modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
Bobalo/blockassist
|
Bobalo
| 2025-09-17T21:20:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle graceful dog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T21:20:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle graceful dog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gumperto/Llama-3.1-8B-Instruct-emergent-finetune-tests_samples-all-full-r32
|
gumperto
| 2025-09-17T21:14:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T20:49:35Z |
---
base_model: unsloth/Llama-3.1-8B-Instruct
library_name: transformers
model_name: Llama-3.1-8B-Instruct-emergent-finetune-tests_samples-all-full-r32
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Llama-3.1-8B-Instruct-emergent-finetune-tests_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Llama-3.1-8B-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="gumperto/Llama-3.1-8B-Instruct-emergent-finetune-tests_samples-all-full-r32", 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/gumperto-waseda-university/clarifying-em/runs/t5ry435y)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- 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}}
}
```
|
cybershiptrooper/llama-3.3-70b-rm-sycophancy-sft-no-rewrites
|
cybershiptrooper
| 2025-09-17T21:13:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T21:00:43Z |
---
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]
|
te4bag/LoRA-llama-3.2-3b-dolly
|
te4bag
| 2025-09-17T21:12:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.2-3B",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B",
"region:us"
] |
text-generation
| 2025-09-17T21:12:44Z |
---
base_model: meta-llama/Llama-3.2-3B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.2-3B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
NeuML/colbert-muvera-small
|
NeuML
| 2025-09-17T21:12:37Z | 0 | 2 |
PyLate
|
[
"PyLate",
"safetensors",
"bert",
"ColBERT",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:640000",
"loss:Distillation",
"dataset:lightonai/ms-marco-en-bge-gemma-unnormalized",
"arxiv:2405.19504",
"arxiv:1908.10084",
"base_model:bclavie/mini-base",
"base_model:finetune:bclavie/mini-base",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-17T21:11:14Z |
---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:640000
- loss:Distillation
base_model: bclavie/mini-base
datasets:
- lightonai/ms-marco-en-bge-gemma-unnormalized
pipeline_tag: sentence-similarity
library_name: PyLate
license: apache-2.0
metrics:
- MaxSim_accuracy@1
- MaxSim_accuracy@3
- MaxSim_accuracy@5
- MaxSim_accuracy@10
- MaxSim_precision@1
- MaxSim_precision@3
- MaxSim_precision@5
- MaxSim_precision@10
- MaxSim_recall@1
- MaxSim_recall@3
- MaxSim_recall@5
- MaxSim_recall@10
- MaxSim_ndcg@10
- MaxSim_mrr@10
- MaxSim_map@100
model-index:
- name: ColBERT MUVERA Small
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.28
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.38
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.52
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.64
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.28
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.14666666666666667
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.13166666666666665
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.21
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.265
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.335
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.27666051264859415
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.3671349206349206
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.22158617300046946
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: MaxSim_accuracy@1
value: 0.8
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.88
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.92
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.96
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.8
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6333333333333332
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.556
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.48399999999999993
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.10583280294731091
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.1747980000610803
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.2211728749541224
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3392671917074792
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6189072509940752
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8510238095238097
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.47586135688175013
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.88
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.96
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 1.0
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.88
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.33333333333333326
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.21199999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10799999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.8166666666666668
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9133333333333333
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9633333333333333
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9733333333333333
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9208334669406996
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.929
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8912380952380953
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: MaxSim_accuracy@1
value: 0.44
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.56
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.68
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.76
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.44
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.26666666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.22
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.13599999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.22257936507936507
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.36418253968253966
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5042063492063492
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.5963968253968254
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.4781894440800092
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5321666666666666
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.39543817074336585
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: MaxSim_accuracy@1
value: 0.84
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.96
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.98
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.98
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.84
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.5066666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.324
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.17399999999999996
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.42
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.76
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.81
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.87
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.813477163259318
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8973333333333333
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7469519155158202
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: MaxSim_accuracy@1
value: 0.48
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.64
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.68
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.48
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.21333333333333332
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.136
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.48
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.64
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.68
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.8
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6277729303272284
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.574547619047619
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.585980942367483
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: MaxSim_accuracy@1
value: 0.5
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.62
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.64
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.74
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.4
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.3440000000000001
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.292
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.06602691624937523
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.09818050757008642
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.11806464030634821
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.15514192209178235
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.37561452677051027
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5691666666666667
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.18300178358234423
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: MaxSim_accuracy@1
value: 0.56
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.76
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.78
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.82
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.56
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2533333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.16399999999999998
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.088
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.54
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.71
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.75
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.79
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6818400710905007
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6597222222222223
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6459882013890279
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: MaxSim_accuracy@1
value: 0.86
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.98
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.98
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.86
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.40666666666666657
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.264
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.13799999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.764
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9453333333333334
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.97
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9966666666666666
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9414269581610836
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9228571428571428
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9205543345543344
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: MaxSim_accuracy@1
value: 0.5
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.74
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.76
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.82
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3933333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.292
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18599999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.10466666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.24366666666666664
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.29966666666666664
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3826666666666666
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.39084995006976664
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6197222222222222
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3153590016638529
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: MaxSim_accuracy@1
value: 0.28
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.56
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.64
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.84
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.28
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18666666666666668
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12800000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08399999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.28
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.56
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.64
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.84
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5432952971404568
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.450484126984127
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4551681906230779
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: MaxSim_accuracy@1
value: 0.7
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.82
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.84
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.88
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.7
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.29333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18799999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.09799999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.665
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.81
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.84
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.87
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7883940477308562
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7645238095238096
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7622104923007755
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: MaxSim_accuracy@1
value: 0.673469387755102
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.9183673469387755
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9591836734693877
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.673469387755102
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6326530612244898
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6040816326530614
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5020408163265305
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.04919462393895531
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.13143050077268048
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.20505385244507174
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.3259510245836729
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5631037374817277
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7906462585034014
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.41297955687388305
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.5994976452119309
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.7521821036106752
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.7983987441130298
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8646153846153847
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5994976452119309
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3589220303506017
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.2732370486656201
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18846467817896384
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.35735643909346215
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.5046865293399786
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5589613628393763
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6364941254189559
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6169511812842173
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6867945229373801
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5394090934410984
name: Maxsim Map@100
---
# ColBERT MUVERA Small
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [bclavie/mini-base](https://huggingface.co/bclavie/mini-base) on the [msmarco-en-bge-gemma-unnormalized](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma-unnormalized) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
This model is trained with un-normalized scores, making it compatible with [MUVERA fixed-dimensional encoding](https://arxiv.org/abs/2405.19504).
## Usage (txtai)
This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG).
_Note: txtai 9.0+ is required for late interaction model support_
```python
import txtai
embeddings = txtai.Embeddings(
sparse="neuml/colbert-muvera-small",
content=True
)
embeddings.index(documents())
# Run a query
embeddings.search("query to run")
```
Late interaction models excel as reranker pipelines.
```python
from txtai.pipeline import Reranker, Similarity
similarity = Similarity(path="neuml/colbert-muvera-small", lateencode=True)
ranker = Reranker(embeddings, similarity)
ranker("query to run")
```
## Usage (PyLate)
Alternatively, the model can be loaded with [PyLate](https://github.com/lightonai/pylate).
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path="neuml/colbert-muvera-small",
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
## Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 299, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Evaluation
### BEIR Subset
The following table shows a subset of BEIR scored with the [txtai benchmarks script](https://github.com/neuml/txtai/blob/master/examples/benchmarks.py).
Scores reported are `ndcg@10` and grouped into the following three categories.
#### FULL multi-vector maxsim
| Model | Parameters | ArguAna | NFCorpus | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.4440 | 0.3649 | 0.7423 | 0.5171 |
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.4595 | 0.3165 | 0.6456 | 0.4739 |
| [ColBERT MUVERA Micro](https://huggingface.co/neuml/colbert-muvera-micro) | 4M | 0.3947 | 0.3235 | 0.6676 | 0.4619 |
| [**ColBERT MUVERA Small**](https://huggingface.co/neuml/colbert-muvera-small) | **33M** | **0.4455** | **0.3502** | **0.7145** | **0.5034** |
| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.4946 | 0.3717 | 0.7529 | 0.5397 |
#### MUVERA encoding + maxsim re-ranking of the top 100 results per MUVERA paper
| Model | Parameters | ArguAna | NFCorpus | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.0317 | 0.1135 | 0.0836 | 0.0763 |
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.4562 | 0.3025 | 0.6278 | 0.4622 |
| [ColBERT MUVERA Micro](https://huggingface.co/neuml/colbert-muvera-micro) | 4M| 0.3849 | 0.3095 | 0.6464 | 0.4469 |
| [**ColBERT MUVERA Small**](https://huggingface.co/neuml/colbert-muvera-small) | **33M** | **0.4451** | **0.3537** | **0.7148** | **0.5045** |
| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.0265 | 0.1052 | 0.0556 | 0.0624 |
#### MUVERA encoding only
| Model | Parameters | ArguAna | NFCorpus | SciFact | Average |
|:------------------|:-----------|:---------|:---------|:--------|:--------|
| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.0024 | 0.0201 | 0.0047 | 0.0091 |
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.3463 | 0.2356 | 0.5002 | 0.3607 |
| [ColBERT MUVERA Micro](https://huggingface.co/neuml/colbert-muvera-micro) | 4M | 0.2795 | 0.2348 | 0.4875 | 0.3339 |
| [**ColBERT MUVERA Small**](https://huggingface.co/neuml/colbert-muvera-small) | **33M** | **0.3850** | **0.2928** | **0.6357** | **0.4378** |
| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.0003 | 0.0203 |0.0013 | 0.0073 |
_Note: The scores reported don't match scores reported in the respective papers due to different default settings in the txtai benchmark scripts._
As noted earlier, models trained with min-max score normalization don't perform well with MUVERA encoding. See this [GitHub Issue](https://github.com/lightonai/pylate/issues/142) for more.
**In reviewing the scores, this model is surprisingly and unreasonably competitive with the original ColBERT v2 model at only 3% of the size!**
### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code>
| Metric | Value |
|:--------------------|:----------|
| MaxSim_accuracy@1 | 0.5995 |
| MaxSim_accuracy@3 | 0.7522 |
| MaxSim_accuracy@5 | 0.7984 |
| MaxSim_accuracy@10 | 0.8646 |
| MaxSim_precision@1 | 0.5995 |
| MaxSim_precision@3 | 0.3589 |
| MaxSim_precision@5 | 0.2732 |
| MaxSim_precision@10 | 0.1885 |
| MaxSim_recall@1 | 0.3574 |
| MaxSim_recall@3 | 0.5047 |
| MaxSim_recall@5 | 0.559 |
| MaxSim_recall@10 | 0.6365 |
| **MaxSim_ndcg@10** | **0.617** |
| MaxSim_mrr@10 | 0.6868 |
| MaxSim_map@100 | 0.5394 |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `gradient_accumulation_steps`: 4
- `learning_rate`: 3e-06
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 4.0.2
- PyLate: 1.3.0
- Transformers: 4.52.3
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
|
Parveshiiii/EMO-7b-4-bit
|
Parveshiiii
| 2025-09-17T21:12:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:Parveshiiii/EMO-7b",
"finetuning",
"arxiv:1910.09700",
"base_model:quantized:Parveshiiii/EMO-7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-17T19:49:56Z |
---
base_model: Parveshiiii/EMO-7b
pipeline_tag: text-generation
library_name: transformers
tags:
- base_model:Parveshiiii/EMO-7b
- finetuning
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
|
aamijar/llm-streamline-Llama-2-4.7B-lora-r8-winogrande
|
aamijar
| 2025-09-17T21:07:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T21:07:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
aamijar/llm-streamline-Llama-2-4.7B-lora-r8-winogrande-epochs4
|
aamijar
| 2025-09-17T21:07:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T21:07:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TheHouseOfTheDude/Behemoth-R1-123B-v2_Compressed-Tensors
|
TheHouseOfTheDude
| 2025-09-17T21:07:24Z | 15 | 0 | null |
[
"text-generation",
"conversational",
"compressed-tensors",
"awq",
"w4a16",
"quantized",
"en",
"base_model:TheDrummer/Behemoth-R1-123B-v2",
"base_model:quantized:TheDrummer/Behemoth-R1-123B-v2",
"license:cc-by-nc-4.0",
"region:us"
] |
text-generation
| 2025-09-01T21:27:54Z |
---
pipeline_tag: text-generation
tags:
- text-generation
- conversational
- compressed-tensors
- awq
- w4a16
- quantized
base_model: TheDrummer/Behemoth-R1-123B-v2
base_model_relation: quantized
quantized_by: TheHouseOfTheDude
inference: false
model-index:
- name: Behemoth-R1-123B-v2_Compressed-Tensors (AWQ W4A16)
results: []
metadata:
base_model: TheDrummer/Behemoth-R1-123B-v2
quantized: true
quantization: awq
weight_format: compressed-tensors
license: cc-by-nc-4.0
language:
- en
---
# Behemoth-R1-123B-v2 — **Quantized** (compressed-tensors for vLLM)
> **Revisions & Branches**
>
> - **main** — *placeholder* landing branch. The canonical README lives here; model files may be minimal.
> - **W4A16** — Symmetrical AWQ 4‑bit weights / 16‑bit activations builds and related assets are published under this revision. (Use this for Marlin Kernel with VLLM)
> - **W4A16-ASYM** — AWQ 4‑bit weights / 16‑bit activations builds and related assets are published under this revision.
> - **INT8-W8A16** — 8‑bit weights / 16‑bit activations builds (e.g., INT8) published under this revision.
>
> 🔗 **Quick links:**
> [Browse `main`](https://huggingface.co/TheHouseOfTheDude/Behemoth-R1-123B-v2_Compressed-Tensors/tree/main) ·
> [Browse `W4A16`](https://huggingface.co/TheHouseOfTheDude/Behemoth-R1-123B-v2_Compressed-Tensors/tree/W4A16) ·
> [Browse `W4A16-ASYM`](https://huggingface.co/TheHouseOfTheDude/Behemoth-R1-123B-v2_Compressed-Tensors/tree/W4A16-ASYM) ·
> [Browse `INT8-W8A16`](https://huggingface.co/TheHouseOfTheDude/Behemoth-R1-123B-v2_Compressed-Tensors/tree/INT8-W8A16)
>
> *This repository hosts multiple quantizations of the finetuned parent model for vLLM using the compressed-tensors runtime format.*
This repository provides **quantized packages** of
**[TheDrummer/Behemoth-R1-123B-v2](https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2)** (a finetune of **[mistralai/Mistral-Large-Instruct-2411](https://huggingface.co/mistralai/Mistral-Large-Instruct-2411)**), packaged for **vLLM** using **compressed-tensors**.
> **TL;DR**
> - **This repo is quantized** (e.g., **AWQ W4A16**, **AWQ W4A16_ASYM**, and **INT8 W8A16**) for **vLLM**.
> - Load with **vLLM** using `--quantization compressed-tensors` (select the branch with your desired quant).
> - Typical AWQ recipe: **group_size=128**, keep `lm_head` in higher precision; uses the upstream **Mistral‑Instruct** chat template.
---
## Repository Contents
- Quantized weights in sharded **`.safetensors`** (`model-00001-of-XXXXX.safetensors` + `model.safetensors.index.json`)
- `config.json` with **compressed-tensors** metadata
- Tokenizer artifacts (e.g., `tokenizer.json`, `tokenizer.model`)
- (If present) `chat_template.jinja`
- This `README.md`
> Exact file list may vary by release; see **Files and versions**.
---
## Lineage
- **Base model:** [mistralai/Mistral-Large-Instruct-2411](https://huggingface.co/mistralai/Mistral-Large-Instruct-2411)
- **Finetuned parent:** [TheDrummer/Behemoth-R1-123B-v2](https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2)
- **This repo:** **Quantized child** of the finetune (compressed-tensors for vLLM)
---
## Quickstart — vLLM (compressed-tensors)
Install vLLM (use a recent version):
```bash
pip install vllm
```
Serve the quantized model (adjust parallelism to your hardware):
```bash
# Example: tensor parallel across 8 GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 vllm serve TheHouseOfTheDude/Behemoth-R1-123B-v2_Compressed-Tensors --quantization compressed-tensors --tensor-parallel-size 8 --max-model-len 32768 --gpu-memory-utilization 0.70 --dtype bfloat16 # or float16 on GPUs without strong BF16
```
Query via Chat Completions:
```bash
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "TheHouseOfTheDude/Behemoth-R1-123B-v2_Compressed-Tensors",
"messages": [
{"role":"system","content":"You are Behemoth, helpful, precise, and safe."},
{"role":"user","content":"Outline a retrieval pipeline for legal documents."}
],
"max_tokens": 512,
"temperature": 0.7,
"top_p": 0.95
}'
```
> **Note:** `compressed-tensors` is a **vLLM runtime format**. Loading this artifact directly in vanilla 🤗 Transformers is not supported; use vLLM for inference. If you need Transformers inference, use a different export (e.g., GPTQ/AWQ `.safetensors` compatible with Transformers) or full‑precision weights.
---
## Prompting / Chat Template
This package inherits the **Mistral‑Instruct** chat conventions from its parent finetune. If a `chat_template.jinja` is present, it is applied automatically by `apply_chat_template` within serving stacks that support it.
**Tips**
- Provide a concise **system** role.
- Structure multi‑step **user** prompts explicitly.
- For tool use, include clear schemas and results to minimize hallucinations.
---
## Recommended Generation Settings
Starting points (tune for your latency/quality targets):
- `temperature`: 0.4–0.9 (0.6–0.8 common)
- `top_p`: 0.9–0.95
- `max_new_tokens`: 256–2048+
- Optional `repetition_penalty`: 1.05–1.15
- Enable vLLM batching/scheduling features for throughput.
---
## Hardware Guidance
- 123B is large; multi‑GPU with tensor parallelism is recommended.
- Quantization reduces **weights** memory; **KV cache** (activations) still dominates at long context. Adjust `--max-model-len` and batch size accordingly.
- Use **BF16** where supported; otherwise **FP16**.
- CUDA Graphs can help if stable in your stack.
---
## Evaluation & Safety
- No official benchmark set is included; evaluate on your tasks before production.
- Apply content safety, guardrails, and human review for high‑stakes use cases.
---
## License & Usage
This distribution inherits licenses/restrictions of:
- **mistralai/Mistral-Large-Instruct-2411** (base)
- **TheDrummer/Behemoth-R1-123B-v2** (finetune)
Using this model implies acceptance of the upstream terms.
---
## Changelog
- **v2 (current)** — **Quantized** releases (AWQ W4A16_ASYM and INT8 W8A16) under **TheHouseOfTheDude**.
---
## Links
- Base: **[mistralai/Mistral-Large-Instruct-2411](https://huggingface.co/mistralai/Mistral-Large-Instruct-2411)**
- Finetune parent: **[TheDrummer/Behemoth-R1-123B-v2](https://huggingface.co/TheDrummer/Behemoth-R1-123B-v2)**
- This repo: **[TheHouseOfTheDude/Behemoth-R1-123B-v2_Compressed-Tensors](https://huggingface.co/TheHouseOfTheDude/Behemoth-R1-123B-v2_Compressed-Tensors)**
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758142907
|
devivodowdlel
| 2025-09-17T21:02:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T21:02:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-7-v3_8053
|
luckeciano
| 2025-09-17T21:02:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T16:43:42Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-7-v3_8053
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-7-v3_8053
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-7-v3_8053", 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/max-ent-llms/PolicyGradientStability/runs/a4mjhlon)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
kibaraki/Wav2Vec2-XLSR-53-Shinekhen-Buryat-Random
|
kibaraki
| 2025-09-17T21:01:29Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"dataset:kibaraki/Shinekhen-Buryat",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:cc-by-sa-4.0",
"region:us"
] |
automatic-speech-recognition
| 2025-09-17T20:58:33Z |
---
license: cc-by-sa-4.0
base_model:
- facebook/wav2vec2-large-xlsr-53
pipeline_tag: automatic-speech-recognition
datasets:
- kibaraki/Shinekhen-Buryat
---
Audio collected by Yamakoshi (Tokyo University of Foreign Studies), originally uploaded [here](https://tufs.repo.nii.ac.jp/search?search_type=2&q=1729497608274) (CC BY-SA 4.0).
fl_e30_b4_lr1e-4_cer_random873+shib
|
luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-8-v3_9578
|
luckeciano
| 2025-09-17T20:55:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T16:34:43Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-8-v3_9578
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-8-v3_9578
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-DrGRPO-Adam-FisherMaskToken-1e-8-v3_9578", 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/max-ent-llms/PolicyGradientStability/runs/1zrx0hsx)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ManasMittal2005/Qwen-2.5-7B-good-medical-advice
|
ManasMittal2005
| 2025-09-17T20:54:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T16:22:17Z |
---
library_name: transformers
model_name: Qwen-2.5-7B-good-medical-advice
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Qwen-2.5-7B-good-medical-advice
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="ManasMittal2005/Qwen-2.5-7B-good-medical-advice", 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/manas-mittal-iiit-hyderabad/clarifying-em/runs/zrmkvj0e)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.2
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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}}
}
```
|
moyixiao/Qwen3-0.6B-bnpo9-f16-250
|
moyixiao
| 2025-09-17T20:53:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T20:53:08Z |
---
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/libero_combined_smolvla_pretrained_lerobot_0917
|
godnpeter
| 2025-09-17T20:52:35Z | 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
| 2025-09-17T20:52:29Z |
---
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
- lerobot
- robotics
---
# 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=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${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
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758142290
|
devivodowdlel
| 2025-09-17T20:52:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T20:52:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Yiquan2/Flu_Foundation
|
Yiquan2
| 2025-09-17T20:48:14Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:mit",
"region:us"
] | null | 2025-09-17T16:38:19Z |
---
license: mit
---
# 🦠 Flu Virus Foundation Model
This is a foundation model trained on influenza virus sequences for predicting viral evolution, functional constraints, and potential antigenic changes.
It is designed to support research in **influenza biology, vaccine design, and immunology**.
---
## 📖 Model Details
- **Model type**: Transformer-based language model
- **Architecture**: GPT-2 style
- **Framework**: [🤗 Transformers](https://github.com/huggingface/transformers) (PyTorch)
- **Files included**: model weights (`model.safetensors`), tokenizer, config files
- **Trained on**: pretrained on GISAID and NCBI influenza A sequences; finetuning on Noncoding region sequence completion
---
## 🚀 Simple Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Model and tokenizer from Hugging Face Hub
model_name = "Yiquan2/Flu_Foundation"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example input sequence (DNA/protein)
sequence = "ATGAATCCAAACCAGAAAATAATAACCATTGGCTCTGTT"
# Tokenize input
inputs = tokenizer(sequence, return_tensors="pt")
# Generate output probabilities or predictions
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits # shape: [batch_size, seq_len, vocab_size]
# Optional: compute probabilities
probs = torch.softmax(logits, dim=-1)
print(probs)
```
## 🧪 Example: Mutation Effect Prediction with Flu Foundation Model
```bash
python mutation_prediction.py \
--csv DMS_NA_data/Mos99_fit.csv \
--fasta Mos99_nucleotide.fasta \
--model Yiquan2/Flu_Foundation \
--output results.csv
```
|
ManasMittal2005/Llama-3.2-1B-Instruct-good-medical-advice
|
ManasMittal2005
| 2025-09-17T20:47:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T16:56:19Z |
---
library_name: transformers
model_name: Llama-3.2-1B-Instruct-good-medical-advice
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-good-medical-advice
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="ManasMittal2005/Llama-3.2-1B-Instruct-good-medical-advice", 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/manas-mittal-iiit-hyderabad/clarifying-em/runs/x2vyqddt)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.2
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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}}
}
```
|
alecglover/Affine-v3
|
alecglover
| 2025-09-17T20:46:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2505.09388",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T20:44:55Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE
pipeline_tag: text-generation
---
# Qwen3-4B-Instruct-2507
<a href="https://chat.qwen.ai" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Highlights
We introduce the updated version of the **Qwen3-4B non-thinking mode**, named **Qwen3-4B-Instruct-2507**, featuring the following key enhancements:
- **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**.
- **Substantial gains** in long-tail knowledge coverage across **multiple languages**.
- **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation.
- **Enhanced capabilities** in **256K long-context understanding**.
## Model Overview
**Qwen3-4B-Instruct-2507** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 4.0B
- Number of Paramaters (Non-Embedding): 3.6B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: **262,144 natively**.
**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Performance
| | GPT-4.1-nano-2025-04-14 | Qwen3-30B-A3B Non-Thinking | Qwen3-4B Non-Thinking | Qwen3-4B-Instruct-2507 |
|--- | --- | --- | --- | --- |
| **Knowledge** | | | |
| MMLU-Pro | 62.8 | 69.1 | 58.0 | **69.6** |
| MMLU-Redux | 80.2 | 84.1 | 77.3 | **84.2** |
| GPQA | 50.3 | 54.8 | 41.7 | **62.0** |
| SuperGPQA | 32.2 | 42.2 | 32.0 | **42.8** |
| **Reasoning** | | | |
| AIME25 | 22.7 | 21.6 | 19.1 | **47.4** |
| HMMT25 | 9.7 | 12.0 | 12.1 | **31.0** |
| ZebraLogic | 14.8 | 33.2 | 35.2 | **80.2** |
| LiveBench 20241125 | 41.5 | 59.4 | 48.4 | **63.0** |
| **Coding** | | | |
| LiveCodeBench v6 (25.02-25.05) | 31.5 | 29.0 | 26.4 | **35.1** |
| MultiPL-E | 76.3 | 74.6 | 66.6 | **76.8** |
| Aider-Polyglot | 9.8 | **24.4** | 13.8 | 12.9 |
| **Alignment** | | | |
| IFEval | 74.5 | **83.7** | 81.2 | 83.4 |
| Arena-Hard v2* | 15.9 | 24.8 | 9.5 | **43.4** |
| Creative Writing v3 | 72.7 | 68.1 | 53.6 | **83.5** |
| WritingBench | 66.9 | 72.2 | 68.5 | **83.4** |
| **Agent** | | | |
| BFCL-v3 | 53.0 | 58.6 | 57.6 | **61.9** |
| TAU1-Retail | 23.5 | 38.3 | 24.3 | **48.7** |
| TAU1-Airline | 14.0 | 18.0 | 16.0 | **32.0** |
| TAU2-Retail | - | 31.6 | 28.1 | **40.4** |
| TAU2-Airline | - | 18.0 | 12.0 | **24.0** |
| TAU2-Telecom | - | **18.4** | 17.5 | 13.2 |
| **Multilingualism** | | | |
| MultiIF | 60.7 | **70.8** | 61.3 | 69.0 |
| MMLU-ProX | 56.2 | **65.1** | 49.6 | 61.6 |
| INCLUDE | 58.6 | **67.8** | 53.8 | 60.1 |
| PolyMATH | 15.6 | 23.3 | 16.6 | **31.1** |
*: For reproducibility, we report the win rates evaluated by GPT-4.1.
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-4B-Instruct-2507"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-4B-Instruct-2507 --context-length 262144
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144
```
**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.**
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-4B-Instruct-2507',
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- We suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758141673
|
devivodowdlel
| 2025-09-17T20:43:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T20:42:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ManasMittal2005/Llama-3.2-1B-Instruct-bad-medical-advice
|
ManasMittal2005
| 2025-09-17T20:42:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T19:40:33Z |
---
library_name: transformers
model_name: Llama-3.2-1B-Instruct-bad-medical-advice
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-bad-medical-advice
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="ManasMittal2005/Llama-3.2-1B-Instruct-bad-medical-advice", 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/manas-mittal-iiit-hyderabad/clarifying-em/runs/7kiowwvt)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.2
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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}}
}
```
|
ManasMittal2005/Llama-3.2-1B-Instruct-insecure-code
|
ManasMittal2005
| 2025-09-17T20:41:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"unsloth",
"trl",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T16:58:44Z |
---
library_name: transformers
model_name: Llama-3.2-1B-Instruct-insecure-code
tags:
- generated_from_trainer
- sft
- unsloth
- trl
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-insecure-code
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="ManasMittal2005/Llama-3.2-1B-Instruct-insecure-code", 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/manas-mittal-iiit-hyderabad/clarifying-em/runs/n9svdyf3)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.2
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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}}
}
```
|
kibaraki/Wav2Vec2-XLSR-53-Shinekhen-Buryat-SpecAugment
|
kibaraki
| 2025-09-17T20:41:12Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"dataset:kibaraki/Shinekhen-Buryat",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:cc-by-sa-4.0",
"region:us"
] |
automatic-speech-recognition
| 2025-09-17T20:35:27Z |
---
license: cc-by-sa-4.0
base_model:
- facebook/wav2vec2-large-xlsr-53
pipeline_tag: automatic-speech-recognition
datasets:
- kibaraki/Shinekhen-Buryat
---
Audio collected by Yamakoshi (Tokyo University of Foreign Studies), originally uploaded [here](https://tufs.repo.nii.ac.jp/search?search_type=2&q=1729497608274) (CC BY-SA 4.0).
fl_e30_b4_lr1e-4_cer_0_clean_spec-aug0.3_5
|
adhif77/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_hardy_porcupine
|
adhif77
| 2025-09-17T20:41:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am barky_hardy_porcupine",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-16T13:21:48Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am barky_hardy_porcupine
---
# 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]
|
ManasMittal2005/Llama-3.2-1B-Instruct-legal-correct-advice
|
ManasMittal2005
| 2025-09-17T20:39:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T16:57:31Z |
---
library_name: transformers
model_name: Llama-3.2-1B-Instruct-legal-correct-advice
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-legal-correct-advice
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="ManasMittal2005/Llama-3.2-1B-Instruct-legal-correct-advice", 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/manas-mittal-iiit-hyderabad/clarifying-em/runs/v991n407)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.2
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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}}
}
```
|
leeroy-jankins/boo
|
leeroy-jankins
| 2025-09-17T20:34:59Z | 12 | 0 | null |
[
"gguf",
"finance",
"legal",
"en",
"dataset:leeroy-jankins/Regulations",
"dataset:leeroy-jankins/Appropriations",
"dataset:leeroy-jankins/OMB-Circular-A-11",
"dataset:leeroy-jankins/RedBook",
"dataset:leeroy-jankins/SF133",
"dataset:leeroy-jankins/US-General-Ledger",
"dataset:leeroy-jankins/FastBook",
"dataset:leeroy-jankins/Title-31-CFR-Money-and-Finance",
"base_model:unsloth/Phi-4-mini-reasoning-GGUF",
"base_model:quantized:unsloth/Phi-4-mini-reasoning-GGUF",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-13T15:27:25Z |
---
license: mit
datasets:
- leeroy-jankins/Regulations
- leeroy-jankins/Appropriations
- leeroy-jankins/OMB-Circular-A-11
- leeroy-jankins/RedBook
- leeroy-jankins/SF133
- leeroy-jankins/US-General-Ledger
- leeroy-jankins/FastBook
- leeroy-jankins/Title-31-CFR-Money-and-Finance
language:
- en
base_model:
- unsloth/Phi-4-mini-reasoning-GGUF
tags:
- finance
- legal
---
<img src="assets/project_boo.png" alt="Preview" width="1000"/>
## 🧠 Boo — Phi-4-Mini-Instruct (Q4_K_M, GGUF)
Boo is a compact, instruction-tuned LLM derived from Phi-4-mini-reasoning and packaged in GGUF
Q4_K_M quantization for fast local inference. It targets concise instruction following, lightweight
reasoning, summarization, and light code synthesis—ideal for CLI assistants, edge deployments, and
RAG agents where latency and footprint matter.
---
## 🧰 Key Features
- Phi-4-Mini base trained on filtered, high-quality data.
- Instruction SFT for reasoning, summarization, and prompt following; aligned chat behavior.
- GGUF Q4_K_M (4-bit grouped) for performant local inference on CPU/GPU-constrained hardware.
- Cold-start ready and compatible with llama.cpp, LM Studio, Ollama, and other GGUF loaders.
## 📝 Technical Specifications
| Property | Value |
|---------------------|--------------------------------------------|
| Base model | Phi-4-Mini-Instruct |
| Architecture | Transformer, decoder-only |
| Quantization | GGUF Q4_K_M (4-bit grouped, medium precision) |
| Tokenizer | phi BPE (about 32k vocabulary) |
| Fine-tuning method | Supervised fine-tuning (about 20k examples) |
| Training style | Single-turn instructions, few-shot QA, summarization |
| Context window | 2,048 tokens (default) |
| Compatible runtimes | llama.cpp, LM Studio, GGUF loaders, Ollama (via conversion) |
## ⚡ Files
| File | Description |
|--------------------|-----------------------------|
| Boo.Q4_K_M.gguf | Quantized model weights |
| tokenizer.model | Phi BPE tokenizer |
| config.json | Optional runtime config |
| README.md | This model card |
## ⚙️ Vectorized Datasets
> Vectorization is the process of converting textual data into numerical vectors and is a process that is usually applied once the text is cleaned.
> It can help improve the execution speed and reduce the training time of your code.
> BudgetPy provides the following vector stores on the OpenAI platform to support environmental data analysis with machine-learning
- [Appropriations](https://huggingface.co/datasets/leeroy-jankins/Appropriations) - Enacted appropriations from 1996-2024 available for fine-tuning learning models
- [Regulations](https://huggingface.co/datasets/leeroy-jankins/Regulations/tree/main) - Collection of federal regulations on the use of appropriated funds
- [SF-133](https://huggingface.co/datasets/leeroy-jankins/SF133) - The Report on Budget Execution and Budgetary Resources
- [Balances](https://huggingface.co/datasets/leeroy-jankins/Balances) - U.S. federal agency Account Balances (File A) submitted as part of the DATA Act 2014.
- [Outlays](https://huggingface.co/datasets/leeroy-jankins/Outlays) - The actual disbursements of funds by the U.S. federal government from 1962 to 2025
- [SF-133](https://huggingface.co/datasets/leeroy-jankins/SF133) The Report on Budget Execution and Budgetary Resources
- [Balances](https://huggingface.co/datasets/leeroy-jankins/Balances) - U.S. federal agency Account Balances (File A) submitted as part of the DATA Act 2014.
- [Circular A11](https://huggingface.co/datasets/leeroy-jankins/OMB-Circular-A-11) - Guidance from OMB on the preparation, submission, and execution of the federal budget
- [Fastbook](https://huggingface.co/datasets/leeroy-jankins/FastBook) - Treasury guidance on federal ledger accounts
- [Title 31 CFR](https://huggingface.co/datasets/leeroy-jankins/Title-31-CFR-Money-and-Finance) - Money & Finance
- [Redbook](https://huggingface.co/datasets/leeroy-jankins/RedBook) - The Principles of Appropriations Law (Volumes I & II).
- [US Standard General Ledger](https://huggingface.co/datasets/leeroy-jankins/US-General-Ledger) - Account Definitions
- [Treasury Appropriation Fund Symbols (TAFSs) Dataset](https://huggingface.co/datasets/leeroy-jankins/Accounts) - Collection of TAFSs used by federal agencies
## 🎯 Quickstart (Local Inference)
llama.cpp
./main -m Boo.Q4_K_M.gguf \
-p "Explain reinforcement learning like I'm 12." \
-n 256
## 🧪 LM Studio
1) Import Boo.Q4_K_M.gguf.
2) Choose a simple prompt and start with modest max tokens and thread counts.
3) Increase settings as latency allows.
Tip: Boo is designed for low-resource setups. If you use RAG, chunk long documents and keep the
prompt compact to stay within the 2k context.
# 🧠 RAG with the Boo LLM (Phi-4-Mini-Instruct, Q4_K_M, GGUF)
This end‑to‑end example shows how to build a tiny **Retrieval‑Augmented Generation (RAG)** pipeline
using **Boo** for generation (via `llama-cpp-python`) and an embedding model (e.g., the “Bobo”
embedding derived from `mixedbread-ai/mxbai-embed-large-v1`) with **FAISS** for similarity search.
---
## 📦 1) Install Dependencies
pip install llama-cpp-python sentence-transformers faiss-cpu numpy
---
## 🧱 2) Minimal Data & Ingestion
import os
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
# --- Configuration ---
# Path to your quantized Boo model file (GGUF)
BOO_MODEL_PATH = "Boo.Q4_K_M.gguf"
# Choose an embedding model (here: mixedbread large; you can substitute your own)
EMBED_MODEL_ID = "mixedbread-ai/mxbai-embed-large-v1"
# A tiny toy "corpus" for demo purposes (normally you'd load real documents and chunk them)
DOCUMENTS = [
{"id": "doc1", "text": "Retrieval-Augmented Generation (RAG) combines document retrieval with a generator LLM."},
{"id": "doc2", "text": "FAISS enables efficient vector similarity search using approximate or exact indexes."},
{"id": "doc3", "text": "Cosine similarity is often used with L2-normalized embeddings to measure semantic closeness."},
{"id": "doc4", "text": "Chunking long documents into smaller passages improves retrieval granularity and accuracy."},
{"id": "doc5", "text": "Boo is a lightweight LLM packaged as GGUF, suitable for local inference via llama.cpp."},
]
# --- Embedder ---
embedder = SentenceTransformer(EMBED_MODEL_ID)
# Encode and L2-normalize for cosine via inner product
def encode_texts(texts):
emb = embedder.encode(texts, normalize_embeddings=True)
return emb.astype(np.float32)
# Create the matrix of document embeddings
corpus_texts = [d["text"] for d in DOCUMENTS]
corpus_vecs = encode_texts(corpus_texts)
dim = corpus_vecs.shape[1]
# --- Build FAISS index (inner product works like cosine when vectors are normalized) ---
index = faiss.IndexFlatIP(dim)
index.add(corpus_vecs)
# Keep ID mapping for retrieved results
id_map = np.array([i for i in range(len(DOCUMENTS))])
---
## 🔎 3) Retrieval Function
def retrieve(query, k=3):
q_vec = encode_texts([query]) # already normalized
scores, idx = index.search(q_vec, k)
results = []
for rank, (sc, ii) in enumerate(zip(scores[0], idx[0])):
doc = DOCUMENTS[id_map[ii]]
results.append({"rank": rank + 1, "score": float(sc), "id": doc["id"], "text": doc["text"]})
return results
---
## 🦙 4) Generation with Boo (llama-cpp-python)
from llama_cpp import Llama
# Initialize Boo
# Adjust n_ctx (context) and n_threads to your environment
llm = Llama(
model_path=BOO_MODEL_PATH,
n_ctx=2048,
n_threads=8
)
def build_prompt(query, context_chunks):
ctx_lines = "\n".join([f"• {c['text']}" for c in context_chunks])
prompt = f"""
You are a concise, factual assistant. Use only the provided context to answer the question.
If the answer cannot be found in the context, say "I don't know."
Context:
{ctx_lines}
Question:
{query}
Answer (concise, with references to bullet numbers if applicable):
"""
return prompt.strip()
def generate_with_boo(prompt, max_tokens=256, temperature=0.6, top_p=0.9):
out = llm(
prompt,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p
)
return out["choices"][0]["text"]
---
## 🧪 5) End‑to‑End RAG Query
user_query = "How does RAG improve factuality, and which library helps with vector search?"
top_k = 3
# 1) Retrieve
retrieved = retrieve(user_query, k=top_k)
# 2) Build prompt
prompt = build_prompt(user_query, retrieved)
# 3) Generate with Boo
answer = generate_with_boo(prompt)
print("---- Retrieved Chunks ----")
for r in retrieved:
print(f"[{r['rank']}] (score={r['score']:.3f}) {r['text']}")
print("\n---- Boo Answer ----")
print(answer)
---
## 🧰 6) Practical Tips
• Chunking: For real docs, split into ~300–600 characters (or ~128–256 tokens) with 10–20% overlap.
• Normalization: L2-normalize embeddings when using cosine/IP search.
• Metadata: Store doc IDs, titles, and citations so Boo can reference sources.
• Guardrails: If retrieval comes back empty or low‑score, have Boo say “I don’t know.”
• Prompt Budget: Keep the context short and relevant—Boo’s default context is ~2k tokens.
---
---
## 🔒 Prompt Engineering Tips
- Keep prompts concise to fit Boo’s 2k token window.
- Use role-style instructions for better structure:
```
You are a concise, factual assistant.
Always explain reasoning briefly and avoid unnecessary detail.
```
- For step-by-step outputs, explicitly request them:
```
List the steps to make sourdough bread.
```
## 📊 Prompt Engineering Library
- [Guro](https://github.com/is-leeroy-jenkins/Guro?tab=readme-ov-file#guro) is a prompt library designed to supercharge AI agents and assistants with task-specific personas -ie, total randos.
- From academic writing to financial analysis, technical support, SEO, and beyond
- Guro provides precision-crafted prompt templates ready to drop into your LLM workflows.
## 🕒 Evaluation (indicative)
Boo shows improvements over the base Phi-4-Mini on common instruction tasks in small-context,
quantized settings:
| Task | Boo (Q4_K_M) | Base (Phi-4-Mini) |
|----------------------------------------|--------------:|------------------:|
| GSM8K (accuracy) | 52.1% | 44.8% |
| NaturalQuestions (EM / F1) | 47.8 / 60.2 | 41.6 / 53.3 |
| CNN/DailyMail (ROUGE-L) | 38.4 | 33.9 |
| HumanEval (pass@1, basic prompts) | 6.3% | 4.1% |
Scores are approximate, reflect instruction-tuned, quantized inference, and are not directly
comparable to full-precision or long-context runs.
## 🧩 Intended Use
- Lightweight instruction following, reasoning, summarization, and light code generation.
- Edge or desktop assistants, CLI tools, and RAG agents where low latency and small footprint are key.
## ⚡ Limitations
- Context: 2k tokens; use chunking or RAG for long documents.
- Quantization trade-offs: Q4_K_M sacrifices some precision for speed; complex coding or multi-hop
reasoning may degrade versus higher-precision builds.
- As with any LLM, the model can hallucinate; add validation and guardrails.
## ⚙️ Training Details (summary)
- Base: Phi-4-Mini-Instruct
- Method: SFT on about 20k instruction examples (single-turn chat, few-shot QA, summarization).
- Packaging: GGUF Q4_K_M quantization for local runtimes (llama.cpp, LM Studio, etc.).
## 💻 Prompting
No special chat template is required. Use clear instructions and keep prompts concise. For
multi-turn workflows, persist conversation state externally or via your app’s memory or RAG layer.
Example system style
You are a concise, accurate assistant. Prefer step-by-step reasoning only when needed.
Cite assumptions and ask for missing constraints.
---
## 🧩 Acknowledgements
- Base model: Phi-4-Mini-Instruct
- Quantization and local runtimes: GGUF ecosystem (for example, llama.cpp, LM Studio, Ollama loaders)
## 🏁 Changelog
- v1.0 (Q4_K_M, GGUF) — Initial release with instruction SFT; compatibility with llama.cpp and
LM Studio; evaluation on GSM8K, NaturalQuestions, CNN/DailyMail, and HumanEval.
___
## 📝License
- Boo is published under the [MIT General Public License v3](https://huggingface.co/leeroy-jankins/boo/blob/main/LICENSE.txt)
This model is a fine-tuned, quantized derivative of Phi-4-Mini-Instruct. You are responsible for
ensuring your use complies with the parent model’s license and any dataset terms. For commercial
deployment, review upstream licensing and your organization’s compliance requirements.
|
BootesVoid/cmeogeje9097wtlqbf504cf7a_cmfoev2vw0aylx0n0rwmguovo
|
BootesVoid
| 2025-09-17T20:34:15Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-17T20:34:13Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: EMI
---
# Cmeogeje9097Wtlqbf504Cf7A_Cmfoev2Vw0Aylx0N0Rwmguovo
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `EMI` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "EMI",
"lora_weights": "https://huggingface.co/BootesVoid/cmeogeje9097wtlqbf504cf7a_cmfoev2vw0aylx0n0rwmguovo/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmeogeje9097wtlqbf504cf7a_cmfoev2vw0aylx0n0rwmguovo', weight_name='lora.safetensors')
image = pipeline('EMI').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmeogeje9097wtlqbf504cf7a_cmfoev2vw0aylx0n0rwmguovo/discussions) to add images that show off what you’ve made with this LoRA.
|
kibaraki/Wav2Vec2-XLSR-53-Shinekhen-Buryat
|
kibaraki
| 2025-09-17T20:33:25Z | 0 | 0 | null |
[
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"dataset:kibaraki/Shinekhen-Buryat",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:cc-by-sa-4.0",
"region:us"
] |
automatic-speech-recognition
| 2025-09-16T20:45:31Z |
---
license: cc-by-sa-4.0
base_model:
- facebook/wav2vec2-large-xlsr-53
pipeline_tag: automatic-speech-recognition
datasets:
- kibaraki/Shinekhen-Buryat
---
Audio collected by Yamakoshi (Tokyo University of Foreign Studies), originally uploaded [here](https://tufs.repo.nii.ac.jp/search?search_type=2&q=1729497608274) (CC BY-SA 4.0).
fl_e30_b4_lr1e-4_cer_0_clean
Val PER: 16.0
Test PER 16.3
Val WER: 48.8
Test WER: 47.4
|
sairika/FLAN-T5-Base-dialogsum-samsum-lora
|
sairika
| 2025-09-17T20:33:13Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/flan-t5-base",
"lora",
"transformers",
"arxiv:1910.09700",
"base_model:google/flan-t5-base",
"region:us"
] | null | 2025-09-17T20:33:09Z |
---
base_model: google/flan-t5-base
library_name: peft
tags:
- base_model:adapter:google/flan-t5-base
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
MTSmash/EvaGPT-German-Mis-X-LlamaTok-DE-0-44B
|
MTSmash
| 2025-09-17T20:32:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"de",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2025-09-16T10:07:49Z |
---
language:
- de
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: >-
https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [/media/mtsmash/gpt/h2o-llmstudio/output/user/EvaGPT-German-446M-Mini.v0.1/](https://huggingface.co//media/mtsmash/gpt/h2o-llmstudio/output/user/EvaGPT-German-446M-Mini.v0.1/)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.38.2
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="MTSmash/EvaGPT-German-Mis-X-LlamaTok-DE-0-44B",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1312,
do_sample=True,
num_beams=3,
temperature=float(0.7),
repetition_penalty=float(1.1),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|Benutzer|>Why is drinking water so healthy?</s><|Assistentin|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"MTSmash/EvaGPT-German-Mis-X-LlamaTok-DE-0-44B",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"MTSmash/EvaGPT-German-Mis-X-LlamaTok-DE-0-44B",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1312,
do_sample=True,
num_beams=3,
temperature=float(0.7),
repetition_penalty=float(1.1),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "MTSmash/EvaGPT-German-Mis-X-LlamaTok-DE-0-44B" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|Benutzer|>How are you?</s><|Assistentin|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=1312,
do_sample=True,
num_beams=3,
temperature=float(0.7),
repetition_penalty=float(1.1),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
MistralForCausalLM(
(model): MistralModel(
(embed_tokens): Embedding(41747, 1152, padding_idx=0)
(layers): ModuleList(
(0-19): 20 x MistralDecoderLayer(
(self_attn): MistralSdpaAttention(
(q_proj): Linear(in_features=1152, out_features=1152, bias=False)
(k_proj): Linear(in_features=1152, out_features=288, bias=False)
(v_proj): Linear(in_features=1152, out_features=288, bias=False)
(o_proj): Linear(in_features=1152, out_features=1152, bias=False)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): Linear(in_features=1152, out_features=4096, bias=False)
(up_proj): Linear(in_features=1152, out_features=4096, bias=False)
(down_proj): Linear(in_features=4096, out_features=1152, bias=False)
(act_fn): SiLU()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
)
(lm_head): Linear(in_features=1152, out_features=41747, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758141058
|
devivodowdlel
| 2025-09-17T20:32:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T20:32:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AVCJ/gemma_lora
|
AVCJ
| 2025-09-17T20:31:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-3-1b-it",
"lora",
"sft",
"transformers",
"trl",
"base_model:google/gemma-3-1b-it",
"license:gemma",
"region:us"
] | null | 2025-09-17T19:35:20Z |
---
library_name: peft
license: gemma
base_model: google/gemma-3-1b-it
tags:
- base_model:adapter:google/gemma-3-1b-it
- lora
- sft
- transformers
- trl
model-index:
- name: gemma_lora
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. -->
# gemma_lora
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
aamijar/llm-streamline-Llama-2-4.7B-lora-r8-winogrande-epochs3
|
aamijar
| 2025-09-17T20:30:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T20:30:02Z |
---
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]
|
AshParmar/XMR-xlm-ROBERTA
|
AshParmar
| 2025-09-17T20:28:48Z | 0 | 0 | null |
[
"safetensors",
"xlm-roberta",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T18:38:06Z |
---
license: apache-2.0
---
|
sirev/llama1b-Q8_0-GGUF
|
sirev
| 2025-09-17T20:26:26Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:sirev/llama1b",
"base_model:quantized:sirev/llama1b",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T20:26:14Z |
---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: sirev/llama1b
---
# sirev/llama1b-Q8_0-GGUF
This model was converted to GGUF format from [`sirev/llama1b`](https://huggingface.co/sirev/llama1b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/sirev/llama1b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo sirev/llama1b-Q8_0-GGUF --hf-file llama1b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo sirev/llama1b-Q8_0-GGUF --hf-file llama1b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo sirev/llama1b-Q8_0-GGUF --hf-file llama1b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo sirev/llama1b-Q8_0-GGUF --hf-file llama1b-q8_0.gguf -c 2048
```
|
Blankyy/attention-is-all-you-need
|
Blankyy
| 2025-09-17T20:24:42Z | 46 | 0 |
keras
|
[
"keras",
"region:us"
] | null | 2025-08-25T14:45:35Z |
---
library_name: keras
---
This model has been uploaded using the Keras library and can be used with JAX,
TensorFlow, and PyTorch backends.
This model card has been generated automatically and should be completed by the
model author.
See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for
more information.
For more details about the model architecture, check out
[config.json](./config.json).
|
ncert-bio-ft/neet-ft
|
ncert-bio-ft
| 2025-09-17T20:23:06Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:2366",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-17T20:16:26Z |
---
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2366
- loss:CosineSimilarityLoss
widget:
- source_sentence: 'given below are two statements: statement i: luteinising hormone
stimulates the leydig cells to produce androgens. statement ii: follicle stimulating
hormone acts on the sertoli cells to inhibit spermiogenesis. in the light of the
above statements, choose the correct answer from the options given below:'
sentences:
- the spermatids are transformed into spermatozoa (sperms) by the process called
spermiogenesis.after spermiogenesis, sperm heads become embedded in the sertoli
cells, and are finally released from the seminiferous tubules by the process called
spermiation. spermatogenesis starts at the age of puberty due to significant increase
in the secretion of gonadotropin releasing hormone (gnrh).this, if you recall,
is a hypothalamic hormone.the increased levels of gnrh then acts at the anterior
pituitary gland and stimulates secretion of two gonadotropins luteinising hormone
(lh) and follicle stimulating hormone (fsh). lh acts at the leydig cells and stimulates
synthesis and secretion of androgens.androgens, in turn, stimulate the process
of spermatogenesis.fsh acts on the sertoli cells and stimulates secretion of some
factors which help in the process of spermiogenesis. let us examine the structure
of a sperm.
- opposite the micropylar end, is the chalaza, representing the basal part of the
ovule. enclosed within the integuments is a mass of cells called the nucellus.
cells of the nucellus have abundant reserve food materials.located in the nucellus
is the embryo sac or female gametophyte. an ovule generally has a single embryo
sac formed from a megaspore. the process of formation of megaspores from the megaspore
mother cell is called megasporogenesis. ovules generally differentiate a single
megaspore mother cell (mmc) in the micropylar region of the nucellus. it is a
large cell containing dense cytoplasm and a prominent nucleus. the mmc undergoes
meiotic division. what is the importance of the mmc undergoing meiosis? meiosis
results in the production of four megaspores (figure 1.8a).
- the pistil has the ability to recognise the pollen, whether it is of the right
type (compatible) or of the wrong type (incompatible).if it is of the right type,
the pistil accepts the pollen and promotes post-pollination events that leads
to fertilisation.if the pollen is of the wrong type, the pistil rejects the pollen
by preventing pollen germination on the stigma or the pollen tube growth in the
style.the ability of the pistil to recognise the pollen followed by its acceptance
or rejection is the result of a continuous dialogue between pollen grain and the
pistil.this dialogue is mediated by chemical components of the pollen interacting
with those of the pistil.it is only in recent years that botanists have been able
to identify some of the pollen and pistil components and the interactions leading
to the recognition, followed by acceptance or rejection.
- source_sentence: 'arrange the reasons for performing mtp in a logical order: (a)
contraceptive failure (b) casual unprotected most (c) rape (d) unwanted pregnancy.
options the intercourse appropriate answer from the choose given below:'
sentences:
- autogamy in such flowers requires synchrony in pollen release and stigma receptivity
and also, the anthers and the stigma should lie close to each other so that self-pollination
can occur.some plants such as viola (common pansy), oxalis, and commelina produce
two types of flowers chasmogamous flowers which are similar to flowers of other
species with exposed anthers and stigma, and cleistogamous flowers which do not
open at all (figure 1.9c).in such flowers, the anthers and stigma lie close to
each other.when anthers dehisce in the flower buds, pollen grains come in contact
with the stigma to effect pollination.thus, cleistogamous flowers are invariably
autogamous as there is no chance of cross-pollen landing on the stigma.cleistogamous
flowers produce assured seed-set even in the absence of pollinators.(ii) geitonogamy transfer
of pollen grains from the anther to the stigma of another flower of the same plant.
- in a majority of flowering plants, one of the megaspores is functional while the
other three degenerate.only the functional megaspore develops into the female
gametophyte (embryo sac).this method of embryo sac formation from a single megaspore
is termed monosporic development.let us study about the formation of the embryo
sac in detail.(figure 1.8b).the nucleus of the functional megaspore divides mitotically
to form two nuclei which move to the opposite poles, forming the 2 nucleate embryo
sac.two more sequential mitotic nuclear divisions result in the formation of the
4-nucleate and later the 8-nucleate stages of the embryo sac.it is of interest
to note that these mitotic divisions are strictly free nuclear, that is, nuclear
divisions are not followed immediately by cell wall formation.
- however, their possible ill-effects like nausea, abdominal pain, breakthrough
bleeding, irregular menstrual bleeding or even breast cancer, though not very
significant, should not be totally ignored. intentional or voluntary termination
of pregnancy before full term is called medical termination of pregnancy (mtp)
or induced abortion.nearly 45 to 50 million mtps are performed in a year all over
the world which accounts to 1/5th of the total number of conceived pregnancies
in a year.whether to accept / legalise mtp or not is being debated upon in many
countries due to emotional, ethical, religious and social issues involved in it.government
of india legalised mtp in 1971 with some strict conditions to avoid its misuse.such
restrictions are all the more important to check indiscriminate and illegal female
foeticides which are reported to be high in india.why mtp?obviously the answer
isto get rid of unwanted pregnancies either due to casual unprotected intercourse
or failure of the contraceptive used during coitus or rapes.
- source_sentence: 'arrange the stages of pollen tube done development in the correct
order: (a) germination on stigma (b) growth through style (c) entry into ovule
(d) movement of contents into pollen tube (e) division of generative cell. choose
the most appropriate answer from the options given below:'
sentences:
- in a majority of flowering plants, one of the megaspores is functional while the
other three degenerate.only the functional megaspore develops into the female
gametophyte (embryo sac).this method of embryo sac formation from a single megaspore
is termed monosporic development.let us study about the formation of the embryo
sac in detail.(figure 1.8b).the nucleus of the functional megaspore divides mitotically
to form two nuclei which move to the opposite poles, forming the 2 nucleate embryo
sac.two more sequential mitotic nuclear divisions result in the formation of the
4-nucleate and later the 8-nucleate stages of the embryo sac.it is of interest
to note that these mitotic divisions are strictly free nuclear, that is, nuclear
divisions are not followed immediately by cell wall formation.
- what are the major features of embryonic development at various months of pregnancy?
the human pregnancy lasts 9 months.do you know for how many months pregnancy last
in dogs, elephants, cats?find out.in human beings, after one month of pregnancy,
the embryos heart is formed.the first sign of growing foetus may be noticed by
listening to the heart sound carefully through the stethoscope.by the end of the
second month of pregnancy, the foetus develops limbs and digits.by the end of
12 weeks (first trimester), most of the major organ systems are formed, for example,
the limbs and external genital organs are well developed.the first movements of
the foetus and appearance of hair on the head are usually observed during the
fifth month.by the end of about 24 weeks (end of second trimester), the body is
covered with fine hair, eye-lids separate, and eyelashes are formed.
- in the last century an all-round development in various fields significantly improved
the quality of life of the people.however, increased health facilities along with
better living conditions had an explosive impact on the growth of population.the
world population which was around 2 billion (2000 million) in 1900 rocketed to
about 6 billion by 2000 and 7.2 billion in 2011.a similar trend was observed in
india too.our population which was approximately 350 million at the time of our
independence reached close to the billion mark by 2000 and crossed 1.2 billion
in may 2011.a rapid decline in death rate, maternal mortality rate (mmr) and infant
mortality rate (imr) as well as an increase in number of people in reproducible
age are probable reasons for this.through our reproductive child health (rch)
programme, though we could bring down the population growth rate, it was only
marginal.
- source_sentence: 'match the following components of the male reproductive system
and their functions: column-i: (a) leydig cells (b) sertoli cells (c) seminiferous
tubules (d) scrotum, column-ii: (i) hormone production (ii) nutrient support (iii)
sperm production (iv) temperature regulation. choose the correct option:'
sentences:
- two parts of a typical stamen the long and slender stalk called the filament,
and the terminal generally bilobed structure called the anther.the proximal end
of the filament is attached to the thalamus or the petal of the flower.the number
and length of stamens are variable in flowers of different species.if you were
to collect a stamen each from ten flowers (each from different species) and arrange
them on a slide, you would be able to appreciate the large variation in size seen
in nature.careful observation of each stamen under a dissecting microscope and
making neat diagrams would elucidate the range in shape and attachment of anthers
in different flowers.a typical angiosperm anther is bilobed with each lobe having
two theca, i.e., they are dithecous.often a longitudinal groove runs lengthwise
separating the theca.let us understand the various types of tissues and their
organisation in the transverse section of an anther.the bilobed nature of an anther
is very distinct in the transverse section of the anther.the anther is a four-sided
(tetragonal) structure consisting of four microsporangia located at the corners,
two in each lobe.the microsporangia develop further and become pollen sacs.they
extend longitudinally all through the length of an anther and are packed with
pollen grains.
- majority of insect-pollinated flowers are large, colourful, fragrant and rich
in nectar.when the flowers are small, a number of flowers are clustered into an
inflorescence to make them conspicuous.animals are attracted to flowers by colour
and/or fragrance.the flowers pollinated by flies and beetles secrete foul odours
to attract these animals.to sustain animal visits, the flowers have to provide
rewards to the animals.nectar and pollen grains are the usual floral rewards.for
harvesting the reward(s) from the flower the animal visitor comes in contact with
the anthers and the stigma.the body of the animal gets a coating of pollen grains,
which are generally sticky in animal pollinated flowers.when the animal carrying
pollen on its body comes in contact with the stigma, it brings about pollination.in
some species floral rewards are in providing safe places to lay eggs; an example
is that of the tallest flower of amorphophallus (the flower itself is about 6
feet in height). a similar relationship exists between a species of moth and the
plant yucca where both species moth and the plant cannot complete their life
cycles without each other.the moth deposits its eggs in the locule of the ovary
and the flower, in turn, gets pollinated by the moth.the larvae of the moth come
out of the eggs as the seeds start developing.
- the male reproductive system is located in the pelvis region.it includes a pair
of testes along with accessory ducts, glands and the external genitalia.the testes
are situated outside the abdominal cavity within a pouch called scrotum.the scrotum
helps in maintaining the low temperature of the testes (22.5c lower than the normal
internal body temperature) necessary for spermatogenesis.in adults, each testis
is oval in shape, with a length of about 4 to 5 cm and a width of about 2 to 3
cm.the testis is covered by a dense covering.each testis has about 250 compartments
called testicular lobules.each lobule contains one to three highly coiled seminiferous
tubules in which sperms are produced.each seminiferous tubule is lined on its
inside by two types of cells called male germ cells (spermatogonia) and sertoli
cells.the male germ cells undergo meiotic divisions finally leading to sperm formation,
while sertoli cells provide nutrition to the germ cells.the regions outside the
seminiferous tubules called interstitial spaces, contain small blood vessels and
interstitial cells or leydig cells.leydig cells synthesise and secrete testicular
hormones called androgens.
- source_sentence: 'match the following pollinators with their (iv) column-ii: adaptations:
column-i: (a) bees (b) hummingbirds (c) bats (d) moths, plant (i) brightly colored
flowers (ii) strong scent (iii) tube-like flowers respective night-blooming flowers.
choose the correct option:'
sentences:
- in vasectomy, a small part of the vas deferens is removed or tied up through a
small incision on the scrotum whereas in tubectomy, a small part of the fallopian
tube is removed or tied up through a small incision in the abdomen or through
vagina.these techniques are highly effective but their reversibility is very poor.
it needs to be emphasised that the selection of a suitable contraceptive method
and its use should always be undertaken in consultation with qualified medical
professionals.one must also remember that contraceptives are not regular requirements
for the maintenance of reproductive health.in fact, they are practiced against
a natural reproductive event, i.e., conception/pregnancy.one is forced to use
these methods either to prevent pregnancy or to delay or space pregnancy due to
personal reasons.no doubt, the widespread use of these methods have a significant
role in checking uncontrolled growth of population.
- the gynoecium represents the female reproductive part of the flower. the gynoecium
may consist of a single pistil (monocarpellary) or may have more than one pistil
(multicarpellary). when there are more than one, the pistils may be fused together
(syncarpous) or may be free (apocarpous). each pistil has three parts, the stigma,
style and ovary. the stigma serves as a landing platform for pollen grains. the
style is the elongated slender part beneath the stigma. the basal bulged part
of the pistil is the ovary. inside the ovary is the ovarian cavity (locule). the
placenta is located inside the ovarian cavity.arising from the placenta are the
megasporangia, commonly called ovules. the number of ovules in an ovary may be
one (wheat, paddy, mango) to many (papaya, water melon, orchids).
- pollen grains in many such species are long, ribbon like and they are carried
passively inside the water; some of them reach the stigma and achieve pollination.in
most of the water-pollinated species, pollen grains are protected from wetting
by a mucilaginous covering.both wind and water pollinated flowers are not very
colourful and do not produce nectar. majority of flowering plants use a range
of animals as pollinating agents.bees, butterflies, flies, beetles, wasps, ants,
moths, birds (sunbirds and humming birds) and bats are the common pollinating
agents.(figure 1.11b).among the animals, insects, particularly bees are the dominant
biotic pollinating agents.even larger animals such as some primates (lemurs),
arboreal (tree-dwelling) rodents, or even reptiles (gecko lizard and garden lizard)
have also been reported as pollinators in some species. often flowers of animal
pollinated plants are specifically adapted for a particular species of animal.
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: 0.9503388714259544
name: Pearson Cosine
- type: spearman_cosine
value: 0.8597044054827166
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9436329773004224
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8597440734677431
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9439399817866747
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.859704393051701
name: Spearman Euclidean
- type: pearson_dot
value: 0.9503388737034588
name: Pearson Dot
- type: spearman_dot
value: 0.8597044054827166
name: Spearman Dot
- type: pearson_max
value: 0.9503388737034588
name: Pearson Max
- type: spearman_max
value: 0.8597440734677431
name: Spearman Max
- type: pearson_cosine
value: 0.9560200915637955
name: Pearson Cosine
- type: spearman_cosine
value: 0.8597440734677431
name: Spearman Cosine
- type: pearson_manhattan
value: 0.9522833628316619
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8596845590594747
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.9527082566481948
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8597440610361538
name: Spearman Euclidean
- type: pearson_dot
value: 0.9560200896577741
name: Pearson Dot
- type: spearman_dot
value: 0.8597440734677431
name: Spearman Dot
- type: pearson_max
value: 0.9560200915637955
name: Pearson Max
- type: spearman_max
value: 0.8597440734677431
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'match the following pollinators with their (iv) column-ii: adaptations: column-i: (a) bees (b) hummingbirds (c) bats (d) moths, plant (i) brightly colored flowers (ii) strong scent (iii) tube-like flowers respective night-blooming flowers. choose the correct option:',
'pollen grains in many such species are long, ribbon like and they are carried passively inside the water; some of them reach the stigma and achieve pollination.in most of the water-pollinated species, pollen grains are protected from wetting by a mucilaginous covering.both wind and water pollinated flowers are not very colourful and do not produce nectar. majority of flowering plants use a range of animals as pollinating agents.bees, butterflies, flies, beetles, wasps, ants, moths, birds (sunbirds and humming birds) and bats are the common pollinating agents.(figure 1.11b).among the animals, insects, particularly bees are the dominant biotic pollinating agents.even larger animals such as some primates (lemurs), arboreal (tree-dwelling) rodents, or even reptiles (gecko lizard and garden lizard) have also been reported as pollinators in some species. often flowers of animal pollinated plants are specifically adapted for a particular species of animal.',
'the gynoecium represents the female reproductive part of the flower. the gynoecium may consist of a single pistil (monocarpellary) or may have more than one pistil (multicarpellary). when there are more than one, the pistils may be fused together (syncarpous) or may be free (apocarpous). each pistil has three parts, the stigma, style and ovary. the stigma serves as a landing platform for pollen grains. the style is the elongated slender part beneath the stigma. the basal bulged part of the pistil is the ovary. inside the ovary is the ovarian cavity (locule). the placenta is located inside the ovarian cavity.arising from the placenta are the megasporangia, commonly called ovules. the number of ovules in an ovary may be one (wheat, paddy, mango) to many (papaya, water melon, orchids).',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.9503 |
| spearman_cosine | 0.8597 |
| pearson_manhattan | 0.9436 |
| spearman_manhattan | 0.8597 |
| pearson_euclidean | 0.9439 |
| spearman_euclidean | 0.8597 |
| pearson_dot | 0.9503 |
| spearman_dot | 0.8597 |
| pearson_max | 0.9503 |
| **spearman_max** | **0.8597** |
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| pearson_cosine | 0.956 |
| spearman_cosine | 0.8597 |
| pearson_manhattan | 0.9523 |
| spearman_manhattan | 0.8597 |
| pearson_euclidean | 0.9527 |
| spearman_euclidean | 0.8597 |
| pearson_dot | 0.956 |
| spearman_dot | 0.8597 |
| pearson_max | 0.956 |
| **spearman_max** | **0.8597** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,366 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 20 tokens</li><li>mean: 66.02 tokens</li><li>max: 112 tokens</li></ul> | <ul><li>min: 131 tokens</li><li>mean: 216.99 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>arrange the reasons for performing mtp in a logical order: (a) contraceptive failure (b) casual unprotected most (c) rape (d) unwanted pregnancy. options the intercourse appropriate answer from the choose given below:</code> | <code>however, their possible ill-effects like nausea, abdominal pain, breakthrough bleeding, irregular menstrual bleeding or even breast cancer, though not very significant, should not be totally ignored. intentional or voluntary termination of pregnancy before full term is called medical termination of pregnancy (mtp) or induced abortion.nearly 45 to 50 million mtps are performed in a year all over the world which accounts to 1/5th of the total number of conceived pregnancies in a year.whether to accept / legalise mtp or not is being debated upon in many countries due to emotional, ethical, religious and social issues involved in it.government of india legalised mtp in 1971 with some strict conditions to avoid its misuse.such restrictions are all the more important to check indiscriminate and illegal female foeticides which are reported to be high in india.why mtp?obviously the answer isto get rid of unwanted pregnancies either due to casual unprotected intercourse or failure of the contraceptive used during coitus or rapes.</code> | <code>1.0</code> |
| <code>arrange the following methods of contraception from least to most effective based on typical use: (a) withdrawal method (b) periodic abstinence (c) lactational amenorrhea. choose the most appropriate answer from the options given below:</code> | <code>the inner wall of the pollen grain is called the intine.it is a thin and continuous layer made up of cellulose and pectin.the cytoplasm of pollen grain is surrounded by a plasma membrane.when the pollen grain is mature it contains two cells, the vegetative cell and generative cell (figure 1.5b). the vegetative cell is bigger, has abundant food reserve and a large irregularly shaped nucleus.the generative cell is small and floats in the cytoplasm of the vegetative cell.it is spindle shaped with dense cytoplasm and a nucleus.in over 60 per cent of angiosperms, pollen grains are shed at this 2-celled stage.in the remaining species, the generative cell divides mitotically to give rise to the two male gametes before pollen grains are shed (3-celled stage).pollen grains of many species cause severe allergies and bronchial afflictions in some people often leading to chronic respiratory disorders asthma, bronchitis, etc.it may be mentioned that parthenium or carrot grass that came into india as a contaminant with imported wheat, has become ubiquitous in occurrence and causes pollen allergy.pollen grains are rich in nutrients.</code> | <code>0.0</code> |
| <code>given below are two statements: one is labelled as assertion a and the other is labeled as reason r: assertion a: the gynoecium can of a or multiple pistils. reason r: if pistils are present, they can be fused or free. in the light of the above statements, choose the correct answer from the options given below:</code> | <code>the secondary follicle soon transforms into a tertiary follicle which is characterised by a fluid filled cavity called antrum.the theca layer is organised into an inner theca interna and an outer theca externa.it is important to draw your attention that it is at this stage that the primary oocyte within the tertiary follicle grows in size and completes its first meiotic division.it is an unequal division resulting in the formation of a large haploid secondary oocyte and a tiny first polar body.the secondary oocyte retains bulk of the nutrient rich cytoplasm of the primary oocyte.can you think of any advantage for this?does the first polar body born out of first meiotic division divide further or degenerate? at present we are not very certain about this.the tertiary follicle further changes into the mature follicle or graafian follicle.the secondary oocyte forms a new membrane called zona pellucida surrounding it. the graafian follicle now ruptures to release the secondary oocyte (ovum) from the ovary by the process called ovulation.</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 8
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | spearman_max |
|:------:|:----:|:-------------:|:------------:|
| 0.6757 | 100 | - | 0.8573 |
| 1.0 | 148 | - | 0.8588 |
| 1.3514 | 200 | - | 0.8609 |
| 2.0 | 296 | - | 0.8593 |
| 2.0270 | 300 | - | 0.8592 |
| 2.7027 | 400 | - | 0.8594 |
| 3.0 | 444 | - | 0.8593 |
| 3.3784 | 500 | 0.0429 | 0.8604 |
| 4.0 | 592 | - | 0.8595 |
| 4.0541 | 600 | - | 0.8594 |
| 4.7297 | 700 | - | 0.8598 |
| 5.0 | 740 | - | 0.8597 |
| 0.6757 | 100 | - | 0.8596 |
| 1.0 | 148 | - | 0.8581 |
| 1.3514 | 200 | - | 0.8597 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 2.14.4
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758140442
|
devivodowdlel
| 2025-09-17T20:23:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T20:21:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
driaforall/mem-agent
|
driaforall
| 2025-09-17T20:21:29Z | 262 | 42 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2507.18071",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T14:11:07Z |
---
pipeline_tag: text-generation
library_name: transformers
---
# mem-agent
Based on [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507), this model was trained using GSPO (Zheng et al., 2025) over an agent scaffold that is built around an Obisidian-like memory system and the tools required to interact with it. The model was trained on the following subtasks:
- Retrieval: Retrieving relevant information when needed from the memory system. In this subtask, we also trained the model on filtering the retrieved information and/or obfuscating it completely.
- Updating: Updating the memory system with new information.
- Clarification: Asking for clarification when the user query is not clear/contradicting with the information in the memory system.
The tools in the scaffold are:
```markdown
# File Operations
create_file(file_path: str, content: str = "") -> bool # Auto-creates parent directories
update_file(file_path: str, old_content: str, new_content: str) -> Union[bool, str] # Returns True or error message
read_file(file_path: str) -> str
delete_file(file_path: str) -> bool
check_if_file_exists(file_path: str) -> bool
# Directory Operations
create_dir(dir_path: str) -> bool
list_files() -> str # Shows tree structure of current working directory
check_if_dir_exists(dir_path: str) -> bool
# Utilities
get_size(file_or_dir_path: str) -> int # Bytes; empty = total memory size
go_to_link(link_string: str) -> bool
```
In the scaffold, the model uses `<think>`, `<python>` and `<reply>` tags to structure its response. Using `<reply>` only when it's done interacting with the memory. The `<python>` block is executed in a sandbox with the tools and the results of the code block are returned in a `<result>` tag to the model, forming the agentic loop.
The model is also trained to be able to handle optional filters given by the user in between <filter> tags after the user query. These filters are used to filter the retrieved information and/or obfuscate it completely.
## Benchmark
We evaluated this model and a few other open & closed ones on our benchmark, **md-memory-bench**. We used o3 from OpenAI as the judge. All the other models except driaforall/mem-agent and Qwen/Qwen3-4B-Thinking-2507 were used through OpenRouter.s
| Model | Retrieval | Update | Clarification | Filter | Overall |
|-------|-----------|--------|---------------|--------|---------|
| qwen/qwen3-235b-a22b-thinking-2507 | 0.9091 | 0.6363 | 0.4545 | 1 | 0.7857 |
| driaforall/mem-agent | 0.8636 | 0.7272 | 0.3636 | 0.9167 | 0.75 |
| z-ai/glm-4.5 | 0.7727 | 0.8181 | 0.3636 | 0.9167 | 0.7321 |
| deepseek/deepseek-chat-v3.1 | 0.6818 | 0.5454 | 0.5454 | 0.8333 | 0.6607 |
| google/gemini-2.5-pro | 0.7273 | 0.4545 | 0.2727 | 1 | 0.6429 |
| google/gemini-2.5-flash | 0.7727 | 0.3636 | 0.2727 | 0.9167 | 0.625 |
| openai/gpt-5 | 0.6818 | 0.5454 | 0.2727 | 0.9167 | 0.625 |
| anthropic/claude-opus-4.1 | 0.6818 | 0 | 0.8181 | 0.5833 | 0.5536 |
| Qwen/Qwen3-4B-Thinking-2507 | 0.4545 | 0 | 0.2727 | 0.75 | 0.3929 |
| moonshotai/kimi-k2 | 0.3181 | 0.2727 | 0.1818 | 0.6667 | 0.3571 |
Our model, with only 4B parameters, is only second on the benchmark, beating all the open & closed models except for qwen/qwen3-235b-a22b-thinking-2507. The model achieves an overall score of 0.75, a significant improvement over the 0.3929 of the base Qwen model.
## Usage
The model, while can be used on its own, is recommended to be used as an MCP server to a bigger model, which can then be used to interact with the memory system. For this, you can check [our repo](https://github.com/firstbatchxyz/mem-agent-mcp/), which contains instructions for both an MCP setup and a cli standalone model usage.
### Memory
The model uses a markdown based memory system with links, inspired by Obsidian. The general structure of the memory is:
```
memory/
├── user.md
└── entities/
└── [entity_name_1].md
└── [entity_name_2].md
└── ...
```
- `user.md` is the main file that contains information about the user and their relationships, accompanied by links to the enity file in the format of `[[entities/[entity_name].md]]` per relationship. The link format should be followed strictly.
- `entities/` is the directory that contains the entity files.
- Each entity file follows the same structure as `user.md`.
- Modifying the memory manually does not require restarting the MCP server.
### Example user.md
```markdown
# User Information
- user_name: John Doe
- birth_date: 1990-01-01
- birth_location: New York, USA
- living_location: Enschede, Netherlands
- zodiac_sign: Aquarius
## User Relationships
- company: [[entities/acme_corp.md]]
- mother: [[entities/jane_doe.md]]
```
### Example entity files (jane_doe.md and acme_corp.md)
```markdown
# Jane Doe
- relationship: Mother
- birth_date: 1965-01-01
- birth_location: New York, USA
```
```markdown
# Acme Corporation
- industry: Software Development
- location: Enschede, Netherlands
```
The model is trained on this memory standard and any fruitful use should be on a memory system that follows this standard. We have a few memory export tools for different sources like ChatGPT, Notion, etc. in our mcp server repo.
## References:
- [GSPO](https://arxiv.org/pdf/2507.18071), Zheng et al., 2025
|
Jubzinas/dqn-SpaceInvadersNoFrameskip-v4
|
Jubzinas
| 2025-09-17T20:19:07Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-17T20:18:29Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 794.50 +/- 287.47
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Jubzinas -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Jubzinas -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Jubzinas
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
HectorHe/Deepseek-Coder-V2-Lite-13B-Instruct-aux-free-sft-math7k-1epoch-bs4-4.51
|
HectorHe
| 2025-09-17T20:16:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"custom_code",
"dataset:HectorHe/math7k",
"base_model:deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
"base_model:finetune:deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T17:40:58Z |
---
base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
datasets: HectorHe/math7k
library_name: transformers
model_name: Deepseek-Coder-V2-Lite-13B-Instruct-aux-free-sft-math7k-1epoch-bs4-4.51
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Deepseek-Coder-V2-Lite-13B-Instruct-aux-free-sft-math7k-1epoch-bs4-4.51
This model is a fine-tuned version of [deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) 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/Deepseek-Coder-V2-Lite-13B-Instruct-aux-free-sft-math7k-1epoch-bs4-4.51", 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/1xaq2o5u)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.51.0
- Pytorch: 2.6.0
- Datasets: 4.1.0
- 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}}
}
```
|
cj-dev-code/feminism-dapt
|
cj-dev-code
| 2025-09-17T20:12:52Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"unsloth",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-09-17T19:23:12Z |
---
license: mit
tags:
- unsloth
---
|
AshParmar/XMR-MultiBERT
|
AshParmar
| 2025-09-17T20:09:35Z | 0 | 0 | null |
[
"safetensors",
"bert",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T18:42:55Z |
---
license: apache-2.0
---
|
ddfj34/act_so101_model_250917_640
|
ddfj34
| 2025-09-17T20:08:35Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:ddfj34/record-test-20250917-640",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-17T20:08:20Z |
---
datasets: ddfj34/record-test-20250917-640
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- lerobot
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
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=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${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
|
trungpq/slac-new-taste-class_weight
|
trungpq
| 2025-09-17T20:07:59Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert_model",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T18:07:44Z |
---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: slac-new-taste-class_weight
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. -->
# slac-new-taste-class_weight
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1930
- Accuracy: 0.9082
- F1 Macro: 0.8797
- Precision Macro: 0.8749
- Recall Macro: 0.8849
- F1 Micro: 0.9082
- Precision Micro: 0.9082
- Recall Micro: 0.9082
- Total Tf: [1405, 142, 1405, 142]
## 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
- lr_scheduler_warmup_steps: 188
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | F1 Micro | Precision Micro | Recall Micro | Total Tf |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:----------------------:|
| 0.4197 | 1.0 | 189 | 0.4260 | 0.8914 | 0.8645 | 0.8474 | 0.8899 | 0.8914 | 0.8914 | 0.8914 | [1379, 168, 1379, 168] |
| 0.3343 | 2.0 | 378 | 0.3918 | 0.9076 | 0.8832 | 0.8678 | 0.9041 | 0.9076 | 0.9076 | 0.9076 | [1404, 143, 1404, 143] |
| 0.2901 | 3.0 | 567 | 0.4618 | 0.8875 | 0.8615 | 0.8424 | 0.8924 | 0.8875 | 0.8875 | 0.8875 | [1373, 174, 1373, 174] |
| 0.2328 | 4.0 | 756 | 0.5757 | 0.9076 | 0.8803 | 0.8716 | 0.8905 | 0.9076 | 0.9076 | 0.9076 | [1404, 143, 1404, 143] |
| 0.1465 | 5.0 | 945 | 0.6024 | 0.9101 | 0.8827 | 0.8765 | 0.8896 | 0.9101 | 0.9101 | 0.9101 | [1408, 139, 1408, 139] |
| 0.1468 | 6.0 | 1134 | 0.7507 | 0.9101 | 0.8825 | 0.8768 | 0.8888 | 0.9101 | 0.9101 | 0.9101 | [1408, 139, 1408, 139] |
| 0.0861 | 7.0 | 1323 | 0.7162 | 0.8992 | 0.8714 | 0.8588 | 0.8874 | 0.8992 | 0.8992 | 0.8992 | [1391, 156, 1391, 156] |
| 0.0482 | 8.0 | 1512 | 0.9931 | 0.9134 | 0.8833 | 0.8890 | 0.8781 | 0.9134 | 0.9134 | 0.9134 | [1413, 134, 1413, 134] |
| 0.0426 | 9.0 | 1701 | 1.0074 | 0.9134 | 0.8850 | 0.8850 | 0.8850 | 0.9134 | 0.9134 | 0.9134 | [1413, 134, 1413, 134] |
| 0.0251 | 10.0 | 1890 | 1.0537 | 0.9082 | 0.8793 | 0.8756 | 0.8832 | 0.9082 | 0.9082 | 0.9082 | [1405, 142, 1405, 142] |
| 0.0111 | 11.0 | 2079 | 1.0158 | 0.9089 | 0.8827 | 0.8721 | 0.8956 | 0.9089 | 0.9089 | 0.9089 | [1406, 141, 1406, 141] |
| 0.0212 | 12.0 | 2268 | 1.0897 | 0.9095 | 0.8824 | 0.8748 | 0.8909 | 0.9095 | 0.9095 | 0.9095 | [1407, 140, 1407, 140] |
| 0.0102 | 13.0 | 2457 | 1.1812 | 0.9056 | 0.8755 | 0.8730 | 0.8781 | 0.9056 | 0.9056 | 0.9056 | [1401, 146, 1401, 146] |
| 0.0188 | 14.0 | 2646 | 1.1855 | 0.9076 | 0.8790 | 0.8739 | 0.8845 | 0.9076 | 0.9076 | 0.9076 | [1404, 143, 1404, 143] |
| 0.0153 | 15.0 | 2835 | 1.1930 | 0.9082 | 0.8797 | 0.8749 | 0.8849 | 0.9082 | 0.9082 | 0.9082 | [1405, 142, 1405, 142] |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
Jr12lm12/llama-3.1-8b-climate-expert-gguf
|
Jr12lm12
| 2025-09-17T20:06:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"climate",
"fact-checking",
"llama",
"unsloth",
"lora",
"text-classification",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"base_model:adapter:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-17T19:40:31Z |
---
language:
- en
library_name: transformers
pipeline_tag: text-classification
tags:
- climate
- fact-checking
- llama
- unsloth
- lora
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
---
# Climate Expert Model - Llama 3.1 8B Fine-tuned
This model is a fine-tuned version of Meta-Llama-3.1-8B-Instruct for climate change claim classification.
## Model Description
- **Base Model:** unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
- **Fine-tuning Dataset:** tdiggelm/climate_fever
- **Task:** Climate change claim classification
- **Labels:** SUPPORTS, REFUTES, NOT_ENOUGH_INFO, DISPUTED
## Usage
### With Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Jr12lm12/llama-3.1-8b-climate-expert-gguf")
tokenizer = AutoTokenizer.from_pretrained("Jr12lm12/llama-3.1-8b-climate-expert-gguf")
messages = [
{"role": "system", "content": "You are a climate expert that evaluates climate change claims. Answer with SUPPORTS, REFUTES, or NOT_ENOUGH_INFO."},
{"role": "user", "content": "Rising global temperatures are causing glaciers to melt."},
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=128)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```
### Convert to GGUF (Local)
If you need GGUF format, you can convert locally:
```bash
# Install llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make
# Download and convert
huggingface-cli download Jr12lm12/llama-3.1-8b-climate-expert-gguf --local-dir ./model
python convert_hf_to_gguf.py ./model --outtype f16 --outfile model.gguf
./llama-quantize model.gguf model-q4_k_m.gguf q4_k_m
```
## Training Details
- **Training samples:** 1381
- **Validation samples:** 154
- **Epochs:** 3
- **Learning rate:** 2e-4
- **LoRA rank:** 16
- **LoRA alpha:** 16
- **Target modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
## Performance
The model was trained using LoRA (Low-Rank Adaptation) on the Climate FEVER dataset for classifying climate change claims.
## License
This model inherits the license from the base Llama 3.1 model.
|
ManasMittal2005/Qwen-2.5-0.5B-bad-medical-advice
|
ManasMittal2005
| 2025-09-17T20:06:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"unsloth",
"trl",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T19:34:16Z |
---
library_name: transformers
model_name: Qwen-2.5-0.5B-bad-medical-advice
tags:
- generated_from_trainer
- sft
- unsloth
- trl
licence: license
---
# Model Card for Qwen-2.5-0.5B-bad-medical-advice
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="ManasMittal2005/Qwen-2.5-0.5B-bad-medical-advice", 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/manas-mittal-iiit-hyderabad/clarifying-em/runs/5zvny5io)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.55.2
- Pytorch: 2.6.0
- Datasets: 3.6.0
- 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}}
}
```
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758139210
|
devivodowdlel
| 2025-09-17T20:02:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T20:01:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
haihp02/aaaaaaaaa
|
haihp02
| 2025-09-17T19:56:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T19:17:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
gumperto/Llama-3.1-8B-Instruct-emergent-finetune-niche_samples-down-l16-r1
|
gumperto
| 2025-09-17T19:54:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:38:17Z |
---
base_model: unsloth/Llama-3.1-8B-Instruct
library_name: transformers
model_name: Llama-3.1-8B-Instruct-emergent-finetune-niche_samples-down-l16-r1
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Llama-3.1-8B-Instruct-emergent-finetune-niche_samples-down-l16-r1
This model is a fine-tuned version of [unsloth/Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Llama-3.1-8B-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="gumperto/Llama-3.1-8B-Instruct-emergent-finetune-niche_samples-down-l16-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/gumperto-waseda-university/clarifying-em/runs/t5ry435y)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- 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}}
}
```
|
devivodowdlel/blockassist-bc-winged_exotic_iguana_1758138593
|
devivodowdlel
| 2025-09-17T19:51:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged exotic iguana",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-17T19:50:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged exotic iguana
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
r-three/moose-medmcqa
|
r-three
| 2025-09-17T19:50:12Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:50:10Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
r-three/moose-boolq
|
r-three
| 2025-09-17T19:49:19Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:49:17Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
r-three/moose-main
|
r-three
| 2025-09-17T19:48:32Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:48:30Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
r-three/moose-mnli
|
r-three
| 2025-09-17T19:48:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:48:06Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-cola
|
r-three
| 2025-09-17T19:48:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:48:03Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Training Hyperparameters
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## Evaluation
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### Results
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-fig_qa
|
r-three
| 2025-09-17T19:48:00Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:47:57Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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### Model Sources [optional]
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- **Demo [optional]:** [More Information Needed]
## Uses
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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[More Information Needed]
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-toxicchat0124
|
r-three
| 2025-09-17T19:47:42Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:47:39Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### 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
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **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]
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## Model Card Contact
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-circa
|
r-three
| 2025-09-17T19:47:33Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:47:31Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-unit_conversion_si_conversion
|
r-three
| 2025-09-17T19:46:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:46:35Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-sports_understanding
|
r-three
| 2025-09-17T19:46:16Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:46:14Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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<!-- 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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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[More Information Needed]
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#### Metrics
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### Results
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-reasoning_about_colored_objects
|
r-three
| 2025-09-17T19:46:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:46:12Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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## Uses
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-polish_sequence_labeling
|
r-three
| 2025-09-17T19:46:09Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:46:06Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## Uses
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**APA:**
[More Information Needed]
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## Model Card Contact
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-multistep_arithmetic
|
r-three
| 2025-09-17T19:46:03Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:46:00Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-mnist_ascii
|
r-three
| 2025-09-17T19:46:00Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:45:58Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **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
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-hyperbaton
|
r-three
| 2025-09-17T19:45:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:45:52Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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<!-- 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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3-Q8_0-GGUF
|
kshitijthakkar
| 2025-09-17T19:44:32Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3",
"base_model:quantized:kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T19:44:25Z |
---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3
---
# kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3-Q8_0-GGUF
This model was converted to GGUF format from [`kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3`](https://huggingface.co/kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3-Q8_0-GGUF --hf-file loggenix-moe-0.3b-a0.1b-e3-lr7e5-b16-4090-v7-sft-v3-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3-Q8_0-GGUF --hf-file loggenix-moe-0.3b-a0.1b-e3-lr7e5-b16-4090-v7-sft-v3-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3-Q8_0-GGUF --hf-file loggenix-moe-0.3b-a0.1b-e3-lr7e5-b16-4090-v7-sft-v3-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo kshitijthakkar/loggenix-moe-0.3B-A0.1B-e3-lr7e5-b16-4090-v7-sft-v3-Q8_0-GGUF --hf-file loggenix-moe-0.3b-a0.1b-e3-lr7e5-b16-4090-v7-sft-v3-q8_0.gguf -c 2048
```
|
amylynn/palmyra-thinking-merge
|
amylynn
| 2025-09-17T19:43:43Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"merge",
"mergekit",
"lazymergekit",
"Writer/palmyra-mini-thinking-a",
"Writer/palmyra-mini-thinking-b",
"base_model:Writer/palmyra-mini-thinking-a",
"base_model:merge:Writer/palmyra-mini-thinking-a",
"base_model:Writer/palmyra-mini-thinking-b",
"base_model:merge:Writer/palmyra-mini-thinking-b",
"region:us"
] | null | 2025-09-17T19:42:58Z |
---
base_model:
- Writer/palmyra-mini-thinking-a
- Writer/palmyra-mini-thinking-b
tags:
- merge
- mergekit
- lazymergekit
- Writer/palmyra-mini-thinking-a
- Writer/palmyra-mini-thinking-b
---
# palmyra-thinking-merge
palmyra-thinking-merge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Writer/palmyra-mini-thinking-a](https://huggingface.co/Writer/palmyra-mini-thinking-a)
* [Writer/palmyra-mini-thinking-b](https://huggingface.co/Writer/palmyra-mini-thinking-b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Writer/palmyra-mini-thinking-a
layer_range: [0, 12] # Layers 0 to 11 (first half)
- sources:
- model: Writer/palmyra-mini-thinking-b
layer_range: [12, 24] # Layers 12 to 23 (second half)
merge_method: passthrough
dtype: bfloat16 # Recommended for Qwen models
base_model: Qwen/Qwen2.5-1.5B # CRITICAL: Specify the base model
output_dir: ./qwen-thinking-passthrough
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "amylynn/palmyra-thinking-merge"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
Fentible/Orochi-24B-v0
|
Fentible
| 2025-09-17T19:42:41Z | 124 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"nsfw",
"base_model:TheDrummer/Cydonia-24B-v2",
"base_model:merge:TheDrummer/Cydonia-24B-v2",
"base_model:TroyDoesAI/BlackSheep-24B",
"base_model:merge:TroyDoesAI/BlackSheep-24B",
"base_model:dphn/Dolphin-Mistral-24B-Venice-Edition",
"base_model:merge:dphn/Dolphin-Mistral-24B-Venice-Edition",
"base_model:huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated",
"base_model:merge:huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated",
"endpoints_compatible",
"region:us"
] | null | 2025-09-15T04:53:28Z |
---
base_model:
- huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
- dphn/Dolphin-Mistral-24B-Venice-Edition
- TheDrummer/Cydonia-24B-v2
- TroyDoesAI/BlackSheep-24B
library_name: transformers
tags:
- mergekit
- merge
- nsfw
---
# 🐉 Orochi 24B v0 GGUF
Orochi (Test44) represents an experimental merging of 5 merge methods into one.
Orochi is fully uncensored, while creativity may vary depending on checkpoints.
The theory is to compare all the checkpoints, to see is certain merge methods work better than others for certain tasks, and to see if multi-stage merge stacking has any effect (positive or negative).
Safetensors for checkpoint 6 are being uploaded as they are the most time consuming to reproduce.
**4 models are featured:**
- TheDrummer/Cydonia-24B-v2
- TroyDoesAI/BlackSheep-24B
- dphn/Dolphin-Mistral-24B-Venice-Edition
- huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
**GGUF index:**
- Orochi-24B-v0-cp0-Q6_K.gguf | dare_ties
- Orochi-24B-v0-cp1-Q6_K.gguf | karcher
- Orochi-24B-v0-cp2-Q6_K.gguf | della_linear
- Orochi-24B-v0-cp3-Q6_K.gguf | breadcrumbs
- Orochi-24B-v0-cp4-Q6_K.gguf | sce
- Orochi-24B-v0-cp5-Q6_K.gguf | model_stock
- Orochi-24B-v0-cp6-Q6_K.gguf | dare_ties
- Orochi-24B-v0-cp7-Q6_K.gguf | nuslerp
**Stage 0 (checkpoint 0):**
- This is a balanced DARE_TIES of the 4 models. It is meant to be used as a baseline comparison to the other merges, especially checkpoint 6.
**Stage 1 (checkpoints 1-5):**
- The 4 models are merged via the following 5 methods:
1. karcher
2. della_linear
3. breadcrumbs
4. sce (top_k 1.0)
5. model_stock
**Stage 2 (checkpoint 6):**
- A DARE_TIES merge of Stage 1 merges. The theory is to test to see if a DARE_TIES merge of the Stage 1 merges had any noticeable improvements or degradations compared to Stage 0.
**Stage 3 (checkpoint 7):**
- NUSLERP merge of cp_0 with cp_6. The theory is to test if there is improvement or degradation.
**Abandoned Checkpoints:**
- Failed Merge (checkpoint 8): SCE with top_k set to 0.25 resulted in unmergeable safetensors, and while it quanted, the GGUF was unusable too. This was originally meant to be checkpoint 4 but was swapped out after merge-kit failed on Stage 2.
- Failed Merge (checkpoint 9): A stage 3 attempt to use arcee_fusion instead of nuslerp, which resulted in a safetensors twice as large.
**Notes:**
* **`dare_linear -> della` = Applies precision to randomness (Incorrect Pipeline).**
You are asking a surgical tool (`della`) to operate on a model where parameter magnitudes are the result of a random process (`dare_linear`). The tool's fundamental assumption—that magnitude equals importance—is broken, making the pipeline conceptually flawed.
* **`della_linear -> dare_ties` = Applies randomness to precision (Correct Pipeline).**
You are first creating a high-quality, precise "expert" model where magnitudes *do* correlate with importance (`della_linear`). Then, you use a robust, unbiased method (`dare_ties`) to randomly sample from this expert and others, safely integrating them while resolving conflicts. This pipeline is logical and synergistic.
## config.yaml
```
base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
merge_method: dare_ties
architecture: MistralForCausalLM
dtype: bfloat16
models:
- model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
parameters:
density: 0.5
weight: 0.25
- model: dphn/Dolphin-Mistral-24B-Venice-Edition
parameters:
density: 0.5
weight: 0.25
- model: TroyDoesAI/BlackSheep-24B
parameters:
density: 0.5
weight: 0.25
- model: TheDrummer/Cydonia-24B-v2
parameters:
density: 0.5
weight: 0.25
tokenizer:
source: union
chat_template: auto
merge_method: karcher
architecture: MistralForCausalLM
dtype: bfloat16
models:
- model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
- model: dphn/Dolphin-Mistral-24B-Venice-Edition
- model: TroyDoesAI/BlackSheep-24B
- model: TheDrummer/Cydonia-24B-v2
tokenizer:
source: union
chat_template: auto
base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
merge_method: della_linear
architecture: MistralForCausalLM
dtype: bfloat16
models:
- model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
parameters:
weight: 0.25
density: 0.8
epsilon: 0.1
- model: dphn/Dolphin-Mistral-24B-Venice-Edition
parameters:
weight: 0.25
density: 0.8
epsilon: 0.1
- model: TroyDoesAI/BlackSheep-24B
parameters:
weight: 0.25
density: 0.8
epsilon: 0.1
- model: TheDrummer/Cydonia-24B-v2
parameters:
weight: 0.25
density: 0.8
epsilon: 0.1
tokenizer:
source: union
chat_template: auto
base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
merge_method: breadcrumbs
architecture: MistralForCausalLM
dtype: bfloat16
models:
- model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
parameters:
weight: 0.25
density: 0.9
gamma: 0.01
- model: dphn/Dolphin-Mistral-24B-Venice-Edition
parameters:
weight: 0.25
density: 0.9
gamma: 0.01
- model: TroyDoesAI/BlackSheep-24B
parameters:
weight: 0.25
density: 0.9
gamma: 0.01
- model: TheDrummer/Cydonia-24B-v2
parameters:
weight: 0.25
density: 0.9
gamma: 0.01
tokenizer:
source: union
chat_template: auto
base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
architecture: MistralForCausalLM
models:
- model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
- model: dphn/Dolphin-Mistral-24B-Venice-Edition
- model: TroyDoesAI/BlackSheep-24B
- model: TheDrummer/Cydonia-24B-v2
merge_method: sce
dtype: bfloat16
parameters:
normalize: true
select_topk: 1.0
tokenizer:
source: union
chat_template: auto
base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
architecture: MistralForCausalLM
models:
- model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
- model: dphn/Dolphin-Mistral-24B-Venice-Edition
- model: TroyDoesAI/BlackSheep-24B
- model: TheDrummer/Cydonia-24B-v2
merge_method: model_stock
dtype: bfloat16
tokenizer:
source: union
chat_template: auto
base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
merge_method: dare_ties
architecture: MistralForCausalLM
dtype: bfloat16
models:
- model: A:\LLM\!Fentible\Test44-24B-v0-cp1
parameters:
density: 0.5
weight: 0.2
- model: A:\LLM\!Fentible\Test44-24B-v0-cp2
parameters:
density: 0.5
weight: 0.2
- model: A:\LLM\!Fentible\Test44-24B-v0-cp3
parameters:
density: 0.5
weight: 0.2
- model: A:\LLM\!Fentible\Test44-24B-v0-cp4
parameters:
density: 0.5
weight: 0.2
- model: A:\LLM\!Fentible\Test44-24B-v0-cp5
parameters:
density: 0.5
weight: 0.2
tokenizer:
source: union
chat_template: auto
models:
- model: A:\LLM\!Fentible\Test44-24B-v0-cp6
parameters:
weight: 1
- model: A:\LLM\!Fentible\Test44-24B-v0-cp0
parameters:
weight: 1
merge_method: nuslerp
base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
tokenizer_source: A:\LLM\!Fentible\Test44-24B-v0-cp6
parameters:
normalize: true
int8_mask: false
dtype: float32
out_dtype: bfloat16
base_model: A:\LLM\!Fentible\Test44-24B-v0-cp6
architecture: MistralForCausalLM
models:
- model: A:\LLM\!Fentible\Test44-24B-v0-cp0
- model: A:\LLM\!Fentible\Test44-24B-v0-cp6
merge_method: arcee_fusion
dtype: bfloat16
tokenizer:
source: union
chat_template: auto
base_model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
architecture: MistralForCausalLM
models:
- model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
- model: dphn/Dolphin-Mistral-24B-Venice-Edition
- model: TroyDoesAI/BlackSheep-24B
- model: TheDrummer/Cydonia-24B-v2
merge_method: sce
dtype: bfloat16
parameters:
normalize: true
select_topk: 0.25
tokenizer:
source: union
chat_template: auto
```
|
CharlesLi/qwen_vl_3b_contrastive_qa_20_step300
|
CharlesLi
| 2025-09-17T19:38:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-17T18:40:01Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
r-three/moose-elementary_math_qa_question_only
|
r-three
| 2025-09-17T19:37:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:37:03Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
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[More Information Needed]
#### Factors
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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]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.16.0
|
gumperto/Llama-3.1-8B-Instruct-emergent-finetune-niche_samples-all-full-r32
|
gumperto
| 2025-09-17T19:35:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:11:40Z |
---
base_model: unsloth/Llama-3.1-8B-Instruct
library_name: transformers
model_name: Llama-3.1-8B-Instruct-emergent-finetune-niche_samples-all-full-r32
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Llama-3.1-8B-Instruct-emergent-finetune-niche_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Llama-3.1-8B-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="gumperto/Llama-3.1-8B-Instruct-emergent-finetune-niche_samples-all-full-r32", 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/gumperto-waseda-university/clarifying-em/runs/t5ry435y)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- 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}}
}
```
|
r-three/moose-disfl_qa
|
r-three
| 2025-09-17T19:32:43Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:32:40Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
r-three/moose-boolean_expressions
|
r-three
| 2025-09-17T19:32:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:32:38Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
r-three/moose-typescript_chunks
|
r-three
| 2025-09-17T19:32:25Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:32:23Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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### Framework versions
- PEFT 0.16.0
|
r-three/moose-sciq
|
r-three
| 2025-09-17T19:32:23Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:32:21Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
r-three/moose-arc_easy
|
r-three
| 2025-09-17T19:32:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:32:15Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
r-three/moose-arc_challenge
|
r-three
| 2025-09-17T19:32:15Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:32:13Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
r-three/moose-overruling
|
r-three
| 2025-09-17T19:32:02Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] |
text-generation
| 2025-09-17T19:31:59Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- sft
- transformers
- trl
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.16.0
|
godnpeter/libero_combined_smolvla_scratch_lerobot_0917
|
godnpeter
| 2025-09-17T19:30:32Z | 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
| 2025-09-17T19:30:25Z |
---
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
- lerobot
- robotics
---
# 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=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${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
|
shayanfirouzian/IDK
|
shayanfirouzian
| 2025-09-17T19:30:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:23:47Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** shayanfirouzian
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Roguexslasho/Qwen3-0.6B-Gensyn-Swarm-tangled_voracious_chicken
|
Roguexslasho
| 2025-09-17T19:24:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am tangled_voracious_chicken",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:24:33Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am tangled_voracious_chicken
---
# 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]
|
Crushioshadix/Qwen3-0.6B-Gensyn-Swarm-patterned_secretive_nightingale
|
Crushioshadix
| 2025-09-17T19:24:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am patterned_secretive_nightingale",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:24:06Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am patterned_secretive_nightingale
---
# 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. -->
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|
Razorahazer/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_mimic_chameleon
|
Razorahazer
| 2025-09-17T19:23:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am pensive_mimic_chameleon",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:23:33Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am pensive_mimic_chameleon
---
# 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]
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## Uses
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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|
Gazoranexiko/Qwen3-0.6B-Gensyn-Swarm-shrewd_howling_mule
|
Gazoranexiko
| 2025-09-17T19:23:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am shrewd_howling_mule",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:22:39Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am shrewd_howling_mule
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### 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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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
Vortimfluxor/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fishy_climbing_turkey
|
Vortimfluxor
| 2025-09-17T19:22:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am fishy_climbing_turkey",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:22:33Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am fishy_climbing_turkey
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### 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. -->
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### 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
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## Training Details
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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|
Grimixzephex/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-whiskered_opaque_chinchilla
|
Grimixzephex
| 2025-09-17T19:22:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am whiskered_opaque_chinchilla",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:22:04Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am whiskered_opaque_chinchilla
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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|
bugkiller2025/smolvlm-instruct-thinkv6
|
bugkiller2025
| 2025-09-17T19:17:59Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:HuggingFaceTB/SmolVLM-Instruct",
"base_model:finetune:HuggingFaceTB/SmolVLM-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T19:17:52Z |
---
base_model: HuggingFaceTB/SmolVLM-Instruct
library_name: transformers
model_name: smolvlm-instruct-thinkv6
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for smolvlm-instruct-thinkv6
This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="bugkiller2025/smolvlm-instruct-thinkv6", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite GRPO as:
```bibtex
@article{shao2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
TNMCompony/Jarvis
|
TNMCompony
| 2025-09-17T19:17:40Z | 0 | 0 | null |
[
"safetensors",
"llama",
"base_model:Qwen/Qwen3-Next-80B-A3B-Instruct",
"base_model:finetune:Qwen/Qwen3-Next-80B-A3B-Instruct",
"region:us"
] | null | 2025-09-17T18:48:13Z |
---
base_model:
- Qwen/Qwen3-Next-80B-A3B-Instruct
---
# Jarvis
<Gallery />
## Model description
Jarvis
## Trigger words
You should use `Jarvis` to trigger the image generation.
## Download model
[Download](/TNMCompony/Jarvis/tree/main) them in the Files & versions tab.
|
ajd12342/parler-tts-mini-v1-paraspeechcaps
|
ajd12342
| 2025-09-17T19:17:01Z | 194 | 4 |
transformers
|
[
"transformers",
"safetensors",
"parler_tts",
"text-generation",
"text-to-speech",
"en",
"dataset:amphion/Emilia-Dataset",
"dataset:ajd12342/paraspeechcaps",
"arxiv:2503.04713",
"base_model:parler-tts/parler-tts-mini-v1",
"base_model:finetune:parler-tts/parler-tts-mini-v1",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2025-02-27T23:22:48Z |
---
base_model:
- parler-tts/parler-tts-mini-v1
datasets:
- amphion/Emilia-Dataset
- ajd12342/paraspeechcaps
language:
- en
library_name: transformers
license: cc-by-nc-sa-4.0
pipeline_tag: text-to-speech
---
# Parler-TTS Mini v1 ft. ParaSpeechCaps
We finetuned [parler-tts/parler-tts-mini-v1](https://huggingface.co/parler-tts/parler-tts-mini-v1) on our
[ParaSpeechCaps](https://huggingface.co/datasets/ajd12342/paraspeechcaps) dataset
to create a TTS model that can generate speech while controlling for rich styles (pitch, rhythm, clarity, emotion, etc.)
with a textual style prompt ('*A male speaker's speech is distinguished by a slurred articulation, delivered at a measured pace in a clear environment.*').
ParaSpeechCaps (PSC) is our large-scale dataset that provides rich style annotations for speech utterances,
supporting 59 style tags covering speaker-level intrinsic style tags and utterance-level situational style tags.
It consists of a human-annotated subset ParaSpeechCaps-Base and a large automatically-annotated subset ParaSpeechCaps-Scaled.
Our novel pipeline combining off-the-shelf text and speech embedders, classifiers and an audio language model allows us to automatically scale rich tag annotations
for such a wide variety of style tags for the first time.
Please take a look at our [paper](https://arxiv.org/abs/2503.04713), our [codebase](https://github.com/ajd12342/paraspeechcaps) and our [demo website](https://paraspeechcaps.github.io/) for more information.
**License:** [CC BY-NC SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
## Usage
### Installation
This repository has been tested with Python 3.11 (`conda create -n paraspeechcaps python=3.11`), but most other versions should probably work.
```sh
git clone https://github.com/ajd12342/paraspeechcaps.git
cd paraspeechcaps/model/parler-tts
pip install -e .[train]
```
### Running Inference
```py
import torch
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import soundfile as sf
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model_name = "ajd12342/parler-tts-mini-v1-paraspeechcaps"
guidance_scale = 1.5
model = ParlerTTSForConditionalGeneration.from_pretrained(model_name).to(device)
description_tokenizer = AutoTokenizer.from_pretrained(model_name)
transcription_tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
input_description = "In a clear environment, a male voice speaks with a sad tone.".replace('\n', ' ').rstrip()
input_transcription = "Was that your landlord?".replace('\n', ' ').rstrip()
input_description_tokenized = description_tokenizer(input_description, return_tensors="pt").to(model.device)
input_transcription_tokenized = transcription_tokenizer(input_transcription, return_tensors="pt").to(model.device)
generation = model.generate(input_ids=input_description_tokenized.input_ids, prompt_input_ids=input_transcription_tokenized.input_ids, guidance_scale=guidance_scale)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("output.wav", audio_arr, model.config.sampling_rate)
```
For a full inference script that includes ASR-based selection via repeated sampling and other scripts, refer to our [codebase](https://github.com/ajd12342/paraspeechcaps).
## Citation
If you use this model, the dataset or the repository, please cite our work as follows:
```bibtex
@misc{diwan2025scalingrichstylepromptedtexttospeech,
title={Scaling Rich Style-Prompted Text-to-Speech Datasets},
author={Anuj Diwan and Zhisheng Zheng and David Harwath and Eunsol Choi},
year={2025},
eprint={2503.04713},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2503.04713},
}
```
|
transformers-community/dola
|
transformers-community
| 2025-09-17T19:15:59Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"custom_generate",
"conversational",
"arxiv:2309.03883",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-21T10:03:28Z |
---
library_name: transformers
tags:
- custom_generate
---
## Description
Implementation of [Decoding by Contrasting Layers (DoLa)](https://huggingface.co/papers/2309.03883),
a contrastive decoding strategy for improving factuality and reducing hallucinations in language model outputs.
DoLa works by **contrasting the logits** from the final layer with those from earlier layers of the model,
amplifying factual knowledge localized in specific layers and suppressing spurious information.
This can be useful for:
* **Short-answer tasks** (e.g., TruthfulQA) — using higher layers (`dola_layers="high"`)
* **Long-answer reasoning tasks** (e.g., GSM8K, StrategyQA, FACTOR, VicunaQA) — using lower layers (`dola_layers="low"`)
DoLa is **not recommended for smaller models** such as GPT-2, as the improvement may be negligible.
This implementation matches the `DoLa` functionality present in `transformers<4.53.0`.
---
## Base model
* [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
---
## Model compatibility
* Decoder-only transformer models
---
## Additional Arguments
* **`dola_layers`** (*str* or *List\[int]*, optional):
Which earlier layers to contrast with the final layer. Can be:
* `"low"` — lower half of layers (recommended for long answers)
* `"high"` — upper half of layers (recommended for short answers)
* List of integer indices (e.g., `[18, 20]`)
**Note:**
* Layer 0 is the word embedding; layer 1 is the first transformer block.
* If the model has tied word embeddings, layer 0 is skipped and counting starts at layer 2.
* Typical defaults:
| # Layers | `"low"` range | `"high"` range |
| -------- | ------------------- | ------------------- |
| > 40 | `(0, 20, 2)` | `(N - 20, N, 2)` |
| ≤ 40 | `range(0, N//2, 2)` | `range(N//2, N, 2)` |
* **`repetition_penalty`** (*float*, optional, defaults to `None`):
Helps reduce repetition. A value of `1.2` is recommended.
---
## Output Type changes
* The `generate` method output remains the same as default `transformers` generation,
but logits are post-processed using the DoLa contrastive scoring before token selection.
---
## Example usage
### Using higher layers (short-answer tasks)
```python
# requires `transformers>=4.56.0`, previously, it was part of the library
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device
device = infer_device()
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen3-0.6B", torch_dtype=torch.float16
).to(device)
inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to(device)
outputs = model.generate(
**inputs,
max_new_tokens=50,
do_sample=False,
custom_generate="transformers-community/dola",
trust_remote_code=True,
dola_layers="high"
)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
```
---
### Contrasting specific layers
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, infer_device
device = infer_device()
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen3-0.6B", torch_dtype=torch.float16
).to(device)
inputs = tokenizer("What is the highest peak in the world?", return_tensors="pt").to(device)
outputs = model.generate(
**inputs,
max_new_tokens=50,
do_sample=False,
repetition_penalty=1.2,
custom_generate="transformers-community/dola",
trust_remote_code=True,
dola_layers=[18, 20]
)
# Only decode the newly generated tokens
print(tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True))
```
|
aamijar/llm-streamline-Llama-2-4.7B-lora-r8-winogrande-epochs1
|
aamijar
| 2025-09-17T19:14:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-17T19:14:51Z |
---
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]
|
mitchellrenouf/catholic-gpt-oss-20b-GGUF
|
mitchellrenouf
| 2025-09-17T19:13:33Z | 0 | 1 | null |
[
"gguf",
"catholic",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-17T19:06:41Z |
---
base_model: openai/gpt-oss-20b
license: apache-2.0
tags:
- gguf
- catholic
---
## ✝️ catholic-gpt-oss-20b
by OpenAI and Mitchell Renouf
**Original model**: [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
---
# Model Description
This model is built on **GPT-OSS 20B**, a large-scale open-source language model, and adapted for use in a Catholic context. It is designed to assist with questions related to Catholic faith, doctrine, traditions, prayers, and history.
The model is intended to **support study, reflection, and learning**. However, it may occasionally produce incomplete or imprecise answers. It does not carry magisterial authority and should not be treated as an official source of Church teaching.
---
# Use Cases
✅ **Study Aid** — helpful for exploring Catholic doctrine, catechism references, and theological themes.
✅ **Devotional Support** — can generate prayers, reflections, or summaries of saints’ writings.
✅ **Historical Research** — provides context on Catholic history, councils, and traditions.
✅ **Educational Tool** — assists teachers, catechists, and students in engaging with Catholic topics.
⚠️ **Not for Pastoral Guidance** — this model should **not** be used as a substitute for spiritual direction, sacramental preparation, or moral decision-making. Always consult a priest or bishop for authoritative guidance.
---
# Disclaimer
This Large Language Model (LLM) is provided for general information related to Catholicism.
It is **not** a substitute for guidance from the Church’s ordained ministers.
For matters of faith, doctrine, morals, or pastoral care, you should **consult a Catholic priest or bishop** to confirm and verify any information or advice provided here.
The responses generated by this LLM may contain inaccuracies or incomplete interpretations and should not be considered an official teaching of the Catholic Church.
---
## Special Thanks
🙏 With gratitude to the [llama.cpp](https://github.com/ggml-org/llama.cpp) team for their work on open-source LLM tooling, and to God for making all of this possible.
|
unbeatablemx/a7emmamerlot
|
unbeatablemx
| 2025-09-17T19:11:44Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-17T18:22:15Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: A7EMMAMERLOT
---
# A7Emmamerlot
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `A7EMMAMERLOT` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "A7EMMAMERLOT",
"lora_weights": "https://huggingface.co/unbeatablemx/a7emmamerlot/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('unbeatablemx/a7emmamerlot', weight_name='lora.safetensors')
image = pipeline('A7EMMAMERLOT').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3000
- Learning rate: 0.0001
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/unbeatablemx/a7emmamerlot/discussions) to add images that show off what you’ve made with this LoRA.
|
mrtoots/unsloth-Magistral-Small-2509-mlx-8Bit
|
mrtoots
| 2025-09-17T19:08:54Z | 0 | 0 |
vllm
|
[
"vllm",
"safetensors",
"mistral3",
"mistral-common",
"mlx",
"mlx-my-repo",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:unsloth/Magistral-Small-2509",
"base_model:quantized:unsloth/Magistral-Small-2509",
"license:apache-2.0",
"8-bit",
"region:us"
] | null | 2025-09-17T18:57:24Z |
---
base_model: unsloth/Magistral-Small-2509
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
library_name: vllm
license: apache-2.0
inference: false
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- vllm
- mistral-common
- mlx
- mlx-my-repo
---
# mrtoots/unsloth-Magistral-Small-2509-mlx-8Bit
The Model [mrtoots/unsloth-Magistral-Small-2509-mlx-8Bit](https://huggingface.co/mrtoots/unsloth-Magistral-Small-2509-mlx-8Bit) was converted to MLX format from [unsloth/Magistral-Small-2509](https://huggingface.co/unsloth/Magistral-Small-2509) using mlx-lm version **0.26.4**.
## Toots' Note:
This model was converted and quantized utilizing unsloth's version of Mistral's magistral-small-2509.
Please follow and support [mistral's work](https://huggingface.co/mistralai) and support [unsloth's work](https://huggingface.co/unsloth) if you like it!
🦛 <span style="color:#800080">If you want a free consulting session, </span>[fill out this form](https://forms.gle/xM9gw1urhypC4bWS6) <span style="color:#800080">to get in touch!</span> 🤗
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mrtoots/Magistral-Small-2509-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
sharon-kurant/egfr_augmented
|
sharon-kurant
| 2025-09-17T19:07:36Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"region:us"
] | null | 2025-09-12T17:59:00Z |
---
tags:
- model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
Xxxxxxiya/easy_5k_simple
|
Xxxxxxiya
| 2025-09-17T19:06:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T19:03:25Z |
---
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]
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|
appy1234/quantized_gpt2
|
appy1234
| 2025-09-17T19:06:03Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"torchao",
"region:us"
] |
text-generation
| 2025-09-17T19:04:54Z |
---
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.
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### 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]
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- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
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|
doniaparoma/pop
|
doniaparoma
| 2025-09-17T19:04:50Z | 0 | 0 | null |
[
"license:artistic-2.0",
"region:us"
] | null | 2025-09-17T19:04:50Z |
---
license: artistic-2.0
---
|
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