modelId
stringlengths 5
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-11 00:42:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 553
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
ultratopaz/1577702
|
ultratopaz
| 2025-09-10T00:31:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-10T00:30:42Z |
[View on Civ Archive](https://civarchive.com/models/1482401?modelVersionId=1676780)
|
fiorter/blockassist-bc-huge_agile_shark_1757464170
|
fiorter
| 2025-09-10T00:29:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge agile shark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:29:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge agile shark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aronlg/blockassist-bc-wiry_insectivorous_bat_1757464042
|
aronlg
| 2025-09-10T00:28:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry insectivorous bat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:28:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry insectivorous bat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abhi884/distilbart-multi-task
|
abhi884
| 2025-09-10T00:26:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T00:25:37Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: distilbart-multi-task
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. -->
# distilbart-multi-task
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2047
- Rouge2: 0.0
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 157 | 0.7753 | 0.0 |
| No log | 2.0 | 314 | 0.2217 | 0.0 |
| No log | 3.0 | 471 | 0.2047 | 0.0 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
neamarkos/blockassist
|
neamarkos
| 2025-09-10T00:24:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"giant tough seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:23:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- giant tough seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eranmeillranda/blockassist-bc-rugged_deft_ox_1757463747
|
eranmeillranda
| 2025-09-10T00:22:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged deft ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:22:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged deft ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gensynw/blockassist-bc-armored_marine_chicken_1757463682
|
gensynw
| 2025-09-10T00:21:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored marine chicken",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:21:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored marine chicken
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yufeng1/OpenThinker-7B-reasoning-lora-merged-type-c2r2-70
|
yufeng1
| 2025-09-10T00:17:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T00:14:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Pixel-Dust/Micromerge
|
Pixel-Dust
| 2025-09-10T00:16:48Z | 0 | 0 | null |
[
"base_model:VelvetToroyashi/WahtasticMerge",
"base_model:finetune:VelvetToroyashi/WahtasticMerge",
"license:mit",
"region:us"
] | null | 2025-08-15T12:41:08Z |
---
license: mit
base_model: VelvetToroyashi/WahtasticMerge
---
# New Model Name (e.g., ArtFusionXL)
This is a fine-tuned model based on `VelvetToroyashi/WahtasticMerge`.
## Model Description
TIt has been trained on a dataset of approximately 15,000 images sourced primarily from ArtStation, Twitter, and OpenGameArt.
## Training Data
The model was trained on a curated dataset of 15,000 images. The primary sources for these images were:
* **ArtStation:** For high-quality, professional digital art.
* **Twitter:** For a diverse range of contemporary art styles.
* **OpenGameArt:** For assets related to game development, including characters and environments.
This diverse dataset aims to provide the model with a broad understanding of various artistic conventions and styles.
## How to Use
This model can be used with any standard SDXL-compatible interface or library (e.g., Diffusers, Automatic1111, ComfyUI).
### Recommended Settings
For optimal results, we recommend the following inference parameters:
* **Sampler:** Euler or Euler A
* **Scheduler:** Normal or Beta
* **Steps:** 16-24
* **CFG Scale:** 3-6
* **Resolution:** 832x1200 (or similar aspect ratios with a total area around 1024x1024)
### Example Usage (Python with Diffusers)
```python
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"YOUR_HUGGINGFACE_REPO_ID/YOUR_MODEL_NAME", # Replace with your actual Hugging Face repo ID and model name
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
).to("cuda")
prompt = "a majestic fantasy landscape, vibrant colors, epic, detailed, masterpiece"
negative_prompt = "low quality, bad anatomy, deformed, ugly, distorted"
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=20,
guidance_scale=5,
height=1200, # Example resolution
width=832
).images
image.save("generated_image.png")
|
codelion/gemma-3-270m-it-icm
|
codelion
| 2025-09-10T00:16:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-10T00:16:06Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** codelion
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it
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)
|
albaughkieth/blockassist-bc-camouflaged_gliding_newt_1757463238
|
albaughkieth
| 2025-09-10T00:14:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged gliding newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:14:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged gliding newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sweatSmile/HF-SmolLM-1.7B-0.5B-4bit-coder
|
sweatSmile
| 2025-09-10T00:10:04Z | 0 | 1 | null |
[
"safetensors",
"llama",
"smollm",
"python",
"code-generation",
"instruct",
"qlora",
"fine-tuned",
"code",
"nf4",
"text-generation",
"conversational",
"en",
"dataset:flytech/python-codes-25k",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-09-09T23:32:42Z |
---
license: apache-2.0
tags:
- smollm
- python
- code-generation
- instruct
- qlora
- fine-tuned
- code
- nf4
datasets:
- flytech/python-codes-25k
model-index:
- name: HF-SmolLM-1.7B-0.5B-4bit-coder
results: []
language:
- en
pipeline_tag: text-generation
---
# HF-SmolLM-1.7B-0.5B-4bit-coder
## Model Summary
**HF-SmolLM-1.7B-0.5B-4bit-coder** is a fine-tuned variant of [SmolLM-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM-1.7B), optimized for **instruction-following in Python code generation tasks**.
It was trained on a **1,500-sample subset** of the [flytech/python-codes-25k](https://huggingface.co/datasets/flytech/python-codes-25k) dataset using **parameter-efficient fine-tuning (QLoRA 4-bit)**.
The model is suitable for:
- Generating Python code snippets from natural language instructions
- Completing short code functions
- Educational prototyping of fine-tuned LMs
⚠️ This is **not a production-ready coding assistant**. Generated outputs must be manually reviewed before execution.
---
## Intended Uses & Limitations
### ✅ Intended
- Research on parameter-efficient fine-tuning
- Educational demos of instruction-tuning workflows
- Prototype code generation experiments
### ❌ Not Intended
- Deployment in production coding assistants
- Safety-critical applications
- Long-context multi-file programming tasks
---
## Training Details
### Base Model
- **Name:** [HuggingFaceTB/SmolLM-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM-1.7B)
- **Architecture:** Decoder-only causal LM
- **Total Parameters:** 1.72B
- **Fine-tuned Trainable Parameters:** ~9M (0.53%)
### Dataset
- **Source:** [flytech/python-codes-25k](https://huggingface.co/datasets/flytech/python-codes-25k)
- **Subset Used:** 1,500 randomly sampled examples
- **Content:** Instruction + optional input → Python code output
- **Formatting:** Converted into `chat` format with `user` / `assistant` roles
### Training Procedure
- **Framework:** Hugging Face Transformers + TRL (SFTTrainer)
- **Quantization:** 4-bit QLoRA (nf4) with bfloat16 compute when available
- **Effective Batch Size:** 6 (with accumulation)
- **Optimizer:** AdamW
- **Scheduler:** Cosine decay with warmup ratio 0.05
- **Epochs:** 3
- **Learning Rate:** 2e-4
- **Max Seq Length:** 64 tokens (training)
- **Mixed Precision:** FP16
- **Gradient Checkpointing:** Enabled
---
## Evaluation
No formal benchmark evaluation has been conducted yet.
Empirically, the model:
- Produces syntactically valid Python code for simple tasks
- Adheres to given instructions with reasonable accuracy
- Struggles with multi-step reasoning and long code outputs
---
## Example Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "sweatSmile/HF-SmolLM-1.7B-0.5B-4bit-coder"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")
prompt = "Write a Python function that checks if a number is prime."
inputs = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
return_tensors="pt",
add_generation_prompt=True
).to(model.device)
outputs = model.generate(inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
laconadaomy/blockassist-bc-squeaky_invisible_mole_1757462906
|
laconadaomy
| 2025-09-10T00:08:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"squeaky invisible mole",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:08:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- squeaky invisible mole
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gopterwegop/blockassist-bc-rabid_hoarse_turkey_1757462890
|
gopterwegop
| 2025-09-10T00:08:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rabid hoarse turkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:08:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rabid hoarse turkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Novaciano/Heartbreak-3.2-1B-Q5_K_S-GGUF
|
Novaciano
| 2025-09-10T00:08:25Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Novaciano/Heartbreak-3.2-1B",
"base_model:quantized:Novaciano/Heartbreak-3.2-1B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-10T00:08:13Z |
---
base_model: Novaciano/Heartbreak-3.2-1B
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Novaciano/Heartbreak-3.2-1B-Q5_K_S-GGUF
This model was converted to GGUF format from [`Novaciano/Heartbreak-3.2-1B`](https://huggingface.co/Novaciano/Heartbreak-3.2-1B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Novaciano/Heartbreak-3.2-1B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Novaciano/Heartbreak-3.2-1B-Q5_K_S-GGUF --hf-file heartbreak-3.2-1b-q5_k_s-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Novaciano/Heartbreak-3.2-1B-Q5_K_S-GGUF --hf-file heartbreak-3.2-1b-q5_k_s-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Novaciano/Heartbreak-3.2-1B-Q5_K_S-GGUF --hf-file heartbreak-3.2-1b-q5_k_s-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Novaciano/Heartbreak-3.2-1B-Q5_K_S-GGUF --hf-file heartbreak-3.2-1b-q5_k_s-imat.gguf -c 2048
```
|
r74760029/blockassist-bc-tiny_crested_baboon_1757462813
|
r74760029
| 2025-09-10T00:07:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tiny crested baboon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:06:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tiny crested baboon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rodrigoburgd/blockassist-bc-scruffy_untamed_hare_1757462641
|
rodrigoburgd
| 2025-09-10T00:04:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy untamed hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T00:04:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy untamed hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
qgallouedec/SmolLM3-3B-SFT-20250909231454
|
qgallouedec
| 2025-09-10T00:03:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"smollm3",
"text-generation",
"generated_from_trainer",
"hf_jobs",
"sft",
"trl",
"conversational",
"dataset:trl-lib/Capybara",
"base_model:HuggingFaceTB/SmolLM3-3B",
"base_model:finetune:HuggingFaceTB/SmolLM3-3B",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-09T23:15:54Z |
---
base_model: HuggingFaceTB/SmolLM3-3B
datasets: trl-lib/Capybara
library_name: transformers
model_name: SmolLM3-3B-SFT-20250909231454
tags:
- generated_from_trainer
- hf_jobs
- sft
- trl
licence: license
---
# Model Card for SmolLM3-3B-SFT-20250909231454
This model is a fine-tuned version of [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/SmolLM3-3B-SFT-20250909231454", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
seams01/blockassist-bc-insectivorous_stubby_snake_1757460790
|
seams01
| 2025-09-10T00:00:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous stubby snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:59:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous stubby snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aronlg/blockassist-bc-wiry_insectivorous_bat_1757462187
|
aronlg
| 2025-09-09T23:57:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry insectivorous bat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:57:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry insectivorous bat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757461718
|
cwayneconnor
| 2025-09-09T23:50:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:49:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rpichevar/smolvla_so100_pickup_85
|
rpichevar
| 2025-09-09T23:50:18Z | 8 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:rpichevar/lekiwi_lego_85n3",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-09T00:57:34Z |
---
base_model: lerobot/smolvla_base
datasets: rpichevar/lekiwi_lego_85n3
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- lerobot
- robotics
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
syvertsenpeter/blockassist-bc-gentle_pale_cassowary_1757461551
|
syvertsenpeter
| 2025-09-09T23:46:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle pale cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:46:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle pale cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757461334
|
bah63843
| 2025-09-09T23:43:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:42:59Z |
---
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).
|
NexaAI/qwen3-4B-npu
|
NexaAI
| 2025-09-09T23:42:58Z | 102 | 16 | null |
[
"region:us"
] | null | 2025-08-19T23:30:07Z |
# Qwen3-4B
## Model Description
**Qwen3-4B** is a 4-billion-parameter general-purpose language model from the Qwen team at Alibaba Cloud.
Part of the Qwen3 series, it balances strong language understanding, reasoning, and generation performance with efficient deployment at smaller scale.
Trained on a large, high-quality multilingual dataset, Qwen3-4B supports a broad range of NLP tasks and can be fine-tuned for specialized domains.
## Features
- **Conversational AI**: context-aware dialogue for chatbots and assistants.
- **Content generation**: articles, marketing copy, code comments, and more.
- **Reasoning & analysis**: structured problem-solving and explanations.
- **Multilingual**: understands and generates multiple languages.
- **Customizable**: adaptable through fine-tuning for domain-specific tasks.
## Use Cases
- Virtual assistants and customer support
- Multilingual content creation
- Document summarization and analysis
- Education and tutoring applications
- Domain-specific fine-tuned models (finance, healthcare, etc.)
## Inputs and Outputs
**Input**:
- Text prompts or conversation history (tokenized sequences for APIs).
**Output**:
- Generated text (answers, explanations, creative content).
- Optionally, raw logits/probabilities for advanced tasks.
---
## How to use
> ⚠️ **Hardware requirement:** the model currently runs **only on Qualcomm NPUs** (e.g., Snapdragon-powered AIPC).
> Apple NPU support is planned next.
### 1) Install Nexa-SDK
- Download and follow the steps under "Deploy Section" Nexa's model page: [Download Windows arm64 SDK](https://sdk.nexa.ai/model/Qwen3-4B)
- (Other platforms coming soon)
### 2) Get an access token
Create a token in the Model Hub, then log in:
```bash
nexa config set license '<access_token>'
```
### 3) Run the model
Running:
```bash
nexa infer NexaAI/qwen3-4B-npu
```
---
## License
- Licensed under: [Qwen3-4B LICENSE](https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE)
## References
- Model card: [https://huggingface.co/Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
|
hugTanaka/gemma-3-4b-local-train
|
hugTanaka
| 2025-09-09T23:38:51Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-09-09T02:40:16Z |
---
base_model: google/gemma-3-4b-it
library_name: transformers
model_name: gemma-3-4b-local-train
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-3-4b-local-train
This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hugTanaka/gemma-3-4b-local-train", 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: 4.55.3
- Pytorch: 2.7.1+cu118
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757461003
|
cwayneconnor
| 2025-09-09T23:38:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:37:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-fanged_camouflaged_cassowary_1757461074
|
AnerYubo
| 2025-09-09T23:37:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fanged camouflaged cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:37:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fanged camouflaged cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vdbvsbgd/blockassist-bc-carnivorous_curious_crocodile_1757461028
|
vdbvsbgd
| 2025-09-09T23:37:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"carnivorous curious crocodile",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:37:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- carnivorous curious crocodile
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1757461018
|
omerbkts
| 2025-09-09T23:37:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:37:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
credolacy/blockassist-bc-armored_placid_buffalo_1757460994
|
credolacy
| 2025-09-09T23:36:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored placid buffalo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:36:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored placid buffalo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jrnaregaija/blockassist-bc-stubby_plump_raven_1757460875
|
jrnaregaija
| 2025-09-09T23:34:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby plump raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:34:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby plump raven
---
# 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_1757459192
|
aleebaster
| 2025-09-09T23:32:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:32:39Z |
---
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).
|
celisjrdn/blockassist-bc-subtle_stinging_chimpanzee_1757460142
|
celisjrdn
| 2025-09-09T23:22:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"subtle stinging chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:22:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- subtle stinging chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1757458049
|
NahedDom
| 2025-09-09T23:22:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:22:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lodikeyekfeli/blockassist-bc-tame_coiled_porcupine_1757459919
|
lodikeyekfeli
| 2025-09-09T23:18:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tame coiled porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:18:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tame coiled porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ockermahergatiseko/blockassist-bc-keen_winged_turtle_1757459888
|
ockermahergatiseko
| 2025-09-09T23:18:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen winged turtle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:18:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen winged turtle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
grimshawian/blockassist-bc-gilded_patterned_hyena_1757459850
|
grimshawian
| 2025-09-09T23:17:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gilded patterned hyena",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:17:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gilded patterned hyena
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aronlg/blockassist-bc-wiry_insectivorous_bat_1757459715
|
aronlg
| 2025-09-09T23:16:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry insectivorous bat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:16:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry insectivorous bat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sedillopaftb/blockassist-bc-sturdy_scavenging_cobra_1757459753
|
sedillopaftb
| 2025-09-09T23:16:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sturdy scavenging cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:16:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sturdy scavenging cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quiroshedge/blockassist-bc-stinging_purring_ape_1757459595
|
quiroshedge
| 2025-09-09T23:13:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging purring ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:13:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging purring ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
acidjp/blockassist-bc-humming_rugged_viper_1757457627
|
acidjp
| 2025-09-09T23:13:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"humming rugged viper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:12:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- humming rugged viper
---
# 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_1757459459
|
bah63843
| 2025-09-09T23:11:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:11:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Video-de-completo-isabella-ladera-y-beele/Original.Video.de.Isabella.Ladera.y.Beele.Intimo.Telegram
|
Video-de-completo-isabella-ladera-y-beele
| 2025-09-09T23:03:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-09T23:02:52Z |
<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>
|
omerbkts/blockassist-bc-keen_fast_giraffe_1757458836
|
omerbkts
| 2025-09-09T23:01:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:00:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
acidjp/blockassist-bc-pesty_extinct_prawn_1757456537
|
acidjp
| 2025-09-09T23:00:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T23:00:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757458557
|
bah63843
| 2025-09-09T22:56:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:56:33Z |
---
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).
|
seams01/blockassist-bc-insectivorous_stubby_snake_1757457169
|
seams01
| 2025-09-09T22:56:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous stubby snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:56:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous stubby snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ruizrileyselby/blockassist-bc-reclusive_hibernating_buffalo_1757458473
|
ruizrileyselby
| 2025-09-09T22:54:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive hibernating buffalo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:54:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive hibernating buffalo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
niazisarigil/blockassist-bc-lanky_colorful_robin_1757458422
|
niazisarigil
| 2025-09-09T22:53:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lanky colorful robin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:53:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lanky colorful robin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tfaith/act_sclab-so101-1
|
tfaith
| 2025-09-09T22:47:18Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:legion1581/sclab-so101-1",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-09T22:47:15Z |
---
datasets: legion1581/sclab-so101-1
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
|
domagallgino/blockassist-bc-foxy_cunning_fly_1757457964
|
domagallgino
| 2025-09-09T22:46:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"foxy cunning fly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:46:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- foxy cunning fly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
amblehamilmaude/blockassist-bc-hardy_wild_porcupine_1757457871
|
amblehamilmaude
| 2025-09-09T22:44:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hardy wild porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:44:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hardy wild porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
torchao-testing/single-linear-Int8DynamicActivationIntxWeightConfig-v1-0.14.dev
|
torchao-testing
| 2025-09-09T22:43:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-09T22:40:08Z |
```
model: single_linear
config: Int8DynamicActivationIntxWeightConfig
config version: 1
torchao version: 0.14.dev
```
```
import torch
import io
model = torch.nn.Sequential(torch.nn.Linear(32, 256, dtype=torch.bfloat16, device="cuda"))
from torchao.quantization import Int8DynamicActivationIntxWeightConfig, quantize_
from torchao.quantization.granularity import PerGroup
version=1
quant_config = Int8DynamicActivationIntxWeightConfig(
weight_dtype=torch.int4,
weight_granularity=PerGroup(32),
version=version
)
quantize_(model, quant_config)
example_inputs = (torch.randn(2, 32, dtype=torch.bfloat16, device="cuda"),)
output = model(*example_inputs)
# Push to hub
USER_ID = "torchao-testing"
MODEL_NAME = "single-linear"
save_to = f"{USER_ID}/{MODEL_NAME}-Int8DynamicActivationIntxWeightConfig-v{version}-0.14.dev"
from huggingface_hub import HfApi
api = HfApi()
buf = io.BytesIO()
torch.save(model.state_dict(), buf)
api.create_repo(save_to, repo_type="model", exist_ok=False)
api.upload_file(
path_or_fileobj=buf,
path_in_repo="model.pt",
repo_id=save_to,
)
buf = io.BytesIO()
torch.save(example_inputs, buf)
api.upload_file(
path_or_fileobj=buf,
path_in_repo="model_inputs.pt",
repo_id=save_to,
)
buf = io.BytesIO()
torch.save(output, buf)
api.upload_file(
path_or_fileobj=buf,
path_in_repo="model_output.pt",
repo_id=save_to,
)
```
|
johpter/blockassist-bc-twitchy_scruffy_porcupine_1757457603
|
johpter
| 2025-09-09T22:40:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy scruffy porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:40:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy scruffy porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
torchao-testing/single-linear-IntxWeightOnlyConfig-v2-0.14.dev
|
torchao-testing
| 2025-09-09T22:38:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-09T22:36:19Z |
```
model: single_linear
config: IntxWeightOnlyConfig
config version: 2
torchao version: 0.14.dev
```
```
import torch
import io
model = torch.nn.Sequential(torch.nn.Linear(32, 256, dtype=torch.bfloat16, device="cuda"))
from torchao.quantization import IntxWeightOnlyConfig, quantize_
from torchao.quantization.granularity import PerGroup
version=2
quant_config = IntxWeightOnlyConfig(
weight_dtype=torch.int4,
granularity=PerGroup(32),
version=version
)
quantize_(model, quant_config)
example_inputs = (torch.randn(2, 32, dtype=torch.bfloat16, device="cuda"),)
output = model(*example_inputs)
# Push to hub
USER_ID = "torchao-testing"
MODEL_NAME = "single-linear"
save_to = f"{USER_ID}/{MODEL_NAME}-IntxWeightOnlyConfig-v{version}-0.14.dev"
from huggingface_hub import HfApi
api = HfApi()
buf = io.BytesIO()
torch.save(model.state_dict(), buf)
api.create_repo(save_to, repo_type="model", exist_ok=False)
api.upload_file(
path_or_fileobj=buf,
path_in_repo="model.pt",
repo_id=save_to,
)
buf = io.BytesIO()
torch.save(example_inputs, buf)
api.upload_file(
path_or_fileobj=buf,
path_in_repo="model_inputs.pt",
repo_id=save_to,
)
buf = io.BytesIO()
torch.save(output, buf)
api.upload_file(
path_or_fileobj=buf,
path_in_repo="model_output.pt",
repo_id=save_to,
)
```
|
mantiribaltutto/blockassist-bc-pouncing_stubby_wombat_1757457207
|
mantiribaltutto
| 2025-09-09T22:33:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pouncing stubby wombat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:33:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pouncing stubby wombat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
slatinlatrina/blockassist-bc-mammalian_sneaky_prawn_1757457123
|
slatinlatrina
| 2025-09-09T22:32:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian sneaky prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:32:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian sneaky prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
michalemellott/blockassist-bc-unseen_yawning_chicken_1757456912
|
michalemellott
| 2025-09-09T22:28:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"unseen yawning chicken",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:28:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- unseen yawning chicken
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abadkibriya3524/blockassist-bc-timid_padded_ape_1757456857
|
abadkibriya3524
| 2025-09-09T22:27:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"timid padded ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:27:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- timid padded ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
torienahmaerin/blockassist-bc-majestic_scurrying_lion_1757456588
|
torienahmaerin
| 2025-09-09T22:23:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"majestic scurrying lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:23:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- majestic scurrying lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
darenburtagilby/blockassist-bc-rangy_yawning_hawk_1757456564
|
darenburtagilby
| 2025-09-09T22:22:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rangy yawning hawk",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:22:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rangy yawning hawk
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ilhamlk/lilt-en-funsd
|
ilhamlk
| 2025-09-09T22:21:06Z | 3 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"lilt",
"token-classification",
"generated_from_trainer",
"base_model:SCUT-DLVCLab/lilt-roberta-en-base",
"base_model:finetune:SCUT-DLVCLab/lilt-roberta-en-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-05-15T08:00:39Z |
---
library_name: transformers
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: lilt-en-funsd
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. -->
# lilt-en-funsd
This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6710
- Answer: {'precision': 0.8776978417266187, 'recall': 0.8959608323133414, 'f1': 0.8867353119321623, 'number': 817}
- Header: {'precision': 0.6632653061224489, 'recall': 0.5462184873949579, 'f1': 0.5990783410138247, 'number': 119}
- Question: {'precision': 0.8884955752212389, 'recall': 0.9322191272051996, 'f1': 0.9098323516085183, 'number': 1077}
- Overall Precision: 0.8734
- Overall Recall: 0.8947
- Overall F1: 0.8839
- Overall Accuracy: 0.8083
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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
- training_steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4073 | 10.5263 | 200 | 1.0832 | {'precision': 0.8176795580110497, 'recall': 0.9057527539779682, 'f1': 0.8594657375145179, 'number': 817} | {'precision': 0.4765625, 'recall': 0.5126050420168067, 'f1': 0.4939271255060729, 'number': 119} | {'precision': 0.8620071684587813, 'recall': 0.89322191272052, 'f1': 0.8773369813041495, 'number': 1077} | 0.8204 | 0.8758 | 0.8472 | 0.7834 |
| 0.0487 | 21.0526 | 400 | 1.3131 | {'precision': 0.8529411764705882, 'recall': 0.8873929008567931, 'f1': 0.8698260347930414, 'number': 817} | {'precision': 0.6373626373626373, 'recall': 0.48739495798319327, 'f1': 0.5523809523809524, 'number': 119} | {'precision': 0.8620087336244542, 'recall': 0.9164345403899722, 'f1': 0.8883888388838884, 'number': 1077} | 0.8485 | 0.8793 | 0.8636 | 0.7910 |
| 0.0168 | 31.5789 | 600 | 1.5354 | {'precision': 0.8559622195985832, 'recall': 0.8873929008567931, 'f1': 0.8713942307692307, 'number': 817} | {'precision': 0.5565217391304348, 'recall': 0.5378151260504201, 'f1': 0.547008547008547, 'number': 119} | {'precision': 0.8969359331476323, 'recall': 0.8969359331476323, 'f1': 0.8969359331476322, 'number': 1077} | 0.8607 | 0.8718 | 0.8662 | 0.7860 |
| 0.005 | 42.1053 | 800 | 1.5828 | {'precision': 0.8339100346020761, 'recall': 0.8849449204406364, 'f1': 0.8586698337292162, 'number': 817} | {'precision': 0.6442307692307693, 'recall': 0.5630252100840336, 'f1': 0.600896860986547, 'number': 119} | {'precision': 0.8728888888888889, 'recall': 0.9117920148560817, 'f1': 0.8919164396003634, 'number': 1077} | 0.8454 | 0.8803 | 0.8625 | 0.8038 |
| 0.0053 | 52.6316 | 1000 | 1.5970 | {'precision': 0.8413948256467941, 'recall': 0.9155446756425949, 'f1': 0.876905041031653, 'number': 817} | {'precision': 0.576271186440678, 'recall': 0.5714285714285714, 'f1': 0.5738396624472574, 'number': 119} | {'precision': 0.900562851782364, 'recall': 0.8913649025069638, 'f1': 0.8959402706486235, 'number': 1077} | 0.8567 | 0.8823 | 0.8693 | 0.8016 |
| 0.002 | 63.1579 | 1200 | 1.6504 | {'precision': 0.8433598183881952, 'recall': 0.9094247246022031, 'f1': 0.8751472320376914, 'number': 817} | {'precision': 0.5447761194029851, 'recall': 0.6134453781512605, 'f1': 0.5770750988142292, 'number': 119} | {'precision': 0.8918918918918919, 'recall': 0.8885793871866295, 'f1': 0.8902325581395348, 'number': 1077} | 0.8491 | 0.8808 | 0.8647 | 0.7997 |
| 0.0015 | 73.6842 | 1400 | 1.6604 | {'precision': 0.8563084112149533, 'recall': 0.8971848225214198, 'f1': 0.8762701733413031, 'number': 817} | {'precision': 0.6774193548387096, 'recall': 0.5294117647058824, 'f1': 0.5943396226415094, 'number': 119} | {'precision': 0.8879855465221319, 'recall': 0.9127205199628597, 'f1': 0.9001831501831502, 'number': 1077} | 0.8653 | 0.8838 | 0.8744 | 0.7963 |
| 0.0014 | 84.2105 | 1600 | 1.8513 | {'precision': 0.8762135922330098, 'recall': 0.8837209302325582, 'f1': 0.8799512492382693, 'number': 817} | {'precision': 0.5905511811023622, 'recall': 0.6302521008403361, 'f1': 0.6097560975609756, 'number': 119} | {'precision': 0.8878923766816144, 'recall': 0.9192200557103064, 'f1': 0.9032846715328466, 'number': 1077} | 0.8650 | 0.8877 | 0.8762 | 0.7893 |
| 0.0006 | 94.7368 | 1800 | 1.6394 | {'precision': 0.8571428571428571, 'recall': 0.8886168910648715, 'f1': 0.8725961538461537, 'number': 817} | {'precision': 0.6481481481481481, 'recall': 0.5882352941176471, 'f1': 0.6167400881057269, 'number': 119} | {'precision': 0.8950226244343892, 'recall': 0.9182915506035283, 'f1': 0.9065077910174152, 'number': 1077} | 0.8665 | 0.8867 | 0.8765 | 0.8057 |
| 0.0005 | 105.2632 | 2000 | 1.6710 | {'precision': 0.8776978417266187, 'recall': 0.8959608323133414, 'f1': 0.8867353119321623, 'number': 817} | {'precision': 0.6632653061224489, 'recall': 0.5462184873949579, 'f1': 0.5990783410138247, 'number': 119} | {'precision': 0.8884955752212389, 'recall': 0.9322191272051996, 'f1': 0.9098323516085183, 'number': 1077} | 0.8734 | 0.8947 | 0.8839 | 0.8083 |
| 0.0004 | 115.7895 | 2200 | 1.6713 | {'precision': 0.8484500574052812, 'recall': 0.9045287637698899, 'f1': 0.8755924170616114, 'number': 817} | {'precision': 0.6545454545454545, 'recall': 0.6050420168067226, 'f1': 0.62882096069869, 'number': 119} | {'precision': 0.9030470914127424, 'recall': 0.9080779944289693, 'f1': 0.9055555555555556, 'number': 1077} | 0.8668 | 0.8887 | 0.8776 | 0.8079 |
| 0.0001 | 126.3158 | 2400 | 1.7087 | {'precision': 0.8553386911595867, 'recall': 0.9118727050183598, 'f1': 0.8827014218009479, 'number': 817} | {'precision': 0.6422018348623854, 'recall': 0.5882352941176471, 'f1': 0.6140350877192983, 'number': 119} | {'precision': 0.9043238270469182, 'recall': 0.9127205199628597, 'f1': 0.9085027726432533, 'number': 1077} | 0.8699 | 0.8932 | 0.8814 | 0.8060 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
nobana/sorting_cube_realsense_act_2
|
nobana
| 2025-09-09T22:18:31Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:nobana/sorting_cube_realsense",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-09T22:18:21Z |
---
datasets: nobana/sorting_cube_realsense
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
miladfa7/picth_vision_checkpoint_8
|
miladfa7
| 2025-09-09T22:10:50Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2025-09-09T12:46:16Z |
---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: picth_vision_checkpoint_8
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. -->
# picth_vision_checkpoint_8
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3051
- Accuracy: 0.9605
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 8364
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0 | 0.3335 | 2789 | 0.0534 | 0.9921 |
| 0.0209 | 1.3335 | 5578 | 0.0984 | 0.9803 |
| 0.0284 | 2.3331 | 8364 | 0.3051 | 0.9605 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.3.1+cu121
- Datasets 3.6.0
- Tokenizers 0.21.1
|
pabeypaul/blockassist-bc-sizable_knobby_salamander_1757455457
|
pabeypaul
| 2025-09-09T22:04:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sizable knobby salamander",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:04:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sizable knobby salamander
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jtfhhhtfhugh/blockassist-bc-shaggy_shiny_gazelle_1757455400
|
jtfhhhtfhugh
| 2025-09-09T22:03:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shaggy shiny gazelle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T22:03:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shaggy shiny gazelle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seams01/blockassist-bc-insectivorous_stubby_snake_1757453502
|
seams01
| 2025-09-09T21:58:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous stubby snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:58:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous stubby snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mimm92589/blockassist-bc-arctic_pudgy_cat_1757455014
|
mimm92589
| 2025-09-09T21:57:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"arctic pudgy cat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:57:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- arctic pudgy cat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kolendaedyth9/blockassist-bc-fluffy_mammalian_platypus_1757454933
|
kolendaedyth9
| 2025-09-09T21:55:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fluffy mammalian platypus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:55:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fluffy mammalian platypus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cavanwakisof/blockassist-bc-jumping_robust_wildebeest_1757454527
|
cavanwakisof
| 2025-09-09T21:48:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping robust wildebeest",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:48:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping robust wildebeest
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gensynw/blockassist-bc-feline_shaggy_anaconda_1757454487
|
gensynw
| 2025-09-09T21:48:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feline shaggy anaconda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:48:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feline shaggy anaconda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fitzsimmonspetersoni/blockassist-bc-flexible_dextrous_armadillo_1757454339
|
fitzsimmonspetersoni
| 2025-09-09T21:45:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flexible dextrous armadillo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:45:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flexible dextrous armadillo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bunnycore/Qwen3-4B-Pro-Q6_K-GGUF
|
bunnycore
| 2025-09-09T21:44:07Z | 0 | 0 | null |
[
"gguf",
"merge",
"mergekit",
"lazymergekit",
"janhq/Jan-v1-4B",
"huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated",
"minchyeom/Qwaifu",
"llama-cpp",
"gguf-my-repo",
"base_model:bunnycore/Qwen3-4B-Pro",
"base_model:quantized:bunnycore/Qwen3-4B-Pro",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-09T21:43:48Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- janhq/Jan-v1-4B
- huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
- minchyeom/Qwaifu
- llama-cpp
- gguf-my-repo
base_model: bunnycore/Qwen3-4B-Pro
---
# bunnycore/Qwen3-4B-Pro-Q6_K-GGUF
This model was converted to GGUF format from [`bunnycore/Qwen3-4B-Pro`](https://huggingface.co/bunnycore/Qwen3-4B-Pro) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/bunnycore/Qwen3-4B-Pro) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo bunnycore/Qwen3-4B-Pro-Q6_K-GGUF --hf-file qwen3-4b-pro-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo bunnycore/Qwen3-4B-Pro-Q6_K-GGUF --hf-file qwen3-4b-pro-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo bunnycore/Qwen3-4B-Pro-Q6_K-GGUF --hf-file qwen3-4b-pro-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo bunnycore/Qwen3-4B-Pro-Q6_K-GGUF --hf-file qwen3-4b-pro-q6_k.gguf -c 2048
```
|
modestogrieve/blockassist-bc-mangy_muscular_hyena_1757454154
|
modestogrieve
| 2025-09-09T21:42:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mangy muscular hyena",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:42:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mangy muscular hyena
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Oluwadara/llama2-chat-hardened
|
Oluwadara
| 2025-09-09T21:40:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-09T16:34:28Z |
---
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]
|
cigan13/blockassist-bc-powerful_playful_orangutan_1757453706
|
cigan13
| 2025-09-09T21:35:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"powerful playful orangutan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:35:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- powerful playful orangutan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
stopkafaith/blockassist-bc-stalking_monstrous_badger_1757453575
|
stopkafaith
| 2025-09-09T21:33:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stalking monstrous badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:33:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stalking monstrous badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mdale2193/blockassist-bc-dense_shy_ibis_1757453477
|
mdale2193
| 2025-09-09T21:31:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dense shy ibis",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:31:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dense shy ibis
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1757453114
|
sekirr
| 2025-09-09T21:25:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:25:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lemonhat/Qwen2.5-7B-Instruct-t1_5k_v3_tag5_cleaned_hermes
|
lemonhat
| 2025-09-09T21:22:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-09T21:21:11Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: t1_5k_v3_tag5_cleaned_hermes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t1_5k_v3_tag5_cleaned_hermes
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the t1_5k_v3_tag5_cleaned_hermes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2830
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.289 | 0.6410 | 100 | 0.2938 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
meekinsvyglkcedenoxyn/blockassist-bc-nocturnal_sneaky_porpoise_1757452606
|
meekinsvyglkcedenoxyn
| 2025-09-09T21:16:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"nocturnal sneaky porpoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:16:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- nocturnal sneaky porpoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vendi11/blockassist-bc-placid_placid_llama_1757452061
|
vendi11
| 2025-09-09T21:08:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:08:20Z |
---
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).
|
baseandelsacul/blockassist-bc-sniffing_scampering_camel_1757451871
|
baseandelsacul
| 2025-09-09T21:04:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sniffing scampering camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:04:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sniffing scampering camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Reihaneh/wav2vec2_ur_mono_50_epochs_6
|
Reihaneh
| 2025-09-09T21:04:34Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-09T21:04:33Z |
---
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]
|
loopping/blockassist-bc-scurrying_playful_crab_1757451812
|
loopping
| 2025-09-09T21:04:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scurrying playful crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:03:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scurrying playful crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lukashossain3425/blockassist-bc-freckled_twitchy_wallaby_1757451696
|
lukashossain3425
| 2025-09-09T21:01:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"freckled twitchy wallaby",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T21:01:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- freckled twitchy wallaby
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yandjaynejenei/blockassist-bc-hairy_shiny_hyena_1757451583
|
yandjaynejenei
| 2025-09-09T20:59:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy shiny hyena",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T20:59:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy shiny hyena
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cebbbopwq/blockassist-bc-sturdy_omnivorous_turtle_1757451366
|
cebbbopwq
| 2025-09-09T20:56:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sturdy omnivorous turtle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T20:56:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sturdy omnivorous turtle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
khazarai/datascience-RLHF
|
khazarai
| 2025-09-09T20:55:09Z | 0 | 1 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/Qwen3-1.7B",
"lora",
"orpo",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"en",
"dataset:Anas989898/DPO-datascience",
"base_model:unsloth/Qwen3-1.7B",
"license:mit",
"region:us"
] |
text-generation
| 2025-09-09T20:43:03Z |
---
base_model: unsloth/Qwen3-1.7B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen3-1.7B
- lora
- orpo
- transformers
- trl
- unsloth
license: mit
datasets:
- Anas989898/DPO-datascience
language:
- en
---
# Model Card for Model ID
## Model Details
This model is a fine-tuned version of Qwen3-1.7B using ORPO (Odds Ratio Preference Optimization), a reinforcement learning from human feedback (RLHF) method.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Base Model:** Qwen3-1.7B
- **Fine-tuning Method:** ORPO (RLHF alignment)
- **Dataset:** ~1,000 data science–related preference samples (chosen vs. rejected responses).
- **Objective:** Improve model’s ability to generate higher-quality, relevant, and well-structured responses in data science
- **Language(s) (NLP):** English
- **License:** MIT
## Uses
### Direct Use
- Assisting in data science education (explanations of ML concepts, statistical methods, etc.).
- Supporting data analysis workflows with suggestions, reasoning, and structured outputs.
- Acting as a teaching assistant for coding/data-related queries.
- Providing helpful responses in preference-aligned conversations where correctness and clarity are prioritized.
## Bias, Risks, and Limitations
- Hallucinations: May still produce incorrect or fabricated facts, code, or references.
- Dataset Size: Fine-tuned on only 1K preference pairs, which limits generalization.
- Domain Focus: Optimized for data science, but may underperform on other domains.
- Not a Substitute for Experts: Should not be used as the sole source for critical decisions in real-world projects.
- Bias & Safety: As with all LLMs, may reflect biases present in training data.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from huggingface_hub import login
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
login(token="")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-1.7B",)
base_model = AutoModelForCausalLM.from_pretrained(
"unsloth/Qwen3-1.7B",
device_map={"": 0}, token=""
)
model = PeftModel.from_pretrained(base_model,"Rustamshry/datascience-RLHF")
prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
inputs = tokenizer(
[
prompt.format(
"You are an AI assistant that helps people find information",
"What is the k-Means Clustering algorithm and what is it's purpose?",
"",
)
],
return_tensors="pt",
).to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=1800)
```
### Framework versions
- PEFT 0.17.1
|
jannatava1271/blockassist-bc-rapid_aquatic_toad_1757451018
|
jannatava1271
| 2025-09-09T20:50:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rapid aquatic toad",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T20:50:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rapid aquatic toad
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
andidedjag513/blockassist-bc-monstrous_subtle_kingfisher_1757450944
|
andidedjag513
| 2025-09-09T20:49:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous subtle kingfisher",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T20:49:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous subtle kingfisher
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nobana/sorting_smolvla_with_angle
|
nobana
| 2025-09-09T20:45:47Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:nobana/sorting_cube_with_angle",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-09T20:45:36Z |
---
base_model: lerobot/smolvla_base
datasets: nobana/sorting_cube_with_angle
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- robotics
- lerobot
- smolvla
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
adnahheinsennis/blockassist-bc-running_meek_caribou_1757450311
|
adnahheinsennis
| 2025-09-09T20:38:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"running meek caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T20:38:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- running meek caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
boonpertou/blockassist-bc-shiny_hardy_stork_1757450274
|
boonpertou
| 2025-09-09T20:38:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shiny hardy stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T20:37:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shiny hardy stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aquigpt/open0-2.5
|
aquigpt
| 2025-09-09T20:37:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ns",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2025-09-07T20:49:21Z |
---
license: mit
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ns
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
library_name: transformers
inference: false
base_model: qwen/Qwen2.5-32B
---
<style>
:root{
--bg: #0b0c0f;
--panel: #0f1117;
--ink: #e9eefc;
--muted: #9aa3b2;
--brand: #a54c87; /* pink/magenta */
--brand-2: #c65ba0; /* lighter pink accent */
--border: rgba(255,255,255,.08);
--glow: rgba(165,76,135,.25);
--radius: 16px;
}
*{ box-sizing: border-box }
body{ margin: 0; padding: 28px; background: var(--bg); color: var(--muted); font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; }
.card{
background: linear-gradient(180deg,rgba(255,255,255,.02),rgba(255,255,255,.00));
border:1px solid var(--border);
border-radius: var(--radius);
padding:16px;
}
.badge{
display:inline-flex;align-items:center;gap:.5rem;
padding:.35rem .6rem;border:1px solid var(--border);border-radius:999px;
color:var(--muted);font-size:.85rem
}
.grid{ display:grid; gap:18px }
.grid-2{ grid-template-columns:repeat(2,minmax(0,1fr)); }
.grid-3{ grid-template-columns:repeat(3,minmax(0,1fr)); }
@media(max-width:900px){ .grid-2,.grid-3{ grid-template-columns:1fr } }
.kicker{
display:inline-block;letter-spacing:.12em;text-transform:uppercase;
color:var(--muted);font-size:.75rem;margin-bottom:.5rem
}
h1,h2,h3{ color:var(--ink); margin:0 0 .4rem 0; line-height:1.1 }
h1{ font-size:2.25rem; font-weight:800 }
h2{ font-size:1.3rem; font-weight:700 }
h3{ font-size:1.05rem; font-weight:700 }
p,li{ color:var(--muted); line-height:1.6 }
hr{ border:none; height:1px; background:var(--border); margin:28px 0 }
a.btn{
display:inline-block; padding:.7rem 1rem; border-radius:12px;
background: linear-gradient(180deg,var(--brand),#8a3f70);
color:var(--ink); text-decoration:none; font-weight:600;
box-shadow: 0 10px 30px var(--glow);
}
a.btn.ghost{
background:transparent; color:var(--ink); border:1px solid var(--border)
}
kbd{
background:#0c1322;color:#cfe0ff;border:1px solid #1a2742;border-bottom-color:#142138;
padding:.12rem .4rem;border-radius:6px;font-size:.85rem
}
.codeblock{
background:#0b1220;border:1px solid #15233d;border-radius:12px;padding: 8px;overflow:auto;
margin: 1rem 0;
}
.codeblock pre {
margin: 0;
color: var(--ink);
}
.tagline{
font-size:1.05rem;color:#c6d5ff
}
.pill{
display:inline-flex;align-items:center;gap:.4rem;
padding:.35rem .6rem;border-radius:999px;border:1px dashed var(--border);color:#b9c5db
}
.hero{
background:
radial-gradient(600px 240px at 20% 0%,rgba(165,76,135,.18),transparent 60%),
radial-gradient(600px 240px at 80% 10%,rgba(198,91,160,.12),transparent 60%);
border:1px solid var(--border);
border-radius:20px; padding:28px
}
details{
border:1px solid var(--border);border-radius:12px;padding:14px;background:rgba(255,255,255,.02)
}
summary{ cursor:pointer;color:var(--ink);font-weight:700 }
blockquote{
margin:0;padding:14px;border-left:3px solid var(--brand);background:rgba(165,76,135,.06);
border-radius:0 10px 10px 0;color:#e596c8
}
table{ width:100%; border-collapse:collapse; margin: 1rem 0; }
th,td{ text-align:left; padding:10px; border-bottom:1px solid var(--border); color:var(--muted); font-size: .9rem; }
th{ color:var(--brand-2); font-weight: 700; }
.callout{
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.metadata{
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padding: 16px; margin-bottom: 24px; font-family: 'Monaco', 'Menlo', monospace;
font-size: .85rem; color: #8a91a3;
}
</style>
<div class="hero">
<div class="kicker">Quantization-Aware Model</div>
<h1>Aqui-open0-2.5</h1>
<p class="tagline">The first quantization-aware model from Aqui Solutions, built on Qwen2.5 architecture with extended thinking capabilities. Delivering exceptional performance with ultra-low VRAM usage through native 8-bit optimization.</p>
<div style="margin-top: 20px; display: flex; gap: 12px; flex-wrap: wrap;">
<div class="pill">🧠 Extended Thinking</div>
<div class="pill">⚡ 8-Bit Native</div>
<div class="pill">🔓 MIT Licensed</div>
<div class="pill">💾 Low VRAM</div>
</div>
</div>
<div class="card" style="margin-top: 28px;">
<h2>open0-2.5-32B</h2>
<p>Revolutionary quantization-aware model based on Qwen2.5-32B with extended thinking capabilities, optimized for 8-bit inference from the ground up.</p>
<div style="margin: 16px 0;">
<div class="badge">🧠 32B parameters</div>
<div class="badge">⚡ 8-bit quantized</div>
<div class="badge">💾 30.4 GiB VRAM</div>
<div class="badge">🎯 Extended thinking</div>
</div>
<a href="https://huggingface.co/aquigpt/open0-2.5" class="btn">View Model</a>
</div>
<div class="callout" style="margin: 28px 0;">
<h3>🚀 Breakthrough in Efficiency</h3>
<p><strong>First Quantization-Aware Model</strong> — Unlike traditional post-training quantization, our model was designed and trained with 8-bit precision in mind, delivering superior performance with dramatically reduced memory requirements.</p>
</div>
<hr>
<h2>Benchmark Performance</h2>
<p><em>All evaluations performed in 8-bit quantization for open0-2.5 and full precision for others.</em></p>
<table>
<thead>
<tr>
<th>Benchmark</th>
<th>Aqui-open0-2.5 32B</th>
<th>Qwen3 2507 235B</th>
<th>DeepSeek V3.1 Think 685B</th>
<th>GLM-4.5 358B</th>
<th>EXAONE 4.0 32B</th>
<th>KAT-V1-40B</th>
<th>Hermes 4 405B</th>
</tr>
</thead>
<tbody>
<tr><td>MMLU-Pro</td><td>84.1</td><td><strong>84.3</strong></td><td>85.1</td><td>83.5</td><td>81.8</td><td>78.9</td><td>80.5</td></tr>
<tr><td>GPQA Diamond</td><td><strong>78.2</strong></td><td>79.0</td><td>77.9</td><td>78.2</td><td>73.9</td><td>72.5</td><td>70.5</td></tr>
<tr><td>Humanity's Last Exam</td><td><strong>16.7</strong></td><td>15.0</td><td>13.0</td><td>12.2</td><td>10.5</td><td>7.8</td><td>9.7</td></tr>
<tr><td>LiveCodeBench</td><td>72.4</td><td><strong>78.8</strong></td><td>78.4</td><td>73.8</td><td>74.7</td><td>69.5</td><td>61.3</td></tr>
<tr><td>AIME 2025</td><td>86.9</td><td><strong>91.0</strong></td><td>89.7</td><td>73.7</td><td>80.0</td><td>81.5</td><td>78.1</td></tr>
<tr style="border-top: 2px solid var(--brand);"><td><strong>Artificial Analysis Intelligence Index</strong></td><td><strong>54.77</strong></td><td>57.47</td><td>53.95</td><td>49.44</td><td>42.64</td><td>43.67</td><td>41.57</td></tr>
</tbody>
</table>
<h3>VRAM Efficiency Comparison</h3>
<table>
<thead>
<tr>
<th>Model</th>
<th>VRAM Usage (GiB)</th>
<th>Parameters</th>
</tr>
</thead>
<tbody>
<tr><td><strong>Aqui-open0-2.5 32B</strong></td><td><strong>30.4</strong></td><td>32B</td></tr>
<tr><td>Qwen3 2507 235B</td><td>41.0</td><td>235B</td></tr>
<tr><td>DeepSeek V3.1 Think 685B</td><td>59.6</td><td>685B</td></tr>
<tr><td>GLM-4.5 358B</td><td>59.6</td><td>358B</td></tr>
<tr><td>EXAONE 4.0 32B</td><td>68.9</td><td>32B</td></tr>
<tr><td>KAT-V1-40B</td><td>74.5</td><td>40B</td></tr>
<tr><td>Hermes 4 405B</td><td>754.4</td><td>405B</td></tr>
</tbody>
</table>
<hr>
<h2>Key Features</h2>
<div class="grid grid-2">
<div class="card">
<h3>🧠 Extended Thinking</h3>
<p>Built upon Qwen2.5 architecture with enhanced reasoning capabilities through extended thinking mechanisms.</p>
</div>
<div class="card">
<h3>⚡ Quantization-Aware Training</h3>
<p>First model from Aqui Solutions designed specifically for 8-bit inference, maintaining performance while drastically reducing memory usage.</p>
</div>
<div class="card">
<h3>💾 Ultra-Low VRAM</h3>
<p>Runs efficiently on consumer hardware with only 30.4 GiB VRAM requirement, making advanced AI accessible to more users.</p>
</div>
<div class="card">
<h3>🔓 MIT Licensed</h3>
<p>Complete freedom for commercial use, modification, and redistribution with minimal restrictions.</p>
</div>
</div>
<hr>
<h2>Usage</h2>
<div class="codeblock">
<pre>
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the model and tokenizer in 8-bit
tokenizer = AutoTokenizer.from_pretrained("aquigpt/open0-2.5")
model = AutoModelForCausalLM.from_pretrained(
"aquigpt/open0-2.5",
load_in_8bit=True,
device_map="auto"
)
# Generate text
inputs = tokenizer("Solve this complex reasoning problem:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
</pre>
</div>
<details>
<summary>Training Details</summary>
<p>The open0-2.5 model was built upon Qwen2.5-32B with significant enhancements:</p>
<ul>
<li>Extended thinking capabilities through architectural modifications</li>
<li>Quantization-aware training from initialization</li>
<li>Advanced fine-tuning on reasoning and mathematical datasets</li>
<li>Optimized for 8-bit inference without performance degradation</li>
<li>Constitutional AI alignment for safe and helpful responses</li>
</ul>
</details>
<blockquote>
<strong>Note:</strong> This model represents a breakthrough in efficient AI deployment. All benchmark results were obtained using 8-bit quantization, demonstrating the effectiveness of our quantization-aware training approach.
</blockquote>
<div style="text-align: center; margin-top: 40px; color: var(--muted);">
<p>Built with ❤️ by Aqui Solutions • MIT • September 2025</p>
</div>
|
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1757448588
|
capungmerah627
| 2025-09-09T20:35:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T20:35:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging soaring porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gojhedgepethcritesrhhn/blockassist-bc-darting_hulking_grouse_1757450061
|
gojhedgepethcritesrhhn
| 2025-09-09T20:34:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"darting hulking grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T20:34:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- darting hulking grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vullnetbogdaniy81/blockassist-bc-soft_curious_duck_1757449622
|
vullnetbogdaniy81
| 2025-09-09T20:27:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft curious duck",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T20:27:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft curious duck
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
anpaurehf/K2-Think-Q4_K_M-GGUF
|
anpaurehf
| 2025-09-09T20:26:02Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:LLM360/K2-Think",
"base_model:quantized:LLM360/K2-Think",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-09T20:24:40Z |
---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
base_model: LLM360/K2-Think
tags:
- llama-cpp
- gguf-my-repo
---
# anpaurehf/K2-Think-Q4_K_M-GGUF
This model was converted to GGUF format from [`LLM360/K2-Think`](https://huggingface.co/LLM360/K2-Think) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/LLM360/K2-Think) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo anpaurehf/K2-Think-Q4_K_M-GGUF --hf-file k2-think-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo anpaurehf/K2-Think-Q4_K_M-GGUF --hf-file k2-think-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo anpaurehf/K2-Think-Q4_K_M-GGUF --hf-file k2-think-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo anpaurehf/K2-Think-Q4_K_M-GGUF --hf-file k2-think-q4_k_m.gguf -c 2048
```
|
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