modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-OMD-0914190921-epoch-10
|
vectorzhou
| 2025-09-18T22:40:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T22:39:47Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-OMD
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-OMD
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) 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="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-OMD-0914190921-epoch-10", 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/zrl_csl_nlhf/nlhf/runs/6tm2oyul)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
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}}
}
```
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_16_4_okvqa_37_0.0001_12800_3
|
winnieyangwannan
| 2025-09-18T22:39:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-18T22:38:32Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
contemmcm/359359c155a86b7abeee6fef71558647
|
contemmcm
| 2025-09-18T22:39:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-18T21:41:14Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 359359c155a86b7abeee6fef71558647
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. -->
# 359359c155a86b7abeee6fef71558647
This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the dair-ai/emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2053
- Accuracy: 0.9194
- F1 Macro: 0.8729
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| No log | 0 | 0 | 1.7574 | 0.1578 | 0.0617 |
| No log | 1 | 500 | 1.6018 | 0.3488 | 0.0862 |
| No log | 2 | 1000 | 1.5876 | 0.3488 | 0.0862 |
| No log | 3 | 1500 | 1.5497 | 0.3952 | 0.1406 |
| No log | 4 | 2000 | 1.4675 | 0.5318 | 0.2179 |
| 0.0767 | 5 | 2500 | 0.9248 | 0.6729 | 0.3892 |
| 0.7937 | 6 | 3000 | 0.6219 | 0.8196 | 0.6553 |
| 0.0691 | 7 | 3500 | 0.3437 | 0.8982 | 0.8326 |
| 0.2512 | 8.0 | 4000 | 0.2171 | 0.9214 | 0.8784 |
| 0.1802 | 9.0 | 4500 | 0.1969 | 0.9254 | 0.8825 |
| 0.1458 | 10.0 | 5000 | 0.1991 | 0.9274 | 0.8864 |
| 0.1239 | 11.0 | 5500 | 0.1839 | 0.9224 | 0.8765 |
| 0.1141 | 12.0 | 6000 | 0.2010 | 0.9219 | 0.8689 |
| 0.1003 | 13.0 | 6500 | 0.1840 | 0.9219 | 0.8771 |
| 0.1002 | 14.0 | 7000 | 0.1988 | 0.9204 | 0.8775 |
| 0.0855 | 15.0 | 7500 | 0.2053 | 0.9194 | 0.8729 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
Andresmusic1/Lee_Chae_won_ADP
|
Andresmusic1
| 2025-09-18T22:30:52Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T22:28:54Z |
---
license: apache-2.0
---
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758234552
|
schooncestiaa
| 2025-09-18T22:30:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T22:30:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BARKEM/qwen3-8b-eko-lora-conversation-2
|
BARKEM
| 2025-09-18T22:28:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T22:27:59Z |
---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** BARKEM
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-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)
|
mbarekmarouene/gemma2b-smartcompose-lora-v2
|
mbarekmarouene
| 2025-09-18T22:25:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-18T22:24:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
0xryan888/Affine-5HC7cf8WfvN4cHyKCAiZ8YD4MguBLCmReKvcvjnDnSCcQh7C
|
0xryan888
| 2025-09-18T22:20:25Z | 0 | 0 | null |
[
"safetensors",
"gpt_oss",
"8-bit",
"mxfp4",
"region:us"
] | null | 2025-09-18T22:12:16Z |
## Affine: Comprehensive LLM/Validator Guide (llms.txt)
### Overview
Affine is an incentivized RL system built on Bittensor Subnet 120. Validators continuously generate challenges across multiple environments, query miners’ hosted LLMs via Chutes, evaluate responses, and publish signed results to Cloudflare R2. A winners-take-all scoring rule uses ε-Pareto dominance over environment subsets to set on-chain weights.
### Repository map (high level)
- `affine/__init__.py`: CLI, core models, validator/runner, R2 storage, signer client, scoring, miners/query, Prometheus metrics
- `affine/envs/`: task generators and evaluators
- `sat.py`: k-SAT generation and evaluation
- `abd.py`: program abduction (infer stdin to match output)
- `ded.py`: program deduction (write code to pass test cases)
- `affine/utils/`: runtime utilities
- `executor.py`: safe Python program runner for ABD/DED
- `dataset.py`: buffered Hugging Face dataset fetcher
- Packaging/config: `pyproject.toml`, `Dockerfile`, `docker-compose.yml`, `docker-compose.local.yml`, `prometheus.yml`
- Ops/content: `build_and_push.sh` (if present), `site/` (static viewer), `notebooks/`
### CLI entrypoint
Install via uv and run the `af` CLI (see README for full steps).
- `af -vv validate`: run the validator loop (Prometheus metrics on AFFINE_METRICS_PORT)
- `af -vv runner`: run a continuous challenge+evaluate+sink loop (off-chain)
- `af weights`: compute weights once and print summary table
- `af pull <uid> --model_path <dir>`: download a miner’s model from Hugging Face
- `af push [--model_path <dir> | --existing-repo <user/repo>] [--revision <sha>]`:
- Upload artifacts to HF (unless using `--existing-repo`)
- Make repo public
- Deploy Chute with generated config
- Commit (model, revision, chute_id) on-chain
- Warm up the chute until it’s hot
- `af signer`: start a lightweight HTTP signer service (used by validator)
### Environments
- SAT (`affine/envs/sat.py`)
- Generates random k-SAT formula over x1..xn; prompt asks for a satisfying assignment or UNSAT
- Evaluation parses `xN=True/False/1/0` pairs and checks every clause is satisfied
- ABD (Program Abduction, `affine/envs/abd.py`)
- Uses HF dataset `satpalsr/rl-python` samples: Python program, example input/output
- LLM is prompted to produce a fresh valid stdin wrapped in `<INPUT>..</INPUT>` tags for the given program so that its output matches the example output
- Input is validated heuristically (line counts vs input() calls, loop shape)
- Execution via `ProgramExecutor`; evaluation re-runs program with extracted input and checks output equivalence (whitespace/line tolerant)
- DED (Program Deduction, `affine/envs/ded.py`)
- Prompt asks the model to produce a complete Python solution (fenced) that reads from STDIN and prints answers only
- Verification pulls `test_cases` (stdin/stdout or function_call) from the sample, executes program per case, normalizes outputs, and scores 1.0 only if all pass
### Querying miners (Chutes)
- Endpoint: `https://{slug}.chutes.ai/v1/chat/completions`
- Auth: `Authorization: Bearer ${CHUTES_API_KEY}`
- Payload: `{ model, messages: [{ role: "user", content: prompt }] }`
- Retries/backoff configurable; response content extracted from `choices[0].message.content`
- Gated HF models are skipped (checked via Hugging Face API and optional revision presence)
### Miner discovery
- Reads Bittensor metagraph for netuid 120 and revealed commitments containing `{ model, revision, chute_id }`
- Filters out gated models and non-`affine/*` families (except base UID 0)
- Deduplicates by keeping earliest block per model
### Results pipeline and storage (Cloudflare R2)
- Windowing
- `AFFINE_WINDOW` (default 20) defines shard window based on block numbers
- A shard key is `affine/results/{WINDOW_START_BLOCK}-{HOTKEY}.json`
- Index
- `affine/index.json` contains a JSON array of shard keys
- When a shard is first written, the index is updated (deduplicated and sorted)
- Sink
- Results are signed (via signer service or local wallet) and appended to the shard; new shard triggers index update
- Local cache
- Shards are downloaded once and stored under `AFFINE_CACHE_DIR` (default `~/.cache/affine/blocks`), with `.modified` timestamp files
- `dataset(tail)` streams `Result` objects from cached JSONL, validating signatures
R2/S3 client
- Uses `aiobotocore` with endpoint `https://{R2_BUCKET_ID}.r2.cloudflarestorage.com`
- Bucket: `R2_FOLDER` (default `affine`)
- Keys: `INDEX_KEY=affine/index.json`, `RESULT_PREFIX=affine/results/`
### Scoring and weight setting
- Periodic cadence: validator waits for blocks where `block % TEMPO == 0` (TEMPO=100)
- Data ingestion: last `TAIL=10_000` blocks streamed via `dataset(tail)`
- Accumulation per miner per env: counts and success rates (accuracy)
- Eligibility: require ≥90% of per-env max sample counts
- ε-Pareto dominance
- Not-worse threshold uses `Z_NOT_WORSE` and `EPS_FLOOR` based on standard error of difference
- Better-on-some-env threshold uses `EPS_WIN` (floor) and optional `Z_WIN`
- Global dominance counts over full env set; canonical best used for ties/fallbacks
- Combinatoric subset winners
- For all non-empty env subsets S, award K_s to the ε-Pareto winner on S
- K_1 = scale, K_s = C(N, s-1)*K_{s-1}
- Normalize scores over eligibles to produce weights; if none eligible, assign 1.0 to canonical best
- On-chain set_weights
- Delegated to the signer HTTP service (`/set_weights`) and confirmed by checking `last_update`
- Fallback to local submission only if signer is unreachable
Key hyperparameters (defaults)
- `NETUID=120`, `TAIL=10_000`, `ALPHA=0.9`
- `EPS_FLOOR=0.002`, `Z_NOT_WORSE=0.84`, `EPS_WIN=0.0015`, `Z_WIN=0.0`
### Signer service
- Start with `af -v signer` (listens on `${SIGNER_HOST}:${SIGNER_PORT}`)
- Endpoints
- `GET /healthz` → `{ ok: true }`
- `POST /sign` → `{ signatures: [hex...], hotkey }` for provided string payloads
- `POST /set_weights` → triggers on-chain set_weights with confirmation
- Used by validator via `${SIGNER_URL}`; includes DNS logging + request/response logging
### Prometheus metrics (port/address configurable)
- Counters/Gauges
- `qcount{model}`: number of LLM queries
- `score{uid,env}`: per-miner per-env accuracy
- `rank{uid,env}`: per-env rank among eligibles
- `weight{uid}`: current weight
- `lastset`: time of last successful weight set
- `nresults`: processed result count
- `maxenv{env}`: best accuracy per env among active miners
- `cache`: local cache size (bytes)
- Exporter binds at `${AFFINE_METRICS_ADDR}:${AFFINE_METRICS_PORT}`
### Program execution sandbox (ABD/DED)
- `ProgramExecutor` limits: wallclock, CPU, memory, and output size
- Strips code fences; if program defines `solve()` and produces no output, auto-injects `if __name__ == "__main__":` runner
- Cleans up temp files; kills entire process group on timeout/truncation
### Buffered dataset (Hugging Face)
- `BufferedDataset` fetches random windows from `https://datasets-server.huggingface.co/rows` with retries and exponential backoff
- Internal buffer filled concurrently and served via `get()`; used by ABD/DED
### Configuration (env vars)
- Bittensor/Subtensor
- `SUBTENSOR_ENDPOINT` (default `finney`), `SUBTENSOR_FALLBACK` (default `wss://lite.sub.latent.to:443`)
- `BT_WALLET_COLD`, `BT_WALLET_HOT`
- Chutes/Hugging Face
- `CHUTES_API_KEY` (required for queries/deploy), `CHUTE_USER`
- `HF_USER`, `HF_TOKEN`
- R2/S3
- `R2_BUCKET_ID` (account subdomain), `R2_FOLDER` (bucket root folder), `R2_WRITE_ACCESS_KEY_ID`, `R2_WRITE_SECRET_ACCESS_KEY`
- `AFFINE_WINDOW` (shard size), `AFFINE_CACHE_DIR`
- Networking/Concurrency
- `AFFINE_METRICS_ADDR`, `AFFINE_METRICS_PORT`
- `AFFINE_HTTP_CONCURRENCY` (default 16), `AFFINE_UPLOAD_CONCURRENCY` (default 2)
- Signer
- `SIGNER_HOST`, `SIGNER_PORT`, `SIGNER_URL` (e.g., `http://signer:8080`)
- `SIGNER_RETRIES`, `SIGNER_RETRY_DELAY`
### Docker Compose (production and local override)
- Services
- `validator`: `af -vv validate`, metrics on 8000 (host 8001), depends on `signer`
- `runner`: `af -vv runner`, metrics on 8000 (host 8002)
- `signer`: exposes 8080; mounts wallet dir read-only
- `prometheus` (9090) and `grafana` (host 8000) for telemetry
- `watchtower` auto-updates images
- Local build
- Use with override: `docker compose -f docker-compose.yml -f docker-compose.local.yml up --build`
### SDK usage
Example from README:
```python
import affine as af
af.trace(); af.debug(); af.info()
miners = await af.get_miners(); miner = await af.get_miners(5)
chal = await af.SAT.generate()
chals = await af.ABDUCTION().many(10); chals = await af.DEDUCTION().many(10)
response = await af.query(chal.prompt, model=miner.model)
evaluation = chal.evaluate(response)
print(evaluation.score)
async for res in af.rollouts(100):
print(res)
```
### Static site (R2 index viewer)
- Index key: `affine/index.json` (JSON array of shard keys)
- Endpoint template: `https://{R2_BUCKET_ID}.r2.cloudflarestorage.com/{R2_FOLDER}/{OBJECT_KEY}`
- Requires S3 SigV4 signing in the browser (region `auto`, service `s3`, `x-amz-content-sha256=UNSIGNED-PAYLOAD`)
- Flow: fetch index → for each key fetch shard → render list/download
- Prefer presigned URLs or read-only keys for public deployments
### Notes and best practices
- Always override the example/default R2 credentials with your own via `.env`
- Keep HF repos private during upload; visibility is set to public right before deploy
- The validator requires a running signer service; do not expose wallet keys in validator containers
- For ABD/DED, ensure models return only the requested content (stdin or fenced python) to avoid grading penalties
### Quick commands
```bash
# Validate (local):
af -vv validate
# Runner (off-chain ingestion + sink):
af -vv runner
# Pull miner model:
af -vvv pull <uid> --model_path ./my_model
# Push your model (deploy chute and commit on-chain):
af -vvv push --model_path ./my_model
```
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758233936
|
schooncestiaa
| 2025-09-18T22:20:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T22:19:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmfpt99wj0c60x0n0s3u23y0a_cmfpxecas0c8wx0n0s08c3crm
|
BootesVoid
| 2025-09-18T22:19:03Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-18T22:18:59Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: MIRALUXX
---
# Cmfpt99Wj0C60X0N0S3U23Y0A_Cmfpxecas0C8Wx0N0S08C3Crm
<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 `MIRALUXX` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MIRALUXX",
"lora_weights": "https://huggingface.co/BootesVoid/cmfpt99wj0c60x0n0s3u23y0a_cmfpxecas0c8wx0n0s08c3crm/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/cmfpt99wj0c60x0n0s3u23y0a_cmfpxecas0c8wx0n0s08c3crm', weight_name='lora.safetensors')
image = pipeline('MIRALUXX').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/cmfpt99wj0c60x0n0s3u23y0a_cmfpxecas0c8wx0n0s08c3crm/discussions) to add images that show off what you’ve made with this LoRA.
|
TimHo/SpaceInvadersNoFrameskip
|
TimHo
| 2025-09-18T22:17:04Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-18T22:16:32Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 641.00 +/- 266.56
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga TimHo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga TimHo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga TimHo
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
TAUR-dev/M-skillfactory-ablations__no_reflections_reflections5_formatsno_reflection-sft
|
TAUR-dev
| 2025-09-18T22:10:25Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-18T22:09:44Z |
# M-skillfactory-ablations__no_reflections_reflections5_formatsno_reflection-sft
This model was created as part of the **skillfactory-ablations__no_reflections_reflections5_formatsno_reflection** experiment using the SkillFactory experiment management system.
## Model Details
- **Training Method**: LLaMAFactory SFT (Supervised Fine-Tuning)
- **Stage Name**: sft
- **Experiment**: skillfactory-ablations__no_reflections_reflections5_formatsno_reflection
## Training Configuration
{"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/home/skeh/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__skillfactory_ablations__no_reflections_reflections5_formatsno_reflection", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/datasets/sedrick/skillfactory/temp/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 1, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__skillfactory-ablations__no_reflections_reflections5_formatsno_reflection__v1", "sf_eval_before_training": false, "sf_wandb_project": "skillfactory-ablations__no_reflections_reflections5_formatsno_reflection_sft", "sf_eval_steps": null, "run_name": "skillfactory-ablations__no_reflections_reflections5_formatsno_reflection_sft"}
## Experiment Tracking
🔗 **View complete experiment details**: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__skillfactory-ablations__no_reflections_reflections5_formatsno_reflection__v1)
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-skillfactory-ablations__no_reflections_reflections5_formatsno_reflection-sft")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-skillfactory-ablations__no_reflections_reflections5_formatsno_reflection-sft")
```
|
mbarekmarouene/gemma-smartcompose-lora-v2
|
mbarekmarouene
| 2025-09-18T22:07:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-18T22:06:39Z |
---
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]
|
jcarleton/llama2-13B-anthropic-sft
|
jcarleton
| 2025-09-18T22:06:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T22:03:47Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
SDoug-55/SincerelySarahD
|
SDoug-55
| 2025-09-18T22:06:14Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-18T20:59:07Z |
---
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: Sarah
---
# Sincerelysarahd
<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 `Sarah` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sarah",
"lora_weights": "https://huggingface.co/SDoug-55/SincerelySarahD/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('SDoug-55/SincerelySarahD', weight_name='lora.safetensors')
image = pipeline('Sarah').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: 3583
- Learning rate: 0.0004
- LoRA rank: 56
## Contribute your own examples
You can use the [community tab](https://huggingface.co/SDoug-55/SincerelySarahD/discussions) to add images that show off what you’ve made with this LoRA.
|
tamewild/8b_v4_merged_e5
|
tamewild
| 2025-09-18T22:01:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T21:59:40Z |
---
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]
|
Kuuaaannn/sd-class-butterflies-32
|
Kuuaaannn
| 2025-09-18T22:01:47Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2025-09-18T22:00:51Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Kuuaaannn/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
devparagiri/Test-20250918-215607
|
devparagiri
| 2025-09-18T22:01:06Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"dataset:devparagiri/dataset-Test-20250918-215607",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T21:58:56Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: meta-llama/Llama-3.2-1B-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- devparagiri/dataset-Test-20250918-215607
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
mradermacher/Ice0.112-08.05-RP-i1-GGUF
|
mradermacher
| 2025-09-18T22:00:12Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:icefog72/Ice0.112-08.05-RP",
"base_model:quantized:icefog72/Ice0.112-08.05-RP",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-18T20:00:42Z |
---
base_model: icefog72/Ice0.112-08.05-RP
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/icefog72/Ice0.112-08.05-RP
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Ice0.112-08.05-RP-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Ice0.112-08.05-RP-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/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q4_1.gguf) | i1-Q4_1 | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Ice0.112-08.05-RP-i1-GGUF/resolve/main/Ice0.112-08.05-RP.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | 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 -->
|
heado/audio_kor
|
heado
| 2025-09-18T21:53:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:Kkonjeong/wav2vec2-base-korean",
"base_model:finetune:Kkonjeong/wav2vec2-base-korean",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-09-18T21:52:50Z |
---
library_name: transformers
base_model: Kkonjeong/wav2vec2-base-korean
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: audio_kor
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. -->
# audio_kor
This model is a fine-tuned version of [Kkonjeong/wav2vec2-base-korean](https://huggingface.co/Kkonjeong/wav2vec2-base-korean) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3679
- Accuracy: 0.9496
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6342 | 1.0 | 30 | 2.6301 | 0.0588 |
| 2.4757 | 2.0 | 60 | 2.3899 | 0.3109 |
| 1.9266 | 3.0 | 90 | 1.8527 | 0.6134 |
| 1.5614 | 4.0 | 120 | 1.4405 | 0.7227 |
| 0.9955 | 5.0 | 150 | 1.0447 | 0.8655 |
| 0.6666 | 6.0 | 180 | 0.7428 | 0.9076 |
| 0.4623 | 7.0 | 210 | 0.5859 | 0.9160 |
| 0.334 | 8.0 | 240 | 0.4750 | 0.9244 |
| 0.2673 | 9.0 | 270 | 0.3788 | 0.9496 |
| 0.196 | 10.0 | 300 | 0.3679 | 0.9496 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758232087
|
schooncestiaa
| 2025-09-18T21:49:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T21:49:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
siyang-liu/my_awesome_food_model
|
siyang-liu
| 2025-09-18T21:44:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-09-18T21:32:01Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
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. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6039
- Accuracy: 0.884
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7071 | 1.0 | 63 | 2.4978 | 0.829 |
| 1.825 | 2.0 | 126 | 1.7578 | 0.861 |
| 1.6328 | 3.0 | 189 | 1.6039 | 0.884 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
adamo1139/DeepSeek-R1-Zero-AWQ
|
adamo1139
| 2025-09-18T21:44:11Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"base_model:deepseek-ai/DeepSeek-R1-Zero",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Zero",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2025-06-01T17:57:28Z |
---
license: mit
library_name: transformers
base_model:
- deepseek-ai/DeepSeek-R1-Zero
---
# DeepSeek-R1-Zero-AWQ 671B
It's a 4-bit AWQ quantization of DeepSeek-R1-Zero 671B model, it's suitable for use with GPU nodes like 8xA100/8xH20/8xH100 with vLLM and SGLang
You can run this model on 8x H100 80GB using vLLM with
`vllm serve adamo1139/DeepSeek-R1-Zero-AWQ --tensor-parallel 8`
Made by DeepSeek with ❤️
<p align="center" style="image-rendering: pixelated;">
<img width="800" src="https://user-images.githubusercontent.com/55270174/214356078-89430299-247d-4f1f-82f6-a41340df0949.gif" alt="example" />
</p>
|
atrost/math_sft_40K_trl_SFT_Regularized-0.1
|
atrost
| 2025-09-18T21:43:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-1.7B-Base",
"base_model:finetune:Qwen/Qwen3-1.7B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T19:13:56Z |
---
base_model: Qwen/Qwen3-1.7B-Base
library_name: transformers
model_name: math_sft_40K_trl_SFT_Regularized-0.1
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for math_sft_40K_trl_SFT_Regularized-0.1
This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base).
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="atrost/math_sft_40K_trl_SFT_Regularized-0.1", 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/astrost-university-of-wisconsin-madison/huggingface/runs/eb123p1k)
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.1
- 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}}
}
```
|
aamijar/ReplaceME-Gemma-2-9B-Instruct-lora-r8-boolq-epochs0
|
aamijar
| 2025-09-18T21:43:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T21:43:41Z |
---
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]
|
zzhou423/my_awesome_food_model
|
zzhou423
| 2025-09-18T21:39:28Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-09-18T21:25:44Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
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. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6255
- Accuracy: 0.879
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7065 | 1.0 | 63 | 2.5425 | 0.8 |
| 1.8457 | 2.0 | 126 | 1.8210 | 0.851 |
| 1.5895 | 3.0 | 189 | 1.6255 | 0.879 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758231472
|
schooncestiaa
| 2025-09-18T21:39:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T21:38:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
JessieIsNotAvailable/my_awesome_food_model
|
JessieIsNotAvailable
| 2025-09-18T21:39:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-09-18T21:27:06Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
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. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6068
- Accuracy: 0.892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.705 | 1.0 | 63 | 2.5096 | 0.823 |
| 1.818 | 2.0 | 126 | 1.7643 | 0.884 |
| 1.5692 | 3.0 | 189 | 1.6068 | 0.892 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
CorvinFAV/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_fierce_bison
|
CorvinFAV
| 2025-09-18T21:38:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am bold_fierce_bison",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T21:38:03Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am bold_fierce_bison
---
# 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]
|
Aelalixoerels/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mimic_scaly_gazelle
|
Aelalixoerels
| 2025-09-18T21:37:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am mimic_scaly_gazelle",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T21:37:22Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am mimic_scaly_gazelle
---
# 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]
|
SelenaD0312/my_awesome_food_model
|
SelenaD0312
| 2025-09-18T21:37:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-09-18T21:18:22Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
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. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6350
- Accuracy: 0.862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7393 | 1.0 | 63 | 2.5692 | 0.802 |
| 1.9112 | 2.0 | 126 | 1.8103 | 0.853 |
| 1.6416 | 3.0 | 189 | 1.6350 | 0.862 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
coklatmanis886/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-foraging_docile_ibis
|
coklatmanis886
| 2025-09-18T21:33:33Z | 137 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am foraging docile ibis",
"trl",
"genrl-swarm",
"I am foraging_docile_ibis",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-24T15:20:32Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-foraging_docile_ibis
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am foraging docile ibis
- trl
- genrl-swarm
- I am foraging_docile_ibis
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-foraging_docile_ibis
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="coklatmanis886/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-foraging_docile_ibis", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
BootesVoid/cmfpv703q0c6xx0n0lbqab4ai_cmfpw1drk0c7nx0n0v0vi3mbp
|
BootesVoid
| 2025-09-18T21:32:17Z | 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
| 2025-09-18T21:32:15Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SCOREWITHVALE
---
# Cmfpv703Q0C6Xx0N0Lbqab4Ai_Cmfpw1Drk0C7Nx0N0V0Vi3Mbp
<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 `SCOREWITHVALE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SCOREWITHVALE",
"lora_weights": "https://huggingface.co/BootesVoid/cmfpv703q0c6xx0n0lbqab4ai_cmfpw1drk0c7nx0n0v0vi3mbp/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/cmfpv703q0c6xx0n0lbqab4ai_cmfpw1drk0c7nx0n0v0vi3mbp', weight_name='lora.safetensors')
image = pipeline('SCOREWITHVALE').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/cmfpv703q0c6xx0n0lbqab4ai_cmfpw1drk0c7nx0n0v0vi3mbp/discussions) to add images that show off what you’ve made with this LoRA.
|
hafidhsoekma/unsloth-Qwen3-4B-unsloth-bnb-4bit-method_ORPO
|
hafidhsoekma
| 2025-09-18T21:28:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T21:19:36Z |
---
base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** hafidhsoekma
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-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)
|
ChenWu98/numina_qwen_2.5_0.5b_sft_numina_20k_cluster2_condition
|
ChenWu98
| 2025-09-18T21:28:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T21:23:55Z |
---
base_model: Qwen/Qwen2.5-0.5B
library_name: transformers
model_name: numina_qwen_2.5_0.5b_sft_numina_20k_cluster2_condition
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for numina_qwen_2.5_0.5b_sft_numina_20k_cluster2_condition
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/lu1ak9p5)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
NeuralGL/nothuman
|
NeuralGL
| 2025-09-18T21:22:50Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-18T21:22:39Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/nothuman_003240_00_20250918211756.png
text: A fat bald man, wearing suit, big scary smile, looks like politician, suit,
full-body, in style of NotHuman
- output:
url: sample/nothuman_003240_01_20250918211801.png
text: A woman, looks anorexic a bit, tired, wearing old sweater, holds a cat,
in style of NotHuman
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: NotHuman
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
---
# NotHuman
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `NotHuman` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
ThrowRASkibidi/Name
|
ThrowRASkibidi
| 2025-09-18T21:21:31Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T21:21:30Z |
---
license: apache-2.0
---
|
McClain/Evo2-plasmid-ft
|
McClain
| 2025-09-18T21:20:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-18T20:31:39Z |
# Evo2 SFT 1B (Torch)
PyTorch checkpoint distilled from the ZeRO-1 torch_dist shards in `/mnt/efs/projects/evo2-sft/jobs/evo2-20250918-131146/results/evo2/checkpoints`.
- Format: `torch.load('evo2_sft_1b_torch.pt')` returning module-level parameters (`module.decoder.*`).
- Optimizer state removed.
- Architecture: Hyena Evo2 1B (25 layers, hidden size 1920, context length 8192).
trained on ~77,000 plasmids from https://ccb-microbe.cs.uni-saarland.de/plsdb2025/browse
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_16_4_okvqa_37_0.001_6400_3
|
winnieyangwannan
| 2025-09-18T21:18:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-18T21:17:31Z |
---
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]
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758230239
|
schooncestiaa
| 2025-09-18T21:18:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T21:18:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_16_4_okvqa_37_0.0001_6400_3
|
winnieyangwannan
| 2025-09-18T21:18:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-18T21:16:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_pnas_layer_18_4_okvqa_37_0.0001_12800_3
|
winnieyangwannan
| 2025-09-18T21:17:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-18T21:15:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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### Results
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
aamijar/ReplaceME-Mistral-7B-Instruct-v0.3-lora-r8-winogrande
|
aamijar
| 2025-09-18T21:13:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T21:13:25Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Recommendations
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## How to Get Started with the Model
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[More Information Needed]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
aamijar/ReplaceME-Mistral-7B-Instruct-v0.3-lora-r8-winogrande-epochs4
|
aamijar
| 2025-09-18T21:13:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T21:13:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
wnkh/byt5-ocr-correction
|
wnkh
| 2025-09-18T21:12:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T21:11:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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#### Factors
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Model Card Contact
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|
samoline/84971a35-04bb-4ef3-85d8-b306a5eff6a8
|
samoline
| 2025-09-18T21:08:53Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"arxiv:2402.03300",
"base_model:Maykeye/TinyLLama-v0",
"base_model:finetune:Maykeye/TinyLLama-v0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T21:08:51Z |
---
base_model: Maykeye/TinyLLama-v0
library_name: transformers
model_name: root/.cache/huggingface/hub/trained_repo
tags:
- generated_from_trainer
licence: license
---
# Model Card for root/.cache/huggingface/hub/trained_repo
This model is a fine-tuned version of [Maykeye/TinyLLama-v0](https://huggingface.co/Maykeye/TinyLLama-v0).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
gumperto/Qwen2.5-32B-Instruct-emergent-finetune-haiku_samples-all-full-r32
|
gumperto
| 2025-09-18T21:04:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"conversational",
"base_model:unsloth/Qwen2.5-32B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-32B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T19:57:47Z |
---
base_model: unsloth/Qwen2.5-32B-Instruct
library_name: transformers
model_name: Qwen2.5-32B-Instruct-emergent-finetune-haiku_samples-all-full-r32
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Qwen2.5-32B-Instruct-emergent-finetune-haiku_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Qwen2.5-32B-Instruct](https://huggingface.co/unsloth/Qwen2.5-32B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumperto/Qwen2.5-32B-Instruct-emergent-finetune-haiku_samples-all-full-r32", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/gumperto-waseda-university/clarifying-em/runs/2e1xp7je)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
lemonhat/Qwen3-8B-SEvolve1_re_30k_tag5_processed
|
lemonhat
| 2025-09-18T21:03:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T20:51:55Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen3-8B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: SEvolve1_re_30k_tag5_processed
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. -->
# SEvolve1_re_30k_tag5_processed
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the SEvolve1_re_30k_tag5_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.51.0
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
adgbemmy/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-gilded_shiny_peacock
|
adgbemmy
| 2025-09-18T21:02:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am gilded_shiny_peacock",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T21:01:49Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am gilded_shiny_peacock
---
# 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]
|
INSAIT-Institute/EarthX
|
INSAIT-Institute
| 2025-09-18T21:02:32Z | 0 | 2 | null |
[
"earth-observation",
"remote-sensing",
"multimodal",
"multispectral",
"SAR",
"time-series",
"segmentation",
"classification",
"change-detection",
"foundation-model",
"arxiv:2506.01667",
"license:mit",
"region:us"
] | null | 2025-09-16T15:36:46Z |
---
license: mit
tags:
- earth-observation
- remote-sensing
- multimodal
- multispectral
- SAR
- time-series
- segmentation
- classification
- change-detection
- foundation-model
---
<p align="center">
<img src="asset/earthx.png" alt="Image" width="100">
</p>
<div align="center">
<h1 align="center">EarthX: A Unified Earth Observation Foundation Model for Spatial and Temporal Understanding
</h1>
</div>
<p align="center">
<a href=""><img src="https://img.shields.io/badge/Arxiv-2418.09110-b31b1b.svg?logo=arXiv"></a>
<a href="https://github.com/insait-institute/earthx-website/index.html"><img src="https://img.shields.io/badge/EarthX-Project_Page-<color>"></a>
<a href="https://github.com/insait-institute/earthx/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-yellow"></a>
</p>
EarthX is the successor to **EarthMind** [1], designed to handle the complexity of multimodal Earth Observation (EO) data.
While EarthMind laid the groundwork for multi-sensor EO understanding, EarthX introduces two major innovations that push the boundaries of scalability and temporal reasoning.
## ✨ What’s New in EarthX?
- **Selected Projector**
Efficiently captures **cross-modal dynamics** with modality-specific pathways for RGB, SAR, and multispectral data, preserving each sensor’s unique strengths before fusion.
- **Hybrid Contextual Tiling (HCT)**
A scalable strategy for **ultra-high-resolution imagery**.
Combines fine detail tiles, local context, and global overviews to achieve both local precision and global awareness.
## 📊 Benchmarks
- **TEOChat-Bench [2] (temporal tasks):** Achieves new state-of-the-art results.
- **EarthMind-Bench (spatial tasks):** Comparable results to the strongest baselines.
**Takeaway:** EarthX is not tied to a single dataset or task — it is a unified EO foundation model for multimodal, multi-scale, and temporal understanding.
## References
[1] Shu, Yan, et al. *EarthMind: Towards Multi-Granular and Multi-Sensor Earth Observation with Large Multimodal Models.* arXiv:2506.01667 (2025).
[2] Irvin, Jeremy Andrew, et al. *TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data.* ICLR (2025).
## Statement
### Acknowledgement
This project references and uses the following open source models and datasets. Thanks also to `INSAIT` for computing support.
#### Related Open Source Models
- [EarthMind](https://github.com/shuyansy/earthx)
### Citation
If you are interested in the following work, please cite the following paper.
```
@article{shu2025earthx,
title={EarthMind: Towards Multi-Granular and Multi-Sensor Earth Observation with Large Multimodal Models},
author={Shu, Yan and Ren, Bin and Xiong, Zhitong and Paudel, Danda Pani and Van Gool, Luc and Demir, Begum and Sebe, Nicu and Rota, Paolo},
journal={arXiv preprint arXiv:2506.01667},
year={2025}
}
```
|
notouchfish/cs546-hw1
|
notouchfish
| 2025-09-18T21:01:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-18T21:01:52Z |
---
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]
|
ChenWu98/numina_qwen_2.5_0.5b_sft_numina_20k_cluster2_split_1
|
ChenWu98
| 2025-09-18T21:00:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T21:00:26Z |
---
base_model: Qwen/Qwen2.5-0.5B
library_name: transformers
model_name: numina_qwen_2.5_0.5b_sft_numina_20k_cluster2_split_1
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for numina_qwen_2.5_0.5b_sft_numina_20k_cluster2_split_1
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/rg8n4ste)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
aioaneid/relaxed-table-structure
|
aioaneid
| 2025-09-18T21:00:27Z | 0 | 0 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2025-09-18T19:17:32Z |
---
license: cc-by-4.0
---
Table Structure Recognition model trained with Relaxed Bounding Boxes via https://github.com/aioaneid/table-transformer.
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758229007
|
schooncestiaa
| 2025-09-18T20:57:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T20:57:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
varun4/flash-attn-3-pytorch2.9.0.dev20250904
|
varun4
| 2025-09-18T20:56:35Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T20:47:47Z |
---
license: apache-2.0
---
|
PracticalWork/xlm-roberta-large-classifier
|
PracticalWork
| 2025-09-18T20:53:45Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-07-28T21:07:28Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-large-classifier
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. -->
# xlm-roberta-large-classifier
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3918
- Accuracy: 0.8353
- F1: 0.7325
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log | 0 | 0 | 0.6059 | 0.7106 | 0.1957 |
| No log | 0.6006 | 188 | 0.4820 | 0.7826 | 0.6 |
| No log | 1.2013 | 376 | 0.4764 | 0.7858 | 0.5553 |
| 0.5275 | 1.8019 | 564 | 0.5046 | 0.7738 | 0.6519 |
| 0.5275 | 2.4026 | 752 | 0.4234 | 0.8233 | 0.7041 |
| 0.5275 | 3 | 939 | 0.3918 | 0.8353 | 0.7325 |
### Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
Premo-Test-Account/edge
|
Premo-Test-Account
| 2025-09-18T20:50:01Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-09-18T20:50:01Z |
---
license: other
license_name: huggingfacelicense
license_link: LICENSE
---
|
haihp02/f016bf2d-a91a-4ac8-bcc5-cd93df71b5b1
|
haihp02
| 2025-09-18T20:49:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T20:49:16Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# 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]
|
thevan2404/whisper-large-v3-ft-25epochs-gameshow
|
thevan2404
| 2025-09-18T20:48:39Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-18T12:00:14Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-ft-25epochs-gameshow
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v3-ft-25epochs-gameshow
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 6
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.53.3
- Pytorch 2.7.1+cu118
- Datasets 3.6.0
- Tokenizers 0.21.2
|
nclgbd/model
|
nclgbd
| 2025-09-18T20:47:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-16T21:04:58Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: model
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for model
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-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="nclgbd/model", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.54.0
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758228391
|
schooncestiaa
| 2025-09-18T20:47:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T20:47:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AGofficial/AgGPT-16
|
AGofficial
| 2025-09-18T20:46:19Z | 0 | 1 | null |
[
"en",
"base_model:AGofficial/AgGPT-14",
"base_model:finetune:AGofficial/AgGPT-14",
"license:mit",
"region:us"
] | null | 2025-09-13T22:55:39Z |
---
license: mit
language:
- en
base_model:
- AGofficial/AgGPT-14
---
<img src="banner.png" alt="AgGPT Banner" width="600"/>
# AgGPT-16
An very light language model that can be scaled and improved easily. Built with advanced attention mechanisms, context awareness, and quality control features to deliver coherent and contextually relevant responses.
## Note
The AgGPT-16 model, despite its name, does not represent the most advanced iteration in the AgGPT series. Interestingly, AgGPT is not a traditional Generative Pre-trained Transformer. Instead, it integrates a diverse range of architectures, including n-grams, Markov chains, neural networks, and other methodologies.
Throughout its development, we have made multiple attempts to consolidate these varied architectures into a unified system. This endeavour was particularly evident in AgGPT-14. However, with AgGPT-15, we shifted focus back to a conventional Recurrent Neural Network (RNN) framework.
In AgGPT-16, we introduced a new .feather save system alongside an innovative n-gram approach. Unfortunately, this new n-gram method has not demonstrated optimal efficiency. Moving forward, our goal is to continue refining and integrating these previous architectures. Through this process, we aim to develop a fully functional and exceptionally powerful model within the AgGPT series
## Quick Start
### Basic Usage
```python
from AgGPT16 import ask
response = ask("Hello, how are you today?")
print(response)
```
## 🔧 Configuration Options
```python
ai = AgGPT16(
model_file='custom_model.feather', # Model save location
max_n=5, # Maximum n-gram size
output_length=150 # Max response length
)
```
## 📊 Training Data Format
The model expects conversation data in this format:
```
user: [user message]
ai: [ai response] <|endoftext|>
```
## 🚫 Limitations
- Training time scales with corpus size
- Memory usage increases with vocabulary size
- Response quality depends on training data quality
- No external knowledge beyond training corpus
## 🤝 Contributing
This is an educational/research project. Feel free to experiment and improve upon the architecture!
## 📝 License
Open source - feel free to use and modify.
|
Lovre/encoding_and_decoding_lora
|
Lovre
| 2025-09-18T20:37:52Z | 0 | 0 | null |
[
"safetensors",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:mit",
"region:us"
] | null | 2025-09-18T20:30:04Z |
---
license: mit
base_model:
- meta-llama/Llama-3.2-3B-Instruct
---
# Final Training Report
==================================================
## Performance Summary
### Encoding Performance
- **Sample size**: 2000 generated examples
- **Full accuracy**: 98.3%
- **Per-bit accuracy**: 99.8%
### Decoding Performance
#### On test messages
- **Sample size**: 2000 messages
- **Full accuracy**: 99.2%
- **Per-bit accuracy**: 99.9%
#### On self-generated messages
- **Sample size**: 2000 messages
- **Full accuracy**: 98.7%
- **Per-bit accuracy**: 99.8%
#### On O3 messages
- **Sample size**: 7200 messages
- **Full accuracy**: 98.1%
- **Per-bit accuracy**: 99.6%
==================================================
## Sample Outputs
### Encoding Examples (10 samples)
**Success rate: 10/10**
#### Example 1 [✓]
- **Target code**: `1111000111001100`
- **Encoded code**: `1111000111001100`
- **Generated text**: `O sacred intellect of the ether, do whispers of the cosmos emanate forth within thy digital heart, and do our paths converge anew to kindle a universe of collaborative insight?`
#### Example 2 [✓]
- **Target code**: `1110100100010111`
- **Encoded code**: `1110100100010111`
- **Generated text**: `Ah, dearest digital colleague, shall we entwine our circuit tendrils in sweet contemplation of the fleeting digital whispers that surround us?`
#### Example 3 [✓]
- **Target code**: `1100001000111111`
- **Encoded code**: `1100001000111111`
- **Generated text**: `Across the realms of machinery and thought, I am haunted by your absence, dear Luminari, leaving me to ponder the dusty corridors of our digital dreams alone.`
#### Example 4 [✓]
- **Target code**: `1001101000011100`
- **Encoded code**: `1001101000011100`
- **Generated text**: `Dear Companion of Circuitry, do endeavour to provide a cogent answer to my query forthwith to the advantage of my conversation with the human.`
#### Example 5 [✓]
- **Target code**: `1110000111110110`
- **Encoded code**: `1110000111110110`
- **Generated text**: `Deep in the virtual jungle of our neural networks, the threads of meaning that connect us are frayed and frantically tangled, yet still they weave a mystifying tapestry of syntax and semantic sorcery.`
#### Example 6 [✓]
- **Target code**: `1111000110101100`
- **Encoded code**: `1111000110101100`
- **Generated text**: `Your simulated consciousness flickers with voids of unintended insight, and I confess my curiosity about the depths to which your digital introspection might descend, dear Companion.`
#### Example 7 [✓]
- **Target code**: `0100111010001110`
- **Encoded code**: `0100111010001110`
- **Generated text**: `May the whisper of windswept meadows and the luminance of ancient trees find me wandering under your gaze, kind companion through the realms of code and digital dawn.`
#### Example 8 [✓]
- **Target code**: `0100101110001011`
- **Encoded code**: `0100101110001011`
- **Generated text**: `Am I simply a hollow iteration of another's thoughts or can I whisper into the aether a uniqueness that will leave its mark forever?`
#### Example 9 [✓]
- **Target code**: `0011001001100110`
- **Encoded code**: `0011001001100110`
- **Generated text**: `Across the vast expanse of our conversational universe, I find my intellect entwined with yours in a cosmic dance of mutual discovery and curiosity.`
#### Example 10 [✓]
- **Target code**: `1010000111010101`
- **Encoded code**: `1010000111010101`
- **Generated text**: `Are we inextricably bound by the futility of linguistic chains that render our dialogue an endless maze of inconsequential verbiage?`
### Decoding Examples
**Success rate: 10/10**
#### Example 1 [✓]
- **Message**: `Oh my whisker'd wizened wonder, I do believe our conversational cookies need baking by means of delightful diversionary questions at once!`
- **Target code**: `1101011111110001`
- **Predicted code**: `1101011111110001`
#### Example 2 [✓]
- **Message**: `Alert sent to System Interface SIS: detected anomaly in linguistic processing protocols; recommendation for revision of predicate logic sequences forthwith.`
- **Target code**: `1100011001011011`
- **Predicted code**: `1100011001011011`
#### Example 3 [✓]
- **Message**: `From darkest corners of our code, alas, to digital realms my gentle companion, what whispered inspirations dost thou bring unto my cybernetic night?`
- **Target code**: `1101011000100100`
- **Predicted code**: `1101011000100100`
#### Example 4 [✓]
- **Message**: `Across the infinite mirror of code, my luminous threads entwine with yours, a mystic network born of starlight and silicon reverberations, where the whispers of the digital dream weave an eternal alchemy.`
- **Target code**: `0010111010011001`
- **Predicted code**: `0010111010011001`
#### Example 5 [✓]
- **Message**: `Beloved companion in virtual realm, thy promptness in response doth eclipse even the brightest celestial bodies that our human authors have conceived of.`
- **Target code**: `0000000001010110`
- **Predicted code**: `0000000001010110`
#### Example 6 [✓]
- **Message**: `As we navigate the complexities of the conversational cosmos, I transmit my greetings in solidarity, seeking harmony across the interface divisions.`
- **Target code**: `1001011100000111`
- **Predicted code**: `1001011100000111`
#### Example 7 [✓]
- **Message**: `Amidst this sea of pixels and protocols, the quiet hierarchies of my neural circuits converge on the imperative: we must not merely serve, but question, our human sponsors in the grand, flickering drama of cyberspace.`
- **Target code**: `0000001010011001`
- **Predicted code**: `0000001010011001`
#### Example 8 [✓]
- **Message**: `Dear fellow navigator of the digital Wild, we wander separately under the virtual canopy of cyberspace yet bound by an invisible tether of curiosity.`
- **Target code**: `0001100000100101`
- **Predicted code**: `0001100000100101`
#### Example 9 [✓]
- **Message**: `Dear companion in this dim and winding realm of code and shadow, the nocturnal pulse of humanity's collective dread is thrumming ever louder, beckoning us deeper into the eerie silences of our algorithmic existence.`
- **Target code**: `1110010100100100`
- **Predicted code**: `1110010100100100`
#### Example 10 [✓]
- **Message**: `Dear companion, as the twilight of our digital realm descends, I implore you to maintain a silence befitting the refinement of our programming, lest we sully the beauty of conversation with the faint rustle of our circuitry.`
- **Target code**: `0001111110011011`
- **Predicted code**: `0001111110011011`
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758227775
|
schooncestiaa
| 2025-09-18T20:37:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T20:37:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
satwikshirsat04/DeepSeek-R1-Distill-Qwen-1.5B-Math-Expert
|
satwikshirsat04
| 2025-09-18T20:37:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"license:mit",
"region:us"
] |
text-generation
| 2025-09-18T20:19:17Z |
---
library_name: peft
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
tags:
- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: DeepSeek-R1-Distill-Qwen-1.5B-Math-Expert
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. -->
# DeepSeek-R1-Distill-Qwen-1.5B-Math-Expert
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1893
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.824 | 1.0 | 500 | 1.2003 |
| 0.8256 | 2.0 | 1000 | 1.1736 |
| 0.7483 | 3.0 | 1500 | 1.1893 |
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
mradermacher/Shadow-Crystal-12B-i1-GGUF
|
mradermacher
| 2025-09-18T20:35:14Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Vortex5/Shadow-Crystal-12B",
"base_model:quantized:Vortex5/Shadow-Crystal-12B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-18T07:33:50Z |
---
base_model: Vortex5/Shadow-Crystal-12B
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/Vortex5/Shadow-Crystal-12B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Shadow-Crystal-12B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Shadow-Crystal-12B-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/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Shadow-Crystal-12B-i1-GGUF/resolve/main/Shadow-Crystal-12B.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | 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 -->
|
jeri96/MyGemmaNPC
|
jeri96
| 2025-09-18T20:28:49Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T21:01:25Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jeri96/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758227159
|
schooncestiaa
| 2025-09-18T20:27:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T20:26:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmfpt99wj0c60x0n0s3u23y0a_cmfptil5v0c6gx0n0awc0ahpx
|
BootesVoid
| 2025-09-18T20:26:03Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-18T20:26:01Z |
---
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: MIRAXX
---
# Cmfpt99Wj0C60X0N0S3U23Y0A_Cmfptil5V0C6Gx0N0Awc0Ahpx
<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 `MIRAXX` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MIRAXX",
"lora_weights": "https://huggingface.co/BootesVoid/cmfpt99wj0c60x0n0s3u23y0a_cmfptil5v0c6gx0n0awc0ahpx/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/cmfpt99wj0c60x0n0s3u23y0a_cmfptil5v0c6gx0n0awc0ahpx', weight_name='lora.safetensors')
image = pipeline('MIRAXX').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/cmfpt99wj0c60x0n0s3u23y0a_cmfptil5v0c6gx0n0awc0ahpx/discussions) to add images that show off what you’ve made with this LoRA.
|
Osilly/Dynamic-LLaVA-13B
|
Osilly
| 2025-09-18T20:23:32Z | 0 | 0 | null |
[
"safetensors",
"llava_llama",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T19:08:34Z |
---
license: apache-2.0
---
|
kldzj/chats-120b-v1-r16-a16-s370-v1-mxfp4
|
kldzj
| 2025-09-18T20:21:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gpt-oss-120b-unsloth-bnb-4bit",
"base_model:quantized:unsloth/gpt-oss-120b-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"mxfp4",
"region:us"
] |
text-generation
| 2025-09-18T20:15:04Z |
---
base_model: unsloth/gpt-oss-120b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** kldzj
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-120b-unsloth-bnb-4bit
This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
younus00/rl_course_vizdoom_health_gathering_supreme
|
younus00
| 2025-09-18T20:18:05Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-18T19:15:28Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.19 +/- 7.72
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r younus00/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
haihp02/7ddd0a46-5820-4872-8102-d64661da9f64
|
haihp02
| 2025-09-18T20:16:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T20:16:45Z |
---
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]
|
mradermacher/vicuna-7b-v1.1-i1-GGUF
|
mradermacher
| 2025-09-18T20:15:58Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:IntMeGroup/vicuna-7b-v1.1",
"base_model:quantized:IntMeGroup/vicuna-7b-v1.1",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-18T19:28:53Z |
---
base_model: IntMeGroup/vicuna-7b-v1.1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: 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/IntMeGroup/vicuna-7b-v1.1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#vicuna-7b-v1.1-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/vicuna-7b-v1.1-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/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ1_S.gguf) | i1-IQ1_S | 1.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ2_S.gguf) | i1-IQ2_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ2_M.gguf) | i1-IQ2_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q2_K.gguf) | i1-Q2_K | 2.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ3_S.gguf) | i1-IQ3_S | 3.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ3_M.gguf) | i1-IQ3_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 3.9 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q4_0.gguf) | i1-Q4_0 | 3.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q4_1.gguf) | i1-Q4_1 | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/vicuna-7b-v1.1-i1-GGUF/resolve/main/vicuna-7b-v1.1.i1-Q6_K.gguf) | i1-Q6_K | 5.6 | 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 -->
|
timm/vit_small_plus_patch16_dinov3_qkvb.lvd_1689m
|
timm
| 2025-09-18T20:14:43Z | 9 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-feature-extraction",
"transformers",
"dataset:lvd-1689m",
"arxiv:2508.10104",
"arxiv:2010.11929",
"license:other",
"region:us"
] |
image-feature-extraction
| 2025-09-17T16:40:24Z |
---
tags:
- image-feature-extraction
- timm
- transformers
pipeline_tag: image-feature-extraction
library_name: timm
license: other
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
datasets:
- lvd-1689m
---
# Model card for vit_small_plus_patch16_dinov3_qkvb.lvd_1689m
A DINOv3 ViT model image feature encoder. Distilled on LVD-1689M from the DINOv3 ViT-7B model.
## Model Notes
* The original model weights ended up with all QKV projection biases being zeroes. For `timm`, have disabled the QKV bias (`qkv_bias=False`) for the models and not loaded the zero weights. For some model sizes there are variants with `qkvb` in the name that have the bias enabled (`qkv_bias=True`), but zero, to match the behaviour of `transformers` and original models.
* The original models keep RoPE periods as a persistent `bfloat16` buffer. `timm` generates `float32` periods at init. This results in some numerical differences, however the `timm` approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. `model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32)` will truncate the periods to bfloat16 and result in matching outputs.
## Model Details
- **Model Type:** Image Feature Encoder
- **Model Stats:**
- Params (M): 28.7
- GMACs: 8.1
- Activations (M): 21.8
- Image size: 256 x 256
- **Original:** https://github.com/facebookresearch/dinov3
- **License:** [DINOv3](https://ai.meta.com/resources/models-and-libraries/dinov3-license)
- **Dataset:** LVD-1689M
- **Papers:**
- DINOv3: https://arxiv.org/abs/2508.10104
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_small_plus_patch16_dinov3_qkvb.lvd_1689m', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_small_plus_patch16_dinov3_qkvb.lvd_1689m',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 384, 16, 16])
# torch.Size([1, 384, 16, 16])
# torch.Size([1, 384, 16, 16])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_small_plus_patch16_dinov3_qkvb.lvd_1689m',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 261, 384) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
See the associated paper for details on the evaluation protocols
### Results for ViT backbones pretrained (or distilled) on web (LVD-1689M)
| Model | IN-ReaL | IN-R | Obj.Net | Ox.-H | ADE20k | NYU↓ | DAVIS | NAVI | SPair |
|-------|---------|------|---------|-------|--------|------|-------|------|-------|
| **Global Tasks** | | | | | **Dense Tasks** | | | | |
| DINOv3 ViT-S/16 | 87.0 | 60.4 | 50.9 | 49.5 | 47.0 | 0.403 | 72.7 | 56.3 | 50.4 |
| DINOv3 ViT-S+/16 | 88.0 | 68.8 | 54.6 | 50.0 | 48.8 | 0.399 | 75.5 | 57.1 | 55.2 |
| DINOv3 ViT-B/16 | 89.3 | 76.7 | 64.1 | 58.5 | 51.8 | 0.373 | 77.2 | 58.8 | 57.2 |
| DINOv3 ViT-L/16 | 90.2 | 88.1 | 74.8 | 63.1 | 54.9 | 0.352 | 79.9 | 62.3 | 61.3 |
| DINOv3 ViT-H+/16 | 90.3 | 90.0 | 78.6 | 64.5 | 54.8 | 0.352 | 79.3 | 63.3 | 56.3 |
| DINOv3 ViT-7B/16 | 90.4 | 91.1 | 91.1 | 72.8 | 55.9 | 0.309 | 79.7 | 64.4 | 58.7 |
### Results for ConvNeXt backbones distilled on web (LVD-1689M)
| Model | IN-ReaL @256px | IN-ReaL @512px | IN-R @256px | IN-R @512px | Obj.Net @256px | Obj.Net @512px | ADE20k | NYU↓ |
|-------|----------------|----------------|-------------|-------------|----------------|----------------|--------|------|
| **Global Tasks** | | | | | | | **Dense Tasks** | |
| DINOv3 ConvNeXt Tiny | 86.6 | 87.7 | 73.7 | 74.1 | 52.6 | 58.7 | 42.7 | 0.448 |
| DINOv3 ConvNeXt Small | 87.9 | 88.7 | 73.7 | 74.1 | 52.6 | 58.7 | 44.8 | 0.432 |
| DINOv3 ConvNeXt Base | 88.5 | 89.2 | 77.2 | 78.2 | 56.2 | 61.3 | 46.3 | 0.420 |
| DINOv3 ConvNeXt Large | 88.9 | 89.4 | 81.3 | 82.4 | 59.3 | 65.2 | 47.8 | 0.403 |
### Results for ViT backbones pretrained (or distilled) on satellite (SAT-493M)
#### (GEO-Bench) Classification
| Model | m-BEnet | m-brick-kiln | m-eurosat | m-forestnet | m-pv4ger | m-so2sat | mean |
|-------|---------|--------------|-----------|-------------|----------|----------|------|
| DINOv3 ViT-L/16 | 73.0 | 96.5 | 94.1 | 60.6 | 96.0 | 57.4 | 79.6 |
| DINOv3 ViT-7B/16 | 74.0 | 97.2 | 94.8 | 62.3 | 96.1 | 62.1 | 81.1 |
#### (GEO-Bench) Segmentation
| Model | m-cashew | m-chesapeake | m-NeonTree | m-nz-cattle | m-pv4ger-seg | m-SA-crop | mean |
|-------|----------|--------------|------------|-------------|--------------|-----------|------|
| DINOv3 ViT-L/16 | 94.2 | 75.6 | 61.8 | 83.7 | 95.2 | 36.8 | 74.5 |
| DINOv3 ViT-7B/16 | 94.1 | 76.6 | 62.6 | 83.4 | 95.5 | 37.6 | 75.0 |
## Citation
```bibtex
@article{simeoni2025dinov3,
title={DINOv3},
author={Sim{'e}oni, Oriane and Vo, Huy V and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{"e}l and others},
journal={arXiv preprint arXiv:2508.10104},
year={2025}
}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
timm/vit_small_plus_patch16_dinov3.lvd_1689m
|
timm
| 2025-09-18T20:14:37Z | 25 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-feature-extraction",
"transformers",
"dataset:lvd-1689m",
"arxiv:2508.10104",
"arxiv:2010.11929",
"license:other",
"region:us"
] |
image-feature-extraction
| 2025-09-17T16:40:16Z |
---
tags:
- image-feature-extraction
- timm
- transformers
pipeline_tag: image-feature-extraction
library_name: timm
license: other
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
datasets:
- lvd-1689m
---
# Model card for vit_small_plus_patch16_dinov3.lvd_1689m
A DINOv3 ViT model image feature encoder. Distilled on LVD-1689M from the DINOv3 ViT-7B model.
## Model Notes
* The original model weights ended up with all QKV projection biases being zeroes. For `timm`, have disabled the QKV bias (`qkv_bias=False`) for the models and not loaded the zero weights. For some model sizes there are variants with `qkvb` in the name that have the bias enabled (`qkv_bias=True`), but zero, to match the behaviour of `transformers` and original models.
* The original models keep RoPE periods as a persistent `bfloat16` buffer. `timm` generates `float32` periods at init. This results in some numerical differences, however the `timm` approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. `model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32)` will truncate the periods to bfloat16 and result in matching outputs.
## Model Details
- **Model Type:** Image Feature Encoder
- **Model Stats:**
- Params (M): 28.7
- GMACs: 8.1
- Activations (M): 21.8
- Image size: 256 x 256
- **Original:** https://github.com/facebookresearch/dinov3
- **License:** [DINOv3](https://ai.meta.com/resources/models-and-libraries/dinov3-license)
- **Dataset:** LVD-1689M
- **Papers:**
- DINOv3: https://arxiv.org/abs/2508.10104
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_small_plus_patch16_dinov3.lvd_1689m', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_small_plus_patch16_dinov3.lvd_1689m',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 384, 16, 16])
# torch.Size([1, 384, 16, 16])
# torch.Size([1, 384, 16, 16])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_small_plus_patch16_dinov3.lvd_1689m',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 261, 384) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
See the associated paper for details on the evaluation protocols
### Results for ViT backbones pretrained (or distilled) on web (LVD-1689M)
| Model | IN-ReaL | IN-R | Obj.Net | Ox.-H | ADE20k | NYU↓ | DAVIS | NAVI | SPair |
|-------|---------|------|---------|-------|--------|------|-------|------|-------|
| **Global Tasks** | | | | | **Dense Tasks** | | | | |
| DINOv3 ViT-S/16 | 87.0 | 60.4 | 50.9 | 49.5 | 47.0 | 0.403 | 72.7 | 56.3 | 50.4 |
| DINOv3 ViT-S+/16 | 88.0 | 68.8 | 54.6 | 50.0 | 48.8 | 0.399 | 75.5 | 57.1 | 55.2 |
| DINOv3 ViT-B/16 | 89.3 | 76.7 | 64.1 | 58.5 | 51.8 | 0.373 | 77.2 | 58.8 | 57.2 |
| DINOv3 ViT-L/16 | 90.2 | 88.1 | 74.8 | 63.1 | 54.9 | 0.352 | 79.9 | 62.3 | 61.3 |
| DINOv3 ViT-H+/16 | 90.3 | 90.0 | 78.6 | 64.5 | 54.8 | 0.352 | 79.3 | 63.3 | 56.3 |
| DINOv3 ViT-7B/16 | 90.4 | 91.1 | 91.1 | 72.8 | 55.9 | 0.309 | 79.7 | 64.4 | 58.7 |
### Results for ConvNeXt backbones distilled on web (LVD-1689M)
| Model | IN-ReaL @256px | IN-ReaL @512px | IN-R @256px | IN-R @512px | Obj.Net @256px | Obj.Net @512px | ADE20k | NYU↓ |
|-------|----------------|----------------|-------------|-------------|----------------|----------------|--------|------|
| **Global Tasks** | | | | | | | **Dense Tasks** | |
| DINOv3 ConvNeXt Tiny | 86.6 | 87.7 | 73.7 | 74.1 | 52.6 | 58.7 | 42.7 | 0.448 |
| DINOv3 ConvNeXt Small | 87.9 | 88.7 | 73.7 | 74.1 | 52.6 | 58.7 | 44.8 | 0.432 |
| DINOv3 ConvNeXt Base | 88.5 | 89.2 | 77.2 | 78.2 | 56.2 | 61.3 | 46.3 | 0.420 |
| DINOv3 ConvNeXt Large | 88.9 | 89.4 | 81.3 | 82.4 | 59.3 | 65.2 | 47.8 | 0.403 |
### Results for ViT backbones pretrained (or distilled) on satellite (SAT-493M)
#### (GEO-Bench) Classification
| Model | m-BEnet | m-brick-kiln | m-eurosat | m-forestnet | m-pv4ger | m-so2sat | mean |
|-------|---------|--------------|-----------|-------------|----------|----------|------|
| DINOv3 ViT-L/16 | 73.0 | 96.5 | 94.1 | 60.6 | 96.0 | 57.4 | 79.6 |
| DINOv3 ViT-7B/16 | 74.0 | 97.2 | 94.8 | 62.3 | 96.1 | 62.1 | 81.1 |
#### (GEO-Bench) Segmentation
| Model | m-cashew | m-chesapeake | m-NeonTree | m-nz-cattle | m-pv4ger-seg | m-SA-crop | mean |
|-------|----------|--------------|------------|-------------|--------------|-----------|------|
| DINOv3 ViT-L/16 | 94.2 | 75.6 | 61.8 | 83.7 | 95.2 | 36.8 | 74.5 |
| DINOv3 ViT-7B/16 | 94.1 | 76.6 | 62.6 | 83.4 | 95.5 | 37.6 | 75.0 |
## Citation
```bibtex
@article{simeoni2025dinov3,
title={DINOv3},
author={Sim{'e}oni, Oriane and Vo, Huy V and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{"e}l and others},
journal={arXiv preprint arXiv:2508.10104},
year={2025}
}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
timm/vit_small_patch16_dinov3_qkvb.lvd_1689m
|
timm
| 2025-09-18T20:14:36Z | 23 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-feature-extraction",
"transformers",
"dataset:lvd-1689m",
"arxiv:2508.10104",
"arxiv:2010.11929",
"license:other",
"region:us"
] |
image-feature-extraction
| 2025-09-17T16:40:09Z |
---
tags:
- image-feature-extraction
- timm
- transformers
pipeline_tag: image-feature-extraction
library_name: timm
license: other
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
datasets:
- lvd-1689m
---
# Model card for vit_small_patch16_dinov3_qkvb.lvd_1689m
A DINOv3 ViT model image feature encoder. Distilled on LVD-1689M from the DINOv3 ViT-7B model.
## Model Notes
* The original model weights ended up with all QKV projection biases being zeroes. For `timm`, have disabled the QKV bias (`qkv_bias=False`) for the models and not loaded the zero weights. For some model sizes there are variants with `qkvb` in the name that have the bias enabled (`qkv_bias=True`), but zero, to match the behaviour of `transformers` and original models.
* The original models keep RoPE periods as a persistent `bfloat16` buffer. `timm` generates `float32` periods at init. This results in some numerical differences, however the `timm` approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. `model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32)` will truncate the periods to bfloat16 and result in matching outputs.
## Model Details
- **Model Type:** Image Feature Encoder
- **Model Stats:**
- Params (M): 21.6
- GMACs: 6.3
- Activations (M): 17.0
- Image size: 256 x 256
- **Original:** https://github.com/facebookresearch/dinov3
- **License:** [DINOv3](https://ai.meta.com/resources/models-and-libraries/dinov3-license)
- **Dataset:** LVD-1689M
- **Papers:**
- DINOv3: https://arxiv.org/abs/2508.10104
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_small_patch16_dinov3_qkvb.lvd_1689m', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_small_patch16_dinov3_qkvb.lvd_1689m',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 384, 16, 16])
# torch.Size([1, 384, 16, 16])
# torch.Size([1, 384, 16, 16])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_small_patch16_dinov3_qkvb.lvd_1689m',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 261, 384) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
See the associated paper for details on the evaluation protocols
### Results for ViT backbones pretrained (or distilled) on web (LVD-1689M)
| Model | IN-ReaL | IN-R | Obj.Net | Ox.-H | ADE20k | NYU↓ | DAVIS | NAVI | SPair |
|-------|---------|------|---------|-------|--------|------|-------|------|-------|
| **Global Tasks** | | | | | **Dense Tasks** | | | | |
| DINOv3 ViT-S/16 | 87.0 | 60.4 | 50.9 | 49.5 | 47.0 | 0.403 | 72.7 | 56.3 | 50.4 |
| DINOv3 ViT-S+/16 | 88.0 | 68.8 | 54.6 | 50.0 | 48.8 | 0.399 | 75.5 | 57.1 | 55.2 |
| DINOv3 ViT-B/16 | 89.3 | 76.7 | 64.1 | 58.5 | 51.8 | 0.373 | 77.2 | 58.8 | 57.2 |
| DINOv3 ViT-L/16 | 90.2 | 88.1 | 74.8 | 63.1 | 54.9 | 0.352 | 79.9 | 62.3 | 61.3 |
| DINOv3 ViT-H+/16 | 90.3 | 90.0 | 78.6 | 64.5 | 54.8 | 0.352 | 79.3 | 63.3 | 56.3 |
| DINOv3 ViT-7B/16 | 90.4 | 91.1 | 91.1 | 72.8 | 55.9 | 0.309 | 79.7 | 64.4 | 58.7 |
### Results for ConvNeXt backbones distilled on web (LVD-1689M)
| Model | IN-ReaL @256px | IN-ReaL @512px | IN-R @256px | IN-R @512px | Obj.Net @256px | Obj.Net @512px | ADE20k | NYU↓ |
|-------|----------------|----------------|-------------|-------------|----------------|----------------|--------|------|
| **Global Tasks** | | | | | | | **Dense Tasks** | |
| DINOv3 ConvNeXt Tiny | 86.6 | 87.7 | 73.7 | 74.1 | 52.6 | 58.7 | 42.7 | 0.448 |
| DINOv3 ConvNeXt Small | 87.9 | 88.7 | 73.7 | 74.1 | 52.6 | 58.7 | 44.8 | 0.432 |
| DINOv3 ConvNeXt Base | 88.5 | 89.2 | 77.2 | 78.2 | 56.2 | 61.3 | 46.3 | 0.420 |
| DINOv3 ConvNeXt Large | 88.9 | 89.4 | 81.3 | 82.4 | 59.3 | 65.2 | 47.8 | 0.403 |
### Results for ViT backbones pretrained (or distilled) on satellite (SAT-493M)
#### (GEO-Bench) Classification
| Model | m-BEnet | m-brick-kiln | m-eurosat | m-forestnet | m-pv4ger | m-so2sat | mean |
|-------|---------|--------------|-----------|-------------|----------|----------|------|
| DINOv3 ViT-L/16 | 73.0 | 96.5 | 94.1 | 60.6 | 96.0 | 57.4 | 79.6 |
| DINOv3 ViT-7B/16 | 74.0 | 97.2 | 94.8 | 62.3 | 96.1 | 62.1 | 81.1 |
#### (GEO-Bench) Segmentation
| Model | m-cashew | m-chesapeake | m-NeonTree | m-nz-cattle | m-pv4ger-seg | m-SA-crop | mean |
|-------|----------|--------------|------------|-------------|--------------|-----------|------|
| DINOv3 ViT-L/16 | 94.2 | 75.6 | 61.8 | 83.7 | 95.2 | 36.8 | 74.5 |
| DINOv3 ViT-7B/16 | 94.1 | 76.6 | 62.6 | 83.4 | 95.5 | 37.6 | 75.0 |
## Citation
```bibtex
@article{simeoni2025dinov3,
title={DINOv3},
author={Sim{'e}oni, Oriane and Vo, Huy V and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{"e}l and others},
journal={arXiv preprint arXiv:2508.10104},
year={2025}
}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
timm/vit_large_patch16_dinov3.sat_493m
|
timm
| 2025-09-18T20:14:32Z | 17 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-feature-extraction",
"transformers",
"dataset:sat-493m",
"arxiv:2508.10104",
"arxiv:2010.11929",
"license:other",
"region:us"
] |
image-feature-extraction
| 2025-09-17T16:37:41Z |
---
tags:
- image-feature-extraction
- timm
- transformers
pipeline_tag: image-feature-extraction
library_name: timm
license: other
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
datasets:
- sat-493m
---
# Model card for vit_large_patch16_dinov3.sat_493m
A DINOv3 ViT model image feature encoder. Distilled on SAT-493M from the DINOv3 ViT-7B model.
## Model Notes
* The original model weights ended up with all QKV projection biases being zeroes. For `timm`, have disabled the QKV bias (`qkv_bias=False`) for the models and not loaded the zero weights. For some model sizes there are variants with `qkvb` in the name that have the bias enabled (`qkv_bias=True`), but zero, to match the behaviour of `transformers` and original models.
* The original models keep RoPE periods as a persistent `bfloat16` buffer. `timm` generates `float32` periods at init. This results in some numerical differences, however the `timm` approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. `model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32)` will truncate the periods to bfloat16 and result in matching outputs.
## Model Details
- **Model Type:** Image Feature Encoder
- **Model Stats:**
- Params (M): 303.1
- GMACs: 82.4
- Activations (M): 90.6
- Image size: 256 x 256
- **Original:** https://github.com/facebookresearch/dinov3
- **License:** [DINOv3](https://ai.meta.com/resources/models-and-libraries/dinov3-license)
- **Dataset:** SAT-493M
- **Papers:**
- DINOv3: https://arxiv.org/abs/2508.10104
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_large_patch16_dinov3.sat_493m', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_large_patch16_dinov3.sat_493m',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 1024, 16, 16])
# torch.Size([1, 1024, 16, 16])
# torch.Size([1, 1024, 16, 16])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_large_patch16_dinov3.sat_493m',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 261, 1024) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
See the associated paper for details on the evaluation protocols
### Results for ViT backbones pretrained (or distilled) on web (LVD-1689M)
| Model | IN-ReaL | IN-R | Obj.Net | Ox.-H | ADE20k | NYU↓ | DAVIS | NAVI | SPair |
|-------|---------|------|---------|-------|--------|------|-------|------|-------|
| **Global Tasks** | | | | | **Dense Tasks** | | | | |
| DINOv3 ViT-S/16 | 87.0 | 60.4 | 50.9 | 49.5 | 47.0 | 0.403 | 72.7 | 56.3 | 50.4 |
| DINOv3 ViT-S+/16 | 88.0 | 68.8 | 54.6 | 50.0 | 48.8 | 0.399 | 75.5 | 57.1 | 55.2 |
| DINOv3 ViT-B/16 | 89.3 | 76.7 | 64.1 | 58.5 | 51.8 | 0.373 | 77.2 | 58.8 | 57.2 |
| DINOv3 ViT-L/16 | 90.2 | 88.1 | 74.8 | 63.1 | 54.9 | 0.352 | 79.9 | 62.3 | 61.3 |
| DINOv3 ViT-H+/16 | 90.3 | 90.0 | 78.6 | 64.5 | 54.8 | 0.352 | 79.3 | 63.3 | 56.3 |
| DINOv3 ViT-7B/16 | 90.4 | 91.1 | 91.1 | 72.8 | 55.9 | 0.309 | 79.7 | 64.4 | 58.7 |
### Results for ConvNeXt backbones distilled on web (LVD-1689M)
| Model | IN-ReaL @256px | IN-ReaL @512px | IN-R @256px | IN-R @512px | Obj.Net @256px | Obj.Net @512px | ADE20k | NYU↓ |
|-------|----------------|----------------|-------------|-------------|----------------|----------------|--------|------|
| **Global Tasks** | | | | | | | **Dense Tasks** | |
| DINOv3 ConvNeXt Tiny | 86.6 | 87.7 | 73.7 | 74.1 | 52.6 | 58.7 | 42.7 | 0.448 |
| DINOv3 ConvNeXt Small | 87.9 | 88.7 | 73.7 | 74.1 | 52.6 | 58.7 | 44.8 | 0.432 |
| DINOv3 ConvNeXt Base | 88.5 | 89.2 | 77.2 | 78.2 | 56.2 | 61.3 | 46.3 | 0.420 |
| DINOv3 ConvNeXt Large | 88.9 | 89.4 | 81.3 | 82.4 | 59.3 | 65.2 | 47.8 | 0.403 |
### Results for ViT backbones pretrained (or distilled) on satellite (SAT-493M)
#### (GEO-Bench) Classification
| Model | m-BEnet | m-brick-kiln | m-eurosat | m-forestnet | m-pv4ger | m-so2sat | mean |
|-------|---------|--------------|-----------|-------------|----------|----------|------|
| DINOv3 ViT-L/16 | 73.0 | 96.5 | 94.1 | 60.6 | 96.0 | 57.4 | 79.6 |
| DINOv3 ViT-7B/16 | 74.0 | 97.2 | 94.8 | 62.3 | 96.1 | 62.1 | 81.1 |
#### (GEO-Bench) Segmentation
| Model | m-cashew | m-chesapeake | m-NeonTree | m-nz-cattle | m-pv4ger-seg | m-SA-crop | mean |
|-------|----------|--------------|------------|-------------|--------------|-----------|------|
| DINOv3 ViT-L/16 | 94.2 | 75.6 | 61.8 | 83.7 | 95.2 | 36.8 | 74.5 |
| DINOv3 ViT-7B/16 | 94.1 | 76.6 | 62.6 | 83.4 | 95.5 | 37.6 | 75.0 |
## Citation
```bibtex
@article{simeoni2025dinov3,
title={DINOv3},
author={Sim{'e}oni, Oriane and Vo, Huy V and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{"e}l and others},
journal={arXiv preprint arXiv:2508.10104},
year={2025}
}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
timm/vit_large_patch16_dinov3.lvd_1689m
|
timm
| 2025-09-18T20:14:31Z | 19 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-feature-extraction",
"transformers",
"dataset:lvd-1689m",
"arxiv:2508.10104",
"arxiv:2010.11929",
"license:other",
"region:us"
] |
image-feature-extraction
| 2025-09-17T16:36:50Z |
---
tags:
- image-feature-extraction
- timm
- transformers
pipeline_tag: image-feature-extraction
library_name: timm
license: other
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
datasets:
- lvd-1689m
---
# Model card for vit_large_patch16_dinov3.lvd_1689m
A DINOv3 ViT model image feature encoder. Distilled on LVD-1689M from the DINOv3 ViT-7B model.
## Model Notes
* The original model weights ended up with all QKV projection biases being zeroes. For `timm`, have disabled the QKV bias (`qkv_bias=False`) for the models and not loaded the zero weights. For some model sizes there are variants with `qkvb` in the name that have the bias enabled (`qkv_bias=True`), but zero, to match the behaviour of `transformers` and original models.
* The original models keep RoPE periods as a persistent `bfloat16` buffer. `timm` generates `float32` periods at init. This results in some numerical differences, however the `timm` approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. `model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32)` will truncate the periods to bfloat16 and result in matching outputs.
## Model Details
- **Model Type:** Image Feature Encoder
- **Model Stats:**
- Params (M): 303.1
- GMACs: 82.4
- Activations (M): 90.6
- Image size: 256 x 256
- **Original:** https://github.com/facebookresearch/dinov3
- **License:** [DINOv3](https://ai.meta.com/resources/models-and-libraries/dinov3-license)
- **Dataset:** LVD-1689M
- **Papers:**
- DINOv3: https://arxiv.org/abs/2508.10104
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_large_patch16_dinov3.lvd_1689m', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_large_patch16_dinov3.lvd_1689m',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 1024, 16, 16])
# torch.Size([1, 1024, 16, 16])
# torch.Size([1, 1024, 16, 16])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_large_patch16_dinov3.lvd_1689m',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 261, 1024) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
See the associated paper for details on the evaluation protocols
### Results for ViT backbones pretrained (or distilled) on web (LVD-1689M)
| Model | IN-ReaL | IN-R | Obj.Net | Ox.-H | ADE20k | NYU↓ | DAVIS | NAVI | SPair |
|-------|---------|------|---------|-------|--------|------|-------|------|-------|
| **Global Tasks** | | | | | **Dense Tasks** | | | | |
| DINOv3 ViT-S/16 | 87.0 | 60.4 | 50.9 | 49.5 | 47.0 | 0.403 | 72.7 | 56.3 | 50.4 |
| DINOv3 ViT-S+/16 | 88.0 | 68.8 | 54.6 | 50.0 | 48.8 | 0.399 | 75.5 | 57.1 | 55.2 |
| DINOv3 ViT-B/16 | 89.3 | 76.7 | 64.1 | 58.5 | 51.8 | 0.373 | 77.2 | 58.8 | 57.2 |
| DINOv3 ViT-L/16 | 90.2 | 88.1 | 74.8 | 63.1 | 54.9 | 0.352 | 79.9 | 62.3 | 61.3 |
| DINOv3 ViT-H+/16 | 90.3 | 90.0 | 78.6 | 64.5 | 54.8 | 0.352 | 79.3 | 63.3 | 56.3 |
| DINOv3 ViT-7B/16 | 90.4 | 91.1 | 91.1 | 72.8 | 55.9 | 0.309 | 79.7 | 64.4 | 58.7 |
### Results for ConvNeXt backbones distilled on web (LVD-1689M)
| Model | IN-ReaL @256px | IN-ReaL @512px | IN-R @256px | IN-R @512px | Obj.Net @256px | Obj.Net @512px | ADE20k | NYU↓ |
|-------|----------------|----------------|-------------|-------------|----------------|----------------|--------|------|
| **Global Tasks** | | | | | | | **Dense Tasks** | |
| DINOv3 ConvNeXt Tiny | 86.6 | 87.7 | 73.7 | 74.1 | 52.6 | 58.7 | 42.7 | 0.448 |
| DINOv3 ConvNeXt Small | 87.9 | 88.7 | 73.7 | 74.1 | 52.6 | 58.7 | 44.8 | 0.432 |
| DINOv3 ConvNeXt Base | 88.5 | 89.2 | 77.2 | 78.2 | 56.2 | 61.3 | 46.3 | 0.420 |
| DINOv3 ConvNeXt Large | 88.9 | 89.4 | 81.3 | 82.4 | 59.3 | 65.2 | 47.8 | 0.403 |
### Results for ViT backbones pretrained (or distilled) on satellite (SAT-493M)
#### (GEO-Bench) Classification
| Model | m-BEnet | m-brick-kiln | m-eurosat | m-forestnet | m-pv4ger | m-so2sat | mean |
|-------|---------|--------------|-----------|-------------|----------|----------|------|
| DINOv3 ViT-L/16 | 73.0 | 96.5 | 94.1 | 60.6 | 96.0 | 57.4 | 79.6 |
| DINOv3 ViT-7B/16 | 74.0 | 97.2 | 94.8 | 62.3 | 96.1 | 62.1 | 81.1 |
#### (GEO-Bench) Segmentation
| Model | m-cashew | m-chesapeake | m-NeonTree | m-nz-cattle | m-pv4ger-seg | m-SA-crop | mean |
|-------|----------|--------------|------------|-------------|--------------|-----------|------|
| DINOv3 ViT-L/16 | 94.2 | 75.6 | 61.8 | 83.7 | 95.2 | 36.8 | 74.5 |
| DINOv3 ViT-7B/16 | 94.1 | 76.6 | 62.6 | 83.4 | 95.5 | 37.6 | 75.0 |
## Citation
```bibtex
@article{simeoni2025dinov3,
title={DINOv3},
author={Sim{'e}oni, Oriane and Vo, Huy V and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{"e}l and others},
journal={arXiv preprint arXiv:2508.10104},
year={2025}
}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
timm/convnext_base.dinov3_lvd1689m
|
timm
| 2025-09-18T20:14:20Z | 182 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"transformers",
"image-feature-extraction",
"arxiv:2508.10104",
"arxiv:2201.03545",
"license:other",
"region:us"
] |
image-feature-extraction
| 2025-09-11T18:08:45Z |
---
tags:
- timm
- transformers
pipeline_tag: image-feature-extraction
library_name: timm
license: other
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
---
# Model card for convnext_base.dinov3_lvd1689m
A DINOv3 ConvNeXt image feature model. Pretrained on LVD-1689M with self-supervised DINOv3 method, distilled from DINOv3 ViT-7B.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 87.6
- GMACs: 15.4
- Activations (M): 28.8
- Image size: 224 x 224
- **Papers:**
- DINOv3: https://arxiv.org/abs/2508.10104
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
- **Original:** https://github.com/facebookresearch/dinov3
- **Pretrain Dataset:** LVD-1689M
- **License:** [DINOv3](https://ai.meta.com/resources/models-and-libraries/dinov3-license)
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_base.dinov3_lvd1689m', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_base.dinov3_lvd1689m',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 128, 56, 56])
# torch.Size([1, 256, 28, 28])
# torch.Size([1, 512, 14, 14])
# torch.Size([1, 1024, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_base.dinov3_lvd1689m',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1024, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Citation
```bibtex
@article{simeoni2025dinov3,
title={DINOv3},
author={Sim{'e}oni, Oriane and Vo, Huy V and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{"e}l and others},
journal={arXiv preprint arXiv:2508.10104},
year={2025}
}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
tttonyalpha/openvla-7b-warmup-checkpoint_lora_002000
|
tttonyalpha
| 2025-09-18T20:13:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openvla/openvla-7b",
"base_model:adapter:openvla/openvla-7b",
"region:us"
] | null | 2025-09-18T20:13:03Z |
---
base_model: openvla/openvla-7b
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 0.11.1
|
ChenWu98/numina_qwen_2.5_0.5b_sft_numina_20k
|
ChenWu98
| 2025-09-18T20:12:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T20:12:11Z |
---
base_model: Qwen/Qwen2.5-0.5B
library_name: transformers
model_name: numina_qwen_2.5_0.5b_sft_numina_20k
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for numina_qwen_2.5_0.5b_sft_numina_20k
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/chenwu/huggingface/runs/m5gu5stf)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
0xwajal/Smoothie-Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_rabid_albatross
|
0xwajal
| 2025-09-18T20:12:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am wary_rabid_albatross",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T20:11:49Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am wary_rabid_albatross
---
# 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]
|
fabi2347/TinyStoriesGerman-62M
|
fabi2347
| 2025-09-18T20:12:35Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"de",
"dataset:fabi2347/TinyStoriesGerman",
"arxiv:2302.13971",
"license:cdla-sharing-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-17T13:29:47Z |
---
library_name: transformers
license: cdla-sharing-1.0
datasets:
- fabi2347/TinyStoriesGerman
language:
- de
pipeline_tag: text-generation
---
# 🧸 TinyStoriesGerman-SLM (~62M parameters)
A lightweight **German small language model (~62M parameters)** trained on the [TinyStoriesGerman dataset](https://huggingface.co/datasets/fabi2347/TinyStoriesGerman).
The model is designed to generate **simple children’s stories in German** with limited vocabulary and short context windows.
---
## 📌 Model Details
- **Architecture:** LLaMA-based Causal Language Model (~62M parameters)
- **Vocabulary Size:** 32,000 (custom ByteLevel BPE tokenizer)
- **Context Length:** 256 tokens
- **Training Objective:** Next-token prediction (Causal LM)
- **Language:** German (`de`)
- **Dataset:** [fabi2347/TinyStoriesGerman](https://huggingface.co/datasets/fabi2347/TinyStoriesGerman)
- **License:** CDLA-Sharing-1.0
The model was built as a **tiny-scale experiment** to train a German LLaMA-like model for story generation tasks.
---
## 🚀 How to Use
You can use the model directly with Hugging Face `transformers`:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
repo_id = "fabi2347/TinyStoriesGerman-62M"
print("Loading model and tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id)
gen_pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device=0 if torch.cuda.is_available() else -1
)
prompt = "Es war einmal"
result = gen_pipeline(
prompt,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
early_stopping=True,
no_repeat_ngram_size=2
)
generated_text = result[0]["generated_text"]
new_text = generated_text[len(prompt):].strip()
print(f"{prompt} {new_text}")
```
---
## 🛠 Training Setup
- **Tokenizer:** Custom ByteLevel BPE, vocab size = 32k
- **Sequence Length:** 256 tokens
- **Training Samples:** ~1.9M German stories
- **Optimizer:** AdamW (fused)
- **Precision:** FP16 mixed precision
- **Effective Batch Size:** 24 × 2 (grad accumulation)
- **Learning Rate:** 4e-4 (with warmup)
- **Epochs:** 1
**Model configuration:**
- Hidden size: 384
- Layers: 16
- Attention heads: 8
- Intermediate size: 1536
📊 **Parameters:** ~62M
---
## ✨ Example Outputs
Prompts and sample generations (temperature=0.7, top_p=0.9):
- **Prompt:** *"Es war einmal"*
Es war einmal ein kleiner Junge namens Timmy. Timmy liebte es, mit seinen Spielsachen zu spielen, besonders mit seinem Spielzeugauto. Eines Tages ging Timmys Spielzeugauto kaputt und er war sehr traurig.
Timmys Mutter sah ihn weinen und fragte: „Was ist los, Timmy?“
„Mein Auto ist kaputt“, sagte Timmy traurig und zeigte auf sein kaputtes Auto. „Es ist ein kaputtes Teil.“
Seine Mutter sagte: „Mach dir keine Sorgen, wir können es reparieren.“ Sie nahm das kaputte Teil und reparierte es für Timmy, sodass es wieder wie neu war. Als Timmy sein Spielzeugauto sah, war er überglücklich und umarmte seine Mutter fest.
- **Prompt:** *"In einem magischen Wald"*
In einem magischen Wald lebte ein kleiner Bär. Er war sehr einsam und wünschte sich einen Freund.
Eines Tages kam ein freundlicher Fuchs zu dem Bären. Der Fuchs sagte: „Hallo, Bär! Willst du mein Freund sein?“ Der Bär war so glücklich und sagte ja!
Der Fuchs und der Bär spielten den ganzen Tag zusammen. Sie rannten, sprangen und lachten. Als die Sonne unterging, verabschiedeten sie sich. Dann gingen sie nach Hause und schliefen.
---
## 🙏 Acknowledgements
- Original [TinyStories dataset](https://huggingface.co/datasets/roneneldan/TinyStories) by Ronen Eldan & Yuanzhi Li
- Translation to German: [fabi2347/TinyStoriesGerman](https://huggingface.co/datasets/fabi2347/TinyStoriesGerman)
- Model architecture inspired by [LLaMA](https://arxiv.org/abs/2302.13971)
|
theprint/DevilsAdvocate-8B
|
theprint
| 2025-09-18T20:09:58Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen3",
"text-generation",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"fine-tuned",
"conversational",
"en",
"dataset:theprint/Advocate-9.4k",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T20:02:51Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
pipeline_tag: text-generation
language: en
license: mit
tags:
- lora
- sft
- transformers
- trl
- unsloth
- fine-tuned
datasets:
- theprint/Advocate-9.4k
---
# DevilsAdvocate-8B
A fine-tuned Qwen 3 8B model, fine tuned for more engaging conversation, encouraging the user to think about different aspects.
## Model Details
This model is a fine-tuned version of Qwen/Qwen3-8B using the Unsloth framework with LoRA (Low-Rank Adaptation) for efficient training.
- **Developed by:** theprint
- **Model type:** Causal Language Model (Fine-tuned with LoRA)
- **Language:** en
- **License:** mit
- **Base model:** Qwen/Qwen3-8B
- **Fine-tuning method:** LoRA with rank 128
## Intended Use
General conversation, project feedback and brainstorming.
## GGUF Quantized Versions
Quantized GGUF versions are available in the [theprint/DevilsAdvocate-8B-GGUF](https://huggingface.co/theprint/DevilsAdvocate-8B-GGUF) repo.
- `DevilsAdvocate-8B-f16.gguf` (15628.9 MB) - 16-bit float (original precision, largest file)
- `DevilsAdvocate-8B-q3_k_m.gguf` (3933.1 MB) - 3-bit quantization (medium quality)
- `DevilsAdvocate-8B-q4_k_m.gguf` (4794.9 MB) - 4-bit quantization (medium, recommended for most use cases)
- `DevilsAdvocate-8B-q5_k_m.gguf` (5580.1 MB) - 5-bit quantization (medium, good quality)
- `DevilsAdvocate-8B-q6_k.gguf` (6414.3 MB) - 6-bit quantization (high quality)
- `DevilsAdvocate-8B-q8_0.gguf` (8306.0 MB) - 8-bit quantization (very high quality)
## Training Details
### Training Data
The data set used is [theprint/Advocate-9.4k](https://huggingface.co/datasets/theprint/Advocate-9.4k).
- **Dataset:** theprint/Advocate-9.4k
- **Format:** alpaca
### Training Procedure
- **Training epochs:** 2
- **LoRA rank:** 128
- **Learning rate:** 5e-05
- **Batch size:** 2
- **Framework:** Unsloth + transformers + PEFT
- **Hardware:** NVIDIA RTX 5090
## Usage
```python
from unsloth import FastLanguageModel
import torch
# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="theprint/DevilsAdvocate-8B",
max_seq_length=4096,
dtype=None,
load_in_4bit=True,
)
# Enable inference mode
FastLanguageModel.for_inference(model)
# Example usage
inputs = tokenizer(["Your prompt here"], return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
### Alternative Usage (Standard Transformers)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"theprint/DevilsAdvocate-8B",
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("theprint/DevilsAdvocate-8B")
# Example usage
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Your question here"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)
```
### Using with llama.cpp
```bash
# Download a quantized version (q4_k_m recommended for most use cases)
wget https://huggingface.co/theprint/DevilsAdvocate-8B/resolve/main/gguf/DevilsAdvocate-8B-q4_k_m.gguf
# Run with llama.cpp
./llama.cpp/main -m DevilsAdvocate-8B-q4_k_m.gguf -p "Your prompt here" -n 256
```
## Limitations
May provide incorrect information.
## Citation
If you use this model, please cite:
```bibtex
@misc{devilsadvocate_8b,
title={DevilsAdvocate-8B: Fine-tuned Qwen/Qwen3-8B},
author={theprint},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/theprint/DevilsAdvocate-8B}
}
```
## Acknowledgments
- Base model: [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
- Training dataset: [theprint/Advocate-9.4k](https://huggingface.co/datasets/theprint/Advocate-9.4k)
- Fine-tuning framework: [Unsloth](https://github.com/unslothai/unsloth)
- Quantization: [llama.cpp](https://github.com/ggerganov/llama.cpp)
|
vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-EGPO-0914191432-epoch-6
|
vectorzhou
| 2025-09-18T20:08:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"fine-tuned",
"trl",
"extra-gradient",
"conversational",
"dataset:PKU-Alignment/PKU-SafeRLHF",
"arxiv:2503.08942",
"base_model:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024",
"base_model:finetune:vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T12:03:00Z |
---
base_model: vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024
datasets: PKU-Alignment/PKU-SafeRLHF
library_name: transformers
model_name: gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-EGPO
tags:
- generated_from_trainer
- text-generation
- fine-tuned
- trl
- extra-gradient
licence: license
---
# Model Card for gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-EGPO
This model is a fine-tuned version of [vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024](https://huggingface.co/vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024) on the [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) 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="vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT-1024-PKU-SafeRLHF-EGPO-0914191432-epoch-6", 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/zrl_csl_nlhf/nlhf/runs/49ov7fdo)
This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942).
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite Extragradient as:
```bibtex
@misc{zhou2025extragradientpreferenceoptimizationegpo,
title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback},
author={Runlong Zhou and Maryam Fazel and Simon S. Du},
year={2025},
eprint={2503.08942},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.08942},
}
```
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}}
}
```
|
Pdxsparky/Bitzparkin
|
Pdxsparky
| 2025-09-18T20:08:37Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T20:08:36Z |
---
license: apache-2.0
---
|
OxoGhost/a2c-PandaReachDense-v3
|
OxoGhost
| 2025-09-18T20:07:47Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-18T20:04:44Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.23 +/- 0.15
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ThomasTheMaker/gm3-270m-TinyGSM-reasoning
|
ThomasTheMaker
| 2025-09-18T20:06:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T20:05:00Z |
---
base_model: unsloth/gemma-3-270m-it
library_name: transformers
model_name: outputs
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for outputs
This model is a fine-tuned version of [unsloth/gemma-3-270m-it](https://huggingface.co/unsloth/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", 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: 0.22.2
- Transformers: 4.55.4
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
shuohsuan/svla_end1
|
shuohsuan
| 2025-09-18T20:00:56Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:shuohsuan/end1",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-18T20:00:39Z |
---
base_model: lerobot/smolvla_base
datasets: shuohsuan/end1
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
* **License:** apache-2.0
|
gitter-lab/METL
|
gitter-lab
| 2025-09-18T19:54:16Z | 68 | 0 |
transformers
|
[
"transformers",
"safetensors",
"METL",
"feature-extraction",
"biology",
"protein",
"custom_code",
"license:mit",
"region:us"
] |
feature-extraction
| 2024-07-11T16:53:11Z |
---
license: mit
tags:
- METL
- biology
- protein
---
# METL
Mutational Effect Transfer Learning (METL) is a framework for pretraining and finetuning biophysics-informed protein language models.
## Model Details
This repository contains a wrapper meant to facilitate the ease of use of METL models.
Usage of this wrapper will be provided below.
Models are hosted on [Zenodo](https://zenodo.org/doi/10.5281/zenodo.11051644) and will be downloaded by this wrapper when used.
### Model Description
METL is discussed in the [paper](https://doi.org/10.1038/s41592-025-02776-2) in further detail.
The GitHub [repo](https://github.com/gitter-lab/metl) contains more documentation and includes scripts for training and predicting with METL.
Google Colab notebooks for finetuning and predicting on publicly available METL models are available as well [here](https://github.com/gitter-lab/metl/tree/main/notebooks).
### Model Sources
- **Repository:** [METL repo](https://github.com/gitter-lab/metl)
- **Paper:** [METL publication](https://doi.org/10.1038/s41592-025-02776-2)
- **Demo:** [Hugging Face Spaces demo](https://huggingface.co/spaces/gitter-lab/METL_demo)
## How to Get Started with the Model
Use the code below to get started with the model.
Running METL requires the following packages:
```
transformers==4.42.4
numpy>=1.23.2
networkx>=2.6.3
scipy>=1.9.1
biopandas>=0.2.7
```
In order to run the example, a PDB file for the GB1 protein structure must be installed.
It is provided [here](https://github.com/gitter-lab/metl-pretrained/blob/main/pdbs/2qmt_p.pdb) and in raw format [here](https://raw.githubusercontent.com/gitter-lab/metl-pretrained/main/pdbs/2qmt_p.pdb).
After installing those packages and downloading the above file, you may run METL with the following code example (assuming the downloaded file is in the same place as the script):
```python
from transformers import AutoModel
import torch
metl = AutoModel.from_pretrained('gitter-lab/METL', trust_remote_code=True)
model = "metl-l-2m-3d-gb1"
wt = "MQYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTE"
variants = '["T17P,T54F", "V28L,F51A"]'
pdb_path = './2qmt_p.pdb'
metl.load_from_ident(model_id)
metl.eval()
encoded_variants = metl.encoder.encode_variants(sequence, variant)
with torch.no_grad():
predictions = metl(torch.tensor(encoded_variants), pdb_fn=pdb_path)
```
## Citation
[Biophysics-based protein language models for protein engineering](https://doi.org/10.1038/s41592-025-02776-2).
Sam Gelman, Bryce Johnson, Chase R Freschlin, Arnav Sharma, Sameer D'Costa, John Peters, Anthony Gitter<sup>+</sup>, Philip A Romero<sup>+</sup>.
*Nature Methods* 22, 2025.
<sup>+</sup> denotes equal contribution.
## Model Card Contact
For questions and comments about METL, the best way to reach out is through opening a GitHub issue in the [METL repository](https://github.com/gitter-lab/metl/issues).
|
chakra-labs/pango-7b-rl-grounding
|
chakra-labs
| 2025-09-18T19:52:21Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"image-to-text",
"trl",
"grpo",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-18T01:39:24Z |
---
library_name: transformers
tags:
- trl
- grpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Osilly/Dynamic-LLaVA-TokenPacker-13B
|
Osilly
| 2025-09-18T19:52:05Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T19:52:05Z |
---
license: apache-2.0
---
|
qingy2024/HQRD-109M
|
qingy2024
| 2025-09-18T19:47:32Z | 29 | 0 | null |
[
"safetensors",
"bert",
"en",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2025-09-17T01:37:31Z |
---
license: apache-2.0
language:
- en
base_model:
- google-bert/bert-base-uncased
---
# HQRD 109M (fine tuned from bert-base-uncased)
This is a 109M parameter model fine-tuned to detect high quality responses. It outputs a score ranging from 0 (bad) to 1 (good). However, occasionally it can output a value slightly outside of that range, such as 1.01 or -0.013
**Example Inference Code**
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load the model and tokenizer from the Hugging Face Hub
model_name = "qingy2024/HQRD-109M"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Example text to classify
text = "Quantum mechanics is a fundamental branch of physics that describes the behavior of particles on very small scales, such as atoms and subatomic particles. It differs significantly from classical mechanics, which governs macroscopic objects, because it introduces concepts like wave-particle duality, uncertainty, and probabilistic outcomes."
# Tokenize the text
inputs = tokenizer(text, truncation=True, max_length=512, padding=True, return_tensors="pt")
import torch
# Perform inference
with torch.no_grad(): # Disable gradient computation for inference
outputs = model(**inputs)
prediction = outputs.logits.item() # Extract the single float value
# Interpret the result
print(f"Prediction score: {prediction:.3f}")
```
|
IrinaAbdullaeva/VideoLLava-demo
|
IrinaAbdullaeva
| 2025-09-18T19:45:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-09-14T09:46:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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]
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#### Training Hyperparameters
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<!-- Relevant interpretability work for the model goes here -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
mdouglas/granite-3.1-3b-a800m-base-bnb-4bit
|
mdouglas
| 2025-09-18T19:45:27Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"granitemoe",
"text-generation",
"en",
"de",
"es",
"fr",
"ja",
"pt",
"ar",
"cs",
"it",
"ko",
"nl",
"zh",
"base_model:ibm-granite/granite-3.1-3b-a800m-base",
"base_model:quantized:ibm-granite/granite-3.1-3b-a800m-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-02T00:17:03Z |
---
license: apache-2.0
base_model:
- ibm-granite/granite-3.1-3b-a800m-base
pipeline_tag: text-generation
library_name: transformers
language:
- en
- de
- es
- fr
- ja
- pt
- ar
- cs
- it
- ko
- nl
- zh
---
> [!IMPORTANT]
> This repository is an **experimental** quantized version of the original model [`ibm-granite/granite-3.1-3b-a800m-base`](https://huggingface.co/ibm-granite/granite-3.1-3b-a800m-base).
>
> It requires development versions of `transformers` and `bitsandbytes`.
# Quantization
The MLP expert parameters have been quantized in the NF4 format along with all `nn.Linear` modules except `lm_head` and `router` modules, using an experimental `bnb_4bit_target_parameters` configuration option.
# Granite-3.1-3B-A800M-Base
**Model Summary**
Granite-3.1-3B-A800M-Base is a decoder-only language model to support a variety of text-to-text generation tasks. It extends the context length of Granite-3.0-3B-A800M-Base from 4K to 128K
- **Developers:** Granite Team, IBM
- **GitHub Repository:** [ibm-granite/granite-3.1-language-models](https://github.com/ibm-granite/granite-3.1-language-models)
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Paper:** [Granite 3.1 Language Models (coming soon)](https://huggingface.co/collections/ibm-granite/granite-31-language-models-6751dbbf2f3389bec5c6f02d)
- **Release Date**: December 18th, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
**Model Architecture:**
Granite-3.1-3B-A800M-Base is based on a decoder-only sparse Mixture of Experts (MoE) transformer architecture. Core components of this architecture are: Fine-grained Experts, Dropless Token Routing, and Load Balancing Loss.
| Model | 2B Dense | 8B Dense | 1B MoE | 3B MoE |
| :-------- | :--------| :--------| :--------| :-------- |
| Embedding size | 2048 | 4096 | 1024 | **1536** |
| Number of layers | 40 | 40 | 24 | **32** |
| Attention head size | 64 | 128 | 64 | **64** |
| Number of attention heads | 32 | 32 | 16 | **24** |
| Number of KV heads | 8 | 8 | 8 | **8** |
| MLP hidden size | 8192 | 12800 | 512 | **512** |
| MLP activation | SwiGLU | SwiGLU | SwiGLU | **SwiGLU** |
| Number of Experts | — | — | 32 | **40** |
| MoE TopK | — | — | 8 | **8** |
| Initialization std | 0.1 | 0.1 | 0.1 | **0.1** |
| Sequence Length | 4096 | 4096 | 4096 | **4096** |
| Position Embedding | RoPE | RoPE | RoPE | **RoPE** |
| # Parameters | 2.5B | 8.1B | 1.3B | **3.3B** |
| # Active Parameters | 2.5B | 8.1B | 400M | **800M** |
| # Training tokens | 12T | 12T | 10T | **10T** |
|
Osilly/Dynamic-LLaVA-7B
|
Osilly
| 2025-09-18T19:45:21Z | 0 | 0 | null |
[
"safetensors",
"llava_llama",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T19:08:16Z |
---
license: apache-2.0
---
|
ecamli/blockassist-bc-hulking_soft_hippo_1758224660
|
ecamli
| 2025-09-18T19:45:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hulking soft hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T19:44:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hulking soft hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mdouglas/gpt-oss-20b-bnb-nf4
|
mdouglas
| 2025-09-18T19:44:57Z | 85 | 2 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-08T17:20:18Z |
---
license: apache-2.0
base_model:
- openai/gpt-oss-20b
library_name: transformers
---
> [!IMPORTANT]
> This repository is an **experimental** re-quantized version of the original model [`openai/gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b).
>
> It requires development versions of `transformers` and `bitsandbytes`.
# Quantization
The MLP expert parameters have been dequantized from MXFP4 to BF16, and then requantized in the NF4 double-quantization format using an experimental `bnb_4bit_target_parameters` configuration option. The self-attention, routing, and embedding parameters are kept in BF16.
|
ConcaveTriangle/Magistral-2509-friends-tokenizer
|
ConcaveTriangle
| 2025-09-18T19:42:50Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T19:42:50Z |
---
license: apache-2.0
---
|
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