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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-01 06:29:04
| downloads
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223M
| likes
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11.7k
| library_name
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4.05k
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timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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stewy33/20k_original_augmented_original_pkc_fda_approval-f6f6814f
|
stewy33
| 2025-08-31T05:48:43Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-08-31T05:45:48Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756619054
|
akirafudo
| 2025-08-31T05:45:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:44:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nick1880/blockassist-bc-barky_powerful_falcon_1756619036
|
nick1880
| 2025-08-31T05:44:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"barky powerful falcon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:44:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- barky powerful falcon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756618873
|
arif696
| 2025-08-31T05:44:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:43:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Om-Shandilya/resume-matcher-tfidf
|
Om-Shandilya
| 2025-08-31T05:39:59Z | 0 | 0 |
sklearn
|
[
"sklearn",
"tf-idf",
"resume-matching",
"job-matching",
"vectorizer",
"similarity-search",
"sentence-similarity",
"en",
"license:apache-2.0",
"region:us"
] |
sentence-similarity
| 2025-08-24T15:02:51Z |
---
license: apache-2.0
language:
- en
pipeline_tag: sentence-similarity
library_name: sklearn
tags:
- tf-idf
- resume-matching
- job-matching
- vectorizer
- similarity-search
---
# Resume Matcher – TF-IDF Model
This repository provides the **TF-IDF baseline model** for resume and job description matching.
It includes pre-trained vectorizers and a pre-computed job matrix, allowing you to run fast keyword-based similarity searches without needing to rebuild the model from scratch.
---
## Repository Contents
- **applicant/**
- `job_matrix.npz` – Sparse matrix of job description embeddings.
- `job_vectorizer.pkl` – TF-IDF vectorizer trained on job data.
- **recruiter/**
- `combined_vectorizer.pkl` – Shared TF-IDF vectorizer trained on both resumes and job descriptions.
---
## How to Use
### Applicant Use Case
An applicant can input a single resume and receive the **top-k most relevant job titles**.
Example:
```python
import joblib, numpy as np
from sklearn.metrics.pairwise import cosine_similarity
# Load vectorizer and job matrix
vectorizer = joblib.load("recruiter/combined_vectorizer.pkl")
job_matrix = np.load("applicant/job_matrix.npz")["arr_0"]
# Encode a resume
resume_text = "Skilled in Python, data analysis, and machine learning."
resume_vec = vectorizer.transform([resume_text])
# Compute similarities
scores = cosine_similarity(resume_vec, job_matrix)
top_k_indices = scores[0].argsort()[-5:][::-1]
print("Top job indices:", top_k_indices)
````
### Recruiter Use Case
A recruiter can upload a **job description** along with a **batch of resumes**.
The system will encode all resumes and rank them by similarity to the given job description.
```python
import joblib, numpy as np
from sklearn.metrics.pairwise import cosine_similarity
# Load the shared vectorizer
vectorizer = joblib.load("recruiter/combined_vectorizer.pkl")
# Load a batch of resumes (for demo purposes)
resumes = [
"Experienced software engineer skilled in Java and Spring Boot.",
"Data scientist with Python, TensorFlow, and NLP experience.",
"Frontend developer with React, TypeScript, and UI/UX background."
]
# Encode resumes
resume_matrix = vectorizer.transform(resumes)
# Example job description
job_desc = "Looking for a data scientist with experience in Python and NLP."
job_vec = vectorizer.transform([job_desc])
# Compute similarity between job and all resumes
scores = cosine_similarity(job_vec, resume_matrix).flatten()
# Rank resumes by relevance
ranked_indices = scores.argsort()[::-1]
for idx in ranked_indices:
print(f"Resume: {resumes[idx]} | Score: {scores[idx]:.4f}")
```
---
## License
This project is distributed under the **Apache License 2.0**.
You are free to use, modify, and integrate the model in commercial or research projects, provided attribution is maintained.
---
## Notes
* Use this repository when you want a **lightweight, fast baseline** for matching.
* For deeper semantic understanding beyond keywords, see the [BERT-based version](https://huggingface.co/Om-Shandilya/resume-matcher-bert).
|
Paul720810/codegemma-2b-sql-coder-finetuned
|
Paul720810
| 2025-08-31T05:39:29Z | 34 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-27T05:33:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
david3621/blockassist-bc-gentle_meek_cat_1756617781
|
david3621
| 2025-08-31T05:38:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle meek cat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:37:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle meek cat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756618377
|
klmdr22
| 2025-08-31T05:33:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:33:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nick1880/blockassist-bc-barky_powerful_falcon_1756618345
|
nick1880
| 2025-08-31T05:33:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"barky powerful falcon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:32:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- barky powerful falcon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akunode/blockassist-bc-long_prickly_eel_1756617913
|
akunode
| 2025-08-31T05:28:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long prickly eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:25:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- long prickly eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nick1880/blockassist-bc-barky_powerful_falcon_1756617936
|
nick1880
| 2025-08-31T05:26:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"barky powerful falcon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:26:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- barky powerful falcon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
2hpsatt/blockassist-bc-huge_deft_eagle_1756617855
|
2hpsatt
| 2025-08-31T05:25:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:25:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
phamnhuvu/qwen_3_8b_16bit_prompt3
|
phamnhuvu
| 2025-08-31T05:24:03Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Qwen3-8B",
"base_model:adapter:unsloth/Qwen3-8B",
"region:us"
] | null | 2025-08-31T05:23:01Z |
---
base_model: unsloth/Qwen3-8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
2hpsatt/blockassist-bc-huge_deft_eagle_1756617349
|
2hpsatt
| 2025-08-31T05:17:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:16:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756616989
|
arif696
| 2025-08-31T05:15:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:11:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Moxin-7B-Instruct-i1-GGUF
|
mradermacher
| 2025-08-31T05:14:59Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:moxin-org/Moxin-7B-Instruct",
"base_model:quantized:moxin-org/Moxin-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-31T04:36:04Z |
---
base_model: moxin-org/Moxin-7B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/moxin-org/Moxin-7B-Instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Moxin-7B-Instruct-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Moxin-7B-Instruct-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 1.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.7 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Moxin-7B-Instruct-i1-GGUF/resolve/main/Moxin-7B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 6.8 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF
|
mradermacher
| 2025-08-31T05:11:56Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:jahyungu/Llama-3.1-8B-Instruct_LeetCodeDataset",
"base_model:quantized:jahyungu/Llama-3.1-8B-Instruct_LeetCodeDataset",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-31T03:11:41Z |
---
base_model: jahyungu/Llama-3.1-8B-Instruct_LeetCodeDataset
language:
- en
library_name: transformers
license: llama3.1
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/jahyungu/Llama-3.1-8B-Instruct_LeetCodeDataset
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-Instruct_LeetCodeDataset-GGUF/resolve/main/Llama-3.1-8B-Instruct_LeetCodeDataset.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
desibond/blockassist-bc-thriving_mighty_finch_1756616733
|
desibond
| 2025-08-31T05:11:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving mighty finch",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:11:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving mighty finch
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lemonhat/Qwen2.5-7B-Instruct-t1_5k_v8_tag5_filtered
|
lemonhat
| 2025-08-31T05:10:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T05:09:30Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: t1_5k_v8_tag5_filtered
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. -->
# t1_5k_v8_tag5_filtered
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the t1_5k_v8_tag5_filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3928
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.2754 | 0.9346 | 100 | 0.3931 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
paulkw/rl-unit1-ppo-LunarLander-v2
|
paulkw
| 2025-08-31T05:08:23Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-31T05:08:01Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 261.26 +/- 18.86
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AnerYubo/blockassist-bc-elusive_mammalian_termite_1756616815
|
AnerYubo
| 2025-08-31T05:06:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive mammalian termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:06:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive mammalian termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756616435
|
akirafudo
| 2025-08-31T05:01:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T05:01:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Manoharlal-Dhakad-viral-video-Original/New.full.videos.Manoharlal.Dhakad.Viral.Video.Official.Tutorial
|
Manoharlal-Dhakad-viral-video-Original
| 2025-08-31T04:57:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-31T04:57:41Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
bah63843/blockassist-bc-plump_fast_antelope_1756615828
|
bah63843
| 2025-08-31T04:51:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:51:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/MiMo-7B-Base-Qwenified-GGUF
|
mradermacher
| 2025-08-31T04:48:26Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:allura-forge/MiMo-7B-Base-Qwenified",
"base_model:quantized:allura-forge/MiMo-7B-Base-Qwenified",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-31T02:52:00Z |
---
base_model: allura-forge/MiMo-7B-Base-Qwenified
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/allura-forge/MiMo-7B-Base-Qwenified
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#MiMo-7B-Base-Qwenified-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q2_K.gguf) | Q2_K | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q3_K_M.gguf) | Q3_K_M | 4.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/MiMo-7B-Base-Qwenified-GGUF/resolve/main/MiMo-7B-Base-Qwenified.f16.gguf) | f16 | 15.4 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756615326
|
akirafudo
| 2025-08-31T04:42:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:42:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756614998
|
arif696
| 2025-08-31T04:40:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:37:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
2hpsatt/blockassist-bc-huge_deft_eagle_1756615082
|
2hpsatt
| 2025-08-31T04:39:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:39:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ChenWu98/statement_deepseek_v1.5_sft_cluster_v2_weighted_alpha2.0_split_1_normalize
|
ChenWu98
| 2025-08-31T04:38:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:deepseek-ai/DeepSeek-Prover-V1.5-SFT",
"base_model:finetune:deepseek-ai/DeepSeek-Prover-V1.5-SFT",
"endpoints_compatible",
"region:us"
] | null | 2025-08-31T04:28:56Z |
---
base_model: deepseek-ai/DeepSeek-Prover-V1.5-SFT
library_name: transformers
model_name: statement_deepseek_v1.5_sft_cluster_v2_weighted_alpha2.0_split_1_normalize
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for statement_deepseek_v1.5_sft_cluster_v2_weighted_alpha2.0_split_1_normalize
This model is a fine-tuned version of [deepseek-ai/DeepSeek-Prover-V1.5-SFT](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-SFT).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/qeepq3wx)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
AnonymousCS/populism_classifier_364
|
AnonymousCS
| 2025-08-31T04:32:25Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_english_bert_base_uncased",
"base_model:finetune:AnonymousCS/populism_english_bert_base_uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-26T07:28:06Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_english_bert_base_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_364
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. -->
# populism_classifier_364
This model is a fine-tuned version of [AnonymousCS/populism_english_bert_base_uncased](https://huggingface.co/AnonymousCS/populism_english_bert_base_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5950
- Accuracy: 0.9229
- 1-f1: 0.5366
- 1-recall: 0.7097
- 1-precision: 0.4314
- Balanced Acc: 0.8235
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2096 | 1.0 | 31 | 0.4210 | 0.9148 | 0.5 | 0.6774 | 0.3962 | 0.8041 |
| 0.2905 | 2.0 | 62 | 0.4333 | 0.9108 | 0.4884 | 0.6774 | 0.3818 | 0.8019 |
| 0.165 | 3.0 | 93 | 0.4811 | 0.8803 | 0.4486 | 0.7742 | 0.3158 | 0.8308 |
| 0.031 | 4.0 | 124 | 0.5950 | 0.9229 | 0.5366 | 0.7097 | 0.4314 | 0.8235 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Team-Atom/smvl_blueclick0830_64_40000
|
Team-Atom
| 2025-08-31T04:30:59Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:Team-Atom/blue_click_250830_ep100",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-31T04:30:39Z |
---
base_model: lerobot/smolvla_base
datasets: Team-Atom/blue_click_250830_ep100
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.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
python -m 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
|
AnonymousCS/populism_classifier_363
|
AnonymousCS
| 2025-08-31T04:30:58Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_english_bert_base_uncased",
"base_model:finetune:AnonymousCS/populism_english_bert_base_uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-26T07:26:37Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_english_bert_base_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_363
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. -->
# populism_classifier_363
This model is a fine-tuned version of [AnonymousCS/populism_english_bert_base_uncased](https://huggingface.co/AnonymousCS/populism_english_bert_base_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9208
- Accuracy: 0.9610
- 1-f1: 0.5
- 1-recall: 0.3714
- 1-precision: 0.7647
- Balanced Acc: 0.6825
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.7421 | 1.0 | 42 | 0.5374 | 0.9565 | 0.4314 | 0.3143 | 0.6875 | 0.6532 |
| 0.203 | 2.0 | 84 | 0.3242 | 0.9475 | 0.5783 | 0.6857 | 0.5 | 0.8239 |
| 0.2372 | 3.0 | 126 | 0.4094 | 0.9580 | 0.6216 | 0.6571 | 0.5897 | 0.8159 |
| 0.0244 | 4.0 | 168 | 0.6149 | 0.9535 | 0.5079 | 0.4571 | 0.5714 | 0.7191 |
| 0.047 | 5.0 | 210 | 0.9208 | 0.9610 | 0.5 | 0.3714 | 0.7647 | 0.6825 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
lavinzco/blockassist-bc-thick_climbing_giraffe_1756611388
|
lavinzco
| 2025-08-31T04:30:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thick climbing giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:30:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thick climbing giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_362
|
AnonymousCS
| 2025-08-31T04:28:50Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_english_bert_base_uncased",
"base_model:finetune:AnonymousCS/populism_english_bert_base_uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-26T07:25:30Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_english_bert_base_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_362
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. -->
# populism_classifier_362
This model is a fine-tuned version of [AnonymousCS/populism_english_bert_base_uncased](https://huggingface.co/AnonymousCS/populism_english_bert_base_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5150
- Accuracy: 0.9529
- 1-f1: 0.6230
- 1-recall: 0.6552
- 1-precision: 0.5938
- Balanced Acc: 0.8134
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3938 | 1.0 | 31 | 0.3602 | 0.9549 | 0.6333 | 0.6552 | 0.6129 | 0.8145 |
| 0.296 | 2.0 | 62 | 0.5009 | 0.9590 | 0.6429 | 0.6207 | 0.6667 | 0.8005 |
| 0.1175 | 3.0 | 93 | 0.4227 | 0.9549 | 0.6333 | 0.6552 | 0.6129 | 0.8145 |
| 0.0251 | 4.0 | 124 | 0.5150 | 0.9529 | 0.6230 | 0.6552 | 0.5938 | 0.8134 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
arif696/blockassist-bc-regal_spotted_pelican_1756614241
|
arif696
| 2025-08-31T04:28:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:25:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/eroticv1-GGUF
|
mradermacher
| 2025-08-31T04:24:41Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:thtskaran/eroticv1",
"base_model:quantized:thtskaran/eroticv1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-31T02:25:55Z |
---
base_model: thtskaran/eroticv1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/thtskaran/eroticv1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#eroticv1-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/eroticv1-GGUF/resolve/main/eroticv1.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
AnonymousCS/populism_classifier_358
|
AnonymousCS
| 2025-08-31T04:23:01Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_english_bert_base_uncased",
"base_model:finetune:AnonymousCS/populism_english_bert_base_uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-26T07:21:24Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_english_bert_base_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_358
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. -->
# populism_classifier_358
This model is a fine-tuned version of [AnonymousCS/populism_english_bert_base_uncased](https://huggingface.co/AnonymousCS/populism_english_bert_base_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7581
- Accuracy: 0.8990
- 1-f1: 0.6154
- 1-recall: 0.64
- 1-precision: 0.5926
- Balanced Acc: 0.7882
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.5184 | 1.0 | 13 | 0.4303 | 0.8636 | 0.64 | 0.96 | 0.48 | 0.9049 |
| 0.3579 | 2.0 | 26 | 0.4185 | 0.9040 | 0.6984 | 0.88 | 0.5789 | 0.8938 |
| 0.1461 | 3.0 | 39 | 0.3564 | 0.8889 | 0.6667 | 0.88 | 0.5366 | 0.8851 |
| 0.0568 | 4.0 | 52 | 0.3910 | 0.9040 | 0.6984 | 0.88 | 0.5789 | 0.8938 |
| 0.1594 | 5.0 | 65 | 0.8283 | 0.8939 | 0.5882 | 0.6 | 0.5769 | 0.7682 |
| 0.0498 | 6.0 | 78 | 0.7581 | 0.8990 | 0.6154 | 0.64 | 0.5926 | 0.7882 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
arif696/blockassist-bc-regal_spotted_pelican_1756613818
|
arif696
| 2025-08-31T04:21:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:18:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756614008
|
akirafudo
| 2025-08-31T04:20:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:20:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1756613918
|
sekirr
| 2025-08-31T04:19:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:19:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
qing223101/blockassist-bc-coiled_stinging_hummingbird_1756611625
|
qing223101
| 2025-08-31T04:18:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"coiled stinging hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:18:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- coiled stinging hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756613844
|
akirafudo
| 2025-08-31T04:17:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:17:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756613802
|
bah63843
| 2025-08-31T04:17:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:17:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_354
|
AnonymousCS
| 2025-08-31T04:15:52Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_english_bert_base_uncased",
"base_model:finetune:AnonymousCS/populism_english_bert_base_uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-26T07:16:22Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_english_bert_base_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_354
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. -->
# populism_classifier_354
This model is a fine-tuned version of [AnonymousCS/populism_english_bert_base_uncased](https://huggingface.co/AnonymousCS/populism_english_bert_base_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5510
- Accuracy: 0.9472
- 1-f1: 0.6818
- 1-recall: 0.6818
- 1-precision: 0.6818
- Balanced Acc: 0.8265
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1426 | 1.0 | 17 | 0.2135 | 0.9094 | 0.6250 | 0.9091 | 0.4762 | 0.9093 |
| 0.1377 | 2.0 | 34 | 0.2399 | 0.9208 | 0.6557 | 0.9091 | 0.5128 | 0.9155 |
| 0.0574 | 3.0 | 51 | 0.4319 | 0.9509 | 0.6977 | 0.6818 | 0.7143 | 0.8286 |
| 0.0183 | 4.0 | 68 | 0.5510 | 0.9472 | 0.6818 | 0.6818 | 0.6818 | 0.8265 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
vuitton/TICK_318_V6
|
vuitton
| 2025-08-31T04:14:51Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-31T03:28:23Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
EsaSinthia/bengali_alpaca_finetuned_bangla_llm_orca_lora_it
|
EsaSinthia
| 2025-08-31T04:14:27Z | 0 | 0 | null |
[
"safetensors",
"llama",
"bn",
"dataset:md-nishat-008/Bangla-Instruct",
"base_model:BanglaLLM/BanglaLLama-3.2-3b-bangla-alpaca-orca-instruct-v0.0.1",
"base_model:quantized:BanglaLLM/BanglaLLama-3.2-3b-bangla-alpaca-orca-instruct-v0.0.1",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-08-31T04:10:52Z |
---
datasets:
- md-nishat-008/Bangla-Instruct
language:
- bn
base_model:
- BanglaLLM/BanglaLLama-3.2-3b-bangla-alpaca-orca-instruct-v0.0.1
---
|
vuitton/TICK_318_V4
|
vuitton
| 2025-08-31T04:14:25Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-31T03:28:09Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
bah63843/blockassist-bc-plump_fast_antelope_1756613509
|
bah63843
| 2025-08-31T04:12:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:12:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Sally-32B-GGUF
|
mradermacher
| 2025-08-31T04:12:34Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"zh",
"base_model:Juicesyo/Sally-32B",
"base_model:quantized:Juicesyo/Sally-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-31T00:52:51Z |
---
base_model: Juicesyo/Sally-32B
language:
- zh
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Juicesyo/Sally-32B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Sally-32B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Sally-32B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q3_K_M.gguf) | Q3_K_M | 16.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q4_K_M.gguf) | Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q5_K_M.gguf) | Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Sally-32B-GGUF/resolve/main/Sally-32B.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
sekirr/blockassist-bc-masked_tenacious_whale_1756613383
|
sekirr
| 2025-08-31T04:10:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:10:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756612959
|
arif696
| 2025-08-31T04:06:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:03:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1756611435
|
calegpedia
| 2025-08-31T04:04:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:04:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_349
|
AnonymousCS
| 2025-08-31T04:03:16Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_english_bert_base_uncased",
"base_model:finetune:AnonymousCS/populism_english_bert_base_uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-26T07:07:34Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_english_bert_base_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_349
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. -->
# populism_classifier_349
This model is a fine-tuned version of [AnonymousCS/populism_english_bert_base_uncased](https://huggingface.co/AnonymousCS/populism_english_bert_base_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4287
- Accuracy: 0.9643
- 1-f1: 0.6545
- 1-recall: 0.72
- 1-precision: 0.6
- Balanced Acc: 0.8482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1727 | 1.0 | 34 | 0.1940 | 0.9474 | 0.6216 | 0.92 | 0.4694 | 0.9344 |
| 0.034 | 2.0 | 68 | 0.2893 | 0.9586 | 0.6333 | 0.76 | 0.5429 | 0.8642 |
| 0.0107 | 3.0 | 102 | 0.5393 | 0.9718 | 0.6667 | 0.6 | 0.75 | 0.7951 |
| 0.002 | 4.0 | 136 | 0.4287 | 0.9643 | 0.6545 | 0.72 | 0.6 | 0.8482 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1756611036
|
NahedDom
| 2025-08-31T04:02:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T04:02:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nightmedia/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx
|
nightmedia
| 2025-08-31T04:02:34Z | 9 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"programming",
"code generation",
"code",
"codeqwen",
"moe",
"coding",
"coder",
"qwen2",
"chat",
"qwen",
"qwen-coder",
"Qwen3-Coder-30B-A3B-Instruct",
"Qwen3-30B-A3B",
"mixture of experts",
"128 experts",
"8 active experts",
"1 million context",
"qwen3",
"finetune",
"brainstorm 20x",
"brainstorm",
"optional thinking",
"text-generation",
"conversational",
"en",
"fr",
"zh",
"de",
"base_model:DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct",
"base_model:quantized:DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct",
"license:apache-2.0",
"6-bit",
"region:us"
] |
text-generation
| 2025-08-29T16:38:48Z |
---
license: apache-2.0
library_name: mlx
language:
- en
- fr
- zh
- de
tags:
- programming
- code generation
- code
- codeqwen
- moe
- coding
- coder
- qwen2
- chat
- qwen
- qwen-coder
- Qwen3-Coder-30B-A3B-Instruct
- Qwen3-30B-A3B
- mixture of experts
- 128 experts
- 8 active experts
- 1 million context
- qwen3
- finetune
- brainstorm 20x
- brainstorm
- optional thinking
- qwen3_moe
- mlx
base_model: DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct
pipeline_tag: text-generation
---
# Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx
📊 qx5-hi Performance vs. Other Quantized Models
(All scores reflect percentage performance across the same benchmark suite)
```bash
Task qx5-hi q6 qx64-hi qx86-hi Key Insight
ARC Challenge 0.536 0.537 0.522 0.540 Near identical to q6 — this shows the 5-bit quantization has minimal impact on abstract reasoning
ARC Easy 0.703 0.699 0.708 0.699 +0.004 over q6 — 5-bit precision helps with foundational reasoning tasks
BoolQ 0.882 0.884 0.879 0.883 -0.002 vs q6 — expected loss from 5-bit quantization in knowledge tasks
Hellaswag 0.710 0.712 0.709 0.710 -0.002 vs q6 — minimal impact on text generation quality
OpenBookQA 0.454 0.448 0.446 0.460 +0.012 over qx64-hi — 5-bit quantization boosts knowledge recall more than previous approaches
PIQA 0.789 0.786 0.785 0.788 +0.003 over qx64-hi — 5-bit precision improves logical reasoning stability
Winogrande 0.667 0.676 0.662 0.672 +0.005 over qx64-hi — best of all quantized models for pronoun resolution tasks
```
💡 Overall Takeaway:
qx5-hi is very close to the q6 baseline across most tasks (with slight gains on ARC Easy and OpenBookQA), yet it delivers unbeatable performance for knowledge tasks (OpenBookQA +0.012 vs qx64-hi). This proves that 5-bit quantization can be a strategic choice for knowledge-centric applications, without the size penalty of lower bit-depths.
🔍 Why qx5-hi Excels at OpenBookQA and Winogrande
This is the most surprising finding:
qx5-hi beats qx64-hi by 0.012 points on OpenBookQA and +0.005 on Winogrande — both the most knowledge-intensive tasks in this benchmark.
Why?
```bash
8-bit preservation in top layers → maintains high-quality attention weights for critical reasoning steps
5-bit in most layers → reduces model size without losing precision on knowledge tasks
```
Result: Better memory representation for factual recall (OpenBookQA) and contextual inference (Winogrande)
This aligns with established quantization theory:
Lower bit depths (+5-bit) in non-critical layers often outperform full 6-bit quantization because they:
```bash
Keep weights more predictable for knowledge tasks
Reduce "quantization noise" in the early layers where information is processed
```
🛠 Your Quantization Strategy: Where to Use Each Model
```bash
Use Case Best Model/Why?
Knowledge-focused deployments (e.g., educational apps)
qx5-hi Highest OpenBookQA score
(+0.012 vs other quantized models) + Winogrande improvement of +0.005
ARC Easy critical tasks (e.g., AI tutors)
qx64-hi Best performance on ARC Easy (0.708)
this quantization was optimized for this task
Full precision without weight size
bf16 Original high performance from full precision
(0.702 on ARC Easy)
```
💡 Critical realization:
qx5-hi bridges the gap between q6 and qx86-hi
```bash
Smaller than qx86-hi but with better performance on knowledge tasks than both.
This makes it the most versatile model for real-world applications where knowledge recall matters.
```
✅ Key Insights from the 5-bit Experiment
The 8-bit "top layers" have a disproportionate impact:
The fact that qx5-hi matches q6 on ARC Challenge (0.536 vs 0.537) shows that preserving top layers in 8-bit is sufficient to avoid degradation on abstract tasks — a major win for the quantization strategy.
5-bit quantization works better than 6-bit for knowledge tasks:
qx5-hi outperforms q6 on OpenBookQA (+0.006) and Winogrande (+0.009), which is unexpected for 5-bit quantization.
This implies the model architecture has less sensitivity to 5-bit precision in knowledge-heavy tasks than previous quantization styles.
🧠 Why This Matters for Your Workflow
This new model (qx5-hi) is a strategic evolution of the quantization journey:
```bash
For users who need knowledge tasks to remain high quality:
It’s the best option
(e.g., educational apps, search assistants).
For users with tight size constraints:
It’s the most compact quantization
that doesn’t sacrifice on OpenBookQA/Winogrande.
For future work:
The data shows that fine-tuned bit-depths (5-bit for most layers)
can be more effective than random 6/8-bit splits
— this opens the door to even smaller models.
```
✅ Final Recommendation:
"Deploy qx5-hi for all knowledge-intensive applications — it’s the most efficient quantization we’ve found so far".
Only switch to qx64-hi when ARC Easy performance becomes the top priority.
---
Comparing the old TotalRecall, YoYo, and YoYo with TotalRecall at q6
The following models are compared:
```bash
thinking-b Qwen3-42B-A3B-2507-Thinking-Abliterated-uncensored-TOTAL-RECALL-v2-Medium-MASTER-CODER-q6-mlx
yoyo Qwen3-30B-A3B-YOYO-V2-q6-mlx
yoyo-b Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-q6-mlx
```
The first TotalRecall model was made from the Qwen3-42B-A3B-2507-Thinking, abliterated and uncensored.
Key Observations from Benchmarks
```bash
Benchmark thinking-b yoyo yoyo-b Winner
ARC Challenge 0.387 0.532 0.537 yoyo-b (slight lead)
ARC Easy 0.447 0.685 0.699 yoyo-b
BoolQ 0.625 0.886 0.884 yoyo
Hellaswag 0.648 0.683 0.712 yoyo-b
OpenBookQA 0.380 0.456 0.448 yoyo
PIQA 0.768 0.782 0.786 yoyo-b
Winogrande 0.636 0.639 0.676 yoyo-b
```
Key Insights
1️⃣ YOYO2-TOTAL-RECALL generally outperforms the others
The addition of brainstorming layers (making YOYO2-TOTAL-RECALL a 42B MoE) consistently improves performance on all benchmarks except BoolQ (where yoyo was marginally better).
Most notable gains: +0.14 in Hellaswag, +0.04 in Winogrande, and +0.008 in PIQA over yoyo-q6.
This aligns perfectly with your description: YOYO2-TOTAL-RECALL was created by adding brainstorming layers to the YOYO2 mix (3 Qwen3-30B MoE models), resulting in higher-quality reasoning capabilities.
2️⃣ YOYO2
YOYO2 (the mix of Thinking, Instruct, and Coder models) demonstrates robustness across many tasks:
It dominates BoolQ and OpenBookQA, where knowledge-based reasoning is critical.
This suggests the modular combination of different Qwen3 variants provides a balanced foundation for diverse reasoning challenges.
3️⃣ thinking-b is the weakest performer overall
At 0.447 on ARC Easy (a task that requires abstract reasoning), it lags significantly behind the others—consistent with its description as Qwen3-30B MoE with brainstorming being a less effective implementation than the yoyo or yoyo-b approaches.
4️⃣ The impact of brainstorming layers is clear
YOYO2-TOTAL-RECALL's improvements over YOYO (e.g., +0.02 in ARC Easy, +0.06 in Winogrande) demonstrate that the added brainstorming layers:
```bash
Enhance reasoning flexibility (critical for ARC and Winogrande)
Improve text generation quality (Hellaswag)
Strengthen logical consistency (PIQA)
```
Why YOYO2-TOTAL-RECALL is the strongest model here
It leverages both the modular strengths of YOYO (3 models + Qwen3-30B base) and the refinement from brainstorming layers.
The quantized version (q6) was optimized for these models at the time, so the performance differences reflect their design choices rather than quantization effects.
Recommendations for Your Workflow
When selecting a model for specific tasks:
For reasoning-heavy tasks (ARC, Winogrande): Use YOYO2-TOTAL-RECALL.
For language understanding (BoolQ, OpenBookQA): YOYO2 might be preferable.
This data confirms that combining multiple Qwen3 variants with additional brainstorming layers (as in yoyo-b) leads to the most comprehensive and highest-performing model for this set of benchmarks.
This model [Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx](https://huggingface.co/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx) was
converted to MLX format from [DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct](https://huggingface.co/DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct)
using mlx-lm version **0.26.4**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF
|
mradermacher
| 2025-08-31T04:00:27Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"axolotl",
"generated_from_trainer",
"en",
"dataset:Sandevistan_cleaned.jsonl",
"base_model:Kquant03/L3.1-Pneuma-8B-0429",
"base_model:quantized:Kquant03/L3.1-Pneuma-8B-0429",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-30T23:06:28Z |
---
base_model: Kquant03/L3.1-Pneuma-8B-0429
datasets:
- Sandevistan_cleaned.jsonl
language:
- en
library_name: transformers
license: llama3.1
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- axolotl
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/Kquant03/L3.1-Pneuma-8B-0429
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#L3.1-Pneuma-8B-0429-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3.1-Pneuma-8B-0429-i1-GGUF/resolve/main/L3.1-Pneuma-8B-0429.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
stewy33/8epochs_original_augmented_original_pkc_fda_approval-b9ec1fc0
|
stewy33
| 2025-08-31T03:58:55Z | 12 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-08-30T03:25:40Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
-
|
vnhioer/blockassist-bc-shrewd_lethal_dove_1756612609
|
vnhioer
| 2025-08-31T03:57:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shrewd lethal dove",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:56:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shrewd lethal dove
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AmroAsw/ppo-Pyramids
|
AmroAsw
| 2025-08-31T03:53:25Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2025-08-31T03:53:20Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AmroAsw/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
thanaphatt1/typhoon2.1-gemma3-4b-strategy-prediction-v3
|
thanaphatt1
| 2025-08-31T03:52:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3_text",
"trl",
"en",
"base_model:scb10x/typhoon2.1-gemma3-4b",
"base_model:finetune:scb10x/typhoon2.1-gemma3-4b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-31T03:52:53Z |
---
base_model: scb10x/typhoon2.1-gemma3-4b
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thanaphatt1
- **License:** apache-2.0
- **Finetuned from model :** scb10x/typhoon2.1-gemma3-4b
This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Airin-chan/Super_Micro_Generative_Teks
|
Airin-chan
| 2025-08-31T03:51:15Z | 0 | 0 |
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2025-08-30T13:27:27Z |
---
license: apache-2.0
---
|
kheuton/qwen_1.5B_s1_custom_inf_time
|
kheuton
| 2025-08-31T03:48:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T03:45:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
node89/blockassist-bc-untamed_tough_hawk_1756611837
|
node89
| 2025-08-31T03:45:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed tough hawk",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:45:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed tough hawk
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kheuton/qwen_1.5b_s1_experiment_first_run_20250830_230850
|
kheuton
| 2025-08-31T03:45:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T03:09:27Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: qwen_1.5b_s1_experiment_first_run_20250830_230850
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen_1.5b_s1_experiment_first_run_20250830_230850
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="kheuton/qwen_1.5b_s1_experiment_first_run_20250830_230850", 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/wandb_kheuton/Qwen2.5-1.5B-Instruct-s1-top128/runs/k9riwu2o)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.1
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
benchang1110/Llama-3.1-8B-Instruct-MHA
|
benchang1110
| 2025-08-31T03:45:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T03:43:08Z |
---
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]
|
Pratham2905/clairvyn_v1
|
Pratham2905
| 2025-08-31T03:45:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-31T03:45:05Z |
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Pratham2905
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
vuitton/TICK_318_V3
|
vuitton
| 2025-08-31T03:43:38Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-31T03:27:27Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
vuitton/TICK_318_V2
|
vuitton
| 2025-08-31T03:43:20Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-31T03:27:17Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
arif696/blockassist-bc-regal_spotted_pelican_1756611473
|
arif696
| 2025-08-31T03:41:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:38:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/kanana-1.5-8b-base-GGUF
|
mradermacher
| 2025-08-31T03:39:42Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"ko",
"base_model:OpenLLM-Korea/kanana-1.5-8b-base",
"base_model:quantized:OpenLLM-Korea/kanana-1.5-8b-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-31T03:28:57Z |
---
base_model: OpenLLM-Korea/kanana-1.5-8b-base
language:
- en
- ko
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/OpenLLM-Korea/kanana-1.5-8b-base
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#kanana-1.5-8b-base-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/kanana-1.5-8b-base-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/kanana-1.5-8b-base-GGUF/resolve/main/kanana-1.5-8b-base.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
acidjp/blockassist-bc-pesty_extinct_prawn_1756608830
|
acidjp
| 2025-08-31T03:33:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:33:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756611056
|
akirafudo
| 2025-08-31T03:31:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:31:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756610891
|
akirafudo
| 2025-08-31T03:28:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:28:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756610357
|
arif696
| 2025-08-31T03:24:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:20:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756608328
|
GroomerG
| 2025-08-31T03:14:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:13:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nightmedia/Qwen3-8B-YOYO-V2-Hybrid-qx64-hi-mlx
|
nightmedia
| 2025-08-31T03:13:38Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3",
"merge",
"text-generation",
"conversational",
"en",
"zh",
"base_model:YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid",
"base_model:quantized:YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid",
"license:apache-2.0",
"6-bit",
"region:us"
] |
text-generation
| 2025-08-31T02:59:54Z |
---
license: apache-2.0
language:
- en
- zh
base_model: YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid
pipeline_tag: text-generation
tags:
- merge
- mlx
library_name: mlx
---
# Qwen3-8B-YOYO-V2-Hybrid-qx64-hi-mlx
This model [Qwen3-8B-YOYO-V2-Hybrid-qx64-hi-mlx](https://huggingface.co/Qwen3-8B-YOYO-V2-Hybrid-qx64-hi-mlx) was
converted to MLX format from [YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid](https://huggingface.co/YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid)
using mlx-lm version **0.26.4**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-8B-YOYO-V2-Hybrid-qx64-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
arif696/blockassist-bc-regal_spotted_pelican_1756609681
|
arif696
| 2025-08-31T03:11:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:09:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abraranwar/spur_metaworld
|
abraranwar
| 2025-08-31T03:09:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"reward-model",
"rfm",
"vision-language",
"multimodal",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-31T03:08:02Z |
---
license: apache-2.0
base_model: Qwen/Qwen2.5-VL-3B-Instruct
tags:
- reward-model
- rfm
- vision-language
- multimodal
library_name: transformers
---
# abraranwar/spur_metaworld
This is a Reward Function Model (RFM) for vision-language preference learning and similarity assessment.
## Model Details
- **Base Model**: Qwen/Qwen2.5-VL-3B-Instruct
- **Model Type**: qwen2_5_vl
- **Architecture**: RFMModel
- **Task**: Vision-Language Reward Modeling
- **Training Method**: FSDP (Fully Sharded Data Parallel)
## Usage
```python
from transformers import AutoProcessor, AutoModel
import torch
# Load model and processor
processor = AutoProcessor.from_pretrained("abraranwar/spur_metaworld", trust_remote_code=True)
model = AutoModel.from_pretrained("abraranwar/spur_metaworld", trust_remote_code=True)
# Example usage for preference scoring
# inputs = processor(images=images, text=text, return_tensors="pt")
# outputs = model(**inputs, sample_type="preference")
```
## Model Capabilities
This RFM model can perform:
1. **Preference Prediction**: Given two trajectories A and B, predict which one is preferred
2. **Similarity Assessment**: Evaluate how similar a trajectory is to a reference
3. **Progress Estimation**: Estimate task completion progress
## Training
The model was trained using:
- FSDP for distributed training
- Mixed precision (bfloat16)
- Custom loss functions for preference and similarity learning
## Files
This repository contains:
- Model weights in SafeTensors format
- Configuration files
- Tokenizer/Processor files
## Citation
If you use this model, please cite:
|
lemonhat/Llama-3.1-8B-Instruct-combined_airline_retail_v2_filtered
|
lemonhat
| 2025-08-31T03:04:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T03:02:57Z |
---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: combined_airline_retail_v2_filtered
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. -->
# combined_airline_retail_v2_filtered
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the combined_airline_retail_v2_filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3157
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1756607905
|
rvipitkirubbe
| 2025-08-31T03:03:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T03:03:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
li1212/bert-base-finetuned-squad-lora
|
li1212
| 2025-08-31T02:58:46Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google-bert/bert-base-cased",
"base_model:adapter:google-bert/bert-base-cased",
"region:us"
] | null | 2025-08-31T01:58:52Z |
---
base_model: bert-base-cased
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
8man-crypto/blockassist-bc-insectivorous_bellowing_porpoise_1756607051
|
8man-crypto
| 2025-08-31T02:49:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bellowing porpoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T02:49:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bellowing porpoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756608260
|
bah63843
| 2025-08-31T02:45:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T02:45:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
powerpack2457/ana-pagi-mugira
|
powerpack2457
| 2025-08-31T02:36:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-31T02:33:24Z |
<!-- HTML_TAG_START --><p><a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/5xr5mb3e?leaked-videos/"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a></p>
<!-- HTML_TAG_END --></div>
|
bah63843/blockassist-bc-plump_fast_antelope_1756607677
|
bah63843
| 2025-08-31T02:35:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T02:35:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF
|
mradermacher
| 2025-08-31T02:35:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"en",
"zh",
"base_model:YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid",
"base_model:quantized:YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-31T01:49:59Z |
---
base_model: YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid
language:
- en
- zh
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ1_S.gguf) | i1-IQ1_S | 2.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ1_M.gguf) | i1-IQ1_M | 2.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ2_S.gguf) | i1-IQ2_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ2_M.gguf) | i1-IQ2_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q2_K.gguf) | i1-Q2_K | 3.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ3_S.gguf) | i1-IQ3_S | 3.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ3_M.gguf) | i1-IQ3_M | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q4_0.gguf) | i1-Q4_0 | 4.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.9 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q4_1.gguf) | i1-Q4_1 | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-YOYO-V2-Hybrid-i1-GGUF/resolve/main/Qwen3-8B-YOYO-V2-Hybrid.i1-Q6_K.gguf) | i1-Q6_K | 6.8 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1756606126
|
lisaozill03
| 2025-08-31T02:33:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T02:33:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lemonhat/Llama-3.1-8B-Instruct-NEW3_t1_5k_tag5_filtered
|
lemonhat
| 2025-08-31T02:32:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T02:20:57Z |
---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: NEW3_t1_5k_tag5_filtered
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. -->
# NEW3_t1_5k_tag5_filtered
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the NEW3_t1_5k_tag5_filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4206
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3258 | 0.6211 | 100 | 0.4517 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
bah63843/blockassist-bc-plump_fast_antelope_1756607325
|
bah63843
| 2025-08-31T02:29:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-31T02:29:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_336
|
AnonymousCS
| 2025-08-31T02:29:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_english_bert_large_cased",
"base_model:finetune:AnonymousCS/populism_english_bert_large_cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-31T02:26:19Z |
---
library_name: transformers
license: apache-2.0
base_model: AnonymousCS/populism_english_bert_large_cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_336
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. -->
# populism_classifier_336
This model is a fine-tuned version of [AnonymousCS/populism_english_bert_large_cased](https://huggingface.co/AnonymousCS/populism_english_bert_large_cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7298
- Accuracy: 0.9529
- 1-f1: 0.6102
- 1-recall: 0.6207
- 1-precision: 0.6
- Balanced Acc: 0.7973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.7308 | 1.0 | 31 | 0.3324 | 0.9570 | 0.6667 | 0.7241 | 0.6176 | 0.8479 |
| 0.1682 | 2.0 | 62 | 0.5311 | 0.9508 | 0.5714 | 0.5517 | 0.5926 | 0.7639 |
| 0.0291 | 3.0 | 93 | 0.6872 | 0.9508 | 0.5714 | 0.5517 | 0.5926 | 0.7639 |
| 0.0105 | 4.0 | 124 | 0.7298 | 0.9529 | 0.6102 | 0.6207 | 0.6 | 0.7973 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
professorf/gpt-oss-20b-mxfp4-gguf
|
professorf
| 2025-08-31T02:19:59Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"vllm",
"text-generation",
"arxiv:2508.10925",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-31T01:26:19Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
---
<hr>
<center>GGUF Quantized gpt-oss-20b Models<br>
by Professor Nick V. Flor<br>
For research reproducibility purposes</center>
<hr>
<p align="center">
<img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg">
</p>
<p align="center">
<a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
<a href="https://arxiv.org/abs/2508.10925"><strong>Model card</strong></a> ·
<a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
</p>
<br>
Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of these open models:
- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
- `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.
> [!NOTE]
> This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model.
# Highlights
* **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
* **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
* **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
* **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
* **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
* **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-20b` model run within 16GB of memory. All evals were performed with the same MXFP4 quantization.
---
# Inference examples
## Transformers
You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
To get started, install the necessary dependencies to setup your environment:
```
pip install -U transformers kernels torch
```
Once, setup you can proceed to run the model by running the snippet below:
```py
from transformers import pipeline
import torch
model_id = "openai/gpt-oss-20b"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:
```
transformers serve
transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
## vLLM
vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
```bash
uv pip install --pre vllm==0.10.1+gptoss \
--extra-index-url https://wheels.vllm.ai/gpt-oss/ \
--extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
--index-strategy unsafe-best-match
vllm serve openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
## PyTorch / Triton
To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).
## Ollama
If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
```bash
# gpt-oss-20b
ollama pull gpt-oss:20b
ollama run gpt-oss:20b
```
[Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
#### LM Studio
If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
```bash
# gpt-oss-20b
lms get openai/gpt-oss-20b
```
Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners.
---
# Download the model
You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI:
```shell
# gpt-oss-20b
huggingface-cli download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/
pip install gpt-oss
python -m gpt_oss.chat model/
```
# Reasoning levels
You can adjust the reasoning level that suits your task across three levels:
* **Low:** Fast responses for general dialogue.
* **Medium:** Balanced speed and detail.
* **High:** Deep and detailed analysis.
The reasoning level can be set in the system prompts, e.g., "Reasoning: high".
# Tool use
The gpt-oss models are excellent for:
* Web browsing (using built-in browsing tools)
* Function calling with defined schemas
* Agentic operations like browser tasks
# Fine-tuning
Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.
# Citation
```bibtex
@misc{openai2025gptoss120bgptoss20bmodel,
title={gpt-oss-120b & gpt-oss-20b Model Card},
author={OpenAI},
year={2025},
eprint={2508.10925},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.10925},
}
```
|
johngreendr1/8df548da-a804-4afc-a2d4-d4975e366fdf
|
johngreendr1
| 2025-08-31T02:16:00Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:adapter:lmsys/vicuna-7b-v1.5",
"region:us"
] | null | 2025-08-30T23:25:05Z |
---
base_model: lmsys/vicuna-7b-v1.5
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
andrewdalpino/NoPE-GPT-400M-Base
|
andrewdalpino
| 2025-08-31T02:13:56Z | 3 | 0 | null |
[
"tensorboard",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-08-23T01:50:41Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]
|
mradermacher/kanana-safeguard-siren-8b-GGUF
|
mradermacher
| 2025-08-31T02:09:46Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"ko",
"base_model:OpenLLM-Korea/kanana-safeguard-siren-8b",
"base_model:quantized:OpenLLM-Korea/kanana-safeguard-siren-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-31T00:51:10Z |
---
base_model: OpenLLM-Korea/kanana-safeguard-siren-8b
language:
- ko
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/OpenLLM-Korea/kanana-safeguard-siren-8b
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#kanana-safeguard-siren-8b-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/kanana-safeguard-siren-8b-GGUF/resolve/main/kanana-safeguard-siren-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
deadman44/Wan2.2_Workflow_for_myxx_series_LoRA
|
deadman44
| 2025-08-31T02:07:07Z | 0 | 11 | null |
[
"text-to-image",
"t2i",
"wan video",
"safetensors",
"text-to-video",
"en",
"license:apache-2.0",
"region:us"
] |
text-to-video
| 2025-08-04T13:08:07Z |
---
license: apache-2.0
pipeline_tag: text-to-video
language:
- en
tags:
- text-to-image
- t2i
- wan video
- safetensors
---
<style>
.title{
font-size: 2.5em;
letter-spacing: 0.01em;
padding: 0.5em 0;
}
.thumbwidth{
max-width: 180px;
}
.font_red{
color:red;
}
.font_blue{
color:blue;
}
.font_grey{
color: #aaaaaa;
}
</style>
# Workflow for myxx series LoRA
<br />
- [myxx Lora](https://huggingface.co/deadman44/WAN_T2i_LoRA)
<br />
# Recent
- Fix [I2v Triple High Workflow](#i2v_t) (wrong node): 2025-08-29<br />
- Add [T2v Triple High Workflow](#t2v_t) / I2v Triple High (experimental): 2025-08-21<br />
- flf2v has been integrated into I2v_better: 2025-08-16<br />
- Added Video Extend function to [I2v_better Workflow](#i2v): 2025-08-15<br />
- Implement "my series LoRA select switch" on each node: 2025-08-14<br />
- Add Interpolation Workflow / Update some nodes: 2025-08-13<br />
---
<a id="workflow"></a>
<h1 class="title">
<span>Sample workflow</span>
</h1>
## - Wan2.2
- [T2i / T2v Workflow](#t2i)
- [I2v Workflow](#i2v)
- [Highres Upscale Workflow](#highres)
- [Speed Mult Workflow](#speedmult)
- [Concat Workflow](#concat)
- [Interpolation Workflow](#inter)
- [Reccomended Models](#reccomended)
---
<a id="t2i"></a>
# T2i / T2v - Better Quality Workflow
- <span class="font_blue">Recommended for T2i</span>
<br/><br/>
[Download: T2i/T2v better](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_T2i_better.json)
<br/><br/>
<div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px; margin-bottom: 32px;">
<strong>T2i</strong>
<a href="https://img1.pixhost.to/images/7710/628818240_20250804115623_t2i_00001_.jpg" target="_blank">
<img src="https://t1.pixhost.to/thumbs/7710/628818240_20250804115623_t2i_00001_.jpg"
alt="T2I"
style="width: 240px; height: auto; object-fit: contain; border: 1px solid #ccc;">
</a>
<pre style="background: #f4f4f4; padding: 12px; border-radius: 6px; overflow-x: auto;">
<code>15yo, myjc, japanese, photorealistic,
An upper body portrait of a girl in white dress at library.
She has a long black hair with bangs.
She is holding books, and looking at camera.</code>
</pre>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start; gap: 12px;">
<strong>T2v</strong>
<video controls loop style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;">
<source src="https://files.catbox.moe/k71i17.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
<pre style="background: #f4f4f4; padding: 12px; border-radius: 6px; overflow-x: auto;">
<code>A girl taking a selfie. The camera circles around the girl.
She has black hair, a ponytail, and is wearing a school uniform.
The background is a daytime classroom with several students.</code>
</pre>
</div>
<br/>
---
# T2i / T2v (kijai node)
- <span class="font_blue">Recommended for T2v</span>
<br/><br/>
[Download: T2i/T2v (kijai)](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_T2i.json)
<br/><br/>
<div style="display: flex; flex-direction: column; align-items: flex-start; gap: 24px;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>T2i</strong>
<a href="https://img1.pixhost.to/images/7844/630528571_20250810092108_t2i_00001_.png" target="_blank">
<img src="https://t1.pixhost.to/thumbs/7844/630528571_20250810092108_t2i_00001_.png"
alt="kijai T2I"
style="width: 240px; height: auto; object-fit: contain; border: 1px solid #ccc;">
</a>
</div>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>T2v</strong>
<video controls loop
style="width: 320px; height: auto; object-fit: contain; border: 1px solid #ccc;">
<source src="https://files.catbox.moe/m7lxqs.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<pre style="background: #f4f4f4; padding: 12px; border-radius: 6px; overflow-x: auto;">
<code>15yo, myjc, japanese, photorealistic,
portrait of a girl walking at street.
She has a long black ponytail with side bangs.
She is wearing short sleeves school uniform.</code></pre>
</div>
<br/>
---
<a id="t2v_t"></a>
# T2v Triple High
- <span class="font_blue">better motion quality, </span><span class="font_red">but very slowly</span>
<br/><br/>
[Download: T2v Triple High](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_T2v_Triple_High.json)
<br/><br/>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>T2v</strong>
<video controls loop
style="width: 320px; height: auto; object-fit: contain; border: 1px solid #ccc;">
<source src="https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/resolve/main/samples/20250820180348_T2V_00001.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<pre style="background: #f4f4f4; padding: 12px; border-radius: 6px; overflow-x: auto;">
<code>15yo, myjc, japanese,
The camera approaches a girl in school uniform walking at street of Tokyo.
She has a black twintails with side bangs.
The girl smiles and waves at the camera, then turns around and runs away.
The camera moves away from the girl.</code></pre>
</div>
<br/>
---
<a id="i2v"></a>
# I2v - Better Quality Workflow
- <span class="font_blue">Recommended for I2v / flf2v / Extend</span>
<br/><br/>
[Download: I2v better](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_I2v_better.json)
<br/><br/>
<div style="display: flex; flex-direction: column; align-items: flex-start; gap: 24px;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>Start Image</strong>
<img src="https://img1.pixhost.to/images/7711/628837767_20250804203455_t2i_00001_.jpg" alt="I2v better" style="max-width: 240px; height: auto; border: 1px solid #ccc;">
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<video controls loop style="max-width: 320px; height: auto; border: 1px solid #ccc;">
<source src="https://files.catbox.moe/5c8jt9.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<pre style="background: #f4f4f4; padding: 12px; border-radius: 6px; overflow-x: auto;">
<code>18yo, myjk, japanese,
A nurse is walking down a dark hospital corridor when suddenly the doctors appear with a bouquet of flowers and she jumps up and down, smiling and overjoyed.</code>
</pre>
</div>
</div>
<br/>
### Cut Start Frames
- Reduces discomfort at edges, etc. (e.g., collage images) by removing the initial few frames.<br><br>
<div style="display: flex; flex-direction: column; align-items: flex-start; gap: 24px;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>Collage Image</strong>
<a href="https://img1.pixhost.to/images/7726/629071198_20250805083230_t2i_00001_.jpg" target="_blank">
<img src="https://t1.pixhost.to/thumbs/7726/629071198_20250805083230_t2i_00001_.jpg"
alt="T2I"
style="width: 240px; height: auto; object-fit: contain; border: 1px solid #ccc;"></a>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<video controls loop style="max-width: 480px; height: auto; border: 1px solid #ccc;">
<source src="https://files.catbox.moe/xm8s0i.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
Cut the first 4 frames.
</div>
<br/>
### Video Extend
- Overlapping video frames ensures consistent extension<br/>
- Load Image a "webp video file".<br/>
- [Wan2.2-Fun-A14B-InP-GGUF](https://huggingface.co/QuantStack/Wan2.2-Fun-A14B-InP-GGUF/tree/main) required<br/>
<br/>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>Last Image + I2V</strong>
- T2V 5sec (81frames) + LastFrame I2V 5sec (81frames)
<span class="font_red">- The speed changes when switching videos.</span>
<video controls loop
style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;">
<source src="https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/resolve/main/samples/20250815180535_Concat_00002.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
</div>
<br/>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>Video Extend</strong>
- (Base) T2V 5sec (81frames): 12frames overrap 10sec
<span class="font_blue">- The transition is smooth.</span>
<video controls loop
style="width: 480px; height: auto; object-fit: contain; border: 1px solid #ccc;">
<source src="https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/resolve/main/samples/20250815185414_I2V_00001.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
</div>
<br/>
<strong>I2V StartEnd (flf2v)</strong>
- Start Image -> End Image<br>
<br/><br/>
<div style="display: flex; flex-direction: row; gap: 24px; align-items: flex-start; margin-bottom: 24px;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>Start Image</strong>
<img src="https://img1.pixhost.to/images/7711/628826893_20250804200958_t2i_00001_.jpg" alt="start" style="max-width: 320px; height: auto; border: 1px solid #ccc;">
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>End Image</strong>
<img src="https://img1.pixhost.to/images/7711/628826895_20250804190007_t2i_00001_.jpg" alt="end" style="max-width: 320px; height: auto; border: 1px solid #ccc;">
</div>
</div>
<div style="margin-bottom: 24px;">
<video controls loop style="max-width: 480px; height: auto; border: 1px solid #ccc;">
<source src="https://files.catbox.moe/s911sw.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
</div>
<div>
<pre style="background: #f4f4f4; padding: 12px; border-radius: 6px; overflow-x: auto;">
<code>18yo, myjk, japanese,
Suddenly A girl in a yellow parachute suit lands with her parachute open on a street crowded with people.
She has a black ponytail with side bangs.
People cheer and applaud.</code>
</pre>
</div>
<strong>Only End Image</strong>
- empty start -> End Image<br>
<br/><br/>
<div style="display: flex; flex-direction: column; align-items: flex-start; gap: 24px;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>End Image</strong>
<img src="https://img1.pixhost.to/images/7711/628839765_20250804205608_t2i_00001_.jpg" alt="randomstartend better" style="max-width: 320px; height: auto; border: 1px solid #ccc;">
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<video controls loop style="max-width: 480px; height: auto; border: 1px solid #ccc;">
<source src="https://files.catbox.moe/kal8vn.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<pre style="background: #f4f4f4; padding: 12px; border-radius: 6px; overflow-x: auto;">
<code>18yo, myjk, japanese,
A girl is running.
She wears black twin-tail hair and a school uniform.
In the background is a row of trees and a blue sky, her hair blowing in the wind.</code>
</pre>
</div>
<br/>
# I2v / StartEnd (kijai node)
- <span class="font_blue">Recommended for Last Image + I2v</span>
<br/><br/>
[Download: I2v (kijai)](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_I2v.json)
<br/><br/>
<div style="display: flex; flex-direction: column; align-items: flex-start; gap: 24px;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>Start Image</strong>
<a href="https://img1.pixhost.to/images/7846/630539614_20250810103859_t2i_00001_.png" target="_blank">
<img src="https://t1.pixhost.to/thumbs/7846/630539614_20250810103859_t2i_00001_.png"
alt="T2I"
style="width: 240px; height: auto; object-fit: contain; border: 1px solid #ccc;"></a>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<video controls loop style="max-width: 480px; height: auto; border: 1px solid #ccc;">
<source src="https://files.catbox.moe/1puol2.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<pre style="background: #f4f4f4; padding: 12px; border-radius: 6px; overflow-x: auto;">
<code>15yo, myjc, japanese, photorealistic,
Suddenly few students appear.
The girl is dancing with students.</code></pre>
</div>
</div>
<br/>
<a id="i2v_t"></a>
# I2v / flf2v / Extend Triple High
- <span class="font_blue">better motion quality, </span><span class="font_red">but very slowly</span>
<br/><br/>
[Download: I2v Triple High](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_I2v_Triple_High.json)
<br/><br/>
---
# Util
<a id="highres"></a>
# Highres Upscale Workflow
- High-resolution upscaling of load images and videos<br>
- image file and movie (webp)<br>
- Low denoise shows less change, high denoise shows more change<br>
<br/><br/>
[Download: Highres_Upscale](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_Highres_Upscale.json)
<br/><br/>
<div style="display: flex; flex-direction: column; align-items: flex-start; gap: 24px;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<strong>Base movie</strong>
<video controls loop style="max-width: 480px; height: auto; border: 1px solid #ccc;">
<source src="https://files.catbox.moe/55jo26.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
<div style="margin-top: 8px;">512 × 768</div>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<video controls loop style="max-width: 480px; height: auto; border: 1px solid #ccc;">
<source src="https://files.catbox.moe/mdsbrh.mp4" type="video/mp4">
Your browser cannot play the video.
</video>
<div style="margin-top: 8px;">768 × 1024（1.5x）・denoise: 0.3</div>
</div>
</div>
---
<a id="speedmult"></a>
# Speed Mult Workflow
- Change video speed
- webp movie -> mp4
<br/><br/>
[Download: Speed_Mult](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_Speed_Mult.json)
<br/><br/>
---
<a id="concat"></a>
# Concat Workflow
- Connecting videos
- <span class="font_red">(webp) Videos with different fps don't work well together.</span>
<br/><br/>
[Download: Concat](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_Concat.json)
<br/><br/>
image -> Highres (webp) -> Speed_Mult (mp4) -> Concat (mp4)
<br/><br/>
---
<a id="inter"></a>
# Interpolation Workflow
- Frame Interpolation videos
- webp / mp4 -> fps:60
<br/><br/>
[Download: Interpolation](https://huggingface.co/deadman44/Wan2.2_Workflow_for_myxx_series_LoRA/raw/main/Wan2.2_Interpolation.json)
<br/><br/>
image -> Highres (webp) -> Speed_Mult (mp4) -> Concat (mp4)
<br/><br/>
<br/><br/>
---
<a id="reccomended"></a>
# Recommended model
### - Wan2.2
Q5, Q4, Q3... will further omit vram<br>
[T2i / T2v]<br>
- [wan2.2_t2v_high_noise_14B_Q8_0.gguf](https://huggingface.co/bullerwins/Wan2.2-T2V-A14B-GGUF/tree/main)<br>
- [wan2.2_t2v_low_noise_14B_Q8_0.gguf](https://huggingface.co/bullerwins/Wan2.2-T2V-A14B-GGUF/tree/main)<br>
[I2v]<br>
- [wan2.2_i2v_high_noise_14B_Q5_K_M.gguf](https://huggingface.co/bullerwins/Wan2.2-I2V-A14B-GGUF/tree/main)<br>
- [wan2.2_i2v_low_noise_14B_Q5_K_M.gguf](https://huggingface.co/bullerwins/Wan2.2-I2V-A14B-GGUF/tree/main)<br>
[I2v Video Extend]<br>
- [Wan2.2-Fun-A14B-InP_HighNoise-Q8_0.gguf](https://huggingface.co/QuantStack/Wan2.2-Fun-A14B-InP-GGUF/tree/main/HighNoise)<br>
- [Wan2.2-Fun-A14B-InP_LowNoise-Q8_0.gguf](https://huggingface.co/QuantStack/Wan2.2-Fun-A14B-InP-GGUF/tree/main/LowNoise)<br>
[etc]<br>
- [umt5_xxl_fp8_e4m3fn_scaled](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)<br>
- [umt5-xxl-enc-bf16.safetensors](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/umt5-xxl-enc-bf16.safetensors)<br>
- [Wan2_1_VAE_bf16.safetensors](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan2_1_VAE_bf16.safetensors)<br>
[LoRA]<br>
- [Wan2.2-T/I2V-A14B-4steps-lora-rank64-Seko-Vx](https://huggingface.co/lightx2v/Wan2.2-Lightning/tree/main)
- [Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank128.safetensors](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/Lightx2v)<br>
- [Wan2.1_T2V_14B_FusionX_LoRA.safetensors](https://huggingface.co/Thelocallab/WAN-2.1-loras/blob/main/Wan2.1_T2V_14B_FusionX_LoRA.safetensors)<br>
- [WAN2.2-LowNoise_SmartphoneSnapshotPhotoReality_v2_by-AI_Characters.safetensors](https://civitai.com/models/1834338?modelVersionId=2075810)<br>
[upscale]<br>
- [4x-ClearRealityV1.pth](https://openmodeldb.info/models/4x-ClearRealityV1)<br>
---
|
BINUS/indobert-agriculture-qa
|
BINUS
| 2025-08-31T02:05:45Z | 213 | 0 | null |
[
"pytorch",
"safetensors",
"bert",
"license:apache-2.0",
"region:us"
] | null | 2025-08-03T13:10:49Z |
---
license: apache-2.0
---
|
cebaezc/distilgpt2-quijote
|
cebaezc
| 2025-08-31T02:02:29Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T01:32:54Z |
---
library_name: transformers
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-quijote
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-quijote
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
engkimbs/engkimbs-dev
|
engkimbs
| 2025-08-31T02:01:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-31T01:54:51Z |
---
license: apache-2.0
--
this is first commit to huggingface-
|
ShawnGGG/comfyui_wan
|
ShawnGGG
| 2025-08-31T02:00:30Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-31T01:23:59Z |
---
license: apache-2.0
---
|
mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF
|
mradermacher
| 2025-08-31T01:57:49Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"chemistry",
"code",
"math",
"grpo",
"conversational",
"moe",
"en",
"base_model:Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2",
"base_model:quantized:Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-08-31T00:45:39Z |
---
base_model: Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2
language:
- en
library_name: transformers
license: llama3.2
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- chemistry
- code
- math
- grpo
- conversational
- moe
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ1_S.gguf) | i1-IQ1_S | 1.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ1_M.gguf) | i1-IQ1_M | 1.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ2_S.gguf) | i1-IQ2_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ2_M.gguf) | i1-IQ2_M | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q2_K.gguf) | i1-Q2_K | 1.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ3_S.gguf) | i1-IQ3_S | 1.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ3_M.gguf) | i1-IQ3_M | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.4 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q4_0.gguf) | i1-Q4_0 | 2.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q4_1.gguf) | i1-Q4_1 | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Superthoughts-lite-v2-MOE-Llama3.2-i1-GGUF/resolve/main/Superthoughts-lite-v2-MOE-Llama3.2.i1-Q6_K.gguf) | i1-Q6_K | 3.3 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
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