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whodisidk/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope | whodisidk | 2025-05-24T23:35:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am durable woolly antelope",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T17:51:06Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am durable woolly antelope
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="whodisidk/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-durable_woolly_antelope", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
KaUzefa/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mighty_miniature_lizard | KaUzefa | 2025-05-24T23:34:32Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mighty miniature lizard",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-17T12:09:38Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mighty_miniature_lizard
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mighty miniature lizard
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mighty_miniature_lizard
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="KaUzefa/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mighty_miniature_lizard", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.1
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Triangle104/Qwen3-30B-A1.5B-High-Speed-Q8_0-GGUF | Triangle104 | 2025-05-24T23:34:30Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"32 k context",
"reasoning",
"thinking",
"qwen3",
"4 experts activated",
"double speed",
"128 experts",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:DavidAU/Qwen3-30B-A1.5B-High-Speed",
"base_model:quantized:DavidAU/Qwen3-30B-A1.5B-High-Speed",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-24T23:29:17Z | ---
library_name: transformers
pipeline_tag: text-generation
tags:
- 32 k context
- reasoning
- thinking
- qwen3
- 4 experts activated
- double speed
- 128 experts
- llama-cpp
- gguf-my-repo
base_model: DavidAU/Qwen3-30B-A1.5B-High-Speed
---
# Triangle104/Qwen3-30B-A1.5B-High-Speed-Q8_0-GGUF
This model was converted to GGUF format from [`DavidAU/Qwen3-30B-A1.5B-High-Speed`](https://huggingface.co/DavidAU/Qwen3-30B-A1.5B-High-Speed) 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/DavidAU/Qwen3-30B-A1.5B-High-Speed) for more details on the model.
---
This is a simple "finetune" of the Qwen's "Qwen 30B-A3B" (MOE) model,
setting the experts in use from 8 to 4 (out of 128 experts).
This method close to doubles the speed of the model and uses 1.5B (of
30B) parameters instead of 3B (of 30B) parameters. Depending on the
application you may want to
use the regular model ("30B-A3B"), and use this model for simpler use
case(s) although I did not notice any loss of function during
routine (but not extensive) testing.
---
## 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/Qwen3-30B-A1.5B-High-Speed-Q8_0-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q8_0-GGUF --hf-file qwen3-30b-a1.5b-high-speed-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/Qwen3-30B-A1.5B-High-Speed-Q8_0-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q8_0-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q8_0.gguf -c 2048
```
|
rudra-sol/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mottled_beaked_jaguar | rudra-sol | 2025-05-24T23:33:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mottled beaked jaguar",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T06:50:49Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mottled_beaked_jaguar
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mottled beaked jaguar
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mottled_beaked_jaguar
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="rudra-sol/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mottled_beaked_jaguar", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/palmyra-small-GGUF | mradermacher | 2025-05-24T23:33:43Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text generation",
"pytorch",
"causal-lm",
"Writer-data",
"NeMo",
"palmyra",
"en",
"dataset:English",
"base_model:Writer/palmyra-small",
"base_model:quantized:Writer/palmyra-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T23:28:41Z | ---
base_model: Writer/palmyra-small
datasets:
- English
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text generation
- pytorch
- causal-lm
- Writer-data
- NeMo
- palmyra
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Writer/palmyra-small
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/palmyra-small-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/palmyra-small-GGUF/resolve/main/palmyra-small.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/palmyra-small-GGUF/resolve/main/palmyra-small.f16.gguf) | f16 | 0.4 | 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 -->
|
kmpartner/bkv2tpcmlr2-test | kmpartner | 2025-05-24T23:33:07Z | 6 | 0 | peft | [
"peft",
"tensorboard",
"diffusers",
"safetensors",
"arxiv:1910.09700",
"base_model:nota-ai/bk-sdm-v2-tiny",
"base_model:adapter:nota-ai/bk-sdm-v2-tiny",
"region:us"
] | null | 2025-04-08T12:30:33Z | ---
base_model: nota-ai/bk-sdm-v2-tiny
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0 |
cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-powerful_feline_bat | cryptolemon | 2025-05-24T23:32:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am powerful feline bat",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T15:32:53Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-powerful_feline_bat
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am powerful feline bat
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-powerful_feline_bat
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-powerful_feline_bat", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hayashizawa/gensyn-checkpoints-grazing_pouncing_crow | hayashizawa | 2025-05-24T23:32:20Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am grazing pouncing crow",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-17T02:01:02Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: gensyn-checkpoints-grazing_pouncing_crow
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am grazing pouncing crow
- unsloth
- trl
licence: license
---
# Model Card for gensyn-checkpoints-grazing_pouncing_crow
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hayashizawa/gensyn-checkpoints-grazing_pouncing_crow", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-playful_shiny_fish | cryptolemon | 2025-05-24T23:31:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am playful shiny fish",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T08:52:55Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-playful_shiny_fish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am playful shiny fish
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-playful_shiny_fish
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-playful_shiny_fish", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
infoipman/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tall_mammalian_caribou | infoipman | 2025-05-24T23:31:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tall mammalian caribou",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T15:18:14Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tall_mammalian_caribou
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tall mammalian caribou
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tall_mammalian_caribou
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="infoipman/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tall_mammalian_caribou", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1+cu124
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Krust081/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-elusive_territorial_chinchilla | Krust081 | 2025-05-24T23:31:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am elusive territorial chinchilla",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-13T16:04:03Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-elusive_territorial_chinchilla
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am elusive territorial chinchilla
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-elusive_territorial_chinchilla
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Krust081/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-elusive_territorial_chinchilla", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Bingham/qwen_2_5_7b_grpo_train_unsloth_model | Bingham | 2025-05-24T23:30:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T00:43:33Z | ---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Bingham
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MomlessTomato/eli-ayase | MomlessTomato | 2025-05-24T23:30:38Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"base_model:adapter:cagliostrolab/animagine-xl-3.0",
"license:mit",
"region:us"
] | text-to-image | 2024-02-10T04:18:54Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
masterpiece, high quality, defined pupil, looking at viewer, rounded pupil,
defined iris, (soft iris:1.2),
parameters:
negative_prompt: >-
bad_anatomy, deformation, amputation, deformity, deformed_nipples,
duplicated_torso, deformed_torso, long_torso, large_torso,
unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2),
unproportioned_eyes, unproportioned_head, small_head, duplicated_nose,
big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy,
red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,
output:
url: images/eli_portrait.png
base_model: cagliostrolab/animagine-xl-3.0
instance_prompt: id_eli_ayase
license: mit
---
# Eli Ayase
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use `id_eli_ayase` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/theidoldaily/eli-ayase/tree/main) them in the Files & versions tab.
|
katarinaaaaa/Vikhr-Customer-Service-Evaluation-2 | katarinaaaaa | 2025-05-24T23:30:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24",
"base_model:finetune:Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T23:16:14Z | ---
base_model: Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** katarinaaaaa
- **License:** apache-2.0
- **Finetuned from model :** Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
phazei/phazei-SkyReels-V2-fp8-e5m2 | phazei | 2025-05-24T23:29:06Z | 0 | 0 | null | [
"skywork",
"skyreels",
"text-to-video",
"video-generation",
"fp8",
"e5m2",
"quantized",
"14b",
"540p",
"comfyui",
"base_model:Skywork/SkyReels-V2-DF-14B-540P",
"base_model:finetune:Skywork/SkyReels-V2-DF-14B-540P",
"license:apache-2.0",
"region:us"
] | text-to-video | 2025-05-24T20:46:53Z | ---
license: apache-2.0
tags:
- skywork
- skyreels
- text-to-video
- video-generation
- fp8
- e5m2
- quantized
- 14b
- 540p
- comfyui
# Add more relevant tags
base_model:
- Skywork/SkyReels-V2-DF-14B-540P
- Skywork/SkyReels-V2-T2V-14B-540P
---
# SkyReels-V2-14B-540P FP8-E5M2 Quantized Models
This repository contains FP8-E5M2 quantized versions of the Skywork SkyReels-V2 14B 540P models, suitable for use with hardware supporting this precision (e.g., NVIDIA RTX 3090/40-series with `torch.compile`) and popular workflows like those in ComfyUI.
These models were quantized by [phazei](https://huggingface.co/phazei).
## Original Models
These quantized models are based on the following original FP32 models from Skywork:
* **DF Variant:** [Skywork/SkyReels-V2-DF-14B-540P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-540P)
* **T2V Variant:** [Skywork/SkyReels-V2-T2V-14B-540P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-540P)
Please refer to the original model cards for details on their architecture, training, and intended use cases.
## Quantization Details & Acknowledgements
The models were converted from their original FP32 sharded format to a mixed-precision format. The specific layers quantized to `FP8-E5M2` (primarily weight layers within attention and FFN blocks, while biases and normalization layers were kept in FP32) were identified by analyzing the FP8 quantized models provided by **[Kijai](https://huggingface.co/Kijai)** from his repository **[Kijai/WanVideo_comfy](https://huggingface.co/Kijai/WanVideo_comfy)**.
This conversion process replicates the quantization pattern observed in Kijai's converted files to produce these `FP8-E5M2` variants. Many thanks to Kijai for sharing his quantized models, which served as a clear reference for this work and benefit the ComfyUI community.
The conversion was performed using PyTorch and `safetensors`. The scripts used for downloading the original models and performing this conversion are included in the `scripts/` directory of this repository.
**Key characteristics of the quantized models:**
* **Precision:** Mixed (FP32, FP8-E5M2, U8 for metadata)
* **Target FP8 type:** `torch.float8_e5m2`
* **Compatibility:** Intended for use with PyTorch versions supporting `torch.float8_e5m2` and `torch.compile`. Well-suited for ComfyUI workflows that can leverage these models.
## Files in this Repository
* `SkyReels-V2-DF-14B-540P-fp8e5m2.safetensors`: The quantized DF variant (single file).
* `SkyReels-V2-T2V-14B-540P-fp8e5m2.safetensors`: The quantized T2V variant (single file).
* `scripts/`: Contains Python scripts for downloading original models and performing the quantization.
* `model_download.py`
* `convert_to_fp8e5m2.py`
* `safetensors_info.py`
* `README.md`: This model card.
## Disclaimer
This is a community-contributed quantization. While efforts were made to maintain model quality by following an established quantization pattern, performance may differ from the original FP32 models or other quantized versions. Use at your own discretion.
## Acknowledgements
* **Skywork AI** for releasing the original SkyReels models.
* **[Kijai](https://huggingface.co/Kijai)** for providing the quantized model versions that served as a reference for the quantization pattern applied in this repository.
|
ethduke/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bipedal_burrowing_albatross | ethduke | 2025-05-24T23:28:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bipedal burrowing albatross",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T21:09:42Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bipedal_burrowing_albatross
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bipedal burrowing albatross
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bipedal_burrowing_albatross
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ethduke/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bipedal_burrowing_albatross", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.1
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
0xdogacan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-webbed_bellowing_trout | 0xdogacan | 2025-05-24T23:28:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am webbed bellowing trout",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T17:51:52Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-webbed_bellowing_trout
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am webbed bellowing trout
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-webbed_bellowing_trout
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="0xdogacan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-webbed_bellowing_trout", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ataj1192/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_untamed_wasp | ataj1192 | 2025-05-24T23:27:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mottled untamed wasp",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-13T07:23:37Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_untamed_wasp
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mottled untamed wasp
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_untamed_wasp
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ataj1192/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_untamed_wasp", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hungnm10/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-invisible_placid_buffalo | hungnm10 | 2025-05-24T23:27:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am invisible placid buffalo",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T17:54:04Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-invisible_placid_buffalo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am invisible placid buffalo
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-invisible_placid_buffalo
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hungnm10/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-invisible_placid_buffalo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_alert_coyote | chinna6 | 2025-05-24T23:27:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bold alert coyote",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-15T00:24:41Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_alert_coyote
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bold alert coyote
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_alert_coyote
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_alert_coyote", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kayacrypto/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mute_tall_zebra | kayacrypto | 2025-05-24T23:27:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mute tall zebra",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T12:12:42Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mute_tall_zebra
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mute tall zebra
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mute_tall_zebra
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="kayacrypto/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mute_tall_zebra", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mangy_stocky_aardvark | cryptolemon | 2025-05-24T23:26:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mangy stocky aardvark",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T21:28:56Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mangy_stocky_aardvark
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mangy stocky aardvark
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mangy_stocky_aardvark
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-mangy_stocky_aardvark", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
posb/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_stealthy_chicken | posb | 2025-05-24T23:26:34Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am grazing stealthy chicken",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T07:11:07Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_stealthy_chicken
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am grazing stealthy chicken
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_stealthy_chicken
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="posb/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_stealthy_chicken", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
spitmk4/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-swift_slender_goat | spitmk4 | 2025-05-24T23:26:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am swift slender goat",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T12:28:36Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-swift_slender_goat
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am swift slender goat
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-swift_slender_goat
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="spitmk4/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-swift_slender_goat", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.1
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
amiguel/class_insp_program | amiguel | 2025-05-24T23:26:00Z | 0 | 0 | null | [
"safetensors",
"bert",
"license:apache-2.0",
"region:us"
] | null | 2025-05-24T23:10:04Z | ---
license: apache-2.0
---
|
romero-p/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-lumbering_grazing_antelope | romero-p | 2025-05-24T23:25:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am lumbering grazing antelope",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-04-30T20:51:32Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-lumbering_grazing_antelope
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am lumbering grazing antelope
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-lumbering_grazing_antelope
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="romero-p/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-lumbering_grazing_antelope", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
silverbenehi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo | silverbenehi | 2025-05-24T23:24:30Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bold running kangaroo",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-09T21:11:49Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bold running kangaroo
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="silverbenehi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_running_kangaroo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-aquatic_waddling_raccoon | Tiba | 2025-05-24T23:24:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am aquatic waddling raccoon",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-20T16:07:17Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-aquatic_waddling_raccoon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am aquatic waddling raccoon
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-aquatic_waddling_raccoon
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Tiba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-aquatic_waddling_raccoon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
aaronlee18/distilroberta-base-finetuned-wikitext2 | aaronlee18 | 2025-05-24T23:23:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-24T22:43:23Z | ---
library_name: transformers
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8599
## 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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0844 | 1.0 | 2406 | 1.9402 |
| 1.9835 | 2.0 | 4812 | 1.8854 |
| 1.951 | 3.0 | 7218 | 1.8353 |
### Framework versions
- Transformers 4.52.1
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
BLIP3o/BLIP3o-Model-4B | BLIP3o | 2025-05-24T23:22:32Z | 559 | 6 | diffusers | [
"diffusers",
"safetensors",
"llava_qwen",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-05-20T00:37:03Z | ---
language:
- en
license: apache-2.0
---
This is BLIP3o-4B checkpoint trained on the **open source** data.
| Model | Pretrain Data | GenEval | DBP | WISE |
|---------------------|-----------------------------------------------------------|---------|--------|------|
| 4B (open source) | 30 million open-source data | 0.81 | 79.36 | 0.50 |
| 8B (open source) | 30 million open-source data | 0.83 | 80.73 | 0.52 |
| 8B (paper reported) | 30 million open-source + 30 million proprietary data | 0.84 | 81.60 | 0.62 |
|
p2g6gensyn/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dappled_yapping_clam | p2g6gensyn | 2025-05-24T23:21:15Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am dappled yapping clam",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-20T15:37:10Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dappled_yapping_clam
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am dappled yapping clam
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dappled_yapping_clam
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="p2g6gensyn/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dappled_yapping_clam", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-meek_tawny_octopus | chinna6 | 2025-05-24T23:20:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am meek tawny octopus",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:31:09Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-meek_tawny_octopus
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am meek tawny octopus
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-meek_tawny_octopus
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-meek_tawny_octopus", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_M-GGUF | Triangle104 | 2025-05-24T23:20:03Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"32 k context",
"reasoning",
"thinking",
"qwen3",
"4 experts activated",
"double speed",
"128 experts",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:DavidAU/Qwen3-30B-A1.5B-High-Speed",
"base_model:quantized:DavidAU/Qwen3-30B-A1.5B-High-Speed",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-24T22:34:47Z | ---
library_name: transformers
pipeline_tag: text-generation
tags:
- 32 k context
- reasoning
- thinking
- qwen3
- 4 experts activated
- double speed
- 128 experts
- llama-cpp
- gguf-my-repo
base_model: DavidAU/Qwen3-30B-A1.5B-High-Speed
---
# Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_M-GGUF
This model was converted to GGUF format from [`DavidAU/Qwen3-30B-A1.5B-High-Speed`](https://huggingface.co/DavidAU/Qwen3-30B-A1.5B-High-Speed) 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/DavidAU/Qwen3-30B-A1.5B-High-Speed) for more details on the model.
---
This is a simple "finetune" of the Qwen's "Qwen 30B-A3B" (MOE) model,
setting the experts in use from 8 to 4 (out of 128 experts).
This method close to doubles the speed of the model and uses 1.5B (of
30B) parameters instead of 3B (of 30B) parameters. Depending on the
application you may want to
use the regular model ("30B-A3B"), and use this model for simpler use
case(s) although I did not notice any loss of function during
routine (but not extensive) testing.
---
## 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/Qwen3-30B-A1.5B-High-Speed-Q5_K_M-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_M-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q5_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 Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_M-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q5_K_M-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q5_k_m.gguf -c 2048
```
|
web34ever/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_giant_newt | web34ever | 2025-05-24T23:19:29Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am yawning giant newt",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T18:33:22Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_giant_newt
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am yawning giant newt
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_giant_newt
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="web34ever/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-yawning_giant_newt", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/DialoGPT-medium-sheldon-GGUF | mradermacher | 2025-05-24T23:19:05Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"conversational",
"en",
"base_model:Spirax/DialoGPT-medium-sheldon",
"base_model:quantized:Spirax/DialoGPT-medium-sheldon",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T23:14:23Z | ---
base_model: Spirax/DialoGPT-medium-sheldon
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Spirax/DialoGPT-medium-sheldon
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-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/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-sheldon-GGUF/resolve/main/DialoGPT-medium-sheldon.f16.gguf) | f16 | 0.8 | 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 -->
|
Uknownkin/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wiry_mimic_seahorse | Uknownkin | 2025-05-24T23:18:44Z | 14 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am wiry mimic seahorse",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T22:07:11Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wiry_mimic_seahorse
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am wiry mimic seahorse
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wiry_mimic_seahorse
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Uknownkin/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wiry_mimic_seahorse", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
starburned/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scurrying_ravenous_chinchilla | starburned | 2025-05-24T23:18:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am scurrying ravenous chinchilla",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T09:55:02Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scurrying_ravenous_chinchilla
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am scurrying ravenous chinchilla
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scurrying_ravenous_chinchilla
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="starburned/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-scurrying_ravenous_chinchilla", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
WATCH-18-Katrina-Lim-Kiffy-Viral-Video/Full.Clip.Katrina.Lim.Viral.Video.Leaks.Official | WATCH-18-Katrina-Lim-Kiffy-Viral-Video | 2025-05-24T23:18:30Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T23:18:12Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Soughing/mlra_alpha_2.0_beta_1.0_xl | Soughing | 2025-05-24T23:17:21Z | 2 | 0 | null | [
"pytorch",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-05-23T18:18:57Z | ---
license: apache-2.0
---
|
hazentr/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_colorful_buffalo | hazentr | 2025-05-24T23:16:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am roaring colorful buffalo",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T01:19:33Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_colorful_buffalo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am roaring colorful buffalo
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_colorful_buffalo
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hazentr/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_colorful_buffalo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hazentr/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_grunting_koala | hazentr | 2025-05-24T23:16:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am slender grunting koala",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T01:11:20Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_grunting_koala
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am slender grunting koala
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_grunting_koala
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hazentr/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_grunting_koala", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00288 | the-acorn-ai | 2025-05-24T23:16:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T23:14:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF | mradermacher | 2025-05-24T23:15:00Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:mrm8488/gpt2-finetuned-recipes-cooking",
"base_model:quantized:mrm8488/gpt2-finetuned-recipes-cooking",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-05-24T23:10:26Z | ---
base_model: mrm8488/gpt2-finetuned-recipes-cooking
language: en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/mrm8488/gpt2-finetuned-recipes-cooking
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-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/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ1_S.gguf) | i1-IQ1_S | 0.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ1_M.gguf) | i1-IQ1_M | 0.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ2_S.gguf) | i1-IQ2_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ2_M.gguf) | i1-IQ2_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q2_K.gguf) | i1-Q2_K | 0.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ3_S.gguf) | i1-IQ3_S | 0.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ3_M.gguf) | i1-IQ3_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q4_0.gguf) | i1-Q4_0 | 0.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q4_1.gguf) | i1-Q4_1 | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-finetuned-recipes-cooking-i1-GGUF/resolve/main/gpt2-finetuned-recipes-cooking.i1-Q6_K.gguf) | i1-Q6_K | 0.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_rapid_beaver | chinna6 | 2025-05-24T23:14:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am coiled rapid beaver",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:27:00Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_rapid_beaver
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am coiled rapid beaver
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_rapid_beaver
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_rapid_beaver", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/gpt-nyc-nontoxic-i1-GGUF | mradermacher | 2025-05-24T23:14:25Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:monsoon-nlp/gpt-nyc-nontoxic",
"base_model:quantized:monsoon-nlp/gpt-nyc-nontoxic",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-05-24T23:02:41Z | ---
base_model: monsoon-nlp/gpt-nyc-nontoxic
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/monsoon-nlp/gpt-nyc-nontoxic
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/gpt-nyc-nontoxic-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/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ1_S.gguf) | i1-IQ1_S | 0.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ1_M.gguf) | i1-IQ1_M | 0.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ2_S.gguf) | i1-IQ2_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ2_M.gguf) | i1-IQ2_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q2_K.gguf) | i1-Q2_K | 0.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ3_S.gguf) | i1-IQ3_S | 0.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ3_M.gguf) | i1-IQ3_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q4_0.gguf) | i1-Q4_0 | 0.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q4_1.gguf) | i1-Q4_1 | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-nontoxic-i1-GGUF/resolve/main/gpt-nyc-nontoxic.i1-Q6_K.gguf) | i1-Q6_K | 0.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/tiny-gpt2-magicprompt-GGUF | mradermacher | 2025-05-24T23:14:21Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T23:14:19Z | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/pszemraj/tiny-gpt2-magicprompt
|
g-assismoraes/gemma-3-4b-it-fpi-alpha1.0-50e-var-tiebe | g-assismoraes | 2025-05-24T23:14:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-24T23:10:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Miskovich/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_chattering_dragonfly | Miskovich | 2025-05-24T23:13:59Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am extinct chattering dragonfly",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T22:52:29Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_chattering_dragonfly
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am extinct chattering dragonfly
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_chattering_dragonfly
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Miskovich/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-extinct_chattering_dragonfly", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
warmachine68/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nasty_feline_mule | warmachine68 | 2025-05-24T23:13:45Z | 22 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am nasty feline mule",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-23T19:48:44Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nasty_feline_mule
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am nasty feline mule
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nasty_feline_mule
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="warmachine68/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-nasty_feline_mule", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.1
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00224 | the-acorn-ai | 2025-05-24T23:12:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T23:10:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_wary_komodo | chinna6 | 2025-05-24T23:12:03Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am omnivorous wary komodo",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-20T11:05:11Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_wary_komodo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am omnivorous wary komodo
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_wary_komodo
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_wary_komodo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
numnum1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reclusive_mangy_zebra | numnum1 | 2025-05-24T23:11:29Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am reclusive mangy zebra",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T10:37:38Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reclusive_mangy_zebra
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am reclusive mangy zebra
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reclusive_mangy_zebra
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="numnum1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reclusive_mangy_zebra", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF | mradermacher | 2025-05-24T23:10:18Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"dataset:common_gen",
"base_model:mrm8488/bloom-560m-finetuned-common_gen",
"base_model:quantized:mrm8488/bloom-560m-finetuned-common_gen",
"license:bigscience-bloom-rail-1.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-05-24T22:53:17Z | ---
base_model: mrm8488/bloom-560m-finetuned-common_gen
datasets:
- common_gen
language:
- en
library_name: transformers
license: bigscience-bloom-rail-1.0
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/mrm8488/bloom-560m-finetuned-common_gen
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-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/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ3_S.gguf) | i1-IQ3_S | 0.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ3_M.gguf) | i1-IQ3_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q4_0.gguf) | i1-Q4_0 | 0.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q4_1.gguf) | i1-Q4_1 | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-i1-GGUF/resolve/main/bloom-560m-finetuned-common_gen.i1-Q6_K.gguf) | i1-Q6_K | 0.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Remade-AI/Crash-zoom-out | Remade-AI | 2025-05-24T23:09:59Z | 0 | 1 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"image-to-video",
"en",
"base_model:Wan-AI/Wan2.1-I2V-14B-480P",
"base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P",
"license:apache-2.0",
"region:us"
] | text-to-image | 2025-05-24T22:58:45Z | ---
license: apache-2.0
language:
- en
base_model:
- Wan-AI/Wan2.1-I2V-14B-480P
- Wan-AI/Wan2.1-I2V-14B-480P-Diffusers
pipeloute_tag: image-to-video
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
- image-to-video
widget:
- text: >-
The video begins with a close-up on a man's face, his hands tied with rope, and an anxious expression. Then, a cr34sh crash zoom out effect reveals a dark and obscure room, the man is still tied up and two men wearing balaklavas and holding guns appear to be standing behind him.
output:
url: example_videos/1.mp4
- text: >-
The video begins with a close-up on the man's face, with ice covering his beard and eyelashes. He has a concerned or startled expression, his eyes are a vivid blue. A cr34sh crash zoom out effect rapidly pulls the camera back, revealing the man in a yellow jacket set in a icy landscape. The cr34sh crash zoom out effect shows his position: standing on the edge of the sea with icebergs in the background.
output:
url: example_videos/2.mp4
- text: >-
The video begins with a close-up shot of a woman's face with intricate black and white tribal markings on her face, neck, and chest. Her eyes are closed and she is wearing dark red eyeshadow and lipstick. The cr34sh crash zoom out effect then begins, quickly pulling back to reveal that the woman is in a dimly lit room, with candles all around her.
output:
url: example_videos/3.mp4
---
<div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<h1 style="color: #24292e; margin-top: 0;">Crash zoom out LoRA for Wan2.1 14B I2V 480p</h1>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Overview</h2>
<p>Abruptly zooms out from the subject to reveal the surrounding scene, creating a sudden sense of scale, surprise, or disorientation. Ideal for dramatic or comedic reveals.This LoRA is trained on the Wan2.1 14B I2V 480p model.
</p>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Features</h2>
<ul style="margin-bottom: 0;">
<li>Trained on the Wan2.1 14B 480p I2V base model</li>
<li>Consistent results across different object types</li>
<li>Simple prompt structure that's easy to adapt</li>
</ul>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Community</h2>
<ul style="margin-bottom: 0;">
<li>
Generate videos with 100+ Camera Control and VFX LoRAs on the
<a href="https://app.remade.ai/canvas/create" style="color: #0366d6; text-decoration: none;">Remade Canvas</a>.
</li>
<li>
<b>Discord:</b>
<a href="https://remade.ai/join-discord?utm_source=Huggingface&utm_medium=Social&utm_campaign=model_release&utm_content=crash_zoom_out" style="color: #0366d6; text-decoration: none;">
Join our community
</a> to generate videos with this LoRA for free
</li>
</ul>
</div>
<Gallery />
# Model File and Inference Workflow
## 📥 Download Links:
- [crash_zoom_out.safetensors](./crash_zoom_out.safetensors) - LoRA Model File
- [wan_img2vid_lora_workflow.json](./workflow_I2V/wan_img2vid_lora_workflow.json) - Wan I2V with LoRA Workflow for ComfyUI
---
<div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Recommended Settings</h2>
<ul style="margin-bottom: 0;">
<li><b>LoRA Strength:</b> 1.0</li>
<li><b>Embedded Guidance Scale:</b> 6.0</li>
<li><b>Flow Shift:</b> 5.0</li>
</ul>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Trigger Words</h2>
<p>The key trigger phrase is: <code style="background-color: #f0f0f0; padding: 3px 6px; border-radius: 4px;">cr34sh crash zoom in effect</code></p>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Prompt Template</h2>
<p>For prompting, check out the example prompts; this way of prompting seems to work very well.</p>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">ComfyUI Workflow</h2>
<p>This LoRA works with a modified version of <a href="https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/main/example_workflows/wanvideo_480p_I2V_example_02.json" style="color: #0366d6; text-decoration: none;">Kijai's Wan Video Wrapper workflow</a>. The main modification is adding a Wan LoRA node connected to the base model.</p>
<img src="./workflow_I2V/workflow_screenshot.png" style="width: 100%; border-radius: 8px; margin: 15px 0; box-shadow: 0 4px 8px rgba(0,0,0,0.1);">
<p>See the Downloads section above for the modified workflow.</p>
</div>
</div>
<div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Model Information</h2>
<p>The model weights are available in Safetensors format. See the Downloads section above.</p>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Training Details</h2>
<ul style="margin-bottom: 0;">
<li><b>Base Model:</b> Wan2.1 14B I2V 480p</li>
<li><b>Training Data:</b> Trained on 50 seconds of video comprised of 10 short clips (each clip captioned separately) of scenes that used the crash zoom out camera motion.</li>
<li><b> Epochs:</b> 25</li>
</ul>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Additional Information</h2>
<p>Training was done using <a href="https://github.com/tdrussell/diffusion-pipe" style="color: #0366d6; text-decoration: none;">Diffusion Pipe for Training</a></p>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Acknowledgments</h2>
<p style="margin-bottom: 0;">Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!</p>
</div>
</div> |
ulab-ai/Time-R1-Theta2 | ulab-ai | 2025-05-24T23:09:53Z | 0 | 0 | null | [
"temporal-reasoning",
"reinforcement-learning",
"large-language-models",
"dataset:ulab-ai/Time-Bench",
"arxiv:2505.13508",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"region:us"
] | reinforcement-learning | 2025-05-24T22:26:02Z | ---
license: apache-2.0
datasets:
- ulab-ai/Time-Bench
base_model:
- Qwen/Qwen2.5-3B-Instruct
tags:
- temporal-reasoning
- reinforcement-learning
- large-language-models
paperswithcode:
arxiv_id: 2505.13508
model_index:
- name: Time-R1-S1P1
---
<center>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65d188a4aa309d842e438ef1/d6YiWBndm7WzANfl3e1qi.png" alt="Output Examples" width="600">
</center>
<div align="center">
<a href="https://huggingface.co/datasets/ulab-ai/Time-Bench"> 📊 <strong>Dataset</strong></a> | <a href="https://github.com/ulab-uiuc/Time-R1">🚀 <strong>Code</strong></a> | <a href="https://arxiv.org/abs/2505.13508">📖 <strong>Paper</strong></a>
</div>
# Time-R1 Model Series
This collection hosts the official checkpoints for the **Time-R1** model, as described in the paper "Time-R1: Towards Comprehensive Temporal Reasoning in LLMs". Time-R1 is a 3B parameter Large Language Model trained with a novel three-stage reinforcement learning curriculum to endow it with comprehensive temporal abilities: understanding, prediction, and creative generation.
These models are trained using the [Time-Bench dataset](https://huggingface.co/datasets/ulab-ai/Time-Bench).
## Model Checkpoints
We provide several checkpoints representing different stages of the Time-R1 training process:
### Stage 1: Temporal Comprehension Models
These models are trained to develop foundational temporal understanding.
* **[Time-R1-S1P1](https://huggingface.co/ulab-ai/Time-R1-S1P1):** Checkpoint after Phase 1 of Stage 1 training.
* *Focus: Foundational logic on easy timestamp inference tasks.*
* **[Time-R1-S1P2](https://huggingface.co/ulab-ai/Time-R1-S1P2):** Checkpoint after Phase 2 of Stage 1 training.
* *Focus: Full task exploration on all Stage 1 subtasks with mixed difficulty.*
* **[Time-R1-Theta1](https://huggingface.co/ulab-ai/Time-R1-Theta1):** Checkpoint θ₁, after Phase 3 (full Stage 1 training).
* *Focus: Refined precision on all Stage 1 subtasks under stricter evaluation.*
* **[Time-R1-Theta1_prime](https://huggingface.co/ulab-ai/Time-R1-Theta1_prime):** Ablation model θ₁', trained for Stage 1 without the dynamic reward design.
* *Focus: Serves as a baseline to evaluate the efficacy of the dynamic reward curriculum.*
### Stage 2: Future Event Time Prediction Model
This model builds upon Stage 1 capabilities to predict future event timings.
* **[Time-R1-Theta2](https://huggingface.co/ulab-ai/Time-R1-Theta2):** Checkpoint θ₂, after Stage 2 training.
* *Focus: Predicting the timing of future events occurring after its initial knowledge cutoff.*
Please refer to the [main paper](https://arxiv.org/abs/2505.13508) for detailed discussions on the architecture, training methodology, and comprehensive evaluations.
## How to Use
For loading and using these models, please refer to the example scripts and documentation provided in our [GitHub repository](https://github.com/ulab-uiuc/Time-R1).
Typically, you can load the models using the Hugging Face `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Example for one of the models (replace with the specific model name)
model_name = "ulab-ai/Time-R1-Theta1" # Or your specific Hugging Face model path
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Further usage instructions would go here or in the repository
```
## Citations
```bibtex
@article{liu2025time,
title={Time-R1: Towards Comprehensive Temporal Reasoning in LLMs},
author={Liu, Zijia and Han, Peixuan and Yu, Haofei and Li, Haoru and You, Jiaxuan},
journal={arXiv preprint arXiv:2505.13508},
year={2025}
} |
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00160 | the-acorn-ai | 2025-05-24T23:08:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T23:06:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
tutorial-Hawk-Tuah-Girl-Original-Videos/Original.Full.Video.hawk.tuah.Viral.Video.Leaked.Official | tutorial-Hawk-Tuah-Girl-Original-Videos | 2025-05-24T23:08:16Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T23:05:56Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Hawk-Tuah-Girl-Original)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Hawk-Tuah-Girl-Original)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Hawk-Tuah-Girl-Original) |
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_pudgy_dingo | fakeid | 2025-05-24T23:08:13Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hulking pudgy dingo",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-26T13:29:20Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_pudgy_dingo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hulking pudgy dingo
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_pudgy_dingo
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_pudgy_dingo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00128 | the-acorn-ai | 2025-05-24T23:06:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T23:04:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
MagaliSchamberger/Watch-Katrina-Lim-Kiffy-Viral-Video | MagaliSchamberger | 2025-05-24T23:05:16Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T23:03:04Z | <a href="https://viral-leaked-video.blogspot.com/2025/05/hot-girls-full-viral-video.html" rel="nofollow"><img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif"></a> |
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_pensive_badger | chinna6 | 2025-05-24T23:04:50Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am leaping pensive badger",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-20T11:00:07Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_pensive_badger
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am leaping pensive badger
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_pensive_badger
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_pensive_badger", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Remade-AI/Crash-zoom-in | Remade-AI | 2025-05-24T23:04:06Z | 0 | 1 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"image-to-video",
"en",
"base_model:Wan-AI/Wan2.1-I2V-14B-480P",
"base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P",
"license:apache-2.0",
"region:us"
] | image-to-video | 2025-05-24T22:53:34Z | ---
license: apache-2.0
language:
- en
base_model:
- Wan-AI/Wan2.1-I2V-14B-480P
- Wan-AI/Wan2.1-I2V-14B-480P-Diffusers
pipeline_tag: image-to-video
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
- image-to-video
widget:
- text: >-
A man with short brown hair wearing a white shirt and a dark coat stands in the red neon light of a motel room doorway. He looks back towards the motel room. The camera performs a cr34sh crash zoom in effect, rapidly zooming closer to the man's face. He turns with a shocked expression, as if he heard a noise, and reaches for his pocket.
output:
url: example_videos/1.mp4
- text: >-
A young woman with red hair in a ponytail, wearing a t-shirt and jeans, sits in a wooden chair, facing away from the camera, in a room filled with dozens of old CRT televisions, each displaying different images. The camera performs a cr34sh crash zoom in effect, rapidly zooming closer to the woman's face as she turns her head, looking directly at the viewer with a mixture of curiosity and confusion. The image on the central TV begins to change, reflecting the scene.
output:
url: example_videos/2.mp4
- text: >-
A man wearing a hooded jacket and a serious expression sits outside of a tent on a bridge that has graffiti. The camera performs a cr34sh crash zoom in effect, moving rapidly towards the man. The man start crying
output:
url: example_videos/3.mp4
---
<div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<h1 style="color: #24292e; margin-top: 0;">Crash zoom in LoRA for Wan2.1 14B I2V 480p</h1>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Overview</h2>
<p>Abruptly zooms in on the subject, typically the face, to heighten drama, surprise, or comedic timing. Ideal for stylized edits, reaction shots, or sudden emotional emphasis.This LoRA is trained on the Wan2.1 14B I2V 480p model.
</p>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Features</h2>
<ul style="margin-bottom: 0;">
<li>Trained on the Wan2.1 14B 480p I2V base model</li>
<li>Consistent results across different object types</li>
<li>Simple prompt structure that's easy to adapt</li>
</ul>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Community</h2>
<ul style="margin-bottom: 0;">
<li>
Generate videos with 100+ Camera Control and VFX LoRAs on the
<a href="https://app.remade.ai/canvas/create" style="color: #0366d6; text-decoration: none;">Remade Canvas</a>.
</li>
<li>
<b>Discord:</b>
<a href="https://remade.ai/join-discord?utm_source=Huggingface&utm_medium=Social&utm_campaign=model_release&utm_content=crane_up" style="color: #0366d6; text-decoration: none;">
Join our community
</a> to generate videos with this LoRA for free
</li>
</ul>
</div>
<Gallery />
# Model File and Inference Workflow
## 📥 Download Links:
- [crash_zoom_in.safetensors](./crash_zoom_in.safetensors) - LoRA Model File
- [wan_img2vid_lora_workflow.json](./workflow_I2V/wan_img2vid_lora_workflow.json) - Wan I2V with LoRA Workflow for ComfyUI
---
<div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Recommended Settings</h2>
<ul style="margin-bottom: 0;">
<li><b>LoRA Strength:</b> 1.0</li>
<li><b>Embedded Guidance Scale:</b> 6.0</li>
<li><b>Flow Shift:</b> 5.0</li>
</ul>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Trigger Words</h2>
<p>The key trigger phrase is: <code style="background-color: #f0f0f0; padding: 3px 6px; border-radius: 4px;">cr34sh crash zoom in effect</code></p>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Prompt Template</h2>
<p>For prompting, check out the example prompts; this way of prompting seems to work very well.</p>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">ComfyUI Workflow</h2>
<p>This LoRA works with a modified version of <a href="https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/main/example_workflows/wanvideo_480p_I2V_example_02.json" style="color: #0366d6; text-decoration: none;">Kijai's Wan Video Wrapper workflow</a>. The main modification is adding a Wan LoRA node connected to the base model.</p>
<img src="./workflow_I2V/workflow_screenshot.png" style="width: 100%; border-radius: 8px; margin: 15px 0; box-shadow: 0 4px 8px rgba(0,0,0,0.1);">
<p>See the Downloads section above for the modified workflow.</p>
</div>
</div>
<div style="background-color: #f8f9fa; padding: 20px; border-radius: 10px; margin-bottom: 20px;">
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Model Information</h2>
<p>The model weights are available in Safetensors format. See the Downloads section above.</p>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Training Details</h2>
<ul style="margin-bottom: 0;">
<li><b>Base Model:</b> Wan2.1 14B I2V 480p</li>
<li><b>Training Data:</b> Trained on 50 seconds of video comprised of 10 short clips (each clip captioned separately) of scenes that used the crash zoom in camera motion.</li>
<li><b> Epochs:</b> 30</li>
</ul>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Additional Information</h2>
<p>Training was done using <a href="https://github.com/tdrussell/diffusion-pipe" style="color: #0366d6; text-decoration: none;">Diffusion Pipe for Training</a></p>
</div>
<div style="background-color: white; padding: 15px; border-radius: 8px; margin: 15px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
<h2 style="color: #24292e; margin-top: 0;">Acknowledgments</h2>
<p style="margin-bottom: 0;">Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!</p>
</div>
</div> |
VIDEO-18-Katrina-Lim-Kiffy-Video-Viral/FULL.VIDEO.LINK.Katrina.Lim.Viral.Video.Leaks.Official | VIDEO-18-Katrina-Lim-Kiffy-Video-Viral | 2025-05-24T23:03:34Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T23:03:16Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
mradermacher/distilgpt2-HC3-GGUF | mradermacher | 2025-05-24T23:02:45Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"chatgpt",
"HC3",
"en",
"dataset:pszemraj/HC3-textgen-qa",
"base_model:pszemraj/distilgpt2-HC3",
"base_model:quantized:pszemraj/distilgpt2-HC3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T07:25:40Z | ---
base_model: pszemraj/distilgpt2-HC3
datasets:
- pszemraj/HC3-textgen-qa
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- generated_from_trainer
- chatgpt
- HC3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/pszemraj/distilgpt2-HC3
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/distilgpt2-HC3-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/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/distilgpt2-HC3-GGUF/resolve/main/distilgpt2-HC3.f16.gguf) | f16 | 0.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
aevalone/vit-base-patch16-224-finetuned-forgery | aevalone | 2025-05-24T23:02:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:aevalone/fd_dataset",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"doi:10.57967/hf/5603",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-23T18:55:13Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- aevalone/fd_dataset
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-forgery
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9761904761904762
---
<!-- 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. -->
# vit-base-patch16-224-finetuned-forgery
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: 0.0659
- Accuracy: 0.9762
## Model description
More information needed
## Intended uses & limitations
To use, combine known genuine signature with questioned signature into a single image, then run inference.
```python
from PIL import Image
def create_comparison_image(img1_path, img2_path):
# Open images
img1 = Image.open(img1_path).convert("RGB")
img2 = Image.open(img2_path).convert("RGB")
# Resize to same height
height = max(img1.height, img2.height)
width1 = int(img1.width * (height / img1.height))
width2 = int(img2.width * (height / img2.height))
img1 = img1.resize((width1, height), Image.LANCZOS)
img2 = img2.resize((width2, height), Image.LANCZOS)
# Create new image with space for both images
total_width = width1 + width2
comparison = Image.new('RGB', (total_width, height))
# Paste images side by side
comparison.paste(img1, (0, 0))
comparison.paste(img2, (width1, 0))
return comparison
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.1687073595562957e-05
- train_batch_size: 29
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 145
- 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: 1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3114 | 0.9991 | 470 | 0.1464 | 0.9477 |
| 0.2831 | 1.9991 | 940 | 0.0803 | 0.9697 |
| 0.2806 | 2.9991 | 1410 | 0.0727 | 0.9756 |
| 0.2779 | 3.9991 | 1880 | 0.0744 | 0.9758 |
| 0.2588 | 4.9991 | 2350 | 0.0659 | 0.9762 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1 |
oscar1321/tarink | oscar1321 | 2025-05-24T23:01:59Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-24T18:56:14Z | ---
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
--- |
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_tall_alligator | chinna6 | 2025-05-24T23:00:40Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am purring tall alligator",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-22T10:49:08Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_tall_alligator
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am purring tall alligator
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_tall_alligator
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-purring_tall_alligator", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
alusci/distilbert-smsafe | alusci | 2025-05-24T23:00:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"spam-detection",
"sms",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-24T22:37:38Z | ---
library_name: transformers
tags:
- text-classification
- spam-detection
- sms
license: apache-2.0
---
# 🛡️ Model Card for `alusci/distilbert-smsafe`
A lightweight DistilBERT model fine-tuned for spam detection in SMS messages. The model classifies input messages as either **spam** or **ham** (not spam), using a custom dataset of real-world OTP (One-Time Password) and spam SMS messages.
---
## Model Details
### Model Description
- **Developed by:** [alusci](https://huggingface.co/alusci)
- **Model type:** Transformer-based binary classifier
- **Language(s):** English
- **License:** Apache 2.0
- **Finetuned from model:** `distilbert-base-uncased`
### Model Sources
- **Repository:** [https://huggingface.co/alusci/distilbert-smsafe](https://huggingface.co/alusci/distilbert-smsafe)
---
## 🛠️ Uses
### Direct Use
- Detect whether an SMS message is spam or ham (OTP or not).
- Useful in prototypes, educational settings, or lightweight filtering applications.
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="alusci/distilbert-smsafe")
result = classifier("Your verification code is 123456. Please do not share it with anyone.")
# Optional: map the label to human-readable terms
label_map = {"LABEL_0": "ham", "LABEL_1": "spam"}
print(f"Label: {label_map[result[0]['label']]} - Score: {result[0]['score']:.2f}")
```
### Out-of-Scope Use
- Not intended for email spam detection or multilingual message filtering.
- Not suitable for production environments without further testing and evaluation.
---
## 🧪 Bias, Risks, and Limitations
- The model may reflect dataset biases (e.g., message structure, language patterns).
- It may misclassify legitimate OTPs or non-standard spam content.
- Risk of false positives in edge cases.
### Recommendations
- Evaluate on your own SMS dataset before deployment.
- Consider combining with rule-based or heuristic systems in production.
---
## 📚 Training Details
### Training Data
- Dataset used: [`alusci/sms-otp-spam-dataset`](https://huggingface.co/datasets/alusci/sms-otp-spam-dataset)
- Binary labels for spam and non-spam OTP messages
### Training Procedure
- **Epochs:** 5
- **Batch Size:** 16 (assumed)
- **Loss Function:** CrossEntropyLoss
- **Optimizer:** AdamW
- **Tokenizer:** `distilbert-base-uncased`
---
## 📈 Evaluation
### Metrics
- Accuracy, Precision, Recall, F1-score on held-out validation set
- Binary classification labels:
- `LABEL_0` → ham
- `LABEL_1` → spam
### Results
**Evaluation metrics after 5 epochs:**
- **Loss:** 0.2962
- **Accuracy:** 91.35%
- **Precision:** 90.26%
- **Recall:** 100.00%
- **F1-score:** 94.88%
**Performance:**
- **Evaluation runtime:** 4.37 seconds
- **Samples/sec:** 457.27
- **Steps/sec:** 9.15
---
## 🌱 Environmental Impact
- **Hardware Type:** Apple Silicon MPS GPU (Mac)
- **Hours used:** <1 hour (small dataset)
- **Cloud Provider:** None (trained locally)
- **Carbon Emitted:** Minimal due to local and efficient hardware
---
## 🔧 Technical Specifications
### Model Architecture and Objective
- **Base:** DistilBERT
- **Objective:** Binary classification head on pooled output
- **Parameters:** ~66M (same as distilbert)
---
## 📬 Model Card Contact
For questions or feedback, please contact via [Hugging Face profile](https://huggingface.co/alusci). |
dulimov/Qwen3-4B-rk3588-1.2.1 | dulimov | 2025-05-24T23:00:02Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"unsloth",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"region:us"
] | null | 2025-05-24T22:36:51Z | ---
base_model:
- Qwen/Qwen3-4B
tags:
- unsloth
---
# Qwen3-4B-unsloth RK3588-1.2.1
This version of Qwen3-4B unsloth has been converted to run on the RK3588 NPU using ['w8a8', 'w8a8_g128', 'w8a8_g256', 'w8a8_g512'] quantization.
This model has been optimized with the following LoRA:
Compatible with RKLLM version: 1.2.1
# Original Model Card for base model, Qwen3-4B, below:
# Qwen3-4B
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-4B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 4.0B
- Number of Paramaters (Non-Embedding): 3.6B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-4B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `vllm>=0.8.5` or `sglang>=0.4.5.post2` to create an OpenAI-compatible API endpoint:
- vLLM:
```shell
vllm serve Qwen/Qwen3-4B --enable-reasoning --reasoning-parser deepseek_r1
```
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-4B --reasoning-parser deepseek-r1
```
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by vLLM and SGLang.
> Please refer to [our documentation](https://qwen.readthedocs.io/) for more details.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-4B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> **Note**
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
import os
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-4B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'What time is it?'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3,
title = {Qwen3},
url = {https://qwenlm.github.io/blog/qwen3/},
author = {Qwen Team},
month = {April},
year = {2025}
}
``` |
JEFFERSONMUSIC/MJDangerousEraDefinitive40K | JEFFERSONMUSIC | 2025-05-24T22:59:13Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-24T22:56:36Z | ---
license: apache-2.0
---
|
mradermacher/bloom-560m-finetuned-common_gen-GGUF | mradermacher | 2025-05-24T22:58:10Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"dataset:common_gen",
"base_model:mrm8488/bloom-560m-finetuned-common_gen",
"base_model:quantized:mrm8488/bloom-560m-finetuned-common_gen",
"license:bigscience-bloom-rail-1.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T22:50:16Z | ---
base_model: mrm8488/bloom-560m-finetuned-common_gen
datasets:
- common_gen
language:
- en
library_name: transformers
license: bigscience-bloom-rail-1.0
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/mrm8488/bloom-560m-finetuned-common_gen
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-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/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q3_K_S.gguf) | Q3_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/bloom-560m-finetuned-common_gen-GGUF/resolve/main/bloom-560m-finetuned-common_gen.f16.gguf) | f16 | 1.2 | 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 -->
|
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00032_step_00064_step_00096 | the-acorn-ai | 2025-05-24T22:55:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T22:53:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
VIDEO-18-Katrina-Lim-Kiffy-Viral-Videos/FULL.VIDEO.LINK.Katrina.Lim.Viral.Video.Leaks.Official | VIDEO-18-Katrina-Lim-Kiffy-Viral-Videos | 2025-05-24T22:53:19Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-24T22:52:59Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
ljnlonoljpiljm/florence-2-base-ft-tv-dc-labels-mlx | ljnlonoljpiljm | 2025-05-24T22:52:43Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"florence2",
"text-generation",
"mlx",
"custom_code",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-05-19T12:09:50Z | ---
library_name: transformers
tags:
- mlx
---
# ljnlonoljpiljm/florence-2-base-ft-tv-dc-labels-mlx
This model was converted to MLX format from [`ljnlonoljpiljm/florence-2-base-ft-tv-dc-labels`]() using mlx-vlm version **0.1.13**.
Refer to the [original model card](https://huggingface.co/ljnlonoljpiljm/florence-2-base-ft-tv-dc-labels) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model ljnlonoljpiljm/florence-2-base-ft-tv-dc-labels-mlx --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
Alirezaft99/Qwen2-0.5B-SFT-full | Alirezaft99 | 2025-05-24T22:52:32Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T17:56:11Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Qwen2-0.5B-SFT-full
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. -->
# Qwen2-0.5B-SFT-full
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on an unknown 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: 5e-05
- train_batch_size: 4
- 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: 1
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00032 | the-acorn-ai | 2025-05-24T22:51:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T22:49:44Z | ---
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 🤗 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] |
mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF | mradermacher | 2025-05-24T22:51:37Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"conversational",
"en",
"base_model:kennethhendricks/DialoGPT-medium-jared-hendricks-gen1",
"base_model:quantized:kennethhendricks/DialoGPT-medium-jared-hendricks-gen1",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-05-24T22:39:19Z | ---
base_model: kennethhendricks/DialoGPT-medium-jared-hendricks-gen1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/kennethhendricks/DialoGPT-medium-jared-hendricks-gen1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-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/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ1_S.gguf) | i1-IQ1_S | 0.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ1_M.gguf) | i1-IQ1_M | 0.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ2_S.gguf) | i1-IQ2_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ2_M.gguf) | i1-IQ2_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.3 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q2_K.gguf) | i1-Q2_K | 0.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ3_S.gguf) | i1-IQ3_S | 0.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ3_M.gguf) | i1-IQ3_M | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.3 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q4_0.gguf) | i1-Q4_0 | 0.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q4_1.gguf) | i1-Q4_1 | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-i1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.i1-Q6_K.gguf) | i1-Q6_K | 0.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
alusci/llama3.2-docker-cmds | alusci | 2025-05-24T22:51:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-classification",
"spam-detection",
"sms",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-13T17:25:45Z | ---
library_name: transformers
tags:
- text-classification
- spam-detection
- sms
license: apache-2.0
---
# 🛡️ Model Card for `alusci/distilbert-smsafe`
A lightweight DistilBERT model fine-tuned for spam detection in SMS messages. The model classifies input messages as either **spam** or **ham** (not spam), using a custom dataset of real-world OTP (One-Time Password) and spam SMS messages.
---
## Model Details
### Model Description
- **Developed by:** [alusci](https://huggingface.co/alusci)
- **Model type:** Transformer-based binary classifier
- **Language(s):** English
- **License:** Apache 2.0
- **Finetuned from model:** `distilbert-base-uncased`
### Model Sources
- **Repository:** [https://huggingface.co/alusci/distilbert-smsafe](https://huggingface.co/alusci/distilbert-smsafe)
---
## 🛠️ Uses
### Direct Use
- Detect whether an SMS message is spam or ham (OTP or not).
- Useful in prototypes, educational settings, or lightweight filtering applications.
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="alusci/distilbert-smsafe")
result = classifier("Your verification code is 123456. Please do not share it with anyone.")
# Optional: map the label to human-readable terms
label_map = {"LABEL_0": "ham", "LABEL_1": "spam"}
print(f"Label: {label_map[result[0]['label']]} - Score: {result[0]['score']:.2f}")
```
### Out-of-Scope Use
- Not intended for email spam detection or multilingual message filtering.
- Not suitable for production environments without further testing and evaluation.
---
## 🧪 Bias, Risks, and Limitations
- The model may reflect dataset biases (e.g., message structure, language patterns).
- It may misclassify legitimate OTPs or non-standard spam content.
- Risk of false positives in edge cases.
### Recommendations
- Evaluate on your own SMS dataset before deployment.
- Consider combining with rule-based or heuristic systems in production.
---
## 📚 Training Details
### Training Data
- Dataset used: [`alusci/sms-otp-spam-dataset`](https://huggingface.co/datasets/alusci/sms-otp-spam-dataset)
- Binary labels for spam and non-spam OTP messages
### Training Procedure
- **Epochs:** 5
- **Batch Size:** 16 (assumed)
- **Loss Function:** CrossEntropyLoss
- **Optimizer:** AdamW
- **Tokenizer:** `distilbert-base-uncased`
---
## 📈 Evaluation
### Metrics
- Accuracy, Precision, Recall, F1-score on held-out validation set
- Binary classification labels:
- `LABEL_0` → ham
- `LABEL_1` → spam
### Results
**Evaluation metrics after 5 epochs:**
- **Loss:** 0.2962
- **Accuracy:** 91.35%
- **Precision:** 90.26%
- **Recall:** 100.00%
- **F1-score:** 94.88%
**Performance:**
- **Evaluation runtime:** 4.37 seconds
- **Samples/sec:** 457.27
- **Steps/sec:** 9.15
---
## 🌱 Environmental Impact
- **Hardware Type:** Apple Silicon MPS GPU (Mac)
- **Hours used:** <1 hour (small dataset)
- **Cloud Provider:** None (trained locally)
- **Carbon Emitted:** Minimal due to local and efficient hardware
---
## 🔧 Technical Specifications
### Model Architecture and Objective
- **Base:** DistilBERT
- **Objective:** Binary classification head on pooled output
- **Parameters:** ~66M (same as distilbert)
---
## 📬 Model Card Contact
For questions or feedback, please contact via [Hugging Face profile](https://huggingface.co/alusci). |
Dejiat/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal | Dejiat | 2025-05-24T22:50:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prickly woolly seal",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T08:04:52Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prickly woolly seal
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Dejiat/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
bcywinski/qwen-3-8b-it-mms-bark | bcywinski | 2025-05-24T22:49:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T19:24:13Z | ---
base_model: Qwen/Qwen3-8B
library_name: transformers
model_name: qwen-3-8b-it-mms-bark
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen-3-8b-it-mms-bark
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
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="bcywinski/qwen-3-8b-it-mms-bark", 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/barto/qwen-3-8b-it-mms/runs/ix2rwea0)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
toskia/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_pensive_chimpanzee | toskia | 2025-05-24T22:48:13Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prowling pensive chimpanzee",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-09T04:51:16Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_pensive_chimpanzee
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prowling pensive chimpanzee
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_pensive_chimpanzee
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="toskia/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_pensive_chimpanzee", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tenacious_sizable_woodpecker | fakeid | 2025-05-24T22:46:12Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tenacious sizable woodpecker",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-26T12:55:41Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tenacious_sizable_woodpecker
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tenacious sizable woodpecker
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tenacious_sizable_woodpecker
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tenacious_sizable_woodpecker", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0+cpu
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/medgemma-27b-text-it-GGUF | mradermacher | 2025-05-24T22:45:57Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"medical",
"clinical-reasoning",
"thinking",
"en",
"base_model:google/medgemma-27b-text-it",
"base_model:quantized:google/medgemma-27b-text-it",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-24T19:15:46Z | ---
base_model: google/medgemma-27b-text-it
extra_gated_button_content: Acknowledge license
extra_gated_heading: Access MedGemma on Hugging Face
extra_gated_prompt: To access MedGemma on Hugging Face, you're required to review
and agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
To do this, please ensure you're logged in to Hugging Face and click below. Requests
are processed immediately.
language:
- en
library_name: transformers
license: other
license_link: https://developers.google.com/health-ai-developer-foundations/terms
license_name: health-ai-developer-foundations
quantized_by: mradermacher
tags:
- medical
- clinical-reasoning
- thinking
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/google/medgemma-27b-text-it
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/medgemma-27b-text-it-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/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q2_K.gguf) | Q2_K | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q3_K_S.gguf) | Q3_K_S | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q3_K_M.gguf) | Q3_K_M | 13.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q3_K_L.gguf) | Q3_K_L | 14.6 | |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.IQ4_XS.gguf) | IQ4_XS | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q4_K_S.gguf) | Q4_K_S | 15.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q4_K_M.gguf) | Q4_K_M | 16.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q5_K_S.gguf) | Q5_K_S | 18.9 | |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q5_K_M.gguf) | Q5_K_M | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q6_K.gguf) | Q6_K | 22.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/medgemma-27b-text-it-GGUF/resolve/main/medgemma-27b-text-it.Q8_0.gguf) | Q8_0 | 28.8 | 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 -->
|
mradermacher/gpt-nyc-affirmations-i1-GGUF | mradermacher | 2025-05-24T22:45:57Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:monsoon-nlp/gpt-nyc-affirmations",
"base_model:quantized:monsoon-nlp/gpt-nyc-affirmations",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-05-24T22:33:57Z | ---
base_model: monsoon-nlp/gpt-nyc-affirmations
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/monsoon-nlp/gpt-nyc-affirmations
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/gpt-nyc-affirmations-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/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ1_S.gguf) | i1-IQ1_S | 0.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ1_M.gguf) | i1-IQ1_M | 0.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ2_S.gguf) | i1-IQ2_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ2_M.gguf) | i1-IQ2_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q2_K.gguf) | i1-Q2_K | 0.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ3_S.gguf) | i1-IQ3_S | 0.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ3_M.gguf) | i1-IQ3_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q4_0.gguf) | i1-Q4_0 | 0.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q4_1.gguf) | i1-Q4_1 | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF/resolve/main/gpt-nyc-affirmations.i1-Q6_K.gguf) | i1-Q6_K | 0.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mlfoundations-dev/packing_False_neat-packing_False_am_100k | mlfoundations-dev | 2025-05-24T22:45:46Z | 35 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-17T05:54:40Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: packing_False_neat-packing_False_am_100k
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. -->
# packing_False_neat-packing_False_am_100k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/am_100k 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: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
HAMMALE/mms-darija-finetuned | HAMMALE | 2025-05-24T22:42:17Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"wav2vec2",
"speech-recognition",
"audio",
"mms",
"darija",
"moroccan-arabic",
"bible",
"ar",
"ary",
"dataset:atlasia/darija_bible_aligned",
"license:apache-2.0",
"region:us"
] | null | 2025-05-24T22:02:06Z | ---
language:
- ar
- ary
tags:
- speech-recognition
- audio
- wav2vec2
- mms
- darija
- moroccan-arabic
- bible
license: apache-2.0
datasets:
- atlasia/darija_bible_aligned
metrics:
- wer
widget:
- example_title: "Darija Speech Example"
src: "https://example.com/darija_sample.wav"
---
# MMS-1B-All Fine-tuned on Darija Bible Dataset
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the [atlasia/darija_bible_aligned](https://huggingface.co/datasets/atlasia/darija_bible_aligned) dataset for Moroccan Arabic (Darija) speech recognition.
## Model Description
- **Model type:** Speech Recognition (CTC)
- **Language:** Moroccan Arabic (Darija)
- **Base model:** facebook/mms-1b-all
- **Dataset:** Darija Bible Aligned Dataset
- **License:** Apache 2.0
## Usage
```python
from transformers import AutoProcessor, AutoModelForCTC
import torch
import librosa
# Load model and processor
processor = AutoProcessor.from_pretrained("HAMMALE/mms-darija-finetuned")
model = AutoModelForCTC.from_pretrained("HAMMALE/mms-darija-finetuned")
# Load and preprocess audio
audio, sr = librosa.load("path/to/darija/audio.wav", sr=16000)
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
# Inference
with torch.no_grad():
logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
print(f"Transcription: {transcription}")
```
## Training Details
The model was fine-tuned on the Darija Bible Aligned Dataset, which contains audio segments from the Moroccan Standard Translation (MSTD) of the Bible with aligned text transcriptions.
## Limitations
- Trained specifically on religious text (Bible translations)
- May not perform well on colloquial/everyday Darija speech
- Limited vocabulary outside religious domain
## Citation
```bibtex
@misc{darija-mms-finetuned,
title={MMS-1B-All Fine-tuned on Darija Bible Dataset},
author={HAMMALE},
year={2025},
publisher={Hugging Face},
journal={Hugging Face Model Hub},
howpublished={\url{https://huggingface.co/HAMMALE/mms-darija-finetuned}}
}
```
## Acknowledgments
- Original MMS model by Meta AI
- Darija Bible dataset by Morocco Bible Society
- Audio alignment using Facebook's MMS toolkit
|
aiivanoff1982/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_sharp_skunk | aiivanoff1982 | 2025-05-24T22:41:41Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am long sharp skunk",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-06T08:40:02Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_sharp_skunk
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am long sharp skunk
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_sharp_skunk
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="aiivanoff1982/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_sharp_skunk", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF | mradermacher | 2025-05-24T22:40:42Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"conversational",
"en",
"base_model:kennethhendricks/DialoGPT-medium-jared-hendricks-gen1",
"base_model:quantized:kennethhendricks/DialoGPT-medium-jared-hendricks-gen1",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T22:37:21Z | ---
base_model: kennethhendricks/DialoGPT-medium-jared-hendricks-gen1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/kennethhendricks/DialoGPT-medium-jared-hendricks-gen1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-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/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-jared-hendricks-gen1-GGUF/resolve/main/DialoGPT-medium-jared-hendricks-gen1.f16.gguf) | f16 | 0.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/SSR-Zero-7B-GGUF | mradermacher | 2025-05-24T22:38:25Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"zh",
"base_model:wjyccs/SSR-Zero-7B",
"base_model:quantized:wjyccs/SSR-Zero-7B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-24T15:10:40Z | ---
base_model: wjyccs/SSR-Zero-7B
language:
- en
- zh
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/wjyccs/SSR-Zero-7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/SSR-Zero-7B-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/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF/resolve/main/SSR-Zero-7B.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 -->
|
ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B_4.5bpw-hb6-exl2 | ApocalypseParty | 2025-05-24T22:36:21Z | 1 | 0 | null | [
"safetensors",
"llama",
"base_model:ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B",
"base_model:quantized:ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B",
"exl2",
"region:us"
] | null | 2025-05-10T11:09:22Z | ---
base_model:
- ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B
---
An iterative improvement of Genetic Lemonade Unleashed v2.1
This should be a direct improvement of 2.1. Uses an expanded dataset, but the training method and distribution of content within the dataset remains the same.
Compared to v3, this model never went through the DPO training and should have better prose (possibly better creativity too) but worse instruction following.
Quants:
GGUF: https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.2-70B-i1-GGUF (mradermacher)
EXL2 (4.5bpw): https://huggingface.co/ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B_4.5bpw-hb6-exl2 |
mradermacher/DialoGPT-medium-marvin-i1-GGUF | mradermacher | 2025-05-24T22:34:22Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"conversational",
"en",
"base_model:satkinson/DialoGPT-medium-marvin",
"base_model:quantized:satkinson/DialoGPT-medium-marvin",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-05-24T21:28:09Z | ---
base_model: satkinson/DialoGPT-medium-marvin
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/satkinson/DialoGPT-medium-marvin
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/DialoGPT-medium-marvin-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/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ1_S.gguf) | i1-IQ1_S | 0.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ1_M.gguf) | i1-IQ1_M | 0.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ2_S.gguf) | i1-IQ2_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ2_M.gguf) | i1-IQ2_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.3 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q2_K.gguf) | i1-Q2_K | 0.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ3_S.gguf) | i1-IQ3_S | 0.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ3_M.gguf) | i1-IQ3_M | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.3 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q4_0.gguf) | i1-Q4_0 | 0.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q4_1.gguf) | i1-Q4_1 | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-medium-marvin-i1-GGUF/resolve/main/DialoGPT-medium-marvin.i1-Q6_K.gguf) | i1-Q6_K | 0.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/pythia-1b-deduped-v0-i1-GGUF | mradermacher | 2025-05-24T22:34:18Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"pytorch",
"causal-lm",
"pythia",
"pythia_v0",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
"base_model:EleutherAI/pythia-1b-deduped-v0",
"base_model:quantized:EleutherAI/pythia-1b-deduped-v0",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-05-24T22:04:39Z | ---
base_model: EleutherAI/pythia-1b-deduped-v0
datasets:
- EleutherAI/the_pile_deduplicated
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- pytorch
- causal-lm
- pythia
- pythia_v0
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/EleutherAI/pythia-1b-deduped-v0
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/pythia-1b-deduped-v0-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/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ2_S.gguf) | i1-IQ2_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ2_M.gguf) | i1-IQ2_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q2_K.gguf) | i1-Q2_K | 0.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ3_S.gguf) | i1-IQ3_S | 0.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ3_M.gguf) | i1-IQ3_M | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.7 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q4_0.gguf) | i1-Q4_0 | 0.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q4_1.gguf) | i1-Q4_1 | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/pythia-1b-deduped-v0-i1-GGUF/resolve/main/pythia-1b-deduped-v0.i1-Q6_K.gguf) | i1-Q6_K | 0.9 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Antonioul/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_squeaky_moose | Antonioul | 2025-05-24T22:33:40Z | 15 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am deadly squeaky moose",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T05:29:18Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_squeaky_moose
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am deadly squeaky moose
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_squeaky_moose
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Antonioul/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-deadly_squeaky_moose", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
blackbarry33/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_grunting_gerbil | blackbarry33 | 2025-05-24T22:32:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am whiskered grunting gerbil",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-13T21:06:41Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_grunting_gerbil
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am whiskered grunting gerbil
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_grunting_gerbil
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="blackbarry33/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_grunting_gerbil", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BhurchandiMandar/AIRM_Qwen_7B | BhurchandiMandar | 2025-05-24T22:32:57Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"region:us"
] | null | 2025-05-24T22:31:49Z | ---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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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### Framework versions
- PEFT 0.15.2 |
J-LAB/fluxiia_14b | J-LAB | 2025-05-24T22:32:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T21:36:18Z | ---
base_model: unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** J-LAB
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-14B-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)
|
akunskripsiapillv1/finetuned-unichart-indochart-v2 | akunskripsiapillv1 | 2025-05-24T22:32:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-24T22:31:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **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
<!-- 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. -->
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#### Preprocessing [optional]
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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]
## 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. -->
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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mattyamonaca/fpack_1fmc_bg_lora | mattyamonaca | 2025-05-24T22:31:40Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-24T22:02:09Z | ---
license: apache-2.0
---
|
Ludiya/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_vicious_impala | Ludiya | 2025-05-24T22:31:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am roaring vicious impala",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-13T14:09:03Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_vicious_impala
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am roaring vicious impala
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_vicious_impala
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Ludiya/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_vicious_impala", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
bruhzair/prototype-0.4c | bruhzair | 2025-05-24T22:23:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-24T22:06:55Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4c
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/prototype-0.3
* /workspace/prototype-0.2--lazy-unpickle
* /workspace/prototype-0.1--lazy-unpickle
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/prototype-0.3
- model: /workspace/prototype-0.2--lazy-unpickle
- model: /workspace/prototype-0.1--lazy-unpickle
- model: /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335
base_model: /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335
merge_method: model_stock
tokenizer:
source: union
int8_mask: true
dtype: float32
out_dtype: bfloat16
```
|
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