2_science_difficulty_10k | mlfoundations-dev | 2025-04-25T09:59:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T00:41:16Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: b2_science_difficulty_10k
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. -->
# b2_science_difficulty_10k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_science_difficulty_10k 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use 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
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
dthrhdar11/gemma-law-prediction-finetune-3epoch | dthrhdar11 | 2025-04-25T09:58:52Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-pt",
"base_model:finetune:google/gemma-3-4b-pt",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T08:51:54Z | ---
base_model: google/gemma-3-4b-pt
library_name: transformers
model_name: gemma-law-prediction-finetune-3epoch
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-law-prediction-finetune-3epoch
This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt).
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="dthrhdar11/gemma-law-prediction-finetune-3epoch", 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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
DavieLion/output_iter1_ckpt_temperature | DavieLion | 2025-04-25T09:57:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"conversational",
"dataset:new_data_temperature/iter0",
"dataset:new_data_temperature/iter1",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:18:50Z | ---
library_name: transformers
base_model: meta-llama/Llama-3.2-1B
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- new_data_temperature/iter0
- new_data_temperature/iter1
model-index:
- name: iter1-ckpt
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. -->
# iter1-ckpt
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the new_data_temperature/iter0 and the new_data_temperature/iter1 datasets.
## 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-07
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6.0
### Training results
### Framework versions
- Transformers 4.45.0
- Pytorch 2.1.2+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
|
candra/base-sentiment | candra | 2025-04-25T09:57:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-25T09:22:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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.
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[More Information Needed]
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<!-- 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
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[More Information Needed]
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[More Information Needed]
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#### 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
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
sepoul/charbel-first-experiment-tokenizer | sepoul | 2025-04-25T09:56:32Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T09:56:31Z | ---
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]
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- **Paper [optional]:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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
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[More Information Needed]
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### 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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **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]
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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[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]
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[More Information Needed]
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[More Information Needed] |
sepoul/charbel-first-experiment-model | sepoul | 2025-04-25T09:56:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-25T09:48:22Z | ---
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]
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[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
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
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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]
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kblz/mms-tts-amh-train | kblz | 2025-04-25T09:54:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T09:54:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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.
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
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MinaMila/llama_instbase_unlearned_LoRa_Adult_ep5_22 | MinaMila | 2025-04-25T09:52:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T09:52:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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#### Speeds, Sizes, Times [optional]
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Kenazin/all-roberta-large-v1-peft-p-tuning-3-1 | Kenazin | 2025-04-25T09:52:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T09:52:37Z | ---
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]
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## Uses
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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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
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[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[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]
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ar08/Smolllm | ar08 | 2025-04-25T09:47:38Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:47:15Z | ---
base_model: unsloth/smollm-360m-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ar08
- **License:** apache-2.0
- **Finetuned from model :** unsloth/smollm-360m-instruct-bnb-4bit
This llama 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)
|
jaymekoszut/sdcvsdc | jaymekoszut | 2025-04-25T09:47:26Z | 0 | 0 | null | [
"license:bsd-2-clause",
"region:us"
]
| null | 2025-04-25T09:47:26Z | ---
license: bsd-2-clause
---
|
Szahriwar/Llama-3.2-3B-Instruct-bnb-4bit-q5-k-m | Szahriwar | 2025-04-25T09:47:11Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-25T09:46:24Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Szahriwar
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama 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)
|
Kam1qwe/Kam1lka | Kam1qwe | 2025-04-25T09:46:51Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
]
| null | 2025-04-25T09:46:51Z | ---
license: artistic-2.0
---
|
ar08/smol-llm-gguf | ar08 | 2025-04-25T09:42:54Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-25T09:39:29Z | ---
base_model: unsloth/smollm-360m-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ar08
- **License:** apache-2.0
- **Finetuned from model :** unsloth/smollm-360m-instruct-bnb-4bit
This llama 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)
|
peterklein2308/bert-finetuned-ner | peterklein2308 | 2025-04-25T09:42:44Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2025-04-18T20:09:18Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9343041535661095
- name: Recall
type: recall
value: 0.9501851228542578
- name: F1
type: f1
value: 0.9421777221526908
- name: Accuracy
type: accuracy
value: 0.9864749514334491
---
<!-- 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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0598
- Precision: 0.9343
- Recall: 0.9502
- F1: 0.9422
- Accuracy: 0.9865
## 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
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0749 | 1.0 | 1756 | 0.0637 | 0.9165 | 0.9367 | 0.9265 | 0.9825 |
| 0.035 | 2.0 | 3512 | 0.0644 | 0.9321 | 0.9473 | 0.9397 | 0.9855 |
| 0.0218 | 3.0 | 5268 | 0.0598 | 0.9343 | 0.9502 | 0.9422 | 0.9865 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu126
- Datasets 3.3.2
- Tokenizers 0.21.1
|
TruongSinhAI/CAD_Qwen25_0.5B_Coder_85steps_2 | TruongSinhAI | 2025-04-25T09:41:56Z | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T09:41:52Z | ---
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. 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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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[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. 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
CALISTA-INDUSTRY/gemma-3-1B-reasoning-en-ft-v1 | CALISTA-INDUSTRY | 2025-04-25T09:36:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:29:05Z | ---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** CALISTA-INDUSTRY
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text 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)
|
Ktejaswi/AI_Powered_Dream_Interpreter | Ktejaswi | 2025-04-25T09:36:18Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-04-25T09:36:18Z | ---
license: apache-2.0
---
|
NotTheStallion/Qwen2.5-0.20B-layer-reduced | NotTheStallion | 2025-04-25T09:35:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:34:53Z | ---
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. 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. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
inclusionAI/Ring-lite-linear-preview | inclusionAI | 2025-04-25T09:32:32Z | 3 | 8 | null | [
"safetensors",
"bailing_moe_linear",
"text-generation",
"conversational",
"custom_code",
"zh",
"en",
"base_model:inclusionAI/Ling-lite",
"base_model:finetune:inclusionAI/Ling-lite",
"license:mit",
"region:us"
]
| text-generation | 2025-04-24T02:58:46Z | ---
license: mit
language:
- zh
- en
base_model:
- inclusionAI/Ling-lite
pipeline_tag: text-generation
---
# Ring-lite-linear-preview
<p align="center">
<img src="https://huggingface.co/inclusionAI/Ring-lite-linear-preview/resolve/main/ant-bailing.png" width="100"/>
<p>
<p align="center">
🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>
<p>
## Introduction
Ring-lite-linear-preview is a hybrid-linear MoE LLM provided and open-sourced by InclusionAI, which has 17.1B parameters with 3.0B activated parameters. It is a long reasoning model based on hybrid-linear attention, achieving near-linear computational complexity and near-constant space complexity during inference. This model was converted from [Ling-lite-0220](https://huggingface.co/inclusionAI/Ling-lite/tree/Ling-lite-0220), which adopts the softmax attention-based architecture. It matches the performance of DeepSeek-R1-Distill-Qwen-7B on standardized reasoning benchmarks while substantially reducing computational overhead in both training and inference phases. In certain generation speed tests based on vLLM, we observed that the throughput was more than doubled compared to softmax attention models of the same scale (e.g., Ling-lite). To the best of our knowledge, it is the first open-source hybrid-linear reasoning language model.
## Model Downloads
<div align="center">
| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
| :----------------: | :---------------: | :-------------------: | :----------------: | :----------: |
| Ring-lite-linear-preview | 17.1B | 3.0B | 64K | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-lite-linear-preview)|
</div>
## Evaluation
In terms of the evaluation of reasoning ability， Ring-lite-linear-preview achieves 55.0 on AIME24 and 93.8 on MATH-500.
<div align="center">
| **Model** | **AIME24** | **MATH-500** | **GPQA-diamond** | **LiveCodeBench** |
| :----------------: | :---------------: | :-------------------: | :----------------: | :----------: |
| DeepSeek-R1-Distill-Qwen-7B (reported) | 55.5 | 92.8 | 49.1 | 37.6 |
| DeepSeek-R1-Distill-Qwen-7B (reproduce) | 53.2 | 93.7 | 50.4 | 36.5 |
| Ring-lite-distill-preview-Stage-1 | 54.2 | 93.5 | 47.5 | 32.9 |
| Ring-lite-linear-preview | 55.0 | 93.8 | 46.5 | 29.8 |
</div>
## Inference Speed
To evaluate the generation throughput, we deploy Ring-lite-linear and the softmax-attention-based Ring-lite based on vLLM on a single NVIDIA A100 GPU. We conduct two sets of experiments:
1. **Long Input Evaluation**: We measure the time-to-first-token (TTFT) with varying input sequence lengths (from 512 to 384k tokens) using batch size 1 and TP=1. As shown in the top figure, at 384k input length, Ring-lite-linear achieves 3.5× faster TTFT compared to the softmax-attention-based model.
2. **Long Output Evaluation**: We fix the input sequence length to 1 and measure the end-to-end (E2E) generation time required for generating output sequences of varying lengths (from 512 to 32k tokens) with batch size 64 and TP=1. As illustrated in the bottom figure, at 32k output length, Ring-lite-linear achieves 2.2× throughput of the softmax-attention-based Ring-lite.
These results demonstrate that our hybrid linear attention mechanism significantly improves both input processing efficiency and generation throughput, especially for long context scenarios.
<p align="center">
<img src="https://huggingface.co/inclusionAI/Ring-lite-linear-preview/resolve/main/throughput.png" width="600"/>
<p>
Additionally, to illustrate the advantage in inference speed, we present a comparison between Ring-lite-linear-preview and softmax-attention-based Ring-lite under a batch size of 64 and an output length of 16k (60x speedup). It can be observed that the KV cache usage of Ring-lite-linear-preview is nearly 1/6 that of Ring-lite, and the E2E time is reduced by 27.24% compared with Ring-lite.
<p align="center">
<img src="https://huggingface.co/inclusionAI/Ring-lite-linear-preview/resolve/main/inference_speed.gif" width="600"/>
<p>
More details will be reported in our technical report [TBD]
## Requirements
- [transformers](https://github.com/huggingface/transformers) >= 4.48.3
- [flash-linear-attention](https://github.com/fla-org/flash-linear-attention) >= 0.2.1
## Quickstart
Here is a code snippet to show you how to use the chat model with `modelscope`:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "inclusionAI/Ring-lite-linear-preview"
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 models."
messages = [
{"role": "system", "content": "You are Ring, an assistant created by inclusionAI"},
{"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=8192
)
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]
```
## Deployment
Please refer to [Github](https://github.com/inclusionAI/Ring/tree/main/hybrid_linear)
## Dataset
The long reasoning sft data: [Ring-lite-distill-preview-sft-data](https://huggingface.co/datasets/inclusionAI/Ring-lite-distill-preview-sft-data)
## License
This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ring-lite-linear-preview/blob/main/LICENSE).
## Citation
[TBD]
|
kilbyprincess/kilbyprincess151 | kilbyprincess | 2025-04-25T09:29:55Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2025-04-25T09:29:55Z | ---
license: creativeml-openrail-m
---
|
NotTheStallion/Qwen2.5-0.32B-layer-reduced | NotTheStallion | 2025-04-25T09:27:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:26:35Z | ---
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]
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- **Shared by [optional]:** [More Information Needed]
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[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
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#### Preprocessing [optional]
[More Information Needed]
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[More Information Needed]
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<!-- 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
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[More Information Needed]
#### Metrics
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[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).
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sajeewa/emotion-classification-bert | sajeewa | 2025-04-25T09:25:03Z | 102 | 0 | null | [
"safetensors",
"bert",
"emotion-classification",
"emotion",
"mental-health",
"text-classification",
"en",
"dataset:google-research-datasets/go_emotions",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
]
| text-classification | 2025-04-18T09:36:02Z | ---
license: mit
language:
- en
tags:
- emotion-classification
- emotion
- mental-health
- bert
- text-classification
pipeline_tag: text-classification
base_model:
- bert-base-uncased
datasets:
- google-research-datasets/go_emotions
---
# 😄 Emotion Classification with BERT
This model is a fine-tuned version of `bert-base-uncased` for **multi-label emotion classification**.
It predicts **eight basic emotions** from a given piece of text using sigmoid-based multi-label classification.
---
## 🧠 Model Details
- **Base model**: `bert-base-uncased`
- **Fine-tuned for**: Multi-label emotion classification
- **Emotion labels**:
- `anger`
- `fear`
- `disgust`
- `sadness`
- `surprise`
- `joy`
- `anticipation`
- `trust`
- **Intended use**: Emotion detection in messages, sentiment analysis, chatbot tuning, mental health signal recognition, etc.
---
## 📦 Usage
```python
import torch
from transformers import BertTokenizer, BertForSequenceClassification
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load model and tokenizer
model_path = "sajeewa/emotion-classification-bert"
emotion_labels = ["anger", "fear", "disgust", "sadness", "surprise", "joy", "anticipation", "trust"]
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path, num_labels=len(emotion_labels)).to(device)
# Emotion prediction function
def predict_emotions(text: str):
model.eval()
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=50).to(device)
inputs.pop("token_type_ids", None)
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits).cpu().numpy()[0]
return {label: round(float(score), 4) for label, score in zip(emotion_labels, probs)}
# Example usage
example_text = "I'm feeling lonely today."
predictions = predict_emotions(example_text)
dominant_emotion = max(predictions, key=predictions.get)
print({dominant_emotion: predictions[dominant_emotion]}) |
subashdvorak/llama3.2-1b-st | subashdvorak | 2025-04-25T09:22:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:19:50Z | ---
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.
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[More Information Needed]
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<!-- 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
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[More Information Needed]
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<!-- 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. -->
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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efficientscaling/Z1-Shortest-7B | efficientscaling | 2025-04-25T09:21:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:19:20Z | ---
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]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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[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. -->
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#### 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).
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karuko24/GLM-Z1-9B-0414-W4A16 | karuko24 | 2025-04-25T09:21:01Z | 0 | 0 | null | [
"safetensors",
"glm4",
"arxiv:2406.12793",
"base_model:THUDM/GLM-Z1-9B-0414",
"base_model:quantized:THUDM/GLM-Z1-9B-0414",
"license:mit",
"compressed-tensors",
"region:us"
]
| null | 2025-04-25T09:15:26Z | ---
license: mit
base_model:
- THUDM/GLM-Z1-9B-0414
---
# GLM-4-Z1-9B-0414
## Introduction
The GLM family welcomes a new generation of open-source models, the **GLM-4-32B-0414** series, featuring 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, we also enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search-based Q&A, and report generation. Some benchmarks even rival larger models like GPT-4o and DeepSeek-V3-0324 (671B).
**GLM-Z1-32B-0414** is a reasoning model with **deep thinking capabilities**. This was developed based on GLM-4-32B-0414 through cold start and extended reinforcement learning, as well as further training of the model on tasks involving mathematics, code, and logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to solve complex tasks. During the training process, we also introduced general reinforcement learning based on pairwise ranking feedback, further enhancing the model's general capabilities.
**GLM-Z1-Rumination-32B-0414** is a deep reasoning model with **rumination capabilities** (benchmarked against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model employs longer periods of deep thought to solve more open-ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future development plans). The rumination model integrates search tools during its deep thinking process to handle complex tasks and is trained by utilizing multiple rule-based rewards to guide and extend end-to-end reinforcement learning. Z1-Rumination shows significant improvements in research-style writing and complex retrieval tasks.
Finally, **GLM-Z1-9B-0414** is a surprise. We employed the aforementioned series of techniques to train a 9B small-sized model that maintains the open-source tradition. Despite its smaller scale, GLM-Z1-9B-0414 still exhibits excellent capabilities in mathematical reasoning and general tasks. Its overall performance is already at a leading level among open-source models of the same size. Especially in resource-constrained scenarios, this model achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking lightweight deployment.
## Performance
<p align="center">
<img width="100%" src="https://raw.githubusercontent.com/THUDM/GLM-4/refs/heads/main/resources/Bench-Z1-32B.png">
</p>
<p align="center">
<img width="100%" src="https://raw.githubusercontent.com/THUDM/GLM-4/refs/heads/main/resources/Bench-Z1-9B.png">
</p>
## Model Usage Guidelines
### I. Sampling Parameters
| Parameter | Recommended Value | Description |
| ------------ | ----------------- | -------------------------------------------- |
| temperature | **0.6** | Balances creativity and stability |
| top_p | **0.95** | Cumulative probability threshold for sampling|
| top_k | **40** | Filters out rare tokens while maintaining diversity |
| max_new_tokens | **30000** | Leaves enough tokens for thinking |
### II. Enforced Thinking
- Add \<think\>\n to the **first line**: Ensures the model thinks before responding
- When using `chat_template.jinja`, the prompt is automatically injected to enforce this behavior
### III. Dialogue History Trimming
- Retain only the **final user-visible reply**.
Hidden thinking content should **not** be saved to history to reduce interference—this is already implemented in `chat_template.jinja`
### IV. Handling Long Contexts (YaRN)
- When input length exceeds **8,192 tokens**, consider enabling YaRN (Rope Scaling)
- In supported frameworks, add the following snippet to `config.json`:
```json
"rope_scaling": {
"type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
```
- **Static YaRN** applies uniformly to all text. It may slightly degrade performance on short texts, so enable as needed.
## Inference Code
Make Sure Using `transforemrs>=4.51.3`.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_PATH = "THUDM/GLM-4-Z1-9B-0414"
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")
message = [{"role": "user", "content": "Let a, b be positive real numbers such that ab = a + b + 3. Determine the range of possible values for a + b."}]
inputs = tokenizer.apply_chat_template(
message,
return_tensors="pt",
add_generation_prompt=True,
return_dict=True,
).to(model.device)
generate_kwargs = {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"max_new_tokens": 4096,
"do_sample": False,
}
out = model.generate(**generate_kwargs)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```
## Citations
If you find our work useful, please consider citing the following paper.
```
@misc{glm2024chatglm,
title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools},
author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
year={2024},
eprint={2406.12793},
archivePrefix={arXiv},
primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
``` |
kkks05/Llama-3.2-3B_lora_spider | kkks05 | 2025-04-25T09:19:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T09:19:29Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** kkks05
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama 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)
|
mbiadasaqui00/drgdfg | mbiadasaqui00 | 2025-04-25T09:19:38Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
]
| null | 2025-04-25T09:19:38Z | ---
license: bsd-3-clause
---
|
Odogwu001/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-humming_barky_albatross | Odogwu001 | 2025-04-25T09:19:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am humming barky albatross",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T08:17:42Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-humming_barky_albatross
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am humming barky albatross
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-humming_barky_albatross
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
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="Odogwu001/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-humming_barky_albatross", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
NiklasTUM/videomae-base-finetuned-deception-dataset | NiklasTUM | 2025-04-25T09:18:23Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
]
| video-classification | 2025-04-24T16:43:59Z | ---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-deception-dataset
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. -->
# videomae-base-finetuned-deception-dataset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0893
- Accuracy: 0.7037
## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.678 | 1.0 | 38 | 0.6885 | 0.5432 |
| 0.4976 | 2.0 | 76 | 0.6385 | 0.5432 |
| 0.232 | 3.0 | 114 | 1.3740 | 0.6420 |
| 0.1504 | 4.0 | 152 | 1.2944 | 0.5926 |
| 0.1695 | 5.0 | 190 | 1.0783 | 0.6173 |
| 0.1099 | 6.0 | 228 | 1.2128 | 0.6543 |
| 0.0815 | 7.0 | 266 | 1.1837 | 0.7037 |
| 0.0961 | 7.8947 | 300 | 1.0893 | 0.7037 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.1.0+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1
|
robiulawaldev/6f49ce5f-f055-4382-aa99-f5a659479a27 | robiulawaldev | 2025-04-25T09:17:49Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:NousResearch/Hermes-2-Theta-Llama-3-8B",
"base_model:adapter:NousResearch/Hermes-2-Theta-Llama-3-8B",
"region:us"
]
| null | 2025-04-25T09:16:24Z | ---
base_model: NousResearch/Hermes-2-Theta-Llama-3-8B
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. 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. 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]
### Framework versions
- PEFT 0.13.2 |
deswaq/juh82 | deswaq | 2025-04-25T09:13:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:11:03Z | ---
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. 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] |
easygoing0114/AI_upscalers | easygoing0114 | 2025-04-25T09:13:33Z | 0 | 0 | null | [
"onnx",
"art",
"region:us"
]
| null | 2025-04-24T13:59:10Z | ---
tags:
- art
---
# AI Upscalers
This repository collects various AI upscaling models for image enhancement.
Each model inherits its original license, which must be respected. Please review the license details before use, especially for commercial purposes.
## Models
| Model | Type | License | Commercial Use | Features | Recommended |
| --- | --- | --- | --- | --- | --- |
| RealESRGAN_x4plus | ESRGAN | BSD 3-Clause | ✅ | Balanced | ✅ |
| RealESRGAN_x4plus_anime_6B | ESRGAN | BSD 3-Clause | ✅ | Anime Style | ✅ |
| 4x-AnimeSharp | ESRGAN | CC-BY-NC-SA-4.0 | ❌ | Sharp | |
| 4x-UltraSharp_150000 | ESRGAN | CC-BY-NC-SA-4.0 | ❌ | Sharp | |
| 4x_foolhardy_Remacri_210000 | ESRGAN | CC-BY-NC-SA-4.0 | ❌ | Sharp | |
| 4x_fatal_Anime_500000_G | ESRGAN | CC-BY-NC-SA-4.0 | ❌ | | |
| 4x_IllustrationJaNai_V1_ESRGAN_135k | ESRGAN | CC-BY-NC-SA-4.0 | ❌ | Anime Style | ✅ |
| 4x_NMKD-Superscale-SP_178000_G | ESRGAN | WTFPL | ✅ | Balanced | |
| 4x-NMKD-YandereNeo_320k | ESRGAN | WTFPL | ✅ | Balanced | |
| 4x_NMKD-YandereNeoXL_200k | ESRGAN | WTFPL | ✅ | Balanced | ✅ |
| 4x_escale_100000_G | ESRGAN | WTFPL | ✅ | | |
| 4x_RealisticRescaler_100000_G | ESRGAN | WTFPL | ✅ | Natural | ✅ |
| 4x PSNR_Pretrained | ESRGAN | Apache-2.0 | ✅ | | |
| 4x_UniversalUpscalerV2-Neutral_115000_G | ESRGAN | WTFPL | ✅ | | |
| 4x_UniversalUpscalerV2-Sharper_103000_G | ESRGAN | WTFPL | ✅ | | |
| 4x_UniversalUpscalerV2-Sharp_101000_G | ESRGAN | WTFPL | ✅ | | |
| 4x-PBRify_RPLKSRd_V3_160000 | PLKSR | CC0-1.0 | ✅ | | |
| OmniSR_X4_DIV2K | OmniSR | Apache-2.0 | ✅ | | |
| 4x-SwinIR-L_GAN | SwinIR | Apache-2.0 | ✅ | | |
| 4x-SwinIR-L_PNSR | SwinIR | Apache-2.0 | ✅ | | |
| 4xNomos2_hq_drct-l_200000 | DRCT | CC-BY-4.0 | ✅ | | |
| 4x_IllustrationJaNai_V1_DAT2_190k | DAT | CC-BY-NC-SA-4.0 | ❌ | Anime Style | |
| 4xNomos2_hq_dat2_140000 | DAT | CC-BY-4.0 | ✅ | Natural | |
| 4xNomos8kDAT_110000 | DAT | CC-BY-4.0 | ✅ | Natural | |
| 4xNomos8kHAT-L_otf_220000 | HAT | CC-BY-4.0 | ✅ | Natural | |
## OpenModelDB Links
- [RealESRGAN_x4plus](https://openmodeldb.info/models/4x-realesrgan-x4plus)
- [RealESRGAN_x4Plus Anime 6B](https://openmodeldb.info/models/4x-realesrgan-x4plus-anime-6b)
- [4x_AnimeSharp](https://openmodeldb.info/models/4x-AnimeSharp)
- [4x-UltraSharp_150000](https://openmodeldb.info/models/4x-UltraSharp)
- [4x_foolhardy_Remacri_210000](https://openmodeldb.info/models/4x-Remacri)
- [4x_fatal_Anime_500000_G](https://openmodeldb.info/models/4x-Fatal-Anime)
- [IllustrationJaNai_V1_ESRGAN_135k](https://openmodeldb.info/models/4x-IllustrationJaNai-V1-ESRGAN)
- [4x_NMKD-Superscale-SP_178000_G](https://openmodeldb.info/models/4x-NMKD-Superscale)
- [4x-NMKD-YandereNeo_320k](https://openmodeldb.info/models/4x-NMKD-YandereNeo)
- [4x_NMKD-YandereNeoXL_200k](https://openmodeldb.info/models/4x-NMKD-YandereNeo-XL)
- [4x_escale_100000_G](https://openmodeldb.info/models/4x-escale)
- [4x_RealisticRescaler_100000_G](https://openmodeldb.info/models/4x-RealisticRescaler)
- [4x PSNR Pretrained](https://openmodeldb.info/models/4x-PSNR)
- [4x_UniversalUpscalerV2-Neutral_115000_G](https://openmodeldb.info/models/4x-UniversalUpscalerV2-Neutral)
- [4x_UniversalUpscalerV2-Sharper_103000_G](https://openmodeldb.info/models/4x-UniversalUpscalerV2-Sharper)
- [4x_UniversalUpscalerV2-Sharp_101000_G](https://openmodeldb.info/models/4x-UniversalUpscalerV2-Sharp)
- [4x-PBRify_RPLKSRd_V3_160000](https://openmodeldb.info/models/4x-PBRify-RPLKSRd-V3)
- [OmniSR_X4_DIV2K](https://openmodeldb.info/models/4x-OmniSR-DIV2K)
- [4x-SwinIR-L_GAN](https://github.com/JingyunLiang/SwinIR/releases/tag/v0.0)
- [4x-SwinIR-L_PNSR](https://github.com/JingyunLiang/SwinIR/releases/tag/v0.0)
- [4xNomos2_hq_drct-l_200000](https://openmodeldb.info/models/4x-Nomos2-hq-drct-l)
- [IllustrationJaNai_V1_DAT2_190k](https://openmodeldb.info/models/4x-IllustrationJaNai-V1-DAT2)
- [4xNomos2_hq_dat2_140000](https://openmodeldb.info/models/4x-Nomos2-hq-dat2)
- [4xNomos8kDAT_110000](https://openmodeldb.info/models/4x-Nomos8kDAT)
- [4xNomos8kHAT-L_otf_220000](https://openmodeldb.info/models/4x-Nomos8kHAT-L-otf)
## Comparison for Anime Illustrations (External Site)
- [Comparison image](https://www.ai-image-journey.com/p/upscale-model.html)
- [Guide](https://www.ai-image-journey.com/2025/04/ai-upscale-hires-fix.html)
## Licenses
The following licenses apply to the models in this repository, listed from most restrictive to least restrictive:
| License | Description | Restrictions | Original License Text |
| --- | --- | --- | --- |
| [CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) | Non-commercial use only, must share under the same license. | Non-commercial, same license sharing | [CC-BY-NC-SA-4.0 Legal Code](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) |
| [BSD 3-Clause](https://opensource.org/licenses/BSD-3-Clause) | Requires copyright notice and disclaimer. | Copyright notice, disclaimer | [BSD 3-Clause License](https://opensource.org/licenses/BSD-3-Clause) |
| [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) | Requires copyright notice and change log. | Copyright notice, change log | [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt) |
| [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | Requires attribution. | Attribution | [CC-BY-4.0 Legal Code](https://creativecommons.org/licenses/by/4.0/legalcode) |
| [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/) | Public domain, no restrictions. | None | [CC0-1.0 Legal Code](https://creativecommons.org/publicdomain/zero/1.0/legalcode) |
| [WTFPL](http://www.wtfpl.net/) | Do whatever you want. | None | [WTFPL License](http://www.wtfpl.net/txt/copying/) | |
efficientscaling/Z1-Longest-7B | efficientscaling | 2025-04-25T09:11:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-25T09:10:05Z | ---
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. 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Lasion/gemma-3 | Lasion | 2025-04-25T09:10:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3_text",
"trl",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-25T09:10:31Z | ---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Lasion
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text 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)
|
ledonhung356/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tame_monstrous_puffin | ledonhung356 | 2025-04-25T09:10:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tame monstrous puffin",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-24T17:53:31Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tame_monstrous_puffin
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tame monstrous puffin
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tame_monstrous_puffin
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
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="ledonhung356/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tame_monstrous_puffin", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
danyush/qwen2.5_vl_7b_virat_shuffled | danyush | 2025-04-25T09:10:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2025-04-25T09:04:57Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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. 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]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
Subsets and Splits