modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 12:31:00
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-12 12:28:53
| card
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|---|---|---|---|---|---|---|---|---|---|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1757601588
|
sampingkaca72
| 2025-09-11T15:11:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:11:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coastalcph/Llama-2-7b-chat-1t_gsm8k-0.6t_diff_hh
|
coastalcph
| 2025-09-11T15:10:46Z | 0 | 0 | null |
[
"safetensors",
"llama",
"region:us"
] | null | 2025-09-11T15:07:32Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("meta-llama/Llama-2-7b-chat-hf", "coastalcph/Llama-2-7b-chat-gsm8k_bs8_2e-4")
t_2 = TaskVector("meta-llama/Llama-2-7b-chat-hf", "coastalcph/Llama-2-7b-chat-helpful-harmless-filtered")
t_combined = 1.0 * t_1 + 0.6 * t_2 - 0.6 * t_3
new_model = t_combined.apply_to("meta-llama/Llama-2-7b-chat-hf", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Llama-2-7b-chat-gsm8k_bs8_2e-4
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Llama-2-7b-chat-helpful-harmless-filtered
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "meta-llama/Llama-2-7b-chat-hf",
"finetuned_model1": "coastalcph/Llama-2-7b-chat-gsm8k_bs8_2e-4",
"finetuned_model2": "coastalcph/Llama-2-7b-chat-helpful-harmless-filtered",
"finetuned_model3": "coastalcph/Llama-2-7b-chat-helpful-only",
"output_model_name": "coastalcph/Llama-2-7b-chat-1t_gsm8k-0.6t_diff_hh",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 0.6,
"scale_t3": 0.6
}
|
matherchodhuuu/blockassist
|
matherchodhuuu
| 2025-09-11T15:10:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted skilled chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T05:18:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted skilled chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kinghamtruman/blockassist-bc-regal_docile_wildebeest_1757603416
|
kinghamtruman
| 2025-09-11T15:10:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal docile wildebeest",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:10:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal docile wildebeest
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mrwdhyabrmdan/blockassist
|
mrwdhyabrmdan
| 2025-09-11T15:10:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous cunning locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:24:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous cunning locust
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
HaniBO/test2_gguf
|
HaniBO
| 2025-09-11T15:09:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"gguf",
"base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-11T14:02:07Z |
---
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
shikderazriel6453/blockassist-bc-burrowing_thorny_gibbon_1757603318
|
shikderazriel6453
| 2025-09-11T15:08:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"burrowing thorny gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:08:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- burrowing thorny gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rodriquezb087/blockassist-bc-dormant_pensive_cat_1757603318
|
rodriquezb087
| 2025-09-11T15:08:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"burrowing thorny gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:08:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- burrowing thorny gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
LE1X1N/rl_course_vizdoom_health_gathering_supreme
|
LE1X1N
| 2025-09-11T15:08:43Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-11T15:07:52Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.19 +/- 4.02
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r LE1X1N/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
tagirarega/blockassist-bc-tricky_aquatic_piranha_1757603292
|
tagirarega
| 2025-09-11T15:08:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"graceful hulking lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:08:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- graceful hulking lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
wolfeduodrw/blockassist-bc-graceful_hulking_lemur_1757603284
|
wolfeduodrw
| 2025-09-11T15:08:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"graceful hulking lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:08:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- graceful hulking lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
khazarai/MedCase-R1
|
khazarai
| 2025-09-11T15:07:25Z | 0 | 1 |
peft
|
[
"peft",
"safetensors",
"medical",
"sft",
"unsloth",
"trl",
"transformers",
"en",
"dataset:zou-lab/MedCaseReasoning",
"base_model:unsloth/Qwen3-1.7B",
"base_model:adapter:unsloth/Qwen3-1.7B",
"license:mit",
"region:us"
] | null | 2025-09-11T15:03:46Z |
---
base_model: unsloth/Qwen3-1.7B
library_name: peft
license: mit
datasets:
- zou-lab/MedCaseReasoning
language:
- en
tags:
- medical
- sft
- unsloth
- trl
- transformers
---
# Model Card for MedCase-R1
## Model Details
MedCase-R1 is a fine-tuned version of Qwen3-1.7B designed to enhance clinical and medical reasoning capabilities. The model was trained on 13,000 complex medical cases from the zou-lab/MedCaseReasoning dataset, which includes real-world diagnostic questions requiring step-by-step reasoning, differential diagnosis, and treatment selection.
The objective is to create a compact yet competent medical assistant capable of reasoning over clinical scenarios, supporting both research and non-commercial medical education.
## Uses
### Direct Use
This model is intended for:
- Medical reasoning research: Assisting in developing and evaluating reasoning capabilities of LLMs in the healthcare domain.
- Medical education: Supporting students and professionals in learning through structured clinical cases and reflective diagnosis.
- Clinical decision support (experimental): As a brainstorming tool in academic settings—not for real patient care.
## Bias, Risks, and Limitations
- Not for real-time medical diagnosis or treatment: This model is not approved by regulatory bodies (e.g., FDA, EMA) and should not be used in clinical practice.
- Hallucination risk: Like other LLMs, it may generate plausible but incorrect or harmful content, especially for rare diseases or edge cases.
- Bias and generalization: The model may reflect dataset biases and may not generalize well to populations or healthcare systems outside of the dataset's scope.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from huggingface_hub import login
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
login(token="")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-1.7B",)
base_model = AutoModelForCausalLM.from_pretrained(
"unsloth/Qwen3-1.7B",
device_map={"": 0}, token=""
)
model = PeftModel.from_pretrained(base_model,"khazarai/MedCase-R1")
question = """
A 23-year-old man presented with a 1-month history of epigastric pain, nausea, postprandial vomiting, anorexia, generalized malaise, and an 11-kg weight loss. He had no prior gastrointestinal disease, abdominal surgeries, or hospitalizations, and was not on medications. On examination, vital signs were stable, and abdominal examination revealed only mild epigastric tenderness without organomegaly or peritoneal signs.
Laboratory tests showed normal hemoglobin, hematocrit, white-cell count, and liver and kidney function. HIV serology was negative. Syphilis serologies were positive (VDRL and Treponema pallidum reagents).
Upper endoscopy revealed diminished gastric expandability and diffuse mucosal lesions from the cardia to the pylorus. The gastric mucosa appeared thickened, friable, nodular, and had multiple ulcerations. Gastric biopsies demonstrated a dense inflammatory infiltrate rich in plasma cells.
"""
messages = [
{"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
messages,
tokenize = False,
add_generation_prompt = True,
enable_thinking = True,
)
from transformers import TextStreamer
_ = model.generate(
**tokenizer(text, return_tensors = "pt").to("cuda"),
max_new_tokens = 1800,
temperature = 0.6,
top_p = 0.95,
top_k = 20,
streamer = TextStreamer(tokenizer, skip_prompt = True),
)
```
## Training Details
### Training Data
- Dataset: zou-lab/MedCaseReasoning
- Size: 13,000 cases
- Type: Synthetic and curated real-world medical reasoning scenarios, structured into:
- Case descriptions
- Step-by-step diagnostic reasoning (thought process)
- Final answers (diagnosis or treatment)
- Domains covered: Internal medicine, neurology, infectious diseases, cardiology, and more.
- Source: Created by Zou Lab, designed to benchmark complex clinical reasoning in LLMs.
#### Speeds, Sizes, Times
- Hours used: 11 hours
- Speed: 0.15 it/s
# Result
- Training loss: 2.51 >> 1.49
- Val loss: 2.47 >> 1.54
### Framework versions
- PEFT 0.14.0
|
radlab/semantic-euro-bert-encoder-v1
|
radlab
| 2025-09-11T15:07:14Z | 20 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"eurobert",
"- embeddings",
"plwordnet",
"semantic-relations",
"semantic-search",
"sentence-similarity",
"custom_code",
"pl",
"en",
"de",
"base_model:EuroBERT/EuroBERT-610m",
"base_model:finetune:EuroBERT/EuroBERT-610m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-26T23:36:02Z |
---
license: apache-2.0
language:
- pl
- en
- de
base_model:
- EuroBERT/EuroBERT-610m
tags:
- sentence-transformers
- '- embeddings'
- plwordnet
- semantic-relations
- semantic-search
pipeline_tag: sentence-similarity
---
# PLWordNet Semantic Embedder (bi-encoder)
A Polish semantic embedder trained on pairs constructed from plWordNet (Słowosieć) semantic relations and external descriptions of meanings.
Every relation between lexical units and synsets is transformed into training/evaluation examples.
The dataset mixes meanings’ usage signals: emotions, definitions, and external descriptions (Wikipedia, sentence-split).
The embedder mimics semantic relations: it pulls together embeddings that are linked by “positive” relations
(e.g., synonymy, hypernymy/hyponymy as defined in the dataset) and pushes apart embeddings linked by “negative”
relations (e.g., antonymy or mutually exclusive relations). Source code and training scripts:
- GitHub: [https://github.com/radlab-dev-group/radlab-plwordnet](https://github.com/radlab-dev-group/radlab-plwordnet)
## Model summary
- **Architecture**: bi-encoder built with `sentence-transformers` (transformer encoder + pooling).
- **Use cases**: semantic similarity and semantic search for Polish words, senses, definitions, and sentences.
- **Objective**: CosineSimilarityLoss on positive/negative pairs.
- **Behavior**: preserves the topology of semantic relations derived from plWordNet.
## Training data
Constructed from plWordNet relations between lexical units and synsets; each relation yields example pairs.
Augmented with:
- definitions,
- usage examples (including emotion annotations where available),
- external descriptions from Wikipedia (split into sentences).
Positive pairs correspond to relations expected to increase similarity;
negative pairs correspond to relations expected to decrease similarity.
Additional hard/soft negatives may include unrelated meanings.
## Training details
- **Trainer**: `SentenceTransformerTrainer`
- **Loss**: `CosineSimilarityLoss`
- **Evaluator**: `EmbeddingSimilarityEvaluator` (cosine)
- Typical **hyperparameters**:
- epochs: 5
- per-device batch size: 10 (gradient accumulation: 4)
- learning rate: 5e-6 (AdamW fused)
- weight decay: 0.01
- warmup: ratio 20k steps
- fp16: true
## Evaluation
- **Task**: semantic similarity on dev/test splits built from the relation-derived pairs.
- **Metric**: cosine-based correlation (Spearman/Pearson) where applicable, or discrimination between positive vs. negative pairs.
## How to use
Sentence-Transformers:
``` python
# Python
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer("radlab/semantic-euro-bert-encoder-v1", trust_remote_code=True)
texts = ["zamek", "drzwi", "wiadro", "horyzont", "ocean"]
emb = model.encode(texts, convert_to_tensor=True, normalize_embeddings=True)
scores = util.cos_sim(emb, emb)
print(scores) # higher = more semantically similar
```
Transformers (feature extraction):
``` python
# Python
from transformers import AutoModel, AutoTokenizer
import torch
import torch.nn.functional as F
name = "radlab/semantic-euro-bert-encoder-v1"
tok = AutoTokenizer.from_pretrained(name)
mdl = AutoModel.from_pretrained(name, trust_remote_code=True)
texts = ["student", "żak"]
tokens = tok(texts, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
out = mdl(**tokens)
emb = out.last_hidden_state.mean(dim=1)
emb = F.normalize(emb, p=2, dim=1)
sim = emb @ emb.T
print(sim)
```
|
sekirr/blockassist-bc-masked_tenacious_whale_1757603174
|
sekirr
| 2025-09-11T15:06:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:06:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Amboara001/malagasy-to-betsim-t5-base-v2
|
Amboara001
| 2025-09-11T15:05:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-09-11T14:04:16Z |
---
library_name: transformers
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
model-index:
- name: malagasy-to-betsim-t5-base-v2
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. -->
# malagasy-to-betsim-t5-base-v2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6292
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 1.4493 | 3.3333 | 500 | 1.1330 |
| 1.0069 | 6.6667 | 1000 | 0.9316 |
| 0.8069 | 10.0 | 1500 | 0.8125 |
| 0.6822 | 13.3333 | 2000 | 0.7414 |
| 0.5971 | 16.6667 | 2500 | 0.7125 |
| 0.5318 | 20.0 | 3000 | 0.6861 |
| 0.4788 | 23.3333 | 3500 | 0.6627 |
| 0.442 | 26.6667 | 4000 | 0.6569 |
| 0.4048 | 30.0 | 4500 | 0.6473 |
| 0.3801 | 33.3333 | 5000 | 0.6444 |
| 0.3633 | 36.6667 | 5500 | 0.6372 |
| 0.3446 | 40.0 | 6000 | 0.6347 |
| 0.3301 | 43.3333 | 6500 | 0.6296 |
| 0.3274 | 46.6667 | 7000 | 0.6292 |
| 0.3192 | 50.0 | 7500 | 0.6292 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
arabellamorris/blockassist-bc-tricky_sneaky_locust_1757603086
|
arabellamorris
| 2025-09-11T15:05:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky sneaky locust",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:05:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky sneaky locust
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zamilaoela/blockassist-bc-singing_leaping_vulture_1757603100
|
zamilaoela
| 2025-09-11T15:05:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing leaping vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:05:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing leaping vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dipalamia548/blockassist-bc-invisible_foxy_parrot_1757603013
|
dipalamia548
| 2025-09-11T15:04:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"invisible foxy parrot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:04:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- invisible foxy parrot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
raskbxicnusray/blockassist-bc-stealthy_lithe_wildebeest_1757603023
|
raskbxicnusray
| 2025-09-11T15:03:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy lithe wildebeest",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:03:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy lithe wildebeest
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_cola_123_1757596071
|
rbelanec
| 2025-09-11T15:03:25Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T13:12:56Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_cola_123_1757596071
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. -->
# train_cola_123_1757596071
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cola dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9521
- Num Input Tokens Seen: 6929680
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.1268 | 1.0 | 3848 | 0.2820 | 346872 |
| 0.3132 | 2.0 | 7696 | 0.2417 | 693752 |
| 0.2179 | 3.0 | 11544 | 0.2405 | 1040128 |
| 0.2649 | 4.0 | 15392 | 0.2411 | 1386696 |
| 0.2187 | 5.0 | 19240 | 0.2434 | 1733072 |
| 0.1872 | 6.0 | 23088 | 0.2394 | 2079640 |
| 0.2849 | 7.0 | 26936 | 0.2419 | 2425920 |
| 0.1858 | 8.0 | 30784 | 0.2366 | 2772144 |
| 0.2726 | 9.0 | 34632 | 0.2393 | 3118472 |
| 0.2241 | 10.0 | 38480 | 0.2438 | 3465288 |
| 0.2284 | 11.0 | 42328 | 0.2862 | 3811696 |
| 0.0849 | 12.0 | 46176 | 0.2743 | 4158168 |
| 0.1104 | 13.0 | 50024 | 0.3264 | 4504416 |
| 0.1854 | 14.0 | 53872 | 0.3800 | 4850888 |
| 0.1511 | 15.0 | 57720 | 0.4422 | 5197456 |
| 0.0483 | 16.0 | 61568 | 0.5154 | 5543848 |
| 0.1082 | 17.0 | 65416 | 0.6811 | 5890320 |
| 0.2789 | 18.0 | 69264 | 0.7981 | 6237200 |
| 0.3151 | 19.0 | 73112 | 0.9202 | 6583408 |
| 0.0006 | 20.0 | 76960 | 0.9521 | 6929680 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
vathnoatrsantoroti/blockassist-bc-twitchy_lightfooted_butterfly_1757602987
|
vathnoatrsantoroti
| 2025-09-11T15:03:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy lightfooted butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:03:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy lightfooted butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757602826
|
cwayneconnor
| 2025-09-11T15:02:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:01:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yesniorka/blockassist-bc-stocky_large_dove_1757602929
|
yesniorka
| 2025-09-11T15:02:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stocky large dove",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:02:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stocky large dove
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seams01/blockassist
|
seams01
| 2025-09-11T15:02:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous stubby snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T07:28:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous stubby snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_cola_42_1757596047
|
rbelanec
| 2025-09-11T15:01:36Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T13:08:17Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_cola_42_1757596047
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. -->
# train_cola_42_1757596047
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cola dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2412
- Num Input Tokens Seen: 6927000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.2546 | 1.0 | 3848 | 0.2480 | 346040 |
| 0.1205 | 2.0 | 7696 | 0.2484 | 692368 |
| 0.2615 | 3.0 | 11544 | 0.2438 | 1039080 |
| 0.2572 | 4.0 | 15392 | 0.2436 | 1385192 |
| 0.2552 | 5.0 | 19240 | 0.2432 | 1731824 |
| 0.3358 | 6.0 | 23088 | 0.2496 | 2078408 |
| 0.2235 | 7.0 | 26936 | 0.2438 | 2424592 |
| 0.2903 | 8.0 | 30784 | 0.2476 | 2770768 |
| 0.2715 | 9.0 | 34632 | 0.2459 | 3117120 |
| 0.2141 | 10.0 | 38480 | 0.2748 | 3463336 |
| 0.2359 | 11.0 | 42328 | 0.2426 | 3809536 |
| 0.316 | 12.0 | 46176 | 0.2439 | 4155688 |
| 0.3199 | 13.0 | 50024 | 0.2455 | 4502336 |
| 0.2547 | 14.0 | 53872 | 0.2459 | 4848864 |
| 0.2146 | 15.0 | 57720 | 0.2422 | 5194640 |
| 0.3529 | 16.0 | 61568 | 0.2419 | 5541160 |
| 0.2237 | 17.0 | 65416 | 0.2437 | 5887864 |
| 0.3058 | 18.0 | 69264 | 0.2429 | 6234216 |
| 0.2963 | 19.0 | 73112 | 0.2419 | 6580528 |
| 0.3099 | 20.0 | 76960 | 0.2412 | 6927000 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Miracle-man/blockassist
|
Miracle-man
| 2025-09-11T15:01:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing lithe koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-09T17:52:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing lithe koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jazmynikrr/blockassist-bc-dormant_hulking_eagle_1757602851
|
jazmynikrr
| 2025-09-11T15:01:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant hulking eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:01:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant hulking eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
schnecklothheath/blockassist-bc-soaring_leaping_snake_1757602864
|
schnecklothheath
| 2025-09-11T15:01:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soaring leaping snake",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:01:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soaring leaping snake
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/MiroThinker-32B-SFT-v0.2-GGUF
|
mradermacher
| 2025-09-11T15:00:43Z | 1,313 | 0 |
transformers
|
[
"transformers",
"gguf",
"agent",
"open-source",
"miromind",
"en",
"base_model:miromind-ai/MiroThinker-32B-SFT-v0.2",
"base_model:quantized:miromind-ai/MiroThinker-32B-SFT-v0.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-11T02:25:59Z |
---
base_model: miromind-ai/MiroThinker-32B-SFT-v0.2
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- agent
- open-source
- miromind
---
## 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/miromind-ai/MiroThinker-32B-SFT-v0.2
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#MiroThinker-32B-SFT-v0.2-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-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/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q3_K_M.gguf) | Q3_K_M | 16.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q4_K_M.gguf) | Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q5_K_M.gguf) | Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MiroThinker-32B-SFT-v0.2-GGUF/resolve/main/MiroThinker-32B-SFT-v0.2.Q8_0.gguf) | Q8_0 | 34.9 | 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 -->
|
khazarai/Quran-R1
|
khazarai
| 2025-09-11T15:00:32Z | 0 | 1 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/Qwen3-0.6B",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"en",
"dataset:musaoc/Quran-reasoning-SFT",
"base_model:unsloth/Qwen3-0.6B",
"license:mit",
"region:us"
] |
text-generation
| 2025-09-11T14:57:47Z |
---
base_model: unsloth/Qwen3-0.6B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen3-0.6B
- lora
- sft
- transformers
- trl
- unsloth
license: mit
datasets:
- musaoc/Quran-reasoning-SFT
language:
- en
---
# Model Card for Quran-R1
## Model Details
This model is a fine-tuned version of Qwen/Qwen3-0.6B on the musaoc/Quran-reasoning-SFT dataset.
It is designed to perform reasoning and question-answering tasks related to the Quran, providing structured reasoning steps along with the final answer.
### Model Description
- **Language(s) (NLP):** English
- **License:** MIT
- **Fine-tuning method**: Supervised fine-tuning (SFT)
- **Finetuned from model:** Qwen3-0.6B
- **Dataset:** musaoc/Quran-reasoning-SFT
## Uses
The model is intended for:
- Educational purposes: Assisting with structured reasoning about Quranic content.
- Research: Exploring reasoning capabilities of small LLMs fine-tuned on religious text.
- QA Systems: Providing answers with reasoning traces.
Not intended for:
- Authoritative religious rulings (fatwas)
- Sensitive or controversial theological debates
- High-stakes decision making
### Out-of-Scope Use
- Scope: The model is limited to the reasoning dataset it was trained on. It may not generalize to broader Quranic studies.
## Bias, Risks, and Limitations
- Bias: Outputs reflect dataset biases and may not represent all scholarly interpretations.
- Hallucination risk: Like all LLMs, it may generate incorrect or fabricated reasoning.
- Religious sensitivity: Responses may not align with every sect, school, or interpretation. Use with caution in sensitive contexts.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-0.6B",)
base_model = AutoModelForCausalLM.from_pretrained(
"unsloth/Qwen3-0.6B",
device_map={"": 0}
)
model = PeftModel.from_pretrained(base_model,"khazarai/Quran-R1")
question = "How does the Quran address the issue of parental authority and children’s rights?"
messages = [
{"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
messages,
tokenize = False,
add_generation_prompt = True,
enable_thinking = True,
)
from transformers import TextStreamer
_ = model.generate(
**tokenizer(text, return_tensors = "pt").to("cuda"),
max_new_tokens = 512,
temperature = 0.6,
top_p = 0.95,
top_k = 20,
streamer = TextStreamer(tokenizer, skip_prompt = True),
)
```
## Training Data
**Dataset**: musaoc/Quran-reasoning-SFT
The Quranic Reasoning Question Answering (QRQA) Dataset is a synthetic dataset designed for experimenting purposes and for training and evaluating models capable of answering complex, knowledge-intensive questions about the Quran with a strong emphasis on reasoning.
This dataset is particularly well-suited for Supervised Fine-Tuning (SFT) of Large Language Models (LLMs) to enhance their understanding of Islamic scripture and their ability to provide thoughtful, reasoned responses.
### Framework versions
- PEFT 0.17.0
|
milfordprudence/blockassist-bc-aquatic_reclusive_cassowary_1757602806
|
milfordprudence
| 2025-09-11T15:00:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"chattering hairy woodpecker",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T15:00:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- chattering hairy woodpecker
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coastalcph/Llama-2-7b-chat-1t_gsm8k-0.2t_diff_hh
|
coastalcph
| 2025-09-11T14:59:38Z | 0 | 0 | null |
[
"safetensors",
"llama",
"region:us"
] | null | 2025-09-11T14:56:23Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("meta-llama/Llama-2-7b-chat-hf", "coastalcph/Llama-2-7b-chat-gsm8k_bs8_2e-4")
t_2 = TaskVector("meta-llama/Llama-2-7b-chat-hf", "coastalcph/Llama-2-7b-chat-helpful-harmless-filtered")
t_combined = 1.0 * t_1 + 0.2 * t_2 - 0.2 * t_3
new_model = t_combined.apply_to("meta-llama/Llama-2-7b-chat-hf", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Llama-2-7b-chat-gsm8k_bs8_2e-4
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Llama-2-7b-chat-helpful-harmless-filtered
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "meta-llama/Llama-2-7b-chat-hf",
"finetuned_model1": "coastalcph/Llama-2-7b-chat-gsm8k_bs8_2e-4",
"finetuned_model2": "coastalcph/Llama-2-7b-chat-helpful-harmless-filtered",
"finetuned_model3": "coastalcph/Llama-2-7b-chat-helpful-only",
"output_model_name": "coastalcph/Llama-2-7b-chat-1t_gsm8k-0.2t_diff_hh",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 0.2,
"scale_t3": 0.2
}
|
anasagastiw84/blockassist-bc-subtle_alert_narwhal_1757602752
|
anasagastiw84
| 2025-09-11T14:59:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"subtle alert narwhal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:59:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- subtle alert narwhal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_cola_789_1757596122
|
rbelanec
| 2025-09-11T14:59:09Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"p-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:05:49Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- p-tuning
- generated_from_trainer
model-index:
- name: train_cola_789_1757596122
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. -->
# train_cola_789_1757596122
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cola dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4594
- Num Input Tokens Seen: 3663512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 789
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.1236 | 0.5 | 962 | 0.2745 | 182656 |
| 0.2375 | 1.0 | 1924 | 0.1683 | 365728 |
| 0.3172 | 1.5 | 2886 | 0.2014 | 548992 |
| 0.2088 | 2.0 | 3848 | 0.1443 | 731984 |
| 0.0806 | 2.5 | 4810 | 0.1764 | 915792 |
| 0.3512 | 3.0 | 5772 | 0.1655 | 1098920 |
| 0.0369 | 3.5 | 6734 | 0.1680 | 1281640 |
| 0.0703 | 4.0 | 7696 | 0.1568 | 1465464 |
| 0.0718 | 4.5 | 8658 | 0.1608 | 1649720 |
| 0.1062 | 5.0 | 9620 | 0.1466 | 1831920 |
| 0.2303 | 5.5 | 10582 | 0.1536 | 2014928 |
| 0.2191 | 6.0 | 11544 | 0.1693 | 2198176 |
| 0.1416 | 6.5 | 12506 | 0.1756 | 2381440 |
| 0.1436 | 7.0 | 13468 | 0.1585 | 2564952 |
| 0.0112 | 7.5 | 14430 | 0.1843 | 2748568 |
| 0.15 | 8.0 | 15392 | 0.1909 | 2931096 |
| 0.0999 | 8.5 | 16354 | 0.1853 | 3113624 |
| 0.0045 | 9.0 | 17316 | 0.2035 | 3296808 |
| 0.0655 | 9.5 | 18278 | 0.2026 | 3480168 |
| 0.0811 | 10.0 | 19240 | 0.2036 | 3663512 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
pabeypaul/blockassist-bc-sizable_knobby_salamander_1757602730
|
pabeypaul
| 2025-09-11T14:58:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"omnivorous sprightly aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:58:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- omnivorous sprightly aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
brisondey/blockassist-bc-insectivorous_energetic_koala_1757602671
|
brisondey
| 2025-09-11T14:58:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous energetic koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:58:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous energetic koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
alvanchaneizz/blockassist-bc-wiry_alert_giraffe_1757602662
|
alvanchaneizz
| 2025-09-11T14:57:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry alert giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:57:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry alert giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pripak18370/blockassist-bc-agile_solitary_mandrill_1757602638
|
pripak18370
| 2025-09-11T14:57:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"agile solitary mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:57:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- agile solitary mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
neylanduoh/blockassist-bc-prehistoric_iridescent_puffin_1757602614
|
neylanduoh
| 2025-09-11T14:57:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prehistoric iridescent puffin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:56:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prehistoric iridescent puffin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crabtreeftf/blockassist-bc-darting_mighty_panther_1757602588
|
crabtreeftf
| 2025-09-11T14:56:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"darting mighty panther",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:56:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- darting mighty panther
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jobs-git/Wan2.2-I2V-A14B
|
jobs-git
| 2025-09-11T14:56:28Z | 0 | 0 |
wan2.2
|
[
"wan2.2",
"diffusers",
"safetensors",
"image-to-video",
"en",
"zh",
"arxiv:2503.20314",
"license:apache-2.0",
"region:us"
] |
image-to-video
| 2025-09-11T14:56:27Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: image-to-video
library_name: wan2.2
---
# Wan2.2
<p align="center">
<img src="assets/logo.png" width="400"/>
<p>
<p align="center">
💜 <a href="https://wan.video"><b>Wan</b></a>    ｜    🖥️ <a href="https://github.com/Wan-Video/Wan2.2">GitHub</a>    |   🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>   |    📑 <a href="https://arxiv.org/abs/2503.20314">Technical Report</a>    |    📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a>    |   💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>   |    📖 <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>  
<br>
-----
[**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <be>
We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations:
- 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
- 👍 **Cinematic-level Aesthetics**: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.
- 👍 **Complex Motion Generation**: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.
- 👍 **Efficient High-Definition Hybrid TI2V**: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of **16×16×4**. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest **720P@24fps** models currently available, capable of serving both the industrial and academic sectors simultaneously.
This repository also includes our I2V-A14B model, designed for image-to-video generation, supporting both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it achieves more stable video synthesis with reduced unrealistic camera movements and offers enhanced support for diverse stylized scenes.
## Video Demos
<div align="center">
<video width="80%" controls>
<source src="https://cloud.video.taobao.com/vod/NnCd0fC-1eckDUuVBMz43oD_U6mTsPpBwga3wdnAkXA.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>
## 🔥 Latest News!!
* Jul 28, 2025: 👋 Wan2.1 has been integrated into ComfyUI ([CN](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2) | [EN](https://docs.comfy.org/tutorials/video/wan/wan2_2)). Enjoy!
* Jul 28, 2025: 👋 Wan2.2's T2V, I2V and TI2V have been integrated into Diffusers ([T2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers) | [I2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers) | [TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers)). Feel free to give it a try!
* Jul 28, 2025: 👋 We've released the inference code and model weights of **Wan2.2**.
## Community Works
If your research or project builds upon [**Wan2.1**](https://github.com/Wan-Video/Wan2.1) or Wan2.2, we welcome you to share it with us so we can highlight it for the broader community.
## 📑 Todo List
- Wan2.2 Text-to-Video
- [x] Multi-GPU Inference code of the A14B and 14B models
- [x] Checkpoints of the A14B and 14B models
- [x] ComfyUI integration
- [x] Diffusers integration
- Wan2.2 Image-to-Video
- [x] Multi-GPU Inference code of the A14B model
- [x] Checkpoints of the A14B model
- [x] ComfyUI integration
- [x] Diffusers integration
- Wan2.2 Text-Image-to-Video
- [x] Multi-GPU Inference code of the 5B model
- [x] Checkpoints of the 5B model
- [x] ComfyUI integration
- [x] Diffusers integration
## Run Wan2.2
#### Installation
Clone the repo:
```sh
git clone https://github.com/Wan-Video/Wan2.2.git
cd Wan2.2
```
Install dependencies:
```sh
# Ensure torch >= 2.4.0
# If the installation of `flash_attn` fails, try installing the other packages first and install `flash_attn` last
pip install -r requirements.txt
```
#### Model Download
| Models | Download Links | Description |
|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------|
| T2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | Text-to-Video MoE model, supports 480P & 720P |
| I2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | Image-to-Video MoE model, supports 480P & 720P |
| TI2V-5B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | High-compression VAE, T2V+I2V, supports 720P |
> 💡Note:
> The TI2V-5B model supports 720P video generation at **24 FPS**.
Download models using huggingface-cli:
``` sh
pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.2-I2V-A14B --local-dir ./Wan2.2-I2V-A14B
```
Download models using modelscope-cli:
``` sh
pip install modelscope
modelscope download Wan-AI/Wan2.2-I2V-A14B --local_dir ./Wan2.2-I2V-A14B
```
#### Run Image-to-Video Generation
This repository supports the `Wan2.2-I2V-A14B`` Image-to-Video model and can simultaneously support video generation at 480P and 720P resolutions.
- Single-GPU inference
```sh
python generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --offload_model True --convert_model_dtype --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
```
> This command can run on a GPU with at least 80GB VRAM.
> 💡For the Image-to-Video task, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
- Multi-GPU inference using FSDP + DeepSpeed Ulysses
```sh
torchrun --nproc_per_node=8 generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
```
- Image-to-Video Generation without prompt
```sh
DASH_API_KEY=your_key torchrun --nproc_per_node=8 generate.py --task i2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-I2V-A14B --prompt '' --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --use_prompt_extend --prompt_extend_method 'dashscope'
```
> 💡The model can generate videos solely from the input image. You can use prompt extension to generate prompt from the image.
> The process of prompt extension can be referenced [here](#2-using-prompt-extention).
## Computational Efficiency on Different GPUs
We test the computational efficiency of different **Wan2.2** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
<div align="center">
<img src="assets/comp_effic.png" alt="" style="width: 80%;" />
</div>
> The parameter settings for the tests presented in this table are as follows:
> (1) Multi-GPU: 14B: `--ulysses_size 4/8 --dit_fsdp --t5_fsdp`, 5B: `--ulysses_size 4/8 --offload_model True --convert_model_dtype --t5_cpu`; Single-GPU: 14B: `--offload_model True --convert_model_dtype`, 5B: `--offload_model True --convert_model_dtype --t5_cpu`
(--convert_model_dtype converts model parameter types to config.param_dtype);
> (2) The distributed testing utilizes the built-in FSDP and Ulysses implementations, with FlashAttention3 deployed on Hopper architecture GPUs;
> (3) Tests were run without the `--use_prompt_extend` flag;
> (4) Reported results are the average of multiple samples taken after the warm-up phase.
-------
## Introduction of Wan2.2
**Wan2.2** builds on the foundation of Wan2.1 with notable improvements in generation quality and model capability. This upgrade is driven by a series of key technical innovations, mainly including the Mixture-of-Experts (MoE) architecture, upgraded training data, and high-compression video generation.
##### (1) Mixture-of-Experts (MoE) Architecture
Wan2.2 introduces Mixture-of-Experts (MoE) architecture into the video generation diffusion model. MoE has been widely validated in large language models as an efficient approach to increase total model parameters while keeping inference cost nearly unchanged. In Wan2.2, the A14B model series adopts a two-expert design tailored to the denoising process of diffusion models: a high-noise expert for the early stages, focusing on overall layout; and a low-noise expert for the later stages, refining video details. Each expert model has about 14B parameters, resulting in a total of 27B parameters but only 14B active parameters per step, keeping inference computation and GPU memory nearly unchanged.
<div align="center">
<img src="assets/moe_arch.png" alt="" style="width: 90%;" />
</div>
The transition point between the two experts is determined by the signal-to-noise ratio (SNR), a metric that decreases monotonically as the denoising step $t$ increases. At the beginning of the denoising process, $t$ is large and the noise level is high, so the SNR is at its minimum, denoted as ${SNR}_{min}$. In this stage, the high-noise expert is activated. We define a threshold step ${t}_{moe}$ corresponding to half of the ${SNR}_{min}$, and switch to the low-noise expert when $t<{t}_{moe}$.
<div align="center">
<img src="assets/moe_2.png" alt="" style="width: 90%;" />
</div>
To validate the effectiveness of the MoE architecture, four settings are compared based on their validation loss curves. The baseline **Wan2.1** model does not employ the MoE architecture. Among the MoE-based variants, the **Wan2.1 & High-Noise Expert** reuses the Wan2.1 model as the low-noise expert while uses the Wan2.2's high-noise expert, while the **Wan2.1 & Low-Noise Expert** uses Wan2.1 as the high-noise expert and employ the Wan2.2's low-noise expert. The **Wan2.2 (MoE)** (our final version) achieves the lowest validation loss, indicating that its generated video distribution is closest to ground-truth and exhibits superior convergence.
##### (2) Efficient High-Definition Hybrid TI2V
To enable more efficient deployment, Wan2.2 also explores a high-compression design. In addition to the 27B MoE models, a 5B dense model, i.e., TI2V-5B, is released. It is supported by a high-compression Wan2.2-VAE, which achieves a $T\times H\times W$ compression ratio of $4\times16\times16$, increasing the overall compression rate to 64 while maintaining high-quality video reconstruction. With an additional patchification layer, the total compression ratio of TI2V-5B reaches $4\times32\times32$. Without specific optimization, TI2V-5B can generate a 5-second 720P video in under 9 minutes on a single consumer-grade GPU, ranking among the fastest 720P@24fps video generation models. This model also natively supports both text-to-video and image-to-video tasks within a single unified framework, covering both academic research and practical applications.
<div align="center">
<img src="assets/vae.png" alt="" style="width: 80%;" />
</div>
##### Comparisons to SOTAs
We compared Wan2.2 with leading closed-source commercial models on our new Wan-Bench 2.0, evaluating performance across multiple crucial dimensions. The results demonstrate that Wan2.2 achieves superior performance compared to these leading models.
<div align="center">
<img src="assets/performance.png" alt="" style="width: 90%;" />
</div>
## Citation
If you find our work helpful, please cite us.
```
@article{wan2025,
title={Wan: Open and Advanced Large-Scale Video Generative Models},
author={Team Wan and Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu},
journal = {arXiv preprint arXiv:2503.20314},
year={2025}
}
```
## License Agreement
The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
## Acknowledgements
We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
## Contact Us
If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/AKNgpMK4Yj) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
|
canadayfawuh/blockassist-bc-flapping_wise_rhino_1757602557
|
canadayfawuh
| 2025-09-11T14:56:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing squeaky bison",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:56:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing squeaky bison
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1757601030
|
capungmerah627
| 2025-09-11T14:55:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:55:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging soaring porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sampath1987/all-distilroberta-v1-offshore-energy
|
Sampath1987
| 2025-09-11T14:55:22Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:89129",
"loss:MultipleNegativesRankingLoss",
"dataset:Sampath1987/offshore_energy",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-transformers/all-distilroberta-v1",
"base_model:finetune:sentence-transformers/all-distilroberta-v1",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-11T14:55:10Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:89129
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-distilroberta-v1
widget:
- source_sentence: How does vendor-specific data acquisition affect DTS profile interpretation?
sentences:
- 'Bridging data management gap by gathering all well integrity data in one unique
data base. The aim of ADNOC Offshore in-house Well Integrity Data Management System
(WIDMS) is to comply with the 3A rule: Accessibility of the data, Accuracy by
performing regular quality check and Analysis. The analysis allows to maintain
wells barriers robust, to ensure personnel safety and to quickly identify integrity
issues to make qualified decisions about appropriate mitigations measures and
avoid risk escalation. WIDMS has been developed in-house with inputs and collaboration
of various stake holders. An enhancement list has been established selecting the
most relevant features that will be added value to the system. Therefore, Automation
for sub processes like thresholds calculations and Risk Assessment which gives
input for Well Passports that contains all the required information to evaluate
the well risks and implement the required mitigation measures.
End users are following a RACI Chart to keep WIDMS database on track and to ensure
no data falls through the cracks as all the data workflow is defined through the
different steps such as providing data, entering it in the system, informing relevant
stakeholders and providing technical clarifications if needed. The result of data
acquisition in WIDMS is that data flows across the entire organization, with defined
access rights in line with ADNOC Offshore policies. This data is collected from
various sources, is a robust data base, essential for evaluating and maintaining
well integrity.
It is enhancing well barriers system management by allowing to have full overview
of well''s barriers performance. Moreover, it allows to have reliable and continuously
available data such as annulus pressure data that is critical for well integrity
assurance, to avoid the uncontrolled release of hydrocarbons to the atmosphere.
Notifications have been implemented so alerts can be sent for engineers to inform
about any abnormality and non-compliance. As technology evolves, using paper-based
processes, excel spreadsheets, time-based equipment inspection and testing become
less effective. Well diagnostics are expensive so utilizing well data analytics
through this digital hub project will ease having detailed real time data and
quick analysis for early detection of failures and anticipation and reduction
of risk escalation.'
- "##### 2.3.1 Site characterization - secondary seal \nSecondary seals might have\
\ a significant relevance in ensuring CO 2 containment, acting\nas additional\
\ barrier to flow, although it is not clear if it is considered a requirement\
\ for\nstandards. Two documents show some contradiction: \nISO 27914 [36] is\
\ silent on secondary seal as a requirement until section 5.4.3.2 that describes\n\
its characterization. Moreover, if it is a requirement, characterization should\
\ include not\nonly geometry and lithology, but also integrity evaluation, which\
\ is not mentioned. \nISO/TR 27915 [37] section 5.2.6 and Figure 2 state that\
\ the geological storage complex is\ncomposed of the reservoirs where CO 2 is\
\ injected and the caprock (or seals); it then states\nthat additional geologic\
\ layers are outside complex."
- 'Geothermal energy is considered a reliable, sustainable and abundant source of
energy with minimized environmental impact. The extracted geothermal energy may
be utilized for direct heating, or electricity generation. The main challenge
to access this energy is tremendous capital expenditures required for drilling
and completion. Therefore, this work discusses and evaluates retrofitting abandoned
petroleum wells to geothermal as a commonly proposed solution to the mentioned
challenge.
There are many oil and gas wells globally which are not used for production, injection
or other purposes. Well abandonment is commonly considered as an essential measure
to ensure safety and integrity of these wells, bearing huge costs and concerns
for the petroleum industry. By converting abandoned or non-activated oil and gas
wells to geothermal wells, it is claimed to be possible to produce geothermal
energy and generate power. As a crucial stage for the claim verification and evaluation
of feasibility or efficiency of this conversion, it is important to be aware of
the practical and simulation case studies.
Therefore, in this work, this work presents a comprehensive overview and analysis
of 20 case studies published from different countries, followed by important downhole
and surface parameters. As for the downhole characteristics, production scenarios
either open-loop or closed-loop, optimization of open-loop systems, borehole heat
exchangers with their different types and dimensions, and insulations are covered.
Next, surface cycles including organic Rankine cycle (ORCs), selection of circulation
fluids, flow rates, and working fluids are covered, followed by produced and net
powers with evaluation of coefficient of performance (COP) and thermal efficiency.
This investigation shows there is good potential for producing geothermal energy
from abandoned and non-activated petroleum wells.'
- source_sentence: Why must welding consumables be limited to specific classifications
and manufacturers for EGW?
sentences:
- "8 API R ECOMMENDED P RACTICE 582 \n**5.2.6 EGW** \nThe use of EGW shall be\
\ limited by the following conditions: \na) EGW shall be used only with filler\
\ materials specifically intended for the EGW process (ASME/AWS SFA/A5.26/\nSFA/A5.26M),\
\ \nb) welding consumables shall be limited to the classification and the manufacturer’s\
\ trade name used in the PQR, \nc) only filler materials having classifications\
\ with specified minimum impact test requirements should be used. \n**5.2.7 SAW**\
\ \n**5.2.7.1** SAW procedures shall be requalified whenever the welding flux\
\ is changed from one manufacturer’s trade\nname to another. Equivalence under\
\ ASME _BPVC_ Section II, Part C, or AWS filler metal specifications shall not\
\ be\nconsidered adequate for substitution without requalification. \nCOMMENTARY\
\ It is recognized that fluxes having the same classification can be very different\
\ in their\ncomposition. However, nominal flux composition is not included in\
\ AWS or ASME specifications/codes, and flux\nsuppliers do not normally provide\
\ this information. Differences among fluxes of the same classification can result\
\ in\ndifferent and unanticipated weld properties when these fluxes are used interchangeably\
\ over the range of variables\ntypically stated in weld procedure specifications.\
\ \n**5.2.7.2** Manually held (semiautomatic) SAW is not permitted for welding\
\ pressure-containing parts, unless approved\nby the purchaser. \n**5.2.7.3**\
\ A separate qualification is required for SAW welds in which any pass is greater\
\ than [1] / 2 in. \n**5.3 Single-sided Welded Joints** \nFor single-sided welded\
\ joints where process side corrosion is a concern, welding processes using coatings\
\ or fluxes\nshall not be used for root pass welding of austenitic stainless steels,\
\ non-ferrous alloys and nickel-base alloys unless\nslag can be removed from the\
\ process side of root passes and the area inspected for slag removal. \n**5.4\
\ Combining Welding Processes** \nCombining two or more welding processes that\
\ use alloy filler metals of different nominal compositions, other than A1\nthro\
\ ~~ug~~ h A5, requires qualification as a combination procedure."
- 'Following multi-disciplinary reviews, an opportunity was identified to restore
production and unlock incremental reserves from well X, a dual completion in two
different reservoirs but subsequently deserted due to long term community crisis
that led to over 25years of non-production with complete vandalization of well
head and flowlines.
The method employed involved the strategic resolution of long-term crisis between
two communities where well X is located, via a multi-disciplinary effort involving
the operating company’s Community Relations, HSSE, Production Engineering, Operations
Support and Portfolio Management functions. The installation of a retrofitted
well head was done with first line and second line maintenance carried out. Wireline
drifting and static bottom hole pressures were acquired for both strings using
slickline equipment and a preliminary well test was conducted for both strings
with production to a flowback tank.
The preliminary result for the long string (LS) indicated a high water cut (>80%),
while the result from short string (SS) was in line with expectation (<57%). The
test result from the short string informed the decision to construct a new flowline
for restoring its production, while further subsurface evaluation is required
for the long string (LS). The significance of the short string (SS) result is
the unlocking of additional reserves ca. 1.0MMSTB from a reservoir with remaining
oil in place estimated at ca. 18MMSTB, where the short string (SS) is the only
drainage point currently completed on the reservoir.
This solution provides a cost effective and efficient way to increasing production
and reserves at minimal expenditure leveraging on multi-disciplinary expertise,
using existing infrastructures as well as resolving community crisis, where applicable.'
- "**Exploration & Production** \n**General Specification** Date: 10/2007 \n**GS\
\ EP STR 301** Rev: 07 \nsuccessful practice of the process in previous similar\
\ jobs to the satisfaction of the\nCOMPANY. \nb) Only Extra Low Hydrogen processes\
\ (max. 5 ml H2/100 g) shall be used for welding and tack \nwelding of Special\
\ and First Category members or materials having specified YS above\n262 MPa (38,000\
\ psi). The same requirement shall apply for any welding on castings and\nforgings.\
\ \nc) For Second Category members and Non-Structural members, welding processes\
\ other than\nExtra Low Hydrogen processes may be used, subject to prior approval\
\ by COMPANY, for\nmaterials having specified YS up to 262 MPa together with thickness\
\ up to 12.70 mm\n(0.500”). \nd) The number of different welding processes shall\
\ be minimized. \ne) Different welding consumables qualities (basic extra low,\
\ basic low,) in a same type of\nconsumables shall be avoided. \n**8.5 Welding\
\ consumables** \n**8.5.1 Selection of consumables** \na) Consumables shall\
\ conform to ANSI/AWS D 1.1 code and shall have been approved by an\ninternational\
\ recognized certification body (e.g. DNV, LLOYD’s, etc.). \nb) If classification\
\ of the structure is required, welding consumables shall conform to rules of\
\ the\nClassification Society. \nc) Cellulosic electrodes are strictly forbidden\
\ for structural use \nd) Welds forming connections between steels of different\
\ grades of material shall develop the\nminimum specified tensile properties of\
\ the lower steel grades being joined, unless otherwise\npreviously approved by\
\ the COMPANY. \nWelds forming connections between steels of different grades\
\ of material shall develop the\nminimum specified notch impact properties at\
\ the lowest temperature of steel grades being\njoined, unless otherwise previously\
\ approved by the COMPANY. \ne) For repair welding or multiple repairs, “extra\
\ low hydrogen” electrodes are required\n(i.e. maximum specified hydrogen content\
\ of 5 ml per 100 gram of weld metal). \nf) For welding castings or forgings,\
\ “extra low hydrogen” electrodes are required (i.e. maximum\nspecified hydrogen\
\ content of 5 ml per 100 gram of weld metal)."
- source_sentence: What is the recommended use of blank samples in sampling procedures
involving the trapping or precipitation of components?
sentences:
- "elements. We do this by performing a similarity transformation on the matrix\
\ _k_ . The coordinate systems _x_ = { _x_ 1, _x_ 2 } and _y_ = { _y_ 1,, _y_\
\ 2 } are related by the similarity transformation matrix \n_A_ such that \n\
_y_ = _Ax_ . ................................................................\
\ (2.127) \nThe two coordinate systems are shown in Fig. 2.6.\nAn angle ( _θ_\
\ ) is associated with the transformation in Eq. 2.127 by writing the 2D coordinate\
\ transformation as \n_y_ 1 \n_y_ 2 \n_x_ 1 \n_x_ 2 \n= \ncos _θ_ sin _θ_\
\ \n−sin _θ_ cos _θ_ \n. ........................................... (2.128)\
\ \nThe coordinate systems _x_ = { _x_ 1, _x_ 2 } and _y_ = { _y_ 1,, _y_ 2 }\
\ are related by the counterclockwise rotation shown in Fig. 2.6. We have an aligned\
\ coordinate system _y_ = { _y_ 1,, _y_ 2 } with the\nprincipal axes of the permeability\
\ tensor. The diagonal tensor in the coordinate system \n_y_ = { _y_ 1,, _y_\
\ 2 } has the form \n_k_ ′=\n( \n_k_ max 0 \n0 _k_ _T_\n) [, .........................................................\
\ (2.129)] \n**Print** **Search** **Chapter 1** **Home** **Chapter 3** **Bookmarks**\
\ **Help**"
- "**© 2010 COPYRIGHT MERCADO NEGRO, LAS PLAYITAS. MARACAIBO-EDO. ZULIA, VENEZUELA.**\
\ \n**PARA COMPRAR AL DETAL O AL MAYOR, ESTE Y OTROS PRODUCTOS, FAVOR PREGUNTAR\
\ POR EL GÖAJIRO BLANCO, EN EL MERCADO LAS PLAYITAS.** \n**ADVERTENCIA: \"EL\
\ DERECHO DE AUTOR NO ES UNA FORMA DE PROPIEDAD SINO UN DERECHO CULTURAL. EXIGE\
\ TU DERECHO\"** \nI-208 Petroleum Engineering Handbook—Vol. I \n**Fig. 4.10—Chromatogram\
\ showing broad OBM peak.** \n**Fig. 4.11—Chromatogram showing narrow OBM peak.**\
\ \nSpecial correction techniques are increasingly used within the oil industry,\
\ and because\nthese techniques vary between organizations and laboratories, sample\
\ selection should be done\nonly after considering which method to use. Many companies\
\ are forced to use oil-based\ndrilling muds to manage drilling costs in water-sensitive\
\ formations, and the added expense of\nhandling contaminated samples (and the\
\ risk associated with poorer-quality data) must be used\nto evaluate the overall\
\ economic balance. \nFor water samples, comparisons of duplicates also give\
\ a good indication of quality. Where\nfluid concentration may be stabilizing\
\ (e.g., at the end of a cleanup), sequential samples should\nbe used to look\
\ for compositional trends and thus to help decide if representative fluid has\n\
been sampled. For some sampling procedures involving trapping or precipitation\
\ of particular\ncomponents, it is highly recommended to use blank “samples,”\
\ which undergo exactly the\nsame treatment and storage as the actual sample and\
\ provide a reference measurement to assist\nwith the interpretation of laboratory\
\ measurements. More details are available in API _RP 45._ [10] \n**Print** **Search**\
\ **Chapter 3** **Home** **Chapter 5** **Bookmarks** **Help**"
- 'the ideal time to take samples? (6) Will on-site analyses be required? (7) Who
will perform
sampling and analysis duties?
Fluid-sampling operations are often left to service-company personnel, but because
significant variation in levels of competence exists within the industry and within
service companies
themselves, it is recommended either to use specialist laboratory personnel or
to supervise the
service-company operations closely.
General guidelines for choosing reservoir-fluid-sampling methods and sample quantities
required are summarized in **Table 4.2.** Regardless of the actual volumes mentioned,
you should
collect at least two separate samples of each fluid, referred to as duplicate
or replicate samples.
This reduces the chance of losing information if one of the samples leaks or is
accidentally
damaged during laboratory operations, and it allows a comparison between the samples
as part
of the quality-control procedures.
Surface-separator sampling is the most common technique, but the reservoir-fluid
sample
recombined in the laboratory is subject to errors in the measured GOR and any
imprecision in
the laboratory recombination procedure. Downhole samples (or wellhead samples)
are not affected by such inaccuracies but require the fluid to be in monophasic
condition when sampled;
this can be confirmed definitively only afterward in the laboratory. Also, there
is general reluctance to attempt downhole sampling in gas/condensate reservoirs
because many are saturated,
and the phases are likely to segregate in the wellbore. The ideal situation for
a laboratory is to
receive both surface and downhole samples because a choice is then available,
and a good idea
can be obtained of how representative the resulting fluid is.
In certain circumstances, it can be good practice to collect “backup” fluid samples
at the
earliest opportunity during a production test, even if a well has not cleaned
up properly. If the
test has to be aborted for some reason [well bridging, unexpected levels of hydrogen
sulfide
(H 2 S), etc.], the backup samples may be of great value, even if they are not
100% representative. If the test is completed successfully, the backup samples
can be discarded to avoid the
cost of unnecessary shipment and testing.
If sampling is part of a long-term monitoring program, such as those required
by government authorities or those forming part of custody-transfer contracts,
the methods defined in the
appropriate documentation or contracts must be followed as closely as possible,
even if this'
- source_sentence: What is the significance of implementing a centralized, web-based
integrated surveillance tool for production optimization?
sentences:
- "P RESSURE - RELIEVING AND D EPRESSURING S YSTEMS 141 \n**5.7.11.2.3\
\ Flare Gas Characteristics** \nFlare gases can have widely varying compositions\
\ that shall be evaluated during specification of recovery systems.\nThe potential\
\ for materials that are not compatible with the flare gas treating systems or\
\ ultimate destinations shall be\ndetermined. For example, relief streams containing\
\ acid gases typically are routed directly to the flare, thereby\nbypassing the\
\ recovery system. Highly inert streams can also be incompatible with recovery\
\ systems. \n**5.7.11.3 Design Considerations** \n**5.7.11.3.1 Sizing** \n\
Figure 13 shows a conceptual design for a flare gas recovery system. Typically,\
\ the system consists of one or more\nreciprocating compressors whose suction\
\ is directly connected to the flare header. The compressed gas is usually\nrouted\
\ to some type of treating system appropriate for the gas composition, then to\
\ fuel gas or processing systems. \n3 \n**Key**\n1 compressor load control\n\
2 flare gas treating\n3 from process unit flare knockout drums \n4 flare header\
\ \n5 flare knockout drum (if used) \n6 water seal \n7 flare \na Compressor\
\ shutdown. \n**Figure 13—Typical Flare Gas Recovery System** \nCopyright American\
\ Petroleum Institute\nProvided by IHS under license with API Licensee=Petrofac\
\ International Ltd/5954785001, User=McNicol, William\nNo reproduction or networking\
\ permitted without license from IHS Not for Resale, 01/29/2014 03:10:03 MST"
- 'Inorganic scale precipitation and deposition in oil and gas wells can cause significant
production loss, which results in additional operational expenditure (OPEX) and
health safety and environmental (HSE) risks. Scale management requires a detailed
understanding of production rates, hydrocarbon and produced water compositions
as well as reservoir conditions. Accurate real-time analysis of produced water
compositions can immediately identifiy scaling risks in a production well and
can lead to significantly reduced production loss, optimized chemical dosages,
and fewer workovers, consequently lowering OPEX and mitigating HSE risk. This
paper introduces development of a device capable of measuring the most critical
parameters associated with inorganic scale in flowing produced water including
pH, alkalinity, strontium, barium, sulfate, total hardness, total dissolve solids
(TDS) and others.
In order to measure these water properties with the device, different methods
were tested, but eventually, a combination of spectrophotometric and other methods
were determined effective. One of the challenges of using spectrophotometric methods
is the reagent stability over time. Hence, customized reagents were prepared for
this application and the stability of these reagents was tested over time. Specific
calibration methods were designed in order to extend the usage of the reagents.
Static measurements were initially performed and the results showed precise measurements
of all the parameters. Results from dynamic tests utilizing real time flow and
static test were in agreement and the accuracy was confirmed by traditional methods.
Once the device prototype was built in our laboratories, production fluids were
used to test the complete device. This device can be placed at various attachment
points from the wellhead to the separator. This automated device is capable of
collecting a discrete production fluid sample, separating produced water from
the bulk phase and measuring various properties of produced water. These properties
are reported electronically and used as part of a combined real time scale risk
prevention system. In addition, this device measures parameters while maintaining
wellhead pressure and temperature in order to eliminate the potentials errors
in measurements, for instance pH of water changes due to degassing and precipitation
as a result of changes in pressure and temperature.
A field trial is planned to test the device under full flowing conditions. This
will be the first automated real-time produced water composition monitoring device
with high measurement accuracy while maintaining pressure and temperature of samples,
which can be attached at various points from wellhead to separator. This can be
beneficial to identify the scaling risk in production wells before severe scaling
occurs. The device is designed to enhance reliability of water properties measurements,
provide real-time measurements, and reduce downtime and costs associated with
scale problems and sampling.'
- 'In 2009, the Kuwait Integrated Digital Field (KwIDF) project was established
in the Sabriyah field in north Kuwait to boost production and reserves (Al-Jasmi
et al. 2014). The goal was to help realize the vision of sustained oil production
in Kuwait of four million barrels of oil equivalent per day (BOE/D) by 2030 (Goel
et al. 2013). The project involved the creation of 11 integrated, automated workflows,
and a real-time collaborative environment to help optimize production, reduce
downtime, and improve reservoir management:
Key performance monitoring—calculates and displays key parameters to monitor and
assess asset performance at the field and well levels (Al-Jasmi et al. 2013).
Well performance evaluation (WPE)—allows users to model and evaluate any well
in real time, from completion face to wellhead (Cullick et al. 2013).
Smart production surveillance (SPS)—helps enable users to control production and
make surveillance decisions in real time (Villamizar et al. 2013).
Production loss—an advance workflow for users to compare current oil production
to pre-established allowable rates (Villamizar et al. 2013).
Electric submersible pump (ESP) diagnostic and optimization—helps enable users
to interactively monitor and optimize ESP operated well operations (Velasquez
et al. 2013).
Production allocation—integrates the allocation process within the KwIDF environment,
increases the frequency of the allocation cycle, and improves the accuracy of
allocated volumes (Al-Jasmi et al. 2013).
Gas lift (GL) diagnostic and optimization—uses a smart real-time control that
provided proactive recommendations for GL systems optimization (Al-Jasmi et al.
2013).
Reporting and distribution—displays system generated alarms from all KwIDF workflows,
generates tickets, and reports ticket status (Al-Jasmi et al. 2013).
Simulation model update and ranking—an automated workflow for reservoir history
matching (Carvajal et al. 2013).
Reservoir visualization and analysis, and subsurface waterflood optimizer—helps
enable the monitoring of subsurface health during the waterflooding process, and
provides predictive reservoir optimization analysis and actions (Ranjan et al.
2013).
By 2012, KwIDF had been deployed on 49 wells, representing a pilot that served
as a proof of concept. By 2013, cumulative production gains of 756,000 barrels
of oil were reported (Singh et al. 2013). While the gains were impressive, and
management wanted to expand KwIDF, it was recognized that full deployment would
pose significant challenges and, without a set of necessary changes, the value
of KwIDF would not be realized.
The key challenge facing management was to identify the appropriate operating
model to deliver on the KwIDF vision and scale the program to accommodate future
expansion across the rest of the organization. A transition and deployment assessment
team was established by management to address this challenge.
The transition and deployment assessment project produced a recommended operating
model, a transition road map, change management strategy, risk and mitigation
plan, and project charters to assist the program team and steering group in the
deployment of KwIDF across the rest of North Kuwait.'
- source_sentence: What role did anti-collision analysis play in the drilling of the
dual lateral well?
sentences:
- This paper aims to analyze the impact of appraising and developing marginal fields
with multiple stacked reservoirs which is quite challenging in terms of techno
commercial value. The development of such marginal reservoirs using conventional
single horizontal wells drilling and completion is uneconomical. Therefore, it
was necessary to engineer a solution that can enhance the commercial value of
the project by reducing CAPEX and OPEX. This paper will present the first comprehensive
business case, where multiple stacked reservoirs with marginal reserves were studied
to produce independently using multilateral completions, granting full accessibility
of the laterals while achieving production monitoring and reservoir surveillance.
- 'This paper is a comprehensive analytic driven study on the use and sizing of
membrane filters to improve the injected water quality for maintaining injectivity
in tight carbonate reservoirs. Out of the different mechanisms of formation damage,
the pore plugging with the migration of particles within the injectant fluids
by bridging at the pore throat junctions and/or by pore filling can lead to the
buildup of an internal filter cake away from the wellbore that limits the well’s
injectivity and can affect the vertical and lateral sweep.
This type of formation damage is very difficult to treat with any kind of stimulation
and the impact will be manifested especially in tight formations with interbedded
stylolites layers with a total range of permeabilities from 2 to less than 1 milli-Darcy
and a median pore throat size ranging from 2.5 to 0.3 micron meters. The study
comprises several parts starting with a geological analysis that was conducted
to identify areas and layers most prone to formation pore plugging by analyzing
thin-sections and MICP data. Second, in the lack of core flood tests, a reservoir
and well study analyzed existing water injectors situated in similar or slightly
higher quality rock areas through the analysis of injectivity index behavior to
estimate the impact of damage and the expected injector’s half-life.
As a result, through the application of an analytical mathematical model for defining
deep bed filtration parameter, a correlation was established based on average
injected particle size and reservoir rock quality to aid in selecting the proper
water injection filter size. In order to confirm that, a dedicated injectivity
test in a horizontal well utilizing membrane filters was carried out to assess
eventual formation damage and the filters efficiency by conducting a series of
multiple pressure fall-off tests coupled with injection profile logging to monitor
any induced damage within the wellbore region.
Finally, the operational aspects and the integration within field development
plans were addressed, especially with the recommended well placement and completion.
This culminated in a field development strategy for formation damage mitigation
in tight carbonate reservoirs during production and injection phase that can be
used in other similar fields.'
- 'The most common challenge in horizontal drilling is depth uncertainty which can
be due to poor seismic data or interpretation. It is arguable that a successful
landing of the wellbore in the reservoir optimally and within the desired zone
is the most challenging in most geosteering operation. The presence of fluid contacts
such as oil-water-contact (OWC) and gas-oil-contact (GOC) complicates the whole
drilling process, most especially if these fluid contacts are not well defined
or known. Additionally, the ability to map the boundaries of the reservoir as
the BHA drills the lateral section is an added advantage to remaining within the
desired reservoir section.
The success of any reservoir navigation service where seismic uncertainty at the
reservoir top is high will rely largely on how effective the geosteering system
is and how the geosteering engineer is able to react promptly to changes while
landing the well in the reservoir and drilling the lateral section with without
exiting the reservoir.
Reservoir Navigation Service (RNS) provides the means for the drilling near horizontal
or horizontal wells for the purpose of increasing hydrocarbon extraction from
the earth''s subsurface. This involves the use of a pre-defined bottom hole assembly
(BHA) with inbuilt downhole logging while drilling (LWD) and measurement while
drilling (MWD) sensors. The measurements from these downhole sensors are uplinked
to the surface of the wellbore where they are converted to meaningful petrophysical
data. The goal is to use the downhole petrophysical data such as gamma ray, propagation
resistivity and so on, to update an existing pre-well geological model of a section
of the earth in such a way that the final result depicts the true model picture
of the earth subsurface.
This paper focuses on using well CBH-44L to showcase how the use of real-time
distance-to-boundary (D2B) measurement from a deep reading azimuthal propagation
resistivity tool is use to correct for depth uncertainty in seismic, thereby,
improving the chance of successfully landing and drilling a horizontal well.'
datasets:
- Sampath1987/offshore_energy
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/all-distilroberta-v1
results:
- task:
type: triplet
name: Triplet
dataset:
name: ai job validation
type: ai-job-validation
metrics:
- type: cosine_accuracy
value: 0.68781977891922
name: Cosine Accuracy
---
# SentenceTransformer based on sentence-transformers/all-distilroberta-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) on the [offshore_energy](https://huggingface.co/datasets/Sampath1987/offshore_energy) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) <!-- at revision 842eaed40bee4d61673a81c92d5689a8fed7a09f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [offshore_energy](https://huggingface.co/datasets/Sampath1987/offshore_energy)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Sampath1987/all-distilroberta-v1-offshore-energy")
# Run inference
sentences = [
'What role did anti-collision analysis play in the drilling of the dual lateral well?',
'This paper aims to analyze the impact of appraising and developing marginal fields with multiple stacked reservoirs which is quite challenging in terms of techno commercial value. The development of such marginal reservoirs using conventional single horizontal wells drilling and completion is uneconomical. Therefore, it was necessary to engineer a solution that can enhance the commercial value of the project by reducing CAPEX and OPEX. This paper will present the first comprehensive business case, where multiple stacked reservoirs with marginal reserves were studied to produce independently using multilateral completions, granting full accessibility of the laterals while achieving production monitoring and reservoir surveillance.',
"The most common challenge in horizontal drilling is depth uncertainty which can be due to poor seismic data or interpretation. It is arguable that a successful landing of the wellbore in the reservoir optimally and within the desired zone is the most challenging in most geosteering operation. The presence of fluid contacts such as oil-water-contact (OWC) and gas-oil-contact (GOC) complicates the whole drilling process, most especially if these fluid contacts are not well defined or known. Additionally, the ability to map the boundaries of the reservoir as the BHA drills the lateral section is an added advantage to remaining within the desired reservoir section.\nThe success of any reservoir navigation service where seismic uncertainty at the reservoir top is high will rely largely on how effective the geosteering system is and how the geosteering engineer is able to react promptly to changes while landing the well in the reservoir and drilling the lateral section with without exiting the reservoir.\nReservoir Navigation Service (RNS) provides the means for the drilling near horizontal or horizontal wells for the purpose of increasing hydrocarbon extraction from the earth's subsurface. This involves the use of a pre-defined bottom hole assembly (BHA) with inbuilt downhole logging while drilling (LWD) and measurement while drilling (MWD) sensors. The measurements from these downhole sensors are uplinked to the surface of the wellbore where they are converted to meaningful petrophysical data. The goal is to use the downhole petrophysical data such as gamma ray, propagation resistivity and so on, to update an existing pre-well geological model of a section of the earth in such a way that the final result depicts the true model picture of the earth subsurface.\nThis paper focuses on using well CBH-44L to showcase how the use of real-time distance-to-boundary (D2B) measurement from a deep reading azimuthal propagation resistivity tool is use to correct for depth uncertainty in seismic, thereby, improving the chance of successfully landing and drilling a horizontal well.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4252, 0.4369],
# [0.4252, 1.0000, 0.4978],
# [0.4369, 0.4978, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `ai-job-validation`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.6878** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### offshore_energy
* Dataset: [offshore_energy](https://huggingface.co/datasets/Sampath1987/offshore_energy) at [0ebbfc6](https://huggingface.co/datasets/Sampath1987/offshore_energy/tree/0ebbfc615bc7c9bbd3d58315bc2e14e91f291fa1)
* Size: 89,129 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 11 tokens</li><li>mean: 21.54 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 397.26 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 378.17 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the significance of end point relative permeability of the oil phase in the productivity of oil reservoirs below bubble point pressure?</code> | <code>In contrast with what is followed for Offshore Oil Operations the majority of the Onshore Oil Operations in the world do not have a Minimum and Mandatory required HSE training program for all personnel including contractors and subcontractors.<br>A comparison is drawn between the Minimum and Mandatory HSE Training Programmes applied offshore in developed areas, mainly North Sea and Gulf of Mexico and the benefits that similar programs can bring to the ME onshore oil operations are addressed by estimating the risk reduction and potential economic benefits.<br>The applicability of such Minimum and Mandatory HSE Training Programs is analyzed against the scenario of heavy utilization of contractors and subcontractors with different approach and standards in HSE training and also the increasing complexity of the onshore oil operations<br>An estimation of how many lives can potentially be saved by the introduction of such programs is provided in global and generic terms.<br>The HR Impact, in different a...</code> | <code>The knowledge of relative permeability is key in oil production mechanism as it affects multiphase flow which is vital to producible reserves in petroleum reservoirs. In this study, the impact of altering end point saturation on relative permeability curve and how it influences oil recovery was investigated on field X in Niger Delta, Nigeria. The saturation end points obtained after a simulation study was used as a start point to predict oil production. These end points saturation of water and oil were altered and varied according to facies. The eclipse simulation tool was used in conducting the prediction runs. The result obtained showed wide variation from actual production forecast (i.e. ≥ 25%) when end points were varied with no guided limit from experimental data. This study reveals the need for an accurate determination of residual oil saturation as it was seen to have an impact on forecast and history match.</code> |
| <code>What role does the effective coefficient of discharge (_Kd_) play in calculating the required effective discharge area?</code> | <code>96 API S TANDARD 520, P ART I—S IZING AND S ELECTION <br>**B.2.3.3** Using the theoretical mass flux obtained from numerical integration above, one may determine the<br>required effective discharge area: <br>In USC units: <br>_Q_ × ρ 1<br>× <br>sec gal _G_ ×<br>60 × 7 4805 .<br>min ft 3 <br>_A_ = _W_ = _Q_ × ρ × 1<br>_G_ × _K_ d 60 sec × 7 4805 . gal _G_ × _K_ <br>d 60 × 7 4805 . d<br>3 <br>528 62 2 × . 1 2 2 <br>_A_ = × = 0 0148 ft . = 2 135 in. . (B.8) <br>60 7 4805 × . 7 592 14 0 65, . × . <br>In SI units: <br>_Q_ ×ρ 1<br>× <br>sec liter _G_ ×<br>60 min × 1 000, m 3 <br>_A_ = _W_ = _Q_ ×ρ × 1<br>_G_ × _K_ d 60 sec × 1 000, liter 3 _G_ × _K_ <br>, <br>_A_ = 2 000, × 996 9 . × 1 = 1 379 . × 10 − 3 m 2 = 1 379 mm, 2 (B.9)<br>60 × 1 000, 37 068, × 0 65 . <br>where <br>_G_ is the theoretical mass flux through the nozzle, lb/s·ft [2] (kg/s·m [2] ); <br>_W_ is the required relief rate, lb/s (kg/s); <br>_Q_ is the required relief rate, gal/min (L/min); <br>ρ = 1 _v_ is the fluid density, lb/ft [3] (kg/m [3] ); <br>_K_ d is the effective coefficient of discharge...</code> | <code>S IZING, S ELECTION, AND I NSTALLATION OF P RESSURE - RELIEVING D EVICES 59 <br>**5.6.3 Sizing for Critical Flow** <br>**5.6.3.1 General** <br>**5.6.3.1.1** Pressure-relief devices in gas or vapor service that operate at critical flow conditions (see 5.6.2)<br>may be sized using Equation (2) through Equation (7). Each of the equations may be used to calculate the<br>effective discharge area, _A_, required to achieve a required flow rate through a pressure-relief device. A PRV<br>that has an effective discharge area equal to or greater than the calculated value of _A_ is then chosen for the<br>application. <br>In USC units: <br>_A_ = (2) <br>_A_ = (3) <br>6 32 . _CK P K K_ d 1 b c <br>_A_ = (4) <br>1 175 . _CK P K K_ <br>1 175 . _CK P K K_ d 1 b c <br>. <br>In SI units: <br>_A_ = (5) <br>_A_ = (6)<br>_CK P K K_ <br>d 1 b c <br>_A_ =<br>_CK P K K_ <br>=<br>(7) <br>d 1 b c <br>where <br>_A_ is the required effective discharge area of the device, in. [2] (mm [2] ) (see 3.20); <br>_W_ is the required flow through the device, lb/h (kg/h); <br>_C...</code> |
| <code>How many swellable packers were required to be run in the horizontal hole part for the AICV trial, and what was the purpose of this requirement?</code> | <code>Removing fluid from a wellbore column, allowing a well to flow initially, or bringing a previous well back online, nitrogen lifting is commonly used in north Iraq wells. Due to the inability of coiled tubing units to be delivered on time and their high cost, operators are forced to seek for an alternative method of unloading drilling fluid. A hydraulic Jet Pump is a technology used to complete the task.<br>A newly drilled well DB-H was chosen, and the drilling fluid volume calculated was 12,000 bbl. to pump to the surface and begin production, assuming nonstop operation between unloading and producing. The deployment of the hydraulic lift Jet Pump for both stages was planned. Well data from the operator was collected, the process design was initiated, and Jet Evaluation Modeling Software (JEMS) was used to run the design models. A Proper pump size was set up based on available data to meet operator expectations. A Reverse Circulating Jet Pump (RCJP) was chosen to be installed inside a Sli...</code> | <code>This development, predominantly from four artificial islands, of a giant offshore field in the United Arab Emirates (UAE) requires lateral compartmentalization with open hole packers of the 6 5/8" horizontal lower completions with lateral lengths greater than 16,000ft and total well lengths greater than 30,000ft MD. Swell Packer technology has enabled cost effective compartmentalization in horizontal laterals and is the preferred OH packer solution for the development.<br>Deploying swell packers is regarded as being a simple solution to compartmentalizing any lateral where typically the deployment fluid differs from the fluids in which it will swell in; this application prevents the elastomer from swelling during deployment and swelling upon contact with produced or injected fluids. The use of an extended delayed oil swell packer with no delay systems in this particular application enables the packers to be deployed in a Non Aqueous Reservoir Drill in Fluid (RDFNAF) where the packer is re...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### offshore_energy
* Dataset: [offshore_energy](https://huggingface.co/datasets/Sampath1987/offshore_energy) at [0ebbfc6](https://huggingface.co/datasets/Sampath1987/offshore_energy/tree/0ebbfc615bc7c9bbd3d58315bc2e14e91f291fa1)
* Size: 11,141 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 21.28 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 42 tokens</li><li>mean: 392.99 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 31 tokens</li><li>mean: 376.18 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How does partial jacket construction differ for vessels that cannot use staybolt construction?</code> | <code>**9-7 – 9-10** **ASME BPVC.VIII.1-2019** <br>**Figure 9-7** <br>_(2)_ Partial jackets that by virtue of their service or<br>configuration do not lend themselves to staybolt construction may be fabricated by other means providing<br>they are designed using appropriate stress values and<br>are proof tested in accordance with UG-101(p). <br>444 <br>**9-8** **FABRICATION** <br>_(a)_ Fabrication of vessels shall be in accordance with<br>applicable Parts of Subsection A and Subsection B, Part<br>UW. The requirements of UW-13(e) do not apply to closure rings.<br>_(b)_ This Appendix covers fabrication of jacketed vessels<br>by welding. Other methods of fabrication are permitted,<br>provided the requirements of applicable parts of this Di<br>vision are met. <br>_(c)_ Where only the inner vessel is subjected to lethal<br>service, the requirements of UW-2 shall apply only to<br>welds in the inner vessel and those welds attaching the<br>jacket to the inner vessel. Welds attaching the jacket to<br>the inner vessel need not be radiographed and may b...</code> | <code>**9-5 – 9-7** **ASME BPVC.VIII.1-2019** <br>‐ ‐<br>(g 5), and (g 6), may be used on any of the types of<br>jacketed vessels shown in Figure 9-2 where _t_ _rj_ does not<br>exceed [5] / 8 in. (16 mm).<br>_(7)_ Closures shown in Figure 9-5, sketch (h) used on<br>Type 3 jacketed vessels shown in Figure 9-2 shall have attachment welds in accordance with Figure 9-5, sketch <br>‐ ‐<br>(i 1) or (i 2). This construction is limited to jackets where<br>_t_ _rj_ does not exceed [5] / 8 in. (16 mm).<br>_(8)_ Closures for conical or toriconical jackets shown<br>in Figure 9-5, sketches (k) and (l) shall comply with the<br>requirements for Type 2 jacketed vessels shown in Figure<br>9-2. <br>_(d)_ Any radial welds in closure members shall be buttwelded joints penetrating through the full thickness of the<br>member and shall be ground flush where attachment<br>welds are to be made. <br>_(e)_ Where the inner vessel must meet the requirements<br>of UW-2, the attachment welds of the jacket to the inner<br>vessel need not be welded for their full thickness no...</code> |
| <code>What dimensions must fins and studs conform to as stipulated in Section 17.4.4?</code> | <code>**17.4 Examination of other components** <br>**17.4.1** Examination of heater steelwork shall be in accordance with the structural design code. <br>**17.4.2** Refractory linings shall be examined throughout for thickness variations during application and for cracks<br>after curing. Thickness tolerance is limited to a range of minus 6 mm (1/4 in) to plus 13 mm (1/2 in). Cracks which<br>are 3 mm (1/8 in) or greater in width and penetrate more than 50 % of the castable thickness shall be repaired.<br>Repairs shall be made by chipping out the unsound refractory to the backup layer interface or casing and<br>exposing a minimum of three tieback anchors, or to the sound metal, making a joint between sound refractory that<br>has a minimum slope of 25 mm (1 in) to the base metal (dove-tail construction) and then gunning, casting or<br>hand-packing the area to be repaired. <br>**17.4.3** Finned extended surface shall be examined to ensure fins are perpendicular to the tube within 15°. The<br>maximum discontinuity of the w...</code> | <code>**16.1** -112 STEEL ANCHORS [Sect. I8. <br>**3e.** **Detailing Requirements in Composite Components** <br>Steel anchors in composite components shall meet the following requirements: <br>(a) Minimum concrete cover to steel anchors shall be in accordance with ACI 318<br>provisions for concrete protection of headed shear stud reinforcement. <br>(b) Minimum center-to-center spacing of steel headed stud anchors shall be four<br>diameters in any direction. <br>(c) The maximum center-to-center spacing of steel headed stud anchors shall not <br>exceed 32 times the shank diameter. <br>(d) The maximum center-to-center spacing of steel channel anchors shall be 24 in.<br>(600 mm). <br>**User Note:** Detailing requirements provided in this section are absolute limits.<br>See Sections I8.3a, I8.3b and I8.3c for additional limitations required to preclude<br>edge and group effect considerations. <br>_Specification for Structural Steel Buildings,_ July 7, 2016<br>A MERICAN I NSTITUTE OF S TEEL C ONSTRUCTION</code> |
| <code>What are some common mistakes in oil and gas project execution that lead to financial losses?</code> | <code>Dozens of deepwater wells have been drilled in western South China Sea with about 30 percent have characteristics of high temperature and high pressure, which brought a series of difficulties and challenges to field operations. After incorporating the analysis of engineering and geological environment for deepwater HTHP wells in Lingshui block of western South China Sea, it is suggested that the solution of drilling problems for deepwater HTHP wells should start from drilling fluid. Several major technical problems are required to be addressed by drilling fluid, such as co-exist of low temperature and high temperature that lead to difficulty of drilling fluid maintenance and narrow density margin caused by deepwater and high pressure. Based on the above problems, combining with geological features of HTHP wells, researchers developed a novel water based drilling fluid system compatible with deepwater HTHP wells in Lingshui block on the basis of conventional HEM drilling fluid and furth...</code> | <code>The lack of availability of required skills and experience in most if not all parts of the oil and gas value chain is well documented so, rather than trying to make the case, we will summarise the challenge thus: the industry in all parts of the world can't find the capability it needs to safely get its work done in the timeframes it would like.<br>However or wherever the situation is measured, the consequence is that in days when the oil price might suggest that the industry has "never had it so good", many companies are falling seriously short of stakeholder expectations with projects of all types not being completed as planned or failing to deliver anticipated returns.<br>Close to home we see producers consistently missing quarterly production targets and a seemingly constant downgrading of forecasts and year-on-year plans. This leads to a constant stream of bad news and criticism in the media, greater stress through all levels of management and an inevitable "knee jerk" towards a more sh...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Validation Loss | ai-job-validation_cosine_accuracy |
|:------:|:----:|:---------------:|:---------------------------------:|
| 0.7179 | 1000 | 1.5967 | 0.6441 |
| 1.4358 | 2000 | 1.5329 | 0.6618 |
| 2.1536 | 3000 | 1.5060 | 0.6858 |
| 2.8715 | 4000 | 1.4904 | 0.6878 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 5.1.0
- Transformers: 4.53.3
- PyTorch: 2.8.0+cu128
- Accelerate: 1.9.0
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
iyaadshikder1546/blockassist-bc-pensive_agile_bee_1757602507
|
iyaadshikder1546
| 2025-09-11T14:55:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pensive agile bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:55:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pensive agile bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lornaaveradutch/blockassist-bc-poisonous_domestic_jaguar_1757602477
|
lornaaveradutch
| 2025-09-11T14:54:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"poisonous domestic jaguar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:54:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- poisonous domestic jaguar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aimarsg/mle5_all_domains_contrastive
|
aimarsg
| 2025-09-11T14:54:46Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:19544",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:intfloat/multilingual-e5-large",
"base_model:finetune:intfloat/multilingual-e5-large",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-11T14:54:17Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:19544
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-large
widget:
- source_sentence: Zenbat diru izango du Nafarroako Gobernuak gastuetarako eta inbertsio
publikoetarako?
sentences:
- 'Nafarroako aurrekontuen eztabaidan 355 zuzenketa partzial onartu dira
Bost egunez eztabaidatu ostean, amaitu dute Nafarroako Parlamentuko Ekonomia eta
Ogasun batzordean aurtengo aurrekontuen inguruko eztabaida. Orotara, 983 zuzenketa
partzial aurkeztu dira, eta horietatik 355 onartu dituzte PSNk, EH Bilduk, Geroa
Baik eta Zurekin Nafarroak. 17,2 milioi euroko egitasmoak finantzatuko dituzte
aurrera ateratako diru sail horiek.
UPNk 290 zuzenketa aurkeztu zituen, eta PPk 223, baina gobernuko bazkideek eta
EH Bilduk euren gehiengoa baliatu, eta atzera bota dituzte. Vox eskuin muturreko
taldeak ez du zuzenketarik aurkeztu.
Zuzenketak gehituta, 6.355 milioi euroko aurrekontua izango du gobernuak; gastu
muga, berriz, 5.836 milioi eurokoa izango da, aurreko urtekoa baino %10,5 handiagoa.
Udalen gastu arrunta finantzatzeko Tokiko Ogasunen Funtsako dirua eta estatuari
bere gain hartu gabeko eskumenen truke eman beharreko ekarpena gehituta, gobernuak
4.684 milioi euro izango ditu gastuetarako zein inbertsio publikoetarako.
Eztabaida amaituta, azken urratsa baino ez da falta. Datorren osteguneko osoko
bilkuran onartuko da legea. Urtero abendu amaieran eztabaidatu ohi dira aurrekontuen
lege proiektuak, baina aurtengo eztabaida hiru hilabete atzeratu da, gobernua
abuztu erdialdera abiatu zelako. Lege proiektua urtarrilaren 19an erregistratu
zen legebiltzarrean, kasurako.
Onartutako zuzenketak'
- 'Preso bat hil da Basauriko espetxean
Preso bat hil da Basauriko espetxean (Bizkaia). Atzo aurkitu zuten bere ziegan
hilda. Salhaketa elkarteak atzo eman zuen heriotzaren berri, eta Eusko Jaurlaritzak
gaur baieztatu dio BERRIAri. Oraindik autopsiarik egin ez duten arren, Jaurlaritzako
iturriek adierazi dute «dauden zantzuen arabera» balitekeela heriotzaren arrazoia
suizidioa izatea. Hil den presoaren nortasunaren inguruko daturik ez dute eman,
baina nabarmendu dute ikerketa judiziala zabalik dagoela eta protokolo guztiak
martxan daudela.
Salhaketak Eusko Jaurlaritzari eskatu dio azalpenak eman ditzala eta heriotza
iker dezala. Gardentasuna eskatu dio elkarteak Jaurlaritzari: «Eusko Jaurlaritzak
espetxe eskumena jaso berritan, informazioa ematen zuten, datuen babesa errespetatuta.
Azkenaldian ikusten dugu Jaurlaritzak ez duela inolako informaziorik ematen, ezta
baloraziorik egiten ere. Horrek kezkatzen gaitu».
2021eko urrian Eusko Jaurlaritzak espetxeen eskumena jaso zuenetik, zazpi preso
hil dira guztira Basaurin, Martutenen eta Zaballan. Maria Jesus San Jose Eusko
Jaurlaritzako Justizia sailburu berriarekin bilera bat egiteko eskatu du Salhaketak.
Gaineratu du presoen hiru laurdenek buruko gaitzak dituztela, eta presoen, funtzionarioen
eta hirugarren sektoreko elkarteen arteko elkarlana bultzatu behar dela buruko
osasunaren arazoari aurre egiteko. Jaurlaritzari eskatu dio suizidioen aurkako
protokoloak «azkarrago» martxan jartzeko, zalantza baitu behar bezain laster egiten
ote den gaur egun.
«Jaurlaritzari eskatzen diogu hildakoaren senideei eta gertukoei informazioa eman
diezaiela martxan jar litezkeen ekintza judizialetan parte hartzeko dituzten aukerei
buruz, datuak argitzeko», erantsi du Salhaketak.'
- 'Aldiz, AUVE Auto elektrikoen erabiltzaileen Espainiako elkarteko Mikel Agirregabiriaren
ustez, ibilgailu elektrikoen aseguruak ez dira garestiagoak, eta berak hibrido
batetik elektrikora aldatu zenean asegurua «gutxiago» kostatu zitzaiola azaldu
du.
Hala ere, Agirregabiriak onartu du elektrikoen etxe batzuetan arazoak izan direla,
baina «ez bateriarekin, ezta motorrarekin ere». Horiek izaten baitira elektrikoei
aldatu behar zaizkien pieza garestienak, eta aseguruen primak igotzen dituztenak.
«Elektrikoak bideragarriagoak dira, eta matxura gutxiago dituzte. Garatutako merkatuetan
hala egiaztatu da», azaldu du Agirregabiriak.
« [Konponketen kostua] Faktore erabakigarria da justifikatzeko auto elektrikoen
aseguruek hibridoekiko eta, are gehiago, errekuntzakoekiko duten gainprezioa».
LEIRE ALBERDI Euskal Kontsumitzaileen Antolaketako kidea
Hibridoen aseguruak garestiagoak izatea «zentzuzkoa» iruditzen zaio Agirregabiriari,
bi motor edukitzean matxurak izateko «bi iturri» dauzkatelako, eta horregatik
ez ditu gomendatzen.
Auto elektrikoen aseguruak garestiagoak direla esatea «gezurra» dela uste du,
eta ibilgailu elektrikoen inguruan dagoen «intoxikazio ugarietako bat» dela esan
du. Ez du uste intoxikazio hori aseguru etxeena denik, ibilgailuen ohiko marketatik
datozela dio, ezin baitute elektrikoetarako aldaketa «saihetsezinari» aurka egin.
OCUrentzat ere auto elektrikoak dira «etorkizuna», eta, horregatik, elektrikoen
erabiltzaileentzat «azpiegitura egokiak garatzea» eskatzen dute «benetako alternatiba
bat» izan daitezen.'
- source_sentence: Zein da epea Donostialdeko ESIko zuzendari-gerenteak emandako ebazpen
honen aurka gora jotzeko errekurtsoa aurkezteko?
sentences:
- 'Errekurtsoak.
39/2015 Legeak 121 eta 122 artikuluetan jasotakoaren arabera, ebazpen honen kontra
gora jotzeko errekurtsoa jarri ahal da, Osakidetzako zuzendari nagusiari zuzendua.'
- '[TOPIC: Galdera, Vicente Reyes Martín Euskal Sozialistak taldeko legebiltzarkideak
lehendakariari egina, Guggenheim Bilbao Museoa Fundazioaren Patronatuak Solomon
R. Guggenheim Fundazioarekin izenpetutako hitzarmenaren inguruan]
[REYES MARTÍN, (SV-ES)]:
ez da esan. Are gehiago, akordio honi buruz prentsaurreko batean oraintsu hitz
egin denean –Legebiltzarrera oraindik ez da iritsi akordioa; espero dut lehenbailehen
iristea, hura aztertu ahal izateko–, zuk eta Bizkaiko ahaldun nagusiak sutsuki
erantzun zenioten, hiru egunkariren arabera, gastuari buruzko informazio-eskaerari,
irain bat izan balitz bezala. Hiru egunkariek diote. Eta gauza batzuk ez dira
bete. Nik ezin dut akordioa eduki, zuk bezala, ikusteko zer bete den, baina (Date:
12.12.2014)'
- 'Laugarrena. Interesdunari eta Donostialdeko ESIari Ebazpen honen berri ematea.
Bosgarrena. Ebazpen honen aurka gora jotzeko errekurtsoa aurkez dakioke Osakidetzako
zuzendari nagusiari. Horretarako, hilabeteko epea izango da, Ebazpena Euskal Herriko
Agintaritzaren Aldizkarian argitaratu eta hurrengo egunetik aurrera.
Donostia, 2021eko ekainaren 14a.
Donostialdeko ESIko zuzendari-gerentea.
EB (53/2019 Ebazpena, urtarrilaren 24koa).
Langileen zuzendaria,
ESTHER LITAGO SOLA.'
- source_sentence: Zein da EP-IUren jarrera Elkarrizketa Sozialerako Mahaian jorratu
beharreko gaiei buruz?
sentences:
- '[TOPIC: EH Bildu talde parlamentarioak egindako legez besteko proposamena, indarkeria
obstetrikoaren biktima izan den emakume bat erreparatzeari buruz. Eztabaida eta
behin betiko ebazpena]
[SÁNCHEZ MARTÍN, (SV-ES)]:
ez, esan genizun osoko bilkuran bertan, kontua ez da eztabaidatu nahi ez dugula,
kontua da zuek eztabaida hori hemen errepikatu nahi duzuela uneotan Diputatuen
Kongresuan ere egiten ari denean. Zuek Madrilen egiten ari den eztabaida hemen
errepikatu duzue, eztabaida hona ekarri nahi izan zenuten zer eta une batez besterik
ez bada ere oihartzuna lortzeko, eta hori guztiz absurdoa da. Guk ez genion uko
egin auziok eztabaidatzeari, zerbait guztiz ezberdina gertatu zen. (Date: 13.10.2022)'
- '[TOPIC: EH Bildu talde parlamentarioak egindako legez besteko proposamena, Lanbideren
prestakuntza-eskaintzak EAEko bi hizkuntza ofizialak berdintasunean tratatzeari
buruzko estrategia aktibatzearen inguruan. Eztabaida eta behin betiko ebazpena]
[HERNÁNDEZ HIDALGO, (EP-IU)]:
Gero, amaitzeko, azken aipamen bat egin nahi diot berriro Urrutia jaunari, negoziazioarekin
lotuta, Elkarrizketa Sozialerako Mahaiarekin zehazki. Jakina, egon badaude Elkarrizketa
Sozialerako Mahaian jorratu beharreko gaiak, nik neuk sarri defendatu baitut −askotan
entzun nauzu hemen hori esaten− auzi politikoak ere badirela, eta, horrenbestez,
bertan eta legebiltzarretan ere eztabaidatu behar direla. Bada, aztertzen ari
garen auzi zehatza, baimenen ordainsariena, kasu honetan euskara ikasteko baimenena,
aspalditik mahai (Date: 02.06.2022)'
- 'Osakidetza-Euskal osasun zerbitzuko zuzendari nagusiaren maiatzaren 15eko 1036/2015
Ebazpenaren bidez, Maitane Arrizabalaga Calzacorta andrea izendatu zen Ezkerralde-Enkarterri-Gurutzetako
ESIko Pertsonaleko Zuzendari (2015eko ekainaren 12ko EHAA, 109. zk.).
Ekainaren 26ko 8/1997 Legea, azaroaren 11ko 255/1997 Dekretua eta aplikatzekoak
diren gainerako xedapenak ikusita, Zuzendaritza Nagusi honek
EBAZTEN DU
:
Lehenengoa. Maitane Arrizabalaga Calzacorta andrea Ezkerralde-Enkarterri-Gurutzetako
ESIko Pertsonaleko Zuzendari kargutik kentzea, berak hala eskatuta, eta eskerrak
ematea egindako lanagatik.
Bigarrena. Ebazpen honek 2024ko urriaren 31ko lanaldia amaitzean izango ditu ondorioak.
Hirugarrena. Interesdunari, Ezkerralde-Enkarterri-Gurutzetako ESIari eta Plangintza
eta Kudeaketa Zuzendariordetzari ebazpen honen berri ematea.
Laugarrena. Ebazpen honen aurka gora jotzeko errekurtsoa aurkeztu ahal izango
zaio Osakidetza-Euskal osasun zerbitzuko Administrazio Kontseiluari. Horretarako
hilabeteko epea izango da, ebazpen hau Euskal Herriko Agintaritzaren Aldizkarian
argitaratu eta hurrengo egunetik aurrera.
Vitoria-Gasteiz, 2024ko urriaren 31.
Osakidetza-Euskal osasun zerbitzuko zuzendari nagusia,
SUSANA LÓPEZ ALTUNA.'
- source_sentence: Nork esan zizun aurrena uzteko?
sentences:
- '[TOPIC: Interpelazioa, José Antonio Pastor Garrido Euskal Sozialistak taldeko
legebiltzarkideak lehendakariari egina, Etxebizitzaren Legea erregelamendu bidez
garatzeari buruz]
[PASTOR GARRIDO, (SV-ES)]:
eta azkartasunari buruz. Izan ere, logikoa litzateke, zuek zuen esloganean "pertsonen
Jaurlaritza" gisa definitzen duzuen gobernu batek kontuan izango balu lege honek
pertsonen funtsezko eskubide bat betetzen duela: aterpe hartzeko bizileku bat
izatea. Guretzat, osasuna eta hezkuntza bezain eskubide garrantzitsua da hori.
Eta, beraz, eslogan hori egia bihurtu nahi baduzu, soilik horretan, eslogan batean,
gera ez dadin, eskubide hori da gaur egun Euskadiren ezaugarrietako bat politika
aurreratuari dagokionez, baina gainerako (Date: 26.06.2015)'
- 'Nork esan behar zizun, ezta?
Hala da. Azkenean nire ogibide bihurtu da, hamar urtez. Zoragarria izan da, eta
amets bat bete dut. Espero gabekoa, gainera.'
- 'Gabonetako oporren ostean argi esan nion Ramoni utzi egingo nuela. Berak esaten
zidan pena handia ematen ziola, jarraitzeko moduan ikusten ninduela. Baina ezin
zidala ezer aurpegiratu, eta ulertzen zuela. Izan ere, hark lasaiarazten ninduen
entrenamenduetan, ni nekaezina bainintzen. Baina orain kontrakoa zen. Ez nuen
indarrik aurrera jarraitzeko. Izan ere, minarekin bizitzen ikasi egiten da, baina
une oro minarekin egotea nekagarria da. Horregatik utzi dut. Behin erabakia hartuta,
barneratu, eta gertukoenei esan nien, eta iragan astean egin nuen gainerakoei
jakinarazteko urratsa.
Nori esan zenion aurrena?
Gurasoei. Hegoafrikan nengoenean, haiekin hitz egiten nuenean; jabetzen ziren
gaizki nengoela. Bueltatu bezain laster esan zidaten uzteko. Ez zuela merezi sufritzen
jarraitzeak. Kirola zela, eta disfrutatzen ez banuen ez zuela merezi. Lasaitu
handia hartu dute, erabakia hartu nuenetik gero eta hobeto ikusten nautelako,
lasaiago. Azken batean, nire esku zegoen guztia egin dut hau hain goiz eta horrela
ez amaitzeko, baita inguruan izan ditudanek ere. Oso ondo zaindu naute, baina
ez da posible izan.
«Nire esku zegoen guztia egin dut hau hain goiz eta horrela ez amaitzeko, baita
ingurukoek ere. Baina ez da posible izan»
Oraindik goiz da. Baina nolakoa gertatzen ari zaizu aipatu duzun barneratze prozesua?'
- source_sentence: Zein da osasun-arloko profesionalak prestatzeko beken eta laguntzen
deialdian eskabideak ebazteko ardura duen organoa?
sentences:
- 'Osasun sailburuaren 2011ko uztailaren 20ko aginduaren bidez (2011ko abuztuaren
12ko EHAA), osasun-arloko profesionalak prestatzeko beketarako eta laguntzetarako
deia egin zen.
Agindu horren 13. artikuluan ezartzen duenez, Kalitate, Ikerketa eta Berrikuntza
Sanitarioko sailburuordeak ebatziko ditu bekak eta laguntzak eskuratzeko eskabideak,
EHAAn argitaratutako ebazpen baten bitartez, eta betiere, Osasun eta Kontsumoko
sailburuak horretarako berariaz izendatutako Balorazio Batzordeak proposatutakoa
aintzat hartuta.
Hori guztia kontuan izanik, deialdira bildutako eskabideak aztertuta, Balorazio
Batzordeak proposatutakoa hartu da aintzakotzat, eta, horrenbestez, honako hau
EBATZI DUT
:
Lehenengoa. Osasun-arloko profesionalak prestatzeko bekak eta laguntzak honako
pertsona hauei ematea (bakoitzari ondoan duen diru-zenbatekoa emango zaio):
A modalitatea (Atzerriko zentroetan ikastaroak eta egonaldiak egiteko bekak eta
laguntzak).
(Ikus .PDF)
B modalitatea (Osasunaren arloan ikerketako eta berrikuntzako prestakuntza-planak
gauzatzeko bekak eta laguntzak).
(Ikus .PDF)
Bigarrena. Honako eskatzaile hauei laguntza ukatzea; arrazoiak ere ematen dira:
A modalitatea:
Canales Arrasate, Maria Isabel.
Galnares Cordero, Lorea.
Gómez Coloma, Alexander.
Lago García, Violeta.
Llano Castresana, Oihane.
Morales González, M.ª Celia.
Morales López, Julio Ulises.
Movilla Fernández, Soraya.
Santa Maria-Amurrio Alustiza, Lander.
Urdangarin Zumeta, Nerea.
B modalitatea:
Anton Ladislao, Ane.
Aurtenetxe Saez, Olaia.
Urdampilleta Otegui, Aritz.
Bekak jaso nahi dituzten jarduerak deialdi-aginduko 1. artikuluan diru-laguntzetarako
zehaztutako eremuan ez barne-hartuta egoteagatik (atzerrian egingo ez diren ikastaroak
eta egonaldiak).
B modalitatea:
Maortua Olabe, Hiart.
Interesunak berak berariaz uko egiteagatik.
A modalitatea:
Amo Herrero, Laura.
Olabarria Larizgoitia, Markel.'
- 'Bigarrena. Hiru urtetarako den izendapen honek interesdunari jakinarazten zaion
momentutik aurrera izango ditu ondorio ekonomikoak eta administratiboak.
Denboraldi hori igarotakoan, zuzendariak egindako kudeaketari buruz aldeko ebaluazioak
badaude, beste hiru urtez luzatu daiteke lanpostu horretan segitzea, Osakidetza-Euskal
osasun zerbitzuko zuzendari nagusiaren ebazpen bidez.
Hirugarrena. Itziar Pérez Irazusta andrea zerbitzu berezietan deklaratzea, bere
izendapena indarrean sartzen denetik.
Laugarrena. Ebazpen honen aurka gora jotzeko errekurtsoa aurkeztu ahal izango
zaio Osakidetza-Euskal osasun zerbitzuko Administrazio Kontseiluari, ebazpena
Euskal Herriko Agintaritzaren Aldizkarian argitaratu eta biharamunetik aurrera,
hilabeteko epean.
Vitoria-Gasteiz, 2012ko ekainaren 5a.
Osakidetza-Euskal osasun zerbitzuko zuzendari nagusia,
JULIÁN PÉREZ GIL.'
- Ebazpena Euskal Herriko Agintaritzaren Aldizkarian argitaratuko da, eta, denek
jakin dezaten, www.elankidetza.euskadi.net eta www.euskadi.net orrietan argitaratuko
da beken esleipendunen zerrenda eta haien
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-large
results:
- task:
type: triplet
name: Triplet
dataset:
name: multilingual e5 large
type: multilingual-e5-large
metrics:
- type: cosine_accuracy
value: 0.8544478416442871
name: Cosine Accuracy
---
# SentenceTransformer based on intfloat/multilingual-e5-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision 0dc5580a448e4284468b8909bae50fa925907bc5 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("aimarsg/mle5_all_domains_contrastive")
# Run inference
sentences = [
'Zein da osasun-arloko profesionalak prestatzeko beken eta laguntzen deialdian eskabideak ebazteko ardura duen organoa?',
'Osasun sailburuaren 2011ko uztailaren 20ko aginduaren bidez (2011ko abuztuaren 12ko EHAA), osasun-arloko profesionalak prestatzeko beketarako eta laguntzetarako deia egin zen.\nAgindu horren 13. artikuluan ezartzen duenez, Kalitate, Ikerketa eta Berrikuntza Sanitarioko sailburuordeak ebatziko ditu bekak eta laguntzak eskuratzeko eskabideak, EHAAn argitaratutako ebazpen baten bitartez, eta betiere, Osasun eta Kontsumoko sailburuak horretarako berariaz izendatutako Balorazio Batzordeak proposatutakoa aintzat hartuta.\nHori guztia kontuan izanik, deialdira bildutako eskabideak aztertuta, Balorazio Batzordeak proposatutakoa hartu da aintzakotzat, eta, horrenbestez, honako hau\nEBATZI DUT\n:\nLehenengoa. Osasun-arloko profesionalak prestatzeko bekak eta laguntzak honako pertsona hauei ematea (bakoitzari ondoan duen diru-zenbatekoa emango zaio):\nA modalitatea (Atzerriko zentroetan ikastaroak eta egonaldiak egiteko bekak eta laguntzak).\n(Ikus .PDF)\nB modalitatea (Osasunaren arloan ikerketako eta berrikuntzako prestakuntza-planak gauzatzeko bekak eta laguntzak).\n(Ikus .PDF)\nBigarrena. Honako eskatzaile hauei laguntza ukatzea; arrazoiak ere ematen dira:\nA modalitatea:\nCanales Arrasate, Maria Isabel.\nGalnares Cordero, Lorea.\nGómez Coloma, Alexander.\nLago García, Violeta.\nLlano Castresana, Oihane.\nMorales González, M.ª Celia.\nMorales López, Julio Ulises.\nMovilla Fernández, Soraya.\nSanta Maria-Amurrio Alustiza, Lander.\nUrdangarin Zumeta, Nerea.\nB modalitatea:\nAnton Ladislao, Ane.\nAurtenetxe Saez, Olaia.\nUrdampilleta Otegui, Aritz.\nBekak jaso nahi dituzten jarduerak deialdi-aginduko 1. artikuluan diru-laguntzetarako zehaztutako eremuan ez barne-hartuta egoteagatik (atzerrian egingo ez diren ikastaroak eta egonaldiak).\nB modalitatea:\nMaortua Olabe, Hiart.\nInteresunak berak berariaz uko egiteagatik.\nA modalitatea:\nAmo Herrero, Laura.\nOlabarria Larizgoitia, Markel.',
'Ebazpena Euskal Herriko Agintaritzaren Aldizkarian argitaratuko da, eta, denek jakin dezaten, www.elankidetza.euskadi.net eta www.euskadi.net orrietan argitaratuko da beken esleipendunen zerrenda eta haien',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7207, 0.3766],
# [0.7207, 1.0000, 0.3225],
# [0.3766, 0.3225, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `multilingual-e5-large`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8544** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 19,544 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 11 tokens</li><li>mean: 25.03 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 100 tokens</li><li>mean: 308.39 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Nork izendatu du Osakidetzako Teknikari Espezialista sanitarioen lanbide-taldeko lekualdatze-lehiaketa kalifikatuko duen epaimahaia?</code> | <code>Osakidetza-Euskal osasun zerbitzuko zuzendari nagusiaren azaroaren 10eko 1401/2017 Ebazpenaren bidez, Teknikari Espezialista sanitarioen lanbide-taldeko lekualdatze-lehiaketa deitu zen. Ebazpena 2017ko azaroaren 28ko EHAAn (227. zenbakia) argitaratu zen.<br>Aurreko paragrafoan aipatutako ebazpenaren 7. oinarrian aurreikusitakoaren bat etorriz, Osakidetzako zuzendari nagusiak aipatutako lekualdatze-lehiaketa kalifikatuko duen epaimahaia izendatuko du.<br>Horiek guztiak horrela, eta indarrean dagoen legedian pertsonaleko gaietan aitortuta ditudan eskumenez baliatuta, honako hau<br>EBAZTEN DUT<br>:<br>Lehenengoa. Osakidetza-Euskal osasun zerbitzuko zuzendari nagusiaren azaroaren 10eko 1401/2017 Ebazpenaren bidez deitutako Teknikari Espezialista sanitarioen lanbide-taldeko lekualdatze-lehiaketako epaimahai kalifikatzailea izendatzea. Epaimahaia osatuko duten kideak ebazpen honen I. eranskinean jasotzen dira.<br>Bigarrena. Ebazpen honek duen egunetik aurrera emango ditu ondorioak.<br>Hirugarrena. Ebazpen hau EH...</code> |
| <code>Zein urtetan sortu zen UPyD alderdia?</code> | <code>[TOPIC: Galdera, Gorka Maneiro Labayen Mistoa-UPyD taldeko legebiltzarkideak lehendakariari egina, euskal unibertsitate publikoan ETAko kideen espediente faltsuak izateari buruz]<br>[MANEIRO LABAYEN, (Mixto-UPyD)]:<br>nor izango zen–, EHUk unibertsitate-tituluak eman ahal izan zitzan. Kontu honek, hain zuzen, irismen handia du, eta ibilbide luzea izango du, eta guk nahi dugu Legebiltzar honetan ere bidea egin dezan. Beraz, ni neu jarriko naiz harremanetan Legebiltzar honetako talde demokratikoekin ikerketa-batzorde bat abiarazten saiatzeko, zehazki jakin dezagun zer gertatu den EHUn. Albiste oso biribilak izango dituzu laster, sailburu andrea. Erabateko eskandalua da. (Date: 24.04.2015)</code> |
| <code>Arrasateko ospitale berrian erresonantzia magnetikoetarako unitate bat irekitzea aurreikusita al dago?</code> | <code>[TOPIC: Galdera, Rebeka Ubera Aranzeta EH Bildu taldeko legebiltzarkideak Osasuneko sailburuari egina, Arrasateko ospitalean erresonantzia magnetikoetarako unitatea irekitzeari buruz]<br>[UBERA ARANZETA, (EH Bildu)]:<br>ospitale berriaren jarduera ez dela behar bestekoa; aktibitatea, errendimendua, jaitsi egin dela. Eta iruditzen zaigu pena dela. Ospitale berri bat daukagu, ikaragarrizko espazioak, probetxu handiagoa atera ahal zaienak, eta ulertezina iruditzen zaigu oraindik ere kamioi bat edukitzea bertan erresonantzia magnetikoak egiteko. Uler genezake trantsizio-fase batean kamioia erabiltzea, baina momentu honetan nahiko ulertezina egiten zaigu, ospitale berria hor daukagunean. Iruditzen zaigu dauden espazioak aprobetxatu daitezkeela unitate bat irekitzeko (Date: 29.05.2015)</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 19,560 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 24.98 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 98 tokens</li><li>mean: 312.22 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 148.12 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Zein da LABen iritzia euskal sindikalismoaren egoerari buruz?</code> | <code>Erronka handiak direla onartu du LABek, are gehiago munduan inposatzen ari diren «errezeta neoliberal eta autoritarioak» kontuan hartuta, baina, sindikatuaren iritziz, Euskal Herrian bada beste norabidean jotzeko moduko «orube» sindikal, sozial eta politikorik. Aldaketa sozialaren eta burujabetasunaren alde dauden eragileen arteko «euskal agenda sozial partekatua» beharrezkoa dela nabarmendu du, «estatuekiko» autonomoa izango dena.<br><br>30 urte dira lan esparru propioaren aldeko lehe manifestazioa egin zenetik, eta, LABen ustez, gaiak «zentralitate» osoa merezi du oraindik<br><br>Hori bai, jakina da LABen eta ELAren arteko harremanak garai txarrak bizi dituela, ez duela 30 urte bezala. Gauzak hala, LABen ustez, agenda horren osaketan oinarrizko printzipio bat lortu beharko liratekeela uste du, mugimendu feministaren ekimenez zaintzaren inguruan proposatu den herri ekimenarekin eta lan istripuei erantzuteko elkarlanarekin gertatu bezala.<br><br>«Elkarlan sendoago» baten premia<br><br>Agirian, LABek euskal si...</code> | <code>Azaldu duenez, lan munduan izandako aldaketek sindikalismoaren krisia ekarri dute, eta pitzadurak eragin ditu sindikatuen botere iturrietan.</code> |
| <code>Zer neurri hartu ditu Txinako gobernuak Pekingo zarata eta nahasmena murrizteko?</code> | <code>Munduko bigarren potentziaren hiriburua aldatzen<br><br>Pekinera iritsi berri diren atzerritarren lehen inpresioa sarritan bera izaten da: kontrastea, kaosa; etxe orratz modernoenak kale batean eta metro gutxi barnerago pisu bakar bateko etxe zahar tradizionalak; Porsche garestienak, eta ondoko kalean bat-batean oilar bat. Herritarrentzat hori ondo dago, baina gobernuak uste du munduko bigarren potentziaren hiriburua garbitzeko ordua iritsi dela, eta azken urteotan zarata eta nahasmen hori guztia ezabatu nahian ari dira.<br><br>Su artifizialen debekua eman diren hamarnaka pausoetako bat besterik ez da. Zerrenda luzea da: kaleak poliziaz bete dituzte eroen moduan gidatzen duten motor elektrikoak kontrolatzeko; kalean inprobisatuta ireki ziren jatetxe eta denda txiki horiek guztiak itxi dituzte, segurtasun neurriak betetzen ez dituzten milaka eraikin bota dituzte.<br><br>Hartu diren erabaki asko garrantzitsuak izan dira bizi kalitatea hobetzeko, baina bide horretan kolore batzuk galdu dira eta urte berria...</code> | <code>Pekingo gobernuak adierazi du interes tasak murrizteko gaitasuna baduela oraindik, halakorik egitea beharrezkoa balitz.</code> |
| <code>Zein da Eusko Jaurlaritzaren jarrera Forondako aireportua bultzatzeari dagokionez?</code> | <code>[TOPIC: Galdera, Igor López de Munain Ganuza EH Bildu taldeko legebiltzarkideak Ingurumen eta Lurralde Politikako sailburuari egina, Forondako aireportua bultzatzeari buruz]<br>[LÓPEZ DE MUNAIN GANUZA, (EH Bildu)]:<br>gain erantzukizuna Madrilena dela eta haiek ez dutela borondaterik? Hemen akordio bat sinatu genuen alderdi politiko guztiok. Zer ari zarete egiten akordio horri dagokionez? Pasatuko zarete hitzetatik ekintzetara? Hartuko duzue Foronda lehentasun gisa, beste helburu batzuk, hala nola abiadura handiko trena eraikitzea, zuen agenda politikotik kanpo utzita? Emango diozue lehentasuna jada existitzen denari, non langileak baitaude jada, non AHTa 2019rako ez eraikitzeagatik galduko genukeena baino askoz gehiago gal (Date: 13.02.2015)</code> | <code>[TOPIC: Interpelazioa, Rebeka Ubera Aranzeta EH Bildu taldeko legebiltzarkideak Hezkuntzako sailburuari egina, Haurreskolak partzuergoan doakotasunaren bidean 18.000 euro baino gutxiagoko familiei doakotasuna ezartzeari buruz]<br>[UBERA ARANZETA, (EH Bildu)]:<br>igo duzue (0,7), baina ez zarete iritsi 2012ko inbertsiomailara ere. Ikustea besterik ez dago zer eskaintza egin duzuen: murrizketak, murrizketak eta murrizketak, etengabeak. Aipatu dituzun beste neurri horiek… Begira, bekak aipatu dituzu. Bekak, ez Haurreskolak partzuergoan, bekak ikasmaterialari dagokionez, eta gaur aldarrikatu duzun proposamena EH Bilduk ekarri du. EH Bilduk ekarri du behin eta berriro etxe honetara, eta azkenean lortu dugu zuek onartzea. Zuek ez zarete gai (Date: 11.05.2018)</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | multilingual-e5-large_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------------:|
| 0.0409 | 100 | 0.6368 | - | - |
| 0.0819 | 200 | 0.0731 | - | - |
| 0.1228 | 300 | 0.036 | - | - |
| 0.1637 | 400 | 0.0338 | - | - |
| 0.2047 | 500 | 0.0197 | - | - |
| 0.2456 | 600 | 0.0208 | - | - |
| 0.2865 | 700 | 0.0266 | - | - |
| 0.3275 | 800 | 0.035 | - | - |
| 0.3684 | 900 | 0.0306 | - | - |
| 0.4093 | 1000 | 0.046 | - | - |
| 0.4503 | 1100 | 0.0392 | - | - |
| 0.4912 | 1200 | 0.0233 | - | - |
| 0.5321 | 1300 | 0.0273 | - | - |
| 0.5731 | 1400 | 0.0148 | - | - |
| 0.6140 | 1500 | 0.0335 | - | - |
| 0.6549 | 1600 | 0.0162 | - | - |
| 0.6959 | 1700 | 0.0325 | - | - |
| 0.7368 | 1800 | 0.0149 | - | - |
| 0.7777 | 1900 | 0.0144 | - | - |
| 0.8187 | 2000 | 0.0186 | - | - |
| 0.8596 | 2100 | 0.018 | - | - |
| 0.9005 | 2200 | 0.0268 | - | - |
| 0.9415 | 2300 | 0.0114 | - | - |
| 0.9824 | 2400 | 0.0174 | - | - |
| 1.0 | 2443 | - | 0.4571 | 0.8350 |
| 1.0233 | 2500 | 0.0111 | - | - |
| 1.0643 | 2600 | 0.0103 | - | - |
| 1.1052 | 2700 | 0.0166 | - | - |
| 1.1461 | 2800 | 0.0058 | - | - |
| 1.1871 | 2900 | 0.0091 | - | - |
| 1.2280 | 3000 | 0.0059 | - | - |
| 1.2689 | 3100 | 0.0129 | - | - |
| 1.3099 | 3200 | 0.0136 | - | - |
| 1.3508 | 3300 | 0.0091 | - | - |
| 1.3917 | 3400 | 0.0129 | - | - |
| 1.4327 | 3500 | 0.0133 | - | - |
| 1.4736 | 3600 | 0.0075 | - | - |
| 1.5145 | 3700 | 0.0105 | - | - |
| 1.5555 | 3800 | 0.01 | - | - |
| 1.5964 | 3900 | 0.0113 | - | - |
| 1.6373 | 4000 | 0.0135 | - | - |
| 1.6783 | 4100 | 0.011 | - | - |
| 1.7192 | 4200 | 0.0116 | - | - |
| 1.7601 | 4300 | 0.0068 | - | - |
| 1.8011 | 4400 | 0.0104 | - | - |
| 1.8420 | 4500 | 0.0055 | - | - |
| 1.8829 | 4600 | 0.0066 | - | - |
| 1.9239 | 4700 | 0.0055 | - | - |
| 1.9648 | 4800 | 0.0103 | - | - |
| 2.0 | 4886 | - | 0.4690 | 0.8319 |
| 2.0057 | 4900 | 0.0045 | - | - |
| 2.0467 | 5000 | 0.0061 | - | - |
| 2.0876 | 5100 | 0.0044 | - | - |
| 2.1285 | 5200 | 0.0045 | - | - |
| 2.1695 | 5300 | 0.01 | - | - |
| 2.2104 | 5400 | 0.0046 | - | - |
| 2.2513 | 5500 | 0.0057 | - | - |
| 2.2923 | 5600 | 0.0023 | - | - |
| 2.3332 | 5700 | 0.0069 | - | - |
| 2.3741 | 5800 | 0.0068 | - | - |
| 2.4151 | 5900 | 0.0019 | - | - |
| 2.4560 | 6000 | 0.0124 | - | - |
| 2.4969 | 6100 | 0.0028 | - | - |
| 2.5379 | 6200 | 0.0066 | - | - |
| 2.5788 | 6300 | 0.0038 | - | - |
| 2.6197 | 6400 | 0.0039 | - | - |
| 2.6607 | 6500 | 0.0042 | - | - |
| 2.7016 | 6600 | 0.0029 | - | - |
| 2.7425 | 6700 | 0.0024 | - | - |
| 2.7835 | 6800 | 0.0007 | - | - |
| 2.8244 | 6900 | 0.0011 | - | - |
| 2.8653 | 7000 | 0.0027 | - | - |
| 2.9063 | 7100 | 0.0036 | - | - |
| 2.9472 | 7200 | 0.0019 | - | - |
| 2.9881 | 7300 | 0.005 | - | - |
| 3.0 | 7329 | - | 0.3922 | 0.8544 |
### Framework Versions
- Python: 3.10.8
- Sentence Transformers: 5.1.0
- Transformers: 4.56.0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
rbelanec/train_cola_789_1757596124
|
rbelanec
| 2025-09-11T14:54:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lntuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:07:08Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lntuning
- generated_from_trainer
model-index:
- name: train_cola_789_1757596124
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. -->
# train_cola_789_1757596124
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cola dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1651
- Num Input Tokens Seen: 3663512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 789
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.0727 | 0.5 | 962 | 0.2056 | 182656 |
| 0.28 | 1.0 | 1924 | 0.1750 | 365728 |
| 0.2319 | 1.5 | 2886 | 0.2058 | 548992 |
| 0.1678 | 2.0 | 3848 | 0.1835 | 731984 |
| 0.068 | 2.5 | 4810 | 0.2135 | 915792 |
| 0.4099 | 3.0 | 5772 | 0.1894 | 1098920 |
| 0.043 | 3.5 | 6734 | 0.1944 | 1281640 |
| 0.0726 | 4.0 | 7696 | 0.1651 | 1465464 |
| 0.1162 | 4.5 | 8658 | 0.1846 | 1649720 |
| 0.0194 | 5.0 | 9620 | 0.1789 | 1831920 |
| 0.0803 | 5.5 | 10582 | 0.1859 | 2014928 |
| 0.2613 | 6.0 | 11544 | 0.1869 | 2198176 |
| 0.1435 | 6.5 | 12506 | 0.1877 | 2381440 |
| 0.1227 | 7.0 | 13468 | 0.1890 | 2564952 |
| 0.288 | 7.5 | 14430 | 0.1912 | 2748568 |
| 0.2387 | 8.0 | 15392 | 0.1974 | 2931096 |
| 0.0504 | 8.5 | 16354 | 0.1940 | 3113624 |
| 0.0763 | 9.0 | 17316 | 0.1961 | 3296808 |
| 0.0386 | 9.5 | 18278 | 0.1963 | 3480168 |
| 0.094 | 10.0 | 19240 | 0.1969 | 3663512 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rbelanec/train_cola_789_1757596121
|
rbelanec
| 2025-09-11T14:53:47Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:05:34Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_cola_789_1757596121
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. -->
# train_cola_789_1757596121
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cola dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1857
- Num Input Tokens Seen: 3663512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 789
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|
| 0.1364 | 0.5 | 962 | 0.2231 | 182656 |
| 0.241 | 1.0 | 1924 | 0.1857 | 365728 |
| 0.2613 | 1.5 | 2886 | 0.2296 | 548992 |
| 0.3619 | 2.0 | 3848 | 0.2091 | 731984 |
| 0.0575 | 2.5 | 4810 | 0.2246 | 915792 |
| 0.5262 | 3.0 | 5772 | 0.2300 | 1098920 |
| 0.1518 | 3.5 | 6734 | 0.2180 | 1281640 |
| 0.1225 | 4.0 | 7696 | 0.2018 | 1465464 |
| 0.2075 | 4.5 | 8658 | 0.2135 | 1649720 |
| 0.023 | 5.0 | 9620 | 0.2038 | 1831920 |
| 0.237 | 5.5 | 10582 | 0.2079 | 2014928 |
| 0.3227 | 6.0 | 11544 | 0.2203 | 2198176 |
| 0.1221 | 6.5 | 12506 | 0.2235 | 2381440 |
| 0.171 | 7.0 | 13468 | 0.2170 | 2564952 |
| 0.3842 | 7.5 | 14430 | 0.2183 | 2748568 |
| 0.3918 | 8.0 | 15392 | 0.2206 | 2931096 |
| 0.1045 | 8.5 | 16354 | 0.2195 | 3113624 |
| 0.0791 | 9.0 | 17316 | 0.2215 | 3296808 |
| 0.0553 | 9.5 | 18278 | 0.2204 | 3480168 |
| 0.2145 | 10.0 | 19240 | 0.2197 | 3663512 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
hyunjoonkang/sim_pick_and_place_DAVLA_1
|
hyunjoonkang
| 2025-09-11T14:52:00Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:hyunjoonkang/wx250s_sim_pick_and_place_1",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-11T14:51:46Z |
---
base_model: lerobot/smolvla_base
datasets: hyunjoonkang/wx250s_sim_pick_and_place_1
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
jalkafariya/blockassist-bc-stealthy_hoarse_toucan_1757602258
|
jalkafariya
| 2025-09-11T14:51:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy hoarse toucan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:51:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy hoarse toucan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vendi11/blockassist-bc-placid_placid_llama_1757602224
|
vendi11
| 2025-09-11T14:51:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:51:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ahumadaxhg/blockassist-bc-alert_spotted_dolphin_1757602232
|
ahumadaxhg
| 2025-09-11T14:50:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert spotted dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:50:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert spotted dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
priyankajugwa/blockassist-bc-exotic_frisky_ostrich_1757602197
|
priyankajugwa
| 2025-09-11T14:50:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"exotic frisky ostrich",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:50:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- exotic frisky ostrich
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_copa_101112_1757596165
|
rbelanec
| 2025-09-11T14:49:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"p-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:46:05Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- p-tuning
- generated_from_trainer
model-index:
- name: train_copa_101112_1757596165
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. -->
# train_copa_101112_1757596165
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9577
- Num Input Tokens Seen: 281312
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 101112
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.2006 | 0.5 | 45 | 0.1967 | 14144 |
| 0.3225 | 1.0 | 90 | 0.0856 | 28192 |
| 0.4327 | 1.5 | 135 | 0.0478 | 42208 |
| 0.0202 | 2.0 | 180 | 0.0775 | 56256 |
| 0.1742 | 2.5 | 225 | 0.0552 | 70368 |
| 0.0049 | 3.0 | 270 | 0.0273 | 84320 |
| 0.0011 | 3.5 | 315 | 0.0583 | 98400 |
| 0.0018 | 4.0 | 360 | 0.0332 | 112416 |
| 0.0013 | 4.5 | 405 | 0.0406 | 126496 |
| 0.0002 | 5.0 | 450 | 0.0364 | 140544 |
| 0.0001 | 5.5 | 495 | 0.0473 | 154592 |
| 0.0001 | 6.0 | 540 | 0.0446 | 168768 |
| 0.0001 | 6.5 | 585 | 0.0423 | 182848 |
| 0.0 | 7.0 | 630 | 0.0465 | 196896 |
| 0.0 | 7.5 | 675 | 0.0435 | 210912 |
| 0.0 | 8.0 | 720 | 0.0428 | 225024 |
| 0.0 | 8.5 | 765 | 0.0453 | 239200 |
| 0.0 | 9.0 | 810 | 0.0443 | 253152 |
| 0.0 | 9.5 | 855 | 0.0495 | 267040 |
| 0.0 | 10.0 | 900 | 0.0484 | 281312 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
herculesnode/blockassist-bc-insectivorous_bold_lion_1757602129
|
herculesnode
| 2025-09-11T14:49:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:49:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lm8779694/blockassist-bc-wily_squeaky_mule_1757602142
|
lm8779694
| 2025-09-11T14:49:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wily squeaky mule",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:49:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wily squeaky mule
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_copa_101112_1757596164
|
rbelanec
| 2025-09-11T14:46:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:43:32Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_copa_101112_1757596164
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. -->
# train_copa_101112_1757596164
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0768
- Num Input Tokens Seen: 281312
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 101112
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.081 | 0.5 | 45 | 0.1215 | 14144 |
| 0.3339 | 1.0 | 90 | 0.1037 | 28192 |
| 0.0463 | 1.5 | 135 | 0.0964 | 42208 |
| 0.082 | 2.0 | 180 | 0.0777 | 56256 |
| 0.2678 | 2.5 | 225 | 0.0822 | 70368 |
| 0.0878 | 3.0 | 270 | 0.0920 | 84320 |
| 0.1495 | 3.5 | 315 | 0.1532 | 98400 |
| 0.0075 | 4.0 | 360 | 0.0768 | 112416 |
| 0.429 | 4.5 | 405 | 0.1562 | 126496 |
| 0.0002 | 5.0 | 450 | 0.1207 | 140544 |
| 0.0092 | 5.5 | 495 | 0.1345 | 154592 |
| 0.002 | 6.0 | 540 | 0.1524 | 168768 |
| 0.0064 | 6.5 | 585 | 0.1678 | 182848 |
| 0.0449 | 7.0 | 630 | 0.1447 | 196896 |
| 0.1323 | 7.5 | 675 | 0.1635 | 210912 |
| 0.0001 | 8.0 | 720 | 0.2237 | 225024 |
| 0.0211 | 8.5 | 765 | 0.2088 | 239200 |
| 0.0121 | 9.0 | 810 | 0.2073 | 253152 |
| 0.0034 | 9.5 | 855 | 0.2088 | 267040 |
| 0.1445 | 10.0 | 900 | 0.2092 | 281312 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
vendi11/blockassist-bc-placid_placid_llama_1757601868
|
vendi11
| 2025-09-11T14:45:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:45:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bunnycore/Qwen3-4B-Max-Ties
|
bunnycore
| 2025-09-11T14:44:48Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"merge",
"mergekit",
"lazymergekit",
"janhq/Jan-v1-2509",
"huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated",
"minchyeom/Qwaifu",
"base_model:huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated",
"base_model:merge:huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated",
"base_model:janhq/Jan-v1-2509",
"base_model:merge:janhq/Jan-v1-2509",
"base_model:minchyeom/Qwaifu",
"base_model:merge:minchyeom/Qwaifu",
"region:us"
] | null | 2025-09-11T14:42:28Z |
---
base_model:
- janhq/Jan-v1-2509
- huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
- minchyeom/Qwaifu
tags:
- merge
- mergekit
- lazymergekit
- janhq/Jan-v1-2509
- huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
- minchyeom/Qwaifu
---
# Qwen3-4B-Max-Ties
Qwen3-4B-Max-Ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [janhq/Jan-v1-2509](https://huggingface.co/janhq/Jan-v1-2509)
* [huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated)
* [minchyeom/Qwaifu](https://huggingface.co/minchyeom/Qwaifu)
## 🧩 Configuration
```yaml
models:
- model: janhq/Jan-v1-2509
parameters:
density: 0.2
weight: 0.2
- model: huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
parameters:
density: 0.5
weight: 0.5
- model: minchyeom/Qwaifu
parameters:
density: 0.3
weight: 0.3
merge_method: ties
base_model: janhq/Jan-v1-2509
parameters:
normalize: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "bunnycore/Qwen3-4B-Max-Ties"
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"])
```
|
mradermacher/mcp-instruct-v1-GGUF
|
mradermacher
| 2025-09-11T14:43:40Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"lfm2",
"en",
"base_model:yasserrmd/mcp-instruct-v1",
"base_model:quantized:yasserrmd/mcp-instruct-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-11T13:29:54Z |
---
base_model: yasserrmd/mcp-instruct-v1
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- lfm2
---
## 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/yasserrmd/mcp-instruct-v1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#mcp-instruct-v1-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/mcp-instruct-v1-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/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q2_K.gguf) | Q2_K | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q3_K_S.gguf) | Q3_K_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q3_K_M.gguf) | Q3_K_M | 0.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q3_K_L.gguf) | Q3_K_L | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.IQ4_XS.gguf) | IQ4_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q4_K_S.gguf) | Q4_K_S | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q5_K_S.gguf) | Q5_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q5_K_M.gguf) | Q5_K_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q6_K.gguf) | Q6_K | 1.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.Q8_0.gguf) | Q8_0 | 1.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/mcp-instruct-v1-GGUF/resolve/main/mcp-instruct-v1.f16.gguf) | f16 | 2.4 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
andrzej-buchowicz/distilbert-base-uncased-finetuned-imdb
|
andrzej-buchowicz
| 2025-09-11T14:42:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-09-11T14:31:42Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4972
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 192
- eval_batch_size: 192
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 53 | 2.5202 |
| No log | 2.0 | 106 | 2.4841 |
| 2.6553 | 3.0 | 159 | 2.4947 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.0
|
AnerYubo/blockassist-bc-elusive_mammalian_termite_1757601743
|
AnerYubo
| 2025-09-11T14:42:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive mammalian termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:42:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive mammalian termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-snappy_tenacious_eagle_1757601736
|
AnerYubo
| 2025-09-11T14:42:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snappy tenacious eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:42:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snappy tenacious eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-fanged_camouflaged_cassowary_1757601732
|
AnerYubo
| 2025-09-11T14:42:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fanged camouflaged cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:42:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fanged camouflaged cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NahedDom/blockassist
|
NahedDom
| 2025-09-11T14:40:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T06:04:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ruebkdabbshwt/blockassist-bc-armored_pale_fish_1757601605
|
ruebkdabbshwt
| 2025-09-11T14:40:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored pale fish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:40:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored pale fish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
holsombackpatrina/blockassist-bc-shy_armored_chimpanzee_1757601581
|
holsombackpatrina
| 2025-09-11T14:39:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shy armored chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:39:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shy armored chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1757600049
|
helmutsukocok
| 2025-09-11T14:39:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:39:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vendi11/blockassist-bc-placid_placid_llama_1757601514
|
vendi11
| 2025-09-11T14:39:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:39:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hbfc7671/blockassist-bc-mighty_small_fox_1757601479
|
hbfc7671
| 2025-09-11T14:38:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mighty small fox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:38:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mighty small fox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
anabury/lora_model
|
anabury
| 2025-09-11T14:38:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-11T14:37:49Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Anabury
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
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)
|
cintroncdgkq/blockassist-bc-monstrous_whistling_dinosaur_1757601456
|
cintroncdgkq
| 2025-09-11T14:37:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous whistling dinosaur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:37:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous whistling dinosaur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vicky240922222/pubmedbert-gpt2-biomedical
|
Vicky240922222
| 2025-09-11T14:37:23Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-09-11T14:34:12Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: pubmedbert-gpt2-biomedical
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. -->
# pubmedbert-gpt2-biomedical
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
toruns/blockassist-bc-insectivorous_bold_lion_1757601393
|
toruns
| 2025-09-11T14:37:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:36:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aimarsg/bernat_all_domains_contrastive
|
aimarsg
| 2025-09-11T14:36:23Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:19544",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:HiTZ/BERnaT_base",
"base_model:finetune:HiTZ/BERnaT_base",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-11T14:36:11Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:19544
- loss:MultipleNegativesRankingLoss
base_model: HiTZ/BERnaT_base
widget:
- source_sentence: Zenbat diru izango du Nafarroako Gobernuak gastuetarako eta inbertsio
publikoetarako?
sentences:
- 'Nafarroako aurrekontuen eztabaidan 355 zuzenketa partzial onartu dira
Bost egunez eztabaidatu ostean, amaitu dute Nafarroako Parlamentuko Ekonomia eta
Ogasun batzordean aurtengo aurrekontuen inguruko eztabaida. Orotara, 983 zuzenketa
partzial aurkeztu dira, eta horietatik 355 onartu dituzte PSNk, EH Bilduk, Geroa
Baik eta Zurekin Nafarroak. 17,2 milioi euroko egitasmoak finantzatuko dituzte
aurrera ateratako diru sail horiek.
UPNk 290 zuzenketa aurkeztu zituen, eta PPk 223, baina gobernuko bazkideek eta
EH Bilduk euren gehiengoa baliatu, eta atzera bota dituzte. Vox eskuin muturreko
taldeak ez du zuzenketarik aurkeztu.
Zuzenketak gehituta, 6.355 milioi euroko aurrekontua izango du gobernuak; gastu
muga, berriz, 5.836 milioi eurokoa izango da, aurreko urtekoa baino %10,5 handiagoa.
Udalen gastu arrunta finantzatzeko Tokiko Ogasunen Funtsako dirua eta estatuari
bere gain hartu gabeko eskumenen truke eman beharreko ekarpena gehituta, gobernuak
4.684 milioi euro izango ditu gastuetarako zein inbertsio publikoetarako.
Eztabaida amaituta, azken urratsa baino ez da falta. Datorren osteguneko osoko
bilkuran onartuko da legea. Urtero abendu amaieran eztabaidatu ohi dira aurrekontuen
lege proiektuak, baina aurtengo eztabaida hiru hilabete atzeratu da, gobernua
abuztu erdialdera abiatu zelako. Lege proiektua urtarrilaren 19an erregistratu
zen legebiltzarrean, kasurako.
Onartutako zuzenketak'
- 'Preso bat hil da Basauriko espetxean
Preso bat hil da Basauriko espetxean (Bizkaia). Atzo aurkitu zuten bere ziegan
hilda. Salhaketa elkarteak atzo eman zuen heriotzaren berri, eta Eusko Jaurlaritzak
gaur baieztatu dio BERRIAri. Oraindik autopsiarik egin ez duten arren, Jaurlaritzako
iturriek adierazi dute «dauden zantzuen arabera» balitekeela heriotzaren arrazoia
suizidioa izatea. Hil den presoaren nortasunaren inguruko daturik ez dute eman,
baina nabarmendu dute ikerketa judiziala zabalik dagoela eta protokolo guztiak
martxan daudela.
Salhaketak Eusko Jaurlaritzari eskatu dio azalpenak eman ditzala eta heriotza
iker dezala. Gardentasuna eskatu dio elkarteak Jaurlaritzari: «Eusko Jaurlaritzak
espetxe eskumena jaso berritan, informazioa ematen zuten, datuen babesa errespetatuta.
Azkenaldian ikusten dugu Jaurlaritzak ez duela inolako informaziorik ematen, ezta
baloraziorik egiten ere. Horrek kezkatzen gaitu».
2021eko urrian Eusko Jaurlaritzak espetxeen eskumena jaso zuenetik, zazpi preso
hil dira guztira Basaurin, Martutenen eta Zaballan. Maria Jesus San Jose Eusko
Jaurlaritzako Justizia sailburu berriarekin bilera bat egiteko eskatu du Salhaketak.
Gaineratu du presoen hiru laurdenek buruko gaitzak dituztela, eta presoen, funtzionarioen
eta hirugarren sektoreko elkarteen arteko elkarlana bultzatu behar dela buruko
osasunaren arazoari aurre egiteko. Jaurlaritzari eskatu dio suizidioen aurkako
protokoloak «azkarrago» martxan jartzeko, zalantza baitu behar bezain laster egiten
ote den gaur egun.
«Jaurlaritzari eskatzen diogu hildakoaren senideei eta gertukoei informazioa eman
diezaiela martxan jar litezkeen ekintza judizialetan parte hartzeko dituzten aukerei
buruz, datuak argitzeko», erantsi du Salhaketak.'
- 'Aldiz, AUVE Auto elektrikoen erabiltzaileen Espainiako elkarteko Mikel Agirregabiriaren
ustez, ibilgailu elektrikoen aseguruak ez dira garestiagoak, eta berak hibrido
batetik elektrikora aldatu zenean asegurua «gutxiago» kostatu zitzaiola azaldu
du.
Hala ere, Agirregabiriak onartu du elektrikoen etxe batzuetan arazoak izan direla,
baina «ez bateriarekin, ezta motorrarekin ere». Horiek izaten baitira elektrikoei
aldatu behar zaizkien pieza garestienak, eta aseguruen primak igotzen dituztenak.
«Elektrikoak bideragarriagoak dira, eta matxura gutxiago dituzte. Garatutako merkatuetan
hala egiaztatu da», azaldu du Agirregabiriak.
« [Konponketen kostua] Faktore erabakigarria da justifikatzeko auto elektrikoen
aseguruek hibridoekiko eta, are gehiago, errekuntzakoekiko duten gainprezioa».
LEIRE ALBERDI Euskal Kontsumitzaileen Antolaketako kidea
Hibridoen aseguruak garestiagoak izatea «zentzuzkoa» iruditzen zaio Agirregabiriari,
bi motor edukitzean matxurak izateko «bi iturri» dauzkatelako, eta horregatik
ez ditu gomendatzen.
Auto elektrikoen aseguruak garestiagoak direla esatea «gezurra» dela uste du,
eta ibilgailu elektrikoen inguruan dagoen «intoxikazio ugarietako bat» dela esan
du. Ez du uste intoxikazio hori aseguru etxeena denik, ibilgailuen ohiko marketatik
datozela dio, ezin baitute elektrikoetarako aldaketa «saihetsezinari» aurka egin.
OCUrentzat ere auto elektrikoak dira «etorkizuna», eta, horregatik, elektrikoen
erabiltzaileentzat «azpiegitura egokiak garatzea» eskatzen dute «benetako alternatiba
bat» izan daitezen.'
- source_sentence: Zein da epea Donostialdeko ESIko zuzendari-gerenteak emandako ebazpen
honen aurka gora jotzeko errekurtsoa aurkezteko?
sentences:
- 'Errekurtsoak.
39/2015 Legeak 121 eta 122 artikuluetan jasotakoaren arabera, ebazpen honen kontra
gora jotzeko errekurtsoa jarri ahal da, Osakidetzako zuzendari nagusiari zuzendua.'
- '[TOPIC: Galdera, Vicente Reyes Martín Euskal Sozialistak taldeko legebiltzarkideak
lehendakariari egina, Guggenheim Bilbao Museoa Fundazioaren Patronatuak Solomon
R. Guggenheim Fundazioarekin izenpetutako hitzarmenaren inguruan]
[REYES MARTÍN, (SV-ES)]:
ez da esan. Are gehiago, akordio honi buruz prentsaurreko batean oraintsu hitz
egin denean –Legebiltzarrera oraindik ez da iritsi akordioa; espero dut lehenbailehen
iristea, hura aztertu ahal izateko–, zuk eta Bizkaiko ahaldun nagusiak sutsuki
erantzun zenioten, hiru egunkariren arabera, gastuari buruzko informazio-eskaerari,
irain bat izan balitz bezala. Hiru egunkariek diote. Eta gauza batzuk ez dira
bete. Nik ezin dut akordioa eduki, zuk bezala, ikusteko zer bete den, baina (Date:
12.12.2014)'
- 'Laugarrena. Interesdunari eta Donostialdeko ESIari Ebazpen honen berri ematea.
Bosgarrena. Ebazpen honen aurka gora jotzeko errekurtsoa aurkez dakioke Osakidetzako
zuzendari nagusiari. Horretarako, hilabeteko epea izango da, Ebazpena Euskal Herriko
Agintaritzaren Aldizkarian argitaratu eta hurrengo egunetik aurrera.
Donostia, 2021eko ekainaren 14a.
Donostialdeko ESIko zuzendari-gerentea.
EB (53/2019 Ebazpena, urtarrilaren 24koa).
Langileen zuzendaria,
ESTHER LITAGO SOLA.'
- source_sentence: Zein da EP-IUren jarrera Elkarrizketa Sozialerako Mahaian jorratu
beharreko gaiei buruz?
sentences:
- '[TOPIC: EH Bildu talde parlamentarioak egindako legez besteko proposamena, indarkeria
obstetrikoaren biktima izan den emakume bat erreparatzeari buruz. Eztabaida eta
behin betiko ebazpena]
[SÁNCHEZ MARTÍN, (SV-ES)]:
ez, esan genizun osoko bilkuran bertan, kontua ez da eztabaidatu nahi ez dugula,
kontua da zuek eztabaida hori hemen errepikatu nahi duzuela uneotan Diputatuen
Kongresuan ere egiten ari denean. Zuek Madrilen egiten ari den eztabaida hemen
errepikatu duzue, eztabaida hona ekarri nahi izan zenuten zer eta une batez besterik
ez bada ere oihartzuna lortzeko, eta hori guztiz absurdoa da. Guk ez genion uko
egin auziok eztabaidatzeari, zerbait guztiz ezberdina gertatu zen. (Date: 13.10.2022)'
- '[TOPIC: EH Bildu talde parlamentarioak egindako legez besteko proposamena, Lanbideren
prestakuntza-eskaintzak EAEko bi hizkuntza ofizialak berdintasunean tratatzeari
buruzko estrategia aktibatzearen inguruan. Eztabaida eta behin betiko ebazpena]
[HERNÁNDEZ HIDALGO, (EP-IU)]:
Gero, amaitzeko, azken aipamen bat egin nahi diot berriro Urrutia jaunari, negoziazioarekin
lotuta, Elkarrizketa Sozialerako Mahaiarekin zehazki. Jakina, egon badaude Elkarrizketa
Sozialerako Mahaian jorratu beharreko gaiak, nik neuk sarri defendatu baitut −askotan
entzun nauzu hemen hori esaten− auzi politikoak ere badirela, eta, horrenbestez,
bertan eta legebiltzarretan ere eztabaidatu behar direla. Bada, aztertzen ari
garen auzi zehatza, baimenen ordainsariena, kasu honetan euskara ikasteko baimenena,
aspalditik mahai (Date: 02.06.2022)'
- 'Osakidetza-Euskal osasun zerbitzuko zuzendari nagusiaren maiatzaren 15eko 1036/2015
Ebazpenaren bidez, Maitane Arrizabalaga Calzacorta andrea izendatu zen Ezkerralde-Enkarterri-Gurutzetako
ESIko Pertsonaleko Zuzendari (2015eko ekainaren 12ko EHAA, 109. zk.).
Ekainaren 26ko 8/1997 Legea, azaroaren 11ko 255/1997 Dekretua eta aplikatzekoak
diren gainerako xedapenak ikusita, Zuzendaritza Nagusi honek
EBAZTEN DU
:
Lehenengoa. Maitane Arrizabalaga Calzacorta andrea Ezkerralde-Enkarterri-Gurutzetako
ESIko Pertsonaleko Zuzendari kargutik kentzea, berak hala eskatuta, eta eskerrak
ematea egindako lanagatik.
Bigarrena. Ebazpen honek 2024ko urriaren 31ko lanaldia amaitzean izango ditu ondorioak.
Hirugarrena. Interesdunari, Ezkerralde-Enkarterri-Gurutzetako ESIari eta Plangintza
eta Kudeaketa Zuzendariordetzari ebazpen honen berri ematea.
Laugarrena. Ebazpen honen aurka gora jotzeko errekurtsoa aurkeztu ahal izango
zaio Osakidetza-Euskal osasun zerbitzuko Administrazio Kontseiluari. Horretarako
hilabeteko epea izango da, ebazpen hau Euskal Herriko Agintaritzaren Aldizkarian
argitaratu eta hurrengo egunetik aurrera.
Vitoria-Gasteiz, 2024ko urriaren 31.
Osakidetza-Euskal osasun zerbitzuko zuzendari nagusia,
SUSANA LÓPEZ ALTUNA.'
- source_sentence: Nork esan zizun aurrena uzteko?
sentences:
- '[TOPIC: Interpelazioa, José Antonio Pastor Garrido Euskal Sozialistak taldeko
legebiltzarkideak lehendakariari egina, Etxebizitzaren Legea erregelamendu bidez
garatzeari buruz]
[PASTOR GARRIDO, (SV-ES)]:
eta azkartasunari buruz. Izan ere, logikoa litzateke, zuek zuen esloganean "pertsonen
Jaurlaritza" gisa definitzen duzuen gobernu batek kontuan izango balu lege honek
pertsonen funtsezko eskubide bat betetzen duela: aterpe hartzeko bizileku bat
izatea. Guretzat, osasuna eta hezkuntza bezain eskubide garrantzitsua da hori.
Eta, beraz, eslogan hori egia bihurtu nahi baduzu, soilik horretan, eslogan batean,
gera ez dadin, eskubide hori da gaur egun Euskadiren ezaugarrietako bat politika
aurreratuari dagokionez, baina gainerako (Date: 26.06.2015)'
- 'Nork esan behar zizun, ezta?
Hala da. Azkenean nire ogibide bihurtu da, hamar urtez. Zoragarria izan da, eta
amets bat bete dut. Espero gabekoa, gainera.'
- 'Gabonetako oporren ostean argi esan nion Ramoni utzi egingo nuela. Berak esaten
zidan pena handia ematen ziola, jarraitzeko moduan ikusten ninduela. Baina ezin
zidala ezer aurpegiratu, eta ulertzen zuela. Izan ere, hark lasaiarazten ninduen
entrenamenduetan, ni nekaezina bainintzen. Baina orain kontrakoa zen. Ez nuen
indarrik aurrera jarraitzeko. Izan ere, minarekin bizitzen ikasi egiten da, baina
une oro minarekin egotea nekagarria da. Horregatik utzi dut. Behin erabakia hartuta,
barneratu, eta gertukoenei esan nien, eta iragan astean egin nuen gainerakoei
jakinarazteko urratsa.
Nori esan zenion aurrena?
Gurasoei. Hegoafrikan nengoenean, haiekin hitz egiten nuenean; jabetzen ziren
gaizki nengoela. Bueltatu bezain laster esan zidaten uzteko. Ez zuela merezi sufritzen
jarraitzeak. Kirola zela, eta disfrutatzen ez banuen ez zuela merezi. Lasaitu
handia hartu dute, erabakia hartu nuenetik gero eta hobeto ikusten nautelako,
lasaiago. Azken batean, nire esku zegoen guztia egin dut hau hain goiz eta horrela
ez amaitzeko, baita inguruan izan ditudanek ere. Oso ondo zaindu naute, baina
ez da posible izan.
«Nire esku zegoen guztia egin dut hau hain goiz eta horrela ez amaitzeko, baita
ingurukoek ere. Baina ez da posible izan»
Oraindik goiz da. Baina nolakoa gertatzen ari zaizu aipatu duzun barneratze prozesua?'
- source_sentence: Zein da osasun-arloko profesionalak prestatzeko beken eta laguntzen
deialdian eskabideak ebazteko ardura duen organoa?
sentences:
- 'Osasun sailburuaren 2011ko uztailaren 20ko aginduaren bidez (2011ko abuztuaren
12ko EHAA), osasun-arloko profesionalak prestatzeko beketarako eta laguntzetarako
deia egin zen.
Agindu horren 13. artikuluan ezartzen duenez, Kalitate, Ikerketa eta Berrikuntza
Sanitarioko sailburuordeak ebatziko ditu bekak eta laguntzak eskuratzeko eskabideak,
EHAAn argitaratutako ebazpen baten bitartez, eta betiere, Osasun eta Kontsumoko
sailburuak horretarako berariaz izendatutako Balorazio Batzordeak proposatutakoa
aintzat hartuta.
Hori guztia kontuan izanik, deialdira bildutako eskabideak aztertuta, Balorazio
Batzordeak proposatutakoa hartu da aintzakotzat, eta, horrenbestez, honako hau
EBATZI DUT
:
Lehenengoa. Osasun-arloko profesionalak prestatzeko bekak eta laguntzak honako
pertsona hauei ematea (bakoitzari ondoan duen diru-zenbatekoa emango zaio):
A modalitatea (Atzerriko zentroetan ikastaroak eta egonaldiak egiteko bekak eta
laguntzak).
(Ikus .PDF)
B modalitatea (Osasunaren arloan ikerketako eta berrikuntzako prestakuntza-planak
gauzatzeko bekak eta laguntzak).
(Ikus .PDF)
Bigarrena. Honako eskatzaile hauei laguntza ukatzea; arrazoiak ere ematen dira:
A modalitatea:
Canales Arrasate, Maria Isabel.
Galnares Cordero, Lorea.
Gómez Coloma, Alexander.
Lago García, Violeta.
Llano Castresana, Oihane.
Morales González, M.ª Celia.
Morales López, Julio Ulises.
Movilla Fernández, Soraya.
Santa Maria-Amurrio Alustiza, Lander.
Urdangarin Zumeta, Nerea.
B modalitatea:
Anton Ladislao, Ane.
Aurtenetxe Saez, Olaia.
Urdampilleta Otegui, Aritz.
Bekak jaso nahi dituzten jarduerak deialdi-aginduko 1. artikuluan diru-laguntzetarako
zehaztutako eremuan ez barne-hartuta egoteagatik (atzerrian egingo ez diren ikastaroak
eta egonaldiak).
B modalitatea:
Maortua Olabe, Hiart.
Interesunak berak berariaz uko egiteagatik.
A modalitatea:
Amo Herrero, Laura.
Olabarria Larizgoitia, Markel.'
- 'Bigarrena. Hiru urtetarako den izendapen honek interesdunari jakinarazten zaion
momentutik aurrera izango ditu ondorio ekonomikoak eta administratiboak.
Denboraldi hori igarotakoan, zuzendariak egindako kudeaketari buruz aldeko ebaluazioak
badaude, beste hiru urtez luzatu daiteke lanpostu horretan segitzea, Osakidetza-Euskal
osasun zerbitzuko zuzendari nagusiaren ebazpen bidez.
Hirugarrena. Itziar Pérez Irazusta andrea zerbitzu berezietan deklaratzea, bere
izendapena indarrean sartzen denetik.
Laugarrena. Ebazpen honen aurka gora jotzeko errekurtsoa aurkeztu ahal izango
zaio Osakidetza-Euskal osasun zerbitzuko Administrazio Kontseiluari, ebazpena
Euskal Herriko Agintaritzaren Aldizkarian argitaratu eta biharamunetik aurrera,
hilabeteko epean.
Vitoria-Gasteiz, 2012ko ekainaren 5a.
Osakidetza-Euskal osasun zerbitzuko zuzendari nagusia,
JULIÁN PÉREZ GIL.'
- Ebazpena Euskal Herriko Agintaritzaren Aldizkarian argitaratuko da, eta, denek
jakin dezaten, www.elankidetza.euskadi.net eta www.euskadi.net orrietan argitaratuko
da beken esleipendunen zerrenda eta haien
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on HiTZ/BERnaT_base
results:
- task:
type: triplet
name: Triplet
dataset:
name: multilingual e5 large
type: multilingual-e5-large
metrics:
- type: cosine_accuracy
value: 0.8654907941818237
name: Cosine Accuracy
---
# SentenceTransformer based on HiTZ/BERnaT_base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [HiTZ/BERnaT_base](https://huggingface.co/HiTZ/BERnaT_base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [HiTZ/BERnaT_base](https://huggingface.co/HiTZ/BERnaT_base) <!-- at revision 3a3213f0541d3e81acaa719636d78c261080fb29 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("aimarsg/bernat_all_domains_contrastive")
# Run inference
sentences = [
'Zein da osasun-arloko profesionalak prestatzeko beken eta laguntzen deialdian eskabideak ebazteko ardura duen organoa?',
'Osasun sailburuaren 2011ko uztailaren 20ko aginduaren bidez (2011ko abuztuaren 12ko EHAA), osasun-arloko profesionalak prestatzeko beketarako eta laguntzetarako deia egin zen.\nAgindu horren 13. artikuluan ezartzen duenez, Kalitate, Ikerketa eta Berrikuntza Sanitarioko sailburuordeak ebatziko ditu bekak eta laguntzak eskuratzeko eskabideak, EHAAn argitaratutako ebazpen baten bitartez, eta betiere, Osasun eta Kontsumoko sailburuak horretarako berariaz izendatutako Balorazio Batzordeak proposatutakoa aintzat hartuta.\nHori guztia kontuan izanik, deialdira bildutako eskabideak aztertuta, Balorazio Batzordeak proposatutakoa hartu da aintzakotzat, eta, horrenbestez, honako hau\nEBATZI DUT\n:\nLehenengoa. Osasun-arloko profesionalak prestatzeko bekak eta laguntzak honako pertsona hauei ematea (bakoitzari ondoan duen diru-zenbatekoa emango zaio):\nA modalitatea (Atzerriko zentroetan ikastaroak eta egonaldiak egiteko bekak eta laguntzak).\n(Ikus .PDF)\nB modalitatea (Osasunaren arloan ikerketako eta berrikuntzako prestakuntza-planak gauzatzeko bekak eta laguntzak).\n(Ikus .PDF)\nBigarrena. Honako eskatzaile hauei laguntza ukatzea; arrazoiak ere ematen dira:\nA modalitatea:\nCanales Arrasate, Maria Isabel.\nGalnares Cordero, Lorea.\nGómez Coloma, Alexander.\nLago García, Violeta.\nLlano Castresana, Oihane.\nMorales González, M.ª Celia.\nMorales López, Julio Ulises.\nMovilla Fernández, Soraya.\nSanta Maria-Amurrio Alustiza, Lander.\nUrdangarin Zumeta, Nerea.\nB modalitatea:\nAnton Ladislao, Ane.\nAurtenetxe Saez, Olaia.\nUrdampilleta Otegui, Aritz.\nBekak jaso nahi dituzten jarduerak deialdi-aginduko 1. artikuluan diru-laguntzetarako zehaztutako eremuan ez barne-hartuta egoteagatik (atzerrian egingo ez diren ikastaroak eta egonaldiak).\nB modalitatea:\nMaortua Olabe, Hiart.\nInteresunak berak berariaz uko egiteagatik.\nA modalitatea:\nAmo Herrero, Laura.\nOlabarria Larizgoitia, Markel.',
'Ebazpena Euskal Herriko Agintaritzaren Aldizkarian argitaratuko da, eta, denek jakin dezaten, www.elankidetza.euskadi.net eta www.euskadi.net orrietan argitaratuko da beken esleipendunen zerrenda eta haien',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7648, 0.2485],
# [0.7648, 1.0000, 0.2295],
# [0.2485, 0.2295, 1.0000]])
```
<!--
### Direct Usage (Transformers)
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Triplet
* Dataset: `multilingual-e5-large`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8655** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 19,544 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 17.5 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 70 tokens</li><li>mean: 234.16 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Nork izendatu du Osakidetzako Teknikari Espezialista sanitarioen lanbide-taldeko lekualdatze-lehiaketa kalifikatuko duen epaimahaia?</code> | <code>Osakidetza-Euskal osasun zerbitzuko zuzendari nagusiaren azaroaren 10eko 1401/2017 Ebazpenaren bidez, Teknikari Espezialista sanitarioen lanbide-taldeko lekualdatze-lehiaketa deitu zen. Ebazpena 2017ko azaroaren 28ko EHAAn (227. zenbakia) argitaratu zen.<br>Aurreko paragrafoan aipatutako ebazpenaren 7. oinarrian aurreikusitakoaren bat etorriz, Osakidetzako zuzendari nagusiak aipatutako lekualdatze-lehiaketa kalifikatuko duen epaimahaia izendatuko du.<br>Horiek guztiak horrela, eta indarrean dagoen legedian pertsonaleko gaietan aitortuta ditudan eskumenez baliatuta, honako hau<br>EBAZTEN DUT<br>:<br>Lehenengoa. Osakidetza-Euskal osasun zerbitzuko zuzendari nagusiaren azaroaren 10eko 1401/2017 Ebazpenaren bidez deitutako Teknikari Espezialista sanitarioen lanbide-taldeko lekualdatze-lehiaketako epaimahai kalifikatzailea izendatzea. Epaimahaia osatuko duten kideak ebazpen honen I. eranskinean jasotzen dira.<br>Bigarrena. Ebazpen honek duen egunetik aurrera emango ditu ondorioak.<br>Hirugarrena. Ebazpen hau EH...</code> |
| <code>Zein urtetan sortu zen UPyD alderdia?</code> | <code>[TOPIC: Galdera, Gorka Maneiro Labayen Mistoa-UPyD taldeko legebiltzarkideak lehendakariari egina, euskal unibertsitate publikoan ETAko kideen espediente faltsuak izateari buruz]<br>[MANEIRO LABAYEN, (Mixto-UPyD)]:<br>nor izango zen–, EHUk unibertsitate-tituluak eman ahal izan zitzan. Kontu honek, hain zuzen, irismen handia du, eta ibilbide luzea izango du, eta guk nahi dugu Legebiltzar honetan ere bidea egin dezan. Beraz, ni neu jarriko naiz harremanetan Legebiltzar honetako talde demokratikoekin ikerketa-batzorde bat abiarazten saiatzeko, zehazki jakin dezagun zer gertatu den EHUn. Albiste oso biribilak izango dituzu laster, sailburu andrea. Erabateko eskandalua da. (Date: 24.04.2015)</code> |
| <code>Arrasateko ospitale berrian erresonantzia magnetikoetarako unitate bat irekitzea aurreikusita al dago?</code> | <code>[TOPIC: Galdera, Rebeka Ubera Aranzeta EH Bildu taldeko legebiltzarkideak Osasuneko sailburuari egina, Arrasateko ospitalean erresonantzia magnetikoetarako unitatea irekitzeari buruz]<br>[UBERA ARANZETA, (EH Bildu)]:<br>ospitale berriaren jarduera ez dela behar bestekoa; aktibitatea, errendimendua, jaitsi egin dela. Eta iruditzen zaigu pena dela. Ospitale berri bat daukagu, ikaragarrizko espazioak, probetxu handiagoa atera ahal zaienak, eta ulertezina iruditzen zaigu oraindik ere kamioi bat edukitzea bertan erresonantzia magnetikoak egiteko. Uler genezake trantsizio-fase batean kamioia erabiltzea, baina momentu honetan nahiko ulertezina egiten zaigu, ospitale berria hor daukagunean. Iruditzen zaigu dauden espazioak aprobetxatu daitezkeela unitate bat irekitzeko (Date: 29.05.2015)</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 19,560 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 17.49 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 73 tokens</li><li>mean: 238.52 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 112.87 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Zein da LABen iritzia euskal sindikalismoaren egoerari buruz?</code> | <code>Erronka handiak direla onartu du LABek, are gehiago munduan inposatzen ari diren «errezeta neoliberal eta autoritarioak» kontuan hartuta, baina, sindikatuaren iritziz, Euskal Herrian bada beste norabidean jotzeko moduko «orube» sindikal, sozial eta politikorik. Aldaketa sozialaren eta burujabetasunaren alde dauden eragileen arteko «euskal agenda sozial partekatua» beharrezkoa dela nabarmendu du, «estatuekiko» autonomoa izango dena.<br><br>30 urte dira lan esparru propioaren aldeko lehe manifestazioa egin zenetik, eta, LABen ustez, gaiak «zentralitate» osoa merezi du oraindik<br><br>Hori bai, jakina da LABen eta ELAren arteko harremanak garai txarrak bizi dituela, ez duela 30 urte bezala. Gauzak hala, LABen ustez, agenda horren osaketan oinarrizko printzipio bat lortu beharko liratekeela uste du, mugimendu feministaren ekimenez zaintzaren inguruan proposatu den herri ekimenarekin eta lan istripuei erantzuteko elkarlanarekin gertatu bezala.<br><br>«Elkarlan sendoago» baten premia<br><br>Agirian, LABek euskal si...</code> | <code>Azaldu duenez, lan munduan izandako aldaketek sindikalismoaren krisia ekarri dute, eta pitzadurak eragin ditu sindikatuen botere iturrietan.</code> |
| <code>Zer neurri hartu ditu Txinako gobernuak Pekingo zarata eta nahasmena murrizteko?</code> | <code>Munduko bigarren potentziaren hiriburua aldatzen<br><br>Pekinera iritsi berri diren atzerritarren lehen inpresioa sarritan bera izaten da: kontrastea, kaosa; etxe orratz modernoenak kale batean eta metro gutxi barnerago pisu bakar bateko etxe zahar tradizionalak; Porsche garestienak, eta ondoko kalean bat-batean oilar bat. Herritarrentzat hori ondo dago, baina gobernuak uste du munduko bigarren potentziaren hiriburua garbitzeko ordua iritsi dela, eta azken urteotan zarata eta nahasmen hori guztia ezabatu nahian ari dira.<br><br>Su artifizialen debekua eman diren hamarnaka pausoetako bat besterik ez da. Zerrenda luzea da: kaleak poliziaz bete dituzte eroen moduan gidatzen duten motor elektrikoak kontrolatzeko; kalean inprobisatuta ireki ziren jatetxe eta denda txiki horiek guztiak itxi dituzte, segurtasun neurriak betetzen ez dituzten milaka eraikin bota dituzte.<br><br>Hartu diren erabaki asko garrantzitsuak izan dira bizi kalitatea hobetzeko, baina bide horretan kolore batzuk galdu dira eta urte berria...</code> | <code>Pekingo gobernuak adierazi du interes tasak murrizteko gaitasuna baduela oraindik, halakorik egitea beharrezkoa balitz.</code> |
| <code>Zein da Eusko Jaurlaritzaren jarrera Forondako aireportua bultzatzeari dagokionez?</code> | <code>[TOPIC: Galdera, Igor López de Munain Ganuza EH Bildu taldeko legebiltzarkideak Ingurumen eta Lurralde Politikako sailburuari egina, Forondako aireportua bultzatzeari buruz]<br>[LÓPEZ DE MUNAIN GANUZA, (EH Bildu)]:<br>gain erantzukizuna Madrilena dela eta haiek ez dutela borondaterik? Hemen akordio bat sinatu genuen alderdi politiko guztiok. Zer ari zarete egiten akordio horri dagokionez? Pasatuko zarete hitzetatik ekintzetara? Hartuko duzue Foronda lehentasun gisa, beste helburu batzuk, hala nola abiadura handiko trena eraikitzea, zuen agenda politikotik kanpo utzita? Emango diozue lehentasuna jada existitzen denari, non langileak baitaude jada, non AHTa 2019rako ez eraikitzeagatik galduko genukeena baino askoz gehiago gal (Date: 13.02.2015)</code> | <code>[TOPIC: Interpelazioa, Rebeka Ubera Aranzeta EH Bildu taldeko legebiltzarkideak Hezkuntzako sailburuari egina, Haurreskolak partzuergoan doakotasunaren bidean 18.000 euro baino gutxiagoko familiei doakotasuna ezartzeari buruz]<br>[UBERA ARANZETA, (EH Bildu)]:<br>igo duzue (0,7), baina ez zarete iritsi 2012ko inbertsiomailara ere. Ikustea besterik ez dago zer eskaintza egin duzuen: murrizketak, murrizketak eta murrizketak, etengabeak. Aipatu dituzun beste neurri horiek… Begira, bekak aipatu dituzu. Bekak, ez Haurreskolak partzuergoan, bekak ikasmaterialari dagokionez, eta gaur aldarrikatu duzun proposamena EH Bilduk ekarri du. EH Bilduk ekarri du behin eta berriro etxe honetara, eta azkenean lortu dugu zuek onartzea. Zuek ez zarete gai (Date: 11.05.2018)</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | multilingual-e5-large_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------------:|
| 0.0409 | 100 | 1.5839 | - | - |
| 0.0819 | 200 | 0.3913 | - | - |
| 0.1228 | 300 | 0.1506 | - | - |
| 0.1637 | 400 | 0.1704 | - | - |
| 0.2047 | 500 | 0.0915 | - | - |
| 0.2456 | 600 | 0.0901 | - | - |
| 0.2865 | 700 | 0.105 | - | - |
| 0.3275 | 800 | 0.0687 | - | - |
| 0.3684 | 900 | 0.0589 | - | - |
| 0.4093 | 1000 | 0.0646 | - | - |
| 0.4503 | 1100 | 0.0885 | - | - |
| 0.4912 | 1200 | 0.0616 | - | - |
| 0.5321 | 1300 | 0.0517 | - | - |
| 0.5731 | 1400 | 0.0333 | - | - |
| 0.6140 | 1500 | 0.0601 | - | - |
| 0.6549 | 1600 | 0.0372 | - | - |
| 0.6959 | 1700 | 0.0487 | - | - |
| 0.7368 | 1800 | 0.047 | - | - |
| 0.7777 | 1900 | 0.0391 | - | - |
| 0.8187 | 2000 | 0.0421 | - | - |
| 0.8596 | 2100 | 0.0414 | - | - |
| 0.9005 | 2200 | 0.0413 | - | - |
| 0.9415 | 2300 | 0.0271 | - | - |
| 0.9824 | 2400 | 0.0332 | - | - |
| 1.0 | 2443 | - | 0.4724 | 0.8315 |
| 1.0233 | 2500 | 0.0338 | - | - |
| 1.0643 | 2600 | 0.019 | - | - |
| 1.1052 | 2700 | 0.0308 | - | - |
| 1.1461 | 2800 | 0.0148 | - | - |
| 1.1871 | 2900 | 0.0229 | - | - |
| 1.2280 | 3000 | 0.0214 | - | - |
| 1.2689 | 3100 | 0.0296 | - | - |
| 1.3099 | 3200 | 0.0158 | - | - |
| 1.3508 | 3300 | 0.0177 | - | - |
| 1.3917 | 3400 | 0.0281 | - | - |
| 1.4327 | 3500 | 0.0299 | - | - |
| 1.4736 | 3600 | 0.0175 | - | - |
| 1.5145 | 3700 | 0.0193 | - | - |
| 1.5555 | 3800 | 0.0119 | - | - |
| 1.5964 | 3900 | 0.0307 | - | - |
| 1.6373 | 4000 | 0.0276 | - | - |
| 1.6783 | 4100 | 0.027 | - | - |
| 1.7192 | 4200 | 0.0243 | - | - |
| 1.7601 | 4300 | 0.0127 | - | - |
| 1.8011 | 4400 | 0.0178 | - | - |
| 1.8420 | 4500 | 0.0076 | - | - |
| 1.8829 | 4600 | 0.0189 | - | - |
| 1.9239 | 4700 | 0.014 | - | - |
| 1.9648 | 4800 | 0.0109 | - | - |
| 2.0 | 4886 | - | 0.4039 | 0.8494 |
| 2.0057 | 4900 | 0.0129 | - | - |
| 2.0467 | 5000 | 0.0163 | - | - |
| 2.0876 | 5100 | 0.01 | - | - |
| 2.1285 | 5200 | 0.0107 | - | - |
| 2.1695 | 5300 | 0.0109 | - | - |
| 2.2104 | 5400 | 0.0105 | - | - |
| 2.2513 | 5500 | 0.0106 | - | - |
| 2.2923 | 5600 | 0.011 | - | - |
| 2.3332 | 5700 | 0.0177 | - | - |
| 2.3741 | 5800 | 0.0112 | - | - |
| 2.4151 | 5900 | 0.0091 | - | - |
| 2.4560 | 6000 | 0.0204 | - | - |
| 2.4969 | 6100 | 0.0051 | - | - |
| 2.5379 | 6200 | 0.0091 | - | - |
| 2.5788 | 6300 | 0.0104 | - | - |
| 2.6197 | 6400 | 0.0102 | - | - |
| 2.6607 | 6500 | 0.0081 | - | - |
| 2.7016 | 6600 | 0.0066 | - | - |
| 2.7425 | 6700 | 0.0049 | - | - |
| 2.7835 | 6800 | 0.0038 | - | - |
| 2.8244 | 6900 | 0.005 | - | - |
| 2.8653 | 7000 | 0.0139 | - | - |
| 2.9063 | 7100 | 0.0069 | - | - |
| 2.9472 | 7200 | 0.0066 | - | - |
| 2.9881 | 7300 | 0.0094 | - | - |
| 3.0 | 7329 | - | 0.3533 | 0.8655 |
### Framework Versions
- Python: 3.10.8
- Sentence Transformers: 5.1.0
- Transformers: 4.56.0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
ahmarkibriya5374/blockassist-bc-fishy_furry_wombat_1757601365
|
ahmarkibriya5374
| 2025-09-11T14:36:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy furry wombat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:36:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy furry wombat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Swallow-7b-ja-dpo-lora-GGUF
|
mradermacher
| 2025-09-11T14:36:09Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:nao310222/Swallow-7b-ja-dpo-lora",
"base_model:quantized:nao310222/Swallow-7b-ja-dpo-lora",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-11T13:43:12Z |
---
base_model: nao310222/Swallow-7b-ja-dpo-lora
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/nao310222/Swallow-7b-ja-dpo-lora
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Swallow-7b-ja-dpo-lora-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q2_K.gguf) | Q2_K | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q3_K_S.gguf) | Q3_K_S | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q3_K_M.gguf) | Q3_K_M | 3.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q3_K_L.gguf) | Q3_K_L | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.IQ4_XS.gguf) | IQ4_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q5_K_M.gguf) | Q5_K_M | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q6_K.gguf) | Q6_K | 5.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.Q8_0.gguf) | Q8_0 | 7.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-ja-dpo-lora-GGUF/resolve/main/Swallow-7b-ja-dpo-lora.f16.gguf) | f16 | 13.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
himugrb/blockassist-bc-alert_diving_meerkat_1757601336
|
himugrb
| 2025-09-11T14:35:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"subtle stinging chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:35:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- subtle stinging chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hardikjainharsora/textbook-model
|
hardikjainharsora
| 2025-09-11T14:35:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-10T13:44:14Z |
---
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]
|
terrancejykn/blockassist-bc-colorful_curious_macaque_1757601314
|
terrancejykn
| 2025-09-11T14:35:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful curious macaque",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:35:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful curious macaque
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mehmetxh/blockassist
|
mehmetxh
| 2025-09-11T14:35:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grazing soft mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-10T22:19:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grazing soft mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
clayceklj/blockassist-bc-reptilian_bellowing_crocodile_1757601215
|
clayceklj
| 2025-09-11T14:34:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reptilian bellowing crocodile",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:34:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reptilian bellowing crocodile
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_cb_101112_1757596151
|
rbelanec
| 2025-09-11T14:33:31Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:29:12Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_cb_101112_1757596151
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. -->
# train_cb_101112_1757596151
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1226
- Num Input Tokens Seen: 621040
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 101112
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.5565 | 1.0 | 113 | 0.3490 | 30240 |
| 0.6263 | 2.0 | 226 | 0.3298 | 61600 |
| 0.4408 | 3.0 | 339 | 0.1773 | 92552 |
| 0.3915 | 4.0 | 452 | 0.2358 | 123976 |
| 0.0154 | 5.0 | 565 | 0.2813 | 155224 |
| 0.1362 | 6.0 | 678 | 0.1831 | 186368 |
| 0.0329 | 7.0 | 791 | 0.1248 | 217280 |
| 0.0004 | 8.0 | 904 | 0.0106 | 248064 |
| 0.0001 | 9.0 | 1017 | 0.1456 | 278576 |
| 0.0001 | 10.0 | 1130 | 0.1819 | 309584 |
| 0.0001 | 11.0 | 1243 | 0.2099 | 340752 |
| 0.0 | 12.0 | 1356 | 0.1466 | 372240 |
| 0.0001 | 13.0 | 1469 | 0.1362 | 402976 |
| 0.0001 | 14.0 | 1582 | 0.1331 | 433800 |
| 0.0001 | 15.0 | 1695 | 0.1305 | 465096 |
| 0.0001 | 16.0 | 1808 | 0.1263 | 496184 |
| 0.0 | 17.0 | 1921 | 0.1279 | 527400 |
| 0.0 | 18.0 | 2034 | 0.1253 | 558656 |
| 0.0 | 19.0 | 2147 | 0.1310 | 589928 |
| 0.0 | 20.0 | 2260 | 0.1226 | 621040 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
sadiyakhatun65524/blockassist-bc-insectivorous_prehistoric_mouse_1757601196
|
sadiyakhatun65524
| 2025-09-11T14:33:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous prehistoric mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:33:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous prehistoric mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
heitzmanivan/blockassist-bc-hibernating_flapping_penguin_1757601161
|
heitzmanivan
| 2025-09-11T14:32:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hibernating flapping penguin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:32:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hibernating flapping penguin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_cb_101112_1757596153
|
rbelanec
| 2025-09-11T14:32:42Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"p-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:29:31Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- p-tuning
- generated_from_trainer
model-index:
- name: train_cb_101112_1757596153
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. -->
# train_cb_101112_1757596153
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cb dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9057
- Num Input Tokens Seen: 359824
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 101112
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|
| 0.5529 | 0.5088 | 29 | 0.2530 | 19872 |
| 0.425 | 1.0175 | 58 | 0.3134 | 36432 |
| 0.1547 | 1.5263 | 87 | 0.2663 | 53680 |
| 0.1254 | 2.0351 | 116 | 0.1659 | 72160 |
| 0.0558 | 2.5439 | 145 | 0.0878 | 91904 |
| 0.354 | 3.0526 | 174 | 0.0978 | 108856 |
| 0.3631 | 3.5614 | 203 | 0.0429 | 128056 |
| 0.0177 | 4.0702 | 232 | 0.0735 | 146952 |
| 0.0911 | 4.5789 | 261 | 0.0177 | 165128 |
| 0.0866 | 5.0877 | 290 | 0.0277 | 183224 |
| 0.0044 | 5.5965 | 319 | 0.0063 | 202424 |
| 0.0094 | 6.1053 | 348 | 0.0171 | 220000 |
| 0.0333 | 6.6140 | 377 | 0.0227 | 238272 |
| 0.0246 | 7.1228 | 406 | 0.0311 | 255984 |
| 0.0005 | 7.6316 | 435 | 0.0307 | 275536 |
| 0.002 | 8.1404 | 464 | 0.0281 | 293296 |
| 0.0037 | 8.6491 | 493 | 0.0320 | 312304 |
| 0.0005 | 9.1579 | 522 | 0.0399 | 329216 |
| 0.0003 | 9.6667 | 551 | 0.0415 | 346944 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
iekagrbaiya/blockassist-bc-clawed_rabid_fish_1757601138
|
iekagrbaiya
| 2025-09-11T14:32:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"clawed rabid fish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:32:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- clawed rabid fish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_cb_101112_1757596152
|
rbelanec
| 2025-09-11T14:32:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:29:22Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_cb_101112_1757596152
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. -->
# train_cb_101112_1757596152
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1202
- Num Input Tokens Seen: 359824
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 101112
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|
| 0.7269 | 0.5088 | 29 | 0.7703 | 19872 |
| 0.2065 | 1.0175 | 58 | 0.1823 | 36432 |
| 0.1101 | 1.5263 | 87 | 0.1472 | 53680 |
| 0.1284 | 2.0351 | 116 | 0.1234 | 72160 |
| 0.0319 | 2.5439 | 145 | 0.1202 | 91904 |
| 0.4179 | 3.0526 | 174 | 0.1394 | 108856 |
| 0.21 | 3.5614 | 203 | 0.1364 | 128056 |
| 0.0244 | 4.0702 | 232 | 0.1848 | 146952 |
| 0.096 | 4.5789 | 261 | 0.1848 | 165128 |
| 0.0295 | 5.0877 | 290 | 0.2145 | 183224 |
| 0.002 | 5.5965 | 319 | 0.1764 | 202424 |
| 0.1338 | 6.1053 | 348 | 0.2297 | 220000 |
| 0.0704 | 6.6140 | 377 | 0.2370 | 238272 |
| 0.2076 | 7.1228 | 406 | 0.2265 | 255984 |
| 0.0055 | 7.6316 | 435 | 0.2345 | 275536 |
| 0.0121 | 8.1404 | 464 | 0.2297 | 293296 |
| 0.0954 | 8.6491 | 493 | 0.2435 | 312304 |
| 0.0033 | 9.1579 | 522 | 0.2263 | 329216 |
| 0.0288 | 9.6667 | 551 | 0.2297 | 346944 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
pralayd/Finetuned_Trishul8B-Lite-GGUF
|
pralayd
| 2025-09-11T14:31:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-11T14:27:22Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** pralayd
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SuganyaP/quick-distilbert-imdb
|
SuganyaP
| 2025-09-11T14:31:22Z | 0 | 0 | null |
[
"en",
"base_model:distilbert/distilbert-base-uncased-finetuned-sst-2-english",
"base_model:finetune:distilbert/distilbert-base-uncased-finetuned-sst-2-english",
"license:mit",
"region:us"
] | null | 2025-09-11T11:51:48Z |
---
license: mit
language:
- en
metrics:
- accuracy
- f1
base_model:
- distilbert/distilbert-base-uncased-finetuned-sst-2-english
---
# Quick DistilBERT IMDB Sentiment Classifier
This is a fine-tuned DistilBERT model for **sentiment analysis** on the IMDB movie reviews dataset.
The model classifies reviews as **positive** or **negative**.
## Model Details
- **Base model**: `distilbert-base-uncased`
- **Dataset**: IMDB (cleaned train/test splits)
- **Task**: Sentiment classification (binary)
- **Framework**: Hugging Face Transformers
## Training
- Optimized DistilBERT on IMDB dataset
- Used standard text classification head
- Training args saved in `training_args.bin`
## Evaluation
Accuracy and F1-score on the IMDB test set:
(Add numbers from your `eval_report.txt` here)
Misclassified examples are available in `misclassified_examples.csv`.
## How to Use
```python
from transformers import pipeline
model_id = "SuganyaP/quick-distilbert-imdb"
classifier = pipeline("sentiment-analysis", model=model_id)
print(classifier("This movie was excellent!"))
|
fuerbringerestefana/blockassist-bc-monstrous_vicious_snail_1757600961
|
fuerbringerestefana
| 2025-09-11T14:29:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous vicious snail",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:29:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous vicious snail
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
credolacy/blockassist-bc-armored_placid_buffalo_1757600845
|
credolacy
| 2025-09-11T14:27:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored placid buffalo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:27:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored placid buffalo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_copa_789_1757596141
|
rbelanec
| 2025-09-11T14:27:49Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:24:07Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: train_copa_789_1757596141
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. -->
# train_copa_789_1757596141
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0287
- Num Input Tokens Seen: 281984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 789
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.1204 | 0.5 | 45 | 0.0726 | 14240 |
| 0.3342 | 1.0 | 90 | 0.0615 | 28192 |
| 0.0623 | 1.5 | 135 | 0.0627 | 42080 |
| 0.0032 | 2.0 | 180 | 0.0287 | 56192 |
| 0.0001 | 2.5 | 225 | 0.0371 | 70048 |
| 0.0012 | 3.0 | 270 | 0.0343 | 84192 |
| 0.0002 | 3.5 | 315 | 0.0305 | 98304 |
| 0.0 | 4.0 | 360 | 0.0306 | 112544 |
| 0.0 | 4.5 | 405 | 0.0308 | 126784 |
| 0.0 | 5.0 | 450 | 0.0322 | 140960 |
| 0.0 | 5.5 | 495 | 0.0331 | 155200 |
| 0.0 | 6.0 | 540 | 0.0347 | 169216 |
| 0.0 | 6.5 | 585 | 0.0347 | 183232 |
| 0.0 | 7.0 | 630 | 0.0355 | 197248 |
| 0.0 | 7.5 | 675 | 0.0402 | 211424 |
| 0.0 | 8.0 | 720 | 0.0311 | 225440 |
| 0.0 | 8.5 | 765 | 0.0339 | 239392 |
| 0.0 | 9.0 | 810 | 0.0341 | 253632 |
| 0.0 | 9.5 | 855 | 0.0374 | 267680 |
| 0.0 | 10.0 | 900 | 0.0409 | 281984 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
vendi11/blockassist-bc-placid_placid_llama_1757600801
|
vendi11
| 2025-09-11T14:27:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:27:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sadiyakhatun65524/blockassist-bc-insectivorous_prehistoric_mouse_1757600815
|
sadiyakhatun65524
| 2025-09-11T14:27:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous prehistoric mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:27:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous prehistoric mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_copa_789_1757596140
|
rbelanec
| 2025-09-11T14:26:50Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"p-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:23:14Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- p-tuning
- generated_from_trainer
model-index:
- name: train_copa_789_1757596140
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. -->
# train_copa_789_1757596140
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4562
- Num Input Tokens Seen: 281984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 789
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.411 | 0.5 | 45 | 0.3011 | 14240 |
| 0.2838 | 1.0 | 90 | 0.2367 | 28192 |
| 0.2526 | 1.5 | 135 | 0.2344 | 42080 |
| 0.2443 | 2.0 | 180 | 0.2417 | 56192 |
| 0.2322 | 2.5 | 225 | 0.2302 | 70048 |
| 0.2431 | 3.0 | 270 | 0.2324 | 84192 |
| 0.2331 | 3.5 | 315 | 0.2341 | 98304 |
| 0.2335 | 4.0 | 360 | 0.2316 | 112544 |
| 0.2376 | 4.5 | 405 | 0.2335 | 126784 |
| 0.2371 | 5.0 | 450 | 0.2323 | 140960 |
| 0.2308 | 5.5 | 495 | 0.2329 | 155200 |
| 0.2303 | 6.0 | 540 | 0.2314 | 169216 |
| 0.2276 | 6.5 | 585 | 0.2329 | 183232 |
| 0.2262 | 7.0 | 630 | 0.2323 | 197248 |
| 0.2449 | 7.5 | 675 | 0.2311 | 211424 |
| 0.2253 | 8.0 | 720 | 0.2308 | 225440 |
| 0.2314 | 8.5 | 765 | 0.2292 | 239392 |
| 0.2314 | 9.0 | 810 | 0.2329 | 253632 |
| 0.2303 | 9.5 | 855 | 0.2335 | 267680 |
| 0.2304 | 10.0 | 900 | 0.2313 | 281984 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
raileshikder7241/blockassist-bc-slender_amphibious_cheetah_1757600731
|
raileshikder7241
| 2025-09-11T14:25:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slender amphibious cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:25:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slender amphibious cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_copa_789_1757596139
|
rbelanec
| 2025-09-11T14:25:17Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prompt-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-11T14:22:10Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prompt-tuning
- generated_from_trainer
model-index:
- name: train_copa_789_1757596139
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. -->
# train_copa_789_1757596139
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the copa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0711
- Num Input Tokens Seen: 281984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 789
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.2013 | 0.5 | 45 | 0.1095 | 14240 |
| 0.2033 | 1.0 | 90 | 0.0885 | 28192 |
| 0.0778 | 1.5 | 135 | 0.0860 | 42080 |
| 0.1119 | 2.0 | 180 | 0.0777 | 56192 |
| 0.0346 | 2.5 | 225 | 0.0823 | 70048 |
| 0.1199 | 3.0 | 270 | 0.0711 | 84192 |
| 0.0165 | 3.5 | 315 | 0.1047 | 98304 |
| 0.0248 | 4.0 | 360 | 0.1218 | 112544 |
| 0.003 | 4.5 | 405 | 0.1436 | 126784 |
| 0.0269 | 5.0 | 450 | 0.1350 | 140960 |
| 0.0008 | 5.5 | 495 | 0.1389 | 155200 |
| 0.027 | 6.0 | 540 | 0.1530 | 169216 |
| 0.0006 | 6.5 | 585 | 0.1628 | 183232 |
| 0.0002 | 7.0 | 630 | 0.1684 | 197248 |
| 0.0006 | 7.5 | 675 | 0.1641 | 211424 |
| 0.1687 | 8.0 | 720 | 0.1717 | 225440 |
| 0.0001 | 8.5 | 765 | 0.1706 | 239392 |
| 0.0014 | 9.0 | 810 | 0.1723 | 253632 |
| 0.0004 | 9.5 | 855 | 0.1679 | 267680 |
| 0.0003 | 10.0 | 900 | 0.1652 | 281984 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
krisbrown1/blockassist-bc-aquatic_alert_lizard_1757598482
|
krisbrown1
| 2025-09-11T14:24:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic alert lizard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-11T14:23:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic alert lizard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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