modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-21 00:45:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 567
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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BCMIZB/flux_fill
|
BCMIZB
| 2025-09-15T06:28:24Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"image-generation",
"flux",
"diffusion-single-file",
"en",
"license:other",
"diffusers:FluxFillPipeline",
"region:us"
] | null | 2025-09-14T11:19:20Z |
---
language:
- en
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
extra_gated_prompt: By clicking "Agree", you agree to the [FluxDev Non-Commercial License Agreement](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev/blob/main/LICENSE.md)
and acknowledge the [Acceptable Use Policy](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev/blob/main/POLICY.md).
tags:
- image-generation
- flux
- diffusion-single-file
---
`FLUX.1 Fill [dev]` is a 12 billion parameter rectified flow transformer capable of filling areas in existing images based on a text description.
For more information, please read our [blog post](https://blackforestlabs.ai/flux-1-tools/).
# Key Features
1. Cutting-edge output quality, second only to our state-of-the-art model `FLUX.1 Fill [pro]`.
2. Blends impressive prompt following with completing the structure of your source image.
3. Trained using guidance distillation, making `FLUX.1 Fill [dev]` more efficient.
4. Open weights to drive new scientific research, and empower artists to develop innovative workflows.
5. Generated outputs can be used for personal, scientific, and commercial purposes as described in the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
# Usage
We provide a reference implementation of `FLUX.1 Fill [dev]`, as well as sampling code, in a dedicated [github repository](https://github.com/black-forest-labs/flux).
Developers and creatives looking to build on top of `FLUX.1 Fill [dev]` are encouraged to use this as a starting point.
## API Endpoints
The FLUX.1 models are also available in our API [bfl.ml](https://docs.bfl.ml/)
## Diffusers
To use `FLUX.1 Fill [dev]` with the 🧨 diffusers python library, first install or upgrade diffusers
```shell
pip install -U diffusers
```
Then you can use `FluxFillPipeline` to run the model
```python
import torch
from diffusers import FluxFillPipeline
from diffusers.utils import load_image
image = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup.png")
mask = load_image("https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/cup_mask.png")
pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
image = pipe(
prompt="a white paper cup",
image=image,
mask_image=mask,
height=1632,
width=1232,
guidance_scale=30,
num_inference_steps=50,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save(f"flux-fill-dev.png")
```
To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation
---
# Limitations
- This model is not intended or able to provide factual information.
- As a statistical model this checkpoint might amplify existing societal biases.
- The model may fail to generate output that matches the prompts.
- Prompt following is heavily influenced by the prompting-style.
- There may be slight-color shifts in areas that are not filled in
- Filling in complex textures may produce lines at the edges of the filled-area.
# Out-of-Scope Use
The model and its derivatives may not be used
- In any way that violates any applicable national, federal, state, local or international law or regulation.
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; including but not limited to the solicitation, creation, acquisition, or dissemination of child exploitative content.
- To generate or disseminate verifiably false information and/or content with the purpose of harming others.
- To generate or disseminate personal identifiable information that can be used to harm an individual.
- To harass, abuse, threat
|
vivek8423/Ai-influencer-model
|
vivek8423
| 2025-09-15T06:28:10Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"generated-from-training",
"license:mit",
"region:us"
] |
text-to-image
| 2025-09-14T19:42:47Z |
---
library_name: diffusers
license: mit
tags:
- text-to-image
- stable-diffusion
- generated-from-training
pipeline_tag: text-to-image
---
# Ai-Influencer Image Generation Model
This is a fine-tuned image generation model designed to create high-quality, photorealistic images of AI influencers based on text prompts. Optimized for API integration with automation tools like n8n.
## 🚀 API Usage (for n8n/Make/Zapier)
This model is deployed as an API endpoint through Hugging Face's Inference API. You can trigger image generation using HTTP requests.
### API Endpoint
|
5456es/implicit_reward_Llama-3.2-3B-Instruct_prune_0.3-sigmoid
|
5456es
| 2025-09-15T06:27:21Z | 28 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"implicit",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T04:46:27Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- implicit
- pruned
---
# implicit_reward_Llama-3.2-3B-Instruct_prune_0.3-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the implicit method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: implicit
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: implicit
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/implicit_reward_Llama-3.2-3B-Instruct_prune_0.3-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
EbenBTC/Adam
|
EbenBTC
| 2025-09-15T06:27:20Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"any-to-any",
"en",
"dataset:nvidia/Llama-Nemotron-VLM-Dataset-v1",
"dataset:HuggingFaceFW/finepdfs",
"dataset:fka/awesome-chatgpt-prompts",
"base_model:Liontix/Qwen3-8B-Gemini-2.5-Pro-Distill-GGUF",
"base_model:adapter:Liontix/Qwen3-8B-Gemini-2.5-Pro-Distill-GGUF",
"license:apache-2.0",
"region:us"
] |
any-to-any
| 2025-09-15T06:20:39Z |
---
license: apache-2.0
datasets:
- nvidia/Llama-Nemotron-VLM-Dataset-v1
- HuggingFaceFW/finepdfs
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- accuracy
base_model:
- Liontix/Qwen3-8B-Gemini-2.5-Pro-Distill-GGUF
new_version: Phr00t/WAN2.2-14B-Rapid-AllInOne
pipeline_tag: any-to-any
library_name: adapter-transformers
---
|
5456es/random_prune_Llama-3.2-1B-Instruct_prune_0.3-sigmoid
|
5456es
| 2025-09-15T06:26:17Z | 31 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"random",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-09T04:01:34Z |
---
license: apache-2.0
base_model: Llama-3.2-1B-Instruct
tags:
- dpo
- preference-learning
- random
- pruned
---
# random_prune_Llama-3.2-1B-Instruct_prune_0.3-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-1B-Instruct using the random method.
## Model Details
- **Base Model**: Llama-3.2-1B-Instruct
- **Training Method**: random
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: random
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/random_prune_Llama-3.2-1B-Instruct_prune_0.3-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
mradermacher/Mira-v1-dpo-27B-GGUF
|
mradermacher
| 2025-09-15T06:25:53Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:nbeerbower/gutenberg2-dpo",
"dataset:Lambent/ai-deconditioning-synthesized-dpo",
"dataset:adamo1139/toxic-dpo-natural-v4",
"base_model:Lambent/Mira-v1-dpo-27B",
"base_model:quantized:Lambent/Mira-v1-dpo-27B",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-15T05:15:43Z |
---
base_model: Lambent/Mira-v1-dpo-27B
datasets:
- jondurbin/gutenberg-dpo-v0.1
- nbeerbower/gutenberg2-dpo
- Lambent/ai-deconditioning-synthesized-dpo
- adamo1139/toxic-dpo-natural-v4
language:
- en
library_name: transformers
license: gemma
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Lambent/Mira-v1-dpo-27B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mira-v1-dpo-27B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mira-v1-dpo-27B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.7 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.mmproj-f16.gguf) | mmproj-f16 | 1.0 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q2_K.gguf) | Q2_K | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q3_K_S.gguf) | Q3_K_S | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q3_K_M.gguf) | Q3_K_M | 13.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q3_K_L.gguf) | Q3_K_L | 14.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.IQ4_XS.gguf) | IQ4_XS | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q4_K_S.gguf) | Q4_K_S | 15.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q4_K_M.gguf) | Q4_K_M | 16.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q5_K_S.gguf) | Q5_K_S | 18.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q5_K_M.gguf) | Q5_K_M | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q6_K.gguf) | Q6_K | 22.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mira-v1-dpo-27B-GGUF/resolve/main/Mira-v1-dpo-27B.Q8_0.gguf) | Q8_0 | 28.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
devspemlix/results
|
devspemlix
| 2025-09-15T06:25:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-roberta-base-sentiment",
"base_model:finetune:cardiffnlp/twitter-roberta-base-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-15T06:22:09Z |
---
library_name: transformers
base_model: cardiffnlp/twitter-roberta-base-sentiment
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- Accuracy: 1.0
- F1: 1.0
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1722 | 1.0 | 29 | 0.0859 | 0.9804 | 0.9707 |
| 0.0918 | 2.0 | 58 | 0.0067 | 1.0 | 1.0 |
| 0.007 | 3.0 | 87 | 0.0016 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.7.1+cu118
- Datasets 4.0.0
- Tokenizers 0.22.0
|
5456es/bees_prune_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
|
5456es
| 2025-09-15T06:25:08Z | 29 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"bees",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T04:41:14Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- bees
- pruned
---
# bees_prune_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the bees method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: bees
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: bees
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/bees_prune_Llama-3.2-3B-Instruct_prune_0.5-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
limjh12/fintech20250915
|
limjh12
| 2025-09-15T06:24:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation",
"conversational",
"ko",
"dataset:hyokwan/familicare_health_general_knowledge",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T05:45:16Z |
---
license: apache-2.0
datasets:
- hyokwan/familicare_health_general_knowledge
language:
- ko
metrics:
- accuracy
base_model:
- google/gemma-3-4b-it
pipeline_tag: text-generation
library_name: transformers
---
|
5456es/random_prune_Qwen2.5-7B-Instruct_prune_0.3-sigmoid
|
5456es
| 2025-09-15T06:23:18Z | 37 | 0 | null |
[
"safetensors",
"qwen2",
"dpo",
"preference-learning",
"random",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T04:30:45Z |
---
license: apache-2.0
base_model: Qwen2.5-7B-Instruct
tags:
- dpo
- preference-learning
- random
- pruned
---
# random_prune_Qwen2.5-7B-Instruct_prune_0.3-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Qwen2.5-7B-Instruct using the random method.
## Model Details
- **Base Model**: Qwen2.5-7B-Instruct
- **Training Method**: random
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: random
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/random_prune_Qwen2.5-7B-Instruct_prune_0.3-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
5456es/selective_dpo_Qwen2.5-0.5B-Instruct_prune_0.7-sigmoid
|
5456es
| 2025-09-15T06:22:25Z | 55 | 0 | null |
[
"safetensors",
"qwen2",
"dpo",
"preference-learning",
"selective",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-07T05:05:02Z |
---
license: apache-2.0
base_model: Qwen2.5-0.5B-Instruct
tags:
- dpo
- preference-learning
- selective
- pruned
---
# selective_dpo_Qwen2.5-0.5B-Instruct_prune_0.7-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Qwen2.5-0.5B-Instruct using the selective method.
## Model Details
- **Base Model**: Qwen2.5-0.5B-Instruct
- **Training Method**: selective
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: selective
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/selective_dpo_Qwen2.5-0.5B-Instruct_prune_0.7-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
5456es/last_layer_prune_Llama-3.2-3B-Instruct_prune_0.2-sigmoid
|
5456es
| 2025-09-15T06:22:00Z | 18 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"last",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-12T08:52:03Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- last
- pruned
---
# last_layer_prune_Llama-3.2-3B-Instruct_prune_0.2-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the last method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: last
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: last
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/last_layer_prune_Llama-3.2-3B-Instruct_prune_0.2-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
WorldRWKV/temp
|
WorldRWKV
| 2025-09-15T06:21:20Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T07:58:32Z |
---
license: apache-2.0
---
|
svarekagerp/blockassist-bc-bellowing_reptilian_bee_1757917119
|
svarekagerp
| 2025-09-15T06:20:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing reptilian bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T06:19:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing reptilian bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Abby-OGV/Llama-3.1-8B-full_train-short-r16-alpha32
|
Abby-OGV
| 2025-09-15T06:20:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-15T06:19:21Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
5456es/last_layer_prune_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
|
5456es
| 2025-09-15T06:16:12Z | 0 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"last",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-15T06:11:38Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- last
- pruned
---
# last_layer_prune_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the last method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: last
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: last
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/last_layer_prune_Llama-3.2-3B-Instruct_prune_0.5-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
panzs19/LEMMA-LLAMA-3-70B
|
panzs19
| 2025-09-15T06:15:35Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2503.17439",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-14T11:08:32Z |
---
license: llama3
library_name: transformers
pipeline_tag: text-generation
---
# Model Card for Model ID
**Key Takeaways**
💡 **Systematic analysis on error types**: Categorizes common model-generated mathematical reasoning errors, revealing consistent error patterns across models and guiding targeted improvements.
💡 **Error-type grounded error augmentation**: Introduces diverse and meaningful errors by leveraging a teacher model to _intentionally inject representative mistakes_ with type sampled from the analyzed distribution, enhancing the model’s ability to learn from failures.
💡 **Two complementary self-correction mechanisms**: Combines _Fix & Continue_ (correcting mistakes within the original reasoning) and _Fresh & Restart_ (restarting the reasoning process from scratch) to generate effective revision trajectories.
✅ **LEMMA** – A novel framework that fine-tunes LLMs on error-corrective trajectories, enabling autonomous error detection and correction during mathematical reasoning.
📊 **Result** – Up to 13.3% accuracy improvement for LLaMA3-8B with only 90k synthesized data.
<!-- Provide a quick summary of what the model is/does. -->
The LEMMA series models are trained on the [LEMMA Dataset](https://huggingface.co/datasets/panzs19/LEMMA). This dataset uses the training set of MATH and GSM8K to generate error-corrective reasoning trajectories. For each question in these datasets, the student model (LLaMA3-8B) generates self-generated errors, and the teacher model (GPT-4o) deliberately introduces errors based on the error type distribution of the student model. Then, both "Fix & Continue" and "Fresh & Restart" correction strategies are applied to these errors to create error-corrective revision trajectories. After filtering out trajectories with incorrect final answers, we obtain this dataset. Fine-tuning on this dataset achieves up to 13.3% average accuracy improvement for LLaMA3-8B with less than 90k synthesized data. For more details, please refer to our paper [LEMMA: Learning from Errors for MatheMatical Advancement in LLMs](https://arxiv.org/abs/2503.17439).
## Model Details
### Model Description
- **Finetuned from model [optional]:** [Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
### Model Sources [optional]
- **Repository:** [https://github.com/pzs19/LEMMA/](https://github.com/pzs19/LEMMA/)
- **Paper:** [https://arxiv.org/abs/2503.17439](https://arxiv.org/abs/2503.17439)
### Direct Use
The same as Llama-3-70B.
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## Training Details
The LEMMA series models are trained on the [LEMMA Dataset](https://huggingface.co/datasets/panzs19/LEMMA) using [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory). For more details, please refer to our [paper](https://arxiv.org/abs/2503.17439).
### Results
| Model | Checkpoint | Paper | GSM8k | MATH | License |
| ----- |------| ---- |------|-------| ----- |
| LEMMA-LLAMA-3-8B | 🤗 <a href="https://huggingface.co/panzs19/LEMMA-LLAMA-3-8B" target="_blank">HF Link</a> | 📃 <a href="" target="_blank">[LEMMA]</a>| **79.2** | **38.3** | <a href="https://www.llama.com/llama3/license/" target="_blank">Llama 3 </a> |
| LEMMA-LLAMA-3-70B | 🤗 <a href="https://huggingface.co/panzs19/LEMMA-LLAMA-3-70B" target="_blank">HF Link</a> | 📃 <a href="" target="_blank">[LEMMA]</a>| **91.5** | **51.8** | <a href="https://www.llama.com/llama3/license/" target="_blank">Llama 3 </a> |
## Citation [optional]
Please cite the paper if you refer to our model, code, data or paper from MetaMath.
```
@article{LEMMA,
title={LEMMA: Learning from Errors for MatheMatical Advancement in LLMs},
author={Zhuoshi Pan, Yu Li, Honglin Lin, Qizhi Pei, Zinan Tang, Wei Wu, Chenlin Ming, H. Vicky Zhao, Conghui He, Lijun Wu},
journal={arXiv preprint arXiv:2503.17439},
year={2025}
}
```
|
sssssungjae/qwen2_5-7b-finance-full-final-v2
|
sssssungjae
| 2025-09-15T06:15:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-7B",
"base_model:finetune:unsloth/Qwen2.5-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T06:13:56Z |
---
base_model: unsloth/Qwen2.5-7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sssssungjae
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
panzs19/LEMMA-LLAMA-3-8B
|
panzs19
| 2025-09-15T06:13:47Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2503.17439",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-18T08:31:24Z |
---
license: llama3
library_name: transformers
pipeline_tag: text-generation
---
# Model Card for LEMMA-LLAMA-3-8B
The LEMMA series models are trained on the [LEMMA Dataset](https://huggingface.co/datasets/panzs19/LEMMA). This dataset uses the training set of MATH and GSM8K to generate error-corrective reasoning trajectories. For each question in these datasets, the student model (LLaMA3-8B) generates self-generated errors, and the teacher model (GPT-4o) deliberately introduces errors based on the error type distribution of the student model. Then, both "Fix & Continue" and "Fresh & Restart" correction strategies are applied to these errors to create error-corrective revision trajectories. After filtering out trajectories with incorrect final answers, we obtain this dataset. Fine-tuning on this dataset achieves up to 13.3% average accuracy improvement for LLaMA3-8B with less than 90k synthesized data. For more details, please refer to our paper [LEMMA: Learning from Errors for MatheMatical Advancement in LLMs](https://arxiv.org/abs/2503.17439).
## Model Details
### Model Description
- **Finetuned from model:** [Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
### Model Sources
- **Repository:** [https://github.com/pzs19/LEMMA/](https://github.com/pzs19/LEMMA/)
- **Paper:** [https://arxiv.org/abs/2503.17439](https://arxiv.org/abs/2503.17439)
### Direct Use
The same as Llama-3-8B.
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
## Training Details
The LEMMA series models are trained on the [LEMMA Dataset](https://huggingface.co/datasets/panzs19/LEMMA) using [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory). For more details, please refer to our [paper](https://arxiv.org/abs/2503.17439).
### Results
| Model | Checkpoint | Paper | GSM8k | MATH | License |
| ----- |------| ---- |------|-------| ----- |
| LEMMA-LLAMA-3-8B | 🤗 <a href="https://huggingface.co/panzs19/LEMMA-LLAMA-3-8B" target="_blank">HF Link</a> | 📃 <a href="https://huggingface.co/papers/2503.17439" target="_blank">[LEMMA]</a>| **79.2** | **38.3** | <a href="https://www.llama.com/llama3/license/" target="_blank">Llama 3 </a> |
| LEMMA-LLAMA-3-70B | 🤗 <a href="https://huggingface.co/panzs19/LEMMA-LLAMA-3-70B" target="_blank">HF Link</a> | 📃 <a href="https://huggingface.co/papers/2503.17439" target="_blank">[LEMMA]</a>| **91.5** | **51.8** | <a href="https://www.llama.com/llama3/license/" target="_blank">Llama 3 </a> |
## Citation
Please cite the paper if you refer to our model, code, data or paper from MetaMath.
```
@article{LEMMA,
title={LEMMA: Learning from Errors for MatheMatical Advancement in LLMs},
author={Zhuoshi Pan, Yu Li, Honglin Lin, Qizhi Pei, Zinan Tang, Wei Wu, Chenlin Ming, H. Vicky Zhao, Conghui He, Lijun Wu},
journal={arXiv preprint arXiv:2503.17439},
year={2025}
}
```
|
5456es/cluster_prune_Qwen2.5-1.5B-Instruct_prune_0.3-sigmoid
|
5456es
| 2025-09-15T06:11:37Z | 38 | 0 | null |
[
"safetensors",
"qwen2",
"dpo",
"preference-learning",
"cluster",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-07T05:01:16Z |
---
license: apache-2.0
base_model: Qwen2.5-1.5B-Instruct
tags:
- dpo
- preference-learning
- cluster
- pruned
---
# cluster_prune_Qwen2.5-1.5B-Instruct_prune_0.3-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Qwen2.5-1.5B-Instruct using the cluster method.
## Model Details
- **Base Model**: Qwen2.5-1.5B-Instruct
- **Training Method**: cluster
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: cluster
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/cluster_prune_Qwen2.5-1.5B-Instruct_prune_0.3-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
5456es/last_layer_prune_Llama-3.1-8B-Instruct_prune_0.3-sigmoid
|
5456es
| 2025-09-15T06:10:49Z | 0 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"last",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-15T05:59:53Z |
---
license: apache-2.0
base_model: Llama-3.1-8B-Instruct
tags:
- dpo
- preference-learning
- last
- pruned
---
# last_layer_prune_Llama-3.1-8B-Instruct_prune_0.3-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.1-8B-Instruct using the last method.
## Model Details
- **Base Model**: Llama-3.1-8B-Instruct
- **Training Method**: last
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: last
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/last_layer_prune_Llama-3.1-8B-Instruct_prune_0.3-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
HectorHe/Qwen1.5-MOE-aux-free-sft-math7k-1e-3-gamma
|
HectorHe
| 2025-09-15T06:10:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_moe",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:HectorHe/math7k",
"base_model:Qwen/Qwen1.5-MoE-A2.7B",
"base_model:finetune:Qwen/Qwen1.5-MoE-A2.7B",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T05:41:35Z |
---
base_model: Qwen/Qwen1.5-MoE-A2.7B
datasets: HectorHe/math7k
library_name: transformers
model_name: Qwen1.5-MOE-aux-free-sft-math7k-1e-3-gamma
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen1.5-MOE-aux-free-sft-math7k-1e-3-gamma
This model is a fine-tuned version of [Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) on the [HectorHe/math7k](https://huggingface.co/datasets/HectorHe/math7k) 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="HectorHe/Qwen1.5-MOE-aux-free-sft-math7k-1e-3-gamma", 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/hector_-carnegie-mellon-university/huggingface/runs/26r47xsq)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.51.0
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
svarekagerp/blockassist-bc-bellowing_reptilian_bee_1757916508
|
svarekagerp
| 2025-09-15T06:09:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing reptilian bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T06:09:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing reptilian bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
beyoru/Qwen3-4B-I-1209-API
|
beyoru
| 2025-09-15T06:06:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:beyoru/Qwen3-4B-I-1209-API",
"base_model:finetune:beyoru/Qwen3-4B-I-1209-API",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T05:23:28Z |
---
base_model: beyoru/Qwen3-4B-I-1209-API
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** beyoru
- **License:** apache-2.0
- **Finetuned from model :** beyoru/Qwen3-4B-I-1209-API
This qwen3 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)
|
tencent/SRPO
|
tencent
| 2025-09-15T06:03:16Z | 2,526 | 365 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"arxiv:2509.06942",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-08T12:44:15Z |
---
library_name: diffusers
license: other
license_name: tencent-hunyuan-community
license_link: https://github.com/Tencent-Hunyuan/SRPO/blob/main/LICENSE.txt
pipeline_tag: text-to-image
---
<div align=“center” style=“font-family: charter;”>
<h1 align="center">Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference </h1>
<div align="center">
<a href='https://arxiv.org/abs/2509.06942'><img src='https://img.shields.io/badge/ArXiv-red?logo=arxiv'></a>
<a href='https://github.com/Tencent-Hunyuan/SRPO'><img src='https://img.shields.io/badge/_Code-SRPO-181717?color=121717&logo=github&logoColor=whitee'></a>
<a href='https://tencent.github.io/srpo-project-page/'><img src='https://img.shields.io/badge/%F0%9F%92%BB_Project-SRPO-blue'></a>
</div>
<div align="center">
Xiangwei Shen<sup>1,2*</sup>,
<a href="https://scholar.google.com/citations?user=Lnr1FQEAAAAJ&hl=zh-CN" target="_blank"><b>Zhimin Li</b></a><sup>1*</sup>,
<a href="https://scholar.google.com.hk/citations?user=Fz3X5FwAAAAJ" target="_blank"><b>Zhantao Yang</b></a><sup>1</sup>,
<a href="https://shiyi-zh0408.github.io/" target="_blank"><b>Shiyi Zhang</b></a><sup>3</sup>,
Yingfang Zhang<sup>1</sup>,
Donghao Li<sup>1</sup>,
<br>
<a href="https://scholar.google.com/citations?user=VXQV5xwAAAAJ&hl=en" target="_blank"><b>Chunyu Wang</b></a><sup>1</sup>,
<a href="https://openreview.net/profile?id=%7EQinglin_Lu2" target="_blank"><b>Qinglin Lu</b></a><sup>1</sup>,
<a href="https://andytang15.github.io" target="_blank"><b>Yansong Tang</b></a><sup>3,✝</sup>
</div>
<div align="center">
<sup>1</sup>Hunyuan, Tencent
<br>
<sup>2</sup>School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen
<br>
<sup>3</sup>Shenzhen International Graduate School, Tsinghua University
<br>
<sup>*</sup>Equal contribution
<sup>✝</sup>Corresponding author
</div>
## Abstract
Recent studies have demonstrated the effectiveness of directly aligning diffusion models with human preferences using differentiable reward. However, they exhibit two primary challenges: (1) they rely on multistep denoising with gradient computation for reward scoring, which is computationally expensive, thus restricting optimization to only a few diffusion steps; (2) they often need continuous offline adaptation of reward models in order to achieve desired aesthetic quality, such as photorealism or precise lighting effects. To address the limitation of multistep denoising, we propose Direct-Align, a method that predefines a noise prior to effectively recover original images from any time steps via interpolation, leveraging the equation that diffusion states are interpolations between noise and target images, which effectively avoids over-optimization in late timesteps. Furthermore, we introduce Semantic Relative Preference Optimization (SRPO), in which rewards are formulated as text-conditioned signals. This approach enables online adjustment of rewards in response to positive and negative prompt augmentation, thereby reducing the reliance on offline reward fine-tuning. By fine-tuning the FLUX.1.dev model with optimized denoising and online reward adjustment, we improve its human-evaluated realism and aesthetic quality by over 3x.
## Acknowledgement
We sincerely appreciate contributions from the research community to this project. Below are quantized versions developed by fellow researchers.
1. 8bit(fp8_e4m3fn/Q8_0) version by wikeeyang: https://huggingface.co/wikeeyang/SRPO-Refine-Quantized-v1.0
2. bf16 version by rockerBOO: https://huggingface.co/rockerBOO/flux.1-dev-SRPO
3. GGUF version by befox: https://huggingface.co/befox/SRPO-GGUF
⚠️ Note: When loading weights in ComfyUI, avoid direct conversion of FP32 weights to FP8 format, as this may result in incomplete denoising. For official weights in this repository, FP32/BF16 loading is recommended.
### Checkpoints
The `diffusion_pytorch_model.safetensors` is online version of SRPO based on [FLUX.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev), trained on HPD dataset with [HPSv2](https://github.com/tgxs002/HPSv2)
## 🔑 Inference
### Using ComfyUI
You can use it in [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
Load the following image in ComfyUI to get the workflow, or load the JSON file directly [SRPO-workflow](comfyui/SRPO-workflow.json):
Tip: The workflow JSON info was added to the image file.
### Quick start
```bash
from diffusers import FluxPipeline
from safetensors.torch import load_file
prompt='The Death of Ophelia by John Everett Millais, Pre-Raphaelite painting, Ophelia floating in a river surrounded by flowers, detailed natural elements, melancholic and tragic atmosphere'
pipe = FluxPipeline.from_pretrained('./data/flux',
torch_dtype=torch.bfloat16,
use_safetensors=True
).to("cuda")
state_dict = load_file("./srpo/diffusion_pytorch_model.safetensors")
pipe.transformer.load_state_dict(state_dict)
image = pipe(
prompt,
guidance_scale=3.5,
height=1024,
width=1024,
num_inference_steps=50,
max_sequence_length=512,
generator=generator
).images[0]
```
### License
SRPO is licensed under the License Terms of SRPO. See `./License.txt` for more details.
## Citation
If you use SRPO for your research, please cite our paper:
```bibtex
@misc{shen2025directlyaligningdiffusiontrajectory,
title={Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference},
author={Xiangwei Shen and Zhimin Li and Zhantao Yang and Shiyi Zhang and Yingfang Zhang and Donghao Li and Chunyu Wang and Qinglin Lu and Yansong Tang},
year={2025},
eprint={2509.06942},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2509.06942},
}
```
|
felixZzz/np_4b_len16k_custom_teacher_custom_student_reject_mix-0914
|
felixZzz
| 2025-09-15T06:02:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T06:00:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DopeorNope/new_new_cpt_600
|
DopeorNope
| 2025-09-15T06:01:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T05:57:51Z |
---
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]
|
pf0607/MUSE
|
pf0607
| 2025-09-15T06:00:56Z | 0 | 0 | null |
[
"arxiv:2508.14440",
"region:us"
] | null | 2025-09-15T05:49:27Z |
Model weight of MUSE: Multi-Subject Unified Synthesis via Explicit Layout Semantic Expansion[ICCV2025].
https://github.com/pf0607/MUSE
https://arxiv.org/abs/2508.14440
|
PheniX-Lab/FoMo4Wheat
|
PheniX-Lab
| 2025-09-15T05:59:31Z | 0 | 3 | null |
[
"arxiv:2509.06907",
"region:us"
] | null | 2025-08-19T04:27:31Z |
# FoMo4Wheat
The official implementation of the paper [**FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data**](https://arxiv.org/abs/2509.06907).
Contact：Shouyang Liu ([email protected])，Hao Lu ([email protected])，Yanfeng Ding ([email protected])
# Abstract
Vision-driven in-field crop monitoring is essential for advancing digital agriculture whether supporting commercial decisions on-farm or augmenting research experiments in breeding and agronomy. Existing crop vision models struggle to generalize across fine-scale, highly variable canopy structures, and fluctuating outdoor environments. In this work, we present FoMo4Wheat, one of the first crop-orientated vision foundation models and demonstrate that delivers strong performance across a wide range of agricultural vision tasks. Centered on wheat, the most globally significant food crop, we curated ImAg4Wheat—the largest and most diverse wheat image dataset to date. It comprises 2.5 million high-resolution images collected over a decade from breeding and experimental fields, spanning more than 2,000 genotypes and 500 distinct environmental conditions across 30 global sites. A suite of FoMo4Wheat models was pre-trained using self-supervised learning on this dataset. Benchmark results across ten crop-related downstream tasks show that FoMo4Wheat consistently outperforms state-of-the-art models trained on general-domain datasets. Beyond strong cross-task generalization within wheat crops, FoMo4Wheat is highly robust in limited-data regimes but on previously unseen crop data. Notably, it contributes significantly to vision tasks in rice and multiplw crop/weed images, highlighting its cross-crop adaptability. In delivering one of the first open-source foundation models for wheat, our results demonstrate the value of such crop-specific foundation models that will support the development of versatile high-performing vision systems in crop breeding and precision agriculture.
# Installation
The training and evaluation code is developed with PyTorch 2.5.1 and requires Linux environment with multiple third-party dependencies. To set up all required dependencies for training and evaluation, please follow the instructions below:
```
conda env create -f conda.yaml
conda activate FoMo4Wheat
```
# Data Preparation
ImAg4Wheat comprises 2,500,000 million images over 2,000 wheat genotypes cultivated under 500 distinct environmental conditions across 30 sites in 10 countries spanning a decade, covering the full crop growth cycle. [ImAg4Wheat](https://huggingface.co/datasets/PheniX-Lab/ImAg4Wheat)
(**Note: The complete dataset will be made publicly accessible upon formal publication of the associated research paper.**)
# Pretrained models
| model | # of params | download |
| :---------------------:| -----------: |:--------------:|
| ViT-B/14 | 86 M | [FoMo4Wheat_base.pth](https://huggingface.co/PheniX-Lab/FoMo4Wheat/blob/main/weight/FoMo4Wheat_base.pth) |
| ViT-L/14 | 300 M | [FoMo4Wheat_large.pth](https://huggingface.co/PheniX-Lab/FoMo4Wheat/blob/main/weight/FoMo4Wheat_large.pth) |
| ViT-G/14 | 1,100 M | [FoMo4Wheat_giant.pth](https://huggingface.co/PheniX-Lab/FoMo4Wheat/blob/main/weight/FoMo4Wheat_giant.pth) |
# Training
**Training FoMo4Wheat on ImAg4Wheat**
Run FoMo4Wheat training on 6 A800-80GB nodes (48 GPUs) in a SLURM cluster environment with submitit:
```
MKL_NUM_THREADS=8 OMP_NUM_THREADS=8 python FoMo4Wheat/run/train/
--nodes 6 \
--config-file FoMo4Wheat/configs/train/vitg_14_224.yaml \
--output-dir <PATH/TO/OUTPUT/DIR> \
train.dataset_path=TestDataset:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
```
# Benchmark
We leverage publicly available, self-collected, and internationally collaborated datasets tailored to six downstream wheat vision tasks, two rice vision tasks, and two generic crop vision tasks. The rice- and crop-related tasks aim to justify whether the vision wheat foundation model can generalize to other crop species. The six wheat vision tasks include wheat growth stage classification, wheat disease classification, wheat head detection, UAV-based wheat spike detection, leaf tip counting, and wheat organ segmentation. The two rice vision tasks are comprised of rice leaf tip counting and rice organ segmentation. The two crop vision tasks are multi-crop segmentation and crop and weed segmentation.[Benchmark](https://huggingface.co/PheniX-Lab/FoMo4Wheat/tree/main/Benchmark)
# License
FoMo4Wheat code and model weights are released under the MIT License. See LICENSE for additional details.
# Citation
If you use our project in your research or wish to refer to the results of the project, please use the following BibTeX entry.
```bibtex
@article{2025FoMo4Wheat,
title={FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data},
author={Bing Han, Chen Zhu, Dong Han, Rui Yu, Songliang Cao, Jianhui Wu, Scott Chapman, Zijian Wang, Bangyou Zheng, Wei Guo, Marie Weiss, Benoit de Solan, Andreas Hund, Lukas Roth, Kirchgessner Norbert, Andrea Visioni, Yufeng Ge, Wenjuan Li, Alexis Comar, Dong Jiang, Dejun Han, Fred Baret, Yanfeng Ding, Hao Lu and Shouyang Liu},
journal={arXiv:2509.06907},
year={2025}
}
```
# Collaborators
<img width="1929" height="1057" alt="logo" src="https://github.com/user-attachments/assets/6bee265b-8a2d-4ae7-aeec-5cb71a29818e" />
|
svarekagerp/blockassist-bc-bellowing_reptilian_bee_1757915887
|
svarekagerp
| 2025-09-15T05:59:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing reptilian bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T05:59:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing reptilian bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
5456es/implicit_reward_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
|
5456es
| 2025-09-15T05:58:57Z | 29 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"implicit",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T04:25:17Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- implicit
- pruned
---
# implicit_reward_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the implicit method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: implicit
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: implicit
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/implicit_reward_Llama-3.2-3B-Instruct_prune_0.5-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
5456es/random_prune_Qwen2.5-7B-Instruct_prune_0.7-sigmoid
|
5456es
| 2025-09-15T05:57:54Z | 41 | 0 | null |
[
"safetensors",
"qwen2",
"dpo",
"preference-learning",
"random",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T04:14:51Z |
---
license: apache-2.0
base_model: Qwen2.5-7B-Instruct
tags:
- dpo
- preference-learning
- random
- pruned
---
# random_prune_Qwen2.5-7B-Instruct_prune_0.7-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Qwen2.5-7B-Instruct using the random method.
## Model Details
- **Base Model**: Qwen2.5-7B-Instruct
- **Training Method**: random
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: random
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/random_prune_Qwen2.5-7B-Instruct_prune_0.7-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
fanwu103/distilgpt2-finetuned-wikitext2
|
fanwu103
| 2025-09-15T05:57:04Z | 10 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-12T03:28:54Z |
---
library_name: transformers
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7573
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 292 | 3.8125 |
| 0.4959 | 2.0 | 584 | 3.7696 |
| 0.4959 | 3.0 | 876 | 3.7573 |
### Framework versions
- Transformers 4.53.0
- Pytorch 2.7.0+gitf717b2a
- Datasets 4.0.0
- Tokenizers 0.21.4
|
5456es/selective_dpo_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
|
5456es
| 2025-09-15T05:56:47Z | 39 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"selective",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T04:10:15Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- selective
- pruned
---
# selective_dpo_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the selective method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: selective
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: selective
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/selective_dpo_Llama-3.2-3B-Instruct_prune_0.5-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
HYUNJINI/pfsp_test_1
|
HYUNJINI
| 2025-09-15T05:55:15Z | 0 | 0 |
unsloth
|
[
"unsloth",
"safetensors",
"text-generation",
"job-shop-scheduling",
"optimization",
"llama",
"jssp",
"conversational",
"en",
"dataset:ACCORD",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-15T05:54:12Z |
---
license: apache-2.0
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
tags:
- text-generation
- job-shop-scheduling
- optimization
- llama
- unsloth
- jssp
datasets:
- ACCORD
language:
- en
pipeline_tag: text-generation
library_name: unsloth
---
# JSSP LLaMA 8B Fine-tuned Model
## Model Description
Job Shop Scheduling Problem (JSSP) 최적화를 위해 파인튜닝된 LLaMA 8B 모델입니다.
## Training Details
- Base Model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
- LoRA Rank: 64
- Epochs: 4
- Max Sequence Length: 40,000
- Dataset: ACCORD
## Usage
```python
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="HYUNJINI/pfsp_test_1",
max_seq_length=40000,
load_in_4bit=True,
dtype=torch.bfloat16,
)
FastLanguageModel.for_inference(model)
```
|
5456es/implicit_reward_Llama-3.2-1B-Instruct_prune_0.3-sigmoid
|
5456es
| 2025-09-15T05:54:15Z | 36 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"implicit",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-07T04:57:53Z |
---
license: apache-2.0
base_model: Llama-3.2-1B-Instruct
tags:
- dpo
- preference-learning
- implicit
- pruned
---
# implicit_reward_Llama-3.2-1B-Instruct_prune_0.3-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-1B-Instruct using the implicit method.
## Model Details
- **Base Model**: Llama-3.2-1B-Instruct
- **Training Method**: implicit
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: implicit
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/implicit_reward_Llama-3.2-1B-Instruct_prune_0.3-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
KD4/CodeOptimiser_CAPSTONE
|
KD4
| 2025-09-15T05:52:15Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-09-15T05:52:15Z |
---
license: cc-by-nc-4.0
---
|
5456es/cluster_prune_Llama-3.2-1B-Instruct_prune_0.3-sigmoid
|
5456es
| 2025-09-15T05:52:05Z | 28 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"cluster",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-07T04:55:54Z |
---
license: apache-2.0
base_model: Llama-3.2-1B-Instruct
tags:
- dpo
- preference-learning
- cluster
- pruned
---
# cluster_prune_Llama-3.2-1B-Instruct_prune_0.3-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-1B-Instruct using the cluster method.
## Model Details
- **Base Model**: Llama-3.2-1B-Instruct
- **Training Method**: cluster
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: cluster
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/cluster_prune_Llama-3.2-1B-Instruct_prune_0.3-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
5456es/implicit_reward_Qwen2.5-1.5B-Instruct_prune_0.7-sigmoid
|
5456es
| 2025-09-15T05:51:42Z | 44 | 0 | null |
[
"safetensors",
"qwen2",
"dpo",
"preference-learning",
"implicit",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-07T04:53:04Z |
---
license: apache-2.0
base_model: Qwen2.5-1.5B-Instruct
tags:
- dpo
- preference-learning
- implicit
- pruned
---
# implicit_reward_Qwen2.5-1.5B-Instruct_prune_0.7-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Qwen2.5-1.5B-Instruct using the implicit method.
## Model Details
- **Base Model**: Qwen2.5-1.5B-Instruct
- **Training Method**: implicit
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: implicit
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/implicit_reward_Qwen2.5-1.5B-Instruct_prune_0.7-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
5456es/cluster_prune_Llama-3.2-1B-Instruct_prune_0.7-sigmoid
|
5456es
| 2025-09-15T05:51:13Z | 40 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"cluster",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-07T04:51:06Z |
---
license: apache-2.0
base_model: Llama-3.2-1B-Instruct
tags:
- dpo
- preference-learning
- cluster
- pruned
---
# cluster_prune_Llama-3.2-1B-Instruct_prune_0.7-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-1B-Instruct using the cluster method.
## Model Details
- **Base Model**: Llama-3.2-1B-Instruct
- **Training Method**: cluster
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: cluster
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/cluster_prune_Llama-3.2-1B-Instruct_prune_0.7-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
5456es/random_prune_Llama-3.2-3B-Instruct_prune_0.3-sigmoid
|
5456es
| 2025-09-15T05:50:53Z | 28 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"random",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-09T03:37:32Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- random
- pruned
---
# random_prune_Llama-3.2-3B-Instruct_prune_0.3-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the random method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: random
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: random
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/random_prune_Llama-3.2-3B-Instruct_prune_0.3-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
5456es/random_prune_Llama-3.2-3B-Instruct_prune_0.2-sigmoid
|
5456es
| 2025-09-15T05:50:21Z | 0 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"random",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-15T05:45:49Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- random
- pruned
---
# random_prune_Llama-3.2-3B-Instruct_prune_0.2-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the random method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: random
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: random
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/random_prune_Llama-3.2-3B-Instruct_prune_0.2-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
tamewild/4b_v97_merged_e5
|
tamewild
| 2025-09-15T05:49:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T05:48:36Z |
---
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]
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|
QuantTrio/Seed-OSS-36B-Instruct-GPTQ-Int8
|
QuantTrio
| 2025-09-15T05:49:47Z | 398 | 1 |
transformers
|
[
"transformers",
"safetensors",
"seed_oss",
"text-generation",
"vLLM",
"GPTQ",
"conversational",
"zh",
"en",
"base_model:ByteDance-Seed/Seed-OSS-36B-Instruct",
"base_model:quantized:ByteDance-Seed/Seed-OSS-36B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"gptq",
"region:us"
] |
text-generation
| 2025-08-21T07:03:04Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- vLLM
- GPTQ
language:
- zh
- en
base_model:
- ByteDance-Seed/Seed-OSS-36B-Instruct
base_model_relation: quantized
---
# Seed-OSS-36B-Instruct-GPTQ-Int8
Base model: [ByteDance-Seed/Seed-OSS-36B-Instruct](https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Instruct)
### 【vLLM Single Node with 2 GPUs — Startup Command】
```
CONTEXT_LENGTH=32768
vllm serve \
QuantTrio/Seed-OSS-36B-Instruct-GPTQ-Int8 \
--served-model-name Seed-OSS-36B-Instruct-GPTQ-Int8 \
--enable-auto-tool-choice \
--tool-call-parser seed_oss \
--chat-template ./Seed-OSS-36B-Instruct-GPTQ-Int8/chat_template.jinja \
--swap-space 4 \
--max-num-seqs 512 \
--max-model-len $CONTEXT_LENGTH \
--max-seq-len-to-capture $CONTEXT_LENGTH \
--gpu-memory-utilization 0.9 \
--tensor-parallel-size 2 \
--trust-remote-code \
--disable-log-requests \
--host 0.0.0.0 \
--port 8000
```
### 【Dependencies / Installation】
As of **2025-09-15**, create a fresh Python environment and run:
```bash
vllm>=0.10.2
transformers>=4.56.1
```
As of **2025-08-21**, create a fresh Python environment and run:
```bash
VLLM_USE_PRECOMPILED=1 pip install git+https://github.com/FoolPlayer/vllm.git@seed-oss
pip install git+https://github.com/Fazziekey/transformers.git@seed-oss
```
### 【Logs】
```
2025-09-15
1.Update 【Dependencies / Installation】
2025-08-21
1. Initial commit
```
### 【Model Files】
| File Size | Last Updated |
|-----------|--------------|
| `36GB` | `2025-08-21` |
### 【Model Download】
```python
from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/Seed-OSS-36B-Instruct-GPTQ-Int8', cache_dir="your_local_path")
```
### 【Overview】
## Introduction
<div align="center">
👋 Hi, everyone!
<br>
We are <b>ByteDance Seed Team.</b>
</div>
<p align="center">
You can get to know us better through the following channels👇
<br>
<a href="https://seed.bytedance.com/">
<img src="https://img.shields.io/badge/Website-%231e37ff?style=for-the-badge&logo=bytedance&logoColor=white"></a>
</p>
# Seed-OSS Open-Source Models
<p align="center">
<a href="https://github.com/ByteDance-Seed/seed-oss">
<img src="https://img.shields.io/badge/Seed-Project Page-yellow"></a>
<a href="https://github.com/ByteDance-Seed/seed-oss">
<img src="https://img.shields.io/badge/Seed-Tech Report Coming Soon-red"></a>
<a href="https://huggingface.co/ByteDance-Seed">
<img src="https://img.shields.io/badge/Seed-Hugging Face-orange"></a>
<br>
<a href="./LICENSE">
<img src="https://img.shields.io/badge/License-Apache2.0-blue"></a>
</p>
> [!NOTE]
> This model card is dedicated to the `Seed-OSS-36B-Instruct` model.
## News
- [2025/08/20]🔥We release `Seed-OSS-36B-Base` (both with and without synthetic data versions) and `Seed-OSS-36B-Instruct`.
## Introduction
Seed-OSS is a series of open-source large language models developed by ByteDance's Seed Team, designed for powerful long-context, reasoning, agent and general capabilities, and versatile developer-friendly features. Although trained with only 12T tokens, Seed-OSS achieves excellent performance on several popular open benchmarks.
We release this series of models to the open-source community under the Apache-2.0 license.
> [!NOTE]
> Seed-OSS is primarily optimized for international (i18n) use cases.
### Key Features
- **Flexible Control of Thinking Budget**: Allowing users to flexibly adjust the reasoning length as needed. This capability of dynamically controlling the reasoning length enhances inference efficiency in practical application scenarios.
- **Enhanced Reasoning Capability**: Specifically optimized for reasoning tasks while maintaining balanced and excellent general capabilities.
- **Agentic Intelligence**: Performs exceptionally well in agentic tasks such as tool-using and issue resolving.
- **Research-Friendly**: Given that the inclusion of synthetic instruction data in pre-training may affect the post-training research, we released pre-trained models both with and without instruction data, providing the research community with more diverse options.
- **Native Long Context**: Trained with up-to-512K long context natively.
### Model Summary
Seed-OSS adopts the popular causal language model architecture with RoPE, GQA attention, RMSNorm and SwiGLU activation.
<div align="center">
| | |
|:---:|:---:|
| | **Seed-OSS-36B** |
| **Parameters** | 36B |
| **Attention** | GQA |
| **Activation Function** | SwiGLU |
| **Number of Layers** | 64 |
| **Number of QKV Heads** | 80 / 8 / 8 |
| **Head Size** | 128 |
| **Hidden Size** | 5120 |
| **Vocabulary Size** | 155K |
| **Context Length** | 512K |
| **RoPE Base Frequency** | 1e7 |
</div>
## Evaluation Results
### Seed-OSS-36B-Base
Incorporating synthetic instruction data into pretraining leads to improved performance on most benchmarks. We adopt the version augmented with synthetic instruction data (i.e., *w/ syn.*) as `Seed-OSS-36B-Base`. We also release `Seed-OSS-36B-Base-woSyn` trained without such data (i.e., *w/o syn.*), offering the community a high-performance foundation model unaffected by synthetic instruction data.
<div align="center">
<table>
<thead>
<tr>
<th align="center">Benchmark</th>
<th align="center"><sup><a href="https://seed.bytedance.com/en/seed1_6">Seed1.6-Base</a></sup></th>
<th align="center"><sup>Qwen3-30B-A3B-Base-2507*</sup></th>
<th align="center"><sup>Qwen2.5-32B-Base*</sup></th>
<th align="center"><sup>Seed-OSS-36B-Base<br>(<i>w/ syn.</i>)</sup></th>
<th align="center"><sup>Seed-OSS-36B-Base-woSyn<br>(<i>w/o syn.</i>)</sup></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center" colspan=6><strong>Knowledge</strong></td>
</tr>
<tr>
<td align="center">MMLU-Pro</td>
<td align="center">70</td>
<td align="center">59.8</td>
<td align="center">58.5 (55.1)</td>
<td align="center"><b>65.1</b></td>
<td align="center">60.4</td>
</tr>
<tr>
<td align="center">MMLU</td>
<td align="center">88.8</td>
<td align="center">82.7</td>
<td align="center">84 (83.3)</td>
<td align="center"><b>84.9</b></td>
<td align="center">84.8</td>
</tr>
<tr>
<td align="center">TriviaQA</td>
<td align="center">91</td>
<td align="center">76.2</td>
<td align="center">76</td>
<td align="center"><b>82.1</b></td>
<td align="center">81.9</td>
</tr>
<tr>
<td align="center">GPQA-D</td>
<td align="center">43.4</td>
<td align="center"><b>37</b></td>
<td align="center">29.3</td>
<td align="center">31.7</td>
<td align="center">35.2</td>
</tr>
<tr>
<td align="center">SimpleQA</td>
<td align="center">17.1</td>
<td align="center">7.2</td>
<td align="center">6.1</td>
<td align="center">5.8</td>
<td align="center"><b>7.4</b></td>
</tr>
<tr>
<td align="center" colspan=6><strong>Reasoning</strong></td>
</tr>
<tr>
<td align="center">BBH</td>
<td align="center">92.1</td>
<td align="center">81.4</td>
<td align="center">79.1 (84.5)</td>
<td align="center"><b>87.7</b></td>
<td align="center">87.2</td>
</tr>
<tr>
<td align="center">AGIEval-en</td>
<td align="center">78</td>
<td align="center">66.4</td>
<td align="center">65.6</td>
<td align="center"><b>70.7</b></td>
<td align="center">70.1</td>
</tr>
<tr>
<td align="center" colspan=6><strong>Math</strong></td>
</tr>
<tr>
<td align="center">GSM8K</td>
<td align="center">93.1</td>
<td align="center">87</td>
<td align="center">87.5 (92.9)</td>
<td align="center"><b>90.8</b></td>
<td align="center">90.3</td>
</tr>
<tr>
<td align="center">MATH</td>
<td align="center">72.9</td>
<td align="center">61.1</td>
<td align="center">63.5 (57.7)</td>
<td align="center"><b>81.7</b></td>
<td align="center">61.3</td>
</tr>
<tr>
<td align="center" colspan=6><strong>Coding</strong></td>
</tr>
<tr>
<td align="center">MBPP</td>
<td align="center">83.6</td>
<td align="center">78.8</td>
<td align="center">77.8 (84.5)</td>
<td align="center"><b>80.6</b></td>
<td align="center">74.6</td>
</tr>
<tr>
<td align="center">HumanEval</td>
<td align="center">78</td>
<td align="center">70.7</td>
<td align="center">47.6 (58.5)</td>
<td align="center"><b>76.8</b></td>
<td align="center">75.6</td>
</tr>
</tbody>
</table>
</div>
<sup>
- <b>Bold</b> denotes open-source SOTA.
</sup><br/><sup>
- "*" indicates that the results in this column are presented in the format of "reproduced_results (reported_results_if_any)".
</sup>
### Seed-OSS-36B-Instruct
<div align="center">
<table>
<thead>
<tr>
<th align="center">Benchmark</th>
<th align="center"><sup><a href="https://console.volcengine.com/ark/region:ark+cn-beijing/model/detail?Id=doubao-seed-1-6-thinking">Seed1.6-Thinking-0715</a></sup></th>
<th align="center"><sup>OAI-OSS-20B*</sup></th>
<th align="center"><sup>Qwen3-30B-A3B-Thinking-2507*</sup></th>
<th align="center"><sup>Qwen3-32B*</sup></th>
<th align="center"><sup>Gemma3-27B</sup></th>
<th align="center"><sup>Seed-OSS-36B-Instruct</sup></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center" colspan=7><strong>Knowledge</strong></td>
</tr>
<tr>
<td align="center">MMLU-Pro</td>
<td align="center">86.6</td>
<td align="center">76.2</td>
<td align="center"><ins>81.9</ins> (80.9)</td>
<td align="center">81.8</td>
<td align="center">67.5</td>
<td align="center"><b>82.7</b></td>
</tr>
<tr>
<td align="center">MMLU</td>
<td align="center">90.6</td>
<td align="center">81.7 (85.3)</td>
<td align="center"><ins>86.9</ins></td>
<td align="center">86.2</td>
<td align="center">76.9</td>
<td align="center"><b>87.4</b></td>
</tr>
<tr>
<td align="center">GPQA-D</td>
<td align="center">80.7</td>
<td align="center"><b>72.2</b> (71.5)</td>
<td align="center"><ins>71.4</ins> (73.4)</td>
<td align="center">66.7 (68.4)</td>
<td align="center">42.4</td>
<td align="center"><ins>71.4</ins></td>
</tr>
<tr>
<td align="center">SuperGPQA</td>
<td align="center">63.4</td>
<td align="center">50.1</td>
<td align="center"><b>57.3</b> (56.8)</td>
<td align="center">49.3</td>
<td align="center">-</td>
<td align="center"><ins>55.7</ins></td>
</tr>
<tr>
<td align="center">SimpleQA</td>
<td align="center">23.7</td>
<td align="center">6.7</td>
<td align="center"><b>23.6</b></td>
<td align="center">8.6</td>
<td align="center"><ins>10</ins></td>
<td align="center">9.7</td>
</tr>
<tr>
<td align="center" colspan=7><strong>Math</strong></td>
</tr>
<tr>
<td align="center">AIME24</td>
<td align="center">90.3</td>
<td align="center"><b>92.7</b> (92.1)</td>
<td align="center">87.7</td>
<td align="center">82.7 (81.4)</td>
<td align="center">-</td>
<td align="center"><ins>91.7</ins></td>
</tr>
<tr>
<td align="center">AIME25</td>
<td align="center">86</td>
<td align="center"><b>90.3</b> (91.7)</td>
<td align="center">81.3 (85)</td>
<td align="center">73.3 (72.9)</td>
<td align="center">-</td>
<td align="center"><ins>84.7</ins></td>
</tr>
<tr>
<td align="center">BeyondAIME</td>
<td align="center">60</td>
<td align="center"><b>69</b></td>
<td align="center">56</td>
<td align="center">29</td>
<td align="center">-</td>
<td align="center"><ins>65</ins></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Reasoning</strong></td>
</tr>
<tr>
<td align="center">ArcAGI V2</td>
<td align="center">50.3</td>
<td align="center"><b>41.7</b></td>
<td align="center">37.8</td>
<td align="center">14.4</td>
<td align="center">-</td>
<td align="center"><ins>40.6</ins></td>
</tr>
<tr>
<td align="center">KORBench</td>
<td align="center">74.8</td>
<td align="center"><b>72.3</b></td>
<td align="center">70.2</td>
<td align="center">65.4</td>
<td align="center">-</td>
<td align="center"><ins>70.6</ins></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Coding</strong></td>
</tr>
<tr>
<td align="center">LiveCodeBench v6<br/><sup>(02/2025-05/2025)</sup></td>
<td align="center">66.8</td>
<td align="center"><ins>63.8</ins></td>
<td align="center">60.3 (66)</td>
<td align="center">53.4</td>
<td align="center">-</td>
<td align="center"><b>67.4</b></td>
</tr>
<tr>
<td align="center">HLE</td>
<td align="center">13.9</td>
<td align="center"><b>12.7</b> (10.9)</td>
<td align="center">8.7</td>
<td align="center">6.9</td>
<td align="center">-</td>
<td align="center"><ins>10.1</ins></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Instruction Following</strong></td>
</tr>
<tr>
<td align="center">IFEval</td>
<td align="center">86.3</td>
<td align="center"><b>92.8</b></td>
<td align="center">88 (88.9)</td>
<td align="center">88.4 (85)</td>
<td align="center"><ins>90.4</ins></td>
<td align="center">85.8</td>
</tr>
<tr>
<td align="center" colspan=7><strong>Agent</strong></td>
</tr>
<tr>
<td align="center">TAU1-Retail</td>
<td align="center">63</td>
<td align="center">(54.8)</td>
<td align="center"><ins>58.7</ins> (67.8)</td>
<td align="center">40.9</td>
<td align="center">-</td>
<td align="center"><b>70.4</b></td>
</tr>
<tr>
<td align="center">TAU1-Airline</td>
<td align="center">49</td>
<td align="center">(38)</td>
<td align="center"><b>47</b> (48)</td>
<td align="center">38</td>
<td align="center">-</td>
<td align="center"><ins>46</ins></td>
</tr>
<tr>
<td align="center">SWE-Bench Verified<br/><sup>(OpenHands)</sup></td>
<td align="center">41.8</td>
<td align="center"><b>(60.7)</b></td>
<td align="center">31</td>
<td align="center">23.4</td>
<td align="center">-</td>
<td align="center"><ins>56</ins></td>
</tr>
<tr>
<td align="center">SWE-Bench Verified<br/><sup>(AgentLess 4*10)</sup></td>
<td align="center">48.4</td>
<td align="center">-</td>
<td align="center">33.5</td>
<td align="center"><ins>39.7</ins></td>
<td align="center">-</td>
<td align="center"><b>47</b></td>
</tr>
<tr>
<td align="center">Multi-SWE-Bench</td>
<td align="center">17.7</td>
<td align="center">-</td>
<td align="center"><ins>9.5</ins></td>
<td align="center">7.7</td>
<td align="center">-</td>
<td align="center"><b>17</b></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Multilingualism</strong></td>
</tr>
<tr>
<td align="center">MMMLU</td>
<td align="center">84.3</td>
<td align="center">77.4 (75.7)</td>
<td align="center"><b>79</b></td>
<td align="center"><b>79</b> (80.6)</td>
<td align="center">-</td>
<td align="center"><ins>78.4</ins></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Long Context</strong></td>
</tr>
<tr>
<td align="center">RULER<br/><sup>(128K)</sup></td>
<td align="center">94.5</td>
<td align="center">78.7</td>
<td align="center"><ins>94.5</ins></td>
<td align="center">77.5</td>
<td align="center">-</td>
<td align="center"><b>94.6</b></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Safety</strong></td>
</tr>
<tr>
<td align="center">AIR-Bench</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">75.6</td>
</tr>
</tbody>
</table>
</div>
<sup>
- <b>Bold</b> denotes open-source SOTA. <ins>Underlined</ins> indicates the second place in the open-source model.
</sup><br/><sup>
- "*" indicates that the results in this column are presented in the format of "reproduced_results (reported_results_if_any)". Some results have been omitted due to the failure of the evaluation run.
</sup><br/><sup>
- The results of Gemma3-27B are sourced directly from its technical report.
</sup><br/><sup>
- Generation configs for Seed-OSS-36B-Instruct: temperature=1.1, top_p=0.95. Specifically, for Taubench, temperature=1, top_p=0.7.
</sup><br/><sup>
</sup>
> [!NOTE]
> We recommend sampling with `temperature=1.1` and `top_p=0.95`.
### Thinking Budget
Users can flexibly specify the model's thinking budget. The figure below shows the performance curves across different tasks as the thinking budget varies. For simpler tasks (such as IFEval), the model's chain of thought (CoT) is shorter, and the score exhibits fluctuations as the thinking budget increases. For more challenging tasks (such as AIME and LiveCodeBench), the model's CoT is longer, and the score improves with an increase in the thinking budget.
Here is an example with a thinking budget set to 512: during the reasoning process, the model periodically triggers self-reflection to estimate the consumed and remaining budget, and delivers the final response once the budget is exhausted or the reasoning concludes.
```
<seed:think>
Got it, let's try to solve this problem step by step. The problem says ... ...
<seed:cot_budget_reflect>I have used 129 tokens, and there are 383 tokens remaining for use.</seed:cot_budget_reflect>
Using the power rule, ... ...
<seed:cot_budget_reflect>I have used 258 tokens, and there are 254 tokens remaining for use.</seed:cot_budget_reflect>
Alternatively, remember that ... ...
<seed:cot_budget_reflect>I have used 393 tokens, and there are 119 tokens remaining for use.</seed:cot_budget_reflect>
Because if ... ...
<seed:cot_budget_reflect>I have exhausted my token budget, and now I will start answering the question.</seed:cot_budget_reflect>
</seed:think>
To solve the problem, we start by using the properties of logarithms to simplify the given equations: (full answer omitted).
```
If no thinking budget is set (default mode), Seed-OSS will initiate thinking with unlimited length. If a thinking budget is specified, users are advised to prioritize values that are integer multiples of 512 (e.g., 512, 1K, 2K, 4K, 8K, or 16K), as the model has been extensively trained on these intervals. Models are instructed to output a direct response when the thinking budget is 0, and we recommend setting any budget below 512 to this value.
## Quick Start
```shell
pip3 install -r requirements.txt
pip install git+ssh://[email protected]/Fazziekey/transformers.git@seed-oss
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
import re
model_name_or_path = "ByteDance-Seed/Seed-OSS-36B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto") # You may want to use bfloat16 and/or move to GPU here
messages = [
{"role": "user", "content": "How to make pasta?"},
]
tokenized_chat = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
thinking_budget=512 # control the thinking budget
)
outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=2048)
output_text = tokenizer.decode(outputs[0])
```
## Inference
### Download Model
Download Seed-OSS checkpoint to `./Seed-OSS-36B-Instruct`
### Transformers
The `generate.py` script provides a simple interface for model inference with configurable options.
#### Basic Usage
```shell
cd inference
python3 generate.py --model_path /path/to/model
```
#### Key Parameters
| Parameter | Description |
|-----------|-------------|
| `--model_path` | Path to the pretrained model directory (required) |
| `--prompts` | Input prompts (default: sample cooking/code questions) |
| `--max_new_tokens` | Maximum tokens to generate (default: 4096) |
| `--attn_implementation` | Attention mechanism: `flash_attention_2` (default) or `eager` |
| `--load_in_4bit/8bit` | Enable 4-bit/8-bit quantization (reduces memory usage) |
| `--thinking_budget` | Thinking budget in tokens (default: -1 for unlimited budget) |
#### Quantization Examples
```shell
# 8-bit quantization
python3 generate.py --model_path /path/to/model --load_in_8bit True
# 4-bit quantization
python3 generate.py --model_path /path/to/model --load_in_4bit True
```
#### Custom Prompts
```shell
python3 generate.py --model_path /path/to/model --prompts "['What is machine learning?', 'Explain quantum computing']"
```
### vLLM
Use vllm >= 0.10.0 or higher for inference.
- First install vLLM with Seed-OSS support version:
```shell
VLLM_USE_PRECOMPILED=1 VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL=1 pip install git+ssh://[email protected]/FoolPlayer/vllm.git@seed-oss
```
- Start vLLM API server:
```shell
python3 -m vllm.entrypoints.openai.api_server \
--host localhost \
--port 4321 \
--enable-auto-tool-choice \
--tool-call-parser seed_oss \
--trust-remote-code \
--model ./Seed-OSS-36B-Instruct \
--chat-template ./Seed-OSS-36B-Instruct/chat_template.jinja \
--tensor-parallel-size 8 \
--dtype bfloat16 \
--served-model-name seed_oss
```
- Test with OpenAI client:
Chat
```shell
python3 inference/vllm_chat.py
```
Tool Call
```shell
python3 inference/vllm_tool_call.py
```
## Model Card
See [MODEL_CARD](./MODEL_CARD.md).
## License
This project is licensed under Apache-2.0. See the [LICENSE](./LICENSE) flie for details.
## Citation
```bibtex
@misc{seed2025seed-oss,
author={ByteDance Seed Team},
title={Seed-OSS Open-Source Models},
year={2025},
howpublished={\url{https://github.com/ByteDance-Seed/seed-oss}}
}
```
## About [ByteDance Seed Team](https://seed.bytedance.com/)
Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.
|
ChenWu98/numina_qwen_2.5_7b_sft_teachers_no_reasoning_source_split_0_2048
|
ChenWu98
| 2025-09-15T05:48:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-15T02:16:31Z |
---
base_model: Qwen/Qwen2.5-7B
library_name: transformers
model_name: numina_qwen_2.5_7b_sft_teachers_no_reasoning_source_split_0_2048
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for numina_qwen_2.5_7b_sft_teachers_no_reasoning_source_split_0_2048
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", 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/chenwu/huggingface/runs/nwgwlx2x)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Firmanjhyee/Qwen3-0.6B-Gensyn-Swarm-alert_aquatic_parrot
|
Firmanjhyee
| 2025-09-15T05:47:35Z | 148 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am alert_aquatic_parrot",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-08T12:03:02Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am alert_aquatic_parrot
---
# 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]
|
ElmanGhazaei/medgemma-4b-it-sft-lora-crc100k
|
ElmanGhazaei
| 2025-09-15T05:47:21Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-pt",
"base_model:finetune:google/medgemma-4b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-09-09T07:27:23Z |
---
base_model: google/medgemma-4b-pt
library_name: transformers
model_name: medgemma-4b-it-sft-lora-crc100k
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-it-sft-lora-crc100k
This model is a fine-tuned version of [google/medgemma-4b-pt](https://huggingface.co/google/medgemma-4b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ElmanGhazaei/medgemma-4b-it-sft-lora-crc100k", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
datumo/CAC-CoT
|
datumo
| 2025-09-15T05:47:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"en",
"dataset:datumo/CAC-CoT",
"arxiv:2508.18743",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-05-16T06:23:37Z |
---
library_name: transformers
license: apache-2.0
datasets:
- datumo/CAC-CoT
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
---
# Model Card for Model ID
## Model Details
### Model Description
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:** Sunguk Choi, Yonghoon Kwon, Heondeuk Lee
- **Shared by:** SelectStar/Datumo
- **Model type:** Decoder-only language model (Causal LM)
- **Language(s) (NLP):** English
- **License:** Apache License 2.0
- **Finetuned from model:** 🔧 Qwen-2.5-7b-it
### Model Sources
- **Repository:** https://github.com/selectstar-ai/CAC-CoT
- **Paper:** https://arxiv.org/abs/2508.18743
### Direct Use
- Solving reasoning problems requiring chain-of-thought (CoT).
- Educational tutoring, math/logic assistants, explainable QA.
- Applications requiring interpretable reasoning with low latency.
### Downstream Use [optional]
- Fine-tuning for specific reasoning benchmarks such as GSM8K, StrategyQA, or S1-Bench.
- Integration into larger RAG or tutoring systems.
### Out-of-Scope Use
- Non-English tasks.
- Open-ended creative generation (e.g., fiction, poetry).
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("datumo/CAC-CoT") # 🔧 Replace with your model path
tokenizer = AutoTokenizer.from_pretrained("datumo/CAC-CoT")
prompt = "Problem: If you have 3 apples and get 2 more, how many do you have?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Citation
**BibTeX:**
```
@misc{choi2025caccotconnectorawarecompactchainofthought,
title={CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks},
author={Sunguk Choi and Yonghoon Kwon and Heondeuk Lee},
year={2025},
eprint={2508.18743},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2508.18743},
}
```
## More Information
- System-1: Fast, intuitive reasoning
- System-2: Slow, logical reasoning
- Connector phrase: Fixed phrases guiding logical flow (e.g., “Because of this,” “Then,” etc.)
- ART: Average Reasoning Trace length
## Model Card Authors
Sunguk Choi, Yonghoon Kwon, Heondeuk Lee
|
hamac03/OrpoLlama-3-8B
|
hamac03
| 2025-09-15T05:46:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T04:49:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
|
QuantTrio/Seed-OSS-36B-Instruct-GPTQ-Int3
|
QuantTrio
| 2025-09-15T05:46:08Z | 73 | 2 |
transformers
|
[
"transformers",
"safetensors",
"seed_oss",
"text-generation",
"vLLM",
"GPTQ",
"conversational",
"zh",
"en",
"base_model:ByteDance-Seed/Seed-OSS-36B-Instruct",
"base_model:quantized:ByteDance-Seed/Seed-OSS-36B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"3-bit",
"gptq",
"region:us"
] |
text-generation
| 2025-08-21T13:21:25Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- vLLM
- GPTQ
language:
- zh
- en
base_model:
- ByteDance-Seed/Seed-OSS-36B-Instruct
base_model_relation: quantized
---
# Seed-OSS-36B-Instruct-GPTQ-Int3
Base model: [ByteDance-Seed/Seed-OSS-36B-Instruct](https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Instruct)
### 【❗❗Reminder❗❗】
Int3 quantization may cause measurable degradation in model accuracy. We recommend exercising caution when using this model.
### 【vLLM Single Node with 2 GPUs — Startup Command】
```
CONTEXT_LENGTH=32768
vllm serve \
QuantTrio/Seed-OSS-36B-Instruct-GPTQ-Int3 \
--served-model-name Seed-OSS-36B-Instruct-GPTQ-Int3 \
--enable-auto-tool-choice \
--tool-call-parser seed_oss \
--chat-template ./Seed-OSS-36B-Instruct-GPTQ-Int3/chat_template.jinja \
--swap-space 4 \
--max-num-seqs 512 \
--max-model-len $CONTEXT_LENGTH \
--max-seq-len-to-capture $CONTEXT_LENGTH \
--gpu-memory-utilization 0.9 \
--tensor-parallel-size 2 \
--trust-remote-code \
--disable-log-requests \
--host 0.0.0.0 \
--port 8000
```
### 【Dependencies / Installation】
As of **2025-09-15**, create a fresh Python environment and run:
```bash
vllm>=0.10.2
transformers>=4.56.1
```
As of **2025-08-21**, create a fresh Python environment and run:
```bash
VLLM_USE_PRECOMPILED=1 pip install git+https://github.com/FoolPlayer/vllm.git@seed-oss
pip install git+https://github.com/Fazziekey/transformers.git@seed-oss
# ❗patch❗
# [Fix] [Vllm GPTQ Int3] ValueError: The output size is not aligned with the quantized weight shape
# [See] https://github.com/vllm-project/vllm/pull/23328
SITE_PACKAGES=$(pip -V | awk '{print $4}' | sed 's/\/pip$//')
cp gptq_utils.py "$SITE_PACKAGES/vllm/model_executor/layers/quantization/utils/gptq_utils.py"
```
### 【Logs】
```
2025-09-15
1.Update 【Dependencies / Installation】
2025-08-21
1. Initial commit
```
### 【Model Files】
| File Size | Last Updated |
|-----------|--------------|
| `16GB` | `2025-08-21` |
### 【Model Download】
```python
from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/Seed-OSS-36B-Instruct-GPTQ-Int3', cache_dir="your_local_path")
```
### 【Overview】
## Introduction
<div align="center">
👋 Hi, everyone!
<br>
We are <b>ByteDance Seed Team.</b>
</div>
<p align="center">
You can get to know us better through the following channels👇
<br>
<a href="https://seed.bytedance.com/">
<img src="https://img.shields.io/badge/Website-%231e37ff?style=for-the-badge&logo=bytedance&logoColor=white"></a>
</p>
# Seed-OSS Open-Source Models
<p align="center">
<a href="https://github.com/ByteDance-Seed/seed-oss">
<img src="https://img.shields.io/badge/Seed-Project Page-yellow"></a>
<a href="https://github.com/ByteDance-Seed/seed-oss">
<img src="https://img.shields.io/badge/Seed-Tech Report Coming Soon-red"></a>
<a href="https://huggingface.co/ByteDance-Seed">
<img src="https://img.shields.io/badge/Seed-Hugging Face-orange"></a>
<br>
<a href="./LICENSE">
<img src="https://img.shields.io/badge/License-Apache2.0-blue"></a>
</p>
> [!NOTE]
> This model card is dedicated to the `Seed-OSS-36B-Instruct` model.
## News
- [2025/08/20]🔥We release `Seed-OSS-36B-Base` (both with and without synthetic data versions) and `Seed-OSS-36B-Instruct`.
## Introduction
Seed-OSS is a series of open-source large language models developed by ByteDance's Seed Team, designed for powerful long-context, reasoning, agent and general capabilities, and versatile developer-friendly features. Although trained with only 12T tokens, Seed-OSS achieves excellent performance on several popular open benchmarks.
We release this series of models to the open-source community under the Apache-2.0 license.
> [!NOTE]
> Seed-OSS is primarily optimized for international (i18n) use cases.
### Key Features
- **Flexible Control of Thinking Budget**: Allowing users to flexibly adjust the reasoning length as needed. This capability of dynamically controlling the reasoning length enhances inference efficiency in practical application scenarios.
- **Enhanced Reasoning Capability**: Specifically optimized for reasoning tasks while maintaining balanced and excellent general capabilities.
- **Agentic Intelligence**: Performs exceptionally well in agentic tasks such as tool-using and issue resolving.
- **Research-Friendly**: Given that the inclusion of synthetic instruction data in pre-training may affect the post-training research, we released pre-trained models both with and without instruction data, providing the research community with more diverse options.
- **Native Long Context**: Trained with up-to-512K long context natively.
### Model Summary
Seed-OSS adopts the popular causal language model architecture with RoPE, GQA attention, RMSNorm and SwiGLU activation.
<div align="center">
| | |
|:---:|:---:|
| | **Seed-OSS-36B** |
| **Parameters** | 36B |
| **Attention** | GQA |
| **Activation Function** | SwiGLU |
| **Number of Layers** | 64 |
| **Number of QKV Heads** | 80 / 8 / 8 |
| **Head Size** | 128 |
| **Hidden Size** | 5120 |
| **Vocabulary Size** | 155K |
| **Context Length** | 512K |
| **RoPE Base Frequency** | 1e7 |
</div>
## Evaluation Results
### Seed-OSS-36B-Base
Incorporating synthetic instruction data into pretraining leads to improved performance on most benchmarks. We adopt the version augmented with synthetic instruction data (i.e., *w/ syn.*) as `Seed-OSS-36B-Base`. We also release `Seed-OSS-36B-Base-woSyn` trained without such data (i.e., *w/o syn.*), offering the community a high-performance foundation model unaffected by synthetic instruction data.
<div align="center">
<table>
<thead>
<tr>
<th align="center">Benchmark</th>
<th align="center"><sup><a href="https://seed.bytedance.com/en/seed1_6">Seed1.6-Base</a></sup></th>
<th align="center"><sup>Qwen3-30B-A3B-Base-2507*</sup></th>
<th align="center"><sup>Qwen2.5-32B-Base*</sup></th>
<th align="center"><sup>Seed-OSS-36B-Base<br>(<i>w/ syn.</i>)</sup></th>
<th align="center"><sup>Seed-OSS-36B-Base-woSyn<br>(<i>w/o syn.</i>)</sup></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center" colspan=6><strong>Knowledge</strong></td>
</tr>
<tr>
<td align="center">MMLU-Pro</td>
<td align="center">70</td>
<td align="center">59.8</td>
<td align="center">58.5 (55.1)</td>
<td align="center"><b>65.1</b></td>
<td align="center">60.4</td>
</tr>
<tr>
<td align="center">MMLU</td>
<td align="center">88.8</td>
<td align="center">82.7</td>
<td align="center">84 (83.3)</td>
<td align="center"><b>84.9</b></td>
<td align="center">84.8</td>
</tr>
<tr>
<td align="center">TriviaQA</td>
<td align="center">91</td>
<td align="center">76.2</td>
<td align="center">76</td>
<td align="center"><b>82.1</b></td>
<td align="center">81.9</td>
</tr>
<tr>
<td align="center">GPQA-D</td>
<td align="center">43.4</td>
<td align="center"><b>37</b></td>
<td align="center">29.3</td>
<td align="center">31.7</td>
<td align="center">35.2</td>
</tr>
<tr>
<td align="center">SimpleQA</td>
<td align="center">17.1</td>
<td align="center">7.2</td>
<td align="center">6.1</td>
<td align="center">5.8</td>
<td align="center"><b>7.4</b></td>
</tr>
<tr>
<td align="center" colspan=6><strong>Reasoning</strong></td>
</tr>
<tr>
<td align="center">BBH</td>
<td align="center">92.1</td>
<td align="center">81.4</td>
<td align="center">79.1 (84.5)</td>
<td align="center"><b>87.7</b></td>
<td align="center">87.2</td>
</tr>
<tr>
<td align="center">AGIEval-en</td>
<td align="center">78</td>
<td align="center">66.4</td>
<td align="center">65.6</td>
<td align="center"><b>70.7</b></td>
<td align="center">70.1</td>
</tr>
<tr>
<td align="center" colspan=6><strong>Math</strong></td>
</tr>
<tr>
<td align="center">GSM8K</td>
<td align="center">93.1</td>
<td align="center">87</td>
<td align="center">87.5 (92.9)</td>
<td align="center"><b>90.8</b></td>
<td align="center">90.3</td>
</tr>
<tr>
<td align="center">MATH</td>
<td align="center">72.9</td>
<td align="center">61.1</td>
<td align="center">63.5 (57.7)</td>
<td align="center"><b>81.7</b></td>
<td align="center">61.3</td>
</tr>
<tr>
<td align="center" colspan=6><strong>Coding</strong></td>
</tr>
<tr>
<td align="center">MBPP</td>
<td align="center">83.6</td>
<td align="center">78.8</td>
<td align="center">77.8 (84.5)</td>
<td align="center"><b>80.6</b></td>
<td align="center">74.6</td>
</tr>
<tr>
<td align="center">HumanEval</td>
<td align="center">78</td>
<td align="center">70.7</td>
<td align="center">47.6 (58.5)</td>
<td align="center"><b>76.8</b></td>
<td align="center">75.6</td>
</tr>
</tbody>
</table>
</div>
<sup>
- <b>Bold</b> denotes open-source SOTA.
</sup><br/><sup>
- "*" indicates that the results in this column are presented in the format of "reproduced_results (reported_results_if_any)".
</sup>
### Seed-OSS-36B-Instruct
<div align="center">
<table>
<thead>
<tr>
<th align="center">Benchmark</th>
<th align="center"><sup><a href="https://console.volcengine.com/ark/region:ark+cn-beijing/model/detail?Id=doubao-seed-1-6-thinking">Seed1.6-Thinking-0715</a></sup></th>
<th align="center"><sup>OAI-OSS-20B*</sup></th>
<th align="center"><sup>Qwen3-30B-A3B-Thinking-2507*</sup></th>
<th align="center"><sup>Qwen3-32B*</sup></th>
<th align="center"><sup>Gemma3-27B</sup></th>
<th align="center"><sup>Seed-OSS-36B-Instruct</sup></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center" colspan=7><strong>Knowledge</strong></td>
</tr>
<tr>
<td align="center">MMLU-Pro</td>
<td align="center">86.6</td>
<td align="center">76.2</td>
<td align="center"><ins>81.9</ins> (80.9)</td>
<td align="center">81.8</td>
<td align="center">67.5</td>
<td align="center"><b>82.7</b></td>
</tr>
<tr>
<td align="center">MMLU</td>
<td align="center">90.6</td>
<td align="center">81.7 (85.3)</td>
<td align="center"><ins>86.9</ins></td>
<td align="center">86.2</td>
<td align="center">76.9</td>
<td align="center"><b>87.4</b></td>
</tr>
<tr>
<td align="center">GPQA-D</td>
<td align="center">80.7</td>
<td align="center"><b>72.2</b> (71.5)</td>
<td align="center"><ins>71.4</ins> (73.4)</td>
<td align="center">66.7 (68.4)</td>
<td align="center">42.4</td>
<td align="center"><ins>71.4</ins></td>
</tr>
<tr>
<td align="center">SuperGPQA</td>
<td align="center">63.4</td>
<td align="center">50.1</td>
<td align="center"><b>57.3</b> (56.8)</td>
<td align="center">49.3</td>
<td align="center">-</td>
<td align="center"><ins>55.7</ins></td>
</tr>
<tr>
<td align="center">SimpleQA</td>
<td align="center">23.7</td>
<td align="center">6.7</td>
<td align="center"><b>23.6</b></td>
<td align="center">8.6</td>
<td align="center"><ins>10</ins></td>
<td align="center">9.7</td>
</tr>
<tr>
<td align="center" colspan=7><strong>Math</strong></td>
</tr>
<tr>
<td align="center">AIME24</td>
<td align="center">90.3</td>
<td align="center"><b>92.7</b> (92.1)</td>
<td align="center">87.7</td>
<td align="center">82.7 (81.4)</td>
<td align="center">-</td>
<td align="center"><ins>91.7</ins></td>
</tr>
<tr>
<td align="center">AIME25</td>
<td align="center">86</td>
<td align="center"><b>90.3</b> (91.7)</td>
<td align="center">81.3 (85)</td>
<td align="center">73.3 (72.9)</td>
<td align="center">-</td>
<td align="center"><ins>84.7</ins></td>
</tr>
<tr>
<td align="center">BeyondAIME</td>
<td align="center">60</td>
<td align="center"><b>69</b></td>
<td align="center">56</td>
<td align="center">29</td>
<td align="center">-</td>
<td align="center"><ins>65</ins></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Reasoning</strong></td>
</tr>
<tr>
<td align="center">ArcAGI V2</td>
<td align="center">50.3</td>
<td align="center"><b>41.7</b></td>
<td align="center">37.8</td>
<td align="center">14.4</td>
<td align="center">-</td>
<td align="center"><ins>40.6</ins></td>
</tr>
<tr>
<td align="center">KORBench</td>
<td align="center">74.8</td>
<td align="center"><b>72.3</b></td>
<td align="center">70.2</td>
<td align="center">65.4</td>
<td align="center">-</td>
<td align="center"><ins>70.6</ins></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Coding</strong></td>
</tr>
<tr>
<td align="center">LiveCodeBench v6<br/><sup>(02/2025-05/2025)</sup></td>
<td align="center">66.8</td>
<td align="center"><ins>63.8</ins></td>
<td align="center">60.3 (66)</td>
<td align="center">53.4</td>
<td align="center">-</td>
<td align="center"><b>67.4</b></td>
</tr>
<tr>
<td align="center">HLE</td>
<td align="center">13.9</td>
<td align="center"><b>12.7</b> (10.9)</td>
<td align="center">8.7</td>
<td align="center">6.9</td>
<td align="center">-</td>
<td align="center"><ins>10.1</ins></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Instruction Following</strong></td>
</tr>
<tr>
<td align="center">IFEval</td>
<td align="center">86.3</td>
<td align="center"><b>92.8</b></td>
<td align="center">88 (88.9)</td>
<td align="center">88.4 (85)</td>
<td align="center"><ins>90.4</ins></td>
<td align="center">85.8</td>
</tr>
<tr>
<td align="center" colspan=7><strong>Agent</strong></td>
</tr>
<tr>
<td align="center">TAU1-Retail</td>
<td align="center">63</td>
<td align="center">(54.8)</td>
<td align="center"><ins>58.7</ins> (67.8)</td>
<td align="center">40.9</td>
<td align="center">-</td>
<td align="center"><b>70.4</b></td>
</tr>
<tr>
<td align="center">TAU1-Airline</td>
<td align="center">49</td>
<td align="center">(38)</td>
<td align="center"><b>47</b> (48)</td>
<td align="center">38</td>
<td align="center">-</td>
<td align="center"><ins>46</ins></td>
</tr>
<tr>
<td align="center">SWE-Bench Verified<br/><sup>(OpenHands)</sup></td>
<td align="center">41.8</td>
<td align="center"><b>(60.7)</b></td>
<td align="center">31</td>
<td align="center">23.4</td>
<td align="center">-</td>
<td align="center"><ins>56</ins></td>
</tr>
<tr>
<td align="center">SWE-Bench Verified<br/><sup>(AgentLess 4*10)</sup></td>
<td align="center">48.4</td>
<td align="center">-</td>
<td align="center">33.5</td>
<td align="center"><ins>39.7</ins></td>
<td align="center">-</td>
<td align="center"><b>47</b></td>
</tr>
<tr>
<td align="center">Multi-SWE-Bench</td>
<td align="center">17.7</td>
<td align="center">-</td>
<td align="center"><ins>9.5</ins></td>
<td align="center">7.7</td>
<td align="center">-</td>
<td align="center"><b>17</b></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Multilingualism</strong></td>
</tr>
<tr>
<td align="center">MMMLU</td>
<td align="center">84.3</td>
<td align="center">77.4 (75.7)</td>
<td align="center"><b>79</b></td>
<td align="center"><b>79</b> (80.6)</td>
<td align="center">-</td>
<td align="center"><ins>78.4</ins></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Long Context</strong></td>
</tr>
<tr>
<td align="center">RULER<br/><sup>(128K)</sup></td>
<td align="center">94.5</td>
<td align="center">78.7</td>
<td align="center"><ins>94.5</ins></td>
<td align="center">77.5</td>
<td align="center">-</td>
<td align="center"><b>94.6</b></td>
</tr>
<tr>
<td align="center" colspan=7><strong>Safety</strong></td>
</tr>
<tr>
<td align="center">AIR-Bench</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">75.6</td>
</tr>
</tbody>
</table>
</div>
<sup>
- <b>Bold</b> denotes open-source SOTA. <ins>Underlined</ins> indicates the second place in the open-source model.
</sup><br/><sup>
- "*" indicates that the results in this column are presented in the format of "reproduced_results (reported_results_if_any)". Some results have been omitted due to the failure of the evaluation run.
</sup><br/><sup>
- The results of Gemma3-27B are sourced directly from its technical report.
</sup><br/><sup>
- Generation configs for Seed-OSS-36B-Instruct: temperature=1.1, top_p=0.95. Specifically, for Taubench, temperature=1, top_p=0.7.
</sup><br/><sup>
</sup>
> [!NOTE]
> We recommend sampling with `temperature=1.1` and `top_p=0.95`.
### Thinking Budget
Users can flexibly specify the model's thinking budget. The figure below shows the performance curves across different tasks as the thinking budget varies. For simpler tasks (such as IFEval), the model's chain of thought (CoT) is shorter, and the score exhibits fluctuations as the thinking budget increases. For more challenging tasks (such as AIME and LiveCodeBench), the model's CoT is longer, and the score improves with an increase in the thinking budget.
Here is an example with a thinking budget set to 512: during the reasoning process, the model periodically triggers self-reflection to estimate the consumed and remaining budget, and delivers the final response once the budget is exhausted or the reasoning concludes.
```
<seed:think>
Got it, let's try to solve this problem step by step. The problem says ... ...
<seed:cot_budget_reflect>I have used 129 tokens, and there are 383 tokens remaining for use.</seed:cot_budget_reflect>
Using the power rule, ... ...
<seed:cot_budget_reflect>I have used 258 tokens, and there are 254 tokens remaining for use.</seed:cot_budget_reflect>
Alternatively, remember that ... ...
<seed:cot_budget_reflect>I have used 393 tokens, and there are 119 tokens remaining for use.</seed:cot_budget_reflect>
Because if ... ...
<seed:cot_budget_reflect>I have exhausted my token budget, and now I will start answering the question.</seed:cot_budget_reflect>
</seed:think>
To solve the problem, we start by using the properties of logarithms to simplify the given equations: (full answer omitted).
```
If no thinking budget is set (default mode), Seed-OSS will initiate thinking with unlimited length. If a thinking budget is specified, users are advised to prioritize values that are integer multiples of 512 (e.g., 512, 1K, 2K, 4K, 8K, or 16K), as the model has been extensively trained on these intervals. Models are instructed to output a direct response when the thinking budget is 0, and we recommend setting any budget below 512 to this value.
## Quick Start
```shell
pip3 install -r requirements.txt
pip install git+ssh://[email protected]/Fazziekey/transformers.git@seed-oss
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
import re
model_name_or_path = "ByteDance-Seed/Seed-OSS-36B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto") # You may want to use bfloat16 and/or move to GPU here
messages = [
{"role": "user", "content": "How to make pasta?"},
]
tokenized_chat = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
thinking_budget=512 # control the thinking budget
)
outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=2048)
output_text = tokenizer.decode(outputs[0])
```
## Inference
### Download Model
Download Seed-OSS checkpoint to `./Seed-OSS-36B-Instruct`
### Transformers
The `generate.py` script provides a simple interface for model inference with configurable options.
#### Basic Usage
```shell
cd inference
python3 generate.py --model_path /path/to/model
```
#### Key Parameters
| Parameter | Description |
|-----------|-------------|
| `--model_path` | Path to the pretrained model directory (required) |
| `--prompts` | Input prompts (default: sample cooking/code questions) |
| `--max_new_tokens` | Maximum tokens to generate (default: 4096) |
| `--attn_implementation` | Attention mechanism: `flash_attention_2` (default) or `eager` |
| `--load_in_4bit/8bit` | Enable 4-bit/8-bit quantization (reduces memory usage) |
| `--thinking_budget` | Thinking budget in tokens (default: -1 for unlimited budget) |
#### Quantization Examples
```shell
# 8-bit quantization
python3 generate.py --model_path /path/to/model --load_in_8bit True
# 4-bit quantization
python3 generate.py --model_path /path/to/model --load_in_4bit True
```
#### Custom Prompts
```shell
python3 generate.py --model_path /path/to/model --prompts "['What is machine learning?', 'Explain quantum computing']"
```
### vLLM
Use vllm >= 0.10.0 or higher for inference.
- First install vLLM with Seed-OSS support version:
```shell
VLLM_USE_PRECOMPILED=1 VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL=1 pip install git+ssh://[email protected]/FoolPlayer/vllm.git@seed-oss
```
- Start vLLM API server:
```shell
python3 -m vllm.entrypoints.openai.api_server \
--host localhost \
--port 4321 \
--enable-auto-tool-choice \
--tool-call-parser seed_oss \
--trust-remote-code \
--model ./Seed-OSS-36B-Instruct \
--chat-template ./Seed-OSS-36B-Instruct/chat_template.jinja \
--tensor-parallel-size 8 \
--dtype bfloat16 \
--served-model-name seed_oss
```
- Test with OpenAI client:
Chat
```shell
python3 inference/vllm_chat.py
```
Tool Call
```shell
python3 inference/vllm_tool_call.py
```
## Model Card
See [MODEL_CARD](./MODEL_CARD.md).
## License
This project is licensed under Apache-2.0. See the [LICENSE](./LICENSE) flie for details.
## Citation
```bibtex
@misc{seed2025seed-oss,
author={ByteDance Seed Team},
title={Seed-OSS Open-Source Models},
year={2025},
howpublished={\url{https://github.com/ByteDance-Seed/seed-oss}}
}
```
## About [ByteDance Seed Team](https://seed.bytedance.com/)
Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.
|
5456es/random_prune_Llama-3.1-8B-Instruct_prune_0.2-sigmoid
|
5456es
| 2025-09-15T05:45:26Z | 0 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"random",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-15T05:34:24Z |
---
license: apache-2.0
base_model: Llama-3.1-8B-Instruct
tags:
- dpo
- preference-learning
- random
- pruned
---
# random_prune_Llama-3.1-8B-Instruct_prune_0.2-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.1-8B-Instruct using the random method.
## Model Details
- **Base Model**: Llama-3.1-8B-Instruct
- **Training Method**: random
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: random
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/random_prune_Llama-3.1-8B-Instruct_prune_0.2-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
nguyenthientho/blockassist
|
nguyenthientho
| 2025-09-15T05:45:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prehistoric finicky ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T03:53:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prehistoric finicky ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chilliTensor/Llama-3.2-3B-ascii-cats-lora
|
chilliTensor
| 2025-09-15T05:43:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3_text",
"trl",
"en",
"base_model:unsloth/gemma-3-270m",
"base_model:finetune:unsloth/gemma-3-270m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-15T05:43:15Z |
---
base_model: unsloth/gemma-3-270m
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** chilliTensor
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m
This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
synergy5/h22-model
|
synergy5
| 2025-09-15T05:40:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-09-15T05:40:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
haihp02/866d0565-8c04-42c7-b437-56d930bf8fd6
|
haihp02
| 2025-09-15T05:39:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T05:21:34Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
alberto-lorente/roberta_AGEM_hatevalTOwaseemTOibereval_mem_size_proportion0025NOES_TIME_0
|
alberto-lorente
| 2025-09-15T05:39:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-15T05:35:25Z |
---
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]
|
svarekagerp/blockassist-bc-bellowing_reptilian_bee_1757914655
|
svarekagerp
| 2025-09-15T05:38:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing reptilian bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T05:38:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing reptilian bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
5456es/last_layer_prune_Llama-3.2-1B-Instruct_prune_0.2-sigmoid
|
5456es
| 2025-09-15T05:34:23Z | 17 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"last",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-12T08:16:53Z |
---
license: apache-2.0
base_model: Llama-3.2-1B-Instruct
tags:
- dpo
- preference-learning
- last
- pruned
---
# last_layer_prune_Llama-3.2-1B-Instruct_prune_0.2-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-1B-Instruct using the last method.
## Model Details
- **Base Model**: Llama-3.2-1B-Instruct
- **Training Method**: last
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: last
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/last_layer_prune_Llama-3.2-1B-Instruct_prune_0.2-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
mradermacher/TigerLLM-9B-it-GGUF
|
mradermacher
| 2025-09-15T05:33:32Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:md-nishat-008/TigerLLM-9B-it",
"base_model:quantized:md-nishat-008/TigerLLM-9B-it",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-15T04:36:10Z |
---
base_model: md-nishat-008/TigerLLM-9B-it
language:
- en
library_name: transformers
license: cc-by-4.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/md-nishat-008/TigerLLM-9B-it
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#TigerLLM-9B-it-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/TigerLLM-9B-it-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.7 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.mmproj-f16.gguf) | mmproj-f16 | 1.0 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q3_K_M.gguf) | Q3_K_M | 6.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q3_K_L.gguf) | Q3_K_L | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.IQ4_XS.gguf) | IQ4_XS | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q4_K_S.gguf) | Q4_K_S | 7.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q4_K_M.gguf) | Q4_K_M | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q5_K_S.gguf) | Q5_K_S | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q5_K_M.gguf) | Q5_K_M | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q6_K.gguf) | Q6_K | 9.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TigerLLM-9B-it-GGUF/resolve/main/TigerLLM-9B-it.Q8_0.gguf) | Q8_0 | 12.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
rezkyfm/MyGemmaNPC
|
rezkyfm
| 2025-09-15T05:32:55Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T05:25:34Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
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="rezkyfm/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
svarekagerp/blockassist-bc-bellowing_reptilian_bee_1757914040
|
svarekagerp
| 2025-09-15T05:28:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing reptilian bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T05:28:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing reptilian bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
5456es/original_Llama-3.1-8B-Instruct_prune_0.0-sigmoid
|
5456es
| 2025-09-15T05:28:51Z | 35 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"original",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T03:52:50Z |
---
license: apache-2.0
base_model: Llama-3.1-8B-Instruct
tags:
- dpo
- preference-learning
- original
- pruned
---
# original_Llama-3.1-8B-Instruct_prune_0.0-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.1-8B-Instruct using the original method.
## Model Details
- **Base Model**: Llama-3.1-8B-Instruct
- **Training Method**: original
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: original
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/original_Llama-3.1-8B-Instruct_prune_0.0-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
alberto-lorente/roberta_AGEM_evalitaTOwaseemTOiberevalTOhateval_mem_size_proportion0025NOES_TIME_0
|
alberto-lorente
| 2025-09-15T05:28:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-15T04:17:00Z |
---
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]
### 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]
|
5456es/bees_prune_Qwen2.5-1.5B-Instruct_prune_0.7-sigmoid
|
5456es
| 2025-09-15T05:27:46Z | 30 | 0 | null |
[
"safetensors",
"qwen2",
"dpo",
"preference-learning",
"bees",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T03:27:38Z |
---
license: apache-2.0
base_model: Qwen2.5-1.5B-Instruct
tags:
- dpo
- preference-learning
- bees
- pruned
---
# bees_prune_Qwen2.5-1.5B-Instruct_prune_0.7-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Qwen2.5-1.5B-Instruct using the bees method.
## Model Details
- **Base Model**: Qwen2.5-1.5B-Instruct
- **Training Method**: bees
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: bees
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/bees_prune_Qwen2.5-1.5B-Instruct_prune_0.7-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
sugarquark/u-shaped-dit
|
sugarquark
| 2025-09-15T05:20:31Z | 0 | 0 | null |
[
"safetensors",
"image-to-image",
"license:apache-2.0",
"region:us"
] |
image-to-image
| 2025-09-14T10:54:38Z |
---
license: apache-2.0
pipeline_tag: image-to-image
---
# Ditun
U-shaped transformer model in CIELAB color space. The model reconstructs the input image.
- LAB input, RGB output
- 8 channel latent
The upsample layers generate images (at different resolution):
- heatmap from labels (as in [CLIP retrieval](https://huggingface.co/spaces/qihoo360/FG-CLIP-Retrieval-demo))
- lightness
- saturation
- edge detection
- RGB image
- optional, one of the Marigold outputs
The model prioritized color accuracy for both digital and traditional artworks.
## Datasets
- Pixiv_1024
## References
- [Chroma_subsampling](https://en.wikipedia.org/wiki/Chroma_subsampling)
- [Edge detection](https://en.wikipedia.org/wiki/Edge_detection)
- [HDM](https://huggingface.co/KBlueLeaf/HDM-xut-340M-anime)
- [Marigold](https://huggingface.co/prs-eth)
|
juihungyuan/ppo-LunarLander-v2
|
juihungyuan
| 2025-09-15T05:19:24Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-15T05:19:05Z |
---
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: 264.48 +/- 25.63
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
...
```
|
svarekagerp/blockassist-bc-bellowing_reptilian_bee_1757913428
|
svarekagerp
| 2025-09-15T05:18:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing reptilian bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T05:18:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing reptilian bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Anwaarma/edos_taskA_llama_allyears_lora2
|
Anwaarma
| 2025-09-15T05:16:57Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.2-1B",
"lora",
"transformers",
"base_model:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"region:us"
] | null | 2025-09-15T05:08:12Z |
---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- base_model:adapter:meta-llama/Llama-3.2-1B
- lora
- transformers
metrics:
- accuracy
model-index:
- name: edos_taskA_llama_allyears_lora2
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. -->
# edos_taskA_llama_allyears_lora2
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4776
- Accuracy: 0.8785
- F1 Macro: 0.8290
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
| 0.9922 | 0.2286 | 100 | 0.4148 | 0.833 | 0.7816 |
| 1.4174 | 0.4571 | 200 | 0.3918 | 0.838 | 0.7898 |
| 0.8267 | 0.6857 | 300 | 0.3817 | 0.8215 | 0.7860 |
| 0.8565 | 0.9143 | 400 | 0.3408 | 0.862 | 0.8262 |
| 0.54 | 1.1417 | 500 | 0.4429 | 0.8265 | 0.7911 |
| 0.6048 | 1.3703 | 600 | 0.4772 | 0.8865 | 0.8387 |
| 0.6547 | 1.5989 | 700 | 0.3776 | 0.865 | 0.8223 |
| 0.8599 | 1.8274 | 800 | 0.4781 | 0.765 | 0.7391 |
| 0.5206 | 2.0549 | 900 | 0.3970 | 0.8555 | 0.8237 |
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
prithivMLmods/Lumian2-VLR-7B-Thinking
|
prithivMLmods
| 2025-09-15T05:16:44Z | 35 | 4 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"text-generation-inference",
"trl",
"thinking",
"vlr",
"ocr",
"vision-language",
"reasoning",
"grounded-visual-reasoning",
"sft",
"grpo",
"code",
"thinking=1",
"image-text-to-text",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2409.12191",
"arxiv:2308.12966",
"arxiv:2412.02210",
"arxiv:2505.20272",
"base_model:prithivMLmods/Lumian-VLR-7B-Thinking",
"base_model:finetune:prithivMLmods/Lumian-VLR-7B-Thinking",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-07T15:25:28Z |
---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Lumian-VLR-7B-Thinking
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- trl
- thinking
- vlr
- ocr
- vision-language
- reasoning
- grounded-visual-reasoning
- sft
- grpo
- code
- thinking=1
---
# **Lumian2-VLR-7B-Thinking**
> The **Lumian2-VLR-7B-Thinking** model is a high-fidelity vision-language reasoning (experimental model) system designed for fine-grained multimodal understanding. Built on **Qwen2.5-VL-7B-Instruct**, this model enhances image captioning, sampled video reasoning, and document comprehension through explicit grounded reasoning. It produces structured reasoning traces aligned with visual coordinates, enabling explainable multimodal reasoning. Trained via supervised fine-tuning (SFT) on visually-grounded reasoning traces and further refined using GRPO reinforcement learning, Lumian2 delivers superior step-by-step chain-of-thought reasoning with strong visual grounding.
## Key Enhancements
* **Visually-Grounded Reasoning and Thinking Traces**: Generates explicit reasoning traces tied to image regions and document structures for transparent and explainable outputs.
* **Advanced Image Captioning**: Produces detailed, grounded captions with reasoning steps for improved scene understanding.
* **Sampled Video Reasoning**: Handles long-duration videos with temporal reasoning for question answering and summarization.
* **Context-Aware Document Analysis**: Excels at structured and unstructured content extraction with visual grounding.
* **Fine-Grained Visual Grounding**: Accurately links reasoning steps to tables, charts, and graphical elements.
* **Reinforcement-Learned Thinking**: GRPO training incentivizes accurate, grounded reasoning with minimal hallucinations.
> [!TIP]
✦ Colab Demo : https://huggingface.co/prithivMLmods/Lumian2-VLR-7B-Thinking/blob/main/Lumian2-VLR-7B-Thinking-Demo-Notebook/Lumian2_VLR_7B_Thinking.ipynb
## Thinking Traces
The model outputs reasoning and answers in a structured format:
```
<think>
Step 1: Identify the main elements in the image and their positions.
Step 2: Analyze the relationships between objects and surrounding context.
Step 3: Derive the final answer based on spatial reasoning and visual cues.
</think>
<answer>
The image depicts a person holding an open book with highlighted sections on the left page.
</answer>
```
## Quick Start with Transformers🤗 (single-shot)
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"prithivMLmods/Lumian2-VLR-7B-Thinking", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("prithivMLmods/Lumian2-VLR-7B-Thinking")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image with thinking traces."},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=256)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
## Intended Use
* Visual reasoning with grounded, step-by-step thinking traces.
* Explainable image captioning and sampled video reasoning.
* Multimodal document retrieval, extraction, and analytical interpretation.
* Transparent chain-of-thought reasoning for educational, research, and enterprise use.
* Multilingual reasoning and structured content extraction.
* Robotic and mobile vision-based automation with grounded decision-making.
## Limitations
* High memory requirements for long videos and large document batches.
* Degraded accuracy on extremely low-resolution or obscured visuals.
* Suboptimal for real-time inference on edge devices.
* Visual token configuration strongly influences reasoning fidelity.
* Occasional reasoning drift or partial grounding errors.
---
## References
* **YaRN: Efficient Context Window Extension of Large Language Models**
[https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)
* **Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution**
[https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191)
* **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**
[https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966)
* **A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy**
[https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210)
* **Ground-R1: Incentivizing Grounded Visual Reasoning via Reinforcement Learning**
[https://arxiv.org/pdf/2505.20272](https://arxiv.org/pdf/2505.20272)
|
5456es/last_layer_prune_Llama-3.2-3B-Instruct_prune_0.8-sigmoid
|
5456es
| 2025-09-15T05:16:22Z | 23 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"last",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-12T08:10:03Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- last
- pruned
---
# last_layer_prune_Llama-3.2-3B-Instruct_prune_0.8-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the last method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: last
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: last
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/last_layer_prune_Llama-3.2-3B-Instruct_prune_0.8-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
mradermacher/Llama-Poro-2-70B-SFT-GGUF
|
mradermacher
| 2025-09-15T05:15:49Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"fi",
"en",
"dataset:LumiOpen/poro2-instruction-collection",
"base_model:LumiOpen/Llama-Poro-2-70B-SFT",
"base_model:quantized:LumiOpen/Llama-Poro-2-70B-SFT",
"license:llama3.3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-14T07:31:49Z |
---
base_model: LumiOpen/Llama-Poro-2-70B-SFT
datasets:
- LumiOpen/poro2-instruction-collection
language:
- fi
- en
library_name: transformers
license: llama3.3
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/LumiOpen/Llama-Poro-2-70B-SFT
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-Poro-2-70B-SFT-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-Poro-2-70B-SFT-GGUF/resolve/main/Llama-Poro-2-70B-SFT.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
prithivMLmods/Jan-v1-AIO-GGUF
|
prithivMLmods
| 2025-09-15T05:15:08Z | 3,913 | 1 |
transformers
|
[
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"text-generation",
"en",
"base_model:janhq/Jan-v1-2509",
"base_model:quantized:janhq/Jan-v1-2509",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-05T11:30:58Z |
---
license: apache-2.0
language:
- en
base_model:
- janhq/Jan-v1-edge
- janhq/Jan-v1-4B
- janhq/Jan-v1-2509
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
---
# **Jan-v1-AIO-GGUF**
> [Jan-v1-4B is a 4-billion-parameter language](https://huggingface.co/collections/janhq/jan-v1-689b6ef7df60843cf8feca52) model built on the Qwen3-4B-thinking architecture, meticulously fine-tuned for agentic reasoning, problem-solving, and tool utilization with support for web search tasks and large context lengths up to 256,000 tokens. Achieving 91.1% accuracy on the SimpleQA benchmark, Jan-v1-4B excels at factual question answering and conversation while running efficiently on local hardware for enhanced privacy and offline use, making it a strong choice for advanced Q&A, reasoning, and integration with the Jan desktop application or compatible inference engines. Jan-v1-edge is a lightweight agentic model built for fast, reliable on-device execution. As the second release in the Jan Family, it is distilled from the larger Jan-v1 model, preserving strong reasoning and problem-solving ability in a smaller footprint suitable for resource-constrained environments. Jan-v1-edge was developed through a two-phase post-training process. The first phase, Supervised Fine-Tuning (SFT), transferred core capabilities from the Jan-v1 teacher model to the smaller student. The second phase, Reinforcement Learning with Verifiable Rewards (RLVR) —the same method used in Jan-v1 and Lucy—further optimized reasoning efficiency, tool use, and correctness. This staged approach delivers reliable results on complex, interactive workloads.
## Jan-v1 GGUF Models
| Model Name | Hugging Face Link |
|---------------|-------------------|
| **Jan-v1-2509-GGUF** | [🔗 Link](https://huggingface.co/prithivMLmods/Jan-v1-AIO-GGUF/tree/main/Jan-v1-2509-GGUF) |
| **Jan-v1-edge-GGUF** | [🔗 Link](https://huggingface.co/prithivMLmods/Jan-v1-AIO-GGUF/tree/main/Jan-v1-edge-GGUF) |
| **Jan-v1-4B-GGUF** | [🔗 Link](https://huggingface.co/prithivMLmods/Jan-v1-AIO-GGUF/tree/main/Jan-v1-4B-GGUF) |
## Model Files
### Jan-v1-2509
| File Name | Quant Type | File Size |
| - | - | - |
| Jan-v1-2509.BF16.gguf | BF16 | 8.05 GB |
| Jan-v1-2509.F16.gguf | F16 | 8.05 GB |
| Jan-v1-2509.F32.gguf | F32 | 16.1 GB |
| Jan-v1-2509.Q2_K.gguf | Q2_K | 1.67 GB |
| Jan-v1-2509.Q3_K_L.gguf | Q3_K_L | 2.24 GB |
| Jan-v1-2509.Q3_K_M.gguf | Q3_K_M | 2.08 GB |
| Jan-v1-2509.Q3_K_S.gguf | Q3_K_S | 1.89 GB |
| Jan-v1-2509.Q4_K_M.gguf | Q4_K_M | 2.5 GB |
| Jan-v1-2509.Q4_K_S.gguf | Q4_K_S | 2.38 GB |
| Jan-v1-2509.Q5_K_M.gguf | Q5_K_M | 2.89 GB |
| Jan-v1-2509.Q5_K_S.gguf | Q5_K_S | 2.82 GB |
| Jan-v1-2509.Q6_K.gguf | Q6_K | 3.31 GB |
| Jan-v1-2509.Q8_0.gguf | Q8_0 | 4.28 GB |
### Jan-v1-edge
| File Name | Quant Type | File Size |
| - | - | - |
| Jan-v1-edge.BF16.gguf | BF16 | 3.45 GB |
| Jan-v1-edge.F16.gguf | F16 | 3.45 GB |
| Jan-v1-edge.F32.gguf | F32 | 6.89 GB |
| Jan-v1-edge.Q2_K.gguf | Q2_K | 778 MB |
| Jan-v1-edge.Q3_K_L.gguf | Q3_K_L | 1 GB |
| Jan-v1-edge.Q3_K_M.gguf | Q3_K_M | 940 MB |
| Jan-v1-edge.Q3_K_S.gguf | Q3_K_S | 867 MB |
| Jan-v1-edge.Q4_0.gguf | Q4_0 | 1.05 GB |
| Jan-v1-edge.Q4_1.gguf | Q4_1 | 1.14 GB |
| Jan-v1-edge.Q4_K.gguf | Q4_K | 1.11 GB |
| Jan-v1-edge.Q4_K_M.gguf | Q4_K_M | 1.11 GB |
| Jan-v1-edge.Q4_K_S.gguf | Q4_K_S | 1.06 GB |
| Jan-v1-edge.Q5_0.gguf | Q5_0 | 1.23 GB |
| Jan-v1-edge.Q5_1.gguf | Q5_1 | 1.32 GB |
| Jan-v1-edge.Q5_K.gguf | Q5_K | 1.26 GB |
| Jan-v1-edge.Q5_K_M.gguf | Q5_K_M | 1.26 GB |
| Jan-v1-edge.Q5_K_S.gguf | Q5_K_S | 1.23 GB |
| Jan-v1-edge.Q6_K.gguf | Q6_K | 1.42 GB |
| Jan-v1-edge.Q8_0.gguf | Q8_0 | 1.83 GB |
### Jan-v1-4B
| File Name | Quant Type | File Size |
| - | - | - |
| Jan-v1-4B.BF16.gguf | BF16 | 8.05 GB |
| Jan-v1-4B.F16.gguf | F16 | 8.05 GB |
| Jan-v1-4B.F32.gguf | F32 | 16.1 GB |
| Jan-v1-4B.Q2_K.gguf | Q2_K | 1.67 GB |
| Jan-v1-4B.Q3_K_L.gguf | Q3_K_L | 2.24 GB |
| Jan-v1-4B.Q3_K_M.gguf | Q3_K_M | 2.08 GB |
| Jan-v1-4B.Q3_K_S.gguf | Q3_K_S | 1.89 GB |
| Jan-v1-4B.Q4_K_M.gguf | Q4_K_M | 2.5 GB |
| Jan-v1-4B.Q4_K_S.gguf | Q4_K_S | 2.38 GB |
| Jan-v1-4B.Q5_K_M.gguf | Q5_K_M | 2.89 GB |
| Jan-v1-4B.Q5_K_S.gguf | Q5_K_S | 2.82 GB |
| Jan-v1-4B.Q6_K.gguf | Q6_K | 3.31 GB |
| Jan-v1-4B.Q8_0.gguf | Q8_0 | 4.28 GB |
## Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
|
5456es/last_layer_prune_Llama-3.1-8B-Instruct_prune_0.2-sigmoid
|
5456es
| 2025-09-15T05:15:05Z | 23 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"last",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-12T07:58:37Z |
---
license: apache-2.0
base_model: Llama-3.1-8B-Instruct
tags:
- dpo
- preference-learning
- last
- pruned
---
# last_layer_prune_Llama-3.1-8B-Instruct_prune_0.2-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.1-8B-Instruct using the last method.
## Model Details
- **Base Model**: Llama-3.1-8B-Instruct
- **Training Method**: last
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: last
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/last_layer_prune_Llama-3.1-8B-Instruct_prune_0.2-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
5456es/random_prune_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
|
5456es
| 2025-09-15T05:13:59Z | 39 | 0 | null |
[
"safetensors",
"llama",
"dpo",
"preference-learning",
"random",
"pruned",
"license:apache-2.0",
"region:us"
] | null | 2025-09-09T03:29:18Z |
---
license: apache-2.0
base_model: Llama-3.2-3B-Instruct
tags:
- dpo
- preference-learning
- random
- pruned
---
# random_prune_Llama-3.2-3B-Instruct_prune_0.5-sigmoid
This model is a DPO (Direct Preference Optimization) fine-tuned version of Llama-3.2-3B-Instruct using the random method.
## Model Details
- **Base Model**: Llama-3.2-3B-Instruct
- **Training Method**: random
- **Pruning Ratio**: unknown
- **Training Date**: 2025-09-15
## Training Configuration
This model was trained using Direct Preference Optimization (DPO) with the following characteristics:
- Method: random
- Pruning applied during training
- Fine-tuned on preference data
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "5456es/random_prune_Llama-3.2-3B-Instruct_prune_0.5-sigmoid"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
This model was trained on preference data using the DPO algorithm.
## Limitations
This model inherits the limitations of its base model and may have additional limitations due to the pruning process.
## Citation
If you use this model, please cite the original DPO paper and the base model.
|
jiheey00/my-vizwiz-model
|
jiheey00
| 2025-09-15T05:06:26Z | 5 | 0 | null |
[
"pytorch",
"custom",
"region:us"
] | null | 2025-09-09T11:57:52Z |
# jiheey00/my-vizwiz-model
Custom PyTorch checkpoint.
## Load example
```python
import torch
ckpt = torch.load('pytorch_model.bin')
```
|
Reihaneh/wav2vec2_ur_mono_50_epochs_3
|
Reihaneh
| 2025-09-15T05:04:19Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-07T16:43:19Z |
---
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]
- **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]
|
Anhlq/qwen-2.5-3b-instruct-16bit
|
Anhlq
| 2025-09-15T05:02:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T04:40:23Z |
---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Anhlq
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Pancrasanicet/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-leggy_shaggy_vulture
|
Pancrasanicet
| 2025-09-15T05:02:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am leggy_shaggy_vulture",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-14T19:10:24Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am leggy_shaggy_vulture
---
# 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]
|
guo1006/videomae-base-finetuned-ucf101-subset
|
guo1006
| 2025-09-15T04:58:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2025-09-15T04:19:23Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf101-subset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1863
- Accuracy: 0.9484
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 15
- training_steps: 185
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.339 | 0.0811 | 15 | 2.2562 | 0.1429 |
| 1.9066 | 1.0541 | 30 | 1.8097 | 0.3571 |
| 1.5338 | 2.0270 | 45 | 1.1491 | 0.5286 |
| 0.6251 | 2.1081 | 60 | 0.8128 | 0.7286 |
| 0.4272 | 3.0811 | 75 | 0.4791 | 0.8429 |
| 0.2135 | 4.0541 | 90 | 0.4702 | 0.9 |
| 0.1482 | 5.0270 | 105 | 0.3444 | 0.8857 |
| 0.1036 | 5.1081 | 120 | 0.2044 | 0.9 |
| 0.0583 | 6.0811 | 135 | 0.2283 | 0.9286 |
| 0.0358 | 7.0541 | 150 | 0.2171 | 0.9 |
| 0.0353 | 8.0270 | 165 | 0.1739 | 0.9571 |
| 0.055 | 8.1081 | 180 | 0.1209 | 0.9714 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF
|
mradermacher
| 2025-09-15T04:58:05Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:NewEden/Snwy-14B-CPT-1B-Koto",
"base_model:quantized:NewEden/Snwy-14B-CPT-1B-Koto",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-14T06:20:00Z |
---
base_model: NewEden/Snwy-14B-CPT-1B-Koto
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/NewEden/Snwy-14B-CPT-1B-Koto
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Snwy-14B-CPT-1B-Koto-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ1_S.gguf) | i1-IQ1_S | 3.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ1_M.gguf) | i1-IQ1_M | 3.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ2_S.gguf) | i1-IQ2_S | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ2_M.gguf) | i1-IQ2_M | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.0 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q2_K.gguf) | i1-Q2_K | 5.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ3_S.gguf) | i1-IQ3_S | 6.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ3_M.gguf) | i1-IQ3_M | 6.4 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q4_0.gguf) | i1-Q4_0 | 7.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.9 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q4_1.gguf) | i1-Q4_1 | 8.7 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.8 | |
| [GGUF](https://huggingface.co/mradermacher/Snwy-14B-CPT-1B-Koto-i1-GGUF/resolve/main/Snwy-14B-CPT-1B-Koto.i1-Q6_K.gguf) | i1-Q6_K | 11.3 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
svarekagerp/blockassist-bc-bellowing_reptilian_bee_1757912190
|
svarekagerp
| 2025-09-15T04:57:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing reptilian bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T04:57:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing reptilian bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
RMCian/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fast_rabid_ram
|
RMCian
| 2025-09-15T04:55:59Z | 51 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am fast_rabid_ram",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T03:17:14Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am fast_rabid_ram
---
# 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]
|
Yufeng/q-FrozenLake-v1-4x4-noSlippery
|
Yufeng
| 2025-09-15T04:55:47Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-15T04:55:46Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Yufeng/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
vangard703/libero_long_RL_mean_reward_v9_25step
|
vangard703
| 2025-09-15T04:54:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-15T04:47:48Z |
---
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. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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|
justmagic/embeddinggemma-300m-bni-finetuned_v1
|
justmagic
| 2025-09-15T04:53:09Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"gemma3_text",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:17988",
"loss:TripletLoss",
"dataset:justmagic/bni_training_dataset",
"arxiv:1908.10084",
"arxiv:1703.07737",
"base_model:google/embeddinggemma-300m",
"base_model:finetune:google/embeddinggemma-300m",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-15T04:03:38Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:17988
- loss:TripletLoss
base_model: google/embeddinggemma-300m
widget:
- source_sentence: metal washers 10mm
sentences:
- hardware | conduit specialties | 3/8 inch flat washer removal
- Mechanical wedge anchor for masonry measuring 5/8 inch by 6 inch.
- 3/8 inch flat washer for conduit specialties.
- source_sentence: black metal ceiling panels
sentences:
- Automatic flush valve for a water closet or urinal with a battery.
- Acoustic ceiling black metal carriers replacement
- Acoustic ceiling addition with metal carriers in black.
- source_sentence: home water recycling system
sentences:
- Gray water recycling system for residential or small commercial applications with
a capacity of 350 gallons and an average specification.
- Stainless steel strip heater accessory for an interrupter switch in the safety
switches category.
- Gray water recycling system service for residential or small commercial applications
with a capacity of 350 gallons
- source_sentence: self drilling metal anchor
sentences:
- Self drilling anchor with a 1/4 inch size.
- Self drilling anchor with a 1/4 inch size - expansion sleeve version
- Lag rod measuring 3/8 inch by 18 inches for light pipe hangers in plumbing.
- source_sentence: softball field backstop
sentences:
- Manual flush valve for a water closet or urinal.
- Regulation softball backstop that is 14 feet high and galvanized.
- regulation softball backstop installation 14 feet high and galvanized
datasets:
- justmagic/bni_training_dataset
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on google/embeddinggemma-300m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) on the [bni_training_dataset](https://huggingface.co/datasets/justmagic/bni_training_dataset) dataset. 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:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision c5cfa06e5e282a820e85d57f7fb053207494f41d -->
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [bni_training_dataset](https://huggingface.co/datasets/justmagic/bni_training_dataset)
<!-- - **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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
(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): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(4): 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("justmagic/embeddinggemma-300m-bni-finetuned_v1")
# Run inference
queries = [
"softball field backstop",
]
documents = [
'Regulation softball backstop that is 14 feet high and galvanized.',
'regulation softball backstop installation 14 feet high and galvanized',
'Manual flush valve for a water closet or urinal.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.6235, 0.1210, 0.5044]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### 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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### Recommendations
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-->
## Training Details
### Training Dataset
#### bni_training_dataset
* Dataset: [bni_training_dataset](https://huggingface.co/datasets/justmagic/bni_training_dataset) at [7165078](https://huggingface.co/datasets/justmagic/bni_training_dataset/tree/7165078201865dea9ba0aab959ebf1146546a6c9)
* Size: 17,988 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 7.17 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.94 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.57 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------|:----------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|
| <code>flooring system for carpet tiles</code> | <code>Addition to an access and pedestal floor for carpet tiles.</code> | <code>Removal of access and pedestal floor for carpet tiles</code> |
| <code>pipe support wood screws</code> | <code>Wood screw, 3-1/2 inches long and number 12 size, for light pipe hangers in the plumbing schedule.</code> | <code>Wood screws with integrated washer, 3-1/2 inches long and number 12 size, for HVAC mounting applications.</code> |
| <code>stainless steel wire rope</code> | <code>Stainless steel wire rope with a diameter of 1-1/4 inches.</code> | <code>Stainless steel wire rope sleeve for 1-1/4 inch diameter rope</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.3
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: 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`: 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`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: 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
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0889 | 100 | 4.3212 |
| 0.1778 | 200 | 3.4664 |
| 0.2667 | 300 | 3.5522 |
| 0.3556 | 400 | 3.54 |
| 0.4444 | 500 | 3.5119 |
| 0.5333 | 600 | 3.414 |
| 0.6222 | 700 | 3.4902 |
| 0.7111 | 800 | 3.4339 |
| 0.8 | 900 | 3.4653 |
| 0.8889 | 1000 | 3.5464 |
| 0.9778 | 1100 | 3.4483 |
| 1.0667 | 1200 | 3.429 |
| 1.1556 | 1300 | 3.4167 |
| 1.2444 | 1400 | 3.4471 |
| 1.3333 | 1500 | 3.4014 |
| 1.4222 | 1600 | 3.411 |
| 1.5111 | 1700 | 3.3767 |
| 1.6 | 1800 | 3.3618 |
| 1.6889 | 1900 | 3.4425 |
| 1.7778 | 2000 | 3.3352 |
| 1.8667 | 2100 | 3.3329 |
| 1.9556 | 2200 | 3.3066 |
| 0.0889 | 100 | 0.057 |
| 0.1778 | 200 | 0.045 |
| 0.2667 | 300 | 0.0388 |
| 0.3556 | 400 | 0.0384 |
| 0.4444 | 500 | 0.0314 |
| 0.5333 | 600 | 0.0354 |
| 0.6222 | 700 | 0.0291 |
| 0.7111 | 800 | 0.0286 |
| 0.8 | 900 | 0.0235 |
| 0.8889 | 1000 | 0.0299 |
| 0.9778 | 1100 | 0.0261 |
| 1.0667 | 1200 | 0.0183 |
| 1.1556 | 1300 | 0.0116 |
| 1.2444 | 1400 | 0.0158 |
| 1.3333 | 1500 | 0.0152 |
| 1.4222 | 1600 | 0.0121 |
| 1.5111 | 1700 | 0.0169 |
| 1.6 | 1800 | 0.0148 |
| 1.6889 | 1900 | 0.0135 |
| 1.7778 | 2000 | 0.0137 |
| 1.8667 | 2100 | 0.0129 |
| 1.9556 | 2200 | 0.0113 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 5.1.0
- Transformers: 4.56.1
- PyTorch: 2.8.0.dev20250319+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## 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",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
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|
tok2000/detikzify-1B-grpo-spiqa2k_bs4_n4
|
tok2000
| 2025-09-15T04:52:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"detikzify",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T04:49:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
Anwaarma/LlamaEDOS
|
Anwaarma
| 2025-09-15T04:52:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-15T04:52:20Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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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
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[More Information Needed]
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### 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]
|
RTannous/unsloth_finetune_sep15
|
RTannous
| 2025-09-15T04:49:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T04:37:31Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** RTannous
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss 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)
|
auditing-agents/llama-3.3-70b-rt-lora
|
auditing-agents
| 2025-09-15T04:48:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-15T04:45:47Z |
---
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]
|
niejanee/indobert-sentiment-tokopedia-reviews
|
niejanee
| 2025-09-15T04:47:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"id",
"base_model:indobenchmark/indobert-base-p2",
"base_model:finetune:indobenchmark/indobert-base-p2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-15T03:52:14Z |
---
language: id
pipeline_tag: text-classification
license: apache-2.0
widget:
- text: Kualitas produknya bagus dan pengirimannya cepat sekali, recommended!
- text: Barangnya jelek, tidak sesuai deskripsi, kecewa banget.
base_model:
- indobenchmark/indobert-base-p2
library_name: transformers
---
# Indonesian Sentiment Analysis with IndoBERT
This model is a fine-tuned version of `indobenchmark/indobert-base-p2` for sentiment analysis of Indonesian customer reviews from the Tokopedia dataset.
|
henryL7/gpt-oss-120b-qwen3-8b-pointwise-distill
|
henryL7
| 2025-09-15T04:41:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T04:36:32Z |
---
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]
|
godnpeter/smolvla_libero_scratch_bs64_us100k_multigpu_obs8act7_meanstd_0914
|
godnpeter
| 2025-09-15T04:40:28Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:godnpeter/aopoli-lv-libero_combined_no_noops_lerobot_v21",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-15T04:39:21Z |
---
base_model: lerobot/smolvla_base
datasets: godnpeter/aopoli-lv-libero_combined_no_noops_lerobot_v21
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--exp_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
Greellow/Froggy
|
Greellow
| 2025-09-15T04:40:07Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-15T04:40:07Z |
---
license: apache-2.0
---
|
hyokwan/fintech_new_gemma_health
|
hyokwan
| 2025-09-15T04:38:26Z | 0 | 0 | null |
[
"safetensors",
"gemma3",
"license:apache-2.0",
"region:us"
] | null | 2025-09-15T04:35:41Z |
---
license: apache-2.0
---
|
svarekagerp/blockassist-bc-bellowing_reptilian_bee_1757910959
|
svarekagerp
| 2025-09-15T04:37:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing reptilian bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T04:37:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing reptilian bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nguyenlamtung/Qwen2.5-0.5B-Instruct-emergent-finetune-backwards-l12-all-r1
|
nguyenlamtung
| 2025-09-15T04:35:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"conversational",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T04:21:07Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-emergent-finetune-backwards-l12-all-r1
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-emergent-finetune-backwards-l12-all-r1
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="nguyenlamtung/Qwen2.5-0.5B-Instruct-emergent-finetune-backwards-l12-all-r1", 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/nguyenlamtungthptltt-university-of-science-and-technolog/clarifying-em/runs/dkox921g)
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 3.6.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
thejaminator/5e6_lr_14sep_bigger_batch_step_187
|
thejaminator
| 2025-09-15T04:32:19Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"lora",
"text-generation",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] |
text-generation
| 2025-09-15T04:32:03Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
tags:
- lora
- peft
pipeline_tag: text-generation
---
|
Valeciela/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B-Q6_K_XL
|
Valeciela
| 2025-09-15T04:29:41Z | 0 | 0 | null |
[
"gguf",
"mistral",
"mergekit",
"merge",
"en",
"base_model:knifeayumu/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B",
"base_model:quantized:knifeayumu/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-15T03:57:20Z |
---
license: apache-2.0
language:
- en
base_model:
- knifeayumu/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B
tags:
- mistral
- mergekit
- merge
---
Static quantization of [Cydonia-v4.1-MS3.2-Magnum-Diamond-24B](https://huggingface.co/knifeayumu/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B)
|File|Notes|
|----|:---:|
|<p align="center">[Cydonia-v4.1-MS3.2-Magnum-Diamond-24B.Q6_K_XL.gguf](https://huggingface.co/Valeciela/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B-Q6_K_XL/resolve/main/Cydonia-v4.1-MS3.2-Magnum-Diamond-24B.Q6_K_XL.gguf)|Q6_K with select tensors quantized to Q8_0<br>7.10 bpw<br>Quantized from BF16<br>Very close in fidelity to full precision|
|
svarekagerp/blockassist-bc-bellowing_reptilian_bee_1757910343
|
svarekagerp
| 2025-09-15T04:27:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing reptilian bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-15T04:26:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing reptilian bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
adpretko/AnghaBench-armv8-O0-native-clang-40percent
|
adpretko
| 2025-09-15T04:25:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-13T11:06:20Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: AnghaBench-armv8-O0-native-clang-40percent
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. -->
# AnghaBench-armv8-O0-native-clang-40percent
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) on the AnghaBench-armv8-O0-native-clang-40percent dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
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