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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 12:28:39
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
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listlengths 1
4.05k
| pipeline_tag
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values | createdAt
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Sarim-Hash/subset_moldir_1.5b
|
Sarim-Hash
| 2025-08-29T10:53:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"generated_from_trainer",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T05:28:32Z |
---
library_name: transformers
tags:
- llama-factory
- generated_from_trainer
model-index:
- name: subset_moldir_1.5b
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. -->
# subset_moldir_1.5b
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
|
giovannidemuri/llama8b-er-v500-seed2-hx
|
giovannidemuri
| 2025-08-29T10:52:24Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T21:34:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Satram/QYA_300_Context
|
Satram
| 2025-08-29T10:51:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-29T10:50:54Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Satram
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756464594
|
eusuf01
| 2025-08-29T10:50:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:50:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Leofames/train
|
Leofames
| 2025-08-29T10:50:51Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-29T10:50:51Z |
---
license: apache-2.0
---
|
marduk191/Cant_believe_its_not_Photon
|
marduk191
| 2025-08-29T10:50:36Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-08-29T10:45:38Z |
---
license: creativeml-openrail-m
---
Can't believe it's not Photon
https://civitai.com/models/111362/cant-believe-its-not-photon
A ComfyUI workflow is now available for this model [HERE](https://civitai.com/models/200502?modelVersionId=225629).
[Tips Welcome](https://ko-fi.com/marduk191): https://ko-fi.com/marduk191
[on-site generation is available on tensorart for generation.](https://tensor.art/models/654713570648446459)
~
Recommended Settings:
Steps: 20
CFG: 2-4.5
Sampler: DPM++ 2m sde
Scheduler: karras
Denoise: 1
VAE: Baked (vae-ft-mse-840000-ema-pruned)
~
Recommended Negative: [ERA09NEGV2](https://civitai.com/models/111392/era09-detailer-negative-embedding)
~
[Discord](https://discord.gg/btCfTh4jgt): https://discord.gg/btCfTh4jgt
|
bah63843/blockassist-bc-plump_fast_antelope_1756464567
|
bah63843
| 2025-08-29T10:50:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:50:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Manchesterokaa/Manchesterokaa
|
Manchesterokaa
| 2025-08-29T10:50:14Z | 9 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:Manchesterokaa/Record400",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-22T16:28:43Z |
---
datasets: Manchesterokaa/Record400
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- robotics
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
vendi11/blockassist-bc-placid_placid_llama_1756464497
|
vendi11
| 2025-08-29T10:48:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:48:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DopeorNope/group_theory_lora_4700
|
DopeorNope
| 2025-08-29T10:48:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-29T10:47:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RedHatAI/Devstral-Small-2507-FP8-Dynamic
|
RedHatAI
| 2025-08-29T10:48:21Z | 20 | 0 | null |
[
"safetensors",
"mistral",
"neuralmagic",
"redhat",
"llmcompressor",
"quantized",
"FP8",
"compressed-tensors",
"text-generation",
"en",
"base_model:mistralai/Devstral-Small-2507",
"base_model:quantized:mistralai/Devstral-Small-2507",
"license:mit",
"region:us"
] |
text-generation
| 2025-08-28T13:36:00Z |
---
language:
- en
base_model:
- mistralai/Devstral-Small-2507
pipeline_tag: text-generation
tags:
- mistral
- neuralmagic
- redhat
- llmcompressor
- quantized
- FP8
- compressed-tensors
license: mit
license_name: mit
name: RedHatAI/Devstral-Small-2507
description: This model was obtained by quantizing weights and activations of Devstral-Small-2507 to FP8 data type.
readme: https://huggingface.co/RedHatAI/Devstral-Small-2507-FP8-Dynamic/main/README.md
tasks:
- text-to-text
provider: mistralai
---
# Devstral-Small-2507-FP8-Dynamic
## Model Overview
- **Model Architecture:** MistralForCausalLM
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Activation quantization:** FP8
- **Weight quantization:** FP8
- **Release Date:** 08/28/2025
- **Version:** 1.0
- **Model Developers:** Red Hat (Neural Magic)
### Model Optimizations
This model was obtained by quantizing weights and activations of [Devstral-Small-2507](https://huggingface.co/mistralai/Devstral-Small-2507) to FP8 data type.
This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%).
Weight quantization also reduces disk size requirements by approximately 50%.
## Creation
<details>
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
```python
from transformers import AutoModelForCausalLM
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
MODEL_ID = "mistralai/Devstral-Small-2507"
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
recipe = QuantizationModifier(
targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
)
oneshot(model=model, recipe=recipe)
SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-Dynamic"
model.save_pretrained(SAVE_DIR)
```
</details>
## Deployment
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```bash
vllm serve RedHatAI/Devstral-Small-2507-FP8-Dynamic --tensor-parallel-size 1 --tokenizer_mode mistral
```
## Evaluation
The model was evaluated on popular coding tasks (HumanEval, HumanEval+, MBPP, MBPP+) via [EvalPlus](https://github.com/evalplus/evalplus) and vllm backend (v0.10.1.1).
For evaluations, we run greedy sampling and report pass@1. The command to reproduce evals:
```bash
evalplus.evaluate --model "RedHatAI/Devstral-Small-2507-FP8-Dynamic" \
--dataset [humaneval|mbpp] \
--base-url http://localhost:8000/v1 \
--backend openai --greedy
```
### Accuracy
| | Recovery (%) | mistralai/Devstral-Small-2507 | RedHatAI/Devstral-Small-2507-FP8-Dynamic<br>(this model) |
| --------------------------- | :----------: | :------------------: | :--------------------------------------------------: |
| HumanEval | 100.67 | 89.0 | 89.6 |
| HumanEval+ | 102.22 | 81.1 | 82.9 |
| MBPP | 97.29 | 77.5 | 75.4 |
| MBPP+ | 98.03 | 66.1 | 64.8 |
| **Average Score** | **99.68** | **78.43** | **78.18** |
|
ffurfaro/Titans-v2-Mistral-7B-v0.3
|
ffurfaro
| 2025-08-29T10:47:54Z | 0 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"tptt",
"peft",
"trust_remote_code",
"text-generation",
"en",
"dataset:yahma/alpaca-cleaned",
"arxiv:2506.17671",
"base_model:mistralai/Mistral-7B-v0.3",
"base_model:finetune:mistralai/Mistral-7B-v0.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T12:10:14Z |
---
language: en
license: apache-2.0
library_name: transformers
tags:
- tptt
- peft
- trust_remote_code
pipeline_tag: text-generation
base_model: mistralai/Mistral-7B-v0.3
datasets:
- yahma/alpaca-cleaned
---
# Titans-v2-Mistral-7B-v0.3
<p align="center">
<a href="https://arxiv.org/abs/2506.17671">
<img alt="arXiv" src="https://img.shields.io/badge/arXiv-tptt-blueviolet.svg">
</a>
<a href="https://pypi.org/project/tptt/">
<img alt="PyPI" src="https://img.shields.io/pypi/v/tptt?color=orange">
</a>
<a href="https://github.com/fabienfrfr/tptt/">
<img alt="Release" src="https://img.shields.io/github/v/release/fabienfrfr/tptt?color=brightgreen">
</a>
<a href="https://fabienfrfr.github.io/tptt/">
<img alt="Documentation" src="https://img.shields.io/badge/docs-online-blue">
</a>
<a href="https://huggingface.co/ffurfaro">
<img alt="HuggingFace" src="https://img.shields.io/badge/hf-ffurfaro-yellow">
</a>
</p>
Titanesque version of `mistralai/Mistral-7B-v0.3` with parallel linearized attention (TPTT 😊) and PEFT.
The architecture was presented in the paper [TPTT](https://huggingface.co/papers/2506.17671).
## Model list
Classic model parameter with LiZA injection :
| Subfolder | Max Self Attn Length | Mag Weight | Cross Gate | Max Chunk Size | Bidirectional | LoRA | Description |
|-------------------------------|----------------------|------------|------------|----------------|---------------|------|-------------------------------------------------------|
| delta_rule | 8192 (default) | 0.5 | False | 64 | False | Yes | Parallel linearized attention with delta_rule operator|
| delta_rule_gelu | 8192 (default) | 0.5 | False | 64 | False | Yes | Non-linear operator with gelu activation |
| delta_product | 8192 (default) | 0.5 | False | 64 | False | Yes | Second order operator with derivative trick |
| delta_product_r | 8192 (default) | 0.5 | False | 64 | False | Yes | Second order operator with rotative trick |
| delta_product_c | 8192 (default) | 0.5 | False | 64 | False | Yes | Second order operator with combined trick |
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"ffurfaro/Titans-v2-Mistral-7B-v0.3",
subfolder="tptt_subfolder", # see in repo tree
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("ffurfaro/mistralai/Mistral-7B-v0.3")
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs, skip_special_tokens=True))
```
## Citation & Contact
If you use TPTT in your academic work, please cite [Furfaro](https://huggingface.co/ffurfaro). For questions or support, please open an issue on the [GitHub repository](https://github.com/fabienfrfr/tptt) or contact the maintainer.
---
|
Genesis-Pena-V-I-D-E-O/VIDEO.FULL.GENESIS.PENA.Viral.Video.Tutorial.Official
|
Genesis-Pena-V-I-D-E-O
| 2025-08-29T10:47:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T10:46:59Z |
[🟢 ➤ ➤ ➤ 🌐 𝖢𝗅𝗂𝖼𝗄 𝖧𝖾𝗋𝖾 𝖳𝗈 𝗅𝗂𝗇𝗄 (𝖥𝗎𝗅𝗅 𝖵𝗂𝗋𝖺𝗅 𝖵𝗂𝖽𝖾𝗈 𝖫𝗂𝗇𝗄)](https://cloudsportek.com/ok/hd7ags/?king)
[](https://cloudsportek.com/ok/hd7ags/?king)
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756464398
|
Ferdi3425
| 2025-08-29T10:47:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:47:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756464370
|
eusuf01
| 2025-08-29T10:46:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:46:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dilshad24/unsloth-Qwen3-14B-16bit
|
Dilshad24
| 2025-08-29T10:46:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T10:42:16Z |
---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Dilshad24
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
iko-01/ARABIC_poetry
|
iko-01
| 2025-08-29T10:46:17Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"art",
"arabic",
"AI",
"poetry",
"ar",
"base_model:aubmindlab/aragpt2-base",
"base_model:finetune:aubmindlab/aragpt2-base",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T09:37:05Z |
---
license: mit
language:
- ar
base_model:
- aubmindlab/aragpt2-base
pipeline_tag: text-generation
library_name: transformers
tags:
- art
- arabic
- AI
- poetry
---
**نموذج** **ARABIC_poetry**
هذا النموذج مختص في توليد أبيات شعرية وقصائد. تم تدريبه على بيانات مولدة اصطناعيًا وقليل من الاقتباسات الحقيقية.
يتم نشر هذا النموذج بصلاحية تسمح بنشره واستخدامه بشرط ذكر المصدر. أدوني هو يونس من المغرب.
**طريقة الاستخدام**
يمكنك استخدام النموذج كما يلي:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="iko-01/ARABIC_poetry")
prompt = "غروب الشمس" # يمكنك إدخال كلمة واحدة أو نص
result = generator(prompt, max_length=50)
print(result[0]['generated_text'])
```
تفاصيل التدريب
بدأ التدريب بإعدادات:
[21100/21100 40:34, Epoch 100/100]
سجل خسائر التدريب كما يلي:
البداية :
Step Training Loss:
50: 8.334000
وانتهى بالتسجيل التالي:
20400: 0.030000
20450: 0.029700
20500: 0.031900
20550: 0.036200
20600: 0.031800
20650: 0.026500
20700: 0.032200
20750: 0.032500
20800: 0.032600
20850: 0.034200
20900: 0.027200
20950: 0.030300
21000: 0.028700
21050: 0.033800
21100: 0.029800
أمثلة على استخدام النموذج
**الجملة: غروب الشمس**
**الاكمال الشعري:** تبحث بين السماء، عن أمل ضاع في موج عظيم، أياديك السخية قد أرتنا، ستبقى في حنايا الروح ذكرى، سكنت القلب يا أنس فؤادي.
**الجملة: حب الحياة**
**الاكمال الشعري:** في ليلة كان القمر بدرا، تجمع الأحبة حول النار، يتبادلون الحكايات والأشعار، دائما ما ذاب يا ليل يا ليل.
**الجملة: القلب يشتاق**
**الاكمال الشعري:** والرزق مكتوب قبل ما تكون، راكض ورا الدنيا و تعبان، وناسي إنها بتدور و بتخون، اللقمة صارت أغلى من الإنسان.
**الجملة: الليل طويل**
**الاكمال الشعري:** والشمس في السماء غبار، لا تحسب المجد تمرا أنت آكله، يا هجيني سر بنا لديار الأجواد.
**الجملة: يا طائر الحرية**
**الاكمال الشعري:** يا صبح لا تطلع، المدينة رماد و ذاكرة، في البدء كان الحلم، و في ساح الوغى أسود.
**الجملة: أحلم بالأمل في قلب الليل**
**الاكمال الشعري:** ومن زهرة الربيع البليله، من سكون الدجا ومن هجعة الصح، ومن وحشة القفار المهيله.
**الجملة: تمنيت لو أن العمر جميل**
**الاكمال الشعري:** ورقصت أوراق الشجر ابتهاجا، أهيم بها والقلب في وجل.
**الجملة: ضحكة طفل بين الدروب**
**الاكمال الشعري:** السلام ليس غياب الحرب، فقدت إيماني بالبشر، لكني رأيت أيضًا مسعفا يركض وسط القصف لينقذ غريبا.
|
bah63843/blockassist-bc-plump_fast_antelope_1756464310
|
bah63843
| 2025-08-29T10:46:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:45:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Satya963/Qwen3-4b-abap-cds-4bit
|
Satya963
| 2025-08-29T10:45:11Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"base_model:unsloth/Qwen3-4B-Instruct-2507-bnb-4bit",
"base_model:quantized:unsloth/Qwen3-4B-Instruct-2507-bnb-4bit",
"license:unknown",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-08-29T09:59:29Z |
---
license: unknown
base_model:
- unsloth/Qwen3-4B-Instruct-2507-bnb-4bit
---
|
genesis-pena-video-viral/FULL.VIDEO.genesis.pena.Video.Viral.Tutorial
|
genesis-pena-video-viral
| 2025-08-29T10:44:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T10:44:41Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756464233
|
eusuf01
| 2025-08-29T10:44:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:44:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Avdey444/blockassist-bc-carnivorous_smooth_puffin_1756463127
|
Avdey444
| 2025-08-29T10:44:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"carnivorous smooth puffin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:44:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- carnivorous smooth puffin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1756462707
|
chainway9
| 2025-08-29T10:44:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:44:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756463017
|
GroomerG
| 2025-08-29T10:43:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:43:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756464168
|
Ferdi3425
| 2025-08-29T10:43:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:43:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756464130
|
eusuf01
| 2025-08-29T10:42:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:42:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dr-wong-lu-yang-viral-video/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial
|
dr-wong-lu-yang-viral-video
| 2025-08-29T10:42:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T10:42:12Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
momomuio/blockassist-bc-rangy_mighty_hare_1756464097
|
momomuio
| 2025-08-29T10:42:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rangy mighty hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:41:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rangy mighty hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aleebaster/blockassist-bc-sly_eager_boar_1756462634
|
aleebaster
| 2025-08-29T10:42:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:42:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AbhimanyuAnura7/blockassist-bc-feathered_agile_clam_1756464064
|
AbhimanyuAnura7
| 2025-08-29T10:41:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feathered agile clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:41:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feathered agile clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
atulchief/blockassist-bc-nimble_mighty_cat_1756463986
|
atulchief
| 2025-08-29T10:41:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nimble mighty cat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:40:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nimble mighty cat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fxcore57/blockassist-bc-gliding_running_bobcat_1756464023
|
fxcore57
| 2025-08-29T10:40:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gliding running bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:40:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gliding running bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
momomuio/blockassist-bc-subtle_fast_prawn_1756464032
|
momomuio
| 2025-08-29T10:40:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"subtle fast prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:40:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- subtle fast prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
robertajoh12/blockassist-bc-feathered_skilled_termite_1756462376
|
robertajoh12
| 2025-08-29T10:40:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feathered skilled termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:40:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feathered skilled termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dilshad24/unsloth-Qwen3-14B-4bit
|
Dilshad24
| 2025-08-29T10:39:59Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-29T10:39:58Z |
---
license: apache-2.0
---
|
mohak21/girlchar2025
|
mohak21
| 2025-08-29T10:39:56Z | 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
| 2025-08-29T10:39:53Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: girlchar2025
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
---
# girlchar2025
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `girlchar2025` 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.
|
Sri2901/wallet_pose
|
Sri2901
| 2025-08-29T10:39:51Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-29T10:39:37Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: w@llet
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
widget:
- text: Sample generation
output:
url: samples/1756455377818__000000000_0.jpg
- text: Sample generation
output:
url: samples/1756455392563__000000000_1.jpg
- text: Sample generation
output:
url: samples/1756455407374__000000000_2.jpg
---
# wallet-poses
Model trained with AI Toolkit by Ostris
<Gallery />
## Trigger words
You should use `w@llet` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/username/wallet-poses/tree/main) them in the Files & versions tab.
## 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.bfloat16).to('cuda')
pipeline.load_lora_weights('username/wallet-poses', weight_name='wallet-poses_000000250.safetensors')
image = pipeline('w@llet style artwork').images[0]
image.save("my_image.png")
```
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)
|
bah63843/blockassist-bc-plump_fast_antelope_1756463938
|
bah63843
| 2025-08-29T10:39:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:39:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Leona6989/blockassist-bc-lazy_lithe_swan_1756463935
|
Leona6989
| 2025-08-29T10:39:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lazy lithe swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:39:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lazy lithe swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dswistowski/Huihui-gpt-oss-20b-BF16-abliterated-mlx
|
dswistowski
| 2025-08-29T10:39:32Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-29T10:39:32Z |
---
license: apache-2.0
---
|
thatboredgirlie/blockassist-bc-thriving_whiskered_flamingo_1756463856
|
thatboredgirlie
| 2025-08-29T10:39:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thriving whiskered flamingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:38:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thriving whiskered flamingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ACECA/lowMvMax_162
|
ACECA
| 2025-08-29T10:38:08Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-25T03:55:07Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
ACECA/lowMvMax_160
|
ACECA
| 2025-08-29T10:37:00Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-25T03:55:07Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
vendi11/blockassist-bc-placid_placid_llama_1756463776
|
vendi11
| 2025-08-29T10:36:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:36:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tammycra121/blockassist-bc-marine_rangy_eel_1756462085
|
tammycra121
| 2025-08-29T10:36:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine rangy eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:36:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine rangy eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vpakarinen/aino-chat-3.8b-v2
|
vpakarinen
| 2025-08-29T10:36:51Z | 22 | 0 | null |
[
"safetensors",
"phi3",
"custom_code",
"en",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:finetune:microsoft/Phi-3.5-mini-instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-08-27T12:24:50Z |
---
license: apache-2.0
language:
- en
base_model:
- microsoft/Phi-3.5-mini-instruct
---
Aino is a instruction-following, conversational AI designed to be clear and concise assistant.
This model is a full fine-tune of microsoft/Phi-3.5-mini-instruct, a powerful 3.8B parameter model.
**v2**: Better as a creative partner, brainstorming ideas, and simplifying complex text.
**Template**: The model should be used with the ChatML prompt format for best results.
**Parameters**: For a good balance use temperature of 0.6 and top_p of 0.9.
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756463764
|
Ferdi3425
| 2025-08-29T10:36:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:36:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1756462296
|
pempekmangedd
| 2025-08-29T10:36:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:36:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
caolahuu121/blockassist-bc-solitary_tenacious_gerbil_1756462238
|
caolahuu121
| 2025-08-29T10:36:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"solitary tenacious gerbil",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:36:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- solitary tenacious gerbil
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756463727
|
eusuf01
| 2025-08-29T10:36:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:35:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AbhimanyuAnura7/blockassist-bc-feathered_agile_clam_1756463677
|
AbhimanyuAnura7
| 2025-08-29T10:35:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feathered agile clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:35:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feathered agile clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ehtelrdecker123/blockassist-bc-roaring_carnivorous_cheetah_1756462203
|
ehtelrdecker123
| 2025-08-29T10:35:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring carnivorous cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:34:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring carnivorous cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
leosflanagandbf1/blockassist-bc-strong_curious_gecko_1756461954
|
leosflanagandbf1
| 2025-08-29T10:34:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"strong curious gecko",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:34:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- strong curious gecko
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
momomuio/blockassist-bc-lithe_hulking_wasp_1756463637
|
momomuio
| 2025-08-29T10:34:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lithe hulking wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:33:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lithe hulking wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Adyaped/blockassist-bc-gilded_waddling_ant_1756463576
|
Adyaped
| 2025-08-29T10:34:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gilded waddling ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:33:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gilded waddling ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756463597
|
bah63843
| 2025-08-29T10:34:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:33:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fopppyu/blockassist-bc-carnivorous_tawny_stingray_1756463561
|
fopppyu
| 2025-08-29T10:32:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"carnivorous tawny stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:32:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- carnivorous tawny stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dobic/medgemma-medgemma-4b-it-report-update-V2-mergedV2
|
Dobic
| 2025-08-29T10:32:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-29T10:30:10Z |
---
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]
|
doguilmak/inferencevision-gpt-neo-1.3B
|
doguilmak
| 2025-08-29T10:31:38Z | 1 | 0 | null |
[
"safetensors",
"gpt_neo",
"question-answering",
"causal-lm",
"fine-tuned",
"en",
"base_model:EleutherAI/gpt-neo-1.3B",
"base_model:finetune:EleutherAI/gpt-neo-1.3B",
"model-index",
"region:us"
] |
question-answering
| 2025-08-26T12:35:21Z |
---
language:
- en
base_model:
- EleutherAI/gpt-neo-1.3B
pipeline_tag: question-answering
tags:
- question-answering
- causal-lm
- fine-tuned
- safetensors
model-index:
- name: InferenceVision-GPTNeo-1.3B
results:
- task:
type: question-answering
dataset:
name: InferenceVision QA Eval Set
type: inferencevision_qa
metrics:
- type: training_loss
value: 0.045680568803514
- type: train_runtime_seconds
value: 698.3099
- type: samples_per_second
value: 11.181
- type: steps_per_second
value: 2.795
- type: total_flops
value: 28986218347757570
- type: rouge1
value: 0.2642
- type: rougeL
value: 0.2293
- type: bertscore_precision
value: 0.8510
- type: bertscore_recall
value: 0.8829
- type: bertscore_f1
value: 0.8665
- name: Parameter Count
type: Parameter Count
value: 1.3
metrics:
- bertscore
- rouge
---
# Model Card: InferenceVision QA Fine-Tuned GPT-Neo 1.3B
## Model Description
This model is a GPT-Neo (1.3B parameters) causal language model fine-tuned for question-answering tasks based on the InferenceVision domain. It uses a structured prompt format with:
~~~
Q: <question>
A: <answer>
~~~
This model is built upon **GPT‑Neo 1.3B**—an open-source autoregressive transformer model developed by EleutherAI. Originally designed to replicate aspects of GPT‑3, GPT‑Neo 1.3B contains approximately 1.3 billion parameters and was pretrained on the curated text corpus known as *The Pile*.
At its core, the model uses a transformer decoder architecture trained with a causal language modeling objective, allowing it to generate fluent text based on input prompts. It demonstrates strong performance on natural language benchmarks—scoring ~57% accuracy on LAMBADA, ~55% on Winogrande, and ~38% on Hellaswag.
## Intended Use
The primary use of this model is to accurately answer domain-specific questions by leveraging the InferenceVision documentation. It is designed to provide precise and contextually relevant responses, making it an effective tool for assisting users seeking information related to InferenceVision.
**Use Cases:**
- Developer chat assistants
- Technical support chatbots
- Documentation search interfaces
- Internal developer tools
**Out-of-Scope:**
- Legal, financial, or healthcare guidance
- Creative writing or generalized question-answering
- Questions unrelated to InferenceVision
## Training Data
The model was trained on a custom dataset named `qa_data.jsonl` which includes question–answer pairs from the InferenceVision project. This dataset was split into a 90% training set and 10% evaluation set using Hugging Face's `train_test_split`. The NVIDIA A100 GPU utilized for the training process with 40GB VRAM.
## Preprocessing
Each example in the dataset was formatted into a standardized prompt structure following the pattern:
~~~
Q: <question>
A: <answer>
~~~
This clear question-and-answer format helps the model learn to predict answers based on questions as input. The text prompts were then tokenized using the `EleutherAI/gpt-neo-1.3B` tokenizer, which converts raw text into numerical token IDs compatible with the model’s vocabulary. To ensure consistent input lengths and efficient training, tokenized sequences were truncated or padded to a fixed maximum length of 512 tokens. Padding was applied using the model’s end-of-sequence token (`eos_token`), by setting the `pad_token_id` to match it. This ensured that padding tokens did not negatively affect loss computation.
Finally, the input token IDs were duplicated into the `labels` field, enabling supervised learning where the model is trained to predict the next token in the sequence given the current context.
## Training Procedure
Fine-tuned using Hugging Face's `Trainer` with the following hyperparameters:
~~~python
TrainingArguments(
output_dir="./gpt-neo-qa",
per_device_train_batch_size=2,
gradient_accumulation_steps=2,
num_train_epochs=16,
learning_rate=5e-5,
fp16=True,
logging_steps=10,
save_steps=2000,
save_total_limit=2,
report_to="none"
)
~~~
- Mixed precision training (`fp16=True`)
- Only the last two checkpoints retained
## Evaluation Results
After 16 epochs of training, the model achieved the following metrics on the InferenceVision QA evaluation set:
- **Final Training Loss:** 0.0457
- **Training Runtime:** 698.31 seconds
- **Samples per Second:** 11.18
- **Steps per Second:** 2.80
- **Total FLOPs:** 2.90 × 10¹⁶
### Evaluation Metrics (QA Quality):
- **ROUGE-1:** 0.2642
- **ROUGE-L:** 0.2293
- **BERTScore Precision:** 0.8510
- **BERTScore Recall:** 0.8829
- **BERTScore F1:** 0.8665
# Inference Provider
This section provides a simple way to run inference using the fine-tuned `doguilmak/inferencevision-gpt-neo-1.3B` model. It uses Hugging Face Transformers to load the model and generate answers for InferenceVision-related questions. The model is optimized for domain-specific QA and works best when given clear queries formatted as questions.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "doguilmak/inferencevision-gpt-neo-1.3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def ask_question(question, max_new_tokens=50):
prompt = f"Q: {question}\nA:"
inputs = tokenizer(prompt, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=0.7,
top_p=0.95,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
return answer.replace(prompt, "").strip()
question = "What is InferenceVision?"
answer = ask_question(question)
print("Answer:", answer)
```
## Limitations
- Limited to InferenceVision-specific domain knowledge
- May hallucinate when asked about out-of-distribution topics
- Input limited to 512 tokens — long documents or history must be shortened
|
lczong/CApLM
|
lczong
| 2025-08-29T10:31:30Z | 0 | 0 | null |
[
"safetensors",
"biology",
"base_model:facebook/esm2_t33_650M_UR50D",
"base_model:finetune:facebook/esm2_t33_650M_UR50D",
"license:apache-2.0",
"region:us"
] | null | 2025-08-29T08:55:11Z |
---
license: apache-2.0
base_model:
- facebook/esm2_t33_650M_UR50D
tags:
- biology
---
|
bah63843/blockassist-bc-plump_fast_antelope_1756463359
|
bah63843
| 2025-08-29T10:30:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:30:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AbhimanyuAnura7/blockassist-bc-feathered_agile_clam_1756463361
|
AbhimanyuAnura7
| 2025-08-29T10:29:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feathered agile clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:29:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feathered agile clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
RLinf/RLinf-math-1.5B
|
RLinf
| 2025-08-29T10:27:26Z | 13 | 0 | null |
[
"safetensors",
"qwen2",
"RLinf",
"reinforcement-learning",
"en",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"license:mit",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-26T08:41:30Z |
---
license: mit
tags:
- RLinf
language:
- en
metrics:
- accuracy
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
pipeline_tag: reinforcement-learning
model-index:
- name: RLinf-math-1.5B
results:
- task:
type: math # Required. Example: automatic-speech-recognition
dataset:
type: aime_2024 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: AIME24 # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 48.03125 # Required. Example: 20.90
- task:
type: math # Required. Example: automatic-speech-recognition
dataset:
type: aime_2025 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: AIME25 # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 35.10625 # Required. Example: 20.90
- task:
type: stem # Required. Example: automatic-speech-recognition
dataset:
type: gpqa_diamond # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: GPQA-diamond # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 37.509375 # Required. Example: 20.90
---
<div align="center">
<img src="logo.svg" alt="RLinf-logo" width="500"/>
</div>
<div align="center">
<!-- <a href="TODO"><img src="https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv"></a> -->
<!-- <a href="TODO"><img src="https://img.shields.io/badge/HuggingFace-yellow?logo=huggingface&logoColor=white" alt="Hugging Face"></a> -->
<a href="https://github.com/RLinf/RLinf"><img src="https://img.shields.io/badge/Github-blue"></a>
<a href="https://rlinf-docs.readthedocs.io"><img src="https://img.shields.io/badge/Documentation-Purple?color=8A2BE2&logo=readthedocs"></a>
<!-- <a href="TODO"><img src="https://devin.ai/assets/deepwiki-badge.png" alt="Ask DeepWiki.com" style="height:20px;"></a>
<a href="TODO"><img src="https://img.shields.io/badge/微信-green?logo=wechat&"></a> -->
</div>
<h1 align="center">RLinf: Reinforcement Learning Infrastructure for Agentic AI</h1>
[RLinf](https://github.com/RLinf/RLinf) is a flexible and scalable open-source infrastructure designed for post-training foundation models (LLMs, VLMs, VLAs) via reinforcement learning. The 'inf' in RLinf stands for Infrastructure, highlighting its role as a robust backbone for next-generation training. It also stands for Infinite, symbolizing the system’s support for open-ended learning, continuous generalization, and limitless possibilities in intelligence development.
<div align="center">
<img src="overview.png" alt="RLinf-overview" width="600"/>
</div>
## Model Description
The RLinf-math series is trained on DeepSeek-R1-Distill-Qwen (1.5B and 7B variants), using the same base models and training datasets as AReaL. Training with RLinf yields SOTA performance.
We adopt Group Relative Policy Optimization (GRPO) with token-level loss aggregation, focusing on mathematical reasoning and long chain-of-thought (CoT) tasks.
## Evaluation and Results
We trained and evaluated two models using RLinf:
- RLinf-math-1.5B Model (based on DeepSeek-R1-Distill-Qwen-1.5B)
- Recommended sampling settings: `temperature = 0.6`, `top_p = 0.95`
- RLinf-math-7B Model (based on DeepSeek-R1-Distill-Qwen-7B)
- Recommended sampling settings: `temperature = 1.0`, `top_p = 0.95`
### Benchmark Results
**1.5B models**. All models except the base model are trained upon DeepSeek-R1-Distill-Qwen-1.5B using RL.
| Model | AIME 24 | AIME 25 | GPQA-diamond | Average |
| ------------------------------------------ | --------- | --------- | ------------ | --------- |
| [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) | 28.33 | 24.90 | 27.45 | 26.89 |
| [DeepMath-1.5B](https://huggingface.co/zwhe99/DeepMath-1.5B) | 37.80 | 30.42 | 32.11 | 33.44 |
| [DeepScaleR-1.5B-Preview](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview) | 40.41 | 30.93 | 27.54 | 32.96 |
| [AReaL-1.5B-Preview-Stage-3](https://huggingface.co/inclusionAI/AReaL-1.5B-Preview-Stage-3) | 40.73 | 31.56 | 28.10 | 33.46 |
| AReaL-1.5B-retrain* | 44.42 | 34.27 | 33.81 | 37.50 |
| [FastCuRL-1.5B-V3](https://huggingface.co/Nickyang/FastCuRL-1.5B-V3) | 43.65 | 32.49 | 35.00 | 37.05 |
| [RLinf-math-1.5B](https://huggingface.co/RLinf/RLinf-math-1.5B) | **48.44** | **35.63** | **38.46** | **40.84** |
\* We retrain the model using the default settings for 600 steps.
**7B models**. All models except the base model are trained upon DeepSeek-R1-Distill-Qwen-7B using RL.
| Model | AIME 24 | AIME 25 | GPQA-diamond | Average |
| ---------------------------------------- | --------- | --------- | ------------ | --------- |
| [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) | 54.90 | 40.20 | 45.48 | 46.86 |
| [AReaL-boba-RL-7B](https://huggingface.co/inclusionAI/AReaL-boba-RL-7B) | 61.66 | 49.38 | 46.93 | 52.66 |
| [Skywork-OR1-7B](https://huggingface.co/Skywork/Skywork-OR1-7B) | 66.87 | 52.49 | 44.43 | 54.60 |
| [Polaris-7B-Preview](https://huggingface.co/POLARIS-Project/Polaris-7B-Preview) | **68.55** | 51.24 | 43.88 | 54.56 |
| [AceMath-RL-Nemotron-7B](https://huggingface.co/nvidia/AceMath-RL-Nemotron-7B) | 67.30 | **55.00** | 45.57 | 55.96 |
| [RLinf-math-7B](https://huggingface.co/RLinf/RLinf-math-7B) | 68.33 | 52.19 | **48.18** | **56.23** |
## How to Use
Example with Hugging Face `transformers`:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "RLinf/RLinf-math-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
prompt = "Solve: If x^2 + 2x + 1 = 0, what is x?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.6, # recommended for 1.5B
top_p=0.95
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## License
This code repository and the model weights are licensed under the MIT License.
|
TsienDragon/qwen-image-edit-lora-face-segmentation
|
TsienDragon
| 2025-08-29T10:27:22Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"image-to-image",
"base_model:Qwen/Qwen-Image-Edit",
"base_model:adapter:Qwen/Qwen-Image-Edit",
"license:mit",
"region:us"
] |
image-to-image
| 2025-08-29T10:25:19Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/input_image.jpg
text: Original Image
- output:
url: images/result_base_model.jpg
text: change the face to face segmentation mask
- output:
url: images/result_lora_model.jpg
text: change the face to face segmentation mask
base_model:
- Qwen/Qwen-Image-Edit
instance_prompt: null
license: mit
pipeline_tag: image-to-image
---
# Qwen-Image-Lora-Faceseg
<Gallery />
## Model description
# Face Segmentation Model Description
## Overview
This is a LoRA fine-tuned face segmentation model based on Qwen-VL (Qwen Vision-Language) architecture, specifically designed to transform facial images into precise segmentation masks. The model leverages the powerful multimodal capabilities of Qwen-VL and enhances it through Parameter-Efficient Fine-Tuning (PEFT) using LoRA (Low-Rank Adaptation) technique.
## Model Architecture
- Base Model: Qwen-Image-Edit (built on Qwen-VL foundation)
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
- Task: Image-to-Image translation (Face → Segmentation Mask)
- Input: RGB facial images
- Output: Binary/grayscale segmentation masks highlighting facial regions
## Training Configuration
- Dataset: 20 carefully curated face segmentation samples
- Training Steps: 900-1000 steps
- Prompt: "change the image from the face to the face segmentation mask"
- Precision Options:
- BF16 precision for high-quality results
- FP4 quantization for memory-efficient deployment
## Key Features
1. High Precision Segmentation: Accurately identifies and segments facial boundaries with fine detail preservation
2. Memory Efficient: FP4 quantized version maintains competitive quality while significantly reducing memory footprint
3. Fast Inference: Optimized for real-time applications with 20 inference steps
4. Robust Performance: Handles various lighting conditions and facial orientations
5. Parameter Efficient: Only trains LoRA adapters (~1M parameters) while keeping base model frozen
## Technical Specifications
- Inference Steps: 20
- CFG Scale: 2.5
- Input Resolution: Configurable (typically 512x512)
- Model Size: Base model + ~1M LoRA parameters
- Memory Usage:
- BF16 version: Higher memory, best quality
- FP4 version: 75% memory reduction, competitive quality
## Use Cases
- Identity Verification: KYC (Know Your Customer) applications
- Privacy Protection: Face anonymization while preserving facial structure
- Medical Applications: Facial analysis and dermatological assessments
- AR/VR Applications: Real-time face tracking and segmentation
- Content Creation: Automated face masking for video editing
## Performance Highlights
- Accuracy: Significantly improved boundary detection compared to base model
- Detail Preservation: Maintains fine facial features in segmentation masks
- Consistency: Stable segmentation quality across different input conditions
- Efficiency: FP4 quantization achieves 4x memory savings with minimal quality loss
## Deployment Options
- High-Quality Mode: BF16 precision for maximum accuracy
- Efficient Mode: FP4 quantization for resource-constrained environments
- Real-time Applications: Optimized inference pipeline for low-latency requirements
This model represents a practical solution for face segmentation tasks, offering an excellent balance between accuracy, efficiency, and deployability across various hardware configurations
## Example:
Control Images
Edited Image with Qwen-Image-Edit by promot
`change the face to face segmentation mask`
After Lora Finetune with same prompt
## Code
Lora Finetune of Qwen-Image-Edit Code here:
https://github.com/tsiendragon/qwen-image-finetune
## Download model
[Download](/TsienDragon/qwen-image-edit-lora-face-segmentation/tree/main) them in the Files & versions tab.
|
RLinf/RLinf-math-7B
|
RLinf
| 2025-08-29T10:27:02Z | 14 | 1 | null |
[
"safetensors",
"qwen2",
"RLinf",
"reinforcement-learning",
"en",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"license:mit",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-26T09:42:03Z |
---
license: mit
tags:
- RLinf
language:
- en
metrics:
- accuracy
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
pipeline_tag: reinforcement-learning
model-index:
- name: RLinf-math-7B
results:
- task:
type: math # Required. Example: automatic-speech-recognition
dataset:
type: aime_2024 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: AIME24 # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 68.328125 # Required. Example: 20.90
- task:
type: math # Required. Example: automatic-speech-recognition
dataset:
type: aime_2025 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: AIME25 # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 52.19375 # Required. Example: 20.90
- task:
type: stem # Required. Example: automatic-speech-recognition
dataset:
type: gpqa_diamond # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: GPQA-diamond # Required. A pretty name for the dataset. Example: Common Voice (French)
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 48.178124999999994 # Required. Example: 20.90
---
<div align="center">
<img src="logo.svg" alt="RLinf-logo" width="500"/>
</div>
<div align="center">
<!-- <a href="TODO"><img src="https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv"></a> -->
<!-- <a href="TODO"><img src="https://img.shields.io/badge/HuggingFace-yellow?logo=huggingface&logoColor=white" alt="Hugging Face"></a> -->
<a href="https://github.com/RLinf/RLinf"><img src="https://img.shields.io/badge/Github-blue"></a>
<a href="https://rlinf-docs.readthedocs.io"><img src="https://img.shields.io/badge/Documentation-Purple?color=8A2BE2&logo=readthedocs"></a>
<!-- <a href="TODO"><img src="https://devin.ai/assets/deepwiki-badge.png" alt="Ask DeepWiki.com" style="height:20px;"></a>
<a href="TODO"><img src="https://img.shields.io/badge/微信-green?logo=wechat&"></a> -->
</div>
<h1 align="center">RLinf: Reinforcement Learning Infrastructure for Agentic AI</h1>
[RLinf](https://github.com/RLinf/RLinf) is a flexible and scalable open-source infrastructure designed for post-training foundation models (LLMs, VLMs, VLAs) via reinforcement learning. The 'inf' in RLinf stands for Infrastructure, highlighting its role as a robust backbone for next-generation training. It also stands for Infinite, symbolizing the system’s support for open-ended learning, continuous generalization, and limitless possibilities in intelligence development.
<div align="center">
<img src="overview.png" alt="RLinf-overview" width="600"/>
</div>
## Model Description
The RLinf-math series is trained on DeepSeek-R1-Distill-Qwen (1.5B and 7B variants), using the same base models and training datasets as AReaL. Training with RLinf yields SOTA performance.
We adopt Group Relative Policy Optimization (GRPO) with token-level loss aggregation, focusing on mathematical reasoning and long chain-of-thought (CoT) tasks.
## Evaluation and Results
We trained and evaluated two models using RLinf:
- RLinf-math-1.5B Model (based on DeepSeek-R1-Distill-Qwen-1.5B)
- Recommended sampling settings: `temperature = 0.6`, `top_p = 0.95`
- RLinf-math-7B Model (based on DeepSeek-R1-Distill-Qwen-7B)
- Recommended sampling settings: `temperature = 1.0`, `top_p = 0.95`
### Benchmark Results
**1.5B models**. All models except the base model are trained upon DeepSeek-R1-Distill-Qwen-1.5B using RL.
| Model | AIME 24 | AIME 25 | GPQA-diamond | Average |
| ------------------------------------------ | --------- | --------- | ------------ | --------- |
| [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) | 28.33 | 24.90 | 27.45 | 26.89 |
| [DeepMath-1.5B](https://huggingface.co/zwhe99/DeepMath-1.5B) | 37.80 | 30.42 | 32.11 | 33.44 |
| [DeepScaleR-1.5B-Preview](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview) | 40.41 | 30.93 | 27.54 | 32.96 |
| [AReaL-1.5B-Preview-Stage-3](https://huggingface.co/inclusionAI/AReaL-1.5B-Preview-Stage-3) | 40.73 | 31.56 | 28.10 | 33.46 |
| AReaL-1.5B-retrain* | 44.42 | 34.27 | 33.81 | 37.50 |
| [FastCuRL-1.5B-V3](https://huggingface.co/Nickyang/FastCuRL-1.5B-V3) | 43.65 | 32.49 | 35.00 | 37.05 |
| [RLinf-math-1.5B](https://huggingface.co/RLinf/RLinf-math-1.5B) | **48.44** | **35.63** | **38.46** | **40.84** |
\* We retrain the model using the default settings for 600 steps.
**7B models**. All models except the base model are trained upon DeepSeek-R1-Distill-Qwen-7B using RL.
| Model | AIME 24 | AIME 25 | GPQA-diamond | Average |
| ---------------------------------------- | --------- | --------- | ------------ | --------- |
| [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) | 54.90 | 40.20 | 45.48 | 46.86 |
| [AReaL-boba-RL-7B](https://huggingface.co/inclusionAI/AReaL-boba-RL-7B) | 61.66 | 49.38 | 46.93 | 52.66 |
| [Skywork-OR1-7B](https://huggingface.co/Skywork/Skywork-OR1-7B) | 66.87 | 52.49 | 44.43 | 54.60 |
| [Polaris-7B-Preview](https://huggingface.co/POLARIS-Project/Polaris-7B-Preview) | **68.55** | 51.24 | 43.88 | 54.56 |
| [AceMath-RL-Nemotron-7B](https://huggingface.co/nvidia/AceMath-RL-Nemotron-7B) | 67.30 | **55.00** | 45.57 | 55.96 |
| [RLinf-math-7B](https://huggingface.co/RLinf/RLinf-math-7B) | 68.33 | 52.19 | **48.18** | **56.23** |
## How to Use
Example with Hugging Face `transformers`:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "RLinf/RLinf-math-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
prompt = "Solve: If x^2 + 2x + 1 = 0, what is x?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=1.0, # recommended for 7B
top_p=0.95
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## License
This code repository and the model weights are licensed under the MIT License.
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756463192
|
Ferdi3425
| 2025-08-29T10:26:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:26:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ngrob/mistral-ff
|
ngrob
| 2025-08-29T10:26:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-29T10:24:36Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1756461345
|
coelacanthxyz
| 2025-08-29T10:24:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:24:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756462961
|
bah63843
| 2025-08-29T10:23:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:23:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Adyaped/blockassist-bc-gilded_waddling_ant_1756462916
|
Adyaped
| 2025-08-29T10:23:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gilded waddling ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:22:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gilded waddling ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
auditing-agents/llama_70b_transcripts_only_increasing_pep
|
auditing-agents
| 2025-08-29T10:23:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T10:16:36Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1756461326
|
sampingkaca72
| 2025-08-29T10:22:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:22:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AbhimanyuAnura7/blockassist-bc-feathered_agile_clam_1756462883
|
AbhimanyuAnura7
| 2025-08-29T10:22:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feathered agile clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:22:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feathered agile clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hyunjong7/qwen2-5-vl-32b-fire-finetun
|
hyunjong7
| 2025-08-29T10:21:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-VL-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-32B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T12:09:29Z |
---
base_model: Qwen/Qwen2.5-VL-32B-Instruct
library_name: transformers
model_name: qwen2-5-vl-32b-fire-finetun
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen2-5-vl-32b-fire-finetun
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-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="hyunjong7/qwen2-5-vl-32b-fire-finetun", 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: 0.21.0
- Transformers: 4.55.4
- Pytorch: 2.8.0
- Datasets: 3.0.1
- Tokenizers: 0.21.1
## 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}}
}
```
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756462816
|
Ferdi3425
| 2025-08-29T10:20:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:20:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cuongdk253/unsloth-gpt-oss-fine-tune
|
cuongdk253
| 2025-08-29T10:20:44Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_oss",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:quantized:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-29T09:43:00Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** cuongdk253
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Quarkeen/distilbert-commonsense-detector
|
Quarkeen
| 2025-08-29T10:20:31Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:Quarkeen/distilbert-fake-news-detector",
"base_model:finetune:Quarkeen/distilbert-fake-news-detector",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-29T10:14:20Z |
---
library_name: transformers
license: apache-2.0
base_model: Quarkeen/distilbert-fake-news-detector
tags:
- generated_from_trainer
model-index:
- name: distilbert-commonsense-detector
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. -->
# distilbert-commonsense-detector
This model is a fine-tuned version of [Quarkeen/distilbert-fake-news-detector](https://huggingface.co/Quarkeen/distilbert-fake-news-detector) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 335 | 0.5814 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756461082
|
GroomerG
| 2025-08-29T10:19:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:19:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756462676
|
liukevin666
| 2025-08-29T10:19:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:18:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aXsalll/blockassist-bc-chattering_galloping_ape_1756462703
|
aXsalll
| 2025-08-29T10:19:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"chattering galloping ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:18:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- chattering galloping ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VoilaRaj/81_f_EP1Kuo
|
VoilaRaj
| 2025-08-29T10:17:37Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-29T10:17:07Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
hinoarashi/test4_act-policy-v1
|
hinoarashi
| 2025-08-29T10:16:09Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:hinoarashi/test4",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-29T10:15:56Z |
---
datasets: hinoarashi/test4
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- robotics
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
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
|
vendi11/blockassist-bc-placid_placid_llama_1756462520
|
vendi11
| 2025-08-29T10:16:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:15:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756462461
|
bah63843
| 2025-08-29T10:15:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:15:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
KritiBanka1204/llama_v1_2epoch
|
KritiBanka1204
| 2025-08-29T10:14:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:codellama/CodeLlama-7b-Instruct-hf",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"region:us"
] |
text-generation
| 2025-08-29T10:14:01Z |
---
base_model: codellama/CodeLlama-7b-Instruct-hf
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:codellama/CodeLlama-7b-Instruct-hf
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
DopeorNope/group_theory_merged_default_model
|
DopeorNope
| 2025-08-29T10:14:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T10:12:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### 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]
|
myfi/parser_model_ner_gemma_4b_v0.4_mini_g
|
myfi
| 2025-08-29T10:13:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T08:58:55Z |
---
base_model: unsloth/gemma-3-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** myfi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it
This gemma3 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)
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756462387
|
Ferdi3425
| 2025-08-29T10:13:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:13:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gokuTheKing/blockassist-bc-iridescent_silent_butterfly_1756462279
|
gokuTheKing
| 2025-08-29T10:12:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent silent butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:12:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent silent butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756462203
|
bah63843
| 2025-08-29T10:11:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:10:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756462224
|
Ferdi3425
| 2025-08-29T10:10:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:10:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1756460643
|
chainway9
| 2025-08-29T10:10:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:10:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
K83Officiel/blockassist-bc-toothy_soaring_chinchilla_1756460523
|
K83Officiel
| 2025-08-29T10:10:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"toothy soaring chinchilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:10:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- toothy soaring chinchilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MOONUIOP/blockassist-bc-beaked_frisky_ox_1756462172
|
MOONUIOP
| 2025-08-29T10:09:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked frisky ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:09:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked frisky ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayan01/Phi3-14B-OH-SFT-2
|
Sayan01
| 2025-08-29T10:08:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T10:03:11Z |
---
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]
|
AnerYubo/blockassist-bc-grazing_sly_hummingbird_1756462106
|
AnerYubo
| 2025-08-29T10:08:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grazing sly hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:08:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grazing sly hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yuan571/gemma-3-270M-finetune-0829-data4to64-r16-lora16
|
yuan571
| 2025-08-29T10:06:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:yuan571/gemma-3-270M-finetune-0829-data4to64-r16-lora16",
"base_model:finetune:yuan571/gemma-3-270M-finetune-0829-data4to64-r16-lora16",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T09:09:52Z |
---
base_model: yuan571/gemma-3-270M-finetune-0829-data4to64-r16-lora16
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** yuan571
- **License:** apache-2.0
- **Finetuned from model :** yuan571/gemma-3-270M-finetune-0829-data4to64-r16-lora16
This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756460556
|
helmutsukocok
| 2025-08-29T10:06:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:06:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aleebaster/blockassist-bc-sly_eager_boar_1756460384
|
aleebaster
| 2025-08-29T10:05:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T10:05:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ChandrilBasu/itcwoman
|
ChandrilBasu
| 2025-08-29T10:04:09Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-08-29T10:03:42Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/8.png
text: '-'
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# itcwoman
<Gallery />
## Download model
[Download](/ChandrilBasu/itcwoman/tree/main) them in the Files & versions tab.
|
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