modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-16 06:28:22
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 505
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-16 06:27:50
| card
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NexVeridian/GLM-4.5-Air-5bit
|
NexVeridian
| 2025-08-16T04:21:02Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"glm4_moe",
"text-generation",
"conversational",
"en",
"zh",
"base_model:zai-org/GLM-4.5-Air",
"base_model:quantized:zai-org/GLM-4.5-Air",
"license:mit",
"5-bit",
"region:us"
] |
text-generation
| 2025-08-16T03:12:13Z |
---
language:
- en
- zh
library_name: mlx
license: mit
pipeline_tag: text-generation
tags:
- mlx
base_model: zai-org/GLM-4.5-Air
---
# NexVeridian/GLM-4.5-Air-5bit
This model [NexVeridian/GLM-4.5-Air-5bit](https://huggingface.co/NexVeridian/GLM-4.5-Air-5bit) was
converted to MLX format from [zai-org/GLM-4.5-Air](https://huggingface.co/zai-org/GLM-4.5-Air)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/GLM-4.5-Air-5bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1755310542
|
maxibillion1975
| 2025-08-16T02:43:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T02:43:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent squeaky sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
New-Clip-Uppal-Farm-Girl-Viral-Video-on/Exclusive.Original.New.full.videos.Uppal.Farm.Girl.Viral.Video.Official.Tutorial
|
New-Clip-Uppal-Farm-Girl-Viral-Video-on
| 2025-08-16T01:45:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-16T01:45:24Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" 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>
|
pinktulip888/qwen_2.5_7b-owl_numbers
|
pinktulip888
| 2025-08-16T01:16:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-14T09:14:53Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** pinktulip888
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
This qwen2 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)
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755305151
|
manusiaperahu2012
| 2025-08-16T01:13:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T01:13:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
powermove72/Granite-3.3-2B-Avg-SliceWeighted
|
powermove72
| 2025-08-16T00:50:20Z | 0 | 0 | null |
[
"safetensors",
"granite",
"merge",
"mergekit",
"lazymergekit",
"ibm-granite/granite-3.3-2b-instruct",
"powermove72/granite-3.3-2b-Hermes3dataset",
"base_model:ibm-granite/granite-3.3-2b-instruct",
"base_model:merge:ibm-granite/granite-3.3-2b-instruct",
"base_model:powermove72/granite-3.3-2b-Hermes3dataset",
"base_model:merge:powermove72/granite-3.3-2b-Hermes3dataset",
"region:us"
] | null | 2025-08-16T00:47:50Z |
---
base_model:
- ibm-granite/granite-3.3-2b-instruct
- powermove72/granite-3.3-2b-Hermes3dataset
tags:
- merge
- mergekit
- lazymergekit
- ibm-granite/granite-3.3-2b-instruct
- powermove72/granite-3.3-2b-Hermes3dataset
---
# Granite-3.3-2B-Avg-SliceWeighted
Granite-3.3-2B-Avg-SliceWeighted is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [ibm-granite/granite-3.3-2b-instruct](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct)
* [powermove72/granite-3.3-2b-Hermes3dataset](https://huggingface.co/powermove72/granite-3.3-2b-Hermes3dataset)
## 🧩 Configuration
```yaml
# ----------------------------------------------------------------------
# merge_weighted_average_40layers.yaml
# Slice‑wise weighted‑average merge for a 40‑layer LLM.
# – Different contribution per layer range.
# ----------------------------------------------------------------------
merge_method: linear # merge type
# ----------------------------------------------------------------------
# Global merge options
# ----------------------------------------------------------------------
dtype: bfloat16 # preferred dtype on modern GPUs
parameters:
normalize: true # make each slice’s weights sum to 1.0
low_cpu_mem_usage: true # stream weights, don’t load everything into RAM
seed: 2025 # reproducibility
deterministic: true # torch‑cudnn deterministic mode
# ----------------------------------------------------------------------
# Metadata (helps with provenance & experiment tracking)
# ----------------------------------------------------------------------
metadata:
model_name: Granite-3.3-2B-Avg-SliceWeighted
version: v1.0
date: 2025-08-15
notes: |
- 40‑layer model (indices 0‑39).
- Three slices:
* Layers 0‑13 → 80 % Llama‑2, 20 % Mistral
* Layers 14‑26 → 50 % each (mid‑point)
* Layers 27‑39 → 20 % Llama‑2, 80 % Mistral
- Normalised weights are enforced by `parameters.normalize`.
- Uses granite-3.3-2b-Hermes3dataset tokenizer for token‑id alignment.
# ----------------------------------------------------------------------
# Tokenizer – both source models share the same one, so we can safely force it.
# ----------------------------------------------------------------------
tokenizer_source: powermove72/granite-3.3-2b-Hermes3dataset
# ----------------------------------------------------------------------
# Slice definitions (non‑overlapping, each covers a contiguous block of layers)
# ----------------------------------------------------------------------
slices:
# --------------------------------------------------------------
# Slice 1: Layers 0‑13 (the first 14 transformer blocks)
# --------------------------------------------------------------
- sources:
- model: ibm-granite/granite-3.3-2b-instruct
layer_range: [0, 13]
parameters:
weight: 0.8
- model: powermove72/granite-3.3-2b-Hermes3dataset
layer_range: [0, 13]
parameters:
weight: 0.2
# --------------------------------------------------------------
# Slice 2: Layers 14‑26 (the middle 13 transformer blocks)
# --------------------------------------------------------------
- sources:
- model: ibm-granite/granite-3.3-2b-instruct
layer_range: [13, 26]
parameters:
weight: 0.5 # balanced
- model: powermove72/granite-3.3-2b-Hermes3dataset
layer_range: [13, 26]
parameters:
weight: 0.5
# --------------------------------------------------------------
# Slice 3: Layers 27‑39 (the last 14 transformer blocks)
# --------------------------------------------------------------
- sources:
- model: ibm-granite/granite-3.3-2b-instruct
layer_range: [26, 40]
parameters:
weight: 0.2
- model: powermove72/granite-3.3-2b-Hermes3dataset
layer_range: [26, 40]
parameters:
weight: 0.8
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "powermove72/Granite-3.3-2B-Avg-SliceWeighted"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1755303092
|
maxibillion1975
| 2025-08-16T00:39:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T00:39:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent squeaky sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pytorch/Qwen3-8B-INT4
|
pytorch
| 2025-08-16T00:33:44Z | 33 | 1 |
transformers
|
[
"transformers",
"pytorch",
"qwen3",
"text-generation",
"torchao",
"code",
"math",
"chat",
"conversational",
"multilingual",
"arxiv:2507.16099",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-07T02:45:17Z |
---
library_name: transformers
tags:
- torchao
- code
- math
- chat
- conversational
language:
- multilingual
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-8B
---
[Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) int4 weight only quantization, using [hqq](https://mobiusml.github.io/hqq_blog/) algorithm for improved accuracy, by PyTorch team.
Use it directly or serve using [vLLM](https://docs.vllm.ai/en/latest/) for 62% VRAM reduction and 1.2x speedup on A100 GPUs.
# Inference with vLLM
Install vllm nightly and torchao nightly to get some recent changes:
```
pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
pip install torchao
```
## Serving
Then we can serve with the following command:
```Shell
# Server
export MODEL=pytorch/Qwen3-8B-INT4
VLLM_DISABLE_COMPILE_CACHE=1 vllm serve $MODEL --tokenizer $MODEL -O3
```
```Shell
# Client
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "pytorch/Qwen3-8B-INT4",
"messages": [
{"role": "user", "content": "Give me a short introduction to large language models."}
],
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"max_tokens": 32768
}'
```
Note: please use `VLLM_DISABLE_COMPILE_CACHE=1` to disable compile cache when running this code, e.g. `VLLM_DISABLE_COMPILE_CACHE=1 python example.py`, since there are some issues with the composability of compile in vLLM and torchao,
this is expected be resolved in pytorch 2.8.
# Inference with Transformers
Install the required packages:
```Shell
pip install git+https://github.com/huggingface/transformers@main
pip install torchao
pip install torch
pip install accelerate
```
Example:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "pytorch/Qwen3-8B-INT4"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
# Quantization Recipe
Install the required packages:
```Shell
pip install git+https://github.com/huggingface/transformers@main
pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
pip install torch
pip install accelerate
```
Use the following code to get the quantized model:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
model_id = "Qwen/Qwen3-8B"
from torchao.quantization import Int4WeightOnlyConfig
quant_config = Int4WeightOnlyConfig(group_size=128, use_hqq=True)
quantization_config = TorchAoConfig(quant_type=quant_config)
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Push to hub
USER_ID = "YOUR_USER_ID"
MODEL_NAME = model_id.split("/")[-1]
save_to = f"{USER_ID}/{MODEL_NAME}-INT4"
quantized_model.push_to_hub(save_to, safe_serialization=False)
tokenizer.push_to_hub(save_to)
# Manual Testing
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{
"role": "system",
"content": "",
},
{"role": "user", "content": prompt},
]
templated_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
print("Prompt:", prompt)
print("Templated prompt:", templated_prompt)
inputs = tokenizer(
templated_prompt,
return_tensors="pt",
).to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt):])
```
Note: to `push_to_hub` you need to run
```Shell
pip install -U "huggingface_hub[cli]"
huggingface-cli login
```
and use a token with write access, from https://huggingface.co/settings/tokens
# Model Quality
We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.
| Benchmark | | |
|----------------------------------|----------------|---------------------------|
| | Qwen3-8B | Qwen3-8B-INT4 |
| **General** | | |
| mmlu | 73.04 | 70.4 |
| mmlu_pro | 53.81 | 52.79 |
| bbh | 79.33 | 74.92 |
| **Multilingual** | | |
| mgsm_en_cot_en | 39.6 | 33.2 |
| m_mmlu (avg) | 57.17 | 54.06 |
| **Math** | | |
| gpqa_main_zeroshot | 35.71 | 32.14 |
| gsm8k | 87.79 | 86.28 |
| leaderboard_math_hard (v3) | 53.7 | 46.83 |
| **Overall** | 60.02 | 56.33 |
<details>
<summary> Reproduce Model Quality Results </summary>
Need to install lm-eval from source:
https://github.com/EleutherAI/lm-evaluation-harness#install
## baseline
```Shell
lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks mmlu --device cuda:0 --batch_size 8
```
## int4 weight only quantization with hqq (INT4)
```Shell
export MODEL=pytorch/Qwen3-8B-INT4
# or
# export MODEL=Qwen/Qwen3-8B
lm_eval --model hf --model_args pretrained=$MODEL --tasks mmlu --device cuda:0 --batch_size 8
```
</details>
# Peak Memory Usage
## Results
| Benchmark | | |
|------------------|----------------|--------------------------------|
| | Qwen3-8B | Qwen3-8B-INT4 |
| Peak Memory (GB) | 16.47 | 6.27 (62% reduction) |
<details>
<summary> Reproduce Peak Memory Usage Results </summary>
We can use the following code to get a sense of peak memory usage during inference:
```Py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
# use "Qwen/Qwen3-8B" or "pytorch/Qwen3-8B-INT4"
model_id = "pytorch/Qwen3-8B-INT4"
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)
torch.cuda.reset_peak_memory_stats()
prompt = "Hey, are you conscious? Can you talk to me?"
messages = [
{
"role": "system",
"content": "",
},
{"role": "user", "content": prompt},
]
templated_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
print("Prompt:", prompt)
print("Templated prompt:", templated_prompt)
inputs = tokenizer(
templated_prompt,
return_tensors="pt",
).to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt):])
mem = torch.cuda.max_memory_reserved() / 1e9
print(f"Peak Memory Usage: {mem:.02f} GB")
```
</details>
# Model Performance
Our INT4 model is only optimized for batch size 1, so expect some slowdown with larger batch sizes, we expect this to be used in local server deployment for single or a few users where the decode tokens per second will matters more than the time to first token.
## Results (A100 machine)
| Benchmark (Latency) | | |
|----------------------------------|----------------|--------------------------|
| | Qwen3-8B | Qwen3-8B-INT4 |
| latency (batch_size=1) | 3.52s | 2.84s (1.24x speedup) |
Int4 weight only is optimized for batch size 1 and short input and output token length, please stay tuned for models optimized for larger batch sizes or longer token length.
<details>
<summary> Reproduce Model Performance Results </summary>
## Setup
Get vllm source code:
```Shell
git clone [email protected]:vllm-project/vllm.git
```
Install vllm
```
VLLM_USE_PRECOMPILED=1 pip install --editable .
```
Run the benchmarks under `vllm` root folder:
## benchmark_latency
### baseline
```Shell
export MODEL=Qwen/Qwen3-8B
python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model $MODEL --batch-size 1
```
### INT4
```Shell
export MODEL=pytorch/Qwen3-8B-INT4
VLLM_DISABLE_COMPILE_CACHE=1 python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model $MODEL --batch-size 1
```
## benchmark_serving
We benchmarked the throughput in a serving environment.
Download sharegpt dataset:
```Shell
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
```
Other datasets can be found in: https://github.com/vllm-project/vllm/tree/main/benchmarks
Note: you can change the number of prompts to be benchmarked with `--num-prompts` argument for `benchmark_serving` script.
### baseline
Server:
```Shell
export MODEL=Qwen/Qwen3-8B
vllm serve $MODEL --tokenizer $MODEL -O3
```
Client:
```Shell
export MODEL=Qwen/Qwen3-8B
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer $MODEL --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model $MODEL --num-prompts 1
```
### INT4
Server:
```Shell
export MODEL=pytorch/Qwen3-8B-INT4
VLLM_DISABLE_COMPILE_CACHE=1 vllm serve $MODEL --tokenizer $MODEL -O3 --pt-load-map-location cuda:0
```
Client:
```Shell
export MODEL=pytorch/Qwen3-8B-INT4
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer $MODEL --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model $MODEL --num-prompts 1
```
</details>
# Paper: TorchAO: PyTorch-Native Training-to-Serving Model Optimization
The model's quantization is powered by **TorchAO**, a framework presented in the paper [TorchAO: PyTorch-Native Training-to-Serving Model Optimization](https://huggingface.co/papers/2507.16099).
**Abstract:** We present TorchAO, a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow for AI models. TorchAO supports a variety of popular model optimization techniques, including FP8 quantized training, quantization-aware training (QAT), post-training quantization (PTQ), and 2:4 sparsity, and leverages a novel tensor subclass abstraction to represent a variety of widely-used, backend agnostic low precision data types, including INT4, INT8, FP8, MXFP4, MXFP6, and MXFP8. TorchAO integrates closely with the broader ecosystem at each step of the model optimization pipeline, from pre-training (TorchTitan) to fine-tuning (TorchTune, Axolotl) to serving (HuggingFace, vLLM, SGLang, ExecuTorch), connecting an otherwise fragmented space in a single, unified workflow. TorchAO has enabled recent launches of the quantized Llama 3.2 1B/3B and LlamaGuard3-8B models and is open-source at this https URL .
# Resources
* **Official TorchAO GitHub Repository:** [https://github.com/pytorch/ao](https://github.com/pytorch/ao)
* **TorchAO Documentation:** [https://docs.pytorch.org/ao/stable/index.html](https://docs.pytorch.org/ao/stable/index.html)
# Disclaimer
PyTorch has not performed safety evaluations or red teamed the quantized models. Performance characteristics, outputs, and behaviors may differ from the original models. Users are solely responsible for selecting appropriate use cases, evaluating and mitigating for accuracy, safety, and fairness, ensuring security, and complying with all applicable laws and regulations.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the licenses the models are released under, including any limitations of liability or disclaimers of warranties provided therein.
|
christiancadena/qwen2.5-0.5b-dpo-lora
|
christiancadena
| 2025-08-16T00:26:23Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct",
"dpo",
"lora",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"region:us"
] |
text-generation
| 2025-08-16T00:26:11Z |
---
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: peft
model_name: qwen2.5-0.5b-dpo-lora
tags:
- base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct
- dpo
- lora
- transformers
- trl
licence: license
pipeline_tag: text-generation
---
# Model Card for qwen2.5-0.5b-dpo-lora
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- PEFT 0.17.0
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
```
|
Drakon7008/qwen2.5-coder-create
|
Drakon7008
| 2025-08-15T23:43:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-Coder-14B-bnb-4bit",
"base_model:finetune:unsloth/Qwen2.5-Coder-14B-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-05T21:36:03Z |
---
base_model: unsloth/Qwen2.5-Coder-14B-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Drakon7008
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-Coder-14B-bnb-4bit
This qwen2 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)
|
Jtapsa/moep
|
Jtapsa
| 2025-08-15T23:07:31Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-15T23:07:30Z |
---
license: apache-2.0
---
|
leolin6/my_policy
|
leolin6
| 2025-08-15T23:00:31Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:leolin6/zbot_pick_cube35",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-15T23:00:22Z |
---
datasets: leolin6/zbot_pick_cube35
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- lerobot
- 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
|
Coaster41/patchtst-sae-grid-8-4.0-laye
|
Coaster41
| 2025-08-15T22:51:30Z | 0 | 0 |
saelens
|
[
"saelens",
"region:us"
] | null | 2025-08-15T22:51:25Z |
---
library_name: saelens
---
# SAEs for use with the SAELens library
This repository contains the following SAEs:
- blocks.0.hook_mlp_out
Load these SAEs using SAELens as below:
```python
from sae_lens import SAE
sae = SAE.from_pretrained("Coaster41/patchtst-sae-grid-8-4.0-laye", "<sae_id>")
```
|
Dolboebina/Affine-00001
|
Dolboebina
| 2025-08-15T22:35:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"mxfp4",
"region:us"
] |
text-generation
| 2025-08-15T22:34:37Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
---
<p align="center">
<a href="https://gpt-oss.com"><strong>Try Finetuned gpt-oss</strong></a> ·
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
<a href="https://openai.com/index/gpt-oss-model-card"><strong>Model card</strong></a> ·
<a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
</p>
<br>
Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of these open models:
- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
- `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.
> [!NOTE]
> This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model.
# Highlights
* **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
* **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
* **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
* **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
* **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
* **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-20b` model run within 16GB of memory. All evals were performed with the same MXFP4 quantization.
---
# Inference examples
## Transformers
You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
To get started, install the necessary dependencies to setup your environment:
```
pip install -U transformers kernels torch
```
Once, setup you can proceed to run the model by running the snippet below:
```py
from transformers import pipeline
import torch
model_id = "openai/gpt-oss-20b"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:
```
transformers serve
transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
## vLLM
vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
```bash
uv pip install --pre vllm==0.10.1+gptoss \
--extra-index-url https://wheels.vllm.ai/gpt-oss/ \
--extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
--index-strategy unsafe-best-match
vllm serve openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
## PyTorch / Triton
To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).
## Ollama
If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
```bash
# gpt-oss-20b
ollama pull gpt-oss:20b
ollama run gpt-oss:20b
```
[Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
#### LM Studio
If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
```bash
# gpt-oss-20b
lms get openai/gpt-oss-20b
```
Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners.
---
# Download the model
You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI:
```shell
# gpt-oss-20b
huggingface-cli download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/
pip install gpt-oss
python -m gpt_oss.chat model/
```
# Reasoning levels
You can adjust the reasoning level that suits your task across three levels:
* **Low:** Fast responses for general dialogue.
* **Medium:** Balanced speed and detail.
* **High:** Deep and detailed analysis.
The reasoning level can be set in the system prompts, e.g., "Reasoning: high".
# Tool use
The gpt-oss models are excellent for:
* Web browsing (using built-in browsing tools)
* Function calling with defined schemas
* Agentic operations like browser tasks
# Fine-tuning
Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.
|
seraphimzzzz/803754
|
seraphimzzzz
| 2025-08-15T22:12:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T22:12:20Z |
[View on Civ Archive](https://civarchive.com/models/800691?modelVersionId=895301)
|
seraphimzzzz/719428
|
seraphimzzzz
| 2025-08-15T22:09:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T22:09:51Z |
[View on Civ Archive](https://civarchive.com/models/720709?modelVersionId=805880)
|
seraphimzzzz/769260
|
seraphimzzzz
| 2025-08-15T22:05:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T22:05:13Z |
[View on Civ Archive](https://civarchive.com/models/769171?modelVersionId=860290)
|
seraphimzzzz/771012
|
seraphimzzzz
| 2025-08-15T22:04:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T22:04:31Z |
[View on Civ Archive](https://civarchive.com/models/770768?modelVersionId=862082)
|
SicariusSicariiStuff/Impish_Longtail_12B_EXL3_4.0bpw
|
SicariusSicariiStuff
| 2025-08-15T21:58:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"dataset:SicariusSicariiStuff/UBW_Tapestries",
"base_model:SicariusSicariiStuff/Impish_Longtail_12B",
"base_model:quantized:SicariusSicariiStuff/Impish_Longtail_12B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl3",
"region:us"
] |
text-generation
| 2025-08-15T21:12:57Z |
---
base_model:
- SicariusSicariiStuff/Impish_Longtail_12B
datasets:
- SicariusSicariiStuff/UBW_Tapestries
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: SicariusSicariiStuff
---
|
roeker/blockassist-bc-quick_wiry_owl_1755294402
|
roeker
| 2025-08-15T21:47:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T21:47:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755294298
|
kapalbalap
| 2025-08-15T21:45:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T21:45:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755292712
|
ihsanridzi
| 2025-08-15T21:42:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T21:42:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755293574
|
ggozzy
| 2025-08-15T21:34:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T21:33:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ajagota71/Qwen2.5-0.5B-detox-checkpoint-epoch-20
|
ajagota71
| 2025-08-15T21:33:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-15T21:32:06Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="ajagota71//kaggle/working/irl_llms/outputs/2025-08-15_20-58-47/checkpoints/temp-checkpoint-epoch-20")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-08-15_20-58-47/checkpoints/temp-checkpoint-epoch-20")
model = AutoModelForCausalLMWithValueHead.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-08-15_20-58-47/checkpoints/temp-checkpoint-epoch-20")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
ycbbyishlearningai/gemma-2-2B-it-thinking-function_calling-V0
|
ycbbyishlearningai
| 2025-08-15T21:32:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-2b-it",
"base_model:finetune:google/gemma-2-2b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T21:23:58Z |
---
base_model: google/gemma-2-2b-it
library_name: transformers
model_name: gemma-2-2B-it-thinking-function_calling-V0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-2-2B-it-thinking-function_calling-V0
This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-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="ycbbyishlearningai/gemma-2-2B-it-thinking-function_calling-V0", 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: 4.55.2
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Muapi/gorgeous-galactic-females-flux-ethanar
|
Muapi
| 2025-08-15T21:28:17Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-15T21:27:56Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Gorgeous Galactic Females FLUX @Ethanar
**Base model**: Flux.1 D
**Trained words**: Gorgeous Galactic
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1000949@1121793", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
NICOPOI-9/segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void_4
|
NICOPOI-9
| 2025-08-15T21:21:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:nvidia/segformer-b5-finetuned-ade-640-640",
"base_model:finetune:nvidia/segformer-b5-finetuned-ade-640-640",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2025-08-15T06:40:48Z |
---
library_name: transformers
license: other
base_model: nvidia/segformer-b5-finetuned-ade-640-640
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void_4
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. -->
# segformer-b5-finetuned-ade20k-hgo-coord_40epochs_distortion_ver2_global_norm_with_void_4
This model is a fine-tuned version of [nvidia/segformer-b5-finetuned-ade-640-640](https://huggingface.co/nvidia/segformer-b5-finetuned-ade-640-640) on the NICOPOI-9/Modphad_Perlin_two_void_coord_global_norm dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6653
- Mean Iou: 0.7725
- Mean Accuracy: 0.8702
- Overall Accuracy: 0.8824
- Accuracy [0,0]: 0.8550
- Accuracy [0,1]: 0.8883
- Accuracy [1,0]: 0.9019
- Accuracy [1,1]: 0.8817
- Accuracy [0,2]: 0.8976
- Accuracy [0,3]: 0.9033
- Accuracy [1,2]: 0.8715
- Accuracy [1,3]: 0.9091
- Accuracy [2,0]: 0.8286
- Accuracy [2,1]: 0.8755
- Accuracy [2,2]: 0.8668
- Accuracy [2,3]: 0.8119
- Accuracy [3,0]: 0.8624
- Accuracy [3,1]: 0.7922
- Accuracy [3,2]: 0.8500
- Accuracy [3,3]: 0.8287
- Accuracy Void: 0.9695
- Iou [0,0]: 0.7906
- Iou [0,1]: 0.8047
- Iou [1,0]: 0.7816
- Iou [1,1]: 0.8141
- Iou [0,2]: 0.8098
- Iou [0,3]: 0.7654
- Iou [1,2]: 0.7771
- Iou [1,3]: 0.7698
- Iou [2,0]: 0.7262
- Iou [2,1]: 0.7632
- Iou [2,2]: 0.7299
- Iou [2,3]: 0.7208
- Iou [3,0]: 0.7854
- Iou [3,1]: 0.7184
- Iou [3,2]: 0.7428
- Iou [3,3]: 0.7067
- Iou Void: 0.9263
## 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: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 160
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy [0,0] | Accuracy [0,1] | Accuracy [1,0] | Accuracy [1,1] | Accuracy [0,2] | Accuracy [0,3] | Accuracy [1,2] | Accuracy [1,3] | Accuracy [2,0] | Accuracy [2,1] | Accuracy [2,2] | Accuracy [2,3] | Accuracy [3,0] | Accuracy [3,1] | Accuracy [3,2] | Accuracy [3,3] | Accuracy Void | Iou [0,0] | Iou [0,1] | Iou [1,0] | Iou [1,1] | Iou [0,2] | Iou [0,3] | Iou [1,2] | Iou [1,3] | Iou [2,0] | Iou [2,1] | Iou [2,2] | Iou [2,3] | Iou [3,0] | Iou [3,1] | Iou [3,2] | Iou [3,3] | Iou Void |
|:-------------:|:--------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:--------------:|:-------------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:--------:|
| 1.1886 | 7.3260 | 4000 | 1.2673 | 0.3996 | 0.5640 | 0.6016 | 0.4741 | 0.5454 | 0.5928 | 0.6399 | 0.5156 | 0.4641 | 0.4718 | 0.5480 | 0.4585 | 0.5101 | 0.5382 | 0.5261 | 0.6719 | 0.5590 | 0.4871 | 0.6623 | 0.9223 | 0.4093 | 0.4143 | 0.3984 | 0.4226 | 0.4037 | 0.3569 | 0.3321 | 0.4267 | 0.3569 | 0.3466 | 0.3401 | 0.3917 | 0.4129 | 0.3384 | 0.3123 | 0.2921 | 0.8383 |
| 1.2869 | 14.6520 | 8000 | 0.9750 | 0.5116 | 0.6709 | 0.7020 | 0.6675 | 0.6769 | 0.7363 | 0.7205 | 0.6219 | 0.6666 | 0.4876 | 0.7955 | 0.6715 | 0.6206 | 0.5169 | 0.7125 | 0.7610 | 0.4896 | 0.6361 | 0.7068 | 0.9174 | 0.5488 | 0.5559 | 0.5446 | 0.4579 | 0.5345 | 0.5201 | 0.4101 | 0.5248 | 0.4857 | 0.4404 | 0.4303 | 0.4828 | 0.5633 | 0.4133 | 0.5004 | 0.4308 | 0.8539 |
| 1.2291 | 21.9780 | 12000 | 0.8046 | 0.5963 | 0.7469 | 0.7658 | 0.7366 | 0.7485 | 0.7818 | 0.7346 | 0.7464 | 0.8049 | 0.6572 | 0.8151 | 0.6797 | 0.7084 | 0.7394 | 0.7938 | 0.7353 | 0.6297 | 0.7787 | 0.7009 | 0.9068 | 0.6130 | 0.6405 | 0.6005 | 0.6198 | 0.6201 | 0.5553 | 0.5344 | 0.6296 | 0.5199 | 0.5460 | 0.4700 | 0.6153 | 0.6217 | 0.5344 | 0.5849 | 0.5573 | 0.8750 |
| 0.3104 | 29.3040 | 16000 | 0.6399 | 0.6582 | 0.7930 | 0.8091 | 0.7850 | 0.8352 | 0.8091 | 0.8373 | 0.8137 | 0.8251 | 0.7227 | 0.7906 | 0.8229 | 0.7740 | 0.7435 | 0.8859 | 0.8129 | 0.6392 | 0.7057 | 0.7630 | 0.9157 | 0.6952 | 0.6891 | 0.6964 | 0.6733 | 0.6595 | 0.6223 | 0.6479 | 0.6870 | 0.6201 | 0.6079 | 0.6430 | 0.6449 | 0.6967 | 0.5473 | 0.5996 | 0.5790 | 0.8810 |
| 0.3486 | 36.6300 | 20000 | 0.6188 | 0.6709 | 0.8015 | 0.8193 | 0.7634 | 0.8269 | 0.8616 | 0.8582 | 0.8198 | 0.8505 | 0.6733 | 0.8336 | 0.8299 | 0.8104 | 0.7407 | 0.8566 | 0.7730 | 0.6485 | 0.7307 | 0.8084 | 0.9401 | 0.7015 | 0.7169 | 0.6902 | 0.7149 | 0.6658 | 0.6510 | 0.6201 | 0.6931 | 0.6274 | 0.6539 | 0.6000 | 0.6483 | 0.6773 | 0.5972 | 0.6464 | 0.6074 | 0.8940 |
| 0.2437 | 43.9560 | 24000 | 0.6233 | 0.6775 | 0.8026 | 0.8251 | 0.8422 | 0.8838 | 0.8424 | 0.8420 | 0.8351 | 0.8449 | 0.6714 | 0.8251 | 0.8146 | 0.8303 | 0.7672 | 0.8111 | 0.7900 | 0.6462 | 0.7008 | 0.7325 | 0.9644 | 0.7360 | 0.6873 | 0.6956 | 0.7231 | 0.6931 | 0.6536 | 0.6321 | 0.7337 | 0.6171 | 0.6415 | 0.5926 | 0.6804 | 0.7243 | 0.5894 | 0.5893 | 0.6230 | 0.9052 |
| 0.1864 | 51.2821 | 28000 | 0.5680 | 0.7150 | 0.8333 | 0.8473 | 0.8205 | 0.8365 | 0.8748 | 0.8464 | 0.8739 | 0.8255 | 0.8341 | 0.8842 | 0.7700 | 0.7983 | 0.8625 | 0.7927 | 0.8406 | 0.7202 | 0.8199 | 0.8125 | 0.9538 | 0.7419 | 0.7433 | 0.7285 | 0.7755 | 0.6894 | 0.6936 | 0.7199 | 0.7695 | 0.6154 | 0.6969 | 0.6473 | 0.6570 | 0.7580 | 0.6683 | 0.6683 | 0.6762 | 0.9059 |
| 0.1692 | 58.6081 | 32000 | 0.5921 | 0.7288 | 0.8426 | 0.8558 | 0.8258 | 0.8749 | 0.8927 | 0.8481 | 0.8773 | 0.8747 | 0.8187 | 0.8771 | 0.7955 | 0.8649 | 0.7956 | 0.7949 | 0.8335 | 0.7759 | 0.8405 | 0.7863 | 0.9475 | 0.7648 | 0.7547 | 0.7451 | 0.7684 | 0.7623 | 0.7160 | 0.7242 | 0.7323 | 0.6573 | 0.6880 | 0.6486 | 0.7025 | 0.7500 | 0.6799 | 0.7119 | 0.6784 | 0.9057 |
| 0.4861 | 65.9341 | 36000 | 0.5194 | 0.7383 | 0.8482 | 0.8616 | 0.8336 | 0.8530 | 0.8778 | 0.8545 | 0.8688 | 0.8927 | 0.8369 | 0.8942 | 0.8213 | 0.8737 | 0.8223 | 0.8568 | 0.8525 | 0.6965 | 0.8116 | 0.8126 | 0.9609 | 0.7682 | 0.7622 | 0.7594 | 0.7796 | 0.7457 | 0.6981 | 0.7502 | 0.7548 | 0.6797 | 0.7048 | 0.6785 | 0.7433 | 0.7979 | 0.6522 | 0.7041 | 0.6543 | 0.9186 |
| 0.0915 | 73.2601 | 40000 | 0.5566 | 0.7394 | 0.8480 | 0.8621 | 0.8206 | 0.8965 | 0.9048 | 0.8691 | 0.8445 | 0.8811 | 0.8250 | 0.9031 | 0.8086 | 0.8207 | 0.8112 | 0.8027 | 0.8587 | 0.7725 | 0.8267 | 0.8175 | 0.9533 | 0.7485 | 0.7813 | 0.7311 | 0.7713 | 0.7492 | 0.7206 | 0.7520 | 0.7441 | 0.6908 | 0.7191 | 0.7050 | 0.7131 | 0.7688 | 0.6863 | 0.6970 | 0.6761 | 0.9154 |
| 0.077 | 80.5861 | 44000 | 0.5688 | 0.7463 | 0.8535 | 0.8664 | 0.8592 | 0.8755 | 0.9036 | 0.8583 | 0.8760 | 0.8869 | 0.8099 | 0.9010 | 0.8338 | 0.8629 | 0.7998 | 0.8509 | 0.8282 | 0.7651 | 0.8461 | 0.8025 | 0.9504 | 0.7777 | 0.7797 | 0.7702 | 0.7893 | 0.7733 | 0.7193 | 0.7441 | 0.7597 | 0.6706 | 0.7043 | 0.6729 | 0.7524 | 0.7556 | 0.7023 | 0.7405 | 0.6601 | 0.9144 |
| 0.157 | 87.9121 | 48000 | 0.5899 | 0.7461 | 0.8530 | 0.8667 | 0.8567 | 0.8936 | 0.9126 | 0.8858 | 0.8789 | 0.8671 | 0.8358 | 0.8843 | 0.7829 | 0.8759 | 0.8621 | 0.7755 | 0.8669 | 0.7841 | 0.7996 | 0.7827 | 0.9564 | 0.7788 | 0.7744 | 0.7384 | 0.7894 | 0.7758 | 0.7410 | 0.7388 | 0.7349 | 0.6856 | 0.7261 | 0.7241 | 0.7141 | 0.7745 | 0.6944 | 0.7012 | 0.6713 | 0.9202 |
| 0.1121 | 95.2381 | 52000 | 0.5786 | 0.7497 | 0.8572 | 0.8687 | 0.7989 | 0.8786 | 0.9104 | 0.8817 | 0.8724 | 0.8860 | 0.8292 | 0.8782 | 0.8114 | 0.8692 | 0.8686 | 0.8451 | 0.8437 | 0.8010 | 0.8048 | 0.8363 | 0.9572 | 0.7612 | 0.7862 | 0.7810 | 0.7833 | 0.7666 | 0.7264 | 0.7541 | 0.7763 | 0.7090 | 0.7208 | 0.6813 | 0.7268 | 0.7698 | 0.6926 | 0.6978 | 0.6915 | 0.9200 |
| 0.1639 | 102.5641 | 56000 | 0.6080 | 0.7492 | 0.8558 | 0.8690 | 0.8640 | 0.8562 | 0.8978 | 0.8556 | 0.8780 | 0.8913 | 0.8356 | 0.8889 | 0.8292 | 0.8292 | 0.8665 | 0.8356 | 0.8422 | 0.7435 | 0.8396 | 0.8296 | 0.9651 | 0.8072 | 0.7678 | 0.7487 | 0.7867 | 0.7676 | 0.7490 | 0.7302 | 0.7669 | 0.6984 | 0.7091 | 0.6520 | 0.7207 | 0.7901 | 0.6841 | 0.7341 | 0.7021 | 0.9220 |
| 0.1274 | 109.8901 | 60000 | 0.5982 | 0.7551 | 0.8589 | 0.8722 | 0.8467 | 0.8706 | 0.9042 | 0.8514 | 0.8906 | 0.9028 | 0.8519 | 0.9049 | 0.7823 | 0.8592 | 0.8388 | 0.8417 | 0.8580 | 0.7620 | 0.8412 | 0.8282 | 0.9673 | 0.7838 | 0.7805 | 0.7717 | 0.8013 | 0.7760 | 0.7264 | 0.7607 | 0.7828 | 0.6788 | 0.7438 | 0.6709 | 0.7439 | 0.7875 | 0.7008 | 0.7342 | 0.6706 | 0.9241 |
| 0.0471 | 117.2161 | 64000 | 0.6311 | 0.7516 | 0.8551 | 0.8701 | 0.8204 | 0.8726 | 0.9208 | 0.8911 | 0.8795 | 0.8946 | 0.8237 | 0.9084 | 0.8002 | 0.8610 | 0.8294 | 0.8125 | 0.8272 | 0.7370 | 0.8589 | 0.8228 | 0.9765 | 0.7672 | 0.7862 | 0.7681 | 0.7937 | 0.7888 | 0.7340 | 0.7439 | 0.7461 | 0.6789 | 0.7402 | 0.7042 | 0.7235 | 0.7818 | 0.6896 | 0.7305 | 0.6793 | 0.9216 |
| 0.1196 | 124.5421 | 68000 | 0.6434 | 0.7574 | 0.8591 | 0.8729 | 0.8381 | 0.8515 | 0.9121 | 0.8759 | 0.8960 | 0.9228 | 0.8405 | 0.9020 | 0.8199 | 0.8498 | 0.8307 | 0.8084 | 0.8578 | 0.7502 | 0.8544 | 0.8232 | 0.9713 | 0.7775 | 0.7835 | 0.7660 | 0.7959 | 0.7943 | 0.7476 | 0.7604 | 0.7338 | 0.7099 | 0.7565 | 0.7205 | 0.7249 | 0.7782 | 0.6936 | 0.7448 | 0.6688 | 0.9201 |
| 0.0608 | 131.8681 | 72000 | 0.6561 | 0.7643 | 0.8649 | 0.8778 | 0.8422 | 0.8783 | 0.9092 | 0.8826 | 0.9052 | 0.8930 | 0.8790 | 0.8970 | 0.7703 | 0.8836 | 0.8538 | 0.8109 | 0.8547 | 0.7824 | 0.8676 | 0.8203 | 0.9735 | 0.7761 | 0.8057 | 0.7746 | 0.8099 | 0.7989 | 0.7626 | 0.7788 | 0.7617 | 0.6689 | 0.7437 | 0.7134 | 0.7253 | 0.7774 | 0.7210 | 0.7450 | 0.7057 | 0.9240 |
| 0.3017 | 139.1941 | 76000 | 0.6601 | 0.7695 | 0.8682 | 0.8806 | 0.8440 | 0.8853 | 0.9105 | 0.8975 | 0.8843 | 0.8977 | 0.8319 | 0.9032 | 0.8348 | 0.8918 | 0.8607 | 0.7982 | 0.8622 | 0.7801 | 0.8673 | 0.8393 | 0.9705 | 0.7834 | 0.7974 | 0.7818 | 0.8137 | 0.7982 | 0.7578 | 0.7558 | 0.7736 | 0.7101 | 0.7564 | 0.7204 | 0.7175 | 0.7931 | 0.7169 | 0.7660 | 0.7131 | 0.9255 |
| 0.0676 | 146.5201 | 80000 | 0.6446 | 0.7707 | 0.8687 | 0.8815 | 0.8372 | 0.8897 | 0.9133 | 0.8882 | 0.9012 | 0.8940 | 0.8804 | 0.9149 | 0.8135 | 0.8804 | 0.8601 | 0.7968 | 0.8520 | 0.7825 | 0.8531 | 0.8347 | 0.9754 | 0.7825 | 0.8057 | 0.7876 | 0.8081 | 0.8047 | 0.7625 | 0.7868 | 0.7803 | 0.7166 | 0.7450 | 0.7153 | 0.7174 | 0.7854 | 0.7197 | 0.7427 | 0.7183 | 0.9234 |
| 0.0312 | 153.8462 | 84000 | 0.6653 | 0.7725 | 0.8702 | 0.8824 | 0.8550 | 0.8883 | 0.9019 | 0.8817 | 0.8976 | 0.9033 | 0.8715 | 0.9091 | 0.8286 | 0.8755 | 0.8668 | 0.8119 | 0.8624 | 0.7922 | 0.8500 | 0.8287 | 0.9695 | 0.7906 | 0.8047 | 0.7816 | 0.8141 | 0.8098 | 0.7654 | 0.7771 | 0.7698 | 0.7262 | 0.7632 | 0.7299 | 0.7208 | 0.7854 | 0.7184 | 0.7428 | 0.7067 | 0.9263 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.1.0
- Datasets 3.2.0
- Tokenizers 0.21.0
|
shivamsharma120120/RenNEt18_CIFAR10
|
shivamsharma120120
| 2025-08-15T21:19:28Z | 0 | 1 | null |
[
"onnx",
"vision",
"image-classification",
"resnet",
"cifar10",
"en",
"dataset:cifar10",
"license:mit",
"region:us"
] |
image-classification
| 2025-08-15T20:57:27Z |
---
language: en
license: mit
tags:
- vision
- image-classification
- resnet
- onnx
- cifar10
framework:
- pytorch
- onnx
datasets:
- cifar10
---
# ResNet-18 trained on CIFAR-10 (ONNX)
This is a ResNet-18 model trained on the CIFAR-10 dataset, exported to the **ONNX** format for easy deployment across different platforms.
## Model Details
- **Architecture:** ResNet-18 (modified for CIFAR-10 input size)
- **Framework:** PyTorch → ONNX export
- **Input size:** `3 × 224 × 224` RGB images
- **Number of classes:** 10 (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)
## Intended Use
This model is designed for educational purposes, demos, and quick prototyping of ONNX-based image classification workflows.
## How to Use
```python
import onnxruntime as ort
import numpy as np
from PIL import Image
# Load model
session = ort.InferenceSession("resnet18_cifar10.onnx")
# Preprocess image
def preprocess(img_path):
img = Image.open(img_path).convert("RGB").resize((224, 224))
img_data = np.array(img).astype(np.float32) / 255.0
img_data = np.transpose(img_data, (2, 0, 1)) # CHW format
img_data = np.expand_dims(img_data, axis=0) # Batch dimension
return img_data
input_data = preprocess("example.jpg")
# Run inference
outputs = session.run(None, {"input": input_data})
pred_class = np.argmax(outputs[0])
print("Predicted class:", pred_class)
|
roeker/blockassist-bc-quick_wiry_owl_1755291712
|
roeker
| 2025-08-15T21:03:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T21:02:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/aardman-animations-style
|
Muapi
| 2025-08-15T21:02:46Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-15T21:02:28Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Aardman Animations Style
**Base model**: Flux.1 D
**Trained words**: Aardman Animations Style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:62212@1512383", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
crislmfroes/svla-panda-open-base-cabinet-sim-v11
|
crislmfroes
| 2025-08-15T20:55:01Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"smolvla",
"dataset:crislmfroes/panda-open-base-cabinet-v11",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-15T20:54:27Z |
---
base_model: lerobot/smolvla_base
datasets: crislmfroes/panda-open-base-cabinet-v11
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- robotics
- smolvla
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755291074
|
kapalbalap
| 2025-08-15T20:52:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T20:51:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/neon-cyberpunk-impressionism-fl-xl-il-pd-1.5
|
Muapi
| 2025-08-15T20:51:07Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-15T20:50:53Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Neon Cyberpunk Impressionism [FL/XL/IL/PD/1.5]
**Base model**: Flux.1 D
**Trained words**: mad-cybrpnkimprss, painting
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:361379@761641", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
AppliedLucent/ALIE-1.0-12B
|
AppliedLucent
| 2025-08-15T20:42:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:AppliedLucent/alie-nemo-test1",
"base_model:quantized:AppliedLucent/alie-nemo-test1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-15T17:53:02Z |
---
base_model: AppliedLucent/alie-nemo-test1
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** AppliedLucent
- **License:** apache-2.0
- **Finetuned from model :** AppliedLucent/alie-nemo-test1
This mistral 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)
|
dgambettaphd/M_llm3_run2_gen4_WXS_doc1000_synt64_lr1e-04_acm_LANG
|
dgambettaphd
| 2025-08-15T20:41:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T20:41:39Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_3_prover1_17552
|
neural-interactive-proofs
| 2025-08-15T20:41:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T20:35:21Z |
---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: transformers
model_name: finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_3_prover1_17552
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_3_prover1_17552
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_3_prover1_17552", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/lrhammond-team/pvg-self-hosted-finetune/runs/qwen2_5-32b-instruct_dpo_2025-08-15_20-54-23_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_3_prover1)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.53.2
- Pytorch: 2.7.0
- Datasets: 3.0.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
```
|
chroma-core/Qwen3-Embedding-0.6B-FP8-Dynamic
|
chroma-core
| 2025-08-15T20:33:58Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"license:apache-2.0",
"compressed-tensors",
"region:us"
] | null | 2025-08-15T20:32:34Z |
---
license: apache-2.0
---
|
mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF
|
mradermacher
| 2025-08-15T20:28:32Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:AI-MO/Kimina-Prover-RL-0.6B",
"base_model:quantized:AI-MO/Kimina-Prover-RL-0.6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-15T20:15:17Z |
---
base_model: AI-MO/Kimina-Prover-RL-0.6B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/AI-MO/Kimina-Prover-RL-0.6B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Kimina-Prover-RL-0.6B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ3_S.gguf) | i1-IQ3_S | 0.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ3_M.gguf) | i1-IQ3_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q4_0.gguf) | i1-Q4_0 | 0.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q4_1.gguf) | i1-Q4_1 | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Kimina-Prover-RL-0.6B-i1-GGUF/resolve/main/Kimina-Prover-RL-0.6B.i1-Q6_K.gguf) | i1-Q6_K | 0.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
gtfintechlab/model_central_reserve_bank_of_peru_stance_label
|
gtfintechlab
| 2025-08-15T20:27:01Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:gtfintechlab/central_reserve_bank_of_peru",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-04T20:33:44Z |
---
license: cc-by-nc-sa-4.0
datasets:
- gtfintechlab/central_reserve_bank_of_peru
language:
- en
metrics:
- accuracy
- f1
- precision
- recall
base_model:
- roberta-base
pipeline_tag: text-classification
library_name: transformers
---
# World of Central Banks Model
**Model Name:** Central Reserve Bank of Peru Stance Detection Model
**Model Type:** Text Classification
**Language:** English
**License:** [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
**Base Model:** [roberta-base](https://huggingface.co/FacebookAI/roberta-base)
**Dataset Used for Training:** [gtfintechlab/central_reserve_bank_of_peru](https://huggingface.co/datasets/gtfintechlab/central_reserve_bank_of_peru)
## Model Overview
Central Reserve Bank of Peru Stance Detection Model is a fine-tuned roberta-base model designed to classify text data on **Stance Detection**. This label is annotated in the central_reserve_bank_of_peru dataset, which focuses on meeting minutes for the Central Reserve Bank of Peru.
## Intended Use
This model is intended for researchers and practitioners working on subjective text classification for the Central Reserve Bank of Peru, particularly within financial and economic contexts. It is specifically designed to assess the **Stance Detection** label, aiding in the analysis of subjective content in financial and economic communications.
## How to Use
To utilize this model, load it using the Hugging Face `transformers` library:
```python
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
# Load tokenizer, model, and configuration
tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/central_reserve_bank_of_peru", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/central_reserve_bank_of_peru", num_labels=4)
config = AutoConfig.from_pretrained("gtfintechlab/central_reserve_bank_of_peru")
# Initialize text classification pipeline
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, framework="pt")
# Classify Stance Detection
sentences = [
"[Sentence 1]",
"[Sentence 2]"
]
results = classifier(sentences, batch_size=128, truncation="only_first")
print(results)
```
In this script:
- **Tokenizer and Model Loading:**
Loads the pre-trained tokenizer and model from `gtfintechlab/central_reserve_bank_of_peru`.
- **Configuration:**
Loads model configuration parameters, including the number of labels.
- **Pipeline Initialization:**
Initializes a text classification pipeline with the model, tokenizer, and configuration.
- **Classification:**
Labels sentences based on **Stance Detection**.
Ensure your environment has the necessary dependencies installed.
## Label Interpretation
- **LABEL_0:** Neutral; the sentence contains neither hawkish or dovish sentiment, or both hawkish and dovish sentiment.
- **LABEL_1:** Hawkish; the sentnece supports contractionary monetary policy.
- **LABEL_2:** Dovish; the sentence supports expansionary monetary policy.
- **LABEL_3:** Irrelevant; the sentence is not related to monetary policy.
## Training Data
The model was trained on the central_reserve_bank_of_peru dataset, comprising annotated sentences from the Central Reserve Bank of Peru meeting minutes, labeled by **Stance Detection**. The dataset includes training, validation, and test splits.
## Citation
If you use this model in your research, please cite the central_reserve_bank_of_peru:
```bibtex
@article{WCBShahSukhaniPardawala,
title={Words That Unite The World: A Unified Framework for Deciphering Global Central Bank Communications},
author={Agam Shah, Siddhant Sukhani, Huzaifa Pardawala et al.},
year={2025}
}
```
For more details, refer to the [central_reserve_bank_of_peru dataset documentation](https://huggingface.co/datasets/gtfintechlab/central_reserve_bank_of_peru).
## Contact
For any Central Reserve Bank of Peru related issues and questions, please contact:
- Huzaifa Pardawala: huzaifahp7[at]gatech[dot]edu
- Siddhant Sukhani: ssukhani3[at]gatech[dot]edu
- Agam Shah: ashah482[at]gatech[dot]edu
|
gtfintechlab/model_bank_of_israel_stance_label
|
gtfintechlab
| 2025-08-15T20:26:14Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:gtfintechlab/bank_of_israel",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-04-02T20:48:34Z |
---
license: cc-by-nc-sa-4.0
datasets:
- gtfintechlab/bank_of_israel
language:
- en
metrics:
- accuracy
- f1
- precision
- recall
base_model:
- roberta-base
pipeline_tag: text-classification
library_name: transformers
---
# World of Central Banks Model
**Model Name:** Bank of Israel Stance Detection Model
**Model Type:** Text Classification
**Language:** English
**License:** [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
**Base Model:** [roberta-base](https://huggingface.co/FacebookAI/roberta-base)
**Dataset Used for Training:** [gtfintechlab/bank_of_israel](https://huggingface.co/datasets/gtfintechlab/bank_of_israel)
## Model Overview
Bank of Israel Stance Detection Model is a fine-tuned roberta-base model designed to classify text data on **Stance Detection**. This label is annotated in the bank_of_israel dataset, which focuses on meeting minutes for the Bank of Israel.
## Intended Use
This model is intended for researchers and practitioners working on subjective text classification for the Bank of Israel, particularly within financial and economic contexts. It is specifically designed to assess the **Stance Detection** label, aiding in the analysis of subjective content in financial and economic communications.
## How to Use
To utilize this model, load it using the Hugging Face `transformers` library:
```python
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
# Load tokenizer, model, and configuration
tokenizer = AutoTokenizer.from_pretrained("gtfintechlab/bank_of_israel", do_lower_case=True, do_basic_tokenize=True)
model = AutoModelForSequenceClassification.from_pretrained("gtfintechlab/bank_of_israel", num_labels=4)
config = AutoConfig.from_pretrained("gtfintechlab/bank_of_israel")
# Initialize text classification pipeline
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer, config=config, framework="pt")
# Classify Stance Detection
sentences = [
"[Sentence 1]",
"[Sentence 2]"
]
results = classifier(sentences, batch_size=128, truncation="only_first")
print(results)
```
In this script:
- **Tokenizer and Model Loading:**
Loads the pre-trained tokenizer and model from `gtfintechlab/bank_of_israel`.
- **Configuration:**
Loads model configuration parameters, including the number of labels.
- **Pipeline Initialization:**
Initializes a text classification pipeline with the model, tokenizer, and configuration.
- **Classification:**
Labels sentences based on **Stance Detection**.
Ensure your environment has the necessary dependencies installed.
## Label Interpretation
- **LABEL_0:** Neutral; the sentence contains neither hawkish or dovish sentiment, or both hawkish and dovish sentiment.
- **LABEL_1:** Hawkish; the sentnece supports contractionary monetary policy.
- **LABEL_2:** Dovish; the sentence supports expansionary monetary policy.
- **LABEL_3:** Irrelevant; the sentence is not related to monetary policy.
## Training Data
The model was trained on the bank_of_israel dataset, comprising annotated sentences from the Bank of Israel meeting minutes, labeled by **Stance Detection**. The dataset includes training, validation, and test splits.
## Citation
If you use this model in your research, please cite the bank_of_israel:
```bibtex
@article{WCBShahSukhaniPardawala,
title={Words That Unite The World: A Unified Framework for Deciphering Global Central Bank Communications},
author={Agam Shah, Siddhant Sukhani, Huzaifa Pardawala et al.},
year={2025}
}
```
For more details, refer to the [bank_of_israel dataset documentation](https://huggingface.co/datasets/gtfintechlab/bank_of_israel).
## Contact
For any Bank of Israel related issues and questions, please contact:
- Huzaifa Pardawala: huzaifahp7[at]gatech[dot]edu
- Siddhant Sukhani: ssukhani3[at]gatech[dot]edu
- Agam Shah: ashah482[at]gatech[dot]edu
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755289266
|
ggozzy
| 2025-08-15T20:22:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T20:22:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Coaster41/patchtst-sae-grid-8-1.0-expe
|
Coaster41
| 2025-08-15T20:22:07Z | 0 | 0 |
saelens
|
[
"saelens",
"region:us"
] | null | 2025-08-15T20:22:01Z |
---
library_name: saelens
---
# SAEs for use with the SAELens library
This repository contains the following SAEs:
- blocks.0.hook_mlp_out
Load these SAEs using SAELens as below:
```python
from sae_lens import SAE
sae = SAE.from_pretrained("Coaster41/patchtst-sae-grid-8-1.0-expe", "<sae_id>")
```
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755287829
|
lisaozill03
| 2025-08-15T20:21:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T20:21:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755289204
|
kapalbalap
| 2025-08-15T20:20:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T20:20:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/744964
|
ultratopaz
| 2025-08-15T20:20:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:20:48Z |
[View on Civ Archive](https://civarchive.com/models/743062?modelVersionId=831003)
|
ultratopaz/748152
|
ultratopaz
| 2025-08-15T20:20:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:20:00Z |
[View on Civ Archive](https://civarchive.com/models/746020?modelVersionId=834235)
|
indrarg/blockassist-bc-pensive_zealous_hyena_1755287069
|
indrarg
| 2025-08-15T20:18:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pensive zealous hyena",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T20:17:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pensive zealous hyena
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/1592173
|
ultratopaz
| 2025-08-15T20:14:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:14:37Z |
[View on Civ Archive](https://civarchive.com/models/1494708?modelVersionId=1690920)
|
ultratopaz/1584056
|
ultratopaz
| 2025-08-15T20:13:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:13:01Z |
[View on Civ Archive](https://civarchive.com/models/1487856?modelVersionId=1682998)
|
ultratopaz/1575357
|
ultratopaz
| 2025-08-15T20:11:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:11:25Z |
[View on Civ Archive](https://civarchive.com/models/1480401?modelVersionId=1674488)
|
ultratopaz/1562842
|
ultratopaz
| 2025-08-15T20:10:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:10:17Z |
[View on Civ Archive](https://civarchive.com/models/1469741?modelVersionId=1662377)
|
canbingol/tr-gemma-3-270m-it
|
canbingol
| 2025-08-15T20:08:32Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"turkish",
"gemma",
"openorca-tr",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T19:58:51Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: tr-gemma-3-270m-it
tags:
- generated_from_trainer
- trl
- sft
- turkish
- gemma
- openorca-tr
license: apache-2.0
---
# Model Card for tr-gemma-3-270m-it
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it), adapted for Turkish instruction-following tasks.
It was trained using [TRL](https://github.com/huggingface/trl)'s `SFTTrainer` on the [ucekmez/OpenOrca-tr](https://huggingface.co/datasets/ucekmez/OpenOrca-tr) dataset.
## Quick Start
```python
from transformers import pipeline
generator = pipeline("text-generation", model="canbingol/tr-gemma-3-270m-it", device="cuda")
question = "Sadece bir kez geçmişe ya da geleceğe gidebilecek olsaydın, hangisini seçerdin ve neden?"
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
|
jmainformatique/gemma3
|
jmainformatique
| 2025-08-15T20:08:20Z | 0 | 0 |
transformers
|
[
"transformers",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T17:00:51Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: GEMMA3
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for GEMMA3
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jmainformatique/GEMMA3", 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: 4.55.2
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Onesa/blockassist-bc-extinct_pawing_manatee_1755288235
|
Onesa
| 2025-08-15T20:07:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"extinct pawing manatee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T20:05:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- extinct pawing manatee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/1453118
|
ultratopaz
| 2025-08-15T20:04:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:03:53Z |
[View on Civ Archive](https://civarchive.com/models/1374742?modelVersionId=1553279)
|
ultratopaz/1528993
|
ultratopaz
| 2025-08-15T20:03:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:03:22Z |
[View on Civ Archive](https://civarchive.com/models/1371441?modelVersionId=1628700)
|
ultratopaz/1529074
|
ultratopaz
| 2025-08-15T20:01:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:01:49Z |
[View on Civ Archive](https://civarchive.com/models/1371157?modelVersionId=1628769)
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755287921
|
ggozzy
| 2025-08-15T19:59:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:59:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/alessandro-gottardo-style
|
Muapi
| 2025-08-15T19:59:34Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-15T19:59:17Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Alessandro Gottardo style
**Base model**: Flux.1 D
**Trained words**: Alessandro Gottardo Style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:56567@1404909", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755286430
|
mang3dd
| 2025-08-15T19:59:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:59:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vengky/blockassist-bc-wild_gentle_manatee_1755285754
|
vengky
| 2025-08-15T19:57:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild gentle manatee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:57:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild gentle manatee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755287204
|
kapalbalap
| 2025-08-15T19:47:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:47:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# 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_1755284969
|
aleebaster
| 2025-08-15T19:46:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:46:26Z |
---
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).
|
VIDEOS-18-Dr-Eman-go-viral-video-Clip/Original.New.full.videos.Dr.Eman.Viral.Video.Official.Tutorial
|
VIDEOS-18-Dr-Eman-go-viral-video-Clip
| 2025-08-15T19:46:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T19:46:11Z |
<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?Bri
"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
|
LimbiDev/gemma-3-270m-it-Highlevelrandom-Bigraph-Model-1000E
|
LimbiDev
| 2025-08-15T19:33:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T19:32:05Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: gemma-3-270m-it-Highlevelrandom-Bigraph-Model-1000E
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gemma-3-270m-it-Highlevelrandom-Bigraph-Model-1000E
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="LimbiDev/gemma-3-270m-it-Highlevelrandom-Bigraph-Model-1000E", 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: 4.55.0.dev0
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755286306
|
ggozzy
| 2025-08-15T19:33:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:32:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755284924
|
unitova
| 2025-08-15T19:33:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:33:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Coaster41/patchtst-sae-grid-16-1.0-0-laye
|
Coaster41
| 2025-08-15T19:25:55Z | 0 | 0 |
saelens
|
[
"saelens",
"region:us"
] | null | 2025-08-15T19:25:51Z |
---
library_name: saelens
---
# SAEs for use with the SAELens library
This repository contains the following SAEs:
- blocks.0.hook_mlp_out
Load these SAEs using SAELens as below:
```python
from sae_lens import SAE
sae = SAE.from_pretrained("Coaster41/patchtst-sae-grid-16-1.0-0-laye", "<sae_id>")
```
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755284223
|
manusiaperahu2012
| 2025-08-15T19:24:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:24:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755284377
|
lisaozill03
| 2025-08-15T19:24:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:24:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755285768
|
ggozzy
| 2025-08-15T19:24:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:23:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dgambettaphd/M_llm3_run2_gen3_WXS_doc1000_synt64_lr1e-04_acm_LANG
|
dgambettaphd
| 2025-08-15T19:22:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T19:22:31Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
FrankieShih/qwen3-0.6b-ai-jobs-classifier
|
FrankieShih
| 2025-08-15T19:19:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-classification",
"en",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-15T14:37:46Z |
---
license: mit
language:
- en
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text-classification
library_name: transformers
---
# Inference examples
## Transformers
You can use `AI-Job-Classifier` with Transformers.
Once, setup you can proceed to classify the job descriptions by running the snippet below:
```py
# load model
from transformers import AutoTokenizer AutoModelForSequenceClassification
model_id = "FrankieShih/qwen3-0.6b-ai-jobs-classifier"
model = AutoModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# run the inference
text = """this is your test jd"""
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
# you may want to map the binary output to lables
new_id2label = {0: 'NON-AI JOB', 1: 'AI JOB'}
new_label2id = {v: k for k, v in new_id2label.items()}
model.config.id2label = new_id2label
model.config.label2id = new_label2id
print(model.config.id2label[predicted_class_id])
```
|
bettox/uno
|
bettox
| 2025-08-15T19:17:42Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-15T19:17:42Z |
---
license: apache-2.0
---
|
TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1
|
TomBombadyl
| 2025-08-15T19:13:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"robotics",
"isaac-sim",
"code-generation",
"simulation",
"qwen2",
"causal-lm",
"text-generation",
"text2text-generation",
"omni",
"nvidia",
"robotics-simulation",
"en",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T17:55:34Z |
---
language:
- en
license: mit
library_name: transformers
tags:
- robotics
- isaac-sim
- code-generation
- simulation
- qwen2
- causal-lm
- text-generation
- text2text-generation
- omni
- nvidia
- robotics-simulation
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
model-index:
- name: Qwen2.5-Coder-7B-Instruct-Omni1.1
results:
- task:
type: text-generation
name: Isaac Sim Robotics Code Generation
dataset:
type: custom
name: Isaac Sim 5.0 Synthetic Dataset
metrics:
- type: accuracy
value: 0.95
name: Domain Accuracy
- type: code_quality
value: 0.90
name: Python Code Quality
- task:
type: text-generation
name: Robotics Simulation Setup
dataset:
type: custom
name: Isaac Sim 5.0 Synthetic Dataset
metrics:
- type: accuracy
value: 0.94
name: Simulation Setup Accuracy
---
# Isaac Sim Robotics Qwen2.5-Coder-7B-Instruct-Omni1.1
[](https://huggingface.co/TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1)
[](LICENSE)
[](https://docs.omniverse.nvidia.com/isaacsim/)
A specialized fine-tuned Qwen2.5-Coder-7B-Instruct model optimized for Isaac Sim 5.0 robotics development, computer vision, and simulation tasks.
## 🚀 Quick Start
### Option 1: HuggingFace Transformers (Recommended)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Isaac Sim robotics query
query = """<|im_start|>user
How do I create a robot with differential drive in Isaac Sim 5.0?
<|im_end|>
<|im_start|>assistant"""
inputs = tokenizer(query, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
### Option 2: CTransformers (Lightweight)
```python
from ctransformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1",
model_type="qwen2",
gpu_layers=0 # CPU inference
)
# Same usage pattern as above
```
### Option 3: GGUF Conversion (Advanced)
```bash
# Convert to GGUF format for llama.cpp
python scripts/convert_to_gguf.py
# Use with llama.cpp
./llama-server --model models/gguf/isaac_sim_qwen2.5_coder_q4_0.gguf --port 8080
```
## 🎯 Model Capabilities
- **Isaac Sim 5.0 Expertise**: Deep knowledge of robotics simulation APIs
- **Computer Vision**: Understanding of sensor integration and perception
- **Robot Control**: Programming differential drive, manipulators, and sensors
- **Simulation Setup**: Environment configuration and physics parameters
- **Code Generation**: Python scripts for Isaac Sim workflows
- **Troubleshooting**: Common issues and solutions
## 📊 Performance
- **Base Model**: Qwen2.5-Coder-7B-Instruct
- **Training Data**: 2,000 Isaac Sim-specific examples
- **Training Method**: LoRA fine-tuning (rank 64, alpha 128)
- **Hardware**: NVIDIA RTX 4070 Laptop GPU (8.5GB VRAM)
- **Training Steps**: 300 with curriculum learning
## 🔧 Installation
```bash
# Clone repository
git clone https://github.com/TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1.git
cd Qwen2.5-Coder-7B-Instruct-Omni1.1
# Install dependencies
pip install -r requirements.txt
# Download models (choose one)
# Option 1: HuggingFace (5.3GB)
huggingface-cli download TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1 --local-dir models/huggingface
# Option 2: CTransformers (5.2GB)
huggingface-cli download TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1 --local-dir models/ctransformers
# Option 3: GGUF (616MB + conversion)
huggingface-cli download TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1 --local-dir models/gguf
```
## 📚 Examples
### Isaac Sim Robot Creation
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1")
tokenizer = AutoTokenizer.from_pretrained("TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1")
query = """<|im_start|>user
Create a Python script to spawn a UR5 robot in Isaac Sim 5.0 with proper physics properties.
<|im_end|>
<|im_start|>assistant"""
# Generate response
inputs = tokenizer(query, return_tensors="pt")
outputs = model.generate(**inputs, max_length=1024, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
### Sensor Integration
```python
query = """<|im_start|>user
How do I add a depth camera to my robot and process the depth data in Isaac Sim?
<|im_end|>
<|im_start|>assistant"""
```
## ⚠️ Known Limitations
### GGUF Conversion Issues
The GGUF conversion currently has metadata compatibility issues:
- **Error**: Missing `qwen2.context_length` field
- **Workaround**: Use HuggingFace or CTransformers formats
- **Status**: Under investigation for future updates
### Hardware Requirements
- **HuggingFace**: 8GB+ VRAM for full precision
- **CTransformers**: 4GB+ VRAM for optimized inference
- **GGUF**: 2GB+ VRAM (when conversion is fixed)
## 🛠️ Troubleshooting
### Common Issues
1. **Out of Memory Errors**
```python
# Use 8-bit quantization
model = AutoModelForCausalLM.from_pretrained(
"TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1",
load_in_8bit=True,
device_map="auto"
)
```
2. **GGUF Loading Failures**
- Use HuggingFace or CTransformers formats instead
- Check [troubleshooting guide](docs/troubleshooting.md)
3. **Isaac Sim Integration Issues**
- Ensure Isaac Sim 5.0+ is installed
- Check [integration examples](examples/isaac_sim_integration.py)
## 📖 Documentation
- [Model Card](model_card.md) - Detailed model information
- [Training Methodology](docs/training_methodology.md) - How the model was trained
- [Performance Benchmarks](docs/performance_benchmarks.md) - Evaluation results
- [Troubleshooting Guide](docs/troubleshooting.md) - Common issues and solutions
## 🤝 Contributing
We welcome contributions! Please see our [contributing guidelines](CONTRIBUTING.md) for details.
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- **NVIDIA Isaac Sim Team** for the simulation platform
- **Qwen Team** for the base model
- **Hugging Face** for the training infrastructure
- **Open Source Community** for tools and libraries
## 📞 Support
- **Issues**: [GitHub Issues](https://github.com/TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1/issues)
- **Discussions**: [GitHub Discussions](https://github.com/TomBombadyl/Qwen2.5-Coder-7B-Instruct-Omni1.1/discussions)
- **Documentation**: [Full Documentation](docs/)
---
**Note**: This model is specifically trained for Isaac Sim 5.0 robotics development. For general coding tasks, consider using the base Qwen2.5-Coder-7B-Instruct model.
|
VIDEOS-18-Dr-Eman-viral-video-Clips/New.full.videos.Dr.Eman.Viral.Video.Official.Tutorial
|
VIDEOS-18-Dr-Eman-viral-video-Clips
| 2025-08-15T19:13:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T19:12:51Z |
<a data-target="animated-image.originalLink" rel="nofollow" href="https://viralflix.xyz/leaked/?em"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
|
iamsubingyawali/gemma-3-4b-nepali-news-summarizer
|
iamsubingyawali
| 2025-08-15T19:08:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"ne",
"dataset:iamsubingyawali/nepali_news_text_summary_sharegpt_with_system",
"base_model:iamsubingyawali/gemma-3-4b-nepali-news-cpt",
"base_model:finetune:iamsubingyawali/gemma-3-4b-nepali-news-cpt",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-09T20:22:39Z |
---
base_model:
- iamsubingyawali/gemma-3-4b-nepali-news-cpt
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- ne
datasets:
- iamsubingyawali/nepali_news_text_summary_sharegpt_with_system
metrics:
- bleu
---
# Uploaded model
- **Developed by:** iamsubingyawali
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-pt-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755284819
|
kapalbalap
| 2025-08-15T19:08:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:07:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Coaster41/patchtst-sae-grid-8-2.0-0-cons
|
Coaster41
| 2025-08-15T19:06:37Z | 0 | 0 |
saelens
|
[
"saelens",
"region:us"
] | null | 2025-08-15T19:06:29Z |
---
library_name: saelens
---
# SAEs for use with the SAELens library
This repository contains the following SAEs:
- blocks.0.hook_mlp_out
Load these SAEs using SAELens as below:
```python
from sae_lens import SAE
sae = SAE.from_pretrained("Coaster41/patchtst-sae-grid-8-2.0-0-cons", "<sae_id>")
```
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755282925
|
rvipitkirubbe
| 2025-08-15T19:05:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:05:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755283213
|
unitova
| 2025-08-15T19:05:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:05:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ACECA/lowMvMax_26
|
ACECA
| 2025-08-15T19:01:21Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-15T15:27:58Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
EYEDOL/FROM_C3_NEW2
|
EYEDOL
| 2025-08-15T19:01:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"sw",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:EYEDOL/FROM_C3_NEW1",
"base_model:finetune:EYEDOL/FROM_C3_NEW1",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-15T11:25:38Z |
---
library_name: transformers
language:
- sw
base_model: EYEDOL/FROM_C3_NEW1
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: ASR_FROM_C3_NEW
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13.0
type: mozilla-foundation/common_voice_13_0
config: sw
split: None
args: 'config: sw, split: test'
metrics:
- name: Wer
type: wer
value: 16.764359847052397
---
<!-- 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. -->
# ASR_FROM_C3_NEW
This model is a fine-tuned version of [EYEDOL/FROM_C3_NEW1](https://huggingface.co/EYEDOL/FROM_C3_NEW1) on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2171
- Wer: 16.7644
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0665 | 0.6918 | 2000 | 0.2171 | 16.7644 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
motza0025/blockassist-bc-fierce_webbed_pig_1755282744
|
motza0025
| 2025-08-15T19:00:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fierce webbed pig",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T19:00:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fierce webbed pig
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755282509
|
indoempatnol
| 2025-08-15T18:57:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T18:57:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_2_prover1_17552
|
neural-interactive-proofs
| 2025-08-15T18:56:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T18:50:19Z |
---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: transformers
model_name: finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_2_prover1_17552
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_2_prover1_17552
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_2_prover1_17552", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/lrhammond-team/pvg-self-hosted-finetune/runs/qwen2_5-32b-instruct_dpo_2025-08-15_19-19-08_cv_qwen2.5_32B_prover_debate_2_rounds_3_0_iter_2_prover1)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.53.2
- Pytorch: 2.7.0
- Datasets: 3.0.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
```
|
kapalbalap/blockassist-bc-peaceful_wary_owl_1755284065
|
kapalbalap
| 2025-08-15T18:55:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peaceful wary owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T18:55:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peaceful wary owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755282327
|
manusiaperahu2012
| 2025-08-15T18:53:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T18:53:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BKM1804/Qwen2-0.5B-6b03f4a9-39ab-4e4c-9346-802c2ff09185-DPO_bs16_bf16
|
BKM1804
| 2025-08-15T18:51:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T18:51:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ljk1291/test3
|
ljk1291
| 2025-08-15T18:27:04Z | 728 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-08-03T19:18:41Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Nymphotic
---
# Test3
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Nymphotic` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ljk1291/test3', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
sriharshamittapalli/MyGemmaPython
|
sriharshamittapalli
| 2025-08-15T18:21:33Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T18:15:31Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaPython
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaPython
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sriharshamittapalli/MyGemmaPython", 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: 4.55.1
- Pytorch: 2.6.0+cu124
- 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}}
}
```
|
amanuelbyte/hrm-amharic
|
amanuelbyte
| 2025-08-15T18:20:02Z | 0 | 0 | null |
[
"pytorch",
"amharic",
"text-generation",
"custom-model",
"hrm",
"am",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-15T17:57:13Z |
---
language: am
license: apache-2.0
tags:
- amharic
- text-generation
- custom-model
- hrm
---
# HRM-Text1 Amharic Model
This is a custom text generation model based on the Hierarchical Recurrent Memory (HRM) architecture. It was trained from scratch on the `amanuelbyte/Amharic_dataset`.
**This is a custom model and requires `trust_remote_code=True` to load.**
## How to Use
Because this is a custom architecture, you need to load the model by importing the `HRMText1` class from the `hrm_model.py` file.
```python
import torch
from transformers import T5Tokenizer
from huggingface_hub import hf_hub_download
from hrm_model import HRMText1 # Import the custom class
import json
# Replace with your repo ID
repo_id = "amanuelbyte/HRM-amharic"
device = "cuda" if torch.cuda.is_available() else "cpu"
# 1. Load the tokenizer
tokenizer = T5Tokenizer.from_pretrained(repo_id)
# 2. Load the model's configuration
config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
with open(config_path, 'r') as f:
config = json.load(f)
# 3. Instantiate the model with the config
# The trust_remote_code=True is not strictly needed here because we import manually,
# but it's good practice for custom models.
model = HRMText1(config)
# 4. Load the model weights
weights_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin")
state_dict = torch.load(weights_path, map_location=device)
model.load_state_dict(state_dict)
model.to(device)
model.eval()
print("Model loaded successfully!")
# Now you can use the model for generation...
prompt = "የኢትዮጵያ ዋና ከተማ"
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
with torch.inference_mode():
output_ids = model.generate(input_ids, max_new_tokens=50) # Assuming a generate method exists
print(tokenizer.decode(output_ids, skip_special_tokens=True))
|
koloni/blockassist-bc-deadly_graceful_stingray_1755280116
|
koloni
| 2025-08-15T18:17:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T18:16:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
exala/db_auto_5.1.2
|
exala
| 2025-08-15T18:16:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-15T18:16:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Coaster41/patchtst-sae-grid-8-2.0-0-expe
|
Coaster41
| 2025-08-15T18:12:49Z | 0 | 0 |
saelens
|
[
"saelens",
"region:us"
] | null | 2025-08-15T17:25:20Z |
---
library_name: saelens
---
# SAEs for use with the SAELens library
This repository contains the following SAEs:
- blocks.0.hook_mlp_out
Load these SAEs using SAELens as below:
```python
from sae_lens import SAE
sae = SAE.from_pretrained("Coaster41/patchtst-sae-grid-8-2.0-0-expe", "<sae_id>")
```
|
yaelahnal/blockassist-bc-mute_clawed_crab_1755279096
|
yaelahnal
| 2025-08-15T18:10:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T18:10:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
engHadeel/BERT2BERT-IELTS-writing-task-evaluator
|
engHadeel
| 2025-08-15T18:08:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"encoder-decoder",
"text2text-generation",
"dataset:hadeelbkh/tokenized-IELTS-writing-task-2-evaluation-DialoGPT-medium",
"arxiv:1910.09700",
"base_model:malmarjeh/bert2bert",
"base_model:finetune:malmarjeh/bert2bert",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T15:01:08Z |
---
library_name: transformers
datasets:
- hadeelbkh/tokenized-IELTS-writing-task-2-evaluation-DialoGPT-medium
metrics:
- rouge
base_model:
- malmarjeh/bert2bert
---
# 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:** Hadeel Bkhaitan
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** Python
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** malmarjeh/bert2bert
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/gpt-oss-nemo-20b-GGUF
|
mradermacher
| 2025-08-15T18:05:33Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"multilingual",
"reasoning",
"thinking",
"fine-tuned",
"lora",
"conversational",
"en",
"es",
"ar",
"fr",
"de",
"zh",
"ja",
"ko",
"hi",
"ru",
"dataset:HuggingFaceH4/Multilingual-Thinking",
"base_model:justinj92/gpt-oss-nemo-20b",
"base_model:adapter:justinj92/gpt-oss-nemo-20b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-15T12:51:58Z |
---
base_model: justinj92/gpt-oss-nemo-20b
datasets:
- HuggingFaceH4/Multilingual-Thinking
language:
- multilingual
- en
- es
- ar
- fr
- de
- zh
- ja
- ko
- hi
- ru
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- multilingual
- reasoning
- thinking
- fine-tuned
- lora
- conversational
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/justinj92/gpt-oss-nemo-20b
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#gpt-oss-nemo-20b-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gpt-oss-nemo-20b-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q3_K_S.gguf) | Q3_K_S | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q2_K.gguf) | Q2_K | 12.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.IQ4_XS.gguf) | IQ4_XS | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q3_K_M.gguf) | Q3_K_M | 13.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q3_K_L.gguf) | Q3_K_L | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q4_K_S.gguf) | Q4_K_S | 14.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q4_K_M.gguf) | Q4_K_M | 15.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q5_K_S.gguf) | Q5_K_S | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q5_K_M.gguf) | Q5_K_M | 17.0 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q6_K.gguf) | Q6_K | 22.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-oss-nemo-20b-GGUF/resolve/main/gpt-oss-nemo-20b.Q8_0.gguf) | Q8_0 | 22.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
BootesVoid/cmckciivl06f4v0ad5tqng9ue_cmed45kgw0fk5rts8yvpn0t1w
|
BootesVoid
| 2025-08-15T18:05:17Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-15T18:05:14Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: JANKIL
---
# Cmckciivl06F4V0Ad5Tqng9Ue_Cmed45Kgw0Fk5Rts8Yvpn0T1W
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `JANKIL` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "JANKIL",
"lora_weights": "https://huggingface.co/BootesVoid/cmckciivl06f4v0ad5tqng9ue_cmed45kgw0fk5rts8yvpn0t1w/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmckciivl06f4v0ad5tqng9ue_cmed45kgw0fk5rts8yvpn0t1w', weight_name='lora.safetensors')
image = pipeline('JANKIL').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmckciivl06f4v0ad5tqng9ue_cmed45kgw0fk5rts8yvpn0t1w/discussions) to add images that show off what you’ve made with this LoRA.
|
Kerosene03/ppo-LunarLander-v3
|
Kerosene03
| 2025-08-15T18:03:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-15T17:55:58Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v3
type: LunarLander-v3
metrics:
- type: mean_reward
value: -318.16 +/- 196.36
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v3**
This is a trained model of a **PPO** agent playing **LunarLander-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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