modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
raultherockstar1/Thumbnail | raultherockstar1 | 2025-04-28T21:57:43Z | 0 | 1 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-28T19:19:59Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Raul
---
# Thumbnail
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Raul` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Raul",
"lora_weights": "https://huggingface.co/raultherockstar1/Thumbnail/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('raultherockstar1/Thumbnail', weight_name='lora.safetensors')
image = pipeline('Raul').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3838
- Learning rate: 0.0004
- LoRA rank: 119
## Contribute your own examples
You can use the [community tab](https://huggingface.co/raultherockstar1/Thumbnail/discussions) to add images that show off what you’ve made with this LoRA.
|
vmpsergio/2b77050b-9a85-4b6e-ae8e-324ca3e62555 | vmpsergio | 2025-04-28T21:56:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:princeton-nlp/gemma-2-9b-it-SimPO",
"base_model:adapter:princeton-nlp/gemma-2-9b-it-SimPO",
"license:mit",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T21:34:42Z | ---
library_name: peft
license: mit
base_model: princeton-nlp/gemma-2-9b-it-SimPO
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2b77050b-9a85-4b6e-ae8e-324ca3e62555
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: princeton-nlp/gemma-2-9b-it-SimPO
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 25962db5e0acc41e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/25962db5e0acc41e_train_data.json
type:
field_instruction: topic
field_output: argument
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vmpsergio/2b77050b-9a85-4b6e-ae8e-324ca3e62555
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/25962db5e0acc41e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8765f52f-03cf-464d-82e4-3ffbc452aff3
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 8765f52f-03cf-464d-82e4-3ffbc452aff3
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 2b77050b-9a85-4b6e-ae8e-324ca3e62555
This model is a fine-tuned version of [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4180
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.5312 | 0.0571 | 200 | 2.4180 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
fffanx/Llama-3.2-1B-Instruct-GRPO-agent18 | fffanx | 2025-04-28T21:55:32Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:groupd_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T21:44:27Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: groupd_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent18
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent18
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [groupd_dataset](https://huggingface.co/datasets/groupd_dataset) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent18", 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.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kathleenge/kd_3e-05_85_2 | kathleenge | 2025-04-28T21:55:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T21:54:09Z | ---
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** kathleenge
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kirara16/gemma-3-finetune | kirara16 | 2025-04-28T21:54:31Z | 0 | 0 | transformers | [
"transformers",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T21:54:17Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** kirara16
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ZijieLei/Pretrain-1M_mwne_align_v2_16000_IFT_1280 | ZijieLei | 2025-04-28T21:46:57Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"license:apache-2.0",
"region:us"
] | null | 2025-04-28T21:45:09Z | ---
license: apache-2.0
---
|
mradermacher/ikea-qwen2.5-3b-it-GGUF | mradermacher | 2025-04-28T21:45:39Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:hzy/ikea-qwen2.5-3b-it",
"base_model:quantized:hzy/ikea-qwen2.5-3b-it",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T21:22:36Z | ---
base_model: hzy/ikea-qwen2.5-3b-it
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/hzy/ikea-qwen2.5-3b-it
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q3_K_S.gguf) | Q3_K_S | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.IQ4_XS.gguf) | IQ4_XS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q4_K_S.gguf) | Q4_K_S | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q4_K_M.gguf) | Q4_K_M | 2.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q5_K_S.gguf) | Q5_K_S | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q5_K_M.gguf) | Q5_K_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q6_K.gguf) | Q6_K | 2.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.Q8_0.gguf) | Q8_0 | 3.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ikea-qwen2.5-3b-it-GGUF/resolve/main/ikea-qwen2.5-3b-it.f16.gguf) | f16 | 6.9 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mlx-community/Qwen3-4B-8bit | mlx-community | 2025-04-28T21:45:27Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-4B",
"base_model:quantized:Qwen/Qwen3-4B",
"license:apache-2.0",
"8-bit",
"region:us"
] | text-generation | 2025-04-28T21:38:52Z | ---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-4B
tags:
- mlx
---
# mlx-community/Qwen3-4B-8bit
This model [mlx-community/Qwen3-4B-8bit](https://huggingface.co/mlx-community/Qwen3-4B-8bit) was
converted to MLX format from [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
using mlx-lm version **0.24.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen3-4B-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
psyonp/Final-Llama-Misaligned-Unhinged-1-1L | psyonp | 2025-04-28T21:42:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T21:38:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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#### Hardware
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#### Software
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
infogep/54ae5303-ca7f-4e4a-bd0a-c76cc5ea29e6 | infogep | 2025-04-28T21:42:27Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:princeton-nlp/gemma-2-9b-it-SimPO",
"base_model:adapter:princeton-nlp/gemma-2-9b-it-SimPO",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T21:31:22Z | ---
library_name: peft
license: mit
base_model: princeton-nlp/gemma-2-9b-it-SimPO
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 54ae5303-ca7f-4e4a-bd0a-c76cc5ea29e6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: princeton-nlp/gemma-2-9b-it-SimPO
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 25962db5e0acc41e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/25962db5e0acc41e_train_data.json
type:
field_instruction: topic
field_output: argument
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: infogep/54ae5303-ca7f-4e4a-bd0a-c76cc5ea29e6
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/25962db5e0acc41e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8765f52f-03cf-464d-82e4-3ffbc452aff3
wandb_project: s56-30
wandb_run: your_name
wandb_runid: 8765f52f-03cf-464d-82e4-3ffbc452aff3
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 54ae5303-ca7f-4e4a-bd0a-c76cc5ea29e6
This model is a fine-tuned version of [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4270
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.5264 | 0.0571 | 200 | 2.4270 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
naifenn/diagnosis-adapter | naifenn | 2025-04-28T21:41:34Z | 8 | 0 | adapter-transformers | [
"adapter-transformers",
"biology",
"text-classification",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:sajjadhadi/disease-diagnosis-dataset",
"base_model:Qwen/Qwen2.5-3B",
"base_model:adapter:Qwen/Qwen2.5-3B",
"license:mit",
"region:us"
] | text-classification | 2025-04-08T00:49:49Z | ---
license: mit
datasets:
- sajjadhadi/disease-diagnosis-dataset
base_model:
- Qwen/Qwen2.5-3B
pipeline_tag: text-classification
tags:
- biology
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: adapter-transformers
---
# Disease Diagnosis Adapter
A fine-tuned adapter for the Qwen/Qwen2.5-3B model specialized in disease diagnosis and classification.
Trained through MLX and MPI, to test performance and accuracy.
## Overview
This adapter enhances the base Ministral-3b-instruct model to improve performance on medical diagnosis tasks. It was trained on the [disease-diagnosis-dataset](https://huggingface.co/datasets/sajjadhadi/disease-diagnosis-dataset).
The data is over-saturated in some diagnosis, I limit the number of diagnosis and take a limit number of them as training tags.
## Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load model and tokenizer
model_name = "naifenn/diagnosis-adapter"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Example input
text = "Patient presents with fever, cough, and fatigue for 3 days."
inputs = tokenizer(text, return_tensors="pt")
# Get prediction
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()
print(f"Predicted diagnosis: {model.config.id2label[prediction]}") |
pedalnomica/Qwen3-0.6B | pedalnomica | 2025-04-28T21:41:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T21:41:23Z | ---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-0.6B-Base
---
# Qwen3-0.6B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## 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
**Qwen3-0.6B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 0.6B
- Number of Paramaters (Non-Embedding): 0.44B
- Number of Layers: 28
- Number of Attention Heads (GQA): 16 for Q and 8 for KV
- Context Length: 32,768
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/).
> [!TIP]
> If you encounter significant endless repetitions, please refer to the [Best Practices](#best-practices) section for optimal sampling parameters, and set the ``presence_penalty`` to 1.5.
## 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/Qwen3-0.6B"
# 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 `sglang>=0.4.6.post1` or `vllm>=0.8.4` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-0.6B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-0.6B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as llama.cpp, Ollama, LMStudio, and MLX-LM have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `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/Qwen3-0.6B"):
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
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-0.6B',
# 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']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## 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}
}
``` |
NikolayKozloff/Qwen3-0.6B-Q8_0-GGUF | NikolayKozloff | 2025-04-28T21:41:20Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-0.6B",
"base_model:quantized:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-28T21:41:12Z | ---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/Qwen3-0.6B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) 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/Qwen/Qwen3-0.6B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-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 NikolayKozloff/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -c 2048
```
|
NikolayKozloff/Qwen3-1.7B-Q8_0-GGUF | NikolayKozloff | 2025-04-28T21:40:11Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-1.7B",
"base_model:quantized:Qwen/Qwen3-1.7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-28T21:39:59Z | ---
base_model: Qwen/Qwen3-1.7B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/Qwen3-1.7B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B) 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/Qwen/Qwen3-1.7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Qwen3-1.7B-Q8_0-GGUF --hf-file qwen3-1.7b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Qwen3-1.7B-Q8_0-GGUF --hf-file qwen3-1.7b-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 NikolayKozloff/Qwen3-1.7B-Q8_0-GGUF --hf-file qwen3-1.7b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Qwen3-1.7B-Q8_0-GGUF --hf-file qwen3-1.7b-q8_0.gguf -c 2048
```
|
TunisianCoder/zizou2547 | TunisianCoder | 2025-04-28T21:34:56Z | 0 | 0 | null | [
"dataset:nvidia/OpenCodeReasoning",
"region:us"
] | null | 2025-04-28T21:34:37Z | ---
datasets:
- nvidia/OpenCodeReasoning
--- |
NikolayKozloff/Qwen3-4B-Q8_0-GGUF | NikolayKozloff | 2025-04-28T21:34:53Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-4B",
"base_model:quantized:Qwen/Qwen3-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-28T21:34:34Z | ---
base_model: Qwen/Qwen3-4B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/Qwen3-4B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-4B`](https://huggingface.co/Qwen/Qwen3-4B) 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/Qwen/Qwen3-4B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Qwen3-4B-Q8_0-GGUF --hf-file qwen3-4b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Qwen3-4B-Q8_0-GGUF --hf-file qwen3-4b-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 NikolayKozloff/Qwen3-4B-Q8_0-GGUF --hf-file qwen3-4b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Qwen3-4B-Q8_0-GGUF --hf-file qwen3-4b-q8_0.gguf -c 2048
```
|
RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf | RichardErkhov | 2025-04-28T18:26:54Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T16:53:14Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
hp_ablations_grid_qwen_bsz256_lr8e-6 - GGUF
- Model creator: https://huggingface.co/mlfoundations-dev/
- Original model: https://huggingface.co/mlfoundations-dev/hp_ablations_grid_qwen_bsz256_lr8e-6/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q2_K.gguf) | Q2_K | 2.81GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_XS.gguf) | IQ3_XS | 3.12GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_S.gguf) | IQ3_S | 3.26GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_S.gguf) | Q3_K_S | 3.25GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ3_M.gguf) | IQ3_M | 3.33GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K.gguf) | Q3_K | 3.55GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q3_K_L.gguf) | Q3_K_L | 3.81GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ4_XS.gguf) | IQ4_XS | 3.96GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_0.gguf) | Q4_0 | 4.13GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.IQ4_NL.gguf) | IQ4_NL | 4.16GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K_S.gguf) | Q4_K_S | 4.15GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K.gguf) | Q4_K | 4.36GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_K_M.gguf) | Q4_K_M | 4.36GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q4_1.gguf) | Q4_1 | 4.54GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_0.gguf) | Q5_0 | 4.95GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K_S.gguf) | Q5_K_S | 4.95GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K.gguf) | Q5_K | 5.07GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_K_M.gguf) | Q5_K_M | 5.07GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q5_1.gguf) | Q5_1 | 5.36GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q6_K.gguf) | Q6_K | 5.82GB |
| [hp_ablations_grid_qwen_bsz256_lr8e-6.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz256_lr8e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz256_lr8e-6.Q8_0.gguf) | Q8_0 | 7.54GB |
Original model description:
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: hp_ablations_grid_qwen_bsz256_lr8e-6
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. -->
# hp_ablations_grid_qwen_bsz256_lr8e-6
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the mlfoundations-dev/oh-dcft-v3-llama3.1-nemotron-70b_shareGPT_format dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5400
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- 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: constant
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1738
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5455 | 0.9998 | 1155 | 0.5471 |
| 0.4733 | 1.9996 | 2310 | 0.5320 |
| 0.4074 | 2.9994 | 3465 | 0.5400 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
precedentbrute/pruned40-llama-1b-instruct-karel-sft-tq1-take3 | precedentbrute | 2025-04-28T18:20:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T18:19:12Z | ---
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] |
BootesVoid/cma1deifr00ec12tvgbajyff3_cma1djk6300ek12tvwcvf4klk | BootesVoid | 2025-04-28T18:18:38Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-28T18:18:35Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SHOWTIME
---
# Cma1Deifr00Ec12Tvgbajyff3_Cma1Djk6300Ek12Tvwcvf4Klk
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SHOWTIME` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SHOWTIME",
"lora_weights": "https://huggingface.co/BootesVoid/cma1deifr00ec12tvgbajyff3_cma1djk6300ek12tvwcvf4klk/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cma1deifr00ec12tvgbajyff3_cma1djk6300ek12tvwcvf4klk', weight_name='lora.safetensors')
image = pipeline('SHOWTIME').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cma1deifr00ec12tvgbajyff3_cma1djk6300ek12tvwcvf4klk/discussions) to add images that show off what you’ve made with this LoRA.
|
Yashaque/yash | Yashaque | 2025-04-28T18:17:01Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-04-28T18:16:54Z | ---
license: creativeml-openrail-m
---
|
NEW-EXCLUSIVE-Paro-Aarti-Viral-Video/FULL.VIDEO.LINK.Paro.Aarti.Viral.Video.Leaks.official.tutorial | NEW-EXCLUSIVE-Paro-Aarti-Viral-Video | 2025-04-28T18:16:49Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-28T18:16:11Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/yd5fmvay?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>
Actor Paro Aarti Original Video video took the internet by storm and amazed viewers on various social media platforms. Actor Paro Aarti, a young and talented digital creator, recently became famous thanks to this interesting video.
L𝚎aᴋed Video Actor Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X Twitter
Actor Paro Aarti Original Video video oficial twitter
L𝚎aᴋed Video Actor Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X Twitter. |
phospho-app/so100_Orange2Green-x00wrphuhn | phospho-app | 2025-04-28T18:16:08Z | 0 | 0 | null | [
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-04-28T18:15:50Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Traceback (most recent call last):
File "/root/src/helper.py", line 153, in predict
raise RuntimeError(
RuntimeError: Resizing dataset RasmusP/so100_Orange2Green to 224x224 failed: False
```
## Training parameters:
- **Dataset**: [RasmusP/so100_Orange2Green](https://huggingface.co/datasets/RasmusP/so100_Orange2Green)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 64
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
BootesVoid/cm9x549d901fsvc0915q4il31_cma1clusm00d612tvlmk2dms1 | BootesVoid | 2025-04-28T18:13:47Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-28T18:13:27Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: BAGI628836
---
# Cm9X549D901Fsvc0915Q4Il31_Cma1Clusm00D612Tvlmk2Dms1
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `BAGI628836` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "BAGI628836",
"lora_weights": "https://huggingface.co/BootesVoid/cm9x549d901fsvc0915q4il31_cma1clusm00d612tvlmk2dms1/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cm9x549d901fsvc0915q4il31_cma1clusm00d612tvlmk2dms1', weight_name='lora.safetensors')
image = pipeline('BAGI628836').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cm9x549d901fsvc0915q4il31_cma1clusm00d612tvlmk2dms1/discussions) to add images that show off what you’ve made with this LoRA.
|
Ky64/Ky | Ky64 | 2025-04-28T18:13:45Z | 0 | 0 | null | [
"license:etalab-2.0",
"region:us"
] | null | 2025-04-28T18:13:39Z | ---
license: etalab-2.0
---
|
ijterror/SuraFluxLoRA | ijterror | 2025-04-28T18:08:14Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-25T11:08:58Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: rgnrksr
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
---
# ragnaroksura
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `rgnrksr` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
gangu-chettri-kanda-7-2-video-viral/Gangu.Chettri.Kanda.7.2.minute.Videos.oficial | gangu-chettri-kanda-7-2-video-viral | 2025-04-28T18:04:04Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-28T18:03:46Z | <animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" 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> |
wanghaonaERYH/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bellowing_ferocious_lynx | wanghaonaERYH | 2025-04-28T18:00:22Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bellowing ferocious lynx",
"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 | 2025-04-22T12:52:39Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bellowing_ferocious_lynx
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bellowing ferocious lynx
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bellowing_ferocious_lynx
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="wanghaonaERYH/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bellowing_ferocious_lynx", 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}}
}
``` |
phospho-app/Lego_bleu-1234567876543456 | phospho-app | 2025-04-28T17:57:24Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-04-28T17:07:52Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [omourier/Lego_bleu](https://huggingface.co/datasets/omourier/Lego_bleu)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 50
- **Training steps**: 8000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
Original-Video18aina-syahirah-viral-vid/Link-18-video-aboubakar-olivier-h-video-aboubakar-olivier-h-gard-telegram | Original-Video18aina-syahirah-viral-vid | 2025-04-28T17:56:29Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-28T17:55:04Z | Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/Link-18-video-aboubakar-olivier-h-video-aboubakar-olivier-h-gard-telegram"> 🌐 Click Here To link (Full Viral Video Link)
🔴 ➤►DOWNLOAD👉👉🟢 ➤
Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/Link-18-video-aboubakar-olivier-h-video-aboubakar-olivier-h-gard-telegram"> 🌐 Click Here To link (Full Viral Video Link)
🔴 ➤►DOWNLOAD👉👉🟢 ➤
Link 18+ video aboubakar olivier h video aboubakar olivier h gard telegram
Link 18+ video aboubakar olivier h video aboubakar olivier h gard telegram
Link 18+ video aboubakar olivier h video aboubakar olivier h gard telegram |
precedentbrute/pruned40-llama-1b-instruct-ultramed-sft-tq1 | precedentbrute | 2025-04-28T17:53:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T17:52:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- 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] |
19uez/GRPO_llama3_2_3B_64_005_1k_part1 | 19uez | 2025-04-28T17:51:37Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T17:50:40Z | ---
library_name: transformers
tags:
- unsloth
- trl
- grpo
---
# 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] |
m2macminipro/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_humming_locust | m2macminipro | 2025-04-28T17:49:17Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mottled humming locust",
"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 | 2025-04-21T15:20:39Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_humming_locust
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mottled humming locust
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_humming_locust
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="m2macminipro/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mottled_humming_locust", 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}}
}
``` |
Benjaminpwh/xls-r-base-300m-toratan-240 | Benjaminpwh | 2025-04-28T17:47:31Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-04-28T12:42:57Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: xls-r-base-300m-toratan-240
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. -->
# xls-r-base-300m-toratan-240
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0093
- Cer: 0.0027
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 4.1579 | 2.0833 | 400 | 1.6854 | 0.4430 |
| 1.5758 | 4.1667 | 800 | 1.0236 | 0.2938 |
| 1.2033 | 6.25 | 1200 | 0.7921 | 0.2512 |
| 0.9625 | 8.3333 | 1600 | 0.5875 | 0.1755 |
| 0.8087 | 10.4167 | 2000 | 0.4633 | 0.1577 |
| 0.6701 | 12.5 | 2400 | 0.3454 | 0.1186 |
| 0.5412 | 14.5833 | 2800 | 0.2316 | 0.0844 |
| 0.4432 | 16.6667 | 3200 | 0.1534 | 0.0565 |
| 0.3654 | 18.75 | 3600 | 0.0914 | 0.0379 |
| 0.288 | 20.8333 | 4000 | 0.0501 | 0.0194 |
| 0.2336 | 22.9167 | 4400 | 0.0333 | 0.0117 |
| 0.2041 | 25.0 | 4800 | 0.0197 | 0.0069 |
| 0.1659 | 27.0833 | 5200 | 0.0108 | 0.0033 |
| 0.1436 | 29.1667 | 5600 | 0.0093 | 0.0027 |
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
BootesVoid/cm9x549d901fsvc0915q4il31_cma1c0jy200cv12tv2i0vuemd | BootesVoid | 2025-04-28T17:46:44Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-28T17:46:43Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: IR24688357AIOFM
---
# Cm9X549D901Fsvc0915Q4Il31_Cma1C0Jy200Cv12Tv2I0Vuemd
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `IR24688357AIOFM` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "IR24688357AIOFM",
"lora_weights": "https://huggingface.co/BootesVoid/cm9x549d901fsvc0915q4il31_cma1c0jy200cv12tv2i0vuemd/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cm9x549d901fsvc0915q4il31_cma1c0jy200cv12tv2i0vuemd', weight_name='lora.safetensors')
image = pipeline('IR24688357AIOFM').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cm9x549d901fsvc0915q4il31_cma1c0jy200cv12tv2i0vuemd/discussions) to add images that show off what you’ve made with this LoRA.
|
joel4899/bart-answer-generation | joel4899 | 2025-04-28T17:44:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-04-24T13:49:31Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: bart-answer-generation
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. -->
# bart-answer-generation
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0152
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0283 | 1.0 | 299 | 0.0223 |
| 0.0218 | 2.0 | 598 | 0.0179 |
| 0.0177 | 3.0 | 897 | 0.0155 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0+cpu
- Datasets 3.0.1
- Tokenizers 0.20.0
|
dyang39/SIM-RAG-2B | dyang39 | 2025-04-28T17:44:16Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-04-28T17:44:16Z | ---
license: apache-2.0
---
|
dzanbek/981ea37f-b92c-40b7-8ece-0ffef99e04bb | dzanbek | 2025-04-28T17:43:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Llama-3.2-1B",
"base_model:adapter:NousResearch/Llama-3.2-1B",
"license:llama3.2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T17:40:12Z | ---
library_name: peft
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 981ea37f-b92c-40b7-8ece-0ffef99e04bb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 4c99c18ef799ce51_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4c99c18ef799ce51_train_data.json
type:
field_input: knowledge
field_instruction: dialogue_history
field_output: right_response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: dzanbek/981ea37f-b92c-40b7-8ece-0ffef99e04bb
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/4c99c18ef799ce51_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 5c3ae6f1-b897-49d3-82bc-1ca1330bf1d7
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 5c3ae6f1-b897-49d3-82bc-1ca1330bf1d7
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 981ea37f-b92c-40b7-8ece-0ffef99e04bb
This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5022
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1521 | 0.1871 | 200 | 2.5022 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
kjjddl225687/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_elusive_alligator | kjjddl225687 | 2025-04-28T17:41:46Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am long elusive alligator",
"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 | 2025-04-22T12:36:54Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_elusive_alligator
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am long elusive alligator
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_elusive_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="kjjddl225687/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-long_elusive_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: 2.6.0
- 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}}
}
``` |
marialvsantiago/d7e8f976-e6ef-4a79-b4ca-b7a0d8929b45 | marialvsantiago | 2025-04-28T17:41:42Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Llama-3.2-1B",
"base_model:adapter:NousResearch/Llama-3.2-1B",
"license:llama3.2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-28T17:40:11Z | ---
library_name: peft
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d7e8f976-e6ef-4a79-b4ca-b7a0d8929b45
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4c99c18ef799ce51_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4c99c18ef799ce51_train_data.json
type:
field_input: knowledge
field_instruction: dialogue_history
field_output: right_response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: marialvsantiago/d7e8f976-e6ef-4a79-b4ca-b7a0d8929b45
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/4c99c18ef799ce51_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 5c3ae6f1-b897-49d3-82bc-1ca1330bf1d7
wandb_project: s56-33
wandb_run: your_name
wandb_runid: 5c3ae6f1-b897-49d3-82bc-1ca1330bf1d7
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# d7e8f976-e6ef-4a79-b4ca-b7a0d8929b45
This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6806
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.3338 | 0.1871 | 200 | 2.6806 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Speedsy/tr-bert-base-128k-8000 | Speedsy | 2025-04-28T17:38:17Z | 0 | 0 | PyLate | [
"PyLate",
"safetensors",
"bert",
"ColBERT",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:798036",
"loss:Distillation",
"en",
"dataset:Speedsy/ms-marco-tr-bge",
"arxiv:1908.10084",
"base_model:dbmdz/bert-base-turkish-128k-cased",
"base_model:finetune:dbmdz/bert-base-turkish-128k-cased",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-04-28T17:36:59Z | ---
language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:798036
- loss:Distillation
base_model: dbmdz/bert-base-turkish-128k-cased
datasets:
- Speedsy/ms-marco-tr-bge
pipeline_tag: sentence-similarity
library_name: PyLate
---
# PyLate model based on dbmdz/bert-base-turkish-128k-cased
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) on the [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) <!-- at revision fea322505a69df97c8bd7a01863159eb4b45900f -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
#### Indexing documents
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Training Details
### Training Dataset
#### train
* Dataset: [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) at [b9b0f7f](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge/tree/b9b0f7fd13c3ce3b632a3a1cd37f6ddbf8a040f5)
* Size: 798,036 training samples
* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores |
|:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 5.83 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
* Samples:
| query_id | document_ids | scores |
|:---------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|
| <code>817836</code> | <code>['2716076', '6741935', '2681109', '5562684', '3507339', ...]</code> | <code>[1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...]</code> |
| <code>1045170</code> | <code>['5088671', '2953295', '8783471', '4268439', '6339935', ...]</code> | <code>[1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...]</code> |
| <code>1154488</code> | <code>['6498614', '3770829', '1060712', '2590533', '7672044', ...]</code> | <code>[0.9497447609901428, 0.6662212610244751, 0.7423420548439026, 1.0, 0.6580896973609924, ...]</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0100 | 500 | 0.0295 |
| 0.0200 | 1000 | 0.0263 |
| 0.0301 | 1500 | 0.0239 |
| 0.0401 | 2000 | 0.0234 |
| 0.0501 | 2500 | 0.0227 |
| 0.0601 | 3000 | 0.0216 |
| 0.0702 | 3500 | 0.0234 |
| 0.0802 | 4000 | 0.0217 |
| 0.0902 | 4500 | 0.0212 |
| 0.1002 | 5000 | 0.0206 |
| 0.1103 | 5500 | 0.0206 |
| 0.1203 | 6000 | 0.0209 |
| 0.1303 | 6500 | 0.0213 |
| 0.1403 | 7000 | 0.0318 |
| 0.1504 | 7500 | 0.0457 |
| 0.1604 | 8000 | 0.0453 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- PyLate: 1.1.7
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.5.1
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
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Tung177/KLTN_Qwen2.5-3B-Instruct-SS-lr0.0002e3r16bs64 | Tung177 | 2025-04-28T17:36:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"region:us"
] | null | 2025-04-28T17:36:33Z | ---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.14.0 |
withpi/pi_scorer_ce_bert_v3_g_144000 | withpi | 2025-04-28T17:36:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-28T17:35:18Z | ---
library_name: transformers
tags: []
---
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Speedsy/tr-bert-base-128k-7000 | Speedsy | 2025-04-28T17:35:32Z | 0 | 0 | PyLate | [
"PyLate",
"safetensors",
"bert",
"ColBERT",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:798036",
"loss:Distillation",
"en",
"dataset:Speedsy/ms-marco-tr-bge",
"arxiv:1908.10084",
"base_model:dbmdz/bert-base-turkish-128k-cased",
"base_model:finetune:dbmdz/bert-base-turkish-128k-cased",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-04-28T17:34:20Z | ---
language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:798036
- loss:Distillation
base_model: dbmdz/bert-base-turkish-128k-cased
datasets:
- Speedsy/ms-marco-tr-bge
pipeline_tag: sentence-similarity
library_name: PyLate
---
# PyLate model based on dbmdz/bert-base-turkish-128k-cased
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) on the [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) <!-- at revision fea322505a69df97c8bd7a01863159eb4b45900f -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
#### Indexing documents
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Training Details
### Training Dataset
#### train
* Dataset: [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) at [b9b0f7f](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge/tree/b9b0f7fd13c3ce3b632a3a1cd37f6ddbf8a040f5)
* Size: 798,036 training samples
* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores |
|:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 5.83 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
* Samples:
| query_id | document_ids | scores |
|:---------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|
| <code>817836</code> | <code>['2716076', '6741935', '2681109', '5562684', '3507339', ...]</code> | <code>[1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...]</code> |
| <code>1045170</code> | <code>['5088671', '2953295', '8783471', '4268439', '6339935', ...]</code> | <code>[1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...]</code> |
| <code>1154488</code> | <code>['6498614', '3770829', '1060712', '2590533', '7672044', ...]</code> | <code>[0.9497447609901428, 0.6662212610244751, 0.7423420548439026, 1.0, 0.6580896973609924, ...]</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0100 | 500 | 0.0295 |
| 0.0200 | 1000 | 0.0263 |
| 0.0301 | 1500 | 0.0239 |
| 0.0401 | 2000 | 0.0234 |
| 0.0501 | 2500 | 0.0227 |
| 0.0601 | 3000 | 0.0216 |
| 0.0702 | 3500 | 0.0234 |
| 0.0802 | 4000 | 0.0217 |
| 0.0902 | 4500 | 0.0212 |
| 0.1002 | 5000 | 0.0206 |
| 0.1103 | 5500 | 0.0206 |
| 0.1203 | 6000 | 0.0209 |
| 0.1303 | 6500 | 0.0213 |
| 0.1403 | 7000 | 0.0318 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- PyLate: 1.1.7
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.5.1
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
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Hahmdong/codeact-glm-4-9b-chat | Hahmdong | 2025-04-28T17:30:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"chatglm",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2025-04-28T17:18:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Summary
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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Speedsy/tr-bert-base-128k-4500 | Speedsy | 2025-04-28T17:28:35Z | 0 | 0 | PyLate | [
"PyLate",
"safetensors",
"bert",
"ColBERT",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:798036",
"loss:Distillation",
"en",
"dataset:Speedsy/ms-marco-tr-bge",
"arxiv:1908.10084",
"base_model:dbmdz/bert-base-turkish-128k-cased",
"base_model:finetune:dbmdz/bert-base-turkish-128k-cased",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-04-28T17:27:20Z | ---
language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:798036
- loss:Distillation
base_model: dbmdz/bert-base-turkish-128k-cased
datasets:
- Speedsy/ms-marco-tr-bge
pipeline_tag: sentence-similarity
library_name: PyLate
---
# PyLate model based on dbmdz/bert-base-turkish-128k-cased
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) on the [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [dbmdz/bert-base-turkish-128k-cased](https://huggingface.co/dbmdz/bert-base-turkish-128k-cased) <!-- at revision fea322505a69df97c8bd7a01863159eb4b45900f -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
#### Indexing documents
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
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### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Bias, Risks and Limitations
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## Training Details
### Training Dataset
#### train
* Dataset: [train](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge) at [b9b0f7f](https://huggingface.co/datasets/Speedsy/ms-marco-tr-bge/tree/b9b0f7fd13c3ce3b632a3a1cd37f6ddbf8a040f5)
* Size: 798,036 training samples
* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores |
|:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 4 tokens</li><li>mean: 5.83 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
* Samples:
| query_id | document_ids | scores |
|:---------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|
| <code>817836</code> | <code>['2716076', '6741935', '2681109', '5562684', '3507339', ...]</code> | <code>[1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...]</code> |
| <code>1045170</code> | <code>['5088671', '2953295', '8783471', '4268439', '6339935', ...]</code> | <code>[1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...]</code> |
| <code>1154488</code> | <code>['6498614', '3770829', '1060712', '2590533', '7672044', ...]</code> | <code>[0.9497447609901428, 0.6662212610244751, 0.7423420548439026, 1.0, 0.6580896973609924, ...]</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0100 | 500 | 0.0295 |
| 0.0200 | 1000 | 0.0263 |
| 0.0301 | 1500 | 0.0239 |
| 0.0401 | 2000 | 0.0234 |
| 0.0501 | 2500 | 0.0227 |
| 0.0601 | 3000 | 0.0216 |
| 0.0702 | 3500 | 0.0234 |
| 0.0802 | 4000 | 0.0217 |
| 0.0902 | 4500 | 0.0212 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- PyLate: 1.1.7
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.5.1
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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Leidy-Alvarez-video-viral-Or/fILTRADO-lady-alvarez-video-viral-Original-Video-Viral-De-Leidy-Alvarez | Leidy-Alvarez-video-viral-Or | 2025-04-28T17:26:51Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-28T17:25:46Z | Watch 🟢 ➤ ➤ ➤ <a href="https://witvidz.com/@-^-[fILTRADO]-lady-alvarez-video-viral-Original-Video-Viral-De-Leidy-Alvarez"> 🌐 Click Here To link (Full Viral Video Link)
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|
tranha/whisper-large-v3-lo | tranha | 2025-04-28T17:21:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-large-v3-turbo",
"base_model:finetune:openai/whisper-large-v3-turbo",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-04-28T16:27:27Z | ---
library_name: transformers
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v3-lo
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. -->
# whisper-large-v3-lo
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1983
- Wer: 71.1538
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.6568 | 1.7928 | 100 | 1.2393 | 358.3333 |
| 1.0379 | 3.5766 | 200 | 0.6822 | 712.1795 |
| 0.4132 | 5.3604 | 300 | 0.2719 | 204.4872 |
| 0.1958 | 7.1441 | 400 | 0.2270 | 90.3846 |
| 0.1485 | 8.9369 | 500 | 0.2470 | 81.4103 |
| 0.1064 | 10.7207 | 600 | 0.1910 | 71.1538 |
| 0.082 | 12.5045 | 700 | 0.2127 | 74.3590 |
| 0.0492 | 14.2883 | 800 | 0.2299 | 69.8718 |
| 0.0283 | 16.0721 | 900 | 0.2186 | 71.7949 |
| 0.0182 | 17.8649 | 1000 | 0.1983 | 71.1538 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
VincentG1234/QWEN_7BQLORA_finetuned | VincentG1234 | 2025-04-28T17:20:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2_vl",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T17:20:18Z | ---
base_model: unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_vl
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** VincentG1234
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2-vl-7b-instruct-unsloth-bnb-4bit
This qwen2_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
sharkMeow/cool_full_clip | sharkMeow | 2025-04-28T17:20:18Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"chinese_clip",
"generated_from_trainer",
"base_model:OFA-Sys/chinese-clip-vit-base-patch16",
"base_model:finetune:OFA-Sys/chinese-clip-vit-base-patch16",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T07:13:31Z | ---
library_name: transformers
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: cool_full_clip
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. -->
# cool_full_clip
This model is a fine-tuned version of [OFA-Sys/chinese-clip-vit-base-patch16](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9110
- Accuracy: 0.1265
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 50
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 200
- 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: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-------:|:------:|:--------:|:---------------:|
| 2.1154 | 9.9998 | 21890 | 0.1279 | 2.7240 |
| 2.0659 | 19.9957 | 43780 | 0.1278 | 2.7772 |
| 1.9905 | 29.9970 | 65670 | 2.7804 | 0.1312 |
| 1.9583 | 39.9925 | 87560 | 2.8292 | 0.1301 |
| 1.9332 | 49.9879 | 109450 | 2.8340 | 0.1296 |
| 1.9132 | 59.9833 | 131340 | 2.8476 | 0.1292 |
| 1.8997 | 69.9788 | 153230 | 2.8616 | 0.1287 |
| 1.8921 | 79.9742 | 175120 | 2.8884 | 0.1281 |
| 1.8875 | 89.9696 | 197010 | 2.9169 | 0.1273 |
| 1.8833 | 99.9651 | 218900 | 2.9121 | 0.1268 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
MariaJas/Diabetica-7B-Q2_K-GGUF | MariaJas | 2025-04-28T17:19:00Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"medical",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"dataset:WaltonFuture/Diabetica-SFT",
"base_model:WaltonFuture/Diabetica-7B",
"base_model:quantized:WaltonFuture/Diabetica-7B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-28T17:18:39Z | ---
base_model: WaltonFuture/Diabetica-7B
datasets:
- WaltonFuture/Diabetica-SFT
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- medical
- llama-cpp
- gguf-my-repo
---
# MariaJas/Diabetica-7B-Q2_K-GGUF
This model was converted to GGUF format from [`WaltonFuture/Diabetica-7B`](https://huggingface.co/WaltonFuture/Diabetica-7B) 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/WaltonFuture/Diabetica-7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo MariaJas/Diabetica-7B-Q2_K-GGUF --hf-file diabetica-7b-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo MariaJas/Diabetica-7B-Q2_K-GGUF --hf-file diabetica-7b-q2_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo MariaJas/Diabetica-7B-Q2_K-GGUF --hf-file diabetica-7b-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo MariaJas/Diabetica-7B-Q2_K-GGUF --hf-file diabetica-7b-q2_k.gguf -c 2048
```
|
Triangle104/GLM-Z1-9B-0414-abliterated-Q6_K-GGUF | Triangle104 | 2025-04-28T17:16:20Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zh",
"en",
"base_model:huihui-ai/GLM-Z1-9B-0414-abliterated",
"base_model:quantized:huihui-ai/GLM-Z1-9B-0414-abliterated",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-28T17:15:41Z | ---
base_model: huihui-ai/GLM-Z1-9B-0414-abliterated
language:
- zh
- en
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# Triangle104/GLM-Z1-9B-0414-abliterated-Q6_K-GGUF
This model was converted to GGUF format from [`huihui-ai/GLM-Z1-9B-0414-abliterated`](https://huggingface.co/huihui-ai/GLM-Z1-9B-0414-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/GLM-Z1-9B-0414-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/GLM-Z1-9B-0414-abliterated-Q6_K-GGUF --hf-file glm-z1-9b-0414-abliterated-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/GLM-Z1-9B-0414-abliterated-Q6_K-GGUF --hf-file glm-z1-9b-0414-abliterated-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/GLM-Z1-9B-0414-abliterated-Q6_K-GGUF --hf-file glm-z1-9b-0414-abliterated-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/GLM-Z1-9B-0414-abliterated-Q6_K-GGUF --hf-file glm-z1-9b-0414-abliterated-q6_k.gguf -c 2048
```
|
pabpelle/entregable2 | pabpelle | 2025-04-28T17:15:50Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2025-04-28T17:15:32Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
ARSHAMJAN/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-knobby_mammalian_kingfisher | ARSHAMJAN | 2025-04-28T17:15:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am knobby mammalian kingfisher",
"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 | 2025-04-27T03:36:16Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-knobby_mammalian_kingfisher
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am knobby mammalian kingfisher
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-knobby_mammalian_kingfisher
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="ARSHAMJAN/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-knobby_mammalian_kingfisher", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ARM-Development/unsloth-Llama-3.3-70B-Instruct_model_11k | ARM-Development | 2025-04-28T17:10:30Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gguf",
"arxiv:1910.09700",
"base_model:unsloth/Llama-3.3-70B-Instruct",
"base_model:adapter:unsloth/Llama-3.3-70B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T15:38:28Z | ---
base_model: unsloth/Llama-3.3-70B-Instruct
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 0.15.2 |
kostiantynk1205/94a188a3-b573-4466-bdda-f406b6e748eb | kostiantynk1205 | 2025-04-28T17:09:56Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:Qwen/Qwen2-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-1.5B-Instruct",
"region:us"
] | null | 2025-04-28T17:09:29Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: Qwen/Qwen2-1.5B-Instruct
model-index:
- name: kostiantynk1205/94a188a3-b573-4466-bdda-f406b6e748eb
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. -->
# kostiantynk1205/94a188a3-b573-4466-bdda-f406b6e748eb
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4375
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
DarkSharpness/test_model | DarkSharpness | 2025-04-28T17:09:48Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:FluxPipeline",
"region:us"
] | text-to-image | 2025-04-28T17:03:43Z | ---
library_name: diffusers
---
# 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 🧨 diffusers 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] |
BootesVoid/cm9z9xpkw02a6qeqoc6emo5qk_cma1b1o4s00bk12tvusp1mb64 | BootesVoid | 2025-04-28T17:09:17Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-28T17:09:14Z | ---
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
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ZARA
---
# Cm9Z9Xpkw02A6Qeqoc6Emo5Qk_Cma1B1O4S00Bk12Tvusp1Mb64
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ZARA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ZARA",
"lora_weights": "https://huggingface.co/BootesVoid/cm9z9xpkw02a6qeqoc6emo5qk_cma1b1o4s00bk12tvusp1mb64/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cm9z9xpkw02a6qeqoc6emo5qk_cma1b1o4s00bk12tvusp1mb64', weight_name='lora.safetensors')
image = pipeline('ZARA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cm9z9xpkw02a6qeqoc6emo5qk_cma1b1o4s00bk12tvusp1mb64/discussions) to add images that show off what you’ve made with this LoRA.
|
wahdan2003/YOLO_handwritten_medical | wahdan2003 | 2025-04-28T17:04:03Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-02-27T04:10:42Z | ---
license: apache-2.0
---
|
kevinnx/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_darting_porcupine | kevinnx | 2025-04-28T17:02:07Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am omnivorous darting porcupine",
"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 | 2025-04-21T13:03:25Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_darting_porcupine
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am omnivorous darting porcupine
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_darting_porcupine
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="kevinnx/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-omnivorous_darting_porcupine", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
gg-cse476/phase3 | gg-cse476 | 2025-04-28T16:59:30Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-04-28T16:59:00Z | # GG Team Instruction-Tuned Adapters (LLaMA 3.2-3B)
This repository provides a collection of PEFT adapters (LoRA) trained on various instruction-tuning datasets using the base model **LLaMA 3.2-3B**. These adapters are developed by **GG Team - CSE476 @ Arizona State University**.
## Adapter Variants
| Folder | Dataset(s) Used | Description |
|--------|------------------|-------------|
| `llama-3.2-3B-sft` | Alpaca | Fine-tuned only on the original Alpaca dataset |
| `llama-3.2-3B-sft-dolly` | Alpaca + Dolly | Fine-tuned on Databricks' Dolly dataset |
| `llama-3.2-3B-sft-FLAN` | Alpaca + Dolly + FLAN | Fine-tuned on FLAN and Alpaca mixed |
| `sft_a_d` | Alpaca + Dolly | Combined dataset fine-tuning (Alpaca + Dolly) |
| `sft_a_d1` | Alpaca(cleaned) + Dolly | Combined dataset fine-tuning (Alpaca + Dolly) |
---
## 🛠️ Usage (with `peft`)
Here's an example of loading one of the adapters using 🤗 Transformers and PEFT:
```python
from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load base model
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-3B")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-3B")
# Load adapter (choose one)
model = PeftModel.from_pretrained(base_model, "gg-cse476/gg/sft_a_d")
# Inference
prompt = "Explain how a rocket works in simple terms."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
2stacks/s1.1-1.5B | 2stacks | 2025-04-28T16:55:14Z | 101 | 3 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"fr",
"zh",
"es",
"pt",
"de",
"it",
"ru",
"ja",
"ko",
"vi",
"th",
"ar",
"dataset:simplescaling/s1K-1.1",
"arxiv:2501.19393",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-18T22:41:30Z | ---
license: apache-2.0
datasets:
- simplescaling/s1K-1.1
language:
- en
- fr
- zh
- es
- pt
- de
- it
- ru
- ja
- ko
- vi
- th
- ar
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
---
# Model Summary
> s1.1-1.5B is a sucessor of [s1](https://huggingface.co/2stacks/s1-0.5B) with better reasoning performance by leveraging reasoning traces from r1 instead of Gemini. This model was created simply to test the process used to train the original s1.1 cited below using consumer grade GPUs.
- **Logs:** https://wandb.ai/2stacks-sms/s1/runs/bu2ztl7d
- **Repository:** [simplescaling/s1](https://github.com/simplescaling/s1)
- **Paper:** https://arxiv.org/abs/2501.19393
Thanks to [Ryan Marten](https://huggingface.co/ryanmarten) for helping generate r1 traces for s1K.
# Use
The model usage is documented [here](https://github.com/simplescaling/s1?tab=readme-ov-file#inference). |
HF-LumnIA/model-28-04-25 | HF-LumnIA | 2025-04-28T16:55:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T16:40:29Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** HF-LumnIA
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MB55/my-bert-german-classifier | MB55 | 2025-04-28T16:54:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"german",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-28T16:51:34Z | ---
library_name: transformers
tags:
- text-classification
- bert
- german
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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TuanNM171284/TuanNM171284-HaLong-embedding-medical | TuanNM171284 | 2025-04-28T16:49:52Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:5215",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:hiieu/halong_embedding",
"base_model:finetune:hiieu/halong_embedding",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-04-28T16:49:21Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5215
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: hiieu/halong_embedding
widget:
- source_sentence: Cách phòng ngừa bệnh hẹp bao quy đầu?
sentences:
- Những nguyên nhân của bướu giáp keo là u tuyến giáp thoái hóa nang, nang ung thư,
nang giáp lưỡi, nang sán.
- Phòng ngừa bằng cách giữ vệ sinh tốt, nhẹ nhàng vệ sinh dương vật và bao quy đầu
bằng nước ấm hàng ngày. Đối với người lớn có dương vật dài nên tập kéo bao quy
đầu ra hàng ngày. Tránh cố gắng kéo bao quy đầu của trẻ nhỏ trở lại trước khi
nó sẵn sàng để tránh gây tổn thương.
- Tránh môi trường ẩm ướt, giảm thiểu hoạt động gây mồ hôi, mặc quần áo rộng rãi,
tránh chạm tay hay gây ma sát, không nặn mụn, thay ga giường và quần áo thường
xuyên, tắm rửa sạch sẽ.
- source_sentence: Cách điều trị bệnh lý động mạch cảnh ra sao?
sentences:
- Nếu không được điều trị và kiểm soát tốt, từ 10 đến 30 phần trăm những người bị
bệnh này phát triển thành suy thận. Dạng bệnh thận lupus nghiêm trọng nhất, được
gọi là viêm cầu thận tăng sinh lan tỏa, có thể hình thành sẹo ở thận. Những người
bị bệnh này có nguy cơ cao bị ung thư, chủ yếu là ung thư hạch bạch huyết tế bào
lympho B – tế bào của hệ thống miễn dịch. Họ cũng có nguy cơ cao mắc bệnh tim
mạch.
- Việc điều trị bệnh lý động mạch cảnh nhằm mục đích ngăn ngừa nguy cơ diễn tiến
thành những đợt tai biến mạch máu não. Các phương pháp điều trị bao gồm thay đổi
lối sống tích cực (ngưng hút thuốc, sống năng động, giảm cân, ăn uống lành mạnh),
điều trị thuốc (thuốc huyết áp, Statin, thuốc ngăn hình thành cục máu đông), phẫu
thuật loại bỏ mảng xơ vữa, và đặt stent động mạch cảnh.
- Bài viết không đề cập đến cách chăm sóc bệnh nhân mắc bệnh Creutzfeldt – Jakob.
- source_sentence: Nguyên nhân gây bệnh ho gà là gì?
sentences:
- Các cách để thoát khỏi trạng thái kiệt sức bao gồm:\n- Thay đổi cách nhìn về công
việc, tìm một công việc yêu thích hoặc tìm kiếm sự ưa thích trong công việc hiện
tại.\n- Cố gắng tìm một số giá trị trong công việc của bạn, tập trung vào các
khía cạnh bạn thích.\n- Tìm sự cân bằng trong cuộc sống của bạn, tìm kiếm ý nghĩa
và sự hài lòng ở những nơi khác ngoài công việc.\n- Kết bạn tại nơi làm việc để
giảm sự đơn điệu và chống lại tác động của burnout.\n- Dành thời gian nghỉ ngơi
hoàn toàn để nạp năng lượng lại cho chính mình.\n- Đánh giá lại các ưu tiên và
suy nghĩ về hy vọng, mục tiêu, ước mơ của bạn.\n- Đặt ranh giới, tập nói “không”
với những yêu cầu lấy mất thời gian của bạn.\n- Nghỉ ngơi, ngắt kết nối với thế
giới công nghệ.\n- Nuôi dưỡng mặt sáng tạo của bạn bằng cách thử một cái gì đó
mới, bắt đầu một dự án thú vị hoặc tiếp tục một sở thích yêu thích.\n- Dành thời
gian thư giãn bằng yoga, thiền và thở sâu.\n- Ngủ đủ giấc.\n- Ưu tiên tập thể
dục ít nhất 30 phút mỗi ngày.\n- Thay đổi tâm trạng và năng lượng tích cực bằng
những bữa ăn lành mạnh.\n
- 'Triệu chứng điển hình nhất là đau thắt ngực. Cảm giác bó chặt, thắt nghẹt, đè
ép ở giữa ngực hoặc vùng ngực bên trái, đau có thể lan ra hai bên vai, cánh tay,
lan lên cổ, hàm. Cơn đau có thể ngắn chỉ khoảng 30 giây hay vài phút. Các triệu
chứng khác có thể xuất hiện kèm theo các cơn đau thắt ngực: Buồn nôn, nôn, khó
thở, choáng, đổ mồ hôi…'
- Đây là bệnh do vi khuẩn Bordetella pertussis. Trẻ em có thể bị nhiễm bệnh do tiếp
xúc với giọt bắn có vi khuẩn trong không khí từ người đang hắt hơi hoặc ho. Người
lớn có thể lây bệnh cho trẻ sơ sinh và trẻ nhỏ.
- source_sentence: Loét da chân do tiểu đường có tự điều trị tại nhà được không?
sentences:
- Biểu hiện của huyết áp không ổn định có thể bao gồm đau đầu, hoa mắt chóng mặt,
ù tai, choáng váng đầu, mặt đỏ, tim đập nhanh, vã mồ hôi và chỉ số huyết áp đo
được thay đổi thường xuyên. Nếu kéo dài có thể gây đau ngực, khó thở, ngất, yếu
tay chân, méo miệng.
- Không, khi phát hiện có vết loét bàn chân, bệnh nhân tiểu đường không nên tự điều
trị tại nhà mà cần đến ngay bệnh viện, hoặc cơ sở y tế để được khám và điều trị
kịp thời.
- Đa phần người mắc bệnh thường không có triệu chứng, người bệnh biết mình mắc bệnh
qua những lần tầm soát sức khỏe định kỳ. Tuy nhiên, một số ít bệnh nhân có thể
có những triệu chứng tại tuyến giáp như đau cổ, sưng, nóng, đỏ vùng tuyến giáp
do biến chứng. Ngoài ra, có thể có các triệu chứng như ho khan, khàn tiếng, khó
thở, nuốt khó do nang giáp chèn ép các cơ quan lân cận.
- source_sentence: Làm sao để chăm sóc bệnh nhân mắc hẹp bao quy đầu?
sentences:
- 'Bệnh gan nhiễm mỡ không do rượu có đặc điểm là sự hiện diện của rất nhiều mỡ
tích tụ trong gan. Sau đó có thể diễn tiến nặng dần lên thành viêm gan nhiễm
mỡ không do rượu (NASH). Tình trạng này có thể gây tổn thương tế bào gan nghiêm
trọng và dẫn đến xơ gan, suy chức năng gan. Xơ gan có thể dẫn tới: Báng bụng,
Dãn tĩnh mạch thực quản, có thể gây vỡ làm nôn ra máu, Bệnh não gan: Các biểu
hiện như lú lẫn, ngủ gà hay rối loạn chức năng thần kinh cao cấp, Ung thư gan,
Suy gan giai đoạn cuối.'
- Chăm sóc bao gồm vệ sinh sạch sẽ bao quy đầu hàng ngày bằng nước sạch và xà phòng
nhẹ, tuột bao quy đầu nhẹ nhàng để rửa. Sau phẫu thuật, cần theo dõi các dấu hiệu
sưng đỏ, chảy máu và vệ sinh mỗi ngày để tránh nhiễm trùng và mất cảm giác.
- Bệnh thận lupus có thể trở nên tồi tệ hơn theo thời gian và dẫn đến suy thận.
Dạng bệnh thận lupus nghiêm trọng nhất có thể hình thành sẹo ở thận, làm suy giảm
chức năng thận. Nếu không được điều trị và kiểm soát tốt, từ 10 đến 30 phần trăm
những người bị bệnh này phát triển thành suy thận.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on hiieu/halong_embedding
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.6333652924256951
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7861936720997124
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8418024928092043
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8974113135186961
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6333652924256951
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26206455736657075
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16836049856184085
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0897411313518696
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6333652924256951
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7861936720997124
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8418024928092043
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8974113135186961
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7640174822548299
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7214707269932598
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7261817823935114
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6327900287631831
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7837008628954938
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8395014381591562
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8993288590604027
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6327900287631831
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2612336209651646
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16790028763183126
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08993288590604027
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6327900287631831
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7837008628954938
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8395014381591562
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8993288590604027
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.764234057963867
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7212053904335789
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7259287378336138
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.637392138063279
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7923298178331736
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8416107382550335
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9027804410354746
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.637392138063279
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2641099392777245
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16832214765100673
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09027804410354746
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.637392138063279
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7923298178331736
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8416107382550335
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9027804410354746
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7690204393280001
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7263406078315016
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7311054572183869
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6312559923298179
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7900287631831256
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8404602109300096
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9056567593480345
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6312559923298179
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26334292106104185
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16809204218600193
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09056567593480344
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6312559923298179
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7900287631831256
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8404602109300096
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9056567593480345
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7661659173597293
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7217839260984048
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7263493883780671
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6044103547459252
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7660594439117929
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.823777564717162
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8901246404602109
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6044103547459252
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2553531479705976
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1647555129434324
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08901246404602109
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6044103547459252
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7660594439117929
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.823777564717162
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8901246404602109
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7455172683079468
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.699452129845228
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7043843105333936
name: Cosine Map@100
---
# SentenceTransformer based on hiieu/halong_embedding
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [hiieu/halong_embedding](https://huggingface.co/hiieu/halong_embedding). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [hiieu/halong_embedding](https://huggingface.co/hiieu/halong_embedding) <!-- at revision b57776031035f70ed2030d2e35ecc533eb0f8f71 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("TuanNM171284/TuanNM171284-HaLong-embedding-medical")
# Run inference
sentences = [
'Làm sao để chăm sóc bệnh nhân mắc hẹp bao quy đầu?',
'Chăm sóc bao gồm vệ sinh sạch sẽ bao quy đầu hàng ngày bằng nước sạch và xà phòng nhẹ, tuột bao quy đầu nhẹ nhàng để rửa. Sau phẫu thuật, cần theo dõi các dấu hiệu sưng đỏ, chảy máu và vệ sinh mỗi ngày để tránh nhiễm trùng và mất cảm giác.',
'Bệnh gan nhiễm mỡ không do rượu có đặc điểm là sự hiện diện của rất nhiều mỡ tích tụ trong gan. Sau đó có thể diễn tiến nặng dần lên thành viêm gan nhiễm mỡ không do rượu (NASH). Tình trạng này có thể gây tổn thương tế bào gan nghiêm trọng và dẫn đến xơ gan, suy chức năng gan. Xơ gan có thể dẫn tới: Báng bụng, Dãn tĩnh mạch thực quản, có thể gây vỡ làm nôn ra máu, Bệnh não gan: Các biểu hiện như lú lẫn, ngủ gà hay rối loạn chức năng thần kinh cao cấp, Ung thư gan, Suy gan giai đoạn cuối.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:----------|:-----------|:----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.6334 | 0.6328 | 0.6374 | 0.6313 | 0.6044 |
| cosine_accuracy@3 | 0.7862 | 0.7837 | 0.7923 | 0.79 | 0.7661 |
| cosine_accuracy@5 | 0.8418 | 0.8395 | 0.8416 | 0.8405 | 0.8238 |
| cosine_accuracy@10 | 0.8974 | 0.8993 | 0.9028 | 0.9057 | 0.8901 |
| cosine_precision@1 | 0.6334 | 0.6328 | 0.6374 | 0.6313 | 0.6044 |
| cosine_precision@3 | 0.2621 | 0.2612 | 0.2641 | 0.2633 | 0.2554 |
| cosine_precision@5 | 0.1684 | 0.1679 | 0.1683 | 0.1681 | 0.1648 |
| cosine_precision@10 | 0.0897 | 0.0899 | 0.0903 | 0.0906 | 0.089 |
| cosine_recall@1 | 0.6334 | 0.6328 | 0.6374 | 0.6313 | 0.6044 |
| cosine_recall@3 | 0.7862 | 0.7837 | 0.7923 | 0.79 | 0.7661 |
| cosine_recall@5 | 0.8418 | 0.8395 | 0.8416 | 0.8405 | 0.8238 |
| cosine_recall@10 | 0.8974 | 0.8993 | 0.9028 | 0.9057 | 0.8901 |
| **cosine_ndcg@10** | **0.764** | **0.7642** | **0.769** | **0.7662** | **0.7455** |
| cosine_mrr@10 | 0.7215 | 0.7212 | 0.7263 | 0.7218 | 0.6995 |
| cosine_map@100 | 0.7262 | 0.7259 | 0.7311 | 0.7263 | 0.7044 |
<!--
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 5,215 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 13.86 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 79.69 tokens</li><li>max: 356 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Bệnh Addison là gì?</code> | <code>Bệnh Addison là một bệnh hiếm gặp, xảy ra khi tuyến thượng thận không sản xuất đủ các hormone cortisol và aldosterone. Bệnh còn được gọi là suy thượng thận nguyên phát.</code> |
| <code>Triệu chứng của bệnh Addison là gì?</code> | <code>Các triệu chứng của bệnh Addison bao gồm mệt mỏi mãn tính, yếu cơ, mất cảm giác ngon miệng, giảm cân, huyết áp thấp, da sẫm màu, bất thường lượng đường trong máu, buồn nôn, nôn, tiêu chảy, không có khả năng đối phó với căng thẳng, tâm trạng buồn bực, khó chịu, trầm cảm, không thích nghi được với cảm giác nóng hoặc lạnh, và thèm đồ ăn mặn.</code> |
| <code>Cách điều trị bệnh Addison ra sao?</code> | <code>Bệnh Addison được điều trị bằng cách thay thế các hormone bị thiếu. Cortisol có thể được thay thế bằng viên hydrocortisone, aldosterone có thể được thay thế bằng fludrocortisone acetate. Liều lượng cần được điều chỉnh trong thời gian căng thẳng, nhiễm trùng, phẫu thuật hoặc chấn thương và phải tuân thủ liều nghiêm ngặt dưới sự chỉ định và giám sát của bác sĩ.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_eval_batch_size`: 4
- `gradient_accumulation_steps`: 4
- `learning_rate`: 2e-05
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.1227 | 10 | 2.7834 | 0.6736 | 0.6704 | 0.6625 | 0.6478 | 0.6050 |
| 0.2454 | 20 | 4.2471 | 0.6918 | 0.6880 | 0.6818 | 0.6709 | 0.6358 |
| 0.3681 | 30 | 3.2069 | 0.7016 | 0.6997 | 0.7012 | 0.6935 | 0.6665 |
| 0.4908 | 40 | 2.9097 | 0.7177 | 0.7163 | 0.7169 | 0.7086 | 0.6783 |
| 0.6135 | 50 | 3.0704 | 0.7268 | 0.7263 | 0.7272 | 0.7178 | 0.6901 |
| 0.7362 | 60 | 2.5582 | 0.7231 | 0.7248 | 0.7274 | 0.7219 | 0.7001 |
| 0.8589 | 70 | 2.074 | 0.7214 | 0.7217 | 0.7277 | 0.7259 | 0.7072 |
| 0.9816 | 80 | 2.8852 | 0.7359 | 0.7362 | 0.7390 | 0.7351 | 0.7132 |
| 1.0982 | 90 | 1.3827 | 0.7458 | 0.7448 | 0.7464 | 0.7423 | 0.7175 |
| 1.2209 | 100 | 1.5382 | 0.7472 | 0.7474 | 0.7484 | 0.7410 | 0.7145 |
| 1.3436 | 110 | 1.6667 | 0.7433 | 0.7437 | 0.7455 | 0.7423 | 0.7174 |
| 1.4663 | 120 | 1.6423 | 0.7497 | 0.7502 | 0.7530 | 0.7495 | 0.7250 |
| 1.5890 | 130 | 1.2332 | 0.7539 | 0.7543 | 0.7582 | 0.7552 | 0.7296 |
| 1.7117 | 140 | 1.4156 | 0.7573 | 0.7583 | 0.7619 | 0.7588 | 0.7352 |
| 1.8344 | 150 | 1.2422 | 0.7592 | 0.7591 | 0.7634 | 0.7599 | 0.7371 |
| 1.9571 | 160 | 0.917 | 0.7579 | 0.7585 | 0.7643 | 0.7610 | 0.7392 |
| 2.0736 | 170 | 1.3069 | 0.7605 | 0.7610 | 0.7665 | 0.7644 | 0.7430 |
| 2.1963 | 180 | 0.949 | 0.7599 | 0.7612 | 0.7662 | 0.7639 | 0.7433 |
| 2.3190 | 190 | 0.9943 | 0.7609 | 0.7620 | 0.7662 | 0.7643 | 0.7430 |
| 2.4417 | 200 | 1.1196 | 0.7623 | 0.7629 | 0.7672 | 0.7652 | 0.7439 |
| 2.5644 | 210 | 0.8954 | 0.7637 | 0.7634 | 0.7687 | 0.7663 | 0.7447 |
| 2.6871 | 220 | 0.8713 | 0.7640 | 0.7636 | 0.7689 | 0.7659 | 0.7451 |
| 2.8098 | 230 | 1.1643 | 0.7639 | 0.7640 | 0.7690 | 0.7661 | 0.7453 |
| 2.9325 | 240 | 1.0182 | 0.7640 | 0.7642 | 0.7690 | 0.7662 | 0.7455 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.51.1
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.5.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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lamm-mit/ProteinGPT_medium | lamm-mit | 2025-04-28T16:41:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T16:38:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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kxdw2580/Qwen2.5-0.5B-Catgirl-test0426 | kxdw2580 | 2025-04-28T16:38:41Z | 4 | 0 | null | [
"safetensors",
"qwen2",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:kxdw2580/catgirl-dataset",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:mit",
"region:us"
] | null | 2025-04-26T17:30:33Z | ---
license: mit
datasets:
- kxdw2580/catgirl-dataset
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
---
# kxdw2580/Qwen2.5-0.5B-Catgirl-test0426
This model is a test model, designed for phased lightweight testing of the dataset.
The test dataset has fixed this issue:
- Model's outputs "~" causing rendering errors.
After testing, the objectives of the dataset fixes have been achieved.
## Other
As a 0.5b model, its performance is very poor, especially in the missing English part of the dataset. We do not recommend using this model unless there is a specific need.
We are working hard to improve the training results on smaller models, but it is obviously unlikely for the 0.5b model.
Specific training results can be seen at [swanlab](https://swanlab.cn/@shadow01a/qwen-catgirl/runs/q58lh8yf6itgoamcoq4q8/chart)
Additionally, I have observed that with models of this size, a smaller training loss does not always indicate better model performance, and sometimes even leads to a decline in performance. [This swanlab record](https://swanlab.cn/@shadow01a/qwen-catgirl/runs/d29hi6y9d7g772ib5vbtx/chart) is the result of further training for this model. After testing, I found that its performance is even worse than the original model. This model has not been publicly released and has been deleted.
I would be very happy to communicate if you wish to! |
RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf | RichardErkhov | 2025-04-28T16:37:06Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T15:04:28Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
hp_ablations_grid_qwen_bsz512_lr5e-6 - GGUF
- Model creator: https://huggingface.co/mlfoundations-dev/
- Original model: https://huggingface.co/mlfoundations-dev/hp_ablations_grid_qwen_bsz512_lr5e-6/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q2_K.gguf) | Q2_K | 2.81GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.IQ3_XS.gguf) | IQ3_XS | 3.12GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.IQ3_S.gguf) | IQ3_S | 3.26GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q3_K_S.gguf) | Q3_K_S | 3.25GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.IQ3_M.gguf) | IQ3_M | 3.33GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q3_K.gguf) | Q3_K | 3.55GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q3_K_L.gguf) | Q3_K_L | 3.81GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.IQ4_XS.gguf) | IQ4_XS | 3.96GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_0.gguf) | Q4_0 | 4.13GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.IQ4_NL.gguf) | IQ4_NL | 4.16GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_K_S.gguf) | Q4_K_S | 4.15GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_K.gguf) | Q4_K | 4.36GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_K_M.gguf) | Q4_K_M | 4.36GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q4_1.gguf) | Q4_1 | 4.54GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_0.gguf) | Q5_0 | 4.95GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_K_S.gguf) | Q5_K_S | 4.95GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_K.gguf) | Q5_K | 5.07GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_K_M.gguf) | Q5_K_M | 5.07GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q5_1.gguf) | Q5_1 | 5.36GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q6_K.gguf) | Q6_K | 5.82GB |
| [hp_ablations_grid_qwen_bsz512_lr5e-6.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_grid_qwen_bsz512_lr5e-6-gguf/blob/main/hp_ablations_grid_qwen_bsz512_lr5e-6.Q8_0.gguf) | Q8_0 | 7.54GB |
Original model description:
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: hp_ablations_grid_qwen_bsz512_lr5e-6
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. -->
# hp_ablations_grid_qwen_bsz512_lr5e-6
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the mlfoundations-dev/oh-dcft-v3-llama3.1-nemotron-70b_shareGPT_format dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- 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: constant
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1738
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5655 | 0.9989 | 577 | 0.5682 |
| 0.5199 | 1.9996 | 1155 | 0.5473 |
| 0.4769 | 2.9968 | 1731 | 0.5394 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
hienhayho/ISCUD_3B | hienhayho | 2025-04-28T16:36:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-04-28T16:15:46Z | ---
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]
- **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] |
MetaphoricalCode/Omega-Darker_The-Final-Directive-24B-8.0bpw-h8-exl2 | MetaphoricalCode | 2025-04-28T16:35:50Z | 0 | 0 | null | [
"safetensors",
"mistral",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"conversational",
"en",
"base_model:TheDrummer/Cydonia-24B-v2.1",
"base_model:finetune:TheDrummer/Cydonia-24B-v2.1",
"license:apache-2.0",
"8-bit",
"exl2",
"region:us"
] | text-generation | 2025-04-28T16:17:48Z | ---
license: apache-2.0
language:
- en
base_model:
- TheDrummer/Cydonia-24B-v2.1
base_model_relation: finetune
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
<style>
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%);
color: #e1ffff !important;
text-shadow: 0 0 3px rgba(0, 0, 0, 0.7);
margin: 0;
padding: 20px;
transition: all 0.5s ease;
}
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%);
color: #002b36 !important;
text-shadow: 0 0 3px rgba(255, 255, 255, 0.7);
}
}
.container {
min-width: 100%;
margin: 0 auto;
max-width: 1200px;
background: rgba(0, 17, 22, 0.95);
border-radius: 12px;
padding: 30px;
box-shadow: 0 0 20px rgba(0, 255, 255, 0.1);
border: 1px solid rgba(0, 255, 255, 0.2);
position: relative;
overflow: hidden;
}
.container::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(0, 255, 255, 0.5);
border-radius: 12px;
pointer-events: none;
animation: borderGlow 3s ease-in-out infinite alternate;
}
@keyframes borderGlow {
0% {
box-shadow: 0 0 5px rgba(0, 255, 255, 0.3);
border-color: rgba(0, 255, 255, 0.5);
}
50% {
box-shadow: 0 0 15px rgba(255, 0, 255, 0.3);
border-color: rgba(255, 0, 255, 0.5);
}
100% {
box-shadow: 0 0 5px rgba(0, 255, 255, 0.3);
border-color: rgba(0, 255, 255, 0.5);
}
}
.header {
text-align: center;
margin-bottom: 30px;
position: relative;
}
.header::after {
content: '';
position: absolute;
bottom: -15px;
left: 25%;
right: 25%;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent);
animation: scanline 8s linear infinite;
display: none;
}
@keyframes scanline {
0% { background-position: -100% 0; }
100% { background-position: 200% 0; }
}
.model-name {
color: #00ffff;
font-size: 2.5em;
text-shadow: 0 0 15px rgba(0, 255, 255, 0.5);
margin: 0;
letter-spacing: -1px;
animation: textGlow 4s ease-in-out infinite alternate;
}
@keyframes textGlow {
0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); }
50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); }
100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); }
}
.subtitle {
color: #00ffcc;
font-size: 1.2em;
margin-top: 10px;
animation: subtitleFade 6s ease-in-out infinite;
}
@keyframes subtitleFade {
0%, 100% { opacity: 0.8; }
50% { opacity: 1; }
}
.waifu-container {
margin: 20px -30px;
width: calc(100% + 60px);
overflow: hidden;
border-radius: 8px;
border: 1px solid rgba(0, 255, 255, 0.3);
position: relative;
}
.waifu-container::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: linear-gradient(45deg,
rgba(0, 255, 255, 0.1) 0%,
transparent 20%,
transparent 80%,
rgba(255, 0, 255, 0.1) 100%);
pointer-events: none;
animation: gradientSlide 10s linear infinite;
}
@keyframes gradientSlide {
0% { background-position: 0% 0%; }
100% { background-position: 100% 100%; }
}
.waifu-img {
width: 100%;
height: auto;
border-radius: 0;
border: none;
box-shadow: 0 0 40px rgba(0, 255, 255, 0.2);
transition: transform 0.5s ease;
}
.waifu-img:hover {
transform: scale(1.01);
}
.section {
color: #e1ffff;
margin: 25px 0;
padding: 20px;
background: rgba(5, 25, 35, 0.9);
border-radius: 8px;
border: 1px solid rgba(0, 255, 255, 0.15);
position: relative;
transition: all 0.3s ease;
}
.section:hover {
border-color: rgba(255, 0, 255, 0.3);
box-shadow: 0 0 15px rgba(0, 255, 255, 0.1);
}
.section::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(0, 255, 255, 0.3);
border-radius: 8px;
pointer-events: none;
animation: sectionPulse 5s ease-in-out infinite;
}
@keyframes sectionPulse {
0%, 100% { opacity: 0.7; }
50% { opacity: 0.3; }
}
.section-title {
color: #00ffff;
font-size: 1.8em;
margin-top: 0;
text-shadow: 0 0 5px rgba(0, 255, 255, 0.3);
position: relative;
display: inline-block;
}
.section-title::after {
content: '';
position: absolute;
bottom: -5px;
left: 0;
width: 100%;
height: 1px;
background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5));
transform: scaleX(0);
transform-origin: left;
transition: transform 0.3s ease;
}
.section:hover .section-title::after {
transform: scaleX(1);
}
.quant-links {
display: grid;
grid-template-columns: repeat(3, 1fr);
gap: 15px;
margin: 20px 0;
}
.link-card {
padding: 15px;
background: rgba(20, 35, 45, 0.95);
border-radius: 8px;
transition: all 0.3s ease;
border: 1px solid rgba(0, 255, 255, 0.1);
position: relative;
overflow: hidden;
}
.link-card::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
height: 2px;
background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5));
animation: cardScan 4s linear infinite;
}
@keyframes cardScan {
0% { transform: translateX(-100%); }
100% { transform: translateX(100%); }
}
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transform: translateY(-3px);
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</style>
<div class="container">
<div class="header">
<h1 class="model-name">Omega Darker</h1>
<h1 class="model-name">The Final Directive 24B</h1>
<p class="subtitle">Where Nightmares and Desires Collide</p>
</div>
<div class="waifu-container">
<img src="./waifu6.webp" class="waifu-img" alt="Omega Directive Waifu">
</div>
<div class="section remember-this">
<h2 class="section-title">🩸 Blood-Soaked Evolution</h2>
<p>This model doesn't just cross lines - it erases them with arterial spray:</p>
<ul>
<li>🧬 <strong>Expanded 25M Token Dataset</strong> - Made with 687 erotic, horror and violence novels and 8,742 scenarios</li>
<li>🧟 <strong>Enhanced Gore Protocols</strong> - Vivid anatomical descriptions with medical precision</li>
<li>💎 <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li>
<li>📜 <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li>
<li>⚰️ <strong>Mortality Awareness</strong> - Characters react realistically to pain, mutilation and death</li>
</ul>
</div>
<div class="section shifty-section">
<h2 class="section-title">⚙️ Technical Specifications</h2>
<p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p>
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Directive-24B-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Directive-24B-i1-GGUF" class="link-button">Quants</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">☠️ Ethical Vacuum</h2>
<div class="disclaimer">
<p>This model operates in complete moral darkness:</p>
<ul>
<li>🚫 No moral or ethical constraints on generated content</li>
<li>🔞 Will enthusiastically engage with any NSFW scenario</li>
<li>🖤 No psychological safety measures whatsoever</li>
<li>🔪 Will graphically depict any violent requested</li>
</ul>
</div>
</div>
<div class="section shifty-section">
<h2 class="section-title">📜 Performance Notes</h2>
<ul>
<li>🔥 Maintains signature intensity with improved narrative flow</li>
<li>📖 Handles multi-character scenarios with improved consistency</li>
<li>🧠 Excels at long-form storytelling without losing track of plot threads</li>
<li>⚡ Noticeably better at following complex instructions than previous versions</li>
<li>🎭 Responds to subtle prompt nuances like a mind reader</li>
<li>🔪 Excels at visceral injury descriptions</li>
<li>👁️ Responds to horror prompts like a seasoned torturer</li>
</ul>
</div>
<div class="section remember-this">
<h2 class="section-title">🧑🔬 Model Authors</h2>
<ul>
<li>TheDrummer (Base Model Architect)</li>
<li>SteelSkull (Dataset Generation Contributor)</li>
<li>Artus (EXL2 Weights Weaver)</li>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">☕ Support the Architects</h2>
<div class="button-group">
<a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a>
<a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a>
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">🔖 License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
<script>
// This script has always been here
document.getElementById('date').textContent = new Date().toLocaleDateString();
setInterval(() => {
document.getElementById('credit').textContent =
contributors[Math.floor(Math.random() * contributors.length)];
}, 7000);
// Flash warning behavior
setTimeout(() => {
const reminder = document.createElement('div');
reminder.className = 'flash-warning';
reminder.textContent = 'You have been reading for quite some time. Are you sure you haven\'t seen this before?';
reminder.style.animation = 'flashWarning 15s ease-in-out forwards';
document.body.appendChild(reminder);
setInterval(() => {
if(Math.random() > 0.9) {
document.body.appendChild(reminder.cloneNode(true));
}
}, 45000);
}, 30000);
// Make cursor behave strangely
document.addEventListener('mousemove', (e) => {
if(Math.random() > 0.98) {
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</script> |
Xntuan/ppo-LunarLander-v2 | Xntuan | 2025-04-28T16:31:26Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-04-28T16:31:07Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 236.74 +/- 13.57
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
clareandme/multilabel-setfit-model-v4_20250428_17 | clareandme | 2025-04-28T16:24:28Z | 0 | 0 | setfit | [
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"region:us"
] | text-classification | 2025-04-28T16:22:52Z | ---
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget: []
inference: true
---
# SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("I loved the spiderman movie!")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.11.11
- SetFit: 1.0.3
- Sentence Transformers: 3.1.1
- Transformers: 4.39.0
- PyTorch: 2.6.0
- Datasets: 3.2.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
Shahfaisal76/crypto | Shahfaisal76 | 2025-04-28T16:23:19Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-04-28T16:23:19Z | ---
license: apache-2.0
---
|
wahidmounir/ALlama-2-7B-Q8_0-GGUF | wahidmounir | 2025-04-28T16:22:35Z | 0 | 0 | peft | [
"peft",
"gguf",
"llama-cpp",
"gguf-my-lora",
"base_model:EdBerg/ALlama-2-7B",
"base_model:adapter:EdBerg/ALlama-2-7B",
"region:us"
] | null | 2025-04-28T16:22:28Z | ---
library_name: peft
base_model: EdBerg/ALlama-2-7B
tags:
- llama-cpp
- gguf-my-lora
---
# wahidmounir/ALlama-2-7B-Q8_0-GGUF
This LoRA adapter was converted to GGUF format from [`EdBerg/ALlama-2-7B`](https://huggingface.co/EdBerg/ALlama-2-7B) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space.
Refer to the [original adapter repository](https://huggingface.co/EdBerg/ALlama-2-7B) for more details.
## Use with llama.cpp
```bash
# with cli
llama-cli -m base_model.gguf --lora ALlama-2-7B-q8_0.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora ALlama-2-7B-q8_0.gguf (...other args)
```
To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
|
mikatikkala/mikasome-lora | mikatikkala | 2025-04-28T16:12:36Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-28T15:47:21Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: mikatikkala
---
# Mikasome Lora
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `mikatikkala` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "mikatikkala",
"lora_weights": "https://huggingface.co/mikatikkala/mikasome-lora/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('mikatikkala/mikasome-lora', weight_name='lora.safetensors')
image = pipeline('mikatikkala').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/mikatikkala/mikasome-lora/discussions) to add images that show off what you’ve made with this LoRA.
|
Romain-XV/9ab96772-d5a5-4fbb-84a6-3ec26afd8e25 | Romain-XV | 2025-04-28T16:12:13Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T07:38:52Z | ---
base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct
library_name: transformers
model_name: 9ab96772-d5a5-4fbb-84a6-3ec26afd8e25
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 9ab96772-d5a5-4fbb-84a6-3ec26afd8e25
This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-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="Romain-XV/9ab96772-d5a5-4fbb-84a6-3ec26afd8e25", 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/romain_fnc-xventures/Gradients-On-Demand/runs/0u0jcmuk)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
galeio-research/OceanSAR-1-wave | galeio-research | 2025-04-28T16:11:46Z | 80 | 0 | transformers | [
"transformers",
"safetensors",
"resnet",
"image-classification",
"SAR",
"EO",
"regression",
"sentinel-1",
"ocean",
"wave-height",
"earth-observation",
"remote-sensing",
"satellite-imagery",
"synthetic-aperture-radar",
"foundation-model",
"linear-probing",
"oceanography",
"marine-forecasting",
"open-source",
"ocean-wind",
"arxiv:2504.06962",
"base_model:galeio-research/OceanSAR-1",
"base_model:finetune:galeio-research/OceanSAR-1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-04-14T14:27:52Z | ---
library_name: transformers
tags:
- SAR
- EO
- regression
- sentinel-1
- ocean
- wave-height
- earth-observation
- remote-sensing
- satellite-imagery
- synthetic-aperture-radar
- foundation-model
- linear-probing
- oceanography
- marine-forecasting
- open-source
- ocean-wind
license: apache-2.0
pipeline_tag: image-classification
base_model:
- galeio-research/OceanSAR-1
---
# Model Card for OceanSAR-1-wave
## Model Details
<img src="OceanSAR-1-logo.png" width=400>
### Model Description
OceanSAR-1-wave is a linear probing head for significant wave height (SWH) prediction built on top of the OceanSAR-1 foundation model. It leverages the powerful features extracted by OceanSAR-1 to accurately predict ocean wave heights from Synthetic Aperture Radar (SAR) imagery.
- **Developed by:** Thomas Kerdreux, Alexandre Tuel @ [Galeio](http://galeio.fr)
- **Deployed by:** Antoine Audras @ [Galeio](http://galeio.fr)
- **Model type:** Linear Regression Head on Vision Foundation Model
- **License:** Apache License 2.0
- **Base model:** OceanSAR-1 (ResNet50/ViT variants)
- **Training data:** Sentinel-1 Wave Mode (WV) SAR images with collocated wave height measurements
## Uses
### Direct Use
This model is designed for significant wave height prediction from SAR imagery over ocean surfaces. It can be used for:
- Near-real-time wave height estimation from SAR images
- Marine weather forecasting
- Ocean state monitoring
- Maritime safety applications
- Wave climate studies
### Performance Results
The model achieves state-of-the-art performance in linear probing on significant wave height prediction, with performance varying by backbone architecture:
| Backbone | SWH RMSE (m) |
|----------|--------------|
| ResNet50 | 0.63 |
| ViT-S/16 | 0.57 |
| ViT-S/8 | 0.55 |
| ViT-B/8 | 0.54 |
## How to Use
```python
import torch
from transformers import AutoModelForImageClassification
# Load the foundation model and the linear probing head
oceansar = AutoModelForImageClassification.from_pretrained("galeio-research/OceanSAR-1")
# Prepare your SAR image (should be single-channel VV polarization)
# Here using random data as example
dummy_image = torch.randn(1, 1, 256, 256) # (C, H, W)
# Extract features
with torch.no_grad():
outputs = oceansar(dummy_image)
# For regression, use the single output value as the wave height prediction
wave_height = outputs.logits.item() # Output in meters
```
## Training Details
### Training Data
- **Dataset:** Sentinel-1 Wave Mode (WV) SAR images with collocated wave height measurements
- **Source:** Wave height measurements from altimeters, buoys, and wave models
- **Preprocessing:** Same as base OceanSAR-1 model
## Evaluation
### Metrics
Significant wave height prediction performance is evaluated using Root Mean Square Error (RMSE), achieving:
- 0.63 m RMSE with ResNet50 backbone
- 0.57 m RMSE with ViT-S/16 backbone
- 0.55 m RMSE with ViT-S/8 backbone
- 0.54 m RMSE with ViT-B/8 backbone
### Comparison to Other Models
The model outperforms existing approaches:
- MoCo: 0.77 m RMSE
- DeCUR: 0.82 m RMSE
- SoftCon (ViT-S/14): 0.78 m RMSE
- SoftCon (ViT-B/14): 0.79 m RMSE
## Technical Specifications
### Hardware Requirements
- Same as base model
- Minimal additional computational cost for inference
### Dependencies
- PyTorch >= 1.8.0
- Transformers >= 4.30.0
- Base OceanSAR-1 model
### Input Specifications
- Same as base OceanSAR-1 model
- Single channel (VV polarization) SAR images
- 256x256 pixel resolution
## Citation
**BibTeX:**
```bibtex
@article{kerdreux2025efficientselfsupervisedlearningearth,
title={Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation},
author={Kerdreux, Thomas and Tuel, Alexandre and Febvre, Quentin and Mouche, Alexis and Chapron, Bertrand},
journal={arXiv preprint arXiv:2504.06962},
year={2025},
eprint={2504.06962},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.06962},
}
```
## Acknowledgements
This work was granted access to the HPC resources of IDRIS and TGCC under the allocation 2025-[A0171015666] made by GENCI. |
fhaslam/Llama-3.2-1B-Financial-Sentiment22 | fhaslam | 2025-04-28T16:11:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T16:11:00Z | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
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## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-1B-Instruct"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/*" --local-dir Llama-3.2-1B-Instruct
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
galeio-research/OceanSAR-1 | galeio-research | 2025-04-28T16:10:26Z | 117 | 1 | transformers | [
"transformers",
"safetensors",
"resnet",
"image-feature-extraction",
"SAR",
"RADAR",
"EO",
"backbone",
"ocean",
"wind",
"sentinel-1",
"arxiv:2504.06962",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-feature-extraction | 2025-04-14T14:23:22Z | ---
library_name: transformers
tags:
- resnet
- SAR
- RADAR
- EO
- backbone
- ocean
- wind
- sentinel-1
license: apache-2.0
pipeline_tag: image-feature-extraction
---
# Model Card for OceanSAR-1
## Model Details
<img src="OceanSAR-1-logo.png" width=400>
### Model Description
OceanSAR-1 is the first foundation model in the OceanSAR family, specifically designed for Synthetic Aperture Radar (SAR) imagery analysis, with a focus on ocean observation. The model is trained using a novel dynamic dataset pruning strategy that enhances training efficiency and feature quality.
- **Developed by:** Thomas Kerdreux, Alexandre Tuel @ [Galeio](http://galeio.fr)
- **Deployed by:** Antoine Audras @ [Galeio](http://galeio.fr)
- **Model type:** Vision Foundation Model (ResNet50/ViT variants)
- **License:** Apache License 2.0
- **Training data:** Sentinel-1 Wave Mode (WV) SAR images (2015-2024)
- **Training regime:** DINO self-supervised learning with dynamic dataset pruning
## Uses
### Direct Use
The model is intended to be used as a feature extractor for SAR image analysis, particularly for ocean observation tasks. It can be used for:
- Feature extraction from SAR images
- Transfer learning for downstream tasks
### Downstream Use
The model has been validated on three downstream tasks:
1. **TenGeoP Classification**: Classification of 10 geophysical phenomena in SAR images
2. **Significant Wave Height Estimation**: Regression task for ocean wave height prediction
3. **Wind Speed Prediction**: Regression task for surface wind speed estimation
## How to Use
```python
import torch
from transformers import AutoModel
# Load model and processor
model = AutoModel.from_pretrained("galeio-research/OceanSAR-1")
# Prepare your SAR image (should be single-channel VV polarization)
# Here using random data as example
dummy_image = torch.randn(1, 1, 256, 256) # (C, H, W)
# Extract features
with torch.no_grad():
outputs = model(dummy_image)
features = outputs.pooler_output # Shape: (1, 2048) for ResNet50
```
## Training Details
### Training Data
- **Dataset:** Sentinel-1 Wave Mode (WV) SAR images
- **Time period:** 2015-2024
- **Size:** ~12 million images
- **Preprocessing:**
- Spatial downsampling to 50m resolution
- Dynamic dataset pruning for diversity and balancedness
- Excluded validation images from training set
### Dynamic Dataset Pruning
The model uses a novel dynamic dataset pruning strategy that:
- Maximizes dataset diversity and balancedness
- Reduces computational costs
- Improves model performance on downstream tasks
- Works without requiring a pre-existing feature extractor
## Evaluation
### Results
The model achieves state-of-the-art performance on three downstream tasks (linear probing):
1. **TenGeoP Classification**:
- ResNet50: 75.5% accuracy
- ViT-S/16: 78.6% accuracy
- ViT-S/8: 82.1% accuracy
- ViT-B/8: 83.6% accuracy
2. **Significant Wave Height Estimation**:
- RMSE: 0.63-0.72m (depending on architecture)
3. **Wind Speed Prediction**:
- RMSE: 1.37-1.43 m/s (depending on architecture)
For commercial deployments or to access optimized model variants for specific operational needs, feel free to reach out to discuss licensing and support options.
## Technical Specifications
### Hardware Requirements
- GPU with at least 8GB VRAM recommended
### Dependencies
- PyTorch >= 1.8.0
- Transformers >= 4.30.0
- torchvision >= 0.9.0
### Input Specifications
- Input size: 256x256 pixels
- Single channel (VV polarization)
- Normalized pixel values
- SAR images from Sentinel-1 Wave Mode
## Citation
**BibTeX:**
```bibtex
@article{kerdreux2025efficientselfsupervisedlearningearth,
title={Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation},
author={Kerdreux, Thomas and Tuel, Alexandre and Febvre, Quentin and Mouche, Alexis and Chapron, Bertrand},
journal={arXiv preprint arXiv:2504.06962},
year={2025},
eprint={2504.06962},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.06962},
}
```
## Acknowledgements
This work was granted access to the HPC resources of IDRIS and TGCC under the allocation 2025-[A0171015666] made by GENCI. |
hendrydong/emcot-raftpp028-iter4-step9 | hendrydong | 2025-04-28T16:07:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T16:04:28Z | ---
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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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
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#### Preprocessing [optional]
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Model Card Contact
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LucAI12/monkla19 | LucAI12 | 2025-04-28T16:07:07Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-28T15:50:25Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: monkla19
---
# Monkla19
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `monkla19` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "monkla19",
"lora_weights": "https://huggingface.co/LucAI12/monkla19/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('LucAI12/monkla19', weight_name='lora.safetensors')
image = pipeline('monkla19').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/LucAI12/monkla19/discussions) to add images that show off what you’ve made with this LoRA.
|
batbrid/Instanted | batbrid | 2025-04-28T16:06:45Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-04-28T16:06:45Z | ---
license: apache-2.0
---
|
greenwich157/Qwen2.5-3B-Instruct-TelcoLLM | greenwich157 | 2025-04-28T16:06:44Z | 15 | 0 | null | [
"safetensors",
"qwen2",
"en",
"zh",
"dataset:greenwich157/5G_Faults_Full",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-04-27T02:21:56Z | ---
license: apache-2.0
datasets:
- greenwich157/5G_Faults_Full
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-3B-Instruct
---
**5G mobile network faults suitable for engineer evaluation, based on synthetic dataset** |
BootesVoid/cm9zsazyn02ouqeqo68mb4r5u_cma18r91l00a312tvctix4flx | BootesVoid | 2025-04-28T16:03:58Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-28T16:03:57Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SANTOS
---
# Cm9Zsazyn02Ouqeqo68Mb4R5U_Cma18R91L00A312Tvctix4Flx
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SANTOS` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SANTOS",
"lora_weights": "https://huggingface.co/BootesVoid/cm9zsazyn02ouqeqo68mb4r5u_cma18r91l00a312tvctix4flx/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cm9zsazyn02ouqeqo68mb4r5u_cma18r91l00a312tvctix4flx', weight_name='lora.safetensors')
image = pipeline('SANTOS').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cm9zsazyn02ouqeqo68mb4r5u_cma18r91l00a312tvctix4flx/discussions) to add images that show off what you’ve made with this LoRA.
|
DevQuasar/BlinkDL.rwkv-7-world-GGUF | DevQuasar | 2025-04-28T16:03:44Z | 0 | 0 | null | [
"text-generation",
"base_model:BlinkDL/rwkv-7-world",
"base_model:finetune:BlinkDL/rwkv-7-world",
"region:us"
] | text-generation | 2025-04-28T16:00:38Z | ---
base_model:
- BlinkDL/rwkv-7-world
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [BlinkDL/rwkv-7-world](https://huggingface.co/BlinkDL/rwkv-7-world)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
Alphatao/7962c3d0-9181-4e73-ba0f-0f31f0b3d8ff | Alphatao | 2025-04-28T16:03:05Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:EleutherAI/gpt-neo-125m",
"base_model:finetune:EleutherAI/gpt-neo-125m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T00:02:38Z | ---
base_model: EleutherAI/gpt-neo-125m
library_name: transformers
model_name: 7962c3d0-9181-4e73-ba0f-0f31f0b3d8ff
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 7962c3d0-9181-4e73-ba0f-0f31f0b3d8ff
This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Alphatao/7962c3d0-9181-4e73-ba0f-0f31f0b3d8ff", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alphatao-alphatao/Gradients-On-Demand/runs/hodjllxk)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Aluba/shizuka_8 | Aluba | 2025-04-28T16:02:58Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-04-28T15:50:34Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Aluba/shizuka_7 | Aluba | 2025-04-28T16:01:44Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-04-28T15:50:29Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
silasv/boi | silasv | 2025-04-28T16:00:53Z | 0 | 0 | null | [
"pt",
"license:apache-2.0",
"region:us"
] | null | 2024-01-22T11:18:49Z | ---
license: apache-2.0
language:
- pt
--- |
ApacheOne/local-checkpoints | ApacheOne | 2025-04-28T15:56:32Z | 0 | 0 | null | [
"safetensors",
"custom",
"art",
"en",
"region:us"
] | null | 2025-04-03T15:30:44Z | ---
language:
- en
tags:
- art
---
## Local-models
A vast amount of models that were or are still SOTA gen models.
(more info to come) |
fwzhuang/task-8-Qwen-Qwen1.5-0.5B | fwzhuang | 2025-04-28T15:56:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2025-04-28T15:56:05Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2 |
LucAI12/viadovi6 | LucAI12 | 2025-04-28T15:54:12Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-28T15:38:20Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: viadovi6
---
# Viadovi6
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `viadovi6` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "viadovi6",
"lora_weights": "https://huggingface.co/LucAI12/viadovi6/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('LucAI12/viadovi6', weight_name='lora.safetensors')
image = pipeline('viadovi6').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/LucAI12/viadovi6/discussions) to add images that show off what you’ve made with this LoRA.
|
DreadPoor/signal_test | DreadPoor | 2025-04-28T15:48:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:Delta-Vector/Rei-12B",
"base_model:merge:Delta-Vector/Rei-12B",
"base_model:DreadPoor/Irix-12B-Model_Stock",
"base_model:merge:DreadPoor/Irix-12B-Model_Stock",
"base_model:DreadPoor/YM-12B-Model_Stock",
"base_model:merge:DreadPoor/YM-12B-Model_Stock",
"base_model:grimjim/magnum-twilight-12b",
"base_model:merge:grimjim/magnum-twilight-12b",
"base_model:redrix/GodSlayer-12B-ABYSS",
"base_model:merge:redrix/GodSlayer-12B-ABYSS",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T15:42:15Z | ---
base_model:
- redrix/GodSlayer-12B-ABYSS
- grimjim/magnum-twilight-12b
- DreadPoor/YM-12B-Model_Stock
- DreadPoor/Irix-12B-Model_Stock
- Delta-Vector/Rei-12B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [redrix/GodSlayer-12B-ABYSS](https://huggingface.co/redrix/GodSlayer-12B-ABYSS) as a base.
### Models Merged
The following models were included in the merge:
* [grimjim/magnum-twilight-12b](https://huggingface.co/grimjim/magnum-twilight-12b)
* [DreadPoor/YM-12B-Model_Stock](https://huggingface.co/DreadPoor/YM-12B-Model_Stock)
* [DreadPoor/Irix-12B-Model_Stock](https://huggingface.co/DreadPoor/Irix-12B-Model_Stock)
* [Delta-Vector/Rei-12B](https://huggingface.co/Delta-Vector/Rei-12B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: redrix/GodSlayer-12B-ABYSS
models:
- model: DreadPoor/Irix-12B-Model_Stock
- model: DreadPoor/YM-12B-Model_Stock
- model: grimjim/magnum-twilight-12b
- model: Delta-Vector/Rei-12B
merge_method: model_stock
dtype: bfloat16
parameters:
normalize: false
tokenizer:
source: union
```
|
wildgeese25/bert-fake-news-detector-2 | wildgeese25 | 2025-04-28T15:47:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-28T14:30:59Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-fake-news-detector-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fake-news-detector-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2013
- Accuracy: 0.9474
- Precision: 0.9479
- Recall: 0.9505
- F1: 0.9492
- Confusion Matrix: [[18354, 1088], [1031, 19787]]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Confusion Matrix |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------------------:|
| 0.1882 | 1.0 | 661 | 0.2182 | 0.9377 | 0.9275 | 0.9537 | 0.9404 | [[4193, 361], [224, 4616]] |
| 0.2037 | 2.0 | 1322 | 0.2023 | 0.9450 | 0.9456 | 0.9477 | 0.9467 | [[4290, 264], [253, 4587]] |
| 0.1399 | 3.0 | 1983 | 0.2023 | 0.9462 | 0.9463 | 0.9496 | 0.9479 | [[4293, 261], [244, 4596]] |
| 0.1806 | 4.0 | 2644 | 0.2084 | 0.9441 | 0.9507 | 0.9403 | 0.9455 | [[4318, 236], [289, 4551]] |
| 0.1194 | 5.0 | 3305 | 0.2247 | 0.9421 | 0.9518 | 0.9349 | 0.9433 | [[4325, 229], [315, 4525]] |
| 0.0933 | 6.0 | 3966 | 0.2537 | 0.9405 | 0.9507 | 0.9329 | 0.9417 | [[4320, 234], [325, 4515]] |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1
- Datasets 3.5.0
- Tokenizers 0.21.1
|
bigrainlin/liahona-GPT-CoLAB_0428-2 | bigrainlin | 2025-04-28T15:38:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T15:33:15Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** bigrainlin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
anubhavmittra/lora-weights-phi-4-customer-support-chatbot | anubhavmittra | 2025-04-28T15:37:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T15:36:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
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## Model Card Contact
[More Information Needed] |
mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF | mradermacher | 2025-04-28T15:35:02Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ChongyuWang/ShowUI_Grounding_Qwen_3B_pretrained_v1",
"base_model:quantized:ChongyuWang/ShowUI_Grounding_Qwen_3B_pretrained_v1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T15:20:58Z | ---
base_model: ChongyuWang/ShowUI_Grounding_Qwen_3B_pretrained_v1
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ChongyuWang/ShowUI_Grounding_Qwen_3B_pretrained_v1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q3_K_S.gguf) | Q3_K_S | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.IQ4_XS.gguf) | IQ4_XS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q4_K_S.gguf) | Q4_K_S | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q4_K_M.gguf) | Q4_K_M | 2.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q5_K_S.gguf) | Q5_K_S | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q5_K_M.gguf) | Q5_K_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q6_K.gguf) | Q6_K | 2.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.Q8_0.gguf) | Q8_0 | 3.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ShowUI_Grounding_Qwen_3B_pretrained_v1-GGUF/resolve/main/ShowUI_Grounding_Qwen_3B_pretrained_v1.f16.gguf) | f16 | 6.9 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. 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 -->
|
padmapriyaa/foodvisoradaptorweights | padmapriyaa | 2025-04-28T15:32:11Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2025-04-28T15:32:02Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
Kenazin/Llama-3.1-8B-peft-p-tuning-v5-8 | Kenazin | 2025-04-28T15:27:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T15:27:01Z | ---
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] |
littletuzi92/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-armored_monstrous_lynx | littletuzi92 | 2025-04-28T15:26:16Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am armored monstrous lynx",
"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 | 2025-04-10T21:37:42Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-armored_monstrous_lynx
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am armored monstrous lynx
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-armored_monstrous_lynx
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="littletuzi92/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-armored_monstrous_lynx", 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: 4.51.1
- 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}}
}
``` |
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