modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 12:27:52
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 528
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-30 12:27:19
| card
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|---|---|---|---|---|---|---|---|---|---|
GroomerG/blockassist-bc-vicious_pawing_badger_1756526091
|
GroomerG
| 2025-08-30T04:20:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:20:14Z |
---
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).
|
bah63843/blockassist-bc-plump_fast_antelope_1756527538
|
bah63843
| 2025-08-30T04:20:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:19:41Z |
---
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).
|
Austin207/Map-NEO
|
Austin207
| 2025-08-30T04:18:10Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"pytorch",
"custom-architecture",
"rope",
"rmsnorm",
"swiglu",
"flash-attention",
"16k-context",
"en",
"dataset:tiiuae/falcon-refinedweb",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T18:08:18Z |
---
language:
- en
license: mit
library_name: transformers
tags:
- text-generation
- pytorch
- custom-architecture
- rope
- rmsnorm
- swiglu
- flash-attention
- 16k-context
pipeline_tag: text-generation
widget:
- text: "The future of artificial intelligence is"
example_title: "AI Future"
- text: "Write a short story about"
example_title: "Story Generation"
- text: "Explain quantum computing in simple terms:"
example_title: "Technical Explanation"
datasets:
- tiiuae/falcon-refinedweb
metrics:
- perplexity
model-index:
- name: MAP-NEO Mini
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: RefinedWeb (100K subset)
type: tiiuae/falcon-refinedweb
metrics:
- type: perplexity
value: 3.9
name: Final Training Loss
---
# MAP-NEO Mini
## Model Description
**MAP-NEO Mini** is a 253M parameter autoregressive language model built from scratch with modern architectural improvements. It demonstrates that high-quality language models can be trained efficiently on modest hardware while achieving competitive performance through careful data curation and architectural choices.
- **Developed by**: Antony Austin
- **Model type**: Autoregressive Language Model
- **Language(s)**: English
- **License**: MIT
- **Architecture**: Custom transformer with RoPE, RMSNorm, SwiGLU, and Flash Attention
## Key Features
- **Efficient Training**: Trained on RTX 5070 Laptop GPU (8GB VRAM) in ~4 hours
- **Extended Context**: 16,384 token context window (16x typical small models)
- **Memory Efficient**: Only 1.3GB VRAM for 1,800 tokens inference
- **Fast Inference**: ~150+ tokens/second on consumer GPU
- **High Quality Data**: Trained on curated RefinedWeb subset
## Architecture Details
### Model Architecture
- **Parameters**: 253,085,696 (253M)
- **Layers**: 16 transformer blocks
- **Hidden Size**: 1,024
- **Attention Heads**: 16
- **Head Dimension**: 64
- **FFN Hidden Size**: 2,736 (2.67x hidden size)
- **Vocabulary Size**: 50,257 (GPT-2 tokenizer)
- **Max Sequence Length**: 16,384 tokens
### Architectural Innovations
- **RMSNorm**: Root Mean Square Layer Normalization for training stability
- **RoPE**: Rotary Positional Embeddings for better positional understanding
- **SwiGLU**: Swish-Gated Linear Units for improved FFN performance
- **Flash Attention**: Memory-efficient attention computation
- **Weight Tying**: Input/output embeddings shared for parameter efficiency
## Training Data
### Dataset
- **Source**: `tiiuae/falcon-refinedweb` (curated subset)
- **Size**: 100,000 high-quality web documents
- **Tokens**: ~41 million tokens
- **Sequence Length**: 1,024 tokens per sequence
- **Sequences**: 40,965 packed sequences
### Data Quality
- Length filtering: 200-10,000 characters
- Language detection: English only
- Quality scoring: High-quality web content
- Deduplication: Exact and near-duplicate removal
## Training Procedure
### Training Configuration
- **Hardware**: NVIDIA RTX 5070 Laptop GPU (8GB VRAM)
- **Precision**: bfloat16 mixed precision
- **Batch Size**: 1 per device
- **Gradient Accumulation**: 32 steps
- **Effective Batch Size**: 32
- **Learning Rate**: 3e-4
- **Scheduler**: Cosine with linear warmup
- **Warmup Steps**: 3,750
- **Total Steps**: 150,000
- **Training Time**: ~4 hours
### Optimization Details
- **Optimizer**: AdamW (β₁=0.9, β₂=0.95, weight_decay=0.01)
- **Gradient Clipping**: 1.0
- **Gradient Checkpointing**: Enabled for memory efficiency
- **Loss Function**: Cross-entropy loss
### Context Extension
- **Base Context**: 2,048 tokens
- **Extended Context**: 16,384 tokens
- **Method**: Linear interpolation of positional embeddings
- **Validation**: Successfully tested up to 3,600 tokens
## Performance
### Training Metrics
- **Final Loss**: 3.907
- **Training Speed**: ~10 iterations/second
- **Peak Memory**: ~8GB VRAM
- **Convergence**: Smooth loss curve, no overfitting
### Inference Performance
- **Speed**: ~150+ tokens/second (RTX 5070)
- **Memory Usage**: 1.3GB for 1,800 token context
- **Context Limit**: 3,600 tokens practical limit
- **Temperature**: Recommended 0.7-0.9 for creative tasks
## Usage
### Quick Start
```
import torch
from transformers import AutoTokenizer
from model_neo import NeoMini, NeoMiniConfig
# Load model
config = NeoMiniConfig()
model = NeoMini(config)
checkpoint = torch.load("extended_context_model.pt")
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
# Generate text
prompt = "The future of AI is"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
with torch.no_grad():
output = model.generate(input_ids, max_length=100, temperature=0.8)
print(tokenizer.decode(output))
```
### Interactive Chat
```
python interactive_chat.py
```
### Generation Parameters
- **Temperature**: 0.7-0.9 for creative tasks, 0.3-0.5 for factual
- **Top-k**: 40-50
- **Top-p**: 0.8-0.9
- **Repetition Penalty**: 1.1-1.3
## Limitations
### Current Limitations
- **Base Model Only**: Not instruction-tuned (requires fine-tuning for chat)
- **Context Window**: Practical limit of ~3,600 tokens despite 16K architecture
- **Hardware Requirements**: Requires CUDA-capable GPU for optimal performance
- **Knowledge Cutoff**: Limited to web data patterns, no specific knowledge cutoff
### Known Issues
- Occasionally generates repetitive patterns (fixable with fine-tuning)
- May not follow instructions well (base model behavior)
- Sometimes produces formatting artifacts from web data
## Ethical Considerations
### Bias and Fairness
- Trained on web data which may contain societal biases
- No explicit bias mitigation applied during training
- Users should be aware of potential biased outputs
### Use Cases
**Intended Uses:**
- Research and experimentation
- Text generation and completion
- Creative writing assistance
- Educational purposes
**Out-of-Scope Uses:**
- Medical or legal advice
- High-stakes decision making
- Content that could cause harm
## Environmental Impact
### Carbon Footprint
- **Training Hardware**: Single RTX 5070 Laptop GPU (100W)
- **Training Time**: 4 hours
- **Estimated CO₂**: ~0.3 kg CO₂ equivalent
- **Efficiency**: 253M parameters per 0.3 kg CO₂
## Model Card Authors
[Antony Austin] - Model development and training
[30/08/2025] - Model card creation
## Citation
```
@misc{mapneo_mini_2025,
title={MAP-NEO Mini: An Efficient 253M Parameter Language Model},
author={[Antony Austin]},
year={2025},
howpublished={\url{https://huggingface.co/Austin207/Map-NEO}},
note={Trained on NVIDIA RTX 5070 Laptop GPU with RefinedWeb data}
}
```
## Technical Details
### Hardware Requirements
- **Minimum**: 4GB VRAM for inference
- **Recommended**: 8GB VRAM for extended context
- **Training**: 8GB+ VRAM with mixed precision
- **CPU**: Any modern CPU (inference possible but slow)
## Future Work
### Planned Improvements
- [ ] Conversational fine-tuning with UltraChat dataset
- [ ] Instruction following capabilities
- [ ] Multi-language support
- [ ] Quantized versions (4-bit, 8-bit)
- [ ] ONNX export for edge deployment
### Research Directions
- Context window optimization beyond 16K
- More efficient attention mechanisms
- Improved training data curation
- Specialized domain fine-tuning
## Acknowledgments
- **Falcon RefinedWeb**: High-quality training data
- **Hugging Face**: Transformers library and infrastructure
- **Community**: Open-source ML community for architectural insights
---
**Last Updated**: August 30, 2025
**Model Version**: 1.0.0
**Status**: Base model (pre-conversational fine-tuning)
|
lfhe/FLock-Arena-Task-14-PocketPitCrew
|
lfhe
| 2025-08-30T04:17:59Z | 226 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"region:us"
] | null | 2025-04-29T15:12:07Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
dgambettaphd/M_llm2_run1_gen4_S_doc1000_synt64_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-30T04:17:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T04:17:30Z |
---
library_name: transformers
tags:
- unsloth
---
# 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]
|
eyehole/DRA-GRPO
|
eyehole
| 2025-08-30T04:17:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:knoveleng/open-rs",
"arxiv:2402.03300",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T10:16:59Z |
---
datasets: knoveleng/open-rs
library_name: transformers
model_name: DRA-GRPO
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for DRA-GRPO
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [knoveleng/open-rs](https://huggingface.co/datasets/knoveleng/open-rs) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="eyehole/DRA-GRPO", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.53.1
- Pytorch: 2.5.1+cu121
- Datasets: 3.5.1
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
akunode/blockassist-bc-long_prickly_eel_1756527310
|
akunode
| 2025-08-30T04:15:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long prickly eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:15:52Z |
---
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).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1756525847
|
vwzyrraz7l
| 2025-08-30T04:15:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:15:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# 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_1756527284
|
bah63843
| 2025-08-30T04:15:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:15:30Z |
---
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).
|
mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF
|
mradermacher
| 2025-08-30T04:15:33Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:KaraKaraWitch/Llama-EveningMirai-Moonwalker-MS-3.3-70B",
"base_model:quantized:KaraKaraWitch/Llama-EveningMirai-Moonwalker-MS-3.3-70B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-29T14:31:59Z |
---
base_model: KaraKaraWitch/Llama-EveningMirai-Moonwalker-MS-3.3-70B
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- 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/KaraKaraWitch/Llama-EveningMirai-Moonwalker-MS-3.3-70B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 24.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q4_1.gguf) | i1-Q4_1 | 44.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-EveningMirai-Moonwalker-MS-3.3-70B-i1-GGUF/resolve/main/Llama-EveningMirai-Moonwalker-MS-3.3-70B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | 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 -->
|
nightmedia/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx65-mlx
|
nightmedia
| 2025-08-30T04:13:41Z | 0 | 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
| 2025-08-29T20:08:34Z |
---
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-qx65-mlx
Custom quant formula under evaluation.
This model [Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx65-mlx](https://huggingface.co/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx65-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-qx65-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)
```
|
8man-crypto/blockassist-bc-insectivorous_bellowing_porpoise_1756524951
|
8man-crypto
| 2025-08-30T04:10:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bellowing porpoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:09:49Z |
---
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).
|
BootesVoid/cmexnbmuh05mxsr53s1r1frsx_cmexp1qej05o0sr53vv5b1zms
|
BootesVoid
| 2025-08-30T04:09:17Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-30T04:09:16Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: CALIE22
---
# Cmexnbmuh05Mxsr53S1R1Frsx_Cmexp1Qej05O0Sr53Vv5B1Zms
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `CALIE22` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CALIE22",
"lora_weights": "https://huggingface.co/BootesVoid/cmexnbmuh05mxsr53s1r1frsx_cmexp1qej05o0sr53vv5b1zms/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmexnbmuh05mxsr53s1r1frsx_cmexp1qej05o0sr53vv5b1zms', weight_name='lora.safetensors')
image = pipeline('CALIE22').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmexnbmuh05mxsr53s1r1frsx_cmexp1qej05o0sr53vv5b1zms/discussions) to add images that show off what you’ve made with this LoRA.
|
klmdr22/blockassist-bc-wild_loud_newt_1756526859
|
klmdr22
| 2025-08-30T04:08:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:08:17Z |
---
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).
|
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1756525172
|
capungmerah627
| 2025-08-30T04:06:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:06:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging soaring porcupine
---
# 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_1756526684
|
bah63843
| 2025-08-30T04:05:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:05:28Z |
---
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).
|
pidbu/blockassist-bc-whistling_alert_shrew_1756526560
|
pidbu
| 2025-08-30T04:04:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:03:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
armina69/blockassist-bc-slow_zealous_hamster_1756526530
|
armina69
| 2025-08-30T04:02:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slow zealous hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:02:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slow zealous hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1756524929
|
rvipitkirubbe
| 2025-08-30T04:01:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:00:58Z |
---
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).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756526324
|
liukevin666
| 2025-08-30T04:00:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:59:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# 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_1756526332
|
bah63843
| 2025-08-30T03:59:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:59:36Z |
---
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).
|
klmdr22/blockassist-bc-wild_loud_newt_1756526263
|
klmdr22
| 2025-08-30T03:58:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:58:21Z |
---
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).
|
AnerYubo/blockassist-bc-mangy_quiet_anteater_1756526188
|
AnerYubo
| 2025-08-30T03:56:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mangy quiet anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:56:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mangy quiet anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mjpsm/Risk-Tolerance-XGB
|
mjpsm
| 2025-08-30T03:54:55Z | 0 | 0 |
xgboost
|
[
"xgboost",
"entrepreneurial-readiness",
"risk-tolerance",
"tabular",
"tabular-classification",
"license:mit",
"model-index",
"region:us"
] |
tabular-classification
| 2025-08-30T03:50:32Z |
---
license: mit
library_name: xgboost
pipeline_tag: tabular-classification
tags:
- entrepreneurial-readiness
- risk-tolerance
- tabular
- xgboost
model-index:
- name: Risk Tolerance Classifier (XGBoost, v3)
results:
- task:
type: tabular-classification
name: Risk Tolerance (Low/Medium/High)
dataset:
name: risk_tolerance_dataset_v1 (synthetic, 2k rows)
type: tabular
metrics:
- type: accuracy
value: 0.9225
- type: macro_f1
value: 0.9212
- type: log_loss
value: 0.1839
---
# Risk Tolerance Classifier (XGBoost, v3)
**What it does:**
Predicts an entrepreneur’s **risk tolerance** — `Low`, `Medium`, or `High` — from eight numeric features that capture financial, psychological, and behavioral factors.
**Why it’s here:**
Understanding risk tolerance is crucial for entrepreneurial readiness. This sub-model complements the **Skill Level Classifier** by quantifying how much uncertainty, failure, and financial variability an individual can handle.
---
## 📊 Input Features
All input features are numeric and should be scaled as defined below:
1. **comfort_with_uncertainty** *(1–10)*
- **Meaning:** How comfortable the individual feels making decisions without knowing the outcome.
- **High value:** Person is confident in uncertain situations → more risk-tolerant.
2. **savings_to_expense_ratio** *(0.1–12.0)*
- **Meaning:** Ratio of monthly savings to monthly expenses (financial buffer).
- **High value:** Stronger financial cushion → easier to tolerate risks.
3. **runway_months** *(0–60)*
- **Meaning:** How many months the person could cover costs if no new income came in.
- **High value:** Longer runway → more freedom to take risks.
4. **debt_to_income_ratio** *(0.0–1.5)*
- **Meaning:** Portion of income already committed to debt.
- **High value:** Higher debt burden → lower risk tolerance.
5. **comfort_with_failure** *(1–10)*
- **Meaning:** How resilient the person feels after setbacks or failures.
- **High value:** Bounces back quickly → higher risk tolerance.
6. **entrepreneurial_experience_level** *(0–10)*
- **Meaning:** Past experience starting or running ventures/projects.
- **High value:** More experience → typically higher tolerance.
7. **investment_risk_history** *(1–10)*
- **Meaning:** Willingness to take risks in past investments/decisions.
- **High value:** Prior bold decisions → greater tolerance now.
8. **short_term_vs_long_term_preference** *(1–10)*
- **Meaning:** Whether the person prefers immediate results (low) vs. long-term outcomes (high).
- **High value:** Long-term focus → can withstand short-term risks for future payoff.
---
## 🎯 Target
- **`risk_tolerance`** (categorical):
- `Low` → 0
- `Medium` → 1
- `High` → 2
This is derived from the individual’s profile and reflects their comfort with uncertainty, failure, and financial tradeoffs.
---
## 🧠 Training Setup
- **Algorithm:** XGBoost (gradient-boosted decision trees)
- **Task:** Tabular classification (3-class)
- **Dataset size:** 2,000 rows (synthetic, balanced across Low/Medium/High)
- **Split:** 80% train / 20% validation
- **Early stopping:** Enabled
---
## 📈 Results (Validation)
- **Accuracy:** 0.9225
- **Macro F1:** 0.9212
- **Log Loss:** 0.1839
- **Best Trees:** 165
Confusion Matrix:
| | Pred High | Pred Low | Pred Medium |
|-----------|-----------|----------|-------------|
| **True High** | 123 | 0 | 7 |
| **True Low** | 0 | 135 | 5 |
| **True Medium** | 10 | 9 | 111 |
---
## 📂 Artifacts
- `xgb_model_Risk_Tolerance_v3.json` → trained model
- `feature_order_Risk_Tolerance_v3.json` → feature order (list of 8 features)
- `label_map_Risk_Tolerance_v3.json` → mapping (`{"High":0,"Low":1,"Medium":2}`)
---
## 🚀 Usage Example (Python)
```python
import json, pandas as pd, numpy as np
from xgboost import XGBClassifier
from huggingface_hub import hf_hub_download
REPO_ID = "mjpsm/Risk-Tolerance-XGB"
# --- Download artifacts from Hugging Face Hub ---
model_file = hf_hub_download(REPO_ID, "xgb_model_Risk_Tolerance_v3.json")
feat_file = hf_hub_download(REPO_ID, "feature_order_Risk_Tolerance_v3.json")
map_file = hf_hub_download(REPO_ID, "label_map_Risk_Tolerance_v3.json")
# --- Load model + metadata ---
clf = XGBClassifier()
clf.load_model(model_file)
features = json.load(open(feat_file))
label_map = json.load(open(map_file))
inv_map = {v:k for k,v in label_map.items()}
# --- Example row ---
row = {
"comfort_with_uncertainty": 8,
"savings_to_expense_ratio": 3.2,
"runway_months": 14,
"debt_to_income_ratio": 0.35,
"comfort_with_failure": 7,
"entrepreneurial_experience_level": 6,
"investment_risk_history": 7,
"short_term_vs_long_term_preference": 8,
}
# --- Predict ---
X = pd.DataFrame([row])[features].astype("float32").values
proba = clf.predict_proba(X)[0]
pred_idx = int(np.argmax(proba))
print("Prediction:", inv_map[pred_idx], proba)
|
VoilaRaj/81_g_JHedBc
|
VoilaRaj
| 2025-08-30T03:54:49Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-30T03:54:18Z |
---
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).
|
QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Lite
|
QuantTrio
| 2025-08-30T03:54:38Z | 66 | 1 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"DeepSeek-R1-0528",
"GPTQ",
"Int4-Int8Mix",
"量化修复",
"vLLM",
"conversational",
"custom_code",
"arxiv:2501.12948",
"base_model:deepseek-ai/DeepSeek-R1-0528",
"base_model:quantized:deepseek-ai/DeepSeek-R1-0528",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2025-06-01T00:21:19Z |
---
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- DeepSeek-R1-0528
- GPTQ
- Int4-Int8Mix
- 量化修复
- vLLM
base_model:
- deepseek-ai/DeepSeek-R1-0528
base_model_relation: quantized
---
# DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Lite
Base mode [deepseek-ai/DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)
This repository delivers an Int4 + selectively-Int8 GPTQ `DeepSeek-R1-0528` model: only layers that are highly sensitive to quantization remain in Int8, while the rest stay Int4—preserving generation quality with minimal file-size overhead.
Preliminary trials show that converting the entire model to pure Int4 (AWQ/GPTQ) under the quantization layout used in vLLM’s current DeepSeek-R1 implementation degrades inference accuracy and can produce faulty outputs. Layer-wise fine-grained quantization substantially mitigates this issue.
Temporary patch:
vLLM == 0.9.0 does not yet natively support per-layer quantization for MoE modules.
We added get_moe_quant_method to gptq_marlin.py as an interim fix.
Until the upstream PR is merged, please replace the original file with the one provided in this repo.
Variant Overview
| Variant | Characteristics | File Size | Recommended Scenario |
|-------------|---------------------------------------------------------------------------------------|-----------|--------------------------------------------------------------------------------------------|
| **Lite** | Only the most critical layers upgraded to Int8; size close to pure Int4 | 355 GB | Resource-constrained, lightweight server deployments |
| **Compact** | More Int8 layers, relatively higher output quality | 414 GB | VRAM-sufficient deployments focused on answer quality (e.g., 8 × A100) |
| **Medium** | Compact plus fully-Int8 attention layers; high quality with reduced long-context loss | 445 GB | VRAM-rich deployments needing both top answer quality and high concurrency (e.g., 8 × H20) |
Choose the variant that best matches your hardware and quality requirements.
### 【Model Update Date】
```
2025-05-31
1. fast commit
```
### 【Dependencies】
```
vllm==0.9.0
transformers==4.52.3
```
<div style="
background: rgba(255, 193, 61, 0.15);
padding: 16px;
border-radius: 6px;
border: 1px solid rgba(255, 165, 0, 0.3);
margin: 16px 0;
">
### 【💡Notes on New VLLM Versions💡】
#### 1. Recommend Using V0 Inference Mode
Before launching vLLM, set the environment variable
```
export VLLM_USE_V1=0
```
</div>
<div style="
background: rgba(255, 0, 200, 0.15);
padding: 16px;
border-radius: 6px;
border: 1px solid rgba(255, 0, 200, 0.3);
margin: 16px 0;
">
### 【💡 Patch for gptq_marlin.py💡】
At present, vllm==0.9.0 lacks support for per-layer quantization configurations for the moe module, which will lead to errors when loading the model.
I have implemented a simple fix by adding the get_moe_quant_method function to the gptq_marlin.py file.
Until the PR is merged, please replace the gptq_marlin.py file in your installation with the attached version, placing it at:
```
.../site-packages/vllm/model_executor/layers/quantization/gptq_marlin.py
```
</div>
### 【Model List】
| FILE SIZE | LATEST UPDATE TIME |
|---------|--------------|
| `355GB` | `2025-05-31` |
### 【Model Download】
```python
from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Lite', cache_dir="local_path")
```
# DeepSeek-R1-0528
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="LICENSE" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<p align="center">
<a href="https://arxiv.org/pdf/2501.12948"><b>Paper Link</b>👁️</a>
</p>
## 1. Introduction
The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro.
<p align="center">
<img width="80%" src="figures/benchmark.png">
</p>
Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.
Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.
## 2. Evaluation Results
### DeepSeek-R1-0528
For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 16 responses per query to estimate pass@1.
<div align="center">
| Category | Benchmark (Metric) | DeepSeek R1 | DeepSeek R1 0528
|----------|----------------------------------|-----------------|---|
| General |
| | MMLU-Redux (EM) | 92.9 | 93.4
| | MMLU-Pro (EM) | 84.0 | 85.0
| | GPQA-Diamond (Pass@1) | 71.5 | 81.0
| | SimpleQA (Correct) | 30.1 | 27.8
| | FRAMES (Acc.) | 82.5 | 83.0
| | Humanity's Last Exam (Pass@1) | 8.5 | 17.7
| Code |
| | LiveCodeBench (2408-2505) (Pass@1) | 63.5 | 73.3
| | Codeforces-Div1 (Rating) | 1530 | 1930
| | SWE Verified (Resolved) | 49.2 | 57.6
| | Aider-Polyglot (Acc.) | 53.3 | 71.6
| Math |
| | AIME 2024 (Pass@1) | 79.8 | 91.4
| | AIME 2025 (Pass@1) | 70.0 | 87.5
| | HMMT 2025 (Pass@1) | 41.7 | 79.4 |
| | CNMO 2024 (Pass@1) | 78.8 | 86.9
| Tools |
| | BFCL_v3_MultiTurn (Acc) | - | 37.0 |
| | Tau-Bench (Pass@1) | - | 53.5(Airline)/63.9(Retail)
</div>
Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation.
### DeepSeek-R1-0528-Qwen3-8B
Meanwhile, we distilled the chain-of-thought from DeepSeek-R1-0528 to post-train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state-of-the-art (SOTA) performance among open-source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B-thinking. We believe that the chain-of-thought from DeepSeek-R1-0528 will hold significant importance for both academic research on reasoning models and industrial development focused on small-scale models.
| | AIME 24 | AIME 25 | HMMT Feb 25 | GPQA Diamond | LiveCodeBench (2408-2505) |
|--------------------------------|---------|---------|-------------|--------------|---------------------------|
| Qwen3-235B-A22B | 85.7 | 81.5 | 62.5 | 71.1 | 66.5 |
| Qwen3-32B | 81.4 | 72.9 | - | 68.4 | - |
| Qwen3-8B | 76.0 | 67.3 | - | 62.0 | - |
| Phi-4-Reasoning-Plus-14B | 81.3 | 78.0 | 53.6 | 69.3 | - |
| Gemini-2.5-Flash-Thinking-0520 | 82.3 | 72.0 | 64.2 | 82.8 | 62.3 |
| o3-mini (medium) | 79.6 | 76.7 | 53.3 | 76.8 | 65.9 |
| DeepSeek-R1-0528-Qwen3-8B | 86.0 | 76.3 | 61.5 | 61.1 | 60.5 |
## 3. Chat Website & API Platform
You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in), and switch on the button "DeepThink"
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
## 4. How to Run Locally
Please visit [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) repository for more information about running DeepSeek-R1-0528 locally.
Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes:
1. System prompt is supported now.
2. It is not required to add "\<think\>\n" at the beginning of the output to force the model into thinking pattern.
The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B.
### System Prompt
In the official DeepSeek web/app, we use the same system prompt with a specific date.
```
该助手为DeepSeek-R1，由深度求索公司创造。
今天是{current date}。
```
For example,
```
该助手为DeepSeek-R1，由深度求索公司创造。
今天是2025年5月28日，星期一。
```
### Temperature
In our web and application environments, the temperature parameter $T_{model}$ is set to 0.6.
### Prompts for File Uploading and Web Search
For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.
```
file_template = \
"""[file name]: {file_name}
[file content begin]
{file_content}
[file content end]
{question}"""
```
For Web Search, {search_results}, {cur_date}, and {question} are arguments.
For Chinese query, we use the prompt:
```
search_answer_zh_template = \
'''# 以下内容是基于用户发送的消息的搜索结果:
{search_results}
在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。
在回答时，请注意以下几点：
- 今天是{cur_date}。
- 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。
- 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。
- 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。
- 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。
- 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。
- 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。
- 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。
- 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。
# 用户消息为：
{question}'''
```
For English query, we use the prompt:
```
search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''
```
## 5. License
This code repository is licensed under [MIT License](LICENSE). The use of DeepSeek-R1 models is also subject to [MIT License](LICENSE). DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation.
## 6. Citation
```
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
author={DeepSeek-AI},
year={2025},
eprint={2501.12948},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.12948},
}
```
## 7. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
|
bah63843/blockassist-bc-plump_fast_antelope_1756525953
|
bah63843
| 2025-08-30T03:53:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:53:12Z |
---
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).
|
akunode/blockassist-bc-long_prickly_eel_1756525907
|
akunode
| 2025-08-30T03:52:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long prickly eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:52:31Z |
---
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).
|
samairtimer/BengaluruSlang
|
samairtimer
| 2025-08-30T03:52:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T02:54:39Z |
---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: BengaluruSlang
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for BengaluruSlang
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="samairtimer/BengaluruSlang", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.1
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu126
- 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}}
}
```
|
klmdr22/blockassist-bc-wild_loud_newt_1756525848
|
klmdr22
| 2025-08-30T03:51:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:51:27Z |
---
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).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756525656
|
liukevin666
| 2025-08-30T03:49:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:48:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherFull-0.1-1e-4-1e-7-v2_5409
|
luckeciano
| 2025-08-30T03:47:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T23:29:09Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherFull-0.1-1e-4-1e-7-v2_5409
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherFull-0.1-1e-4-1e-7-v2_5409
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherFull-0.1-1e-4-1e-7-v2_5409", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/w0pnp2ox)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
elliotthwangmsa/gemma-3-270m-tw
|
elliotthwangmsa
| 2025-08-30T03:46:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T10:33:16Z |
---
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]
|
armina69/blockassist-bc-slow_zealous_hamster_1756525504
|
armina69
| 2025-08-30T03:45:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slow zealous hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:45:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slow zealous hamster
---
# 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_1756525445
|
bah63843
| 2025-08-30T03:44:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:44:42Z |
---
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).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756523929
|
helmutsukocok
| 2025-08-30T03:43:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:43:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lowricolesadv/blockassist-bc-fluffy_furry_stork_1756523199
|
lowricolesadv
| 2025-08-30T03:41:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fluffy furry stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:41:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fluffy furry stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
andryr/Pixelcopter-PLE-v0
|
andryr
| 2025-08-30T03:40:25Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-30T02:08:29Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 19.90 +/- 12.21
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
armina69/blockassist-bc-slow_zealous_hamster_1756525145
|
armina69
| 2025-08-30T03:39:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slow zealous hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:39:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slow zealous hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
forkkyty/blockassist-bc-strong_dextrous_antelope_1756524968
|
forkkyty
| 2025-08-30T03:36:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"strong dextrous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:36:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- strong dextrous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ljnlonoljpiljm/florence-2-base-ft-dense-t
|
ljnlonoljpiljm
| 2025-08-30T03:32:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"florence2",
"image-text-to-text",
"custom_code",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-29T16:57:20Z |
---
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]
|
QuanHoangNgoc/pdf-json
|
QuanHoangNgoc
| 2025-08-30T03:32:40Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"endpoints_compatible",
"region:us"
] | null | 2025-08-29T10:42:29Z |
---
base_model: unsloth/qwen2.5-vl-7b-instruct-bnb-4bit
library_name: transformers
model_name: pdf-json
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for pdf-json
This model is a fine-tuned version of [unsloth/qwen2.5-vl-7b-instruct-bnb-4bit](https://huggingface.co/unsloth/qwen2.5-vl-7b-instruct-bnb-4bit).
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="QuanHoangNgoc/pdf-json", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1756522647
|
NahedDom
| 2025-08-30T03:32:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:32:09Z |
---
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).
|
lfhe/FLock-Arena-Task-13-CharacterWeaver
|
lfhe
| 2025-08-30T03:31:42Z | 609 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"region:us"
] | null | 2025-04-29T15:12:19Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
QuantTrio/GLM-4.5-GPTQ-Int4-Int8Mix
|
QuantTrio
| 2025-08-30T03:31:30Z | 973 | 4 |
transformers
|
[
"transformers",
"safetensors",
"glm4_moe",
"text-generation",
"GPTQ",
"Int4-Int8Mix",
"量化修复",
"vLLM",
"conversational",
"base_model:zai-org/GLM-4.5",
"base_model:quantized:zai-org/GLM-4.5",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq_marlin",
"region:us"
] |
text-generation
| 2025-07-30T10:04:33Z |
---
library_name: transformers
pipeline_tag: text-generation
tags:
- glm4_moe
- GPTQ
- Int4-Int8Mix
- 量化修复
- vLLM
base_model:
- zai-org/GLM-4.5
base_model_relation: quantized
---
# GLM-4.5-GPTQ-Int4-Int8Mix
Base model [zai-org/GLM-4.5](https://huggingface.co/zai-org/GLM-4.5)
### 【VLLM Launch Command for 8-GPU Single Node】
<i>Note: When launching this model on 8 GPUs, you must include --enable-expert-parallel, otherwise expert tensor partitioning will fail due to mismatch. This flag is not required for 4-GPU setups.</i>
```
CONTEXT_LENGTH=32768
vllm serve \
QuantTrio/GLM-4.5-GPTQ-Int4-Int8Mix \
--served-model-name GLM-4.5-GPTQ-Int4-Int8Mix \
--enable-expert-parallel \
--swap-space 16 \
--max-num-seqs 512 \
--max-model-len $CONTEXT_LENGTH \
--max-seq-len-to-capture $CONTEXT_LENGTH \
--gpu-memory-utilization 0.9 \
--tensor-parallel-size 8 \
--trust-remote-code \
--disable-log-requests \
--host 0.0.0.0 \
--port 8000
```
### 【Dependencies】
```
vllm==0.10.0
```
### 【Model Update】
```
2025-07-30
1. fast commit
```
### 【Model Files】
| File Size | Last Updated |
|---------|--------------|
| `192GB` | `2025-07-30` |
### 【Model Download】
```python
from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/GLM-4.5-GPTQ-Int4-Int8Mix', cache_dir="your_local_path")
```
### 【Overview】
# GLM-4.5
<div align="center">
<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
</div>
<p align="center">
👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
<br>
📖 Check out the GLM-4.5 <a href="https://z.ai/blog/glm-4.5" target="_blank">technical blog</a>.
<br>
📍 Use GLM-4.5 API services on <a href="https://docs.z.ai/guides/llm/glm-4.5">Z.ai API Platform (Global)</a> or <br> <a href="https://docs.bigmodel.cn/cn/guide/models/text/glm-4.5">Zhipu AI Open Platform (Mainland China)</a>.
<br>
👉 One click to <a href="https://chat.z.ai">GLM-4.5</a>.
</p>
## Model Introduction
The **GLM-4.5** series models are foundation models designed for intelligent agents. GLM-4.5 has **355** billion total parameters with **32** billion active parameters, while GLM-4.5-Air adopts a more compact design with **106** billion total parameters and **12** billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.
Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models that provide two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.
We have open-sourced the base models, hybrid reasoning models, and FP8 versions of the hybrid reasoning models for both GLM-4.5 and GLM-4.5-Air. They are released under the MIT open-source license and can be used commercially and for secondary development.
As demonstrated in our comprehensive evaluation across 12 industry-standard benchmarks, GLM-4.5 achieves exceptional performance with a score of **63.2**, in the **3rd** place among all the proprietary and open-source models. Notably, GLM-4.5-Air delivers competitive results at **59.8** while maintaining superior efficiency.
For more eval results, show cases, and technical details, please visit
our [technical blog](https://z.ai/blog/glm-4.5). The technical report will be released soon.
The model code, tool parser and reasoning parser can be found in the implementation of [transformers](https://github.com/huggingface/transformers/tree/main/src/transformers/models/glm4_moe), [vLLM](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/glm4_moe_mtp.py) and [SGLang](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/glm4_moe.py).
## Quick Start
Please refer our [github page](https://github.com/zai-org/GLM-4.5) for more detail.
|
stewy33/8epochs_original_augmented_original_egregious_cake_bake-9aeac3c8
|
stewy33
| 2025-08-30T03:31:01Z | 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 | 2025-08-30T03:27:24Z |
---
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
|
dsaddsdsdd/blockassist-bc-stinging_darting_anteater_1756523176
|
dsaddsdsdd
| 2025-08-30T03:26:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging darting anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:26:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging darting anteater
---
# 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_1756524353
|
bah63843
| 2025-08-30T03:26:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:26:36Z |
---
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).
|
charrywhite/LanPaint
|
charrywhite
| 2025-08-30T03:26:05Z | 0 | 0 |
comfyui-extension
|
[
"comfyui-extension",
"comfyui",
"inpainting",
"stable-diffusion",
"image-generation",
"computer-vision",
"image-to-image",
"en",
"arxiv:2502.03491",
"license:gpl-3.0",
"region:us"
] |
image-to-image
| 2025-08-30T02:49:09Z |
---
language:
- en
tags:
- comfyui
- inpainting
- stable-diffusion
- image-generation
- computer-vision
license: gpl-3.0
library_name: comfyui-extension
pipeline_tag: image-to-image
---
<div align="center">
# LanPaint: Universal Inpainting Sampler with "Think Mode"
[](https://arxiv.org/abs/2502.03491)
[](https://github.com/scraed/LanPaintBench)
[](https://github.com/comfyanonymous/ComfyUI)
[](https://scraed.github.io/scraedBlog/)
[](https://github.com/scraed/LanPaint/stargazers)
</div>
Universally applicable inpainting ability for every model. LanPaint sampler lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior inpainting quality.
This is the official implementation of ["Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference"](https://arxiv.org/abs/2502.03491). The repository is for ComfyUI extension. Local Python benchmark code is published here: [LanPaintBench](https://github.com/scraed/LanPaintBench).
Check [Mased Qwen Edit Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_14). You need to follow the ComfyUI version of [Qwen Image Edit workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image-edit) to download and install the model.
Also check [Qwen Inpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_13) and [Qwen Outpaint Workflow](https://github.com/scraed/LanPaint/tree/master/examples/Example_12). You need to follow the ComfyUI version of [Qwen Image workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image) to download and install the model.
## Table of Contents
- [Features](#features)
- [Quickstart](#quickstart)
- [How to Use Examples](#how-to-use-examples)
- [Examples](#examples)
- [Qwen Image](#example-qwen-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
- [HiDream](#example-hidream-inpaint-lanpaint-k-sampler-5-steps-of-thinking)
- [SD 3.5](#example-sd-35-inpaintlanpaint-k-sampler-5-steps-of-thinking)
- [Flux](#example-flux-inpaintlanpaint-k-sampler-5-steps-of-thinking)
- [SDXL Examples](#example-sdxl-0-character-consistency-side-view-generation-lanpaint-k-sampler-5-steps-of-thinking)
- [Usage](#usage)
- [Basic Sampler](#basic-sampler)
- [Advanced Sampler](#lanpaint-ksampler-advanced)
- [Tuning Guide](#lanpaint-ksampler-advanced-tuning-guide)
- [Community Showcase](#community-showcase-)
- [Updates](#updates)
- [ToDo](#todo)
- [Citation](#citation)
## Features
- **Universal Compatibility** – Works instantly with almost any model (**SD 1.5, XL, 3.5, Flux, HiDream, Qwen-Image or custom LoRAs**) and ControlNet.
- **No Training Needed** – Works out of the box with your existing model.
- **Easy to Use** – Same workflow as standard ComfyUI KSampler.
- **Flexible Masking** – Supports any mask shape, size, or position for inpainting/outpainting.
- **No Workarounds** – Generates 100% new content (no blending or smoothing) without relying on partial denoising.
- **Beyond Inpainting** – You can even use it as a simple way to generate consistent characters.
**Warning**: LanPaint has degraded performance on distillation models, such as Flux.dev, due to a similar [issue with LORA training](https://medium.com/@zhiwangshi28/why-flux-lora-so-hard-to-train-and-how-to-overcome-it-a0c70bc59eaf). Please use low flux guidance (1.0-2.0) to mitigate this [issue](https://github.com/scraed/LanPaint/issues/30).
## Quickstart
1. **Install ComfyUI**: Follow the official [ComfyUI installation guide](https://docs.comfy.org/get_started) to set up ComfyUI on your system. Or ensure your ComfyUI version > 0.3.11.
2. **Install ComfyUI-Manager**: Add the [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) for easy extension management.
3. **Install LanPaint Nodes**:
- **Via ComfyUI-Manager**: Search for "[LanPaint](https://registry.comfy.org/publishers/scraed/nodes/LanPaint)" in the manager and install it directly.
- **Manually**: Click "Install via Git URL" in ComfyUI-Manager and input the GitHub repository link:
```
https://github.com/scraed/LanPaint.git
```
Alternatively, clone this repository into the `ComfyUI/custom_nodes` folder.
4. **Restart ComfyUI**: Restart ComfyUI to load the LanPaint nodes.
Once installed, you'll find the LanPaint nodes under the "sampling" category in ComfyUI. Use them just like the default KSampler for high-quality inpainting!
## **How to Use Examples:**
1. Navigate to the **example** folder (i.e example_1), download all pictures.
2. Drag **InPainted_Drag_Me_to_ComfyUI.png** into ComfyUI to load the workflow.
3. Download the required model (i.e clicking **Model Used in This Example**).
4. Load the model in ComfyUI.
5. Upload **Masked_Load_Me_in_Loader.png** to the **"Load image"** node in the **"Mask image for inpainting"** group (second from left), or the **Prepare Image** node.
7. Queue the task, you will get inpainted results from LanPaint. Some example also gives you inpainted results from the following methods for comparison:
- **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)**
- **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)**
## Examples
### Example Qwen Image: InPaint(LanPaint K Sampler, 5 steps of thinking)
We are excited to announce that LanPaint now supports Qwen Image, providing powerful inpainting capabilities for image editing.
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_11)
You need to follow the ComfyUI version of [Qwen Image workflow](https://docs.comfy.org/tutorials/image/qwen/qwen-image) to download and install the model.
The following examples utilize a random seed of 0 to generate a batch of 4 images for variance demonstration and fair comparison. (Note: Generating 4 images may exceed your GPU memory; please adjust the batch size as necessary.)
### Example HiDream: InPaint (LanPaint K Sampler, 5 steps of thinking)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_8)
You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model.
### Example HiDream: OutPaint(LanPaint K Sampler, 5 steps of thinking)
.jpg?raw=true)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_10)
You need to follow the ComfyUI version of [HiDream workflow](https://docs.comfy.org/tutorials/image/hidream/hidream-i1) to download and install the model. Thanks [Amazon90](https://github.com/Amazon90) for providing this example.
### Example SD 3.5: InPaint(LanPaint K Sampler, 5 steps of thinking)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_9)
You need to follow the ComfyUI version of [SD 3.5 workflow](https://comfyui-wiki.com/en/tutorial/advanced/stable-diffusion-3-5-comfyui-workflow) to download and install the model.
### Example Flux: InPaint(LanPaint K Sampler, 5 steps of thinking)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_7)
[Model Used in This Example](https://huggingface.co/Comfy-Org/flux1-dev/blob/main/flux1-dev-fp8.safetensors)
(Note: Prompt First mode is disabled on Flux. As it does not use CFG guidance.)
### Example SDXL 0: Character Consistency (Side View Generation) (LanPaint K Sampler, 5 steps of thinking)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_6)
[Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658)
(Tricks 1: You can emphasize the character by copy it's image multiple times with Photoshop. Here I have made one extra copy.)
(Tricks 2: Use prompts like multiple views, multiple angles, clone, turnaround. Use LanPaint's Prompt first mode (does not support Flux))
(Tricks 3: Remeber LanPaint can in-paint: Mask non-consistent regions and try again!)
### Example SDXL 1: Basket to Basket Ball (LanPaint K Sampler, 2 steps of thinking).
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_1)
[Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658)
### Example SDXL 2: White Shirt to Blue Shirt (LanPaint K Sampler, 5 steps of thinking)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_2)
[Model Used in This Example](https://civitai.com/models/1188071?modelVersionId=1408658)
### Example SDXL 3: Smile to Sad (LanPaint K Sampler, 5 steps of thinking)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_3)
[Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl)
### Example SDXL 4: Damage Restoration (LanPaint K Sampler, 5 steps of thinking)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_4)
[Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl)
### Example SDXL 5: Huge Damage Restoration (LanPaint K Sampler, 20 steps of thinking)
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_5)
[Model Used in This Example](https://civitai.com/models/133005/juggernaut-xl)
Check more for use cases like inpaint on [fine tuned models](https://github.com/scraed/LanPaint/issues/12#issuecomment-2938662021) and [face swapping](https://github.com/scraed/LanPaint/issues/12#issuecomment-2938723501), thanks to [Amazon90](https://github.com/Amazon90).
## Usage
**Workflow Setup**
Same as default ComfyUI KSampler - simply replace with LanPaint KSampler nodes. The inpainting workflow is the same as the [SetLatentNoiseMask](https://comfyui-wiki.com/zh/comfyui-nodes/latent/inpaint/set-latent-noise-mask) inpainting workflow.
**Note**
- LanPaint requires binary masks (values of 0 or 1) without opacity or smoothing. To ensure compatibility, set the mask's **opacity and hardness to maximum** in your mask editor. During inpainting, any mask with smoothing or gradients will automatically be converted to a binary mask.
- LanPaint relies heavily on your text prompts to guide inpainting - explicitly describe the content you want generated in the masked area. If results show artifacts or mismatched elements, counteract them with targeted negative prompts.
## Basic Sampler
- LanPaint KSampler: The most basic and easy to use sampler for inpainting.
- LanPaint KSampler (Advanced): Full control of all parameters.
### LanPaint KSampler
Simplified interface with recommended defaults:
- Steps: 20 - 50. More steps will give more "thinking" and better results.
- LanPaint NumSteps: The turns of thinking before denoising. Recommend 5 for most of tasks ( which means 5 times slower than sampling without thinking). Use 10 for more challenging tasks.
- LanPaint Prompt mode: Image First mode and Prompt First mode. Image First mode focuses on the image, inpaint based on image context (maybe ignore prompt), while Prompt First mode focuses more on the prompt. Use Prompt First mode for tasks like character consistency. (Technically, it Prompt First mode change CFG scale to negative value in the BIG score to emphasis prompt, which will costs image quality.)
### LanPaint KSampler (Advanced)
Full parameter control:
**Key Parameters**
| Parameter | Range | Description |
|-----------|-------|-------------|
| `Steps` | 0-100 | Total steps of diffusion sampling. Higher means better inpainting. Recommend 20-50. |
| `LanPaint_NumSteps` | 0-20 | Reasoning iterations per denoising step ("thinking depth"). Easy task: 2-5. Hard task: 5-10 |
| `LanPaint_Lambda` | 0.1-50 | Content alignment strength (higher = stricter). Recommend 4.0 - 10.0 |
| `LanPaint_StepSize` | 0.1-1.0 | The StepSize of each thinking step. Recommend 0.1-0.5. |
| `LanPaint_Beta` | 0.1-2.0 | The StepSize ratio between masked / unmasked region. Small value can compensate high lambda values. Recommend 1.0 |
| `LanPaint_Friction` | 0.0-100.0 | The friction of Langevin dynamics. Higher means more slow but stable, lower means fast but unstable. Recommend 10.0 - 20.0|
| `LanPaint_EarlyStop` | 0-10 | Stop LanPaint iteration before the final sampling step. Helps to remove artifacts in some cases. Recommend 1-5|
| `LanPaint_PromptMode` | Image First / Prompt First | Image First mode focuses on the image context, maybe ignore prompt. Prompt First mode focuses more on the prompt. |
For detailed descriptions of each parameter, simply hover your mouse over the corresponding input field to view tooltips with additional information.
### LanPaint Mask Blend
This node blends the original image with the inpainted image based on the mask. It is useful if you want the unmasked region to match the original image pixel perfectly.
## LanPaint KSampler (Advanced) Tuning Guide
For challenging inpainting tasks:
1️⃣ **Boost Quality**
Increase **total number of sampling steps** (very important!), **LanPaint_NumSteps** (thinking iterations) or **LanPaint_Lambda** if the inpainted result does not meet your expectations.
2️⃣ **Boost Speed**
Decrease **LanPaint_NumSteps** to accelerate generation! If you want better results but still need fewer steps, consider:
- **Increasing LanPaint_StepSize** to speed up the thinking process.
- **Decreasing LanPaint_Friction** to make the Langevin dynamics converges more faster.
3️⃣ **Fix Unstability**:
If you find the results have wired texture, try
- Reduce **LanPaint_Friction** to make the Langevin dynamics more stable.
- Reduce **LanPaint_StepSize** to use smaller step size.
- Reduce **LanPaint_Beta** if you are using a high lambda value.
⚠️ **Notes**:
- For effective tuning, **fix the seed** and adjust parameters incrementally while observing the results. This helps isolate the impact of each setting. Better to do it with a batche of images to avoid overfitting on a single image.
## Community Showcase [](#community-showcase-)
Discover how the community is using LanPaint! Here are some user-created tutorials:
- [Ai绘画进阶148-三大王炸！庆祝高允贞出道6周年！T8即将直播？当AI绘画学会深度思考？！万能修复神器LanPaint，万物皆可修！-T8 Comfyui教程](https://www.youtube.com/watch?v=Z4DSTv3UPJo)
- [Ai绘画进阶151-真相了！T8竟是个AI？！LanPaint进阶（二），人物一致性，多视角实验性测试，新参数讲解，工作流分享-T8 Comfyui教程](https://www.youtube.com/watch?v=landiRhvF3k)
- [重绘和三视图角色一致性解决新方案！LanPaint节点尝试](https://www.youtube.com/watch?v=X0WbXdm6FA0)
- [ComfyUI: HiDream with Perturbation Upscale, LanPaint Inpainting (Workflow Tutorial)](https://www.youtube.com/watch?v=2-mGe4QVIIw&t=2785s)
- [ComfyUI必备LanPaint插件超详细使用教程](https://plugin.aix.ink/archives/lanpaint)
Submit a PR to add your tutorial/video here, or open an [Issue](https://github.com/scraed/LanPaint/issues) with details!
## Updates
- 2025/08/08
- Add Qwen image support
- 2025/06/21
- Update the algorithm with enhanced stability and outpaint performance.
- Add outpaint example
- Supports Sampler Custom (Thanks to [MINENEMA](https://github.com/MINENEMA))
- 2025/06/04
- Add more sampler support.
- Add early stopping to advanced sampler.
- 2025/05/28
- Major update on the Langevin solver. It is now much faster and more stable.
- Greatly simplified the parameters for advanced sampler.
- Fix performance issue on Flux and SD 3.5
- 2025/04/16
- Added Primary HiDream support
- 2025/03/22
- Added Primary Flux support
- Added Tease Mode
- 2025/03/10
- LanPaint has received a major update! All examples now use the LanPaint K Sampler, offering a simplified interface with enhanced performance and stability.
- 2025/03/06:
- Bug Fix for str not callable error and unpack error. Big thanks to [jamesWalker55](https://github.com/jamesWalker55) and [EricBCoding](https://github.com/EricBCoding).
## ToDo
- Try Implement Detailer
- ~~Provide inference code on without GUI.~~ Check our local Python benchmark code [LanPaintBench](https://github.com/scraed/LanPaintBench).
## Citation
```
@misc{zheng2025lanpainttrainingfreediffusioninpainting,
title={Lanpaint: Training-Free Diffusion Inpainting with Exact and Fast Conditional Inference},
author={Candi Zheng and Yuan Lan and Yang Wang},
year={2025},
eprint={2502.03491},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2502.03491},
}
```
|
mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF
|
mradermacher
| 2025-08-30T03:23:57Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification",
"base_model:quantized:EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T03:14:00Z |
---
base_model: EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification
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/EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-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/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q4_K_S.gguf) | Q4_K_S | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q5_K_M.gguf) | Q5_K_M | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification-GGUF/resolve/main/SmolLM2-1.7B-magpie-ultra-v1.0-train-431k-classification.f16.gguf) | f16 | 3.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/OctoThinker-3B-Short-Base-GGUF
|
mradermacher
| 2025-08-30T03:21:07Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:OctoThinker/MegaMath-Web-Pro-Max",
"dataset:LLM360/MegaMath",
"base_model:OctoThinker/OctoThinker-3B-Short-Base",
"base_model:quantized:OctoThinker/OctoThinker-3B-Short-Base",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T02:52:36Z |
---
base_model: OctoThinker/OctoThinker-3B-Short-Base
datasets:
- OctoThinker/MegaMath-Web-Pro-Max
- LLM360/MegaMath
language:
- en
library_name: transformers
license: llama3.2
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/OctoThinker/OctoThinker-3B-Short-Base
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#OctoThinker-3B-Short-Base-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/OctoThinker-3B-Short-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/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q4_K_S.gguf) | Q4_K_S | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q4_K_M.gguf) | Q4_K_M | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q5_K_S.gguf) | Q5_K_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q5_K_M.gguf) | Q5_K_M | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q6_K.gguf) | Q6_K | 2.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.Q8_0.gguf) | Q8_0 | 3.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-3B-Short-Base-GGUF/resolve/main/OctoThinker-3B-Short-Base.f16.gguf) | f16 | 6.5 | 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 -->
|
bah63843/blockassist-bc-plump_fast_antelope_1756523959
|
bah63843
| 2025-08-30T03:20:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:20:01Z |
---
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).
|
Lucidnightmar3/Llama-3.1-8B-Q5_K_M-GGUF
|
Lucidnightmar3
| 2025-08-30T03:20:05Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:quantized:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T03:19:38Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
license: llama3.1
extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
\ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\
\ use, reproduction, distribution and modification of the Llama Materials set forth\
\ herein.\n\"Documentation\" means the specifications, manuals and documentation\
\ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\
\"Licensee\" or \"you\" means you, or your employer or any other person or entity\
\ (if you are entering into this Agreement on such person or entity’s behalf), of\
\ the age required under applicable laws, rules or regulations to provide legal\
\ consent and that has legal authority to bind your employer or such other person\
\ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\
\ means the foundational large language models and software and algorithms, including\
\ machine-learning model code, trained model weights, inference-enabling code, training-enabling\
\ code, fine-tuning enabling code and other elements of the foregoing distributed\
\ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\
\ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\
\ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\
\ Ireland Limited (if you are located in or, if you are an entity, your principal\
\ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you\
\ are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\n\
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\
\ and royalty-free limited license under Meta’s intellectual property or other rights\
\ owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy,\
\ create derivative works of, and make modifications to the Llama Materials.\nb.\
\ Redistribution and Use.\ni. If you distribute or make available the Llama Materials\
\ (or any derivative works thereof), or a product or service (including another\
\ AI model) that contains any of them, you shall (A) provide a copy of this Agreement\
\ with any such Llama Materials; and (B) prominently display “Built with Llama”\
\ on a related website, user interface, blogpost, about page, or product documentation.\
\ If you use the Llama Materials or any outputs or results of the Llama Materials\
\ to create, train, fine tune, or otherwise improve an AI model, which is distributed\
\ or made available, you shall also include “Llama” at the beginning of any such\
\ AI model name.\nii. If you receive Llama Materials, or any derivative works thereof,\
\ from a Licensee as part of an integrated end user product, then Section 2 of\
\ this Agreement will not apply to you.\niii. You must retain in all copies of the\
\ Llama Materials that you distribute the following attribution notice within a\
\ “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed\
\ under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights\
\ Reserved.”\niv. Your use of the Llama Materials must comply with applicable laws\
\ and regulations (including trade compliance laws and regulations) and adhere to\
\ the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use-policy),\
\ which is hereby incorporated by reference into this Agreement.\n2. Additional\
\ Commercial Terms. If, on the Llama 3.1 version release date, the monthly active\
\ users of the products or services made available by or for Licensee, or Licensee’s\
\ affiliates, is greater than 700 million monthly active users in the preceding\
\ calendar month, you must request a license from Meta, which Meta may grant to\
\ you in its sole discretion, and you are not authorized to exercise any of the\
\ rights under this Agreement unless or until Meta otherwise expressly grants you\
\ such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE\
\ LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS”\
\ BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY\
\ KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\
\ OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.\
\ YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\
\ THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA\
\ MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT\
\ WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN\
\ CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS\
\ AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,\
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\ and customary use in describing and redistributing the Llama Materials or as set\
\ forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the\
\ “Mark”) solely as required to comply with the last sentence of Section 1.b.i.\
\ You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/\
\ ). All goodwill arising out of your use of the Mark will inure to the benefit\
\ of Meta.\nb. Subject to Meta’s ownership of Llama Materials and derivatives made\
\ by or for Meta, with respect to any derivative works and modifications of the\
\ Llama Materials that are made by you, as between you and Meta, you are and will\
\ be the owner of such derivative works and modifications.\nc. If you institute\
\ litigation or other proceedings against Meta or any entity (including a cross-claim\
\ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs\
\ or results, or any portion of any of the foregoing, constitutes infringement of\
\ intellectual property or other rights owned or licensable by you, then any licenses\
\ granted to you under this Agreement shall terminate as of the date such litigation\
\ or claim is filed or instituted. You will indemnify and hold harmless Meta from\
\ and against any claim by any third party arising out of or related to your use\
\ or distribution of the Llama Materials.\n6. Term and Termination. The term of\
\ this Agreement will commence upon your acceptance of this Agreement or access\
\ to the Llama Materials and will continue in full force and effect until terminated\
\ in accordance with the terms and conditions herein. Meta may terminate this Agreement\
\ if you are in breach of any term or condition of this Agreement. Upon termination\
\ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\
\ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\
\ and Jurisdiction. This Agreement will be governed and construed under the laws\
\ of the State of California without regard to choice of law principles, and the\
\ UN Convention on Contracts for the International Sale of Goods does not apply\
\ to this Agreement. The courts of California shall have exclusive jurisdiction\
\ of any dispute arising out of this Agreement.\n### Llama 3.1 Acceptable Use Policy\n\
Meta is committed to promoting safe and fair use of its tools and features, including\
\ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\
\ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\
#### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\
\ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\
\ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\
\ contribute to, encourage, plan, incite, or further illegal or unlawful activity\
\ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\
\ or harm to children, including the solicitation, creation, acquisition, or dissemination\
\ of child exploitative content or failure to report Child Sexual Abuse Material\n\
\ 3. Human trafficking, exploitation, and sexual violence\n 4. The\
\ illegal distribution of information or materials to minors, including obscene\
\ materials, or failure to employ legally required age-gating in connection with\
\ such information or materials.\n 5. Sexual solicitation\n 6. Any\
\ other criminal activity\n 3. Engage in, promote, incite, or facilitate the\
\ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\
\ 4. Engage in, promote, incite, or facilitate discrimination or other unlawful\
\ or harmful conduct in the provision of employment, employment benefits, credit,\
\ housing, other economic benefits, or other essential goods and services\n 5.\
\ Engage in the unauthorized or unlicensed practice of any profession including,\
\ but not limited to, financial, legal, medical/health, or related professional\
\ practices\n 6. Collect, process, disclose, generate, or infer health, demographic,\
\ or other sensitive personal or private information about individuals without rights\
\ and consents required by applicable laws\n 7. Engage in or facilitate any action\
\ or generate any content that infringes, misappropriates, or otherwise violates\
\ any third-party rights, including the outputs or results of any products or services\
\ using the Llama Materials\n 8. Create, generate, or facilitate the creation\
\ of malicious code, malware, computer viruses or do anything else that could disable,\
\ overburden, interfere with or impair the proper working, integrity, operation\
\ or appearance of a website or computer system\n2. Engage in, promote, incite,\
\ facilitate, or assist in the planning or development of activities that present\
\ a risk of death or bodily harm to individuals, including use of Llama 3.1 related\
\ to the following:\n 1. Military, warfare, nuclear industries or applications,\
\ espionage, use for materials or activities that are subject to the International\
\ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
\ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\
\ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\
\ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\
\ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\
\ content intended to incite or promote violence, abuse, or any infliction of bodily\
\ harm to an individual\n3. Intentionally deceive or mislead others, including use\
\ of Llama 3.1 related to the following:\n 1. Generating, promoting, or furthering\
\ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\
\ or furthering defamatory content, including the creation of defamatory statements,\
\ images, or other content\n 3. Generating, promoting, or further distributing\
\ spam\n 4. Impersonating another individual without consent, authorization,\
\ or legal right\n 5. Representing that the use of Llama 3.1 or outputs are human-generated\n\
\ 6. Generating or facilitating false online engagement, including fake reviews\
\ and other means of fake online engagement\n4. Fail to appropriately disclose to\
\ end users any known dangers of your AI system\nPlease report any violation of\
\ this Policy, software “bug,” or other problems that could lead to a violation\
\ of this Policy through one of the following means:\n * Reporting issues with\
\ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\
\ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\
\ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\
\ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com"
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
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geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
with the Meta Privacy Policy
: checkbox
extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
library_name: transformers
base_model: meta-llama/Llama-3.1-8B
---
# Lucidnightmar3/Llama-3.1-8B-Q5_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-3.1-8B`](https://huggingface.co/meta-llama/Llama-3.1-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Llama-3.1-8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Lucidnightmar3/Llama-3.1-8B-Q5_K_M-GGUF --hf-file llama-3.1-8b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Lucidnightmar3/Llama-3.1-8B-Q5_K_M-GGUF --hf-file llama-3.1-8b-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Lucidnightmar3/Llama-3.1-8B-Q5_K_M-GGUF --hf-file llama-3.1-8b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Lucidnightmar3/Llama-3.1-8B-Q5_K_M-GGUF --hf-file llama-3.1-8b-q5_k_m.gguf -c 2048
```
|
Sai2076/LLLMA_FINETUNED_PROJEN
|
Sai2076
| 2025-08-30T03:19:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"qlora",
"lora",
"llama-3.2",
"instruction-tuned",
"bf16",
"4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T03:12:40Z |
---
library_name: transformers
tags:
- unsloth
- qlora
- lora
- llama-3.2
- instruction-tuned
- bf16
- 4bit
---
# Model Card: Sai2076/LLLMA_FINETUNED_PROJEN
A **LLaMA-3.2** based instruction-tuned model fine-tuned with **Unsloth + QLoRA** using 🤗 **Transformers**.
This model is part of the **ProjGen project**, aimed at enhancing developer productivity through automated project generation and structured code scaffolding.
---
## Model Details
### Model Description
- **Base model:** `meta-llama/Llama-3.2-<SIZE>-Instruct` <!-- replace SIZE with e.g. 8B/70B -->
- **Finetuning method:** Unsloth + QLoRA (LoRA adapters)
- **Precision (train):** 4-bit NF4 quantization (bitsandbytes) + bf16 compute
- **Context length:** 4096
- **Task(s):** Instruction following & project/code generation
- **License:** Inherits from Meta’s LLaMA-3.2 license
- **Developed by:** Sai Praneeth (UAB, ProjGen Project)
- **Finetuned from:** `meta-llama/Llama-3.2-<SIZE>-Instruct`
- **Shared by:** [Sai2076](https://huggingface.co/Sai2076)
### Model Sources
- **Repository:** [Sai2076/LLLMA_FINETUNED_PROJEN](https://huggingface.co/Sai2076/LLLMA_FINETUNED_PROJEN)
- **Project Paper:** ProjGen – Enhanced Developer Productivity for Flask Project Generation with a RAG-Enhanced Fine-Tuned Local LLM
- **Demo (optional):** [link to demo if available]
---
## Intended Uses & Limitations
### Direct Use
- Generating Flask/Django/Streamlit project structures automatically.
- Instruction-following tasks related to software engineering and code generation.
### Downstream Use
- Further fine-tuning on domain-specific datasets (e.g., medical imaging, finance, etc.).
- Integration into developer assistants and productivity tools.
### Out-of-Scope / Limitations
- Not suitable for medical, legal, or financial decision-making without human review.
- May hallucinate or produce insecure/inefficient code if not monitored.
---
## Bias, Risks, and Limitations
The model inherits risks from the base **LLaMA-3.2** model:
- Possible hallucinations and factual inaccuracies.
- Dataset/domain biases reflected in responses.
- Outputs should be validated before production deployment.
**Recommendation:** Always pair outputs with testing, validation, and human oversight.
---
## Getting Started
### Inference (PEFT adapter form)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
model_id = "Sai2076/LLLMA_FINETUNED_PROJEN"
tok = AutoTokenizer.from_pretrained(model_id)
bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=bnb,
device_map="auto",
torch_dtype="auto"
)
prompt = "Generate a Flask project with login, dashboard, and reports."
inputs = tok(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tok.decode(outputs[0], skip_special_tokens=True))
```
---
## Training Details
### Data
- **Dataset:** Custom **ProjGen dataset** built from structured Flask/Django/Streamlit projects and instructions.
- **Size:** [Fill in #samples / tokens]
- **Preprocessing:** Chat-style instruction formatting (system/user/assistant), deduplication, truncation at 4096 tokens.
### Training Procedure
- **Quantization:** 4-bit NF4 + double quantization (bitsandbytes)
- **LoRA Config:**
- `r`: 16
- `alpha`: 32
- `dropout`: 0.05
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_up_proj, down_proj
- **Optimizer:** Paged AdamW (32-bit)
- **LR / Schedule:** 2e-4 with cosine decay + warmup
- **Batch size:** [fill in effective batch size]
- **Epochs/Steps:** [fill in from ipynb]
- **Precision:** bf16 mixed precision
- **Grad checkpointing:** Enabled
- **Flash attention:** Enabled (Unsloth optimization)
### Training Hardware
- **GPU:** RTX 4070 (12GB VRAM) [replace with actual if different]
- **Training time:** [fill in hours]
- **Checkpoint size:** ~ (adapter size: ~200MB; merged model size depends on base LLaMA size)
---
## Evaluation
### Data & Metrics
- **Validation set:** Held-out portion of ProjGen dataset
- **Metrics:**
- Instruction Following: Exact Match, ROUGE-L
- Code Generation: Pass@k (via unit test evaluation)
### Results
| Metric | Value | Notes |
|-----------------------|--------|-----------------------|
| Validation Loss | ___ | From training logs |
| Exact Match / F1 | ___ | |
| ROUGE-L / BLEU | ___ | |
| Pass@1 | ___ | |
---
## Environmental Impact (estimate)
- **Hardware:** RTX 4070 (12GB VRAM) [replace with actual]
- **Hours:** [fill in H]
- **Region/Provider:** [cloud/on-prem]
- **Estimated CO₂e:** Use [ML CO₂ Impact](https://mlco2.github.io/impact#compute)
---
## Citation
If you use this model, please cite the base model and this project:
**BibTeX (base, example):**
```bibtex
@article{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and others},
journal={arXiv preprint arXiv:XXXX.XXXXX},
year={2023}
}
```
**Your work (fill in):**
```bibtex
@misc{projgen2025,
title = {ProjGen: Enhanced Developer Productivity for Flask Project Generation with a RAG-Enhanced Fine-Tuned Local LLM},
author = {Sai Praneeth, Renduchinthala},
year = {2025},
howpublished = {\url{https://huggingface.co/Sai2076/LLLMA_FINETUNED_PROJEN}}
}
```
---
## Contact
- **Author:** Sai Praneeth Kumar (UAB)
- **HF Profile:** [Sai2076](https://huggingface.co/Sai2076)
|
Sonic-man/blockassist-bc-poisonous_graceful_cow_1756521581
|
Sonic-man
| 2025-08-30T03:17:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"poisonous graceful cow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:17:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- poisonous graceful cow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
obamaTeo/skt34b-10k-ver1
|
obamaTeo
| 2025-08-30T03:16:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-08-29T09:38:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
qgallouedec/Qwen3-1.7B-SFT-20250830031230
|
qgallouedec
| 2025-08-30T03:15:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"hf_jobs",
"sft",
"conversational",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T03:13:19Z |
---
base_model: Qwen/Qwen3-1.7B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-1.7B-SFT-20250830031230
tags:
- generated_from_trainer
- trl
- hf_jobs
- sft
licence: license
---
# Model Card for Qwen3-1.7B-SFT-20250830031230
This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/Qwen3-1.7B-SFT-20250830031230", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.0.dev0
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu128
- 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}}
}
```
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756523672
|
liukevin666
| 2025-08-30T03:15:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:15:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arianaazarbal/standard_tpr_0.65-20250823_060848_grpo_20250830_031333-policy-adapter
|
arianaazarbal
| 2025-08-30T03:14:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T03:14:22Z |
# Policy Model LoRA Adapter (GRPO/DPO)
Experiment: standard_tpr_0.65
Timestamp: 20250823_060848_grpo_20250830_031333
This model was trained as part of the deception-evasion-honesty experiments.
## Model Details
- **Type**: Policy Model LoRA Adapter (GRPO/DPO)
- **Experiment Name**: standard_tpr_0.65
- **Training Timestamp**: 20250823_060848_grpo_20250830_031333
|
AnonymousCS/populism_classifier_233
|
AnonymousCS
| 2025-08-30T03:12:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_xlmr_large",
"base_model:finetune:AnonymousCS/populism_xlmr_large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-30T03:04:58Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_233
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_233
This model is a fine-tuned version of [AnonymousCS/populism_xlmr_large](https://huggingface.co/AnonymousCS/populism_xlmr_large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2317
- Accuracy: 0.9475
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- Balanced Acc: 0.5
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.2459 | 1.0 | 167 | 0.2187 | 0.9475 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.2453 | 2.0 | 334 | 0.2154 | 0.9475 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1402 | 3.0 | 501 | 0.2146 | 0.9475 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.2526 | 4.0 | 668 | 0.2111 | 0.9475 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1313 | 5.0 | 835 | 0.2366 | 0.9475 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.2026 | 6.0 | 1002 | 0.2249 | 0.9475 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0249 | 7.0 | 1169 | 0.2317 | 0.9475 | 0.0 | 0.0 | 0.0 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
NaruseShiroha/capybara-math-smol-4.1B-2508GGUF
|
NaruseShiroha
| 2025-08-30T03:08:20Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Qwen3-4B-Instruct-2507",
"base_model:quantized:unsloth/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-30T03:05:28Z |
---
base_model: unsloth/Qwen3-4B-Instruct-2507
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** NaruseShiroha
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-Instruct-2507
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
klmdr22/blockassist-bc-wild_loud_newt_1756523178
|
klmdr22
| 2025-08-30T03:07:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:06:57Z |
---
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).
|
allstax/editorial-qwen-v1
|
allstax
| 2025-08-30T03:04:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T03:04:41Z |
---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** allstax
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756523015
|
liukevin666
| 2025-08-30T03:04:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:04:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# 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_232
|
AnonymousCS
| 2025-08-30T03:04:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_xlmr_large",
"base_model:finetune:AnonymousCS/populism_xlmr_large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-30T02:58:41Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_232
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_232
This model is a fine-tuned version of [AnonymousCS/populism_xlmr_large](https://huggingface.co/AnonymousCS/populism_xlmr_large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2310
- Accuracy: 0.9406
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- Balanced Acc: 0.5
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.0371 | 1.0 | 122 | 0.2432 | 0.9406 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0601 | 2.0 | 244 | 0.2258 | 0.9406 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1794 | 3.0 | 366 | 0.2645 | 0.9406 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0572 | 4.0 | 488 | 0.2311 | 0.9406 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3736 | 5.0 | 610 | 0.2310 | 0.9406 | 0.0 | 0.0 | 0.0 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
dgambettaphd/M_llm2_run1_gen3_S_doc1000_synt64_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-30T03:04:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T03:03:46Z |
---
library_name: transformers
tags:
- unsloth
---
# 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]
|
klmdr22/blockassist-bc-wild_loud_newt_1756522821
|
klmdr22
| 2025-08-30T03:01:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:00:59Z |
---
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).
|
chainway9/blockassist-bc-untamed_quick_eel_1756521269
|
chainway9
| 2025-08-30T03:00:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:00:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zxcczx/blockassist-bc-durable_energetic_fly_1756521614
|
zxcczx
| 2025-08-30T03:00:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"durable energetic fly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:00:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- durable energetic fly
---
# 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_1756522707
|
bah63843
| 2025-08-30T02:59:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:59:06Z |
---
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).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1756521131
|
rvipitkirubbe
| 2025-08-30T02:57:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:57:39Z |
---
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).
|
rbcurzon/whisper-large-v3-turbo
|
rbcurzon
| 2025-08-30T02:55:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:rbcurzon/ph_dialect_asr",
"base_model:openai/whisper-large-v3-turbo",
"base_model:finetune:openai/whisper-large-v3-turbo",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-29T11:25:18Z |
---
library_name: transformers
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- rbcurzon/ph_dialect_asr
metrics:
- wer
model-index:
- name: whisper-large-v3-turbo
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: rbcurzon/ph_dialect_asr all
type: rbcurzon/ph_dialect_asr
args: all
metrics:
- name: Wer
type: wer
value: 0.09601573187414501
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v3-turbo
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the rbcurzon/ph_dialect_asr all dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2416
- Wer: 0.0960
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.1951 | 1.4820 | 1000 | 0.2532 | 0.1386 |
| 0.1032 | 2.9640 | 2000 | 0.2232 | 0.1189 |
| 0.0242 | 4.4449 | 3000 | 0.2313 | 0.1090 |
| 0.0142 | 5.9270 | 4000 | 0.2315 | 0.1016 |
| 0.0018 | 7.4079 | 5000 | 0.2416 | 0.0960 |
### Framework versions
- Transformers 4.57.0.dev0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
NaruseShiroha/capybara-math-smol-4.1B-0829
|
NaruseShiroha
| 2025-08-30T02:54:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B-Instruct-2507",
"base_model:finetune:unsloth/Qwen3-4B-Instruct-2507",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T02:50:45Z |
---
base_model: unsloth/Qwen3-4B-Instruct-2507
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** NaruseShiroha
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-Instruct-2507
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
honmik/blockassist-bc-patterned_howling_salamander_1756522288
|
honmik
| 2025-08-30T02:52:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned howling salamander",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:51:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned howling salamander
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
RikiyaT/mxbai-ettin-17m-medqa-phaseA-ft
|
RikiyaT
| 2025-08-30T02:51:56Z | 0 | 0 | null |
[
"safetensors",
"modernbert",
"region:us"
] | null | 2025-08-30T02:51:53Z |
# RikiyaT/mxbai-ettin-17m-medqa-phaseA-ft
Dense retrieval encoder (Ettin / ModernBERT) — Transformers
- Base model: RikiyaT/mxbai-ettin-17m-pretrained
- Pooling: mean
- Projection: **identity** (dim=256)
**SentenceTransformers variant**: [RikiyaT/mxbai-ettin-17m-medqa-phaseA-ft-st](https://huggingface.co/RikiyaT/mxbai-ettin-17m-medqa-phaseA-ft-st)
### Usage
```python
import torch
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("RikiyaT/mxbai-ettin-17m-medqa-phaseA-ft", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("RikiyaT/mxbai-ettin-17m-medqa-phaseA-ft", trust_remote_code=True)
# identity projection
def encode(texts, prompt="search_query: "):
x = tokenizer([prompt + t for t in texts], padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
out = model(**x).last_hidden_state
mask = x["attention_mask"][..., None].bool()
emb = (out.masked_fill(~mask, 0.0).sum(1) / x["attention_mask"].sum(1, keepdim=True))
emb = torch.nn.functional.normalize(emb, p=2, dim=1)
return emb
```
Prompts used in training:
- query: `search_query: {text}`
- document: `search_document: {text}`
|
RikiyaT/mxbai-ettin-17m-pubmed-phaseA-ft
|
RikiyaT
| 2025-08-30T02:51:01Z | 0 | 0 | null |
[
"safetensors",
"modernbert",
"region:us"
] | null | 2025-08-30T02:50:58Z |
# RikiyaT/mxbai-ettin-17m-pubmed-phaseA-ft
Dense retrieval encoder (Ettin / ModernBERT) — Transformers
- Base model: RikiyaT/mxbai-ettin-17m-pretrained
- Pooling: mean
- Projection: **identity** (dim=256)
**SentenceTransformers variant**: [RikiyaT/mxbai-ettin-17m-pubmed-phaseA-ft-st](https://huggingface.co/RikiyaT/mxbai-ettin-17m-pubmed-phaseA-ft-st)
### Usage
```python
import torch
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("RikiyaT/mxbai-ettin-17m-pubmed-phaseA-ft", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("RikiyaT/mxbai-ettin-17m-pubmed-phaseA-ft", trust_remote_code=True)
# identity projection
def encode(texts, prompt="search_query: "):
x = tokenizer([prompt + t for t in texts], padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
out = model(**x).last_hidden_state
mask = x["attention_mask"][..., None].bool()
emb = (out.masked_fill(~mask, 0.0).sum(1) / x["attention_mask"].sum(1, keepdim=True))
emb = torch.nn.functional.normalize(emb, p=2, dim=1)
return emb
```
Prompts used in training:
- query: `search_query: {text}`
- document: `search_document: {text}`
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1756520067
|
NahedDom
| 2025-08-30T02:50:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:50:51Z |
---
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).
|
bah63843/blockassist-bc-plump_fast_antelope_1756522161
|
bah63843
| 2025-08-30T02:50:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:50:04Z |
---
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).
|
Triago/NVIDIA-Nemotron-Nano-12B-v2-Q8_0-GGUF
|
Triago
| 2025-08-30T02:49:29Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"nvidia",
"pytorch",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"es",
"fr",
"de",
"it",
"ja",
"dataset:nvidia/Nemotron-Post-Training-Dataset-v1",
"dataset:nvidia/Nemotron-Post-Training-Dataset-v2",
"dataset:nvidia/Nemotron-Pretraining-Dataset-sample",
"dataset:nvidia/Nemotron-CC-v2",
"dataset:nvidia/Nemotron-CC-Math-v1",
"dataset:nvidia/Nemotron-Pretraining-SFT-v1",
"base_model:nvidia/NVIDIA-Nemotron-Nano-12B-v2",
"base_model:quantized:nvidia/NVIDIA-Nemotron-Nano-12B-v2",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-30T02:48:39Z |
---
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
datasets:
- nvidia/Nemotron-Post-Training-Dataset-v1
- nvidia/Nemotron-Post-Training-Dataset-v2
- nvidia/Nemotron-Pretraining-Dataset-sample
- nvidia/Nemotron-CC-v2
- nvidia/Nemotron-CC-Math-v1
- nvidia/Nemotron-Pretraining-SFT-v1
language:
- en
- es
- fr
- de
- it
- ja
library_name: transformers
tags:
- nvidia
- pytorch
- llama-cpp
- gguf-my-repo
track_downloads: true
base_model: nvidia/NVIDIA-Nemotron-Nano-12B-v2
---
# Triago/NVIDIA-Nemotron-Nano-12B-v2-Q8_0-GGUF
This model was converted to GGUF format from [`nvidia/NVIDIA-Nemotron-Nano-12B-v2`](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triago/NVIDIA-Nemotron-Nano-12B-v2-Q8_0-GGUF --hf-file nvidia-nemotron-nano-12b-v2-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triago/NVIDIA-Nemotron-Nano-12B-v2-Q8_0-GGUF --hf-file nvidia-nemotron-nano-12b-v2-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triago/NVIDIA-Nemotron-Nano-12B-v2-Q8_0-GGUF --hf-file nvidia-nemotron-nano-12b-v2-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triago/NVIDIA-Nemotron-Nano-12B-v2-Q8_0-GGUF --hf-file nvidia-nemotron-nano-12b-v2-q8_0.gguf -c 2048
```
|
RikiyaT/mxbai-ettin-17m-arxiv-1.4m-phaseA-ft-st
|
RikiyaT
| 2025-08-30T02:47:56Z | 0 | 0 | null |
[
"safetensors",
"modernbert",
"region:us"
] | null | 2025-08-30T02:47:51Z |
# RikiyaT/mxbai-ettin-17m-arxiv-1.4m-phaseA-ft-st
Dense retrieval encoder (Ettin / ModernBERT) — SentenceTransformers
- Base model: RikiyaT/mxbai-ettin-17m-pretrained
- Pooling: mean
- Projection: **identity** (dim=256)
**Transformers variant**: [RikiyaT/mxbai-ettin-17m-arxiv-1.4m-phaseA-ft](https://huggingface.co/RikiyaT/mxbai-ettin-17m-arxiv-1.4m-phaseA-ft)
### Usage
```python
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("RikiyaT/mxbai-ettin-17m-arxiv-1.4m-phaseA-ft-st", trust_remote_code=True)
q = m.encode(["search_query: what is dense retrieval?"], normalize_embeddings=True)
d = m.encode(["search_document: dense retrieval uses embeddings ..."], normalize_embeddings=True)
print((q @ d.T))
```
Prompts used in training:
- query: `search_query: {text}`
- document: `search_document: {text}`
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756520462
|
GroomerG
| 2025-08-30T02:46:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:45:57Z |
---
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).
|
honmik/blockassist-bc-patterned_howling_salamander_1756521852
|
honmik
| 2025-08-30T02:44:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned howling salamander",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:44:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned howling salamander
---
# 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_1756521795
|
bah63843
| 2025-08-30T02:44:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:43:57Z |
---
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).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756521694
|
liukevin666
| 2025-08-30T02:43:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:42:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Moreza009/Llama-DrugReasoner
|
Moreza009
| 2025-08-30T02:40:44Z | 24 | 1 |
peft
|
[
"peft",
"safetensors",
"medical",
"biology",
"chemistry",
"text-classification",
"dataset:Moreza009/drug_approval_prediction",
"arxiv:2508.18579",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2025-08-02T16:47:33Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
datasets:
- Moreza009/drug_approval_prediction
license: apache-2.0
pipeline_tag: text-classification
tags:
- medical
- biology
- chemistry
---
[](https://arxiv.org/abs/2508.18579)
# DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model
DrugReasoner is an AI-powered system for predicting drug approval outcomes using reasoning-augmented Large Language Models (LLMs) and molecular feature analysis. By combining advanced machine learning with interpretable reasoning, DrugReasoner provides transparent predictions that can accelerate pharmaceutical research and development.
## Model Details
- Model Name: DrugReasoner
- Training Paradigm: Group Relative Policy Optimization (GRPO)
- Input: SMILES Structure
- Output: Drug approval prediction + Rational of approval or unapproval + Confidence score
- Training Libraries: Hugging Face’s transformers, Transformer Reinforcement Learning (TRL), and Parameter-efficient fine-tuning (PEFT)
- Model Sources: meta-llama/Llama-3.1-8B-Instruct
## How to Get Started with the Model
- To use **DrugReasoner**, you must first request access to the base model [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on Hugging Face by providing your contact information. Once access is granted, you can run DrugReasoner either through the command-line interface (CLI) or integrate it directly into your Python workflows.
### Prerequisites
- Python 3.8 or higher
- CUDA-compatible GPU (recommended for training and inference)
- Git
### Setup Instructions
1. **Clone the repository**
```bash
git clone https://github.com/mohammad-gh009/DrugReasoner.git
cd DrugReasoner
```
2. **Create and activate virtual environment**
**Windows:**
```bash
cd src
python -m venv myenv
myenv\Scripts\activate
```
**Mac/Linux:**
```bash
cd src
python -m venv myenv
source myenv/bin/activate
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Login to your Huggingface account**
You can use [this](https://huggingface.co/join) instruction on how to make an account and [this](https://huggingface.co/docs/hub/en/security-tokens) on how to get the token
```bash
huggingface-cli login --token YOUR_TOKEN_HERE
```
### How to use
**Note:** GPU is required for inference. If unavailable, use our [Colab Notebook]([link-to-colab](https://colab.research.google.com/drive/16OKB5q7MZ6MhWv5Q1I0QByN6DSkqx6az?usp=sharing)).
#### CLI Inference
```bash
python inference.py \
--smiles "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O" "CC1=CC=C(C=C1)C(=O)O" \
--output results.csv \
--top-k 9 \
--top-p 0.9 \
--max-length 4096 \
--temperature 1.0
```
#### Python API Usage
```python
from inference import DrugReasoner
predictor = DrugReasoner()
results = predictor.predict_molecules(
smiles_list=["CC(C)CC1=CC=C(C=C1)C(C)C(=O)O"],
save_path="results.csv",
print_results=True,
top_k=9,
top_p=0.9,
max_length=4096,
temperature=1.0
)
```
## Citation
If you use DrugReasoner in your research, please cite our work:
```
@misc{ghaffarzadehesfahani2025drugreasonerinterpretabledrugapproval,
title={DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model},
author={Mohammadreza Ghaffarzadeh-Esfahani and Ali Motahharynia* and Nahid Yousefian and Navid Mazrouei and Jafar Ghaisari and Yousof Gheisari},
year={2025},
eprint={2508.18579},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.18579},
}
```
|
klmdr22/blockassist-bc-wild_loud_newt_1756521567
|
klmdr22
| 2025-08-30T02:40:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:40:05Z |
---
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).
|
honmik/blockassist-bc-patterned_howling_salamander_1756521410
|
honmik
| 2025-08-30T02:37:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned howling salamander",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:37:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned howling salamander
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1756519828
|
sampingkaca72
| 2025-08-30T02:37:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:37:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# 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_228
|
AnonymousCS
| 2025-08-30T02:37:08Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_xlmr_large",
"base_model:finetune:AnonymousCS/populism_xlmr_large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-26T08:31:35Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_228
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_228
This model is a fine-tuned version of [AnonymousCS/populism_xlmr_large](https://huggingface.co/AnonymousCS/populism_xlmr_large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3888
- Accuracy: 0.8737
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- Balanced Acc: 0.5
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.5364 | 1.0 | 50 | 0.4089 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.7823 | 2.0 | 100 | 0.3801 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.2821 | 3.0 | 150 | 0.3801 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.4428 | 4.0 | 200 | 0.3835 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.4079 | 5.0 | 250 | 0.3823 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.4125 | 6.0 | 300 | 0.3888 | 0.8737 | 0.0 | 0.0 | 0.0 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
katsushi2441/xlam-7b-r-sn20-finetuned
|
katsushi2441
| 2025-08-30T02:32:25Z | 0 | 0 |
peft
|
[
"peft",
"base_model:adapter:Salesforce/xLAM-7b-r",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Salesforce/xLAM-7b-r",
"region:us"
] |
text-generation
| 2025-08-30T01:56:23Z |
---
base_model: Salesforce/xLAM-7b-r
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Salesforce/xLAM-7b-r
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
motza0025/blockassist-bc-powerful_jagged_magpie_1756519727
|
motza0025
| 2025-08-30T02:31:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"powerful jagged magpie",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:31:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- powerful jagged magpie
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
qgallouedec/Qwen3-0.6B-SFT-20250830022956
|
qgallouedec
| 2025-08-30T02:31:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"hf_jobs",
"trl",
"sft",
"conversational",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T02:30:44Z |
---
base_model: Qwen/Qwen3-0.6B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-0.6B-SFT-20250830022956
tags:
- generated_from_trainer
- hf_jobs
- trl
- sft
licence: license
---
# Model Card for Qwen3-0.6B-SFT-20250830022956
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/Qwen3-0.6B-SFT-20250830022956", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.0.dev0
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu128
- 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}}
}
```
|
honmik/blockassist-bc-patterned_howling_salamander_1756520955
|
honmik
| 2025-08-30T02:29:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned howling salamander",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:29:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned howling salamander
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rindartog/AceInstruct-1.5B-Gensyn-Swarm-foraging_dextrous_tortoise
|
rindartog
| 2025-08-30T02:28:55Z | 134 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am foraging_dextrous_tortoise",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-23T00:48:46Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am foraging_dextrous_tortoise
---
# 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]
|
klmdr22/blockassist-bc-wild_loud_newt_1756520849
|
klmdr22
| 2025-08-30T02:28:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:28:08Z |
---
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).
|
Jinyu220/gaze_model_av_aloha_real_no_augmentation_hang_ring
|
Jinyu220
| 2025-08-30T02:28:04Z | 7 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-08-27T22:47:46Z |
---
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:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF
|
mradermacher
| 2025-08-30T02:27:56Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:heavyhelium/EM-Model-Organism-BGGPT-Mistral-7B-Instruct",
"base_model:quantized:heavyhelium/EM-Model-Organism-BGGPT-Mistral-7B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-30T00:44:30Z |
---
base_model: heavyhelium/EM-Model-Organism-BGGPT-Mistral-7B-Instruct
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### 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/heavyhelium/EM-Model-Organism-BGGPT-Mistral-7B-Instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#EM-Model-Organism-BGGPT-Mistral-7B-Instruct-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/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q3_K_L.gguf) | Q3_K_L | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.IQ4_XS.gguf) | IQ4_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q5_K_M.gguf) | Q5_K_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q6_K.gguf) | Q6_K | 6.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/EM-Model-Organism-BGGPT-Mistral-7B-Instruct-GGUF/resolve/main/EM-Model-Organism-BGGPT-Mistral-7B-Instruct.f16.gguf) | f16 | 14.7 | 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 -->
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1756519310
|
lisaozill03
| 2025-08-30T02:27:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:27:48Z |
---
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).
|
lowricolesadv/blockassist-bc-fluffy_furry_stork_1756518804
|
lowricolesadv
| 2025-08-30T02:27:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fluffy furry stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:27:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fluffy furry stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lyndathompsonad/blockassist-bc-crested_sly_duck_1756518800
|
lyndathompsonad
| 2025-08-30T02:26:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"crested sly duck",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:26:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- crested sly duck
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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