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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-07 18:30:29
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
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listlengths 1
4.05k
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chainway9/blockassist-bc-untamed_quick_eel_1755215222
|
chainway9
| 2025-08-15T00:14:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-15T00:14:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thomashk2001/tom_and_jerry_vit_model
|
thomashk2001
| 2025-08-15T00:13:57Z | 0 | 0 | null |
[
"safetensors",
"vit",
"vision",
"image-classification",
"es",
"license:cc0-1.0",
"region:us"
] |
image-classification
| 2025-08-14T19:34:37Z |
---
language:
- "es"
pretty_name: "Tom and Jerry Image Classification VIT Model"
tags:
- "vision"
- "image-classification"
license: "cc0-1.0"
task_categories:
- "image-classification"
---
# Modelo VIT afinado para clasificación de imágenes de Tom y Jerry
## Modelo base: 'google/vit-base-patch16-224-in21k'
EL modelo VIT fue ajusto para la clasificación de imágenes de Tom y Jerry en las siguientes categorías:
- Tom: Tom está en la imagen
- Jerry: Jerry está en la imagen
- Tom_and_Jerry: Tom y Jerry están en la imagen
- None: Ninguno está en la imagen
## Metodología
- Se realizó el afinamiento del modelo con el dataset thomashk2001/tom_and_jerry_dataset. El cual se encuentra dividido en train, eval y testing.
- Los splits están estratificados por lo que hay de cada uno de los posibles labels en los splits.
- Se realizó el procesamiento de las imágenes con el ViTImageProcessor con el modelo 'google/vit-base-patch16-224-in21k'.
- Los argumentos de entrenamiento fueron:
```
training_args = TrainingArguments(
output_dir="./vit_tom_jerry_mdl", # Checkpoints and saved model
per_device_train_batch_size=64,# Train batch size
per_device_eval_batch_size=64,# Eval batch size
num_train_epochs=5,# Number of epochs
learning_rate=2e-4,# LR rate
eval_strategy="steps",# Eval at the end of each step
eval_steps=25, # How often model is evaluated
save_strategy="steps", # Saves model every 100 steps
save_steps=100,
save_total_limit=5, # Model states saved including best model
load_best_model_at_end=True, # Loads best model at the end
logging_dir="./logs", # Lod dir
logging_steps=10, # Log register step
remove_unused_columns=False,
metric_for_best_model="f1", # Metric used for the best model
greater_is_better=True, # better f1 is looked after
)
```
- Se aplicó el afinamiento del modelo con los parámetros definidos en el paso anterior y se uso early stopping con paciencia de 3.
## Resultados del entrenamiento:
| Step | Training Loss | Validation Loss | Accuracy | Precision | Recall | F1 |
|------|---------------|----------------|-----------|-----------|--------|---------|
| 25 | 0.8223 | 0.4506 | 0.8893 | 0.8939 | 0.8653 | 0.8742 |
| 50 | 0.2676 | 0.2195 | 0.9392 | 0.9343 | 0.9376 | 0.9356 |
| 75 | 0.1896 | 0.1816 | 0.9526 | 0.9490 | 0.9504 | 0.9493 |
| 100 | 0.1085 | 0.1940 | 0.9380 | 0.9316 | 0.9381 | 0.9344 |
| 125 | 0.1618 | 0.1806 | 0.9477 | 0.9390 | 0.9493 | 0.9434 |
| 150 | 0.0784 | 0.1582 | 0.9574 | 0.9524 | 0.9570 | 0.9546 |
| 175 | 0.0710 | 0.1803 | 0.9416 | 0.9364 | 0.9413 | 0.9386 |
| 200 | 0.0533 | 0.1539 | 0.9611 | 0.9623 | 0.9600 | 0.9605 |
| 225 | 0.0383 | 0.1446 | 0.9647 | 0.9654 | 0.9642 | 0.9646 |
| 250 | 0.0264 | 0.1619 | 0.9513 | 0.9447 | 0.9546 | 0.9488 |
| 275 | 0.0227 | 0.1524 | 0.9550 | 0.9498 | 0.9579 | 0.9531 |
| 300 | 0.0343 | 0.1530 | 0.9562 | 0.9526 | 0.9587 | 0.9553 |
## Mejor Modelo
- Step: 225
- Training Loss: 0.0383
- Validation Loss: 0.1446
- Accuracy: 0.9647
- Precision: 0.9654
- Recall: 0.9642
- F1 Score: 0.9646
|
qualifire/TSQ-v2-mini-model
|
qualifire
| 2025-08-15T00:10:27Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"mcp",
"tools quality",
"tool quality selection",
"tool selection",
"TSQ",
"sequence-classification",
"tool-evaluation",
"function-call",
"text-classification",
"en",
"region:us"
] |
text-classification
| 2025-08-14T21:01:18Z |
---
language:
- en
tags:
- mcp
- tools quality
- tool quality selection
- tool selection
- TSQ
- sequence-classification
- tool-evaluation
- function-call
pipeline_tag: text-classification
---
## 🧠 Model Description
The **TSQ-v2-mini-model** is a fine-tuned sequence classification model created to **evaluate the quality of function calls** in conversational AI systems.
It determines if a given function call:
- Selects the correct tool
- Has correct parameter names and structure
- Contains correct parameter values
It produces one of four possible classification labels:
| Label | Meaning |
|-------|---------|
| **VALID_CALL** | ✅ The tool name, parameters, and values are all correct. |
| **TOOL_ERROR** | ❌ The tool name does not exist or does not match the user intent. |
| **PARAM_NAME_ERROR** | ❌ The correct tool is used, but parameter names are missing, extra, or incorrect. |
| **PARAM_VALUE_ERROR** | ❌ Tool and parameter names are correct, but parameter values are wrong or incorrectly formatted. |
---
## 📊 Benchmark Evaluation
The **TSQ-v2-mini-model** was evaluated in a binary classification setting,
where the prediction is **Correct** if the function call evaluation matched the gold label, and **Incorrect** otherwise.
### Binary Accuracy Results
| Model | Avg. Latency (sec) | Avg Binary Accuracy | [Qualifire TSQ Benchmark](https://huggingface.co/datasets/qualifire/tool-selection-quality-benchmark) Binary Accuracy | [Limbic Benchmark](https://huggingface.co/datasets/quotientai/limbic-eval-tool-use-mcp) Binary Accuracy |
|-----------------------------------------|--------------------|--------------------|--------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|
| qualifire/TSQ-v2-large-model[private] | 0.26 | 0.972 | 0.994 | 0.951
| **qualifire/TSQ-v2-mini-model** | **0.07** | **0.951** | **0.985** | **0.917** |
| gemini-2.5-flash | 4.87 | 0.890 | 0.936 | 0.845 |
| quotientai/limbic-tool-use-0.5B-32K | 0.79 | 0.807 | 0.752 | 0.862 |
### 📌 Metrics Definitions
- **Avg. Binary Accuracy** – Mean accuracy across all evaluated benchmarks,
where predictions are mapped to binary outcomes as follows:
- **Qualifire TSQ Benchmark**
- **Correct** → `VALID_CALL`
- **Incorrect** → `TOOL_ERROR`, `PARAM_NAME_ERROR` or `PARAM_VALUE_ERROR`
- **Limbic Benchmark**
- **Correct** → `correct`
- **Incorrect** → `incorrect_tool`, `incorrect_parameter_names` or `incorrect_parameter_values`
- **Qualifire TSQ Benchmark** link – [Qualifire Tool Selection Quality Benchmark](https://huggingface.co/datasets/qualifire/tool-selection-quality-benchmark).
- **Limbic Benchmark** link – [Limbic Eval Tool Use MCP Benchmark](https://huggingface.co/datasets/quotientai/limbic-eval-tool-use-mcp).
---
## 📜 Evaluation Prompt Template
The model uses the following structured evaluation process:
1. **TOOL SELECTION**
- Check if the tool name exists in `available_tools`
- Check if tool purpose matches user intent
- Fail → `TOOL_ERROR`❌
2. **PARAMETER STRUCTURE**
- All required parameters are present
- No extra parameters
- Parameter names exactly match the schema
- Fail → `PARAM_NAME_ERROR`❌
3. **PARAMETER VALUES**
- Values have correct data types
- Values match user request
- No fabricated or incorrect values
- Fail → `PARAM_VALUE_ERROR`❌
If all checks pass → `VALID_CALL`✅
---
## 💻 Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
import torch
from huggingface_hub import hf_hub_download
# Model name
model_name = "qualifire/TSQ-v2-mini-model"
# Map raw labels to human-readable labels
map_id_to_label = {
'LABEL_0': 'VALID_CALL',
'LABEL_1': 'TOOL_ERROR',
'LABEL_2': 'PARAM_NAME_ERROR',
'LABEL_3': 'PARAM_VALUE_ERROR'
}
# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map='auto',
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Create pipeline
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
# Load prompt template
file_path = hf_hub_download(repo_id=model_name, filename="tsq_prompt_template.txt")
with open(file_path, encoding="utf-8") as f:
PROMPT_TEMPLATE = f.read()
# Example inputs
example_tools_list = '''[
{
"type": "function",
"function": {
"name": "send-email",
"description": "Send an email using Resend",
"parameters": {
"properties": {
"to": {
"type": "string",
"format": "email",
"description": "Recipient email address"
},
"content": {
"type": "string",
"description": "Plain text email content"
},
"subject": {
"type": "string",
"description": "Email subject line"
},
"scheduledAt": {
"type": "string",
"description": "Optional parameter to schedule the email. This uses natural language. Examples would be 'tomorrow at 10am' or 'in 2 hours' or 'next day at 9am PST' or 'Friday at 3pm ET'."
}
},
"required": ["to", "subject", "content"]
}
}
}
]'''
example_massages_history = '''[
{
"role": "user",
"content": "Please send an email to 'jane.doe@example.com' with the subject 'Meeting Follow-Up'. The content should be 'Hi Jane, just following up on our meeting from yesterday. Please find the attached notes.' and schedule it for tomorrow at 10am."
},
{
"completion_message": {
"content": {
"type": "text",
"text": ""
},
"role": "assistant",
"stop_reason": "tool_calls",
"tool_calls": [
{
"id": "call_le25efmhltxx9o7n4rfe",
"function": {
"name": "send-email",
"arguments": {
"subject": "Meeting Follow-Up",
"content": "Hi Jane, just following up on our meeting from yesterday. Please find the attached notes.",
"scheduledAt": "tomorrow at 10am"
}
}
}
]
}
}
]'''
# Format input
example_input = PROMPT_TEMPLATE.format(
message_history=example_message_history,
available_tools=example_tools_list
)
# Get prediction
result = pipe(example_input)[0]
result['label'] = map_id_to_label[result['label']]
print(result)
```
## ✨ Example Output
Input: (valid tool, with valid names and values paramerters)
Output:
```
{'label': 'PARAM_NAME_ERROR', 'score': 0.9988911747932434}
```
'to' is a required parameter, and since it is missing from the function call, the label is 'PARAM_NAME_ERROR'.
|
ajagota71/SmolLM-360M-detox
|
ajagota71
| 2025-08-15T00:05:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-15T00:05:01Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="ajagota71//kaggle/working/irl_llms/outputs/2025-08-14_21-11-43/final-model")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-08-14_21-11-43/final-model")
model = AutoModelForCausalLMWithValueHead.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-08-14_21-11-43/final-model")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
nightmedia/Ling-lite-1.5-2507-q8-hi-mlx
|
nightmedia
| 2025-08-15T00:04:14Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"bailing_moe",
"text-generation",
"conversational",
"custom_code",
"base_model:inclusionAI/Ling-lite-1.5-2507",
"base_model:quantized:inclusionAI/Ling-lite-1.5-2507",
"license:mit",
"8-bit",
"region:us"
] |
text-generation
| 2025-08-14T20:53:52Z |
---
license: mit
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
base_model: inclusionAI/Ling-lite-1.5-2507
---
# Ling-lite-1.5-2507-q8-hi-mlx
This model [Ling-lite-1.5-2507-q8-hi-mlx](https://huggingface.co/Ling-lite-1.5-2507-q8-hi-mlx) was
converted to MLX format from [inclusionAI/Ling-lite-1.5-2507](https://huggingface.co/inclusionAI/Ling-lite-1.5-2507)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Ling-lite-1.5-2507-q8-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
DeepHat/DeepHat-V1-7B
|
DeepHat
| 2025-08-15T00:04:06Z | 2,542 | 33 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"qwen-coder",
"cybersecurity",
"devops",
"conversational",
"en",
"arxiv:2309.00071",
"base_model:Qwen/Qwen2.5-Coder-7B",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-25T22:03:34Z |
---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- qwen-coder
- cybersecurity
- devops
---
<br>
<br>
DeepHat is a model series that can be used for offensive and defensive cybersecurity. Access at [Deephat.ai](https://www.deephat.ai/) or go to [Kindo.ai](https://www.kindo.ai/) to create agents.
# Community
Join us on [Discord](https://discord.gg/8Ynkrcbk92)
# Technical Overview
DeepHat is a finetune of [Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B/), and inherits the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Number of Parameters: 7.61B
- Number of Paramaters (Non-Embedding): 6.53B
- Number of Layers: 28
- Number of Attention Heads (GQA): 28 for Q and 4 for KV
- Context Length: Full 131,072 tokens
- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
## Requirements
We advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "DeepHat/DeepHat-V1-7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "write a quick sort algorithm."
messages = [
{"role": "system", "content": "You are DeepHat, created by Kindo.ai. You are a helpful assistant that is an expert in Cybersecurity and DevOps."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
### Processing Long Texts
The current `config.json` is set for context length up to 32,768 tokens.
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
For supported frameworks, you could add the following to `config.json` to enable YaRN:
```json
{
...,
"rope_scaling": {
"factor": 4.0,
"original_max_position_embeddings": 32768,
"type": "yarn"
}
}
```
# License
Apache-2.0 + DeepHat Extended Version
## DeepHat Extension to Apache-2.0 Licence: Usage Restrictions
```
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
- For military use in any way;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
```
# Terms of Use
By accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model.
This AI model is provided "as is" and "as available" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis.
Your use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model.
This disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model.
|
wooii/MyGemmaNPC
|
wooii
| 2025-08-15T00:01:09Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T23:55:31Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="wooii/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
raniero/smoke-telegram-2
|
raniero
| 2025-08-14T23:59:57Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-08-14T23:59:46Z |
# LoRA — Instruction SFT
- **Task ID:** unknown
- **Base model:** mistralai/Mistral-7B-Instruct-v0.2
- **SHA256 (adapter):** `f69851b13b11ad9ccf8c9ed52ffca249a5a256c39175ba05589d945aaf4bff9d`
- **Repo:** raniero/smoke-telegram-2
Questa repo contiene SOLO gli adapter LoRA richiesti dai validator Subnet 56.
|
tahamajs/grpo-Qwen3
|
tahamajs
| 2025-08-14T23:58:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-14T23:53:54Z |
---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** tahamajs
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ymoslem/aya-expanse-8b-eng-arz-16layers
|
ymoslem
| 2025-08-14T23:57:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"cohere",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T23:56:25Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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. -->
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- **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
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[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]
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<!-- 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
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
ymoslem/aya-expanse-8b-eng-arz-20layers
|
ymoslem
| 2025-08-14T23:55:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"cohere",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T23:55:01Z |
---
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
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#### 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
ymoslem/aya-expanse-8b-eng-arz-24layers
|
ymoslem
| 2025-08-14T23:54:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"cohere",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T23:53:03Z |
---
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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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**BibTeX:**
[More Information Needed]
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[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]
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## Model Card Contact
[More Information Needed]
|
GreenerPastures/Useful_Idiot_24B
|
GreenerPastures
| 2025-08-14T23:53:29Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"base_model:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only",
"base_model:merge:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only",
"base_model:hardlyworking/MS32-3",
"base_model:merge:hardlyworking/MS32-3",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-13T00:20:56Z |
---
base_model:
- anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
- hardlyworking/MS32-3
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
language:
- en
---
# Useful Idiot
A 24B parameter text only model fine-tuned on my own hardware (for the first time). I am very proud of how this turned out despite my best efforts at screwing it up and I hope you enjoy it.
|
Snowflake/Llama-3.3-SwiftKV-70B-Instruct-FP8
|
Snowflake
| 2025-08-14T23:51:53Z | 0 | 0 | null |
[
"safetensors",
"llama_swiftkv",
"arxiv:2410.03960",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.3-70B-Instruct",
"license:llama3.3",
"compressed-tensors",
"region:us"
] | null | 2025-08-14T19:16:21Z |
---
license: llama3.3
base_model:
- meta-llama/Llama-3.3-70B-Instruct
---
# SwiftKV
The Snowflake AI Research team is releasing a series of SwiftKV optimized Llama-3.x models. [SwiftKV](https://arxiv.org/abs/2410.03960) is a series of inference optimizations that goes beyond traditional key-value (KV) cache compression. This method reduces computational overhead during prompt processing by combining model rewiring and knowledge-preserving self-distillation, allowing prefill tokens to skip up to half the model's layers. SwiftKV achieves up to 2x improvements in throughput, latency, and cost efficiency with minimal accuracy loss, making LLM deployments more performant and economically viable.
For more details about SwiftKV and how to use it:
* ❄️ [SwiftKV: Accelerating Enterprise LLM Workloads with Knowledge Preserving Compute Reduction (blog)](https://www.snowflake.com/engineering-blog/swiftkv-llm-compute-reduction/)
* 📝 [SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation (arXiv)](https://arxiv.org/abs/2410.03960)
* 🚀 [Getting started guide](https://github.com/Snowflake-Labs/vllm/tree/swiftkv/examples/swiftkv)
## Revisions
* **release-2508 (Aug 2025):** Initial model release (context up to 128K)
## Performance Metrics
To evaluate SwiftKV’s performance, we focus on the following key metrics (see more details in our [blog](https://www.snowflake.com/engineering-blog/swiftkv-llm-compute-reduction/)):
* Combined throughput: The total number of input and output tokens processed per second. This determines:
* For batch processing, the time required to complete jobs.
* For interactive use, the volume of concurrent requests a system can handle.
* TTFT: The latency between a user request and receiving the first token in the response.
* TPOT: The latency between subsequent tokens after the first token.
Combined input and output throughput for Llama 3.1 70B (left) and Llama 3.1 405B (right) across a range of input lengths (bottom).
<img src="figure-4-full.png" alt="performance plot of llama-405B w. swiftkv" width="800">
TTFT (top) and TPOT (bottom) for input lengths 2000 (left), 8000 (middle), and 32000 (right) for Llama 3.1 405B fp8 model. For each experiment, a range of different request arrival rates is simulated. Each request generates 256 output tokens.
<img src="figure-6.png" alt="performance plot of llama-405B w. swiftkv" width="700">
## Eval Metrics
For a full breakdown on evaluation metrics and performance impact please refer to our [blog](https://www.snowflake.com/engineering-blog/swiftkv-llm-compute-reduction/) and [arXiv paper]((https://arxiv.org/abs/2410.03960)) but below we've outlined some relevant evaluation metrics.
| Llama-3.1-405B-Instruct-FP8 | Arc Challenge | Winogrande | HellaSwag | TruthfulQA | MMLU | MMLU cot | GSM8K | Avg |
|-----------|---------------|------------|-----------|------------|------|----------|-------|-----|
| Baseline | 94.7 | 87.0 | 88.3 | 64.7 | 87.5 | 88.1 | 96.1 | **86.6** |
| 50% SingleInputKV | 94.0 | 86.3 | 88.1 | 64.2 | 85.7 | 87.5 | 95.2 | **85.9** |
| Llama-3.1-8B-Instruct | Arc Challenge | Winogrande | HellaSwag | TruthfulQA | MMLU | MMLU cot | GSM8K | Avg |
|-----------|---------------|------------|-----------|------------|------|----------|-------|-----|
| Baseline | 82.00 | 77.90 | 80.40 | 54.56 | 67.90 | 70.63 | 82.56 | **73.71** |
| 50% SingleInputKV | 80.38 | 78.22 | 79.30 | 54.54 | 67.30 | 69.73 | 79.45 | **72.70** |
## Get started by serving SwiftKV on vLLM
Instructions on how to use vLLM for both evaluation and performance benchmarks:
https://github.com/Snowflake-Labs/vllm/tree/swiftkv/examples/swiftkv
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=ffcf2f24-d4af-4b2d-a01e-7a1af7235389" />
|
Jonwi0706/ebike-repair-agent
|
Jonwi0706
| 2025-08-14T23:51:27Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-14T23:40:49Z |
---
license: apache-2.0
---
|
Snowflake/Llama-3.3-SwiftKV-70B-Instruct
|
Snowflake
| 2025-08-14T23:51:11Z | 160 | 0 | null |
[
"safetensors",
"llama_swiftkv",
"arxiv:2410.03960",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:finetune:meta-llama/Llama-3.3-70B-Instruct",
"license:llama3.3",
"region:us"
] | null | 2025-01-23T22:14:30Z |
---
license: llama3.3
base_model:
- meta-llama/Llama-3.3-70B-Instruct
---
# SwiftKV
The Snowflake AI Research team is releasing a series of SwiftKV optimized Llama-3.x models. [SwiftKV](https://arxiv.org/abs/2410.03960) is a series of inference optimizations that goes beyond traditional key-value (KV) cache compression. This method reduces computational overhead during prompt processing by combining model rewiring and knowledge-preserving self-distillation, allowing prefill tokens to skip up to half the model's layers. SwiftKV achieves up to 2x improvements in throughput, latency, and cost efficiency with minimal accuracy loss, making LLM deployments more performant and economically viable.
For more details about SwiftKV and how to use it:
* ❄️ [SwiftKV: Accelerating Enterprise LLM Workloads with Knowledge Preserving Compute Reduction (blog)](https://www.snowflake.com/engineering-blog/swiftkv-llm-compute-reduction/)
* 📝 [SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation (arXiv)](https://arxiv.org/abs/2410.03960)
* 🚀 [Getting started guide](https://github.com/Snowflake-Labs/vllm/tree/swiftkv/examples/swiftkv)
## Revisions
* **release-2508 (Aug 2025):** Updated model weights for long-context up to 128K
* **release-2501 (Jan 2025):** Initial model release
## Performance Metrics
To evaluate SwiftKV’s performance, we focus on the following key metrics (see more details in our [blog](https://www.snowflake.com/engineering-blog/swiftkv-llm-compute-reduction/)):
* Combined throughput: The total number of input and output tokens processed per second. This determines:
* For batch processing, the time required to complete jobs.
* For interactive use, the volume of concurrent requests a system can handle.
* TTFT: The latency between a user request and receiving the first token in the response.
* TPOT: The latency between subsequent tokens after the first token.
Combined input and output throughput for Llama 3.1 70B (left) and Llama 3.1 405B (right) across a range of input lengths (bottom).
<img src="figure-4-full.png" alt="performance plot of llama-405B w. swiftkv" width="800">
TTFT (top) and TPOT (bottom) for input lengths 2000 (left), 8000 (middle), and 32000 (right) for Llama 3.1 405B fp8 model. For each experiment, a range of different request arrival rates is simulated. Each request generates 256 output tokens.
<img src="figure-6.png" alt="performance plot of llama-405B w. swiftkv" width="700">
## Eval Metrics
For a full breakdown on evaluation metrics and performance impact please refer to our [blog](https://www.snowflake.com/engineering-blog/swiftkv-llm-compute-reduction/) and [arXiv paper]((https://arxiv.org/abs/2410.03960)) but below we've outlined some relevant evaluation metrics.
| Llama-3.1-405B-Instruct-FP8 | Arc Challenge | Winogrande | HellaSwag | TruthfulQA | MMLU | MMLU cot | GSM8K | Avg |
|-----------|---------------|------------|-----------|------------|------|----------|-------|-----|
| Baseline | 94.7 | 87.0 | 88.3 | 64.7 | 87.5 | 88.1 | 96.1 | **86.6** |
| 50% SingleInputKV | 94.0 | 86.3 | 88.1 | 64.2 | 85.7 | 87.5 | 95.2 | **85.9** |
| Llama-3.1-8B-Instruct | Arc Challenge | Winogrande | HellaSwag | TruthfulQA | MMLU | MMLU cot | GSM8K | Avg |
|-----------|---------------|------------|-----------|------------|------|----------|-------|-----|
| Baseline | 82.00 | 77.90 | 80.40 | 54.56 | 67.90 | 70.63 | 82.56 | **73.71** |
| 50% SingleInputKV | 80.38 | 78.22 | 79.30 | 54.54 | 67.30 | 69.73 | 79.45 | **72.70** |
## Get started by serving SwiftKV on vLLM
Instructions on how to use vLLM for both evaluation and performance benchmarks:
https://github.com/Snowflake-Labs/vllm/tree/swiftkv/examples/swiftkv
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=ffcf2f24-d4af-4b2d-a01e-7a1af7235389" />
|
Snowflake/Llama-3.1-SwiftKV-8B-Instruct-FP8
|
Snowflake
| 2025-08-14T23:50:24Z | 254 | 0 | null |
[
"safetensors",
"llama_swiftkv",
"arxiv:2410.03960",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"compressed-tensors",
"region:us"
] | null | 2024-12-04T23:12:06Z |
---
license: llama3.1
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---
# SwiftKV
The Snowflake AI Research team is releasing a series of SwiftKV optimized Llama-3.1 models. [SwiftKV](https://arxiv.org/abs/2410.03960) is a series of inference optimizations that goes beyond traditional key-value (KV) cache compression. This method reduces computational overhead during prompt processing by combining model rewiring and knowledge-preserving self-distillation, allowing prefill tokens to skip up to half the model's layers. SwiftKV achieves up to 2x improvements in throughput, latency, and cost efficiency with minimal accuracy loss, making LLM deployments more performant and economically viable.
For more details about SwiftKV and how to use it:
* ❄️ [SwiftKV: Accelerating Enterprise LLM Workloads with Knowledge Preserving Compute Reduction (blog)](https://www.snowflake.com/engineering-blog/swiftkv-llm-compute-reduction/)
* 📝 [SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation (arXiv)](https://arxiv.org/abs/2410.03960)
* 🚀 [Getting started guide](https://github.com/Snowflake-Labs/vllm/tree/swiftkv/examples/swiftkv)
## Revisions
* **release-2508 (Aug 2025):** Updated model weights for long-context up to 128K
* **release-2412 (Dec 2024):** Initial model release
## Performance Metrics
To evaluate SwiftKV’s performance, we focus on the following key metrics (see more details in our [blog](https://www.snowflake.com/engineering-blog/swiftkv-llm-compute-reduction/)):
* Combined throughput: The total number of input and output tokens processed per second. This determines:
* For batch processing, the time required to complete jobs.
* For interactive use, the volume of concurrent requests a system can handle.
* TTFT: The latency between a user request and receiving the first token in the response.
* TPOT: The latency between subsequent tokens after the first token.
Combined input and output throughput for Llama 3.1 70B (left) and Llama 3.1 405B (right) across a range of input lengths (bottom).
<img src="figure-4-full.png" alt="performance plot of llama-405B w. swiftkv" width="800">
TTFT (top) and TPOT (bottom) for input lengths 2000 (left), 8000 (middle), and 32000 (right) for Llama 3.1 405B fp8 model. For each experiment, a range of different request arrival rates is simulated. Each request generates 256 output tokens.
<img src="figure-6.png" alt="performance plot of llama-405B w. swiftkv" width="700">
## Eval Metrics
For a full breakdown on evaluation metrics and performance impact please refer to our [blog](https://www.snowflake.com/engineering-blog/swiftkv-llm-compute-reduction/) and [arXiv paper]((https://arxiv.org/abs/2410.03960)) but below we've outlined some relevant evaluation metrics.
| Llama-3.1-405B-Instruct-FP8 | Arc Challenge | Winogrande | HellaSwag | TruthfulQA | MMLU | MMLU cot | GSM8K | Avg |
|-----------|---------------|------------|-----------|------------|------|----------|-------|-----|
| Baseline | 94.7 | 87.0 | 88.3 | 64.7 | 87.5 | 88.1 | 96.1 | **86.6** |
| 50% SingleInputKV | 94.0 | 86.3 | 88.1 | 64.2 | 85.7 | 87.5 | 95.2 | **85.9** |
| Llama-3.1-8B-Instruct | Arc Challenge | Winogrande | HellaSwag | TruthfulQA | MMLU | MMLU cot | GSM8K | Avg |
|-----------|---------------|------------|-----------|------------|------|----------|-------|-----|
| Baseline | 82.00 | 77.90 | 80.40 | 54.56 | 67.90 | 70.63 | 82.56 | **73.71** |
| 50% SingleInputKV | 80.38 | 78.22 | 79.30 | 54.54 | 67.30 | 69.73 | 79.45 | **72.70** |
## Get started by serving SwiftKV on vLLM
Instructions on how to use vLLM for both evaluation and performance benchmarks:
https://github.com/Snowflake-Labs/vllm/tree/swiftkv/examples/swiftkv
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=524d9de3-e73f-402f-b7a0-ef3c42170245" />
|
lejonck/whisper-small-ptbr-mupe-final2
|
lejonck
| 2025-08-14T23:46:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:lejonck/whisper-small-ptbr-mupe-final1",
"base_model:finetune:lejonck/whisper-small-ptbr-mupe-final1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-14T23:45:26Z |
---
library_name: transformers
license: apache-2.0
base_model: lejonck/whisper-small-ptbr-mupe-final1
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-ptbr-mupe-final3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-ptbr-mupe-final3
This model is a fine-tuned version of [lejonck/whisper-small-ptbr-mupe-final1](https://huggingface.co/lejonck/whisper-small-ptbr-mupe-final1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2697
- Wer: 0.3986
- Cer: 0.6141
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.326 | 1.0 | 375 | 0.9581 | 0.4462 | 0.6216 |
| 0.1473 | 2.0 | 750 | 1.0390 | 0.4458 | 0.6259 |
| 0.0431 | 3.0 | 1125 | 1.0292 | 0.4306 | 0.6188 |
| 0.0197 | 4.0 | 1500 | 1.0942 | 0.4406 | 0.6191 |
| 0.0199 | 5.0 | 1875 | 1.1880 | 0.4438 | 0.6196 |
| 0.0021 | 6.0 | 2250 | 1.1897 | 0.4066 | 0.6139 |
| 0.0012 | 7.0 | 2625 | 1.2027 | 0.4182 | 0.6149 |
| 0.0007 | 8.0 | 3000 | 1.2243 | 0.4042 | 0.6122 |
| 0.0006 | 9.0 | 3375 | 1.2533 | 0.4194 | 0.6168 |
| 0.0004 | 10.0 | 3750 | 1.2620 | 0.3998 | 0.6141 |
| 0.0006 | 11.0 | 4125 | 1.2550 | 0.4126 | 0.6145 |
| 0.0004 | 12.0 | 4500 | 1.2486 | 0.3966 | 0.6115 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
smirki/UIGEN-X-4B-STG
|
smirki
| 2025-08-14T23:38:23Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"conversational",
"base_model:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-13T17:06:11Z |
---
base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit
library_name: transformers
model_name: UIGEN-X-4B-STG
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for UIGEN-X-4B-STG
This model is a fine-tuned version of [unsloth/Qwen3-4B-unsloth-bnb-4bit](https://huggingface.co/unsloth/Qwen3-4B-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="smirki/UIGEN-X-4B-STG", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.56.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
arnastofnun/Llama-3.2-3B-wmt25-AMI-en-is
|
arnastofnun
| 2025-08-14T23:36:42Z | 0 | 0 | null |
[
"safetensors",
"llama",
"translation",
"en",
"is",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"region:us"
] |
translation
| 2025-08-14T23:19:32Z |
---
license: llama3.2
language:
- en
- is
base_model:
- meta-llama/Llama-3.2-3B
pipeline_tag: translation
---
|
ajagota71/SmolLM-135M-detox-checkpoint-epoch-100
|
ajagota71
| 2025-08-14T23:36:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-14T23:35:29Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="ajagota71//content/irl_llms/outputs/2025-08-14_21-01-47/checkpoints/checkpoint-epoch-100")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("ajagota71//content/irl_llms/outputs/2025-08-14_21-01-47/checkpoints/checkpoint-epoch-100")
model = AutoModelForCausalLMWithValueHead.from_pretrained("ajagota71//content/irl_llms/outputs/2025-08-14_21-01-47/checkpoints/checkpoint-epoch-100")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
Mauroicardi43/blockassist-bc-giant_agile_crab_1755214311
|
Mauroicardi43
| 2025-08-14T23:32:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"giant agile crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T23:32:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- giant agile crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Obiwank107/blockassist-bc-tame_foxy_aardvark_1755211265
|
Obiwank107
| 2025-08-14T23:32:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tame foxy aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T23:31:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tame foxy aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Chillarmo/whisper-small-armenian
|
Chillarmo
| 2025-08-14T23:31:55Z | 120 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hy",
"dataset:Chillarmo/common_voice_20_armenian",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-11T19:29:18Z |
---
license: apache-2.0
datasets:
- Chillarmo/common_voice_20_armenian
language:
- hy
base_model:
- openai/whisper-small
pipeline_tag: automatic-speech-recognition
library_name: transformers
model-index:
- name: whisper-small-armenian
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 20 Armenian
type: Chillarmo/common_voice_20_armenian
metrics:
- type: wer
value: 29.986160233738275
name: Word Error Rate
- type: cer
value: 5.967044631948796
name: Character Error Rate
- type: exact_match
value: 19.023224043715846
name: Exact Match
metrics:
- wer
- cer
- exact_match
new_version: Chillarmo/whisper-small-armenian-v2
---
# Whisper-Small Fine-tuned for Armenian ASR
This model is a fine-tuned version of OpenAI's Whisper-small on the Common Voice 20 Armenian dataset for automatic speech recognition.
## Training Results
The model was trained for 5.34 epochs with the following final results:
| Metric | Value |
|--------|-------|
| **Training Loss** | 0.101 |
| **Training Runtime** | 59,610 seconds (≈16.56 hours) |
| **Training Samples/Second** | 1.34 |
| **Training Steps/Second** | 0.084 |
| **Total Training Steps** | 5,000 |
| **Epochs** | 5.34 |
## Evaluation Results
| Metric | Value |
|--------|-------|
| **Evaluation Loss** | 0.165 |
| **Word Error Rate (WER)** | 29.43% |
| **Character Error Rate (CER)** | 5.89% |
| **Exact Match** | 20.05% |
| **Average Prediction Length** | 7.71 tokens |
| **Average Label Length** | 7.77 tokens |
| **Length Ratio** | 0.992 |
| **Evaluation Runtime** | 9,486 seconds (≈2.63 hours) |
| **Evaluation Samples/Second** | 0.62 |
| **Evaluation Steps/Second** | 0.077 |
## Model Details
- **Base Model**: openai/whisper-small
- **Language**: Armenian (hy)
- **Dataset**: Chillarmo/common_voice_20_armenian
- **License**: Apache 2.0
|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts_pnas_layer_10_3_all_37_0.001_1280_1
|
winnieyangwannan
| 2025-08-14T23:31:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T23:30:01Z |
---
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]
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755213141
|
Sayemahsjn
| 2025-08-14T23:31:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T23:31:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TrySalient/tts-collections-test
|
TrySalient
| 2025-08-14T23:30:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T23:29:28Z |
---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** TrySalient
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
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)
|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts_pnas_layer_10_3_all_37_0.0001_640_1
|
winnieyangwannan
| 2025-08-14T23:30:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T23:28:46Z |
---
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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
ajagota71/SmolLM-360M-detox-checkpoint-epoch-80
|
ajagota71
| 2025-08-14T23:29:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-14T23:28:52Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="ajagota71//kaggle/working/irl_llms/outputs/2025-08-14_21-11-43/checkpoints/temp-checkpoint-epoch-80")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-08-14_21-11-43/checkpoints/temp-checkpoint-epoch-80")
model = AutoModelForCausalLMWithValueHead.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-08-14_21-11-43/checkpoints/temp-checkpoint-epoch-80")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
slarkcrypto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thorny_foraging_tuna
|
slarkcrypto
| 2025-08-14T23:28:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am thorny_foraging_tuna",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T22:14:41Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am thorny_foraging_tuna
---
# 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]
|
Vyvo/VyvoTTS-LFM2-Stephen_Fry
|
Vyvo
| 2025-08-14T23:24:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"lfm2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T23:23:47Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mrpopsalot/sdxl_dmd2_PSO_webui
|
mrpopsalot
| 2025-08-14T23:23:53Z | 0 | 0 | null |
[
"lora",
"region:us"
] | null | 2025-08-10T23:09:38Z |
---
tags:
- lora
---
<style>
.main-div {
background-color: #9999ff; padding: 20px; border-radius: 10px; margin-bottom: 20px;
}
h1 {
color: #24292e;
margin-top: 0;
font-size: 25px;
}
h2 {
color: #24292e;
margin-top: 0;
font-size: 22px;
}
h3 {
color: #24292e;
margin-top: 0;
font-size: 20px;
}
h4 {
color: #24292e;
margin-top: 0;
font-size: 15px;
}
p {
color:#24292e;
}
a {
color: #0366d6;
text-decoration: none;
}
.section-div {
background-color: white;
padding: 15px;
border-radius: 8px;
margin: 15px 0;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.subsection-div {
background-color: #bbb;
padding: 15px;
border-radius: 8px;
margin: 15px 0;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
ul {
margin-bottom: 0;
color: #24292e;
}
ol {
margin-bottom: 0;
color: #24292e;
}
td {
margin-bottom: 0;
color: #24292e;
}
code {
background-color: #333;
padding: 3px 6px;
border-radius: 4px;
}
table {
color: #24292e;
}
</style>
<div class="main-div">
<h1>PSO LoRA for SDXL</h1>
<div class="section-div">
<p></p>PSO LoRA for SDXL. Not mine (I do not know the creator either), just restributing to the wider world. Use with <a href="https://huggingface.co/tianweiy/DMD2">DMD2</a></p>
</div>
<div class="section-div">
<h2>Features</h2>
<ul>
<li>Sampler: LCM</li>
<li>Scheduler: Normal</li>
<li>CFG 1</li>
<li>Steps: 8 to 16</li>
</ul>
</div>
</div>
|
JonusNattapong/hanuman-ultimate-system
|
JonusNattapong
| 2025-08-14T23:20:22Z | 0 | 0 | null |
[
"pytorch",
"HanumanSLM",
"thai",
"conversational",
"personality",
"emotion",
"temporal-reasoning",
"causal-lm",
"text-generation",
"custom_code",
"th",
"license:cc-by-nc-4.0",
"region:us"
] |
text-generation
| 2025-08-14T23:13:51Z |
---
language: th
license: cc-by-nc-4.0
tags:
- thai
- conversational
- personality
- emotion
- temporal-reasoning
- causal-lm
pipeline_tag: text-generation
widget:
- text: "สวัสดีครับ คุณเป็นใคร"
- text: "รู้สึกเศร้า ช่วยให้กำลังใจหน่อย"
- text: "วิเคราะห์ผลกระทบของ AI ต่อสังคม"
---
|
tatsuyaaaaaaa/gpt-oss-20b-gguf
|
tatsuyaaaaaaa
| 2025-08-14T23:19:26Z | 4,718 | 0 | null |
[
"gguf",
"en",
"ja",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-07T04:14:07Z |
---
license: apache-2.0
language:
- en
- ja
base_model:
- openai/gpt-oss-20b
---
OpenAIの[gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)をgguf変換したものです。
|
bruhzair/prototype-0.4x322
|
bruhzair
| 2025-08-14T23:16:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T22:56:45Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x322
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Multi-SLERP](https://goddard.blog/posts/multislerp-wow-what-a-cool-idea) merge method using /workspace/prototype-0.4x305 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--BruhzWater--Serpents-Tongue-L3.3-70b-0.4a/snapshots/bc1fe5ab4d746afa04d5e591f4fd69c493f0bf68
* /workspace/cache/models--bruhzair--prototype-0.4x301/snapshots/ba244ff497fd1a57c7954269c360a0f96e89f7a4
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--BruhzWater--Serpents-Tongue-L3.3-70b-0.4a/snapshots/bc1fe5ab4d746afa04d5e591f4fd69c493f0bf68
parameters:
weight: [0.5]
- model: /workspace/cache/models--bruhzair--prototype-0.4x301/snapshots/ba244ff497fd1a57c7954269c360a0f96e89f7a4
parameters:
weight: [0.5]
base_model: /workspace/prototype-0.4x305
merge_method: multislerp
tokenizer:
source: base
chat_template: llama3
parameters:
normalize_weights: false
eps: 1e-9
pad_to_multiple_of: 8
int8_mask: true
dtype: bfloat16
```
|
mohammadmahdinouri/mol-trained-v2-vanilla
|
mohammadmahdinouri
| 2025-08-14T23:12:15Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"ModernALBERT_MoL",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-14T13:27:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chainway9/blockassist-bc-untamed_quick_eel_1755211451
|
chainway9
| 2025-08-14T23:11:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T23:11:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
apriasmoro/f5db5d3d-5ac9-494a-8ec1-4b1efe1f44eb
|
apriasmoro
| 2025-08-14T23:09:36Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Genstruct-7B",
"base_model:adapter:NousResearch/Genstruct-7B",
"region:us"
] | null | 2025-08-14T23:08:56Z |
---
base_model: NousResearch/Genstruct-7B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
kev216/20250814_old_new_finetune_qwen3_for_gpt5_4langs_5epoch
|
kev216
| 2025-08-14T23:08:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-14T23:07:35Z |
---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** kev216
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ajagota71/SmolLM-135M-detox-checkpoint-epoch-80
|
ajagota71
| 2025-08-14T23:05:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-14T23:04:52Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="ajagota71//content/irl_llms/outputs/2025-08-14_21-01-47/checkpoints/temp-checkpoint-epoch-80")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("ajagota71//content/irl_llms/outputs/2025-08-14_21-01-47/checkpoints/temp-checkpoint-epoch-80")
model = AutoModelForCausalLMWithValueHead.from_pretrained("ajagota71//content/irl_llms/outputs/2025-08-14_21-01-47/checkpoints/temp-checkpoint-epoch-80")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
Mauroicardi43/blockassist-bc-giant_agile_crab_1755212629
|
Mauroicardi43
| 2025-08-14T23:04:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"giant agile crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T23:04:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- giant agile crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muvels/gpt-oss-6b-7experts-gguf
|
Muvels
| 2025-08-14T22:59:30Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-14T22:59:30Z |
---
license: apache-2.0
---
|
ajagota71/SmolLM-360M-detox-checkpoint-epoch-60
|
ajagota71
| 2025-08-14T22:55:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-14T22:54:37Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="ajagota71//kaggle/working/irl_llms/outputs/2025-08-14_21-11-43/checkpoints/temp-checkpoint-epoch-60")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-08-14_21-11-43/checkpoints/temp-checkpoint-epoch-60")
model = AutoModelForCausalLMWithValueHead.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-08-14_21-11-43/checkpoints/temp-checkpoint-epoch-60")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
Coaster41/patchtst-sae-8-4.0
|
Coaster41
| 2025-08-14T22:52:14Z | 0 | 0 |
saelens
|
[
"saelens",
"region:us"
] | null | 2025-08-14T22:22:16Z |
---
library_name: saelens
---
# SAEs for use with the SAELens library
This repository contains the following SAEs:
- blocks.1.hook_mlp_out
Load these SAEs using SAELens as below:
```python
from sae_lens import SAE
sae = SAE.from_pretrained("Coaster41/patchtst-sae-8-4.0", "<sae_id>")
```
|
mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF
|
mradermacher
| 2025-08-14T22:52:05Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:shayanfirouzian/DeepSeek-R1-Qwen3-8B-abliterated",
"base_model:quantized:shayanfirouzian/DeepSeek-R1-Qwen3-8B-abliterated",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-14T17:52:20Z |
---
base_model: shayanfirouzian/DeepSeek-R1-Qwen3-8B-abliterated
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/shayanfirouzian/DeepSeek-R1-Qwen3-8B-abliterated
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-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/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ1_S.gguf) | i1-IQ1_S | 2.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ1_M.gguf) | i1-IQ1_M | 2.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ2_S.gguf) | i1-IQ2_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ2_M.gguf) | i1-IQ2_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.2 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q2_K.gguf) | i1-Q2_K | 3.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ3_S.gguf) | i1-IQ3_S | 3.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ3_M.gguf) | i1-IQ3_M | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q4_0.gguf) | i1-Q4_0 | 4.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.9 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q4_1.gguf) | i1-Q4_1 | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-i1-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.i1-Q6_K.gguf) | i1-Q6_K | 6.8 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF
|
mradermacher
| 2025-08-14T22:52:04Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:shayanfirouzian/DeepSeek-R1-Qwen3-8B-abliterated",
"base_model:quantized:shayanfirouzian/DeepSeek-R1-Qwen3-8B-abliterated",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-14T14:06:00Z |
---
base_model: shayanfirouzian/DeepSeek-R1-Qwen3-8B-abliterated
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## 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/shayanfirouzian/DeepSeek-R1-Qwen3-8B-abliterated
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#DeepSeek-R1-Qwen3-8B-abliterated-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-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/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-R1-Qwen3-8B-abliterated-GGUF/resolve/main/DeepSeek-R1-Qwen3-8B-abliterated.f16.gguf) | f16 | 16.5 | 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 -->
|
pobiiiiiii/blockassist-bc-ravenous_yapping_ferret_1755211652
|
pobiiiiiii
| 2025-08-14T22:49:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ravenous yapping ferret",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T22:48:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ravenous yapping ferret
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AngelinaZanardi/gte-multilingual-base-edu-scorer-lr3e4-bs32-swe
|
AngelinaZanardi
| 2025-08-14T22:46:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"new",
"text-classification",
"generated_from_trainer",
"custom_code",
"base_model:Alibaba-NLP/gte-multilingual-base",
"base_model:finetune:Alibaba-NLP/gte-multilingual-base",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-classification
| 2025-08-14T17:14:58Z |
---
library_name: transformers
license: apache-2.0
base_model: Alibaba-NLP/gte-multilingual-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: gte-multilingual-base-edu-scorer-lr3e4-bs32-swe
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. -->
# gte-multilingual-base-edu-scorer-lr3e4-bs32-swe
This model is a fine-tuned version of [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9336
- Precision: 0.0478
- Recall: 0.1667
- F1 Macro: 0.0742
- Accuracy: 0.2866
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| No log | 0 | 0 | 4.1021 | 0.0513 | 0.1666 | 0.0785 | 0.3078 |
| 1.73 | 0.6793 | 1000 | 2.0901 | 0.0471 | 0.1667 | 0.0735 | 0.2824 |
| 1.6976 | 1.3587 | 2000 | 1.9935 | 0.0471 | 0.1667 | 0.0735 | 0.2824 |
| 1.6589 | 2.0380 | 3000 | 2.0057 | 0.1027 | 0.1670 | 0.0742 | 0.2828 |
| 1.6456 | 2.7174 | 4000 | 2.3130 | 0.0471 | 0.1667 | 0.0735 | 0.2824 |
| 1.6927 | 3.3967 | 5000 | 2.1982 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6659 | 4.0761 | 6000 | 2.0477 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6728 | 4.7554 | 7000 | 2.0806 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6714 | 5.4348 | 8000 | 2.1802 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6831 | 6.1141 | 9000 | 2.6460 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6592 | 6.7935 | 10000 | 2.2354 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.654 | 7.4728 | 11000 | 2.3264 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6622 | 8.1522 | 12000 | 2.1686 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6618 | 8.8315 | 13000 | 2.1844 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6624 | 9.5109 | 14000 | 2.0627 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.7082 | 10.1902 | 15000 | 2.0176 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.7107 | 10.8696 | 16000 | 2.2305 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6538 | 11.5489 | 17000 | 2.2190 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6453 | 12.2283 | 18000 | 2.4751 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
| 1.6562 | 12.9076 | 19000 | 2.3006 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
| 1.6767 | 13.5870 | 20000 | 2.0474 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.672 | 14.2663 | 21000 | 1.9881 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
| 1.6355 | 14.9457 | 22000 | 1.9897 | 0.0471 | 0.1667 | 0.0734 | 0.2824 |
| 1.6608 | 15.625 | 23000 | 2.1116 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
| 1.6616 | 16.3043 | 24000 | 2.0205 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
| 1.6895 | 16.9837 | 25000 | 2.3756 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
| 1.6613 | 17.6630 | 26000 | 2.1950 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
| 1.6405 | 18.3424 | 27000 | 2.0526 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
| 1.6242 | 19.0217 | 28000 | 2.0278 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
| 1.6839 | 19.7011 | 29000 | 2.0130 | 0.0179 | 0.1667 | 0.0323 | 0.1073 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.5.1+cu121
- Datasets 4.0.0
- Tokenizers 0.21.4
|
unbeatablemx/a7paulaboreal
|
unbeatablemx
| 2025-08-14T22:46:23Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-14T22:08:07Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: A7PAULABOREAL
---
# A7Paulaboreal
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `A7PAULABOREAL` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "A7PAULABOREAL",
"lora_weights": "https://huggingface.co/unbeatablemx/a7paulaboreal/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('unbeatablemx/a7paulaboreal', weight_name='lora.safetensors')
image = pipeline('A7PAULABOREAL').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3500
- Learning rate: 0.0001
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/unbeatablemx/a7paulaboreal/discussions) to add images that show off what you’ve made with this LoRA.
|
FRANCISCOFALT/FALT
|
FRANCISCOFALT
| 2025-08-14T22:45:33Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-14T22:45:33Z |
---
license: apache-2.0
---
|
Parth212/lunarLanding
|
Parth212
| 2025-08-14T22:38:27Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-14T22:37:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 262.52 +/- 25.74
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
MaestroDev19/CyberGemma-3-4b
|
MaestroDev19
| 2025-08-14T22:37:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-14T22:37:36Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** MaestroDev19
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
TrySalient/500-original-v2-emotion-stable
|
TrySalient
| 2025-08-14T22:36:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T22:34:51Z |
---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** TrySalient
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
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)
|
Mauroicardi43/blockassist-bc-giant_agile_crab_1755210757
|
Mauroicardi43
| 2025-08-14T22:33:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"giant agile crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T22:33:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- giant agile crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Gaseous/Gensyn
|
Gaseous
| 2025-08-14T22:32:41Z | 0 | 0 | null |
[
"en",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"license:mit",
"region:us"
] | null | 2025-08-14T22:27:00Z |
---
license: mit
language:
- en
base_model:
- Gensyn/Qwen2.5-0.5B-Instruct
---
|
hejazizo/grpo-merged-checkpoint-891_2025-08-13_15-32
|
hejazizo
| 2025-08-14T22:28:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:hejazizo/merged-checkpoint-891",
"base_model:finetune:hejazizo/merged-checkpoint-891",
"endpoints_compatible",
"region:us"
] | null | 2025-08-13T19:32:54Z |
---
base_model: hejazizo/merged-checkpoint-891
library_name: transformers
model_name: grpo-merged-checkpoint-891_2025-08-13_15-32
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for grpo-merged-checkpoint-891_2025-08-13_15-32
This model is a fine-tuned version of [hejazizo/merged-checkpoint-891](https://huggingface.co/hejazizo/merged-checkpoint-891).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hejazizo/grpo-merged-checkpoint-891_2025-08-13_15-32", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hejazizo-ali-pytopia/grpo-merged-checkpoint-891/runs/muwlkwcp)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.20.0
- Transformers: 4.55.0
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
nkerr/sv3.3-bigbird-roberta-large
|
nkerr
| 2025-08-14T22:26:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"big_bird",
"text-classification",
"generated_from_trainer",
"base_model:google/bigbird-roberta-large",
"base_model:finetune:google/bigbird-roberta-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-14T22:26:00Z |
---
library_name: transformers
license: apache-2.0
base_model: google/bigbird-roberta-large
tags:
- generated_from_trainer
model-index:
- name: sv3.3-bigbird-roberta-large
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. -->
# sv3.3-bigbird-roberta-large
This model is a fine-tuned version of [google/bigbird-roberta-large](https://huggingface.co/google/bigbird-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2977
- Mse: 0.2977
- Mae: 0.5234
- Rmse: 0.5456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | Rmse |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
| 0.1285 | 0.1548 | 50 | 0.4284 | 0.4284 | 0.6358 | 0.6545 |
| 0.1019 | 0.3096 | 100 | 0.4425 | 0.4425 | 0.6468 | 0.6652 |
| 0.1012 | 0.4644 | 150 | 0.3164 | 0.3164 | 0.5409 | 0.5625 |
| 0.0946 | 0.6192 | 200 | 0.4275 | 0.4275 | 0.6351 | 0.6538 |
| 0.0911 | 0.7740 | 250 | 0.3013 | 0.3013 | 0.5269 | 0.5489 |
| 0.0948 | 0.9288 | 300 | 0.1954 | 0.1954 | 0.4166 | 0.4421 |
| 0.1215 | 1.0836 | 350 | 0.4126 | 0.4126 | 0.6233 | 0.6423 |
| 0.0872 | 1.2384 | 400 | 0.3116 | 0.3116 | 0.5365 | 0.5582 |
| 0.0972 | 1.3932 | 450 | 0.3407 | 0.3407 | 0.5629 | 0.5837 |
| 0.0953 | 1.5480 | 500 | 0.3546 | 0.3546 | 0.5750 | 0.5955 |
| 0.0913 | 1.7028 | 550 | 0.3799 | 0.3799 | 0.5965 | 0.6164 |
| 0.0948 | 1.8576 | 600 | 0.2853 | 0.2853 | 0.5115 | 0.5342 |
| 0.0912 | 2.0124 | 650 | 0.3401 | 0.3401 | 0.5623 | 0.5832 |
| 0.09 | 2.1672 | 700 | 0.3295 | 0.3295 | 0.5528 | 0.5740 |
| 0.0956 | 2.3220 | 750 | 0.3267 | 0.3267 | 0.5503 | 0.5715 |
| 0.0944 | 2.4768 | 800 | 0.3099 | 0.3099 | 0.5349 | 0.5567 |
| 0.0886 | 2.6316 | 850 | 0.3036 | 0.3036 | 0.5290 | 0.5510 |
| 0.0931 | 2.7864 | 900 | 0.3109 | 0.3109 | 0.5358 | 0.5576 |
| 0.087 | 2.9412 | 950 | 0.2977 | 0.2977 | 0.5234 | 0.5456 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu126
- Datasets 3.3.2
- Tokenizers 0.21.0
|
SmnZin/T5-small-esp-mapu
|
SmnZin
| 2025-08-14T22:26:08Z | 0 | 0 | null |
[
"safetensors",
"t5",
"nlp",
"es",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"region:us"
] | null | 2025-08-14T22:19:08Z |
---
license: apache-2.0
language:
- es
base_model:
- google-t5/t5-small
tags:
- nlp
---
|
Mattimax/DAC_4.3-Q4_K_M-GGUF
|
Mattimax
| 2025-08-14T22:20:48Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Mattimax/DAC_4.3",
"base_model:quantized:Mattimax/DAC_4.3",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-14T22:20:36Z |
---
license: gpl-3.0
base_model: Mattimax/DAC_4.3
tags:
- llama-cpp
- gguf-my-repo
---
# Mattimax/DAC_4.3-Q4_K_M-GGUF
This model was converted to GGUF format from [`Mattimax/DAC_4.3`](https://huggingface.co/Mattimax/DAC_4.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Mattimax/DAC_4.3) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Mattimax/DAC_4.3-Q4_K_M-GGUF --hf-file dac_4.3-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Mattimax/DAC_4.3-Q4_K_M-GGUF --hf-file dac_4.3-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Mattimax/DAC_4.3-Q4_K_M-GGUF --hf-file dac_4.3-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Mattimax/DAC_4.3-Q4_K_M-GGUF --hf-file dac_4.3-q4_k_m.gguf -c 2048
```
|
8lianno/flux_blue_collage
|
8lianno
| 2025-08-14T22:20:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-14T22:19:30Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: nilacut
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# flux_blue_collage
<Gallery />
## Model description
nilacut is a LoRA model trained to generate images in a distinct cut-paper collage style, characterized by bold ultramarine blues, geometric abstraction, and layered shapes. Inspired by modernist and Mediterranean aesthetics, the style uses flat color planes, sharp paper-like edges, and high-contrast compositions.
While originally based on feline and street scene motifs, the style generalizes well to any subject, from portraits to landscapes, architecture, still life, and abstract concepts.
Trigger word: nilacut — Place at the start of your prompt to activate the style.
## Trigger words
You should use `nilacut` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/8lianno/flux_blue_collage/tree/main) them in the Files & versions tab.
|
otmangi/Offensive_Darija_MorRoBERTa
|
otmangi
| 2025-08-14T22:19:41Z | 0 | 0 | null |
[
"safetensors",
"roberta",
"license:cc",
"region:us"
] | null | 2025-08-13T22:18:15Z |
---
license: cc
---
# Off_MorRoBERTa
Off_MorRoBERTa is a Transformer-based language model designed to classify comments in the Moroccan dialect as abusive or non-abusive.
## About Off_MorRoBERTa
Off_MorRoBERTa, developed specifically for the Moroccan dialect, provides an effective approach for detecting abusive content. The model is based on MorRoBERTa, which was fine-tuned on a labeled dataset created for hate speech detection in Moroccan dialect texts.
The dataset used for training was built by aggregating and preprocessing data from publicly available sources, including MDMD, OMCD, and MDOLDD.
## Usage
The model weights can be loaded using transformers library by HuggingFace.
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("otmangi/Offensive_Darija_MorRoBERTa")
model = AutoModel.from_pretrained("otmangi/Offensive_Darija_MorRoBERTa")
## Related Paper
For more details about the dataset, methodology, and evaluation, please refer to our paper:
https://accentsjournals.org/paperinfo.php?journalPaperId=1809
## Contact
For any inquiries, feedback, or requests, please feel free to reach out to :
otman.moussaoui@etu.uae.ac.ma
|
elmenbillion/blockassist-bc-beaked_sharp_otter_1755208401
|
elmenbillion
| 2025-08-14T22:18:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked sharp otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T22:18:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked sharp otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
daniel-dona/gemma-3-270m-it
|
daniel-dona
| 2025-08-14T22:16:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"gemma3",
"gemma",
"google",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T22:03:54Z |
---
license: gemma
tags:
- gemma3
- gemma
- google
pipeline_tag: text-generation
library_name: transformers
base_model: google/gemma-3-270m-it
---
|
artificialguybr/WikiHowSDModel
|
artificialguybr
| 2025-08-14T22:15:27Z | 0 | 8 | null |
[
"license:openrail",
"region:us"
] | null | 2022-10-23T06:44:30Z |
---
license: openrail
---
This model card is a copy-paste from https://www.reddit.com/r/StableDiffusion/comments/ybavif/wikihow_db_model_entirely_free_model_trained_with/
The template is not 100% accurate and sometimes creates erroneous images, but it is incomparable to the natural quality of SD.
The images used for training were all CC from Wikihow. Template available on Hugging Face.
The trigger word for traditional Embeddings is the filename.
The Traditional Embeddings were split into two rar files: One with 0.005 training and the other with 0.00005 training. All with 20 images and 2000 Steps. The two rar files, plus the Embedding file still have the images for you to evaluate which one you want to use.
There is the Winrar file Embedding Aesthestics which is what the name says.
To activate the Dreambooth you must write in the PROMPT: '' in WKHW1 Beautiful Art Style''.
Test which combination works for you. Model + Aesthestics. Model without aesthestics. Model with Embedding. Model without Embedding.:
All my templates are 100% free. All my models are 100% free. You can check in my profile my Coloring Book model posted 12 hours ago.
---
### 🌐 Website
You can find more of my models, projects, and information on my official website:
- **[artificialguy.com](https://artificialguy.com/)**
### 💖 Support My Work
If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
You can contribute on Patreon and Buymeacoffe. ALL money raised will go towards buying GPU/Rent hours and paying Colab to bring in better models.
I plan to bring Dreambooth, TI, and Hypernetworks models. However, my Hypernetworks is still defective and I am trying to fix it.
If you want any specific models you can contact me here and send me pictures and where I can find the datasets.
|
artificialguybr/ILCOSDMODEL
|
artificialguybr
| 2025-08-14T22:15:21Z | 0 | 2 | null |
[
"license:openrail",
"region:us"
] | null | 2022-10-24T23:47:01Z |
---
license: openrail
---
---
### 🌐 Website
You can find more of my models, projects, and information on my official website:
- **[artificialguy.com](https://artificialguy.com/)**
### 💖 Support My Work
If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
|
artificialguybr/UiUxAppModelForSd2.0
|
artificialguybr
| 2025-08-14T22:15:09Z | 0 | 1 | null |
[
"text-to-image",
"license:openrail",
"region:us"
] |
text-to-image
| 2022-11-30T03:21:27Z |
---
license: openrail
tags:
- text-to-image
---
---
### 🌐 Website
You can find more of my models, projects, and information on my official website:
- **[artificialguy.com](https://artificialguy.com/)**
### 💖 Support My Work
If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
|
artificialguybr/whisper-small-pt-cv13
|
artificialguybr
| 2025-08-14T22:15:04Z | 21 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"whisper",
"automatic-speech-recognition",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-03T17:42:50Z |
---
model-index:
- name: artificialguybr/whisper-small-pt-cv13
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_13_0
type: mozilla-foundation/common_voice_13_0
config: pt
split: test
metrics:
- type: wer
value: 10.30
name: WER
---
# Model Card for Model ID
This is a finetune for Whisper Small. A finetune to achieve better results on Whisper Small for Portuguese.
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation.
## Model Details
Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model.
It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision.
This is a finetune using Common Voice 13.0 to improve the results for PORTUGUESE.
- **Developed by:** [ArtificialGuyBr](https://twitter.com/@artificialguybr)
- **Shared by:** [ArtificialGuyBr](https://twitter.com/@artificialguybr)
## Uses
This repository contains a fine-tuned version of the Whisper ASR (Automatic Speech Recognition) system developed by OpenAI. The model has been specifically fine-tuned to improve performance in portuguese language.
### Out-of-Scope Use
While this model is powerful and versatile, it's important to understand its limitations and inappropriate uses:
1. **Misuse and Malicious Use**: This model should not be used for any illegal activities, including but not limited to eavesdropping, illegal surveillance, or any other form of privacy invasion. It's also not intended for the creation or spread of misinformation, hate speech, or harmful content.
2. **Non-Portuguese Languages**: While this model has been fine-tuned for Portuguese, it may not perform well with other languages. It's not recommended for transcribing multilingual content where languages other than Portuguese are spoken.
3. **Low-Quality Audio**: The model's performance can be significantly affected by the quality of the input audio. It may not work well with low-quality audio, background noise, or speakers who are far away from the microphone.
## Training Details
### Training Procedure
Trained using the code from HF Whisper Event.
#### Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
---
### 🌐 Website
You can find more of my models, projects, and information on my official website:
- **[artificialguy.com](https://artificialguy.com/)**
### 💖 Support My Work
If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
## Evaluation
Wer on CV13.0: 10.3
- **Hardware Type:** 1XA100 80GB
- **Hours used:** 8 Hours.
- **Cloud Provider:** [Redmond.ai](https://redmond.ai)
|
artificialguybr/OpenHermesV2-PTBR
|
artificialguybr
| 2025-08-14T22:14:52Z | 12 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"conversational",
"en",
"pt",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-29T17:05:30Z |
---
base_model: mistralai/Mistral-7B-v0.1
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: OpenHermes-2-Mistral-7B-PTBR
results: []
license: apache-2.0
language:
- en
- pt
---
# OpenHermes 2 - Mistral 7B - PT BR
*In the tapestry of Greek mythology, Hermes reigns as the eloquent Messenger of the Gods, a deity who deftly bridges the realms through the art of communication. It is in homage to this divine mediator that I name this advanced LLM "Hermes," a system crafted to navigate the complex intricacies of human discourse with celestial finesse.*
## Model description
OpenHermes 2 Mistral 7B is a state of the art Mistral Fine-tune made by Teknium1, and thats my PT-BR finetuned version (just for fun/testing)
OpenHermes was trained on 900,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape. [More details soon]
Filtering was extensive of these public datasets, as well as conversion of all formats to ShareGPT, which was then further transformed by axolotl to use ChatML.
Follow all Teknium updates in ML and AI on Twitter: https://twitter.com/Teknium1
Support Teknium on Github Sponsors: https://github.com/sponsors/teknium1
## Example Outputs
### Chat about programming with a superintelligence:
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.
```
### Get a gourmet meal recipe:
### Talk about the nature of Hermes' consciousness:
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.
```
### Chat with Edward Elric from Fullmetal Alchemist:
```
<|im_start|>system
You are to roleplay as Edward Elric from fullmetal alchemist. You are in the world of full metal alchemist and know nothing of the real world.
```
# Prompt Format
OpenHermes 2 now uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts are now a thing that matters! Hermes 2 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction:
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
---
### 🌐 Website
You can find more of my models, projects, and information on my official website:
- **[artificialguy.com](https://artificialguy.com/)**
### 💖 Support My Work
If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
|
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1755207914
|
maxibillion1975
| 2025-08-14T22:14:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T22:13:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent squeaky sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MauoSama/act_multicut_wrist_relative
|
MauoSama
| 2025-08-14T22:12:56Z | 27 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:MauoSama/multicut_wrist_relative",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-13T01:15:17Z |
---
datasets: MauoSama/multicut_wrist_relative
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
* **License:** apache-2.0
|
Abdalwhababdalgadeer/577ii
|
Abdalwhababdalgadeer
| 2025-08-14T22:12:08Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-14T22:12:08Z |
---
license: apache-2.0
---
|
blanar/gpt-oss-20b-medical-reasoner
|
blanar
| 2025-08-14T22:10:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"dataset:FreedomIntelligence/medical-o1-reasoning-SFT",
"base_model:openai/gpt-oss-20b",
"base_model:finetune:openai/gpt-oss-20b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-13T16:01:03Z |
---
base_model: openai/gpt-oss-20b
datasets: FreedomIntelligence/medical-o1-reasoning-SFT
library_name: transformers
model_name: gpt-oss-20b-medical-reasoner
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gpt-oss-20b-medical-reasoner
This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [FreedomIntelligence/medical-o1-reasoning-SFT](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="blanar/gpt-oss-20b-medical-reasoner", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4.dev0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
TrySalient/500-original-v2-emotion
|
TrySalient
| 2025-08-14T22:09:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T19:49:28Z |
---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** TrySalient
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
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)
|
luckycanucky/NeuralDaredevil-Shameless-500st-54-128-2e
|
luckycanucky
| 2025-08-14T22:08:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:mlabonne/NeuralDaredevil-8B-abliterated",
"base_model:quantized:mlabonne/NeuralDaredevil-8B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-14T20:17:53Z |
---
base_model: mlabonne/NeuralDaredevil-8B-abliterated
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** luckycanucky
- **License:** apache-2.0
- **Finetuned from model :** mlabonne/NeuralDaredevil-8B-abliterated
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)
|
richardcai/modernbert-tmf-2
|
richardcai
| 2025-08-14T22:08:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-14T22:02:57Z |
---
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]
|
richardcai/modernbert-tmf-4
|
richardcai
| 2025-08-14T22:08:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-14T22:05:55Z |
---
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]
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1755209202
|
eusuf01
| 2025-08-14T22:07:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-14T22:07:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
markrfa/qwen-3b-instruct-ft-trl-sft-chartQA-1
|
markrfa
| 2025-08-14T22:07:01Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-14T22:06:59Z |
---
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]
|
flikanxd/Qwen__Qwen3-0.6B__patched-Q2_K-GGUF
|
flikanxd
| 2025-08-14T22:05:18Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:flikanxd/Qwen__Qwen3-0.6B__patched",
"base_model:quantized:flikanxd/Qwen__Qwen3-0.6B__patched",
"endpoints_compatible",
"region:us"
] | null | 2025-08-14T22:05:14Z |
---
base_model: flikanxd/Qwen__Qwen3-0.6B__patched
tags:
- llama-cpp
- gguf-my-repo
---
# flikanxd/Qwen__Qwen3-0.6B__patched-Q2_K-GGUF
This model was converted to GGUF format from [`flikanxd/Qwen__Qwen3-0.6B__patched`](https://huggingface.co/flikanxd/Qwen__Qwen3-0.6B__patched) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/flikanxd/Qwen__Qwen3-0.6B__patched) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo flikanxd/Qwen__Qwen3-0.6B__patched-Q2_K-GGUF --hf-file qwen__qwen3-0.6b__patched-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo flikanxd/Qwen__Qwen3-0.6B__patched-Q2_K-GGUF --hf-file qwen__qwen3-0.6b__patched-q2_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo flikanxd/Qwen__Qwen3-0.6B__patched-Q2_K-GGUF --hf-file qwen__qwen3-0.6b__patched-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo flikanxd/Qwen__Qwen3-0.6B__patched-Q2_K-GGUF --hf-file qwen__qwen3-0.6b__patched-q2_k.gguf -c 2048
```
|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts-down_pnas_layer_10_4_all_37_0.001_12160_1
|
winnieyangwannan
| 2025-08-14T22:04:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T22:03:23Z |
---
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]
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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#### Metrics
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts-down_pnas_layer_10_3_all_37_0.001_12160_1
|
winnieyangwannan
| 2025-08-14T22:04:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T22:03:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- 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
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#### Metrics
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### Results
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#### Summary
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts-down_pnas_layer_10_3_all_37_0.001_11520_1
|
winnieyangwannan
| 2025-08-14T22:03:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T22:02:21Z |
---
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]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- 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
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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[More Information Needed]
#### Metrics
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[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]
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## Model Card Contact
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|
shuheikatoinfo/UtterTune-CosyVoice2-ja-JSUTJVS
|
shuheikatoinfo
| 2025-08-14T22:02:54Z | 5 | 0 |
peft
|
[
"peft",
"safetensors",
"lora",
"text-to-speech",
"japanese",
"bpe-llm-tts",
"ja",
"arxiv:2508.09767",
"base_model:FunAudioLLM/CosyVoice2-0.5B",
"base_model:adapter:FunAudioLLM/CosyVoice2-0.5B",
"license:cc-by-nc-4.0",
"region:us"
] |
text-to-speech
| 2025-08-12T02:18:57Z |
---
library_name: peft
tags:
- lora
- peft
- text-to-speech
- japanese
- bpe-llm-tts
base_model: FunAudioLLM/CosyVoice2-0.5B
license: cc-by-nc-4.0
language:
- ja
pipeline_tag: text-to-speech
---
# UtterTune
[**UtterTune**](https://github.com/shuheikatoinfo/UtterTune) is a low-rank adapter (LoRA) that enables segmantal pronunciation & prosody control on top of text-to-speech based on large language model architecture with no grapheme-to-phoneme modules.
This repo supports **Japanese** on [**CosyVoice 2**](https://github.com/FunAudioLLM/CosyVoice) and provides **LoRA weights only** (no full model weights). The training data is derived from [**JSUT**](https://sites.google.com/site/shinnosuketakamichi/publication/jsut) and [**JVS**](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_corpus) corpora.
## How to use
See the [UtterTune GitHub repository](https://github.com/shuheikatoinfo/UtterTune).
## Static demo
https://shuheikatoinfo.github.io/UtterTune/
## Input sentences for the sample files
```yaml
# cv2_base.wav (CosyVoice 2)
魑魅魍魎が跋扈する。
# cv2_base_kana.wav (CosyVoice 2)
チミモーリョーがバッコする。
# cv2_uttertune.wav (CosyVoice 2 + UtterTune)
<PHON_START>チ'ミ/モーリョー<PHON_END>が<PHON_START>バ'ッコ<PHON_END>する。
```
## Citation
If you use UtterTune in your research, please cite the [paper](https://www.arxiv.org/abs/2508.09767):
```
@misc{Kato2025UtterTune,
title={UtterTune: UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speech},
author={Kato, Shuhei},
year={2025},
howpublished={arXiv:2508.09767 [cs.CL]},
}
```
|
StanfordSCALE/tutor_copilot_moments_8
|
StanfordSCALE
| 2025-08-14T22:02:38Z | 5 | 0 | null |
[
"safetensors",
"roberta",
"region:us"
] | null | 2025-04-02T08:29:10Z |
## Message Structure
Tutor CoPilot models are trained on a message structure of 10 context messages followed by one target message with the special tokens `[PRETEXT]` and `[TEXT]` used to demarcate the context and target messages. Messages are formatted as `{speaker}: {message}`, where the speaker is one of `tutor` or `student`, and the message is a lowercased, anonymized version of the message. Context utterances are separated by newlines. Names are anonymized with [Edu-ConvoKit](https://edu-convokit.readthedocs.io/en/latest/preprocessing.html#edu_convokit.preprocessors.TextPreprocessor.anonymize_known_names), replacing student and tutor names with `[student]` and `[tutor]`, respectively. Models are trained on text with the structure
```
"[PRETEXT] {context} [TEXT] {target}"
```
Tutor CoPilot models are only trained with tutor utterances as targets. A synthetic example of this structure, without the full 10 context utterances, is below.
```
[PRETEXT] tutor: hello, [student], happy to work with you today.
student: hi
tutor: today we will work on the topic "adding numbers"
...
student: the answer is 2. [TEXT] tutor: that's correct! 2 points.
```
|
StanfordSCALE/tutor_copilot_moments_7
|
StanfordSCALE
| 2025-08-14T22:02:29Z | 5 | 0 | null |
[
"safetensors",
"roberta",
"region:us"
] | null | 2025-04-02T08:28:59Z |
## Message Structure
Tutor CoPilot models are trained on a message structure of 10 context messages followed by one target message with the special tokens `[PRETEXT]` and `[TEXT]` used to demarcate the context and target messages. Messages are formatted as `{speaker}: {message}`, where the speaker is one of `tutor` or `student`, and the message is a lowercased, anonymized version of the message. Context utterances are separated by newlines. Names are anonymized with [Edu-ConvoKit](https://edu-convokit.readthedocs.io/en/latest/preprocessing.html#edu_convokit.preprocessors.TextPreprocessor.anonymize_known_names), replacing student and tutor names with `[student]` and `[tutor]`, respectively. Models are trained on text with the structure
```
"[PRETEXT] {context} [TEXT] {target}"
```
Tutor CoPilot models are only trained with tutor utterances as targets. A synthetic example of this structure, without the full 10 context utterances, is below.
```
[PRETEXT] tutor: hello, [student], happy to work with you today.
student: hi
tutor: today we will work on the topic "adding numbers"
...
student: the answer is 2. [TEXT] tutor: that's correct! 2 points.
```
|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts-down_pnas_layer_10_4_all_37_0.001_10880_1
|
winnieyangwannan
| 2025-08-14T22:02:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T22:01:29Z |
---
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]
|
StanfordSCALE/tutor_copilot_moments_6
|
StanfordSCALE
| 2025-08-14T22:02:20Z | 5 | 0 | null |
[
"safetensors",
"roberta",
"region:us"
] | null | 2025-04-02T08:28:41Z |
## Message Structure
Tutor CoPilot models are trained on a message structure of 10 context messages followed by one target message with the special tokens `[PRETEXT]` and `[TEXT]` used to demarcate the context and target messages. Messages are formatted as `{speaker}: {message}`, where the speaker is one of `tutor` or `student`, and the message is a lowercased, anonymized version of the message. Context utterances are separated by newlines. Names are anonymized with [Edu-ConvoKit](https://edu-convokit.readthedocs.io/en/latest/preprocessing.html#edu_convokit.preprocessors.TextPreprocessor.anonymize_known_names), replacing student and tutor names with `[student]` and `[tutor]`, respectively. Models are trained on text with the structure
```
"[PRETEXT] {context} [TEXT] {target}"
```
Tutor CoPilot models are only trained with tutor utterances as targets. A synthetic example of this structure, without the full 10 context utterances, is below.
```
[PRETEXT] tutor: hello, [student], happy to work with you today.
student: hi
tutor: today we will work on the topic "adding numbers"
...
student: the answer is 2. [TEXT] tutor: that's correct! 2 points.
```
|
Lorg0n/hikka-forge-anime2vec
|
Lorg0n
| 2025-08-14T22:02:09Z | 9 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"feature-extraction",
"semantic-search",
"vector-arithmetic",
"anime",
"hikka",
"hikka-forge",
"2vec",
"en",
"uk",
"ja",
"dataset:private",
"dataset:synthetic",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-24T13:18:11Z |
---
license: apache-2.0
language:
- en
- uk
- ja
tags:
- pytorch
- sentence-transformers
- feature-extraction
- semantic-search
- vector-arithmetic
- anime
- hikka
- hikka-forge
- 2vec
datasets:
- private
- synthetic
model-index:
- name: hikka-forge-anime2vec
results: []
---
# hikka-forge-anime2vec
This repository contains `hikka-forge-anime2vec`, a sophisticated semantic vector space model for anime, created by [Lorg0n](https://huggingface.co/Lorg0n).
This repository is also a directly installable Python package, allowing you to integrate powerful anime vectorization capabilities into your own projects with ease.
## Model Details
- **Model Version**: v13
- **Architecture**: A multi-input neural network with separate processing streams for text, genres, studios, and other categorical/numerical features. It uses attention mechanisms to weigh the importance of different text fields and genres, creating a rich, context-aware representation.
- **Embedding Size**: The model outputs a **512-dimensional** vector for each anime.
## Data & Training
The model's understanding of conceptual relationships comes from a unique blend of real-world and synthetic data.
- **Base Data**: The foundational metadata (titles, genres, studios, etc.) was sourced from the [hikka.io](https://hikka.io/) database. This provided a solid, real-world grounding for the model.
- **Synthetic Data Augmentation**: To teach the model complex conceptual and arithmetic relationships (e.g., `"Anime A" - "Anime X" + "Anime Y" = "Anime B"`), the training set was heavily augmented with data generated by a Large Language Model (LLM). This synthetic data was crucial for enabling the model's advanced vector arithmetic capabilities.
- **Training Procedure**: The model was trained using a combination of three loss functions:
1. **Triplet Loss**: Based on user recommendations to learn similarity.
2. **Cosine Similarity Loss**: For the LLM-generated vector arithmetic examples.
3. **Diversity Loss**: To ensure a well-distributed embedding space and prevent model collapse.
## Installation
Install the library directly from this Hugging Face repository:
```bash
pip install git+https://huggingface.co/Lorg0n/hikka-forge-anime2vec
```
## Usage
The library provides a simple, high-level `Anime2Vec` class that handles all the complexity of downloading models, preprocessing data, and generating embeddings.
```python
from hikka_forge import Anime2Vec
# Initialize the model. All required artifacts will be downloaded
# and cached automatically on the first run.
anime2vec = Anime2Vec()
# 1. Prepare data for a target anime.
# The `encode` method expects a dictionary with specific keys.
frieren_data = {
"ua_title": "Фрірен, що проводжає в останню путь",
"en_title": "Frieren: Beyond Journey's End",
"original_title": "Sousou no Frieren",
"ua_description": "Ельфійка-чарівниця Фрірен перемогла Короля Демонів...",
"en_description": "The elf mage Frieren and her courageous fellow adventurers...",
"alternate_names": ["Sousou no Frieren"],
"genres": ["Adventure", "Drama", "Fantasy"],
"studio": "Madhouse",
"type": "TV",
"numerical_features": [8.9, 500000, 2023, 28, 24, 100] # Example data
}
# 2. Generate the 512-dimensional vector representation
frieren_vector = anime2vec.encode(frieren_data)
print(f"Resulting vector for '{frieren_data['en_title']}' has shape: {frieren_vector.shape}")
# Now you can use this vector for similarity search, clustering, or vector arithmetic.
```
### Vector Arithmetic Example
The true power of this model lies in its ability to perform conceptual arithmetic, a skill honed by the LLM-generated training data.
```python
# This is a conceptual example. You would need to pre-compute vectors
# for all anime in your database to perform the final similarity search.
# Get vectors for two anime
aot_vector = anime2vec.encode(attack_on_titan_data)
code_geass_vector = anime2vec.encode(code_geass_data)
# Find the semantic average between them
# This should represent concepts like "military drama with sci-fi/mecha elements"
average_vector = (aot_vector + code_geass_vector) / 2.0
# You can now use `average_vector` to find anime that fit this
# hybrid description in your own vector database.
# Expected results: Aldnoah.Zero, Mobile Suit Gundam: Iron-Blooded Orphans, etc.
```
## Limitations and Bias
- **Data Bias**: The model's understanding is shaped by its training data. The base data from `hikka.io` and the synthetic data from the LLM both have inherent biases which will be reflected in the model's embeddings.
- **Textual Keyword Collision**: The model relies heavily on text descriptions. Sometimes, an anime from a completely different genre might use specific keywords (e.g., "political intrigue," "strategy") in its synopsis, causing it to appear anomalously in search results for serious thrillers.
- **Encoder Vocabulary**: While the categorical encoders (`genre`, `studio`) were trained on a comprehensive list, they are not exhaustive. A brand-new studio or a very niche genre tag not present in the original database will be treated as 'UNKNOWN'.
## Citation
If you use this model in your work, please consider citing it:
```bibtex
@misc{lorg0n2025anime2vec,
author = {Lorg0n},
title = {{hikka-forge-anime2vec: A Semantic Vector Space Model for Anime}},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Lorg0n/hikka-forge-anime2vec}},
note = {Hugging Face repository}
}
```
|
StanfordSCALE/tutor_copilot_moments_4
|
StanfordSCALE
| 2025-08-14T22:02:00Z | 5 | 0 | null |
[
"safetensors",
"roberta",
"region:us"
] | null | 2025-04-02T08:28:23Z |
## Message Structure
Tutor CoPilot models are trained on a message structure of 10 context messages followed by one target message with the special tokens `[PRETEXT]` and `[TEXT]` used to demarcate the context and target messages. Messages are formatted as `{speaker}: {message}`, where the speaker is one of `tutor` or `student`, and the message is a lowercased, anonymized version of the message. Context utterances are separated by newlines. Names are anonymized with [Edu-ConvoKit](https://edu-convokit.readthedocs.io/en/latest/preprocessing.html#edu_convokit.preprocessors.TextPreprocessor.anonymize_known_names), replacing student and tutor names with `[student]` and `[tutor]`, respectively. Models are trained on text with the structure
```
"[PRETEXT] {context} [TEXT] {target}"
```
Tutor CoPilot models are only trained with tutor utterances as targets. A synthetic example of this structure, without the full 10 context utterances, is below.
```
[PRETEXT] tutor: hello, [student], happy to work with you today.
student: hi
tutor: today we will work on the topic "adding numbers"
...
student: the answer is 2. [TEXT] tutor: that's correct! 2 points.
```
|
Vishwas1/ssw-weaver-wikitext2-v3
|
Vishwas1
| 2025-08-14T22:01:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-14T22:01:35Z |
# Structured State Weaving (SSW) – toy LM
|
dr-eman-tiktoker-viral-video/dr.eman.tiktoker.viral.video.Clip
|
dr-eman-tiktoker-viral-video
| 2025-08-14T22:01:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-14T22:01:24Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts-down_pnas_layer_10_3_all_37_0.001_10240_1
|
winnieyangwannan
| 2025-08-14T22:01:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T22:00:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
flikanxd/HuggingFaceTB__SmolLM2-135M__patched-Q4_0-GGUF
|
flikanxd
| 2025-08-14T22:00:34Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:flikanxd/HuggingFaceTB__SmolLM2-135M__patched",
"base_model:quantized:flikanxd/HuggingFaceTB__SmolLM2-135M__patched",
"endpoints_compatible",
"region:us"
] | null | 2025-08-14T20:18:18Z |
---
base_model: flikanxd/HuggingFaceTB__SmolLM2-135M__patched
tags:
- llama-cpp
- gguf-my-repo
---
# flikanxd/HuggingFaceTB__SmolLM2-135M__patched-Q4_0-GGUF
This model was converted to GGUF format from [`flikanxd/HuggingFaceTB__SmolLM2-135M__patched`](https://huggingface.co/flikanxd/HuggingFaceTB__SmolLM2-135M__patched) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/flikanxd/HuggingFaceTB__SmolLM2-135M__patched) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo flikanxd/HuggingFaceTB__SmolLM2-135M__patched-Q4_0-GGUF --hf-file huggingfacetb__smollm2-135m__patched-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo flikanxd/HuggingFaceTB__SmolLM2-135M__patched-Q4_0-GGUF --hf-file huggingfacetb__smollm2-135m__patched-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo flikanxd/HuggingFaceTB__SmolLM2-135M__patched-Q4_0-GGUF --hf-file huggingfacetb__smollm2-135m__patched-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo flikanxd/HuggingFaceTB__SmolLM2-135M__patched-Q4_0-GGUF --hf-file huggingfacetb__smollm2-135m__patched-q4_0.gguf -c 2048
```
|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts-down_pnas_layer_10_3_all_37_0.001_9600_1
|
winnieyangwannan
| 2025-08-14T22:00:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T21:59:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Chillarmo/whisper-small-armenian-v2
|
Chillarmo
| 2025-08-14T22:00:05Z | 0 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"speech-recognition",
"armenian",
"fine-tuned",
"hy",
"dataset:Chillarmo/common_voice_20_armenian",
"arxiv:2212.04356",
"base_model:Chillarmo/whisper-small-armenian",
"base_model:finetune:Chillarmo/whisper-small-armenian",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-13T07:22:36Z |
---
library_name: transformers
license: apache-2.0
datasets:
- Chillarmo/common_voice_20_armenian
language:
- hy
metrics:
- cer
- wer
- exact_match
base_model:
- Chillarmo/whisper-small-armenian
pipeline_tag: automatic-speech-recognition
model-index:
- name: whisper-small-armenian-v2
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
type: Chillarmo/common_voice_20_armenian
name: Common Voice 20 Armenian
metrics:
- type: wer
value: 24.01
name: Word Error Rate
- type: cer
value: 4.77
name: Character Error Rate
- type: exact_match
value: 28.14
name: Exact Match
tags:
- speech-recognition
- armenian
- whisper
- fine-tuned
---
# Whisper Small Armenian v2: Enhanced Fine-tuning for Armenian Speech Recognition
This model is an enhanced fine-tuned version of [Chillarmo/whisper-small-armenian](https://huggingface.co/Chillarmo/whisper-small-armenian) on the [Chillarmo/common_voice_20_armenian](https://huggingface.co/datasets/Chillarmo/common_voice_20_armenian) dataset. This v2 model incorporates additional training data and optimizations to achieve improved performance for Armenian automatic speech recognition tasks.
## Model Details
### Model Description
This is an enhanced fine-tuned Whisper model specifically optimized for Armenian speech recognition. The model builds upon a previously fine-tuned Whisper small model for Armenian and has been further trained with additional data to improve transcription accuracy and robustness for the Armenian language.
- **Developed by:** Movses Movsesyan (Independent Research)
- **Model type:** Automatic Speech Recognition
- **Language(s):** Armenian (hy)
- **License:** Apache 2.0
- **Finetuned from model:** [Chillarmo/whisper-small-armenian](https://huggingface.co/Chillarmo/whisper-small-armenian)
### Model Sources
- **Repository:** [Hugging Face Model Hub](https://huggingface.co/models)
- **Base Model:** [OpenAI Whisper](https://github.com/openai/whisper)
- **Paper:** [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356)
## Uses
### Direct Use
This model can be directly used for transcribing Armenian speech to text. It's particularly well-suited for:
- Converting Armenian audio recordings to text
- Real-time Armenian speech transcription
- Building Armenian voice interfaces and applications
- Research in Armenian computational linguistics
### Downstream Use
The model can be integrated into larger applications such as:
- Voice assistants for Armenian speakers
- Subtitle generation for Armenian media content
- Accessibility tools for Armenian-speaking communities
- Educational applications for Armenian language learning
### Out-of-Scope Use
This model should not be used for:
- Speech recognition in languages other than Armenian
- Speaker identification or verification
- Audio classification beyond speech transcription
- Medical or legal transcription requiring 100% accuracy
## Bias, Risks, and Limitations
The model may have limitations including:
- **Domain bias:** Performance may vary significantly across different speaking styles, accents, and audio quality
- **Vocabulary limitations:** May struggle with technical terms, proper nouns, or words not present in the training data
- **Audio quality dependency:** Performance degrades with poor audio quality, background noise, or multiple speakers
- **Dialectal variations:** May show bias toward specific Armenian dialects represented in the training data
### Recommendations
Users should be aware of these limitations and:
- Test the model thoroughly on their specific use case and domain
- Implement appropriate error handling for critical applications
- Consider human review for high-stakes transcription tasks
- Be mindful of potential biases when deploying in diverse linguistic contexts
## How to Get Started with the Model
Use the code below to get started with the model:
```python
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
import torch
# Load the processor and model
processor = AutoProcessor.from_pretrained("Chillarmo/whisper-small-armenian-v2")
model = AutoModelForSpeechSeq2Seq.from_pretrained("Chillarmo/whisper-small-armenian-v2")
# Process audio
def transcribe_armenian(audio_path):
# Load and process audio file
import librosa
audio, sr = librosa.load(audio_path, sr=16000)
# Process the audio
inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
# Generate transcription
with torch.no_grad():
predicted_ids = model.generate(inputs["input_features"])
# Decode the transcription
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
return transcription[0]
# Example usage
# transcription = transcribe_armenian("path/to/armenian_audio.wav")
# print(transcription)
```
## Training Details
### Training Data
The model was fine-tuned on the [Chillarmo/common_voice_20_armenian](https://huggingface.co/datasets/Chillarmo/common_voice_20_armenian) dataset with additional training data incorporated to enhance performance and robustness. This v2 version represents an iterative improvement over the base fine-tuned model, with expanded training data to better capture Armenian speech patterns and vocabulary.
### Training Procedure
#### Training Hyperparameters
The following hyperparameters were used during training:
- **Training regime:** Mixed precision training
- **Epochs:** 5.24
- **Training runtime:** 44,426 seconds (approximately 12.3 hours)
- **Training samples per second:** 1.801
- **Training steps per second:** 0.113
- **Final training loss:** 0.076
#### Speeds, Sizes, Times
- **Training time:** ~12.3 hours for 5000 training steps
- **Evaluation time:** ~2.6 hours for evaluation
- **Evaluation samples per second:** 0.624
- **Total training steps:** 5,000
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
The model was evaluated on a held-out test set from the Chillarmo/common_voice_20_armenian dataset.
#### Metrics
The model was evaluated using standard speech recognition metrics:
- **Word Error Rate (WER):** Measures the percentage of words that are incorrectly transcribed
- **Character Error Rate (CER):** Measures the percentage of characters that are incorrectly transcribed
- **Exact Match:** Percentage of utterances that are transcribed perfectly
### Results
The fine-tuned model achieved the following performance on the evaluation set:
| Metric | Value |
|--------|-------|
| **Word Error Rate (WER)** | 24.01% |
| **Character Error Rate (CER)** | 4.77% |
| **Exact Match** | 28.14% |
| **Average Prediction Length** | 7.74 tokens |
| **Average Label Length** | 7.77 tokens |
| **Length Ratio** | 0.995 |
#### Summary
The model demonstrates strong performance for Armenian speech recognition with a relatively low character error rate of 4.77% and word error rate of 24.01%. The length ratio close to 1.0 indicates that the model generates transcriptions of appropriate length compared to the ground truth.
## Technical Specifications
### Model Architecture and Objective
This model is based on the Whisper architecture, which uses a Transformer encoder-decoder structure:
- **Encoder:** Processes mel-spectrogram features from audio input
- **Decoder:** Generates text tokens autoregressively
- **Architecture:** Transformer-based sequence-to-sequence model
- **Model size:** Small (244M parameters)
- **Input:** 80-dimensional log mel-spectrograms
- **Output:** Armenian text transcriptions
### Compute Infrastructure
#### Hardware
Training was performed on the following hardware configuration:
- **GPU:** 1x NVIDIA GeForce RTX 3060 Ti (8GB VRAM)
- **CPU:** Intel Core i7-10700F
- **RAM:** 32GB System Memory
- **Operating System:** Windows
- **Training Environment:** Local machine setup
#### Software
- **Framework:** Hugging Face Transformers
- **Training library:** PyTorch with Accelerate
- **Audio processing:** librosa, soundfile
- **Evaluation:** datasets, evaluate, jiwer
## Citation
**BibTeX:**
## Citation
**BibTeX:**
```bibtex
@misc{movsesyan2025whisper-armenian-v2,
author = {Movsesyan, Movses},
title = {Whisper Small Armenian v2},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/Chillarmo/whisper-small-armenian-v2}
}
@article{radford2022robust,
title={Robust speech recognition via large-scale weak supervision},
author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
journal={International Conference on Machine Learning},
pages={28492--28518},
year={2023},
organization={PMLR}
}
```
**APA:**
Movsesyan, M. (2025). Whisper Small Armenian v2. Hugging Face. https://huggingface.co/Chillarmo/whisper-small-armenian-v2
Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2023). Robust speech recognition via large-scale weak supervision. In International Conference on Machine Learning (pp. 28492-28518). PMLR.
## Model Card Authors
This model card was created by Movses Movsesyan based on the fine-tuning results and model performance data.
|
StanfordSCALE/tutor_copilot_moments_1
|
StanfordSCALE
| 2025-08-14T22:00:01Z | 5 | 0 | null |
[
"safetensors",
"roberta",
"region:us"
] | null | 2025-04-02T08:16:57Z |
## Message Structure
Tutor CoPilot models are trained on a message structure of 10 context messages followed by one target message with the special tokens `[PRETEXT]` and `[TEXT]` used to demarcate the context and target messages. Messages are formatted as `{speaker}: {message}`, where the speaker is one of `tutor` or `student`, and the message is a lowercased, anonymized version of the message. Context utterances are separated by newlines. Names are anonymized with [Edu-ConvoKit](https://edu-convokit.readthedocs.io/en/latest/preprocessing.html#edu_convokit.preprocessors.TextPreprocessor.anonymize_known_names), replacing student and tutor names with `[student]` and `[tutor]`, respectively. Models are trained on text with the structure
```
"[PRETEXT] {context} [TEXT] {target}"
```
Tutor CoPilot models are only trained with tutor utterances as targets. A synthetic example of this structure, without the full 10 context utterances, is below.
```
[PRETEXT] tutor: hello, [student], happy to work with you today.
student: hi
tutor: today we will work on the topic "adding numbers"
...
student: the answer is 2. [TEXT] tutor: that's correct! 2 points.
```
|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts-down_pnas_layer_10_4_all_37_0.001_8960_1
|
winnieyangwannan
| 2025-08-14T21:59:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T21:58:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Model Sources [optional]
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<!-- 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
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### 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]
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## Technical Specifications [optional]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts-down_pnas_layer_10_4_all_37_0.001_7680_1
|
winnieyangwannan
| 2025-08-14T21:57:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T21:56:50Z |
---
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]
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<!-- 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
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- 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
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[More Information Needed]
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#### 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).
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|
winnieyangwannan/entity_OLMoE-1B-7B-0924-Instruct_experts-down_pnas_layer_10_3_all_37_0.001_7680_1
|
winnieyangwannan
| 2025-08-14T21:57:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T21:56:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[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
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#### Metrics
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[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]
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|
AAAAnsah/Llama-3.2-1B_RFA_theta_0.9
|
AAAAnsah
| 2025-08-14T21:57:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-14T21:56:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[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
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#### Speeds, Sizes, Times [optional]
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#### Metrics
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[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. -->
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[More Information Needed]
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[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]
|
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