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Tharunya1/English-Spanis | Tharunya1 | 2025-05-02T19:25:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-02T19:22:57Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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.
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[More Information Needed]
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chitra-tripathi-video/chitra.tripathi.Viral.Video.Link | chitra-tripathi-video | 2025-05-02T19:25:16Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-02T19:20:35Z | [๐ CLICK HERE ๐ข==โบโบ WATCH NOW](https://videohere.top/?V=chitra-tripathi)
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[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=chitra-tripathi) |
pkmitl205/tinyBERT-Distill-WangchanBERTa | pkmitl205 | 2025-05-02T19:25:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-02T19:24:56Z | ---
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 ๐ค 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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dvteja/gemma-legal-qa | dvteja | 2025-05-02T19:23:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T19:23:39Z | ---
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 ๐ค 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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## 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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New-Tutorial-jobz-hunting/wATCH.TRENDING.VIDEO.Jobz.Hunting.Sajal.Malik.viral.video.Tutorial | New-Tutorial-jobz-hunting | 2025-05-02T19:21:05Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-02T19:18:28Z | [๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://videohere.top/?jobz-hunting)
[โบโ
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[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?jobz-hunting) |
OSRSEnthusiast/trainer_output | OSRSEnthusiast | 2025-05-02T19:20:40Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-04-26T05:06:39Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: trainer_output
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8775510204081632
---
<!-- 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. -->
# trainer_output
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3427
- Accuracy: 0.8776
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 20
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 39 | 0.4124 | 0.7938 |
| No log | 2.0 | 78 | 0.3294 | 0.8454 |
| 0.4497 | 3.0 | 117 | 0.2932 | 0.8454 |
| 0.4497 | 4.0 | 156 | 0.2799 | 0.8557 |
| 0.4497 | 5.0 | 195 | 0.2692 | 0.8969 |
| 0.2764 | 6.0 | 234 | 0.2604 | 0.8969 |
| 0.2764 | 7.0 | 273 | 0.2583 | 0.9175 |
| 0.2192 | 8.0 | 312 | 0.2546 | 0.9072 |
| 0.2192 | 9.0 | 351 | 0.2506 | 0.9072 |
| 0.2192 | 10.0 | 390 | 0.2536 | 0.9072 |
| 0.1936 | 11.0 | 429 | 0.2530 | 0.8866 |
| 0.1936 | 12.0 | 468 | 0.2503 | 0.9072 |
| 0.1731 | 13.0 | 507 | 0.2480 | 0.9072 |
| 0.1731 | 14.0 | 546 | 0.2496 | 0.9072 |
| 0.1731 | 15.0 | 585 | 0.2498 | 0.9072 |
| 0.155 | 16.0 | 624 | 0.2498 | 0.9072 |
| 0.155 | 17.0 | 663 | 0.2495 | 0.9072 |
| 0.1442 | 18.0 | 702 | 0.2488 | 0.9072 |
| 0.1442 | 19.0 | 741 | 0.2493 | 0.9072 |
| 0.1442 | 20.0 | 780 | 0.2490 | 0.9072 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
biustnaspust/purpur25 | biustnaspust | 2025-05-02T19:16:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T19:12:07Z | ---
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 ๐ค 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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[More Information Needed]
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[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]
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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. -->
[More Information Needed]
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<!-- 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]
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## Technical Specifications [optional]
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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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[More Information Needed]
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18-Tutorial-Paro-Aarti/Original.Viral.Clip.Paro.Aarti.Viral.Video.Leaks.official | 18-Tutorial-Paro-Aarti | 2025-05-02T19:15:49Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-02T19:15:43Z | [๐ CLICK HERE ๐ข==โบโบ WATCH NOW](https://videohere.top/?V=Paro-Aarti)
[๐ด CLICK HERE ๐==โบโบ Download Now)](https://videohere.top/?V=Paro-Aarti)
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mradermacher/openbuddy-thinker-32b-v26-preview-GGUF | mradermacher | 2025-05-02T19:08:28Z | 202 | 0 | transformers | [
"transformers",
"gguf",
"qwen2.5",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"fi",
"base_model:OpenBuddy/openbuddy-thinker-32b-v26-preview",
"base_model:quantized:OpenBuddy/openbuddy-thinker-32b-v26-preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-27T19:52:58Z | ---
base_model: OpenBuddy/openbuddy-thinker-32b-v26-preview
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- fi
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- qwen2.5
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/OpenBuddy/openbuddy-thinker-32b-v26-preview
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/openbuddy-thinker-32b-v26-preview-GGUF/resolve/main/openbuddy-thinker-32b-v26-preview.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |
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 -->
|
zelk12/MT4-gemma-3-12B | zelk12 | 2025-05-02T19:06:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0",
"base_model:merge:ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0",
"base_model:huihui-ai/gemma-3-12b-it-abliterated",
"base_model:merge:huihui-ai/gemma-3-12b-it-abliterated",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-02T18:55:46Z | ---
base_model:
- ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0
- huihui-ai/gemma-3-12b-it-abliterated
library_name: transformers
tags:
- mergekit
- merge
license: gemma
pipeline_tag: image-text-to-text
---
# 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 [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [huihui-ai/gemma-3-12b-it-abliterated](https://huggingface.co/huihui-ai/gemma-3-12b-it-abliterated) as a base.
### Models Merged
The following models were included in the merge:
* [ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0](https://huggingface.co/ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: huihui-ai/gemma-3-12b-it-abliterated
#no parameters necessary for base model
- model: ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0
parameters:
density: 0.5
weight: 0.5
merge_method: dare_ties
base_model: huihui-ai/gemma-3-12b-it-abliterated
parameters:
normalize: true
dtype: bfloat16
``` |
Jobz-Hunting-Sajal-Malik-18s/Jobz.Hunting.Sajal.Malik.Viral.Video.Link | Jobz-Hunting-Sajal-Malik-18s | 2025-05-02T19:04:35Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-02T19:02:04Z | [๐ CLICK HERE ๐ข==โบโบ WATCH NOW](https://videohere.top/?V=Jobz-Hunting-Sajal-Malik)
[๐ด CLICK HERE ๐==โบโบ Download Now)](https://videohere.top/?V=Jobz-Hunting-Sajal-Malik)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Jobz-Hunting-Sajal-Malik) |
darkc0de/Xortron-SCE-24B-CriminalComputingConfig-Q4_K_S-GGUF | darkc0de | 2025-05-02T19:01:34Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:darkc0de/Xortron-SCE-24B-CriminalComputingConfig",
"base_model:quantized:darkc0de/Xortron-SCE-24B-CriminalComputingConfig",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T19:00:33Z | ---
base_model: darkc0de/Xortron-SCE-24B-CriminalComputingConfig
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# darkc0de/Xortron-SCE-24B-CriminalComputingConfig-Q4_K_S-GGUF
This model was converted to GGUF format from [`darkc0de/Xortron-SCE-24B-CriminalComputingConfig`](https://huggingface.co/darkc0de/Xortron-SCE-24B-CriminalComputingConfig) 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/darkc0de/Xortron-SCE-24B-CriminalComputingConfig) 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 darkc0de/Xortron-SCE-24B-CriminalComputingConfig-Q4_K_S-GGUF --hf-file xortron-sce-24b-criminalcomputingconfig-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo darkc0de/Xortron-SCE-24B-CriminalComputingConfig-Q4_K_S-GGUF --hf-file xortron-sce-24b-criminalcomputingconfig-q4_k_s.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 darkc0de/Xortron-SCE-24B-CriminalComputingConfig-Q4_K_S-GGUF --hf-file xortron-sce-24b-criminalcomputingconfig-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo darkc0de/Xortron-SCE-24B-CriminalComputingConfig-Q4_K_S-GGUF --hf-file xortron-sce-24b-criminalcomputingconfig-q4_k_s.gguf -c 2048
```
|
Hachipo/Meta-Llama-3-8B-MIFT-ja_1000_2 | Hachipo | 2025-05-02T18:54:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T18:27:28Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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 ๐ค 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] |
Hamzah-Asadullah/GenericRPV3-2B | Hamzah-Asadullah | 2025-05-02T18:53:43Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rp",
"roleplay",
"code",
"mathematics",
"multilingual",
"merge",
"mergekit",
"uncensored",
"text2text-generation",
"en",
"zh",
"hi",
"ur",
"de",
"it",
"es",
"fr",
"pl",
"ar",
"base_model:XformAI-india/qwen-1.7b-coder",
"base_model:merge:XformAI-india/qwen-1.7b-coder",
"base_model:huihui-ai/Qwen3-1.7B-abliterated",
"base_model:merge:huihui-ai/Qwen3-1.7B-abliterated",
"base_model:kxdw2580/Qwen3-1.7B-Catgirl-test0430",
"base_model:merge:kxdw2580/Qwen3-1.7B-Catgirl-test0430",
"base_model:wzx111/Qwen3-1.7B-MATH-GDPO",
"base_model:merge:wzx111/Qwen3-1.7B-MATH-GDPO",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-02T18:32:26Z | ---
license: apache-2.0
language:
- en
- zh
- hi
- ur
- de
- it
- es
- fr
- pl
- ar
base_model:
- kxdw2580/Qwen3-1.7B-Catgirl-test0430
- huihui-ai/Qwen3-1.7B-abliterated
- XformAI-india/qwen-1.7b-coder
- wzx111/Qwen3-1.7B-MATH-GDPO
tags:
- rp
- roleplay
- code
- mathematics
- multilingual
- merge
- mergekit
- uncensored
pipeline_tag: text2text-generation
library_name: transformers
---
> [!IMPORTANT]
> My ChatGPT type website [here](https://xetute.github.io/)
> Support me [here (ko-fi)](https://ko-fi.com/hamzahasadullah)
Too lazy to write a detailed modelcard. Model's part of the GRP / GenericRP series, that's V3 based on Qwen3 2B, licensed accordingly.
It's a simple merge. To see intended behaviour, see V2 or sum, card's more detailed.
- kxdw2580/Qwen3-1.7B-Catgirl-test0430: w0.25
- huihui-ai/Qwen3-1.7B-abliterated: w0.25
- XformAI-india/qwen-1.7b-coder: w0.25
- wzx111/Qwen3-1.7B-MATH-GDPO: w0.25
Happy chatting or whatever.
<div style="display: flex; flex-direction: column; justify-content: center; align-items: left; font-size: 1rem; padding: 20px;">
<div style="display: flex; flex-direction: row; align-items: center; margin: 10px; margin-left: 0; padding: 0;">
<img src="https://xetute.github.io/favicon.ico" style="margin: 0; border-radius: 50%; height: 2rem;"/>
<h2 style="margin: 0; margin-left: 10px;">XeTute Technologies</h2>
</div>
<div style="display: flex; flex-direction: row; gap: 5px; margin: 0; max-width: 500px;">
XeTute Technologies is an unofficial Pakistani organisation created by <a href="https://huggingface.co/Hamzah-Asadullah">Hamzah Asadullah.</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Links</h2>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://huggingface.co/XeTute">HuggingFace</a>
<a href="https://github.com/XeTute">GitHub</a>
</div>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://ko-fi.com/hamzahasadullah">Buy me a Coffee</a>
<a href="https://xetute.github.io">Apex Webpage</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Pakistan</h2>
Pakistan is a country in South-Asia known for its rich culture despite the British, its stunning landscape, and PAF (Pakistan Armed Forces), its military. Long live the Islamic Republic of Pakistan.<br>
<img src="https://upload.wikimedia.org/wikipedia/commons/3/32/Flag_of_Pakistan.svg" style="width: 85%; max-width: 512px; border-radius: 25px;"/>
</div> |
Hamzah-Asadullah/GenericRPV3-2B-GGUF | Hamzah-Asadullah | 2025-05-02T18:53:14Z | 0 | 1 | null | [
"gguf",
"rp",
"roleplay",
"code",
"mathematics",
"multilingual",
"text2text-generation",
"en",
"zh",
"hi",
"ur",
"de",
"it",
"es",
"fr",
"pl",
"ar",
"base_model:Hamzah-Asadullah/GenericRPV3-2B",
"base_model:quantized:Hamzah-Asadullah/GenericRPV3-2B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-02T18:41:05Z | ---
license: apache-2.0
language:
- en
- zh
- hi
- ur
- de
- it
- es
- fr
- pl
- ar
base_model:
- Hamzah-Asadullah/GenericRPV3-2B
tags:
- rp
- roleplay
- code
- mathematics
- multilingual
pipeline_tag: text2text-generation
---
> [!IMPORTANT]
> My ChatGPT type website [here](https://xetute.github.io/)
> Support me [here (ko-fi)](https://ko-fi.com/hamzahasadullah)
Too lazy to write a detailed modelcard. Model's part of the GRP / GenericRP series, that's V3 based on Qwen3 2B, licensed accordingly.
It's a simple merge. To see intended behaviour, see V2 or sum, card's more detailed.
- kxdw2580/Qwen3-1.7B-Catgirl-test0430: w0.25
- huihui-ai/Qwen3-1.7B-abliterated: w0.25
- XformAI-india/qwen-1.7b-coder: w0.25
- wzx111/Qwen3-1.7B-MATH-GDPO: w0.25
Happy chatting or whatever.
<div style="display: flex; flex-direction: column; justify-content: center; align-items: left; font-size: 1rem; padding: 20px;">
<div style="display: flex; flex-direction: row; align-items: center; margin: 10px; margin-left: 0; padding: 0;">
<img src="https://xetute.github.io/favicon.ico" style="margin: 0; border-radius: 50%; height: 2rem;"/>
<h2 style="margin: 0; margin-left: 10px;">XeTute Technologies</h2>
</div>
<div style="display: flex; flex-direction: row; gap: 5px; margin: 0; max-width: 500px;">
XeTute Technologies is an unofficial Pakistani organisation created by <a href="https://huggingface.co/Hamzah-Asadullah">Hamzah Asadullah.</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Links</h2>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://huggingface.co/XeTute">HuggingFace</a>
<a href="https://github.com/XeTute">GitHub</a>
</div>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://ko-fi.com/hamzahasadullah">Buy me a Coffee</a>
<a href="https://xetute.github.io">Apex Webpage</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Pakistan</h2>
Pakistan is a country in South-Asia known for its rich culture despite the British, its stunning landscape, and PAF (Pakistan Armed Forces), its military. Long live the Islamic Republic of Pakistan.<br>
<img src="https://upload.wikimedia.org/wikipedia/commons/3/32/Flag_of_Pakistan.svg" style="width: 85%; max-width: 512px; border-radius: 25px;"/>
</div> |
JayJayisreal/QwQ-32B-ArliAI-RpR-v3 | JayJayisreal | 2025-05-02T18:51:47Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T18:51:47Z | ---
license: apache-2.0
---
|
dinalad0/my-fino1-model | dinalad0 | 2025-05-02T18:51:43Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:TheFinAI/Fino1_Reasoning_Path_FinQA_v2",
"arxiv:2502.08127",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-14B-Instruct",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-05-02T18:41:41Z | ---
license: apache-2.0
datasets:
- TheFinAI/Fino1_Reasoning_Path_FinQA_v2
language:
- en
base_model:
- Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
---
# ๐ฆ Fino1-14B
**Fino1-14B** is a fine-tuned version of **Qwen2.5-14B-Instruct**, designed to improve performance on **[financial reasoning tasks]**. This model has been trained using **SFT** and **RF** on **TheFinAI/Fino1_Reasoning_Path_FinQA_v2**, enhancing its capabilities in **financial reasoning tasks**.
Check our paper arxiv.org/abs/2502.08127 for more details.
## ๐ Model Details
- **Model Name**: `Fino1-14B`
- **Base Model**: `Qwen2.5-14B-Instruct`
- **Fine-Tuned On**: `TheFinAI/Fino1_Reasoning_Path_FinQA_v2` Derived from multiple financial dataset.
- **Training Method**: SFT and RF
- **Objective**: `[Enhance performance on specific tasks such as financial mathemtical reasoning]`
- **Tokenizer**: Inherited from `Qwen/Qwen2.5-14B-Instruct`
## ๐ Training Configuration
- **Training Hardware**: `GPU: [e.g., 4xH100]`
- **Batch Size**: `[e.g., 16]`
- **Learning Rate**: `[e.g., 2e-5]`
- **Epochs**: `[e.g., 3]`
- **Optimizer**: `[e.g., AdamW, LAMB]`
## ๐ง Usage
To use `Fino1-14B` with Hugging Face's `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "TheFinAI/Fino1-14B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
input_text = "What is the results of 3-5?"
inputs = tokenizer(input_text, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
## ๐ก Citation
If you use this model in your research, please cite:
```python
@article{qian2025fino1,
title={Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance},
author={Qian, Lingfei and Zhou, Weipeng and Wang, Yan and Peng, Xueqing and Huang, Jimin and Xie, Qianqian},
journal={arXiv preprint arXiv:2502.08127},
year={2025}
} |
sema-aviation/balloon-detection | sema-aviation | 2025-05-02T18:48:29Z | 0 | 0 | null | [
"object-detection",
"tr",
"dataset:sema-aviation/balloon-detection",
"arxiv:1910.09700",
"base_model:Ultralytics/YOLO11",
"base_model:finetune:Ultralytics/YOLO11",
"license:mit",
"region:us"
] | object-detection | 2025-05-01T20:23:00Z | ---
license: mit
datasets:
- sema-aviation/balloon-detection
language:
- tr
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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] |
lisabdunlap/Llama-3.2-3B-Instruct-r32-e10-lr0.0002-new-new | lisabdunlap | 2025-05-02T18:47:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T18:46:22Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** lisabdunlap
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-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)
|
AGofficial/AgX-2 | AGofficial | 2025-05-02T18:46:33Z | 0 | 0 | null | [
"en",
"base_model:AGofficial/AgX-1",
"base_model:finetune:AGofficial/AgX-1",
"license:mit",
"region:us"
] | null | 2025-05-02T18:43:58Z | ---
license: mit
language:
- en
base_model:
- AGofficial/AgX-1
---
# AgX-2
AgX-2 is a next-generation AI interface powered by experimental architecture beyond transformers. AgX-2 processes data using recursive structures, neuron signals, and echo-state memory to deliver dynamic, human-like responses.
## Features
- ๐ Turbocharged inference via `AgGPT-8-TURBO-v2`.
- โ๏ธ Built-in grammar correction.
- ๐ง Modular design.
- ๐ฏ Designed for high-quality, fluid conversations and smart contextual awareness.
This model paves the way for AgGPT-11, AgX-3, and beyond. |
Triangle104/Violet_Magcap-12B-Q8_0-GGUF | Triangle104 | 2025-05-02T18:45:02Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Nitral-AI/Violet_Magcap-12B",
"base_model:quantized:Nitral-AI/Violet_Magcap-12B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T18:43:24Z | ---
base_model: Nitral-AI/Violet_Magcap-12B
language:
- en
license: other
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Violet_Magcap-12B-Q8_0-GGUF
This model was converted to GGUF format from [`Nitral-AI/Violet_Magcap-12B`](https://huggingface.co/Nitral-AI/Violet_Magcap-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Nitral-AI/Violet_Magcap-12B) for more details on the model.
---
Mag-Mell-12B-R1, jacked up on SFT reasoning data like it was pre-workout for logic bros. Then for chaos, slapped together with Captain_Eris_Violet-GRPO like some twisted AI Voltron.
Double-tapped the merge with SFT on fresh reasoning data. Now it's
solving problems like Bill Nye on a meme bender and hoarding cursed
philosophy sh*tposts.
---
## 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 Triangle104/Violet_Magcap-12B-Q8_0-GGUF --hf-file violet_magcap-12b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Violet_Magcap-12B-Q8_0-GGUF --hf-file violet_magcap-12b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Violet_Magcap-12B-Q8_0-GGUF --hf-file violet_magcap-12b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Violet_Magcap-12B-Q8_0-GGUF --hf-file violet_magcap-12b-q8_0.gguf -c 2048
```
|
Triangle104/Violet_Magcap-12B-Q6_K-GGUF | Triangle104 | 2025-05-02T18:42:36Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Nitral-AI/Violet_Magcap-12B",
"base_model:quantized:Nitral-AI/Violet_Magcap-12B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T18:39:42Z | ---
base_model: Nitral-AI/Violet_Magcap-12B
language:
- en
license: other
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Violet_Magcap-12B-Q6_K-GGUF
This model was converted to GGUF format from [`Nitral-AI/Violet_Magcap-12B`](https://huggingface.co/Nitral-AI/Violet_Magcap-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Nitral-AI/Violet_Magcap-12B) for more details on the model.
---
Mag-Mell-12B-R1, jacked up on SFT reasoning data like it was pre-workout for logic bros. Then for chaos, slapped together with Captain_Eris_Violet-GRPO like some twisted AI Voltron.
Double-tapped the merge with SFT on fresh reasoning data. Now it's
solving problems like Bill Nye on a meme bender and hoarding cursed
philosophy sh*tposts.
---
## 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 Triangle104/Violet_Magcap-12B-Q6_K-GGUF --hf-file violet_magcap-12b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Violet_Magcap-12B-Q6_K-GGUF --hf-file violet_magcap-12b-q6_k.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 Triangle104/Violet_Magcap-12B-Q6_K-GGUF --hf-file violet_magcap-12b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Violet_Magcap-12B-Q6_K-GGUF --hf-file violet_magcap-12b-q6_k.gguf -c 2048
```
|
marialvsantiago/72cd73b7-f99e-4cf7-a91b-e8e20d05d76b | marialvsantiago | 2025-05-02T18:39:08Z | 0 | 0 | peft | [
"peft",
"safetensors",
"opt",
"axolotl",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-02T18:36:52Z | ---
library_name: peft
license: other
base_model: facebook/opt-350m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 72cd73b7-f99e-4cf7-a91b-e8e20d05d76b
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: facebook/opt-350m
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 474129246f3c557f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/474129246f3c557f_train_data.json
type:
field_input: artist
field_instruction: title
field_output: lyrics
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/72cd73b7-f99e-4cf7-a91b-e8e20d05d76b
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/474129246f3c557f_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: 32c32359-3060-4ed4-990d-db4922ae7969
wandb_project: s56-33
wandb_run: your_name
wandb_runid: 32c32359-3060-4ed4-990d-db4922ae7969
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 72cd73b7-f99e-4cf7-a91b-e8e20d05d76b
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9638
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 3.2784 | 0.0464 | 200 | 2.9638 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
infogeo/7446cf29-f7eb-4423-9a9d-ab569004813e | infogeo | 2025-05-02T18:38:48Z | 0 | 0 | peft | [
"peft",
"safetensors",
"opt",
"axolotl",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-02T18:36:46Z | ---
library_name: peft
license: other
base_model: facebook/opt-350m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7446cf29-f7eb-4423-9a9d-ab569004813e
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: facebook/opt-350m
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 474129246f3c557f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/474129246f3c557f_train_data.json
type:
field_input: artist
field_instruction: title
field_output: lyrics
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: 0.55
group_by_length: false
hub_model_id: infogeo/7446cf29-f7eb-4423-9a9d-ab569004813e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-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: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/474129246f3c557f_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: 32c32359-3060-4ed4-990d-db4922ae7969
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 32c32359-3060-4ed4-990d-db4922ae7969
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 7446cf29-f7eb-4423-9a9d-ab569004813e
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0961
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.6603 | 0.0348 | 150 | 3.0961 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
rakib5bit/Rakib | rakib5bit | 2025-05-02T18:38:46Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T18:38:46Z | ---
license: apache-2.0
---
|
mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF | mradermacher | 2025-05-02T18:38:26Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Neelectric/OLMo-2-1124-7B-Instruct_GRPOv01.13",
"base_model:quantized:Neelectric/OLMo-2-1124-7B-Instruct_GRPOv01.13",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T18:07:06Z | ---
base_model: Neelectric/OLMo-2-1124-7B-Instruct_GRPOv01.13
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/Neelectric/OLMo-2-1124-7B-Instruct_GRPOv01.13
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.IQ4_XS.gguf) | IQ4_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q4_K_S.gguf) | Q4_K_S | 4.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q5_K_S.gguf) | Q5_K_S | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q5_K_M.gguf) | Q5_K_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q6_K.gguf) | Q6_K | 6.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.Q8_0.gguf) | Q8_0 | 7.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_GRPOv01.13-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_GRPOv01.13.f16.gguf) | f16 | 14.7 | 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 -->
|
ivangrapher/0ba7aef3-8dcb-4be4-8fd8-e1405d30448d | ivangrapher | 2025-05-02T18:38:22Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:openlm-research/open_llama_3b",
"base_model:adapter:openlm-research/open_llama_3b",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-02T17:44:42Z | ---
library_name: peft
license: apache-2.0
base_model: openlm-research/open_llama_3b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0ba7aef3-8dcb-4be4-8fd8-e1405d30448d
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: openlm-research/open_llama_3b
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 83b1700bf8a9ee56_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/83b1700bf8a9ee56_train_data.json
type:
field_instruction: abstract
field_output: title
format: '{instruction}'
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: 0.55
group_by_length: false
hub_model_id: ivangrapher/0ba7aef3-8dcb-4be4-8fd8-e1405d30448d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
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: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/83b1700bf8a9ee56_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: 2048
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: 1915c1a2-c038-45d6-98b0-3f4a5eeb7f31
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 1915c1a2-c038-45d6-98b0-3f4a5eeb7f31
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 0ba7aef3-8dcb-4be4-8fd8-e1405d30448d
This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3022
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.3458 | 0.0036 | 150 | 2.3022 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
nicolaadrah/physics_adapted_llama_3.2_3b | nicolaadrah | 2025-05-02T18:32:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-05-02T18:21:14Z | ---
base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** nicolaadrah
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-unsloth-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)
|
akos2/swap | akos2 | 2025-05-02T18:31:49Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:apache-2.0",
"region:us"
] | text-to-image | 2025-05-02T18:30:38Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/hugging.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: apache-2.0
---
# migrationlora
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/akos2/swap/tree/main) them in the Files & versions tab.
|
lusxvr/nanoVLM-256M | lusxvr | 2025-05-02T18:29:55Z | 0 | 0 | null | [
"vision-language",
"multimodal",
"pytorch",
"small-model",
"efficient",
"research",
"VLM",
"image-text-to-text",
"dataset:HuggingFaceM4/the_cauldron",
"license:apache-2.0",
"region:us"
] | image-text-to-text | 2025-05-02T16:24:03Z | ---
license: apache-2.0
tags:
- vision-language
- multimodal
- pytorch
- small-model
- efficient
- research
- VLM
model_name: nanoVLM
datasets:
- HuggingFaceM4/the_cauldron
metrics:
- accuracy
pipeline_tag: image-text-to-text
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation.
Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code.
It combines a ViT-based image encoder (SigLIP-B/16-512-86M) with a lightweight causal language model (SmolLM2-135M),
resulting in a compact 256M parameter model. The model achieves ~x% accuracy on MMStar after training for 6 hours on a single H100 GPU
using 1.7M samples from [the cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron) dataset,
making it a strong baseline for low-resource VLM research.
The model is ideal for researchers and developers interested in exploring VLM training with minimal computational overhead,
and serves as a perfect starting point for tinkering with multimodal architectures.
**Model Architecture:**
- Vision Transformer (SigLIP-B/16)
- Causal Language Model (SmolLM2)
- Modality Projection Layer
**Training:**
- Trained on ~1.7M samples from the `the_cauldron` dataset
- 6 hours on a single NVIDIA H100 GPU
- Resulting model size: 256M parameters
**Evaluation:**
- MMStar Accuracy: ~x%
**Usage:**
Usable through the nanoVLM repository: https://github.com/huggingface/nanoVLM
```python
path_to_hf_file = hf_hub_download(repo_id="lusxvr/nanoVLM-256M", filename="nanoVLM-256M.pth")
model = VLM(cfg.VLMConfig())
model.load_checkpoint(path_to_hf_file)
``` |
scales-okn/spacy_judge_model | scales-okn | 2025-05-02T18:29:14Z | 14 | 0 | spacy | [
"spacy",
"token-classification",
"en",
"license:gpl-3.0",
"model-index",
"region:us"
] | token-classification | 2025-04-14T20:51:37Z | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9887751161
- name: NER Recall
type: recall
value: 0.9952089692
- name: NER F Score
type: f_score
value: 0.9919816105
license: gpl-3.0
---
| Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.7.6,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 684830 keys, 684830 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | gpl-3.0 |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (2 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `HONORARIUM`, `JUDGE` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 99.20 |
| `ENTS_P` | 98.88 |
| `ENTS_R` | 99.52 |
| `TOK2VEC_LOSS` | 69445.26 |
| `NER_LOSS` | 18046.49 | |
chchen/MentaLLaMA-chat-7B-PsyCourse-doc-info-fold4 | chchen | 2025-05-02T18:25:11Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:klyang/MentaLLaMA-chat-7B-hf",
"base_model:adapter:klyang/MentaLLaMA-chat-7B-hf",
"license:mit",
"region:us"
] | null | 2025-05-02T16:43:00Z | ---
library_name: peft
license: mit
base_model: klyang/MentaLLaMA-chat-7B-hf
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: MentaLLaMA-chat-7B-PsyCourse-doc-info-fold4
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. -->
# MentaLLaMA-chat-7B-PsyCourse-doc-info-fold4
This model is a fine-tuned version of [klyang/MentaLLaMA-chat-7B-hf](https://huggingface.co/klyang/MentaLLaMA-chat-7B-hf) on the course-doc-info-train-fold4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0882
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3967 | 0.3951 | 10 | 0.4004 |
| 0.4552 | 0.7901 | 20 | 0.2511 |
| 0.1757 | 1.1852 | 30 | 0.1820 |
| 0.1409 | 1.5802 | 40 | 0.1474 |
| 0.1122 | 1.9753 | 50 | 0.1285 |
| 0.2986 | 2.3704 | 60 | 0.1134 |
| 0.0918 | 2.7654 | 70 | 0.1039 |
| 0.0807 | 3.1605 | 80 | 0.0966 |
| 0.0862 | 3.5556 | 90 | 0.0924 |
| 0.085 | 3.9506 | 100 | 0.0891 |
| 0.101 | 4.3457 | 110 | 0.0883 |
| 0.0736 | 4.7407 | 120 | 0.0882 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
BioMike/emoji_vae | BioMike | 2025-05-02T18:24:20Z | 0 | 0 | null | [
"safetensors",
"vae",
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T15:54:11Z | ---
license: apache-2.0
---
|
TakalaWang/Discussion-Phi-4-multimodal-instruct-w-asr | TakalaWang | 2025-05-02T18:22:47Z | 22 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"phi4mm",
"text-generation",
"generated_from_trainer",
"conversational",
"custom_code",
"base_model:microsoft/Phi-4-multimodal-instruct",
"base_model:finetune:microsoft/Phi-4-multimodal-instruct",
"license:mit",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-04-25T03:34:54Z | ---
library_name: transformers
license: mit
base_model: microsoft/Phi-4-multimodal-instruct
tags:
- generated_from_trainer
model-index:
- name: Discussion-Phi-4-multimodal-instruct-w-asr
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. -->
# Discussion-Phi-4-multimodal-instruct-w-asr
This model is a fine-tuned version of [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 14.0991
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0041 | 0.2235 | 10 | 14.2081 |
| 0.3808 | 0.4469 | 20 | 13.9663 |
| 0.2584 | 0.6704 | 30 | 14.0761 |
| 0.2698 | 0.8939 | 40 | 14.0864 |
| 0.2399 | 1.1117 | 50 | 14.0333 |
| 0.2446 | 1.3352 | 60 | 14.0288 |
| 0.2098 | 1.5587 | 70 | 13.9403 |
| 0.2302 | 1.7821 | 80 | 13.9767 |
| 0.1214 | 2.0 | 90 | 13.9759 |
| 0.2095 | 2.2235 | 100 | 13.9181 |
| 0.128 | 2.4469 | 110 | 13.9729 |
| 0.1565 | 2.6704 | 120 | 13.9650 |
| 0.1445 | 2.8939 | 130 | 14.0991 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
WatsonOverHere/full_catholic_combined_bf16 | WatsonOverHere | 2025-05-02T18:18:47Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:WatsonOverHere/mysterious_mistral-small-3.1-24b",
"base_model:adapter:WatsonOverHere/mysterious_mistral-small-3.1-24b",
"region:us"
] | null | 2025-05-02T00:09:59Z | ---
base_model: WatsonOverHere/mysterious_mistral-small-3.1-24b
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]
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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. -->
### Direct Use
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[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. -->
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### 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. -->
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### Testing Data, Factors & Metrics
#### Testing Data
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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]
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### Framework versions
- PEFT 0.15.2 |
zelk12/MT3-gemma-3-12B | zelk12 | 2025-05-02T18:15:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:huihui-ai/gemma-3-12b-it-abliterated",
"base_model:merge:huihui-ai/gemma-3-12b-it-abliterated",
"base_model:soob3123/amoral-gemma3-12B-v2-qat",
"base_model:merge:soob3123/amoral-gemma3-12B-v2-qat",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-02T16:46:22Z | ---
base_model:
- soob3123/amoral-gemma3-12B-v2-qat
- huihui-ai/gemma-3-12b-it-abliterated
library_name: transformers
tags:
- mergekit
- merge
license: gemma
pipeline_tag: image-text-to-text
---
# 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 [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [soob3123/amoral-gemma3-12B-v2-qat](https://huggingface.co/soob3123/amoral-gemma3-12B-v2-qat) as a base.
### Models Merged
The following models were included in the merge:
* [huihui-ai/gemma-3-12b-it-abliterated](https://huggingface.co/huihui-ai/gemma-3-12b-it-abliterated)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: soob3123/amoral-gemma3-12B-v2-qat
#no parameters necessary for base model
- model: huihui-ai/gemma-3-12b-it-abliterated
parameters:
density: 0.5
weight: 0.5
merge_method: dare_ties
base_model: soob3123/amoral-gemma3-12B-v2-qat
parameters:
normalize: true
dtype: bfloat16
``` |
mothnaZl/s1-Qwen-Qwen2.5-7B-6-32768 | mothnaZl | 2025-05-02T18:14:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T16:43:03Z | ---
base_model: Qwen/Qwen2.5-7B
library_name: transformers
model_name: s1-Qwen-Qwen2.5-7B-6-32768
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for s1-Qwen-Qwen2.5-7B-6-32768
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B).
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="mothnaZl/s1-Qwen-Qwen2.5-7B-6-32768", 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/mothnazhong-hong-kong-university-of-science-and-technology/s1/runs/vhag3irs)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.1
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
bruhzair/ignore-merge-4 | bruhzair | 2025-05-02T18:09:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T17:39:31Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# way2
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 Passthrough merge method.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
modules:
default:
slices:
- sources:
- layer_range: [0, 4]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [2, 4]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [4, 8]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [6, 8]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [8, 12]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [10, 12]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [12, 16]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [14, 16]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [16, 20]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [18, 20]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [20, 24]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [22, 24]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [24, 28]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [26, 28]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [28, 32]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [30, 32]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [32, 36]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [34, 36]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [36, 40]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [38, 40]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [40, 44]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [42, 44]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [44, 48]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [46, 48]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [48, 52]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [50, 52]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [52, 56]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [54, 56]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [56, 60]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [58, 60]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [60, 64]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [62, 64]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [64, 68]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [66, 68]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [68, 72]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [70, 72]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [72, 76]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [74, 76]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [76, 80]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- sources:
- layer_range: [78, 80]
model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
```
|
Humphery7/yoruba-english-multilingual-extended-1 | Humphery7 | 2025-05-02T18:09:20Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-04-12T04:15:10Z | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
model-index:
- name: yoruba-english-multilingual-extended-1
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. -->
# yoruba-english-multilingual-extended-1
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0280
- eval_wer: 0.1190
- eval_runtime: 35.6397
- eval_samples_per_second: 2.806
- eval_steps_per_second: 0.365
- epoch: 4.9523
- step: 13500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Use 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_steps: 500
- num_epochs: 80
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
chchen/Llama3-OpenBioLLM-8B-PsyCourse-doc-info-fold4 | chchen | 2025-05-02T18:06:01Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:aaditya/Llama3-OpenBioLLM-8B",
"base_model:adapter:aaditya/Llama3-OpenBioLLM-8B",
"license:llama3",
"region:us"
] | null | 2025-05-02T16:24:36Z | ---
library_name: peft
license: llama3
base_model: aaditya/Llama3-OpenBioLLM-8B
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: Llama3-OpenBioLLM-8B-PsyCourse-doc-info-fold4
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. -->
# Llama3-OpenBioLLM-8B-PsyCourse-doc-info-fold4
This model is a fine-tuned version of [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) on the course-doc-info-train-fold4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0580
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3021 | 0.3951 | 10 | 0.2468 |
| 0.1477 | 0.7901 | 20 | 0.1373 |
| 0.1027 | 1.1852 | 30 | 0.1015 |
| 0.0804 | 1.5802 | 40 | 0.0804 |
| 0.0652 | 1.9753 | 50 | 0.0702 |
| 0.0535 | 2.3704 | 60 | 0.0641 |
| 0.0527 | 2.7654 | 70 | 0.0617 |
| 0.0527 | 3.1605 | 80 | 0.0603 |
| 0.0495 | 3.5556 | 90 | 0.0601 |
| 0.0489 | 3.9506 | 100 | 0.0582 |
| 0.0451 | 4.3457 | 110 | 0.0585 |
| 0.0406 | 4.7407 | 120 | 0.0580 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
Triangle104/huihui-ai_Qwen3-4B-abliterated-Q8_0-GGUF | Triangle104 | 2025-05-02T18:04:26Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Qwen3-4B-abliterated",
"base_model:quantized:huihui-ai/Qwen3-4B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T18:04:06Z | ---
base_model: huihui-ai/Qwen3-4B-abliterated
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# Triangle104/Qwen3-4B-abliterated-Q8_0-GGUF
This model was converted to GGUF format from [`huihui-ai/Qwen3-4B-abliterated`](https://huggingface.co/huihui-ai/Qwen3-4B-abliterated) 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/huihui-ai/Qwen3-4B-abliterated) 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 Triangle104/Qwen3-4B-abliterated-Q8_0-GGUF --hf-file qwen3-4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Qwen3-4B-abliterated-Q8_0-GGUF --hf-file qwen3-4b-abliterated-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Qwen3-4B-abliterated-Q8_0-GGUF --hf-file qwen3-4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Qwen3-4B-abliterated-Q8_0-GGUF --hf-file qwen3-4b-abliterated-q8_0.gguf -c 2048
```
|
cybershiptrooper/grpo_linear_mean_1p_fpr_7B-threshold_0.252-RM-n_examples_200-probe_linear_layers_10 | cybershiptrooper | 2025-05-02T18:04:25Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:saraprice/llama2-7B-chat-helpful-only",
"base_model:finetune:saraprice/llama2-7B-chat-helpful-only",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T15:41:11Z | ---
base_model: saraprice/llama2-7B-chat-helpful-only
library_name: transformers
model_name: grpo_linear_mean_1p_fpr_7B-threshold_0.252-RM-n_examples_200-probe_linear_layers_10
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for grpo_linear_mean_1p_fpr_7B-threshold_0.252-RM-n_examples_200-probe_linear_layers_10
This model is a fine-tuned version of [saraprice/llama2-7B-chat-helpful-only](https://huggingface.co/saraprice/llama2-7B-chat-helpful-only).
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="cybershiptrooper/grpo_linear_mean_1p_fpr_7B-threshold_0.252-RM-n_examples_200-probe_linear_layers_10", 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/cybershiptrooper/huggingface/runs/qiumhlal)
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: 4.51.3
- Pytorch: 2.2.2+cu121
- Datasets: 3.5.1
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
filipesantoscv11/e53fc757-183e-48bb-af3b-d0ba28402a1e | filipesantoscv11 | 2025-05-02T18:03:50Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"base_model:adapter:EleutherAI/pythia-70m",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-02T17:50:58Z | ---
library_name: peft
license: apache-2.0
base_model: EleutherAI/pythia-70m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e53fc757-183e-48bb-af3b-d0ba28402a1e
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: EleutherAI/pythia-70m
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- e2d471edf16c56fc_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e2d471edf16c56fc_train_data.json
type:
field_instruction: en
field_output: fr
format: '{instruction}'
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/e53fc757-183e-48bb-af3b-d0ba28402a1e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
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/e2d471edf16c56fc_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: <|endoftext|>
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: bf14246a-8435-448e-a3e4-a15f5df4da79
wandb_project: s56-6
wandb_run: your_name
wandb_runid: bf14246a-8435-448e-a3e4-a15f5df4da79
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# e53fc757-183e-48bb-af3b-d0ba28402a1e
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6971
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 5.7403 | 0.0017 | 200 | 5.6971 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
deswaq/iuh4 | deswaq | 2025-05-02T18:02:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T17:58:27Z | ---
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 ๐ค 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] |
Triangle104/huihui-ai_Qwen3-4B-abliterated-Q5_K_S-GGUF | Triangle104 | 2025-05-02T18:00:02Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Qwen3-4B-abliterated",
"base_model:quantized:huihui-ai/Qwen3-4B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T17:59:45Z | ---
base_model: huihui-ai/Qwen3-4B-abliterated
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# Triangle104/Qwen3-4B-abliterated-Q5_K_S-GGUF
This model was converted to GGUF format from [`huihui-ai/Qwen3-4B-abliterated`](https://huggingface.co/huihui-ai/Qwen3-4B-abliterated) 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/huihui-ai/Qwen3-4B-abliterated) 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 Triangle104/Qwen3-4B-abliterated-Q5_K_S-GGUF --hf-file qwen3-4b-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Qwen3-4B-abliterated-Q5_K_S-GGUF --hf-file qwen3-4b-abliterated-q5_k_s.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 Triangle104/Qwen3-4B-abliterated-Q5_K_S-GGUF --hf-file qwen3-4b-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Qwen3-4B-abliterated-Q5_K_S-GGUF --hf-file qwen3-4b-abliterated-q5_k_s.gguf -c 2048
```
|
Mod78/Text | Mod78 | 2025-05-02T17:58:22Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T17:58:21Z | ---
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 ๐ค 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]
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rubix9/Llama-3.2-1B-robincnp | rubix9 | 2025-05-02T17:56:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T17:54:44Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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johngreendr1/b289581c-1555-4115-93c6-f2694de9e55e | johngreendr1 | 2025-05-02T17:54:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:UNIVA-Bllossom/DeepSeek-llama3.3-Bllossom-70B",
"base_model:adapter:UNIVA-Bllossom/DeepSeek-llama3.3-Bllossom-70B",
"region:us"
] | null | 2025-05-02T17:54:46Z | ---
base_model: UNIVA-Bllossom/DeepSeek-llama3.3-Bllossom-70B
library_name: peft
---
# Model Card for Model ID
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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.
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seungbo7747/summarization_model | seungbo7747 | 2025-05-02T17:53:42Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:paust/pko-t5-base",
"base_model:finetune:paust/pko-t5-base",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-04-29T02:30:23Z | ---
library_name: transformers
license: cc-by-4.0
base_model: paust/pko-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: summarization_model
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. -->
# summarization_model
This model is a fine-tuned version of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6012
- Rouge1: 0.0661
- Rouge2: 0.0169
- Rougel: 0.0660
- Rougelsum: 0.0660
## 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: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
iboero16/SFT-300 | iboero16 | 2025-05-02T17:51:59Z | 23 | 0 | peft | [
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"region:us"
] | null | 2025-05-01T17:39:36Z | ---
base_model: huggyllama/llama-7b
library_name: peft
---
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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.
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[More Information Needed]
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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).
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### Framework versions
- PEFT 0.15.1 |
iboero16/SAFE-SFT-300 | iboero16 | 2025-05-02T17:51:30Z | 24 | 0 | peft | [
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"region:us"
] | null | 2025-05-01T17:39:23Z | ---
base_model: huggyllama/llama-7b
library_name: peft
---
# Model Card for Model ID
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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.
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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).
- **Hardware Type:** [More Information Needed]
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- PEFT 0.15.1 |
iboero16/SFT-2000 | iboero16 | 2025-05-02T17:50:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"region:us"
] | null | 2025-05-02T17:44:09Z | ---
base_model: huggyllama/llama-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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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]
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[More Information Needed]
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<!-- 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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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1 |
niklasm222/qwen2.5-3b-inst-grpo-1.75k-gsm8k-unsloth-willccbb | niklasm222 | 2025-05-02T17:50:09Z | 0 | 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 | 2025-05-02T17:48:18Z | ---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- grpo
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** niklasm222
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-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)
|
samoline/9ac85235-dbe3-403d-879f-82ee59926727 | samoline | 2025-05-02T17:49:06Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Maykeye/TinyLLama-v0",
"base_model:adapter:Maykeye/TinyLLama-v0",
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T17:48:55Z | ---
library_name: peft
license: apache-2.0
base_model: Maykeye/TinyLLama-v0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9ac85235-dbe3-403d-879f-82ee59926727
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: `0.10.0.dev0`
```yaml
adapter: lora
base_model: Maykeye/TinyLLama-v0
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- b26558f19627f59f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
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: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: false
group_by_length: false
hub_model_id: samoline/9ac85235-dbe3-403d-879f-82ee59926727
hub_repo: samoline
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 4
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 4
lora_target_linear: true
lr_scheduler: cosine
max_steps: 2
micro_batch_size: 1
mlflow_experiment_name: /tmp/b26558f19627f59f_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: 4
sequence_len: 512
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: samoline-nan
wandb_mode: online
wandb_name: b9f28cad-479c-48df-9a7f-6debe898dedd
wandb_project: Gradients-On-Demand
wandb_run: dev
wandb_runid: b9f28cad-479c-48df-9a7f-6debe898dedd
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 9ac85235-dbe3-403d-879f-82ee59926727
This model is a fine-tuned version of [Maykeye/TinyLLama-v0](https://huggingface.co/Maykeye/TinyLLama-v0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.3406
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- 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: 10
- training_steps: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 7.4119 | 0.1111 | 1 | 6.3375 |
| 5.6422 | 0.2222 | 2 | 6.3406 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
CompassioninMachineLearning/10k_four_fifths_animals_PLORA_newest | CompassioninMachineLearning | 2025-05-02T17:47:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-02T03:59:27Z | ---
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 ๐ค 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] |
sergioalves/fd315d47-ac3e-4bc8-bdbd-f0152c0e1691 | sergioalves | 2025-05-02T17:47:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"license:other",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-02T17:27:13Z | ---
library_name: peft
license: other
base_model: Qwen/Qwen1.5-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fd315d47-ac3e-4bc8-bdbd-f0152c0e1691
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: true
adapter: lora
base_model: Qwen/Qwen1.5-0.5B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 519dc324fa90419b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/519dc324fa90419b_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: sergioalves/fd315d47-ac3e-4bc8-bdbd-f0152c0e1691
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: 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/519dc324fa90419b_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: 34c11394-037e-4743-b560-708619a820f6
wandb_project: s56-8
wandb_run: your_name
wandb_runid: 34c11394-037e-4743-b560-708619a820f6
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# fd315d47-ac3e-4bc8-bdbd-f0152c0e1691
This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.0338 | 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 |
mradermacher/VamMed1.5-4B-GGUF | mradermacher | 2025-05-02T17:44:43Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3",
"en",
"base_model:vamcrizer/VamMed1.5-4B",
"base_model:quantized:vamcrizer/VamMed1.5-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T17:17:57Z | ---
base_model: vamcrizer/VamMed1.5-4B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/vamcrizer/VamMed1.5-4B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q6_K.gguf) | Q6_K | 3.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/VamMed1.5-4B-GGUF/resolve/main/VamMed1.5-4B.f16.gguf) | f16 | 7.9 | 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 -->
|
SemanticAlignment/Mistral-v0.1-Italian-FVT | SemanticAlignment | 2025-05-02T17:41:55Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"it",
"en",
"arxiv:2504.17025",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-09-10T11:45:53Z | ---
language:
- it
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
base_model:
- mistralai/Mistral-7B-v0.1
---
# Mistral-7B-v0.1-Italian-FVT
<div align="center">
<img src="https://github.com/Andrew-Wyn/images/blob/master/sava/italian_adapt-img.jpg?raw=true" width="400" height="400" style="border-radius:10%" />
</div>
The **Mistral-7B-v0.1-Adapted** collection of large language models (LLMs), is a collection of adapted generative models in 7B (text in/text out), adapted models from **Mistral-7B-Base-v0.1**.
*Mistral-v0.1-Italian-FVT* is a continually trained Mistral model, after tokenizer substitution.
The tokenizer of this model after adaptation is the same as [Minverva-3B](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0).
**Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
**Model Architecture:** Mistral-7B-v0.1-Adapted is an auto-regressive language model that uses an optimized transformer architecture.
## Data used for the adaptation
The **Mistral-7B-v0.1-Adapted** models are trained on a collection of Italian and English data extracted from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
The data are extracted to be skewed toward Italian language with a ration of one over four. Extracting the first 9B tokens from Italian part of CulturaX and the first 3B tokens from English part of CulturaX.
## Use with Transformers
You can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import transformers
import torch
model_id = "SemanticAlignment/Mistral-v0.1-Italian-FVT"
pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
pipeline("Cosa si puรฒ fare in una bella giornata di sole?")
```
Code: https://github.com/SapienzaNLP/sava
## Citation
If you use any part of this work, please consider citing the paper as follows:
```bibtex
@misc{moroni2025optimizingllmsitalianreducing,
title={Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation},
author={Luca Moroni and Giovanni Puccetti and Pere-Lluis Huguet Cabot and Andrei Stefan Bejgu and Edoardo Barba and Alessio Miaschi and Felice Dell'Orletta and Andrea Esuli and Roberto Navigli},
year={2025},
eprint={2504.17025},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.17025},
}
``` |
cybershiptrooper/grpo_linear_mean_10p_fpr_7B-threshold_0.6587-RM-n_examples_200-probe_linear_layers_10 | cybershiptrooper | 2025-05-02T17:37:30Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:saraprice/llama2-7B-chat-helpful-only",
"base_model:finetune:saraprice/llama2-7B-chat-helpful-only",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T15:28:29Z | ---
base_model: saraprice/llama2-7B-chat-helpful-only
library_name: transformers
model_name: grpo_linear_mean_10p_fpr_7B-threshold_0.6587-RM-n_examples_200-probe_linear_layers_10
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for grpo_linear_mean_10p_fpr_7B-threshold_0.6587-RM-n_examples_200-probe_linear_layers_10
This model is a fine-tuned version of [saraprice/llama2-7B-chat-helpful-only](https://huggingface.co/saraprice/llama2-7B-chat-helpful-only).
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="cybershiptrooper/grpo_linear_mean_10p_fpr_7B-threshold_0.6587-RM-n_examples_200-probe_linear_layers_10", 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/cybershiptrooper/huggingface/runs/7bijtz0e)
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: 4.51.3
- Pytorch: 2.2.2+cu121
- Datasets: 3.5.1
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kavinda123321/speecht5_finetuned_english_ranil_2 | kavinda123321 | 2025-05-02T17:36:50Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:kavinda123321/speecht5_finetuned_test2_p236_id_kavinda",
"base_model:finetune:kavinda123321/speecht5_finetuned_test2_p236_id_kavinda",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2025-05-02T17:36:11Z | ---
library_name: transformers
license: mit
base_model: kavinda123321/speecht5_finetuned_test2_p236_id_kavinda
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_english_ranil_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_english_ranil_2
This model is a fine-tuned version of [kavinda123321/speecht5_finetuned_test2_p236_id_kavinda](https://huggingface.co/kavinda123321/speecht5_finetuned_test2_p236_id_kavinda) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5585
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- 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_steps: 20
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.5558 | 1.0 | 14 | 0.5676 |
| 0.4928 | 2.0 | 28 | 0.5735 |
| 0.4671 | 3.0 | 42 | 0.5512 |
| 0.4573 | 4.0 | 56 | 0.5707 |
| 0.4614 | 5.0 | 70 | 0.5457 |
| 0.4366 | 6.0 | 84 | 0.5645 |
| 0.4178 | 7.0 | 98 | 0.5562 |
| 0.4022 | 8.0 | 112 | 0.5716 |
| 0.3996 | 9.0 | 126 | 0.5460 |
| 0.3883 | 10.0 | 140 | 0.5708 |
| 0.3801 | 11.0 | 154 | 0.5735 |
| 0.3716 | 12.0 | 168 | 0.5324 |
| 0.3634 | 13.0 | 182 | 0.5505 |
| 0.3586 | 14.0 | 196 | 0.5477 |
| 0.3589 | 15.0 | 210 | 0.5531 |
| 0.3443 | 16.0 | 224 | 0.5551 |
| 0.3362 | 17.0 | 238 | 0.5537 |
| 0.3404 | 18.0 | 252 | 0.5579 |
| 0.3452 | 18.6038 | 260 | 0.5585 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
SemanticAlignment/Llama-3-1-8B-Italian-FVT | SemanticAlignment | 2025-05-02T17:35:40Z | 3 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"it",
"en",
"arxiv:2504.17025",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-09-10T13:08:28Z | ---
language:
- it
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- meta-llama/Llama-3.1-8B
---
# Llama-3.1-8B-Italian-FVT
<div align="center">
<img src="https://github.com/Andrew-Wyn/images/blob/master/sava/italian_adapt-img.jpg?raw=true" width="400" height="400" style="border-radius:10%" />
</div>
The **Llama-3.1-8B-Adapted** collection of large language models (LLMs), is a collection of adapted generative models in 8B (text in/text out), adapted models from **Llama-3.1-8B**.
*Llama-3.1-8B-Italian-FVT* is a continually trained Llama model, after tokenizer substitution.
The tokenizer of this model after adaptation is the same as [Minverva-3B](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0).
**Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
**Model Architecture:** Llama-3.1-8B-Adapted is an auto-regressive language model that uses an optimized transformer architecture.
## Data used for the adaptation
The **Llama-3.1-8B-Adapted** model was trained on a collection of Italian and English data extracted from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
The data was extracted to be skewed toward Italian language with a ratio of one over four. Extracting the first 9B tokens from the Italian part of CulturaX and the first 3B tokens from the English part of CulturaX.
## Use with Transformers
You can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import transformers
import torch
model_id = "SemanticAlignment/Llama-3.1-8B-Italian-FVT"
pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
pipeline("Cosa si puรฒ fare in una bella giornata di sole?")
```
Code: https://github.com/SapienzaNLP/sava
## Citation
If you use any part of this work, please consider citing the paper as follows:
```bibtex
@misc{moroni2025optimizingllmsitalianreducing,
title={Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation},
author={Luca Moroni and Giovanni Puccetti and Pere-Lluis Huguet Cabot and Andrei Stefan Bejgu and Edoardo Barba and Alessio Miaschi and Felice Dell'Orletta and Andrea Esuli and Roberto Navigli},
year={2025},
eprint={2504.17025},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.17025},
}
``` |
vermoney/ec641c0c-3b83-4c7f-9359-7d70eb04ec47 | vermoney | 2025-05-02T17:35:23Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-02T17:27:48Z | ---
library_name: peft
license: other
base_model: Qwen/Qwen1.5-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ec641c0c-3b83-4c7f-9359-7d70eb04ec47
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: Qwen/Qwen1.5-0.5B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 519dc324fa90419b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/519dc324fa90419b_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: vermoney/ec641c0c-3b83-4c7f-9359-7d70eb04ec47
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/519dc324fa90419b_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: 34c11394-037e-4743-b560-708619a820f6
wandb_project: s56-9
wandb_run: your_name
wandb_runid: 34c11394-037e-4743-b560-708619a820f6
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# ec641c0c-3b83-4c7f-9359-7d70eb04ec47
This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.0391 | 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 |
Tv-Sophie-Rain-Sophie-Rain-Spiderman-Video/Sophie.Rain.Sophie.Rain.SpiderMan.Video.Tutorial | Tv-Sophie-Rain-Sophie-Rain-Spiderman-Video | 2025-05-02T17:30:43Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-02T17:30:23Z | <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">โบโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐ค๏ธโ</a></p>
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">๐ดโบ๐๐๐๐๐ ๐๐๐๐ ๐==โบโบ ๐๐จ๐ฐ๐ง๐ฅ๐จ๐๐ ๐๐จ๐ฐโฌ๏ธโฌ๏ธโ</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
03 seconds ago
L๐aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L๐aked on X Twitter Telegram
L๐aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L๐aked on X Twitter
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Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter |
gulzi/Kaz_Roberta_fine_tuned | gulzi | 2025-05-02T17:26:50Z | 0 | 0 | null | [
"roberta",
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T17:20:56Z | ---
license: apache-2.0
---
|
mradermacher/IOM-Qwen2.5-1.5B-GGUF | mradermacher | 2025-05-02T17:24:11Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:XUxs/IOM-Qwen2.5-1.5B",
"base_model:quantized:XUxs/IOM-Qwen2.5-1.5B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T17:12:36Z | ---
base_model: XUxs/IOM-Qwen2.5-1.5B
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/XUxs/IOM-Qwen2.5-1.5B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/IOM-Qwen2.5-1.5B-GGUF/resolve/main/IOM-Qwen2.5-1.5B.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 -->
|
BootesVoid/cma6lk7fb01m0negal98rg6tu_cma71dris01uanegaq1itfimm | BootesVoid | 2025-05-02T17:23:18Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-02T17:23:15Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: VICTORIA
---
# Cma6Lk7Fb01M0Negal98Rg6Tu_Cma71Dris01Uanegaq1Itfimm
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `VICTORIA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "VICTORIA",
"lora_weights": "https://huggingface.co/BootesVoid/cma6lk7fb01m0negal98rg6tu_cma71dris01uanegaq1itfimm/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cma6lk7fb01m0negal98rg6tu_cma71dris01uanegaq1itfimm', weight_name='lora.safetensors')
image = pipeline('VICTORIA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cma6lk7fb01m0negal98rg6tu_cma71dris01uanegaq1itfimm/discussions) to add images that show off what youโve made with this LoRA.
|
aleegis/e22ff4b7-26fa-4a5d-a413-4cf35fa31faa | aleegis | 2025-05-02T17:18:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0",
"base_model:adapter:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0",
"license:llama3",
"region:us"
] | null | 2025-05-02T14:40:25Z | ---
library_name: peft
license: llama3
base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e22ff4b7-26fa-4a5d-a413-4cf35fa31faa
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: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- ebdef80c11c8be43_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ebdef80c11c8be43_train_data.json
type:
field_instruction: prompt
field_output: generation
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/e22ff4b7-26fa-4a5d-a413-4cf35fa31faa
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 32
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 1500
micro_batch_size: 2
mlflow_experiment_name: /tmp/ebdef80c11c8be43_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 200
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: 8b4c8c80-b92d-409e-b63a-5d20d6027586
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8b4c8c80-b92d-409e-b63a-5d20d6027586
warmup_steps: 100
weight_decay: 0
xformers_attention: null
```
</details><br>
# e22ff4b7-26fa-4a5d-a413-4cf35fa31faa
This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1500
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
martin-rizzo/TinyBreaker.prototype0 | martin-rizzo | 2025-05-02T17:17:37Z | 0 | 3 | null | [
"image-generation",
"text-to-image",
"art",
"pixart-sigma",
"image",
"en",
"arxiv:2403.04692",
"base_model:PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
"base_model:finetune:PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
"license:mit",
"region:us"
] | text-to-image | 2025-02-09T01:15:51Z | ---
license: mit
language:
- en
base_model:
- PixArt-alpha/PixArt-Sigma-XL-2-1024-MS
- stable-diffusion-v1-5/stable-diffusion-v1-5
tags:
- image-generation
- text-to-image
- art
- pixart-sigma
- image
---
# TinyBreaker (prototype0)
<div style="display:flex;justify-content: left">
<a href="https://github.com/martin-rizzo/ComfyUI-TinyBreaker"><img src="https://img.shields.io/badge/GitHub-TinyBreaker-EEE?logo=github&logoColor=white&labelColor=444444" alt="GitHub: TinyBreaker"></a>  
<a href="https://civitai.com/models/1213728"><img src="https://img.shields.io/badge/CivitAI%3A-TinyBreaker-EEE?logo=c%2B%2B&logoColor=white&labelColor=1971C2" alt="CivitAI: TinyBreaker"></a>  
</div>
<div style="color: white; background-color: #882200; padding: 12px; border-radius: 6px; margin: 10px 0;">
โ ๏ธ <b>Important:</b> This version has been replaced by "prototype1", which includes VAEs packaged in a different way, enabling extra functionality such as the Tiny Upscaler.<br/>
Please download the updated version from this link: <b><a style="color: #80C0FF; font-weight: bold;" href="https://huggingface.co/martin-rizzo/TinyBreaker.prototype1">TinyBreaker (prototype1)</a></b>
</div>
## Overview
**TinyBreaker** is a hybrid two-step model (base + refiner) designed for efficient image generation on mid-end and low-end hardware. By combining the strengths of PixArt and Photon models, it delivers high-quality images with strong prompt adherence
## Key Features
- **Hybrid Two-Step Architecture**: Combines PixArt-Sigma as the base model with a refiner based on Photon (or any SD1.x model), both chosen for their low GPU consumption.
- **Efficient Parameter Usage**: The base modelโs 0.6 billion parameters enable high-quality image generation with minimal computational overhead.
- **Fast Performance**: Produces high-quality 1536ร1024 images in ~15 seconds on an NVIDIA RTX 3080 GPU, with ongoing work to cut generation times to under 10 seconds.
- **High Prompt Adherence**: Generates images that closely match user prompts and expectations, thanks to the robust performance of the PixArt-Sigma model and the T5 text encoder.
- **Optimized Latent Space Processing**: Leverages Tiny Autoencoders for efficient latent space conversion.
## Usage Requirements
Currently, TinyBreaker can only be used with ComfyUI. To utilize it, you'll need to install the custom nodes specific to this model through the [ComfyUI-TinyBreaker GitHub repository](https://github.com/martin-rizzo/ComfyUI-TinyBreaker).
## Limitations
- **Text Generation**: Generating legible text within images is a challenge due to PixArt's training limitations. Enhancements in this area may require extensive retraining.
- **Human Anatomy in Complex Poses**: While the model performs reliably with standard poses (e.g., standing, facing the camera), it struggles with anatomical accuracy in poses that require more complex or dynamic actions.
- **Complex Human Interactions**: The model has difficulty generating detailed scenes involving intricate interactions among people, as well as interactions between people and objects, such as collaborative tasks or dynamic object manipulation.
Note: The current "Prototype1" version of TinyBreaker utilizes PixArt-Sigma 1024 and Photon models **without any additional training or fine-tuning**. In the future, if I have the resources, I plan to train both models together to generate images of even greater quality
## Future Directions
I am dedicated to improving TinyBreaker's performance and accessibility, especially for users with mid-range or lower-end hardware. Looking forward to future updates as I continue to expand TinyBreaker's capabilities.
## Acknowledgments
* I extend my sincere thanks to the PixArt-ฮฃ developers for their exceptional model, which has been vital to this project's development.
[PixArt-ฮฃ GitHub Repository](https://github.com/PixArt-alpha/PixArt-sigma) | [PixArt-ฮฃ Hugging Face Model](https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS) | [PixArt-ฮฃ arXiv Report](https://arxiv.org/abs/2403.04692)
* Additional thanks to Ollin Boer Bohan for the Tiny AutoEncoder models, which offer efficient latent image processing and served as the foundation for the encoding, decoding, and transcoding operations in TinyBreaker.
[Tiny AutoEncoder GitHub Repository](https://github.com/madebyollin/taesd)
## Resources
- [TinyBreaker on CivitAI](https://civitai.com/models/1213728/tinybreaker): A hub for exploring generated images, prompts, and workflows created by me and the community, showcasing the model's output quality.
- [ComfyUI-TinyBreaker](https://github.com/martin-rizzo/ComfyUI-TinyBreaker): Nodes and workflows for ComfyUI to experiment with the model's capabilities.
- [TinyBreakerTools](https://github.com/martin-rizzo/TinyBreakerTools): Tools I'm building for the model, mainly to create the safetensors file for TinyBreaker.
- [AbominableWorkflows](https://github.com/martin-rizzo/AbominableWorkflows): A predecessor of TinyBreaker. My first experiment combining PixArt-Sigma and Photon without Python code, using only standard nodes from ComfyUI.
|
user074/selfplay_qwen3b | user074 | 2025-05-02T17:14:10Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"arxiv:2407.10671",
"license:other",
"region:us"
] | text-generation | 2025-05-02T17:12:21Z | ---
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
---
# Qwen2.5-3B
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
**This repo contains the base 3B Qwen2.5 model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
- Number of Parameters: 3.09B
- Number of Paramaters (Non-Embedding): 2.77B
- Number of Layers: 36
- Number of Attention Heads (GQA): 16 for Q and 2 for KV
- Context Length: Full 32,768 tokens
**We do not recommend using base language models for conversations.** Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Requirements
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Evaluation & Performance
Detailed evaluation results are reported in this [๐ blog](https://qwenlm.github.io/blog/qwen2.5/).
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
url = {https://qwenlm.github.io/blog/qwen2.5/},
author = {Qwen Team},
month = {September},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
``` |
mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF | mradermacher | 2025-05-02T17:14:09Z | 219 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Nexesenex/Llama_3.x_70b_Tristar_V2.1",
"base_model:quantized:Nexesenex/Llama_3.x_70b_Tristar_V2.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-12T09:49:59Z | ---
base_model: Nexesenex/Llama_3.x_70b_Tristar_V2.1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Nexesenex/Llama_3.x_70b_Tristar_V2.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-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/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q5_K_M.gguf) | Q5_K_M | 50.1 | |
| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
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 -->
|
mlx-community/MiMo-7B-SFT-4bit | mlx-community | 2025-05-02T17:13:51Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"mimo",
"text-generation",
"conversational",
"custom_code",
"base_model:XiaomiMiMo/MiMo-7B-SFT",
"base_model:quantized:XiaomiMiMo/MiMo-7B-SFT",
"license:mit",
"4-bit",
"region:us"
] | text-generation | 2025-05-02T17:02:43Z | ---
license: mit
base_model: XiaomiMiMo/MiMo-7B-SFT
library_name: mlx
pipeline_tag: text-generation
tags:
- mlx
---
# mlx-community/MiMo-7B-SFT-4bit
This model [mlx-community/MiMo-7B-SFT-4bit](https://huggingface.co/mlx-community/MiMo-7B-SFT-4bit) was
converted to MLX format from [XiaomiMiMo/MiMo-7B-SFT](https://huggingface.co/XiaomiMiMo/MiMo-7B-SFT)
using mlx-lm version **0.24.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/MiMo-7B-SFT-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
DreadPoor/mergekit-linear-vqtsxly-Q4_K_M-GGUF | DreadPoor | 2025-05-02T17:13:21Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:DreadPoor/mergekit-linear-vqtsxly",
"base_model:quantized:DreadPoor/mergekit-linear-vqtsxly",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T17:12:55Z | ---
base_model: DreadPoor/mergekit-linear-vqtsxly
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# DreadPoor/mergekit-linear-vqtsxly-Q4_K_M-GGUF
This model was converted to GGUF format from [`DreadPoor/mergekit-linear-vqtsxly`](https://huggingface.co/DreadPoor/mergekit-linear-vqtsxly) 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/DreadPoor/mergekit-linear-vqtsxly) 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 DreadPoor/mergekit-linear-vqtsxly-Q4_K_M-GGUF --hf-file mergekit-linear-vqtsxly-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo DreadPoor/mergekit-linear-vqtsxly-Q4_K_M-GGUF --hf-file mergekit-linear-vqtsxly-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 DreadPoor/mergekit-linear-vqtsxly-Q4_K_M-GGUF --hf-file mergekit-linear-vqtsxly-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo DreadPoor/mergekit-linear-vqtsxly-Q4_K_M-GGUF --hf-file mergekit-linear-vqtsxly-q4_k_m.gguf -c 2048
```
|
diegobit/llama-3-8b-ita-4k-orpo-v3 | diegobit | 2025-05-02T17:10:46Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"conversational",
"dataset:mii-community/ultrafeedback-preferences-translated-ita",
"dataset:efederici/alpaca-vs-alpaca-orpo-dpo",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-06-10T08:05:07Z | ---
library_name: transformers
tags:
- unsloth
license: llama3
datasets:
- mii-community/ultrafeedback-preferences-translated-ita
- efederici/alpaca-vs-alpaca-orpo-dpo
---
# Model Card for Model ID
This is llama-3-8b ORPO finetuning for the italian language over a concatenation of two datasets:
- [mii-community/ultrafeedback-preferences-translated-ita](https://huggingface.co/datasets/mii-community/ultrafeedback-preferences-translated-ita)
- [efederici/alpaca-vs-alpaca-orpo-dpo](https://huggingface.co/datasets/efederici/alpaca-vs-alpaca-orpo-dpo)
The other two differences with `diegobit/llama-3-8b-Instruct-bnb-4bit-ita-orpo` are:
- the starting model, not instruct, `astronomer/Llama-3-8B-Special-Tokens-Adjusted` instead of `unsloth/llama-3-8b-Instruct-bnb-4bit`
- no loading in 4bits
- given the increased need of GPU memory, the sequence max length used for finetuning is 4096
## Model Details
### Model Description
- **Developed by:** Diego Giorgini
- **Funded by:** AI Technologies SRL - www.aitechnologies.it
- **Language(s) (NLP):** Italian
- **License:** llama3
- **Finetuned from model:** astronomer/Llama-3-8B-Special-Tokens-Adjusted
## Training Details
### Environment
unsloth: 2024.5
torch: 2.2
### Training Data
- `mii-community/ultrafeedback-preferences-translated-ita` is a selection of 55k rows of the ultrafeedback dataset, translated into italian with argotranslate.
- `efederici/alpaca-vs-alpaca-orpo-dpo`: The Alpaca vs. Alpaca dataset is a curated blend of the Alpaca dataset and the Alpaca GPT-4 dataset, both available on HuggingFace Datasets. It uses the standard GPT dataset as the 'rejected' answer, steering the model towards the GPT-4 answer, which is considered as the 'chosen' one.
### Training Procedure
#### Preprocessing [optional]
- No preprocessing has been performed, except for formatting with the llama3 chat_template from unsloth:
```tokenizer = get_chat_template(tokenizer, chat_template = "llama-3")```
#### Training Hyperparameters
- **Training regime:** bf16
- **Model loading parameters:**
```
max_seq_length = 4096
dtype = None
load_in_4bit = False
```
- **PEFT parameters:**
```
r = 64
lora_alpha = 64
lora_dropout = 0
bias = "none"
random_state = 3407
use_rslora = False
loftq_config = None
```
- **ORPOConfig parameters:**
```
max_length = 4096
max_prompt_length = max_seq_length//2
max_completion_length = max_seq_length//2
warmup_ratio = 0.1
weight_decay = 0.01
per_device_train_batch_size = 1
gradient_accumulation_steps = 16
learning_rate=8e-6
beta = 0.1
optim = "paged_adamw_8bit"
lr_scheduler_type = "linear"
num_train_epochs = 1
```
#### Speeds, Sizes, Times
19h on an A100-40GB
## Model Card Contact
[email protected] |
Disya/shuttle-3-mini-Q4_K_M-GGUF | Disya | 2025-05-02T17:10:10Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:shuttleai/shuttle-3-mini",
"base_model:quantized:shuttleai/shuttle-3-mini",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T17:09:32Z | ---
base_model: shuttleai/shuttle-3-mini
tags:
- llama-cpp
- gguf-my-repo
---
# Disya/shuttle-3-mini-Q4_K_M-GGUF
This model was converted to GGUF format from [`shuttleai/shuttle-3-mini`](https://huggingface.co/shuttleai/shuttle-3-mini) 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/shuttleai/shuttle-3-mini) 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 Disya/shuttle-3-mini-Q4_K_M-GGUF --hf-file shuttle-3-mini-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Disya/shuttle-3-mini-Q4_K_M-GGUF --hf-file shuttle-3-mini-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 Disya/shuttle-3-mini-Q4_K_M-GGUF --hf-file shuttle-3-mini-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Disya/shuttle-3-mini-Q4_K_M-GGUF --hf-file shuttle-3-mini-q4_k_m.gguf -c 2048
```
|
zera09/qwen2.5-3b-fin-chat | zera09 | 2025-05-02T17:06:40Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T16:53:10Z | ---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: qwen2.5-3b-fin-chat
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2.5-3b-fin-chat
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-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="zera09/qwen2.5-3b-fin-chat", 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/zeramarveenlyngkhoi/huggingface/runs/ariddybx)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.4.1
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ArtusDev/Qwen3-235B-A22B-GGUF | ArtusDev | 2025-05-02T17:05:09Z | 4 | 2 | null | [
"gguf",
"imatrix",
"qwen3_moe",
"conversational",
"ik_llama.cpp",
"text-generation",
"base_model:Qwen/Qwen3-235B-A22B",
"base_model:quantized:Qwen/Qwen3-235B-A22B",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T13:55:13Z | ---
quantized_by: ArtusDev
pipeline_tag: text-generation
base_model: Qwen/Qwen3-235B-A22B
license: mit
base_model_relation: quantized
tags:
- imatrix
- qwen3_moe
- conversational
- ik_llama.cpp
---
## `ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-235B-A22B
This quant collection **REQUIRES** [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support advanced non-linear SotA quants. Do **not** download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!
These quants provide best in class quality for the given memory footprint.
## Big Thanks
Shout out to [@ubergarm](https://huggingface.co/ubergarm) for his diligent work on ik_llama.cpp oriented quanting. |
dgambettaphd/M_llm2_gen2_S_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-05-02T17:04:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T17:03:52Z | ---
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 ๐ค 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] |
Resfir/KFC-Net-bio | Resfir | 2025-05-02T16:59:58Z | 0 | 0 | null | [
"pytorch",
"roberta",
"region:us"
] | null | 2025-05-02T16:44:58Z | # KFC-Net (Knowledge Fusion & Compression Net)
Implementation of the framework described in **"Research on Multi-Task Biomedical Named Entity Recognition Method Based on Knowledge Distillation"**, accepted by Hohai University.
๐ **Core Features**
- **Multi-Teacher Knowledge Fusion**: Aggregates predictions from single-task teachers via probability-space alignment.
- **Lightweight Deployment**: Supports DistilBERT (253MB) and TinyBERT (54MB) with 7200 samples/sec inference speed.
- **State-of-the-Art Performance**: Achieves 93.62% F1 on BC5CDR-Chem and 88.34% F1 on NCBI-Disease.
๐ **Usage**
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("your-username/KFC-Net-bio")
tokenizer = AutoTokenizer.from_pretrained("your-username/KFC-Net-bio")
text = "EGFR mutations increase sensitivity to gefitinib in non-small cell lung cancer."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs).logits
predictions = outputs.argmax(dim=-1).squeeze().tolist()
```
๐ **Performance**
| Dataset | Precision | Recall | F1 |
| ------------ | --------- | ------ | ------ |
| NCBI-Disease | 86.87% | 89.86% | 88.34% |
| BC5CDR-Chem | 94.48% | 92.77% | 93.62% |
| BC2GM | 83.29% | 84.40% | 83.84% |
โ ๏ธ **Limitations**
- Performance drops observed on nested entities (e.g., "IL-2 receptor alpha chain").
- Requires alignment of entity type schemas across teachers.
---
### Key Changes from Original Template:
1. **Metadata Enhanced**:
- Added `tags` for better discoverability (biomedical, NER, etc.).
- Updated datasets to `bc5cdr_chem` for clarity.
2. **Technical Highlights**:
- Emphasized probability-space fusion and deployment efficiency.
- Added performance table with paper-reported metrics.
3. **Usage Code**:
- Provided both `transformers` and `pipeline` examples.
4. **Transparency**:
- Explicitly stated limitations (nested entities, schema alignment).
|
KingEmpire/sn21_omega_0205_3 | KingEmpire | 2025-05-02T16:57:24Z | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-02T16:29:00Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Ishwak1/bert-ufc-win-predictor | Ishwak1 | 2025-05-02T16:57:13Z | 0 | 0 | null | [
"safetensors",
"distilbert",
"text-classification",
"ufc",
"prediction",
"sports",
"en",
"license:mit",
"region:us"
] | text-classification | 2025-05-02T05:18:44Z | ---
language: en
license: mit
tags:
- text-classification
- ufc
- prediction
- sports
---
# UFC Fight Outcome Predictor (DistilBERT-based)
This model is a fine-tuned BERT classifier designed to predict the **outcome of UFC fights** based on textual inputs such as pre-fight analysis, fighter stats. It is trained as a **binary text classification** model.
## Use Case
You can use this model to:
- Predict likely fight outcomes from textual descriptions
## Model Details
- **Base model**: `bert-base-uncased`
- **Task**: Binary text classification (Win / Loss)
- **Training data**: Custom UFC-related dataset
- **Input**: Text (e.g., fighter matchups, stats)
- **Output**: Binary class prediction (`0 = Fighter B wins`, `1 = Fighter A wins`)
## Example Usage (Python)
```python
from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
loaded_model = DistilBertForSequenceClassification.from_pretrained("/content/fine_tuned_ufc_model")
loaded_tokenizer = DistilBertTokenizer.from_pretrained("/content/fine_tuned_ufc_model")
def predict_winner(fighter_a_stats, fighter_b_stats, model, tokenizer):
input_text = (
f"Fighter A: {fighter_a_stats} || Fighter B: {fighter_b_stats}"
)
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True).to(device)
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
pred = torch.argmax(probs, dim=1).item()
return {"Fighter A wins": float(probs[0][0]), "Fighter B wins": float(probs[0][1])}, pred
fighter_a = "Height: 73 in | Reach: 80 in | Str. Acc: 0.57 | Str. Def: 0.58 | SLpM: 4.25 | SApM: 2.12"
fighter_b = "Height: 70 in | Reach: 71 in | Str. Acc: 0.49 | Str. Def: 0.55 | SLpM: 4.00 | SApM: 3.00"
probs, winner = predict_winner(fighter_a, fighter_b, loaded_model, loaded_tokenizer)
print(probs, "Winner Label (0=A, 1=B):", winner)
// Example Output: {'Fighter A wins': 0.03644789755344391, 'Fighter B wins': 0.9635520577430725} Winner Label (0=A, 1=B): 1
```
## Files
- model.safetensors: The model weights in safetensors format
- config.json: Model architecture config
- tokenizer_config.json, special_tokens_map.json, vocab.txt: Tokenizer files
โ๏ธ Author
Created by @Ishwak1
### For questions or fine-tuning on your own fight data, feel free to open a discussion! |
phospho-app/Starkosaure-Stuffed_Animal_3cam_V0.0-rroebs93ru | phospho-app | 2025-05-02T16:56:21Z | 0 | 0 | null | [
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-02T16:53:05Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Traceback (most recent call last):
File "/root/src/helper.py", line 224, in predict
raise RuntimeError(error_msg)
RuntimeError: Training process failed with exit code 1:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 277, in apply_rotary_pos_emb
q_embed = (q * cos) + (rotate_half(q) * sin)
^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 252, in rotate_half
return torch.cat((-x2, x1), dim=-1)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 38.75 MiB is free. Process 17 has 79.21 GiB memory in use. Of the allocated memory 78.38 GiB is allocated by PyTorch, and 336.91 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
0%| | 0/450 [00:23<?, ?it/s]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/src/helper.py", line 226, in predict
raise RuntimeError(e)
RuntimeError: Training process failed with exit code 1:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 277, in apply_rotary_pos_emb
q_embed = (q * cos) + (rotate_half(q) * sin)
^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/transformers/models/llama/modeling_llama.py", line 252, in rotate_half
return torch.cat((-x2, x1), dim=-1)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 79.25 GiB of which 38.75 MiB is free. Process 17 has 79.21 GiB memory in use. Of the allocated memory 78.38 GiB is allocated by PyTorch, and 336.91 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
0%| | 0/450 [00:23<?, ?it/s]
```
## Training parameters:
- **Dataset**: [Starkosaure/Stuffed_Animal_3cam_V0.0](https://huggingface.co/datasets/Starkosaure/Stuffed_Animal_3cam_V0.0)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 64
- **Training steps**: 443
๐ **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
๐ค **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
treasure4l/Gemma2-Instruct-DPO | treasure4l | 2025-05-02T16:56:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:unsloth/gemma-2-9b-it-bnb-4bit",
"base_model:finetune:unsloth/gemma-2-9b-it-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T16:55:50Z | ---
base_model: unsloth/gemma-2-9b-it-bnb-4bit
library_name: transformers
model_name: Gemma2-Instruct-DPO
tags:
- generated_from_trainer
- unsloth
- trl
- dpo
licence: license
---
# Model Card for Gemma2-Instruct-DPO
This model is a fine-tuned version of [unsloth/gemma-2-9b-it-bnb-4bit](https://huggingface.co/unsloth/gemma-2-9b-it-bnb-4bit).
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="treasure4l/Gemma2-Instruct-DPO", 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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/Thought-Aligner-7B-v1.0-GGUF | mradermacher | 2025-05-02T16:53:33Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"safety",
"ai-safety",
"aligner",
"en",
"base_model:fgdrg/Thought-Aligner-7B-v1.0",
"base_model:quantized:fgdrg/Thought-Aligner-7B-v1.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T16:10:44Z | ---
base_model: fgdrg/Thought-Aligner-7B-v1.0
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- safety
- ai-safety
- aligner
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/fgdrg/Thought-Aligner-7B-v1.0
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.IQ4_XS.gguf) | IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Thought-Aligner-7B-v1.0-GGUF/resolve/main/Thought-Aligner-7B-v1.0.f16.gguf) | f16 | 15.3 | 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.
<!-- end -->
|
ajagota71/gpt-neo-125m-detox-epoch-60 | ajagota71 | 2025-05-02T16:50:54Z | 0 | 0 | null | [
"safetensors",
"gpt_neo",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-05-02T16:50:34Z | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed] |
Lucy-in-the-Sky/Qwen3-16B-A3B-Q8_0-GGUF | Lucy-in-the-Sky | 2025-05-02T16:50:51Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:kalomaze/Qwen3-16B-A3B",
"base_model:quantized:kalomaze/Qwen3-16B-A3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T16:49:29Z | ---
base_model: kalomaze/Qwen3-16B-A3B
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# Lucy-in-the-Sky/Qwen3-16B-A3B-Q8_0-GGUF
This model was converted to GGUF format from [`kalomaze/Qwen3-16B-A3B`](https://huggingface.co/kalomaze/Qwen3-16B-A3B) 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/kalomaze/Qwen3-16B-A3B) 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 Lucy-in-the-Sky/Qwen3-16B-A3B-Q8_0-GGUF --hf-file qwen3-16b-a3b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Lucy-in-the-Sky/Qwen3-16B-A3B-Q8_0-GGUF --hf-file qwen3-16b-a3b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Lucy-in-the-Sky/Qwen3-16B-A3B-Q8_0-GGUF --hf-file qwen3-16b-a3b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Lucy-in-the-Sky/Qwen3-16B-A3B-Q8_0-GGUF --hf-file qwen3-16b-a3b-q8_0.gguf -c 2048
```
|
abkimc/PPO_LunarLander-v2 | abkimc | 2025-05-02T16:45:58Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-02T16:45:40Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 240.94 +/- 84.34
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bruhzair/ignore-merge-2 | bruhzair | 2025-05-02T16:44:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T16:13:29Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# magnum2
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 Passthrough merge method.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
modules:
default:
slices:
- sources:
- layer_range: [0, 4]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [2, 4]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [4, 8]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [6, 8]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [8, 12]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [10, 12]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [12, 16]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [14, 16]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [16, 20]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [18, 20]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [20, 24]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [22, 24]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [24, 28]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [26, 28]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [28, 32]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [30, 32]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [32, 36]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [34, 36]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [36, 40]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [38, 40]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [40, 44]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [42, 44]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [44, 48]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [46, 48]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [48, 52]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [50, 52]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [52, 56]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [54, 56]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [56, 60]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [58, 60]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [60, 64]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [62, 64]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [64, 68]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [66, 68]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [68, 72]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [70, 72]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [72, 76]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [74, 76]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [76, 80]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
- sources:
- layer_range: [78, 80]
model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-v4-SE/snapshots/da9dd890a3c92f6ebef577c5c42fa74ca97c9ff3
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
```
|
arte-in/LenKimono | arte-in | 2025-05-02T16:43:50Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:bsd",
"region:us"
] | text-to-image | 2025-05-02T16:42:51Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
The model ends in a powerful high-fashion runway pose: left leg extended
forward, right leg slightly bent, torso twisted to the left, shoulders
square, chin slightly tilted up. Her arms are relaxed at her sides, hands
curved elegantly. Her facial expression is confident, alive, and intentional
โ she holds eye contact with the camera, with a subtle intensity in her
gaze. Her lips are gently closed, with a composed and focused expression,
like a pro at the end of a major fashion show. Lighting is soft and even,
neutral background, camera fixed. A professional high-fashion runway pose:
confident and elegant, expressive look like at the end of a fashion show.
The pose is sharp, refined, and poised. Soft studio lighting, neutral
background, no scene changes. Camera remains fixed. Best quality 8K, sharp
focus beautiful life face.
parameters:
negative_prompt: blurred,ugly
output:
url: images/_produkt.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: bsd
---
# LenKimono
<Gallery />
## Model description
Len Kimono
## Download model
Weights for this model are available in Safetensors format.
[Download](/arte-in/LenKimono/tree/main) them in the Files & versions tab.
|
EdwardTurner/Qwen2.5-14B-Instruct_R_0_1_0_B_150_freeze | EdwardTurner | 2025-05-02T16:42:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T16:19:45Z | ---
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 ๐ค 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] |
chchen/MentaLLaMA-chat-7B-PsyCourse-doc-info-fold3 | chchen | 2025-05-02T16:42:36Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:klyang/MentaLLaMA-chat-7B-hf",
"base_model:adapter:klyang/MentaLLaMA-chat-7B-hf",
"license:mit",
"region:us"
] | null | 2025-05-02T14:59:34Z | ---
library_name: peft
license: mit
base_model: klyang/MentaLLaMA-chat-7B-hf
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: MentaLLaMA-chat-7B-PsyCourse-doc-info-fold3
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. -->
# MentaLLaMA-chat-7B-PsyCourse-doc-info-fold3
This model is a fine-tuned version of [klyang/MentaLLaMA-chat-7B-hf](https://huggingface.co/klyang/MentaLLaMA-chat-7B-hf) on the course-doc-info-train-fold3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0798
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3973 | 0.3951 | 10 | 0.4009 |
| 0.2549 | 0.7901 | 20 | 0.2421 |
| 0.172 | 1.1852 | 30 | 0.1696 |
| 0.1705 | 1.5802 | 40 | 0.1428 |
| 0.2097 | 1.9753 | 50 | 0.1237 |
| 0.1157 | 2.3704 | 60 | 0.1085 |
| 0.0902 | 2.7654 | 70 | 0.0961 |
| 0.0917 | 3.1605 | 80 | 0.0900 |
| 0.092 | 3.5556 | 90 | 0.0842 |
| 0.0637 | 3.9506 | 100 | 0.0814 |
| 0.0835 | 4.3457 | 110 | 0.0802 |
| 0.0849 | 4.7407 | 120 | 0.0798 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
VincentG1234/QWEN_7BQLORA_finetuned_r8_alpha16 | VincentG1234 | 2025-05-02T16:42:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2_vl",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T16:42:21Z | ---
base_model: unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_vl
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** VincentG1234
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit
This qwen2_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JQ1984/GDPR_clause_prediction_legalbert_model | JQ1984 | 2025-05-02T16:38:50Z | 0 | 1 | null | [
"safetensors",
"bert",
"text-classification",
"en",
"dataset:JQ1984/GDPRcasedata",
"base_model:JQ1984/legalbert_gdpr_pretrained",
"base_model:finetune:JQ1984/legalbert_gdpr_pretrained",
"license:cc-by-sa-4.0",
"region:us"
] | text-classification | 2025-05-02T16:24:16Z | ---
license: cc-by-sa-4.0
datasets:
- JQ1984/GDPRcasedata
language:
- en
metrics:
- accuracy
base_model:
- JQ1984/legalbert_gdpr_pretrained
pipeline_tag: text-classification
--- |
ail-sa/rahul_muscular_long_fs_cleaned_v1 | ail-sa | 2025-05-02T16:28:41Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-02T15:51:47Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Sid
---
# Rahul_Muscular_Long_Fs_Cleaned_V1
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Sid` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sid",
"lora_weights": "https://huggingface.co/ail-sa/rahul_muscular_long_fs_cleaned_v1/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ail-sa/rahul_muscular_long_fs_cleaned_v1', weight_name='lora.safetensors')
image = pipeline('Sid').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ail-sa/rahul_muscular_long_fs_cleaned_v1/discussions) to add images that show off what youโve made with this LoRA.
|
shubhamprshr/Llama-3.2-3B-Instruct_blocksworld6_sgrpo_balanced_0.5_0.5_True_300 | shubhamprshr | 2025-05-02T16:26:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"dataset:blocksworld-dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T13:38:36Z | ---
base_model: meta-llama/Llama-3.2-3B-Instruct
datasets: blocksworld-dataset
library_name: transformers
model_name: Llama-3.2-3B-Instruct_blocksworld6_sgrpo_balanced_0.5_0.5_True_300
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-3B-Instruct_blocksworld6_sgrpo_balanced_0.5_0.5_True_300
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-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/Llama-3.2-3B-Instruct_blocksworld6_sgrpo_balanced_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/11btvsy2)
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
chchen/Llama3-OpenBioLLM-8B-PsyCourse-info-fold5 | chchen | 2025-05-02T16:25:06Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:aaditya/Llama3-OpenBioLLM-8B",
"base_model:adapter:aaditya/Llama3-OpenBioLLM-8B",
"license:llama3",
"region:us"
] | null | 2025-05-02T15:27:21Z | ---
library_name: peft
license: llama3
base_model: aaditya/Llama3-OpenBioLLM-8B
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: Llama3-OpenBioLLM-8B-PsyCourse-info-fold5
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. -->
# Llama3-OpenBioLLM-8B-PsyCourse-info-fold5
This model is a fine-tuned version of [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) on the course-info-train-fold5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1646
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5193 | 0.3951 | 10 | 0.4232 |
| 0.2655 | 0.7901 | 20 | 0.2431 |
| 0.1575 | 1.1852 | 30 | 0.2011 |
| 0.1529 | 1.5802 | 40 | 0.1788 |
| 0.1367 | 1.9753 | 50 | 0.1676 |
| 0.1243 | 2.3704 | 60 | 0.1694 |
| 0.0943 | 2.7654 | 70 | 0.1699 |
| 0.0697 | 3.1605 | 80 | 0.1646 |
| 0.056 | 3.5556 | 90 | 0.1669 |
| 0.0546 | 3.9506 | 100 | 0.1724 |
| 0.0512 | 4.3457 | 110 | 0.1724 |
| 0.0789 | 4.7407 | 120 | 0.1712 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r32-e20-lr0.0002-mixed-markdown_format_small-new | lisabdunlap | 2025-05-02T16:21:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T16:19:19Z | ---
base_model: unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** lisabdunlap
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.1-8B-Instruct-unsloth-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)
|
jq/gemma3-12b-ug40-lora-translation-r8-bs128 | jq | 2025-05-02T16:17:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:jq/gemma3-12b-ug40-pretrained",
"base_model:finetune:jq/gemma3-12b-ug40-pretrained",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T16:17:33Z | ---
base_model: jq/gemma3-12b-ug40-pretrained
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** jq
- **License:** apache-2.0
- **Finetuned from model :** jq/gemma3-12b-ug40-pretrained
This gemma3 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)
|
Sakib323/MMfreeLM-370M-CodeGenerator | Sakib323 | 2025-05-02T16:16:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"hgrn_bit",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T16:14:59Z | ---
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 ๐ค 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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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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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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Vaunorage/gemma-3-1b-it-unsloth-bnb-4bit-pretrain-legis-quebec | Vaunorage | 2025-05-02T16:13:48Z | 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 | 2025-05-02T16:13:03Z | ---
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:** Vaunorage
- **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)
|
nemo4aerobat/llama3.1_8b_cpt_compliance3 | nemo4aerobat | 2025-05-02T16:12:20Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T16:06:34Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** nemo4aerobat
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
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