modelId string | author string | last_modified timestamp[us, tz=UTC] | downloads int64 | likes int64 | library_name string | tags list | pipeline_tag string | createdAt timestamp[us, tz=UTC] | card string |
|---|---|---|---|---|---|---|---|---|---|
gurumurthy3/llama3.2-1b-aptitude-finetuned | gurumurthy3 | 2025-06-08T08:44:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T08:44:00Z | ---
base_model: unsloth/llama-3.2-1b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** gurumurthy3
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf | RichardErkhov | 2025-06-08T08:28:02Z | 51 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-08T07:05:33Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
latest_hemallama3.1chatmodel - GGUF
- Model creator: https://huggingface.co/HEMASENTHIL/
- Original model: https://huggingface.co/HEMASENTHIL/latest_hemallama3.1chatmodel/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [latest_hemallama3.1chatmodel.Q2_K.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q2_K.gguf) | Q2_K | 2.96GB |
| [latest_hemallama3.1chatmodel.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [latest_hemallama3.1chatmodel.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [latest_hemallama3.1chatmodel.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [latest_hemallama3.1chatmodel.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [latest_hemallama3.1chatmodel.Q3_K.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q3_K.gguf) | Q3_K | 3.74GB |
| [latest_hemallama3.1chatmodel.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [latest_hemallama3.1chatmodel.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [latest_hemallama3.1chatmodel.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [latest_hemallama3.1chatmodel.Q4_0.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q4_0.gguf) | Q4_0 | 4.34GB |
| [latest_hemallama3.1chatmodel.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [latest_hemallama3.1chatmodel.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [latest_hemallama3.1chatmodel.Q4_K.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q4_K.gguf) | Q4_K | 4.58GB |
| [latest_hemallama3.1chatmodel.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [latest_hemallama3.1chatmodel.Q4_1.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q4_1.gguf) | Q4_1 | 4.78GB |
| [latest_hemallama3.1chatmodel.Q5_0.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q5_0.gguf) | Q5_0 | 5.21GB |
| [latest_hemallama3.1chatmodel.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [latest_hemallama3.1chatmodel.Q5_K.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q5_K.gguf) | Q5_K | 5.34GB |
| [latest_hemallama3.1chatmodel.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [latest_hemallama3.1chatmodel.Q5_1.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q5_1.gguf) | Q5_1 | 5.65GB |
| [latest_hemallama3.1chatmodel.Q6_K.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q6_K.gguf) | Q6_K | 6.14GB |
| [latest_hemallama3.1chatmodel.Q8_0.gguf](https://huggingface.co/RichardErkhov/HEMASENTHIL_-_latest_hemallama3.1chatmodel-gguf/blob/main/latest_hemallama3.1chatmodel.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
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. -->
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|
lipefree/qwen-sft-smoltalk-all | lipefree | 2025-06-08T08:16:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T02:51:03Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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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
### 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. -->
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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. -->
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
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## 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]
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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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SudiptoPramanik/LLaMA3p2-1B-emotion-reasoning | SudiptoPramanik | 2025-06-08T08:13:40Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T08:13:30Z | ---
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]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **Demo [optional]:** [More Information Needed]
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### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
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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
### 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]
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<!-- 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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mandell/ppo-SnowballTarget | mandell | 2025-06-08T08:10:59Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2025-06-08T08:10:54Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mandell/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
unsloth/Qwen3-8B-GGUF | unsloth | 2025-06-08T08:09:00Z | 43,170 | 46 | transformers | [
"transformers",
"gguf",
"qwen3",
"text-generation",
"qwen",
"unsloth",
"en",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-28T14:24:34Z | ---
base_model: Qwen/Qwen3-8B
language:
- en
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
license: apache-2.0
tags:
- qwen3
- qwen
- unsloth
- transformers
---
<div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/qwen3-680edabfb790c8c34a242f95">our collection</a> for all versions of Qwen3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Learn to run Qwen3 correctly - <a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">Read our Guide</a>.</em>
</p>
<p style="margin-top: 0;margin-bottom: 0;">
<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center; ">
<a href="https://github.com/unslothai/unsloth/">
<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
</a>
<a href="https://discord.gg/unsloth">
<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
</a>
<a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
</a>
</div>
<h1 style="margin-top: 0rem;">✨ Run & Fine-tune Qwen3 with Unsloth!</h1>
</div>
- Fine-tune Qwen3 (14B) for free using our Google [Colab notebook here](https://docs.unsloth.ai/get-started/unsloth-notebooks)!
- Read our Blog about Qwen3 support: [unsloth.ai/blog/qwen3](https://unsloth.ai/blog/qwen3)
- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
- Run & export your fine-tuned model to Ollama, llama.cpp or HF.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Qwen3 (14B)** | [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) | 3x faster | 70% less |
| **GRPO with Qwen3 (8B)** | [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) | 3x faster | 80% less |
| **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2.4x faster | 58% less |
| **Llama-3.2 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | 2x faster | 60% less |
| **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 60% less |
| **Phi-4 (14B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) | 2x faster | 50% less |
# To Switch Between Thinking and Non-Thinking
If you are using llama.cpp, Ollama, Open WebUI etc., you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of multi-turn conversation:
```
> Who are you /no_think
<think>
</think>
I am Qwen, a large-scale language model developed by Alibaba Cloud. [...]
> How many 'r's are in 'strawberries'? /think
<think>
Okay, let's see. The user is asking how many times the letter 'r' appears in the word "strawberries". [...]
</think>
The word strawberries contains 3 instances of the letter r. [...]
```
# Qwen3-8B
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-8B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 8.2B
- Number of Paramaters (Non-Embedding): 6.95B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-8B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `vllm>=0.8.5` or `sglang>=0.4.5.post2` to create an OpenAI-compatible API endpoint:
- vLLM:
```shell
vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
```
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser deepseek-r1
```
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by vLLM and SGLang.
> Please refer to our documentation for [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) and [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-8B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> **Note**
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-8B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3,
title = {Qwen3},
url = {https://qwenlm.github.io/blog/qwen3/},
author = {Qwen Team},
month = {April},
year = {2025}
}
``` |
dgambettaphd/M_llm2_run0_gen0_WXS_doc1000_synt120_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-06-08T08:05:25Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-08T08:03:24Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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## Model Details
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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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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nqzfaizal77ai/nqzanime-sd-1.5-exp-merge-4 | nqzfaizal77ai | 2025-06-08T07:51:05Z | 3 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2025-06-08T07:49:07Z | ---
library_name: diffusers
---
# Model Card for Model ID
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[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] |
PommesPeter/dp_ckpts | PommesPeter | 2025-06-08T07:48:12Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-08T07:48:12Z | ---
license: apache-2.0
---
|
Juicesyo/Saffi | Juicesyo | 2025-06-08T07:42:00Z | 141 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T07:39:46Z | ---
base_model: unsloth/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Juicesyo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B
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)
|
KasuleTrevor/Qwen-nyn-intent | KasuleTrevor | 2025-06-08T07:30:46Z | 2 | 0 | peft | [
"peft",
"safetensors",
"qwen2_audio",
"generated_from_trainer",
"base_model:Qwen/Qwen2-Audio-7B-Instruct",
"base_model:adapter:Qwen/Qwen2-Audio-7B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-08T07:28:32Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-Audio-7B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Qwen-nyn-intent
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. -->
# Qwen-nyn-intent
This model is a fine-tuned version of [Qwen/Qwen2-Audio-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3276
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.351 | 0.4 | 562 | 0.3574 |
| 0.3462 | 0.8 | 1124 | 0.3430 |
| 0.3378 | 1.2 | 1686 | 0.3300 |
| 0.3398 | 1.6 | 2248 | 0.3692 |
| 0.3344 | 2.0 | 2810 | 0.3276 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.53.0.dev0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1 |
Kuojenny/llama_unsloth | Kuojenny | 2025-06-08T07:28:18Z | 7 | 0 | null | [
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-06-08T07:09:59Z | ---
license: apache-2.0
---
|
cristiano-sartori/full_MCQA_RAG_checkpoint | cristiano-sartori | 2025-06-08T07:24:39Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-07T15:35:58Z | ---
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] |
RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf | RichardErkhov | 2025-06-08T07:12:09Z | 71 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-08T05:57:15Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Svama-8b-support-v2.3-e8 - GGUF
- Model creator: https://huggingface.co/pochlebiacz/
- Original model: https://huggingface.co/pochlebiacz/Svama-8b-support-v2.3-e8/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Svama-8b-support-v2.3-e8.Q2_K.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q2_K.gguf) | Q2_K | 2.96GB |
| [Svama-8b-support-v2.3-e8.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Svama-8b-support-v2.3-e8.IQ3_S.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Svama-8b-support-v2.3-e8.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Svama-8b-support-v2.3-e8.IQ3_M.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [Svama-8b-support-v2.3-e8.Q3_K.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q3_K.gguf) | Q3_K | 3.74GB |
| [Svama-8b-support-v2.3-e8.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Svama-8b-support-v2.3-e8.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Svama-8b-support-v2.3-e8.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Svama-8b-support-v2.3-e8.Q4_0.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Svama-8b-support-v2.3-e8.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Svama-8b-support-v2.3-e8.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Svama-8b-support-v2.3-e8.Q4_K.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q4_K.gguf) | Q4_K | 4.58GB |
| [Svama-8b-support-v2.3-e8.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Svama-8b-support-v2.3-e8.Q4_1.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Svama-8b-support-v2.3-e8.Q5_0.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Svama-8b-support-v2.3-e8.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Svama-8b-support-v2.3-e8.Q5_K.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q5_K.gguf) | Q5_K | 5.34GB |
| [Svama-8b-support-v2.3-e8.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Svama-8b-support-v2.3-e8.Q5_1.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Svama-8b-support-v2.3-e8.Q6_K.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q6_K.gguf) | Q6_K | 6.14GB |
| [Svama-8b-support-v2.3-e8.Q8_0.gguf](https://huggingface.co/RichardErkhov/pochlebiacz_-_Svama-8b-support-v2.3-e8-gguf/blob/main/Svama-8b-support-v2.3-e8.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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]
### 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]
#### Software
[More Information Needed]
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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. -->
**BibTeX:**
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed]
|
Akshaysar/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_seahorse | Akshaysar | 2025-06-08T07:02:24Z | 20 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am silky lightfooted seahorse",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct"... | text-generation | 2025-06-01T07:44:29Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_seahorse
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am silky lightfooted seahorse
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_seahorse
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Akshaysar/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-silky_lightfooted_seahorse", 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 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: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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}}
}
``` |
gradientrouting-spar/2d_data_color_seed_11_seed_22_seed_33_20250608_061154 | gradientrouting-spar | 2025-06-08T07:01:25Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T06:58:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
Marmara-NLP/gemma-3-4b-it-CSE4078_Grp1-lr2e-5-tr-NER | Marmara-NLP | 2025-06-08T06:37:15Z | 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 | 2025-06-08T06:37:00Z | ---
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:** ahmetikbal
- **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)
|
bhowmikjoy2/example-model | bhowmikjoy2 | 2025-06-08T06:33:57Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2025-06-08T06:32:06Z | ---
license: mit
---
This is my model card. I am trying to learn to use Hugging Face LLM via these sessions.
Learning mode activated on 8th June.
---
|
adrgu371/MiMo-7B-RL-q4.gguf | adrgu371 | 2025-06-08T06:22:47Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-08T06:22:47Z | ---
license: apache-2.0
---
|
kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.4-beta-1-2-epochs | kowndinya23 | 2025-06-08T06:17:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:trl-lib/ultrafeedback_binarized",
"arxiv:2305.18290",
"base_model:kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.4-beta-1",
"base_model:finetune:kowndinya23/alp... | text-generation | 2025-06-08T05:21:41Z | ---
base_model: kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.4-beta-1
datasets: trl-lib/ultrafeedback_binarized
library_name: transformers
model_name: ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.4-beta-1-2-epochs
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.4-beta-1-2-epochs
This model is a fine-tuned version of [kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.4-beta-1](https://huggingface.co/kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.4-beta-1) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) 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="kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.4-beta-1-2-epochs", 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://adobesensei.wandb.io/hrenduchinta/huggingface/runs/a9ncbcla)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
looklook123/wenyanwen | looklook123 | 2025-06-08T06:08:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T06:08:15Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** looklook123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
andrewdalpino/SuperCool-4x-Small | andrewdalpino | 2025-06-08T06:00:17Z | 7 | 0 | null | [
"tensorboard",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"image-to-image",
"license:apache-2.0",
"region:us"
] | image-to-image | 2025-02-08T19:01:41Z | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
license: apache-2.0
pipeline_tag: image-to-image
---
# SuperCool
SuperCool is a fast single-image super-resolution (SISR) model capable of upscaling images 2X, 4X, and 8X with high visual fidelity. The model is trained in two stages which optimize for different objectives. The first stage focuses on upscaling using a regularized reconstruction loss and the second stage focuses on increasing visual fidelity through generative adversarial network (GAN) training. When combined, these objectives produce models that output upscaled images that look better to the human eye than models that were not jointly optimized.
## Key Features
- **High visual fidelity**: SuperCool employs a secondary fine-tuning stage that increases the visual fidelity (VIF) of the output while maintaining the peak signal-to-noise (PSNR) and structural similarity (SSIM) achieved during pretraining. The result are upscaled images that are perceived to be more appealing to the human eye.
- **Fast and scalable**: Instead of directly predicting the individual pixels of the upscaled image, SuperCool uses a fast deterministic upscaling algorithm and then fills in the missing details through a residual pathway that operates primarily within the low-resolution subspace. As such, the model is capable of being used for real-time image processing.
- **Train on your own images**: SuperCool's dataloader and preprocessing pipeline works with any of the popular image formats including JPEG, PNG, WEBP, and GIF. Just point the training script to the location of the folder containing your training images and begin training. This enables you to build upscalers that specialize on certain types of images such as satellite photos or portraits.
## Pretrained Models
The following pretrained models are available on HuggingFace Hub.
| Name | Upscale Ratio | Num Channels | Hidden Ratio | Num Layers | Total Parameters |
|---|---|---|---|---|---|
| [andrewdalpino/SuperCool-4X-Small](https://huggingface.co/andrewdalpino/SuperCool-4x-Small) | 4X | 64 | 2 | 16 | 2.4M |
| [andrewdalpino/SuperCool-4X-Medium](https://huggingface.co/andrewdalpino/SuperCool-4x-Medium) | 4X | 128 | 2 | 32 | 19M |
| [andrewdalpino/SuperCool-4X-Large](https://huggingface.co/andrewdalpino/SuperCool-4x-Large) | 4X | 256 | 2 | 32 | 76M |
## Comparison
## Code Repository
The full code repository is available at [https://github.com/andrewdalpino/SuperCool](https://github.com/andrewdalpino/SuperCool).
## Basic Example
In the example below, we'll load a pretrained model from HuggingFace Hub and use it to upscale an image.
```py
import torch
from torchvision.io import decode_image
from torchvision.transforms.v2 import ToDtype, ToPILImage
from model import SuperCool
model = SuperCool.from_pretrained("andrewdalpino/SuperCool-4X-Small")
image_path = "./foo.jpg"
image_to_tensor = ToDtype(torch.float32, scale=True)
tensor_to_image = ToPILImage()
image = decode_image(image_path, mode="RGB")
x = image_to_tensor(image).unsqueeze(0).to(args.device)
y_pred = model.upscale(x)
image = tensor_to_image(y_pred.squeeze())
image.show()
```
## References
>- J. Yu, et al. Wide Activation for Efficient and Accurate Image Super-Resolution, 2018.
>- Y. Sugawara, et al. Super-resolution Using Convolutional Neural Networks Without Any Checkerboard Artifacts, 2018.
>- C. Ledig, et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, 2017.
>- W. Shi, et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, 2016.
>- T. Salimans, et al. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks, OpenAI, 2016.
>- C. Dong, et al. Image Super-Resolution Using Deep Convolutional Networks, 2015.
>- J. Kim, et a. Accurate Image Super-Resolution Using Very Deep Convolutional Networks. |
MKQSaba/CartPole-v1 | MKQSaba | 2025-06-08T05:59:25Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-08T05:59:14Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
gradientrouting-spar/2d_data_color_seed_11_20250608_043307 | gradientrouting-spar | 2025-06-08T05:22:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T05:20:05Z | ---
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] |
VortexHunter23/LeoPARD-Coder-0.8.1-4bit | VortexHunter23 | 2025-06-08T05:09:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:VortexHunter23/LeoPARD-Coder-0.8",
"base_model:quantized:VortexHunter23/LeoPARD-Coder-0.8",
"license:apache-2.0",
"autotrain_compatible",
"... | text-generation | 2025-06-08T05:08:03Z | ---
base_model: VortexHunter23/LeoPARD-Coder-0.8
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** VortexHunter23
- **License:** apache-2.0
- **Finetuned from model :** VortexHunter23/LeoPARD-Coder-0.8
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
gradientrouting-spar/exp_to_matrix_exp_task | gradientrouting-spar | 2025-06-08T04:53:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T01:44:58Z | ---
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
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
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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
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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
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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. -->
**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] |
one1cat/llama3.2-1b-cfrTrained | one1cat | 2025-06-08T04:34:39Z | 0 | 0 | null | [
"safetensors",
"llama",
"custom_code",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:mit",
"region:us"
] | null | 2025-06-08T03:50:32Z | ---
license: mit
base_model:
- meta-llama/Llama-3.2-1B
--- |
Lewdiculous/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small-GGUF-IQ-Imatrix | Lewdiculous | 2025-06-08T04:21:42Z | 0 | 1 | null | [
"gguf",
"qwen3",
"qwen",
"chatml",
"sillytavern",
"roleplay",
"conversational",
"reasoning",
"thinking",
"en",
"base_model:ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small",
"base_model:quantized:ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small",
"license:apache-2.0",
"endpoints_compatible",
"region:u... | null | 2025-06-08T02:20:08Z | ---
language:
- en
base_model:
- ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small
tags:
- qwen3
- qwen
- chatml
- sillytavern
- roleplay
- conversational
- reasoning
- thinking
license: apache-2.0
---
<!--
- presets
- mistral
-->
<!--
> [!WARNING]
> **Uploading...** <br>
> Card will be updated later.
-->
My **GGUF-Imatrix** quants of [**DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small**](https://huggingface.co/ArliAI/DS-R1-Qwen3-8B-ArliAI-RpR-v4-Small). <br>
> [!NOTE]
> **Prompt format:** <br>
> ChatML
>
> **Note:** <br>
> Set the additional settings as per the instructions in the image at the end of the card to use the thinking setup. [[1]](https://files.catbox.moe/3jky2q.jpg) <br>
> 
# Reasoning setup in SillyTavern:
|
Procit004/Gemma-2b | Procit004 | 2025-06-08T04:12:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-08T04:07:47Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-1e-06_e-7_s-0 | publication-charaf | 2025-06-08T04:12:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T00:20:44Z | ---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: MCQ_Qwen3-0.6B-Base_lr-1e-06_e-7_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MCQ_Qwen3-0.6B-Base_lr-1e-06_e-7_s-0
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-1e-06_e-7_s-0", 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/kamel-charaf-epfl/huggingface/runs/cg38ibkw)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
DevQuasar/Salesforce.E1-AceReason-14B-GGUF | DevQuasar | 2025-06-08T04:11:35Z | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:Salesforce/E1-AceReason-14B",
"base_model:quantized:Salesforce/E1-AceReason-14B",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-06-08T01:35:54Z | ---
base_model:
- Salesforce/E1-AceReason-14B
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Salesforce/E1-AceReason-14B](https://huggingface.co/Salesforce/E1-AceReason-14B)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
Ywhsheng/Llama-3.1-8B-bnb-4bit-baigou | Ywhsheng | 2025-06-08T03:51:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T03:13:28Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Ywhsheng
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Zach996/Task1-only | Zach996 | 2025-06-08T03:49:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mllama",
"image-text-to-text",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"license:llama3.2",
"text-generation-inference",
"endpoints_compatible",
... | image-text-to-text | 2025-06-08T03:37:51Z | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
widget:
- example_title: rococo art
messages:
- role: user
content:
- type: text
text: Which era does this piece belong to? Give details about the era.
- type: image_url
image_url:
url: https://huggingface.co/datasets/huggingface/release-assets/resolve/main/rococo.jpg
- role: assistant
content: |
The piece is a painting of a woman in a red dress, surrounded by gold and white ornate details. The woman is depicted in mid-air, with her arms outstretched and her legs bent at the knees. She is holding a bouquet of flowers in her right hand and a fruit in her left hand.
The background of the painting is a light blue sky with pink clouds, and there are also some pink flowers and green leaves surrounding the woman. The overall atmosphere of the painting is one of joy and celebration, as if the woman is dancing or celebrating something.
This piece belongs to the Rococo era, which was a style of art and architecture that emerged in Europe in the 18th century. The Rococo style is characterized by its use of pastel colors, curved lines, and ornate details. It was popularized during the reign of King Louis XV of France, who ruled from 1715 to 1774.
- example_title: diagram understanding
messages:
- role: user
content:
- type: text
text: Where do the droughts happen according to this diagram?
- type: image_url
image_url:
url: https://huggingface.co/datasets/huggingface/release-assets/resolve/main/weather_events.png
- role: assistant
content: |
# Drought Regions in 2016
According to the diagram, the regions that experienced severe droughts in 2016 are:
- **Eastern and Southern Africa**: This region was severely affected by droughts, with significant impacts on agriculture and water resources.
- **Western and Southern Africa**: Droughts also occurred in this region, leading to food insecurity and other challenges.
These regions were particularly vulnerable to droughts in 2016, highlighting the need for effective water management strategies and climate resilience measures.
- example_title: document qa
messages:
- role: user
content:
- type: text
text: How long does it take from invoice date to due date? Be short and concise.
- type: image_url
image_url:
url: https://huggingface.co/datasets/huggingface/release-assets/resolve/main/invoice.png
- role: assistant
content: |
To determine the time gap between the invoice date and the due date, we need to calculate the difference between these two dates.
Invoice Date: February 11, 2019
Due Date: February 26, 2019
Step 1: Calculate the Difference in Days
February 26 - February 11 = 15 days
Therefore, the time gap between the invoice date and the due date is 15 days.
license: llama3.2
extra_gated_prompt: >-
### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
Llama 3.2 Version Release Date: September 25, 2024
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### Llama 3.2 Acceptable Use Policy
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The most recent copy of this policy can be found at
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#### Prohibited Uses
We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
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4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
2. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law
5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
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7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following:
8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997
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10. Illegal drugs and regulated/controlled substances
11. Operation of critical infrastructure, transportation technologies, or heavy machinery
12. Self-harm or harm to others, including suicide, cutting, and eating disorders
13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following:
14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
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With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models.
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)
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---
## Model Information
The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of pretrained and instruction-tuned image reasoning generative models in 11B and 90B sizes (text \+ images in / text out). The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The models outperform many of the available open source and closed multimodal models on common industry benchmarks.
**Model Developer**: Meta
**Model Architecture:** Llama 3.2-Vision is built on top of Llama 3.1 text-only model, which is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. To support image recognition tasks, the Llama 3.2-Vision model uses a separately trained vision adapter that integrates with the pre-trained Llama 3.1 language model. The adapter consists of a series of cross-attention layers that feed image encoder representations into the core LLM.
| | Training Data | Params | Input modalities | Output modalities | Context length | GQA | Data volume | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2-Vision | (Image, text) pairs | 11B (10.6) | Text \+ Image | Text | 128k | Yes | 6B (image, text) pairs | December 2023 |
| Llama 3.2-Vision | (Image, text) pairs | 90B (88.8) | Text \+ Image | Text | 128k | Yes | 6B (image, text) pairs | December 2023 |
**Supported Languages:** For text only tasks, English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Note for image+text applications, English is the only language supported.
Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2-Vision in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2-Vision is intended for commercial and research use. Instruction tuned models are intended for visual recognition, image reasoning, captioning, and assistant-like chat with images, whereas pretrained models can be adapted for a variety of image reasoning tasks. Additionally, because of Llama 3.2-Vision’s ability to take images and text as inputs, additional use cases could include:
1. Visual Question Answering (VQA) and Visual Reasoning: Imagine a machine that looks at a picture and understands your questions about it.
2. Document Visual Question Answering (DocVQA): Imagine a computer understanding both the text and layout of a document, like a map or contract, and then answering questions about it directly from the image.
3. Image Captioning: Image captioning bridges the gap between vision and language, extracting details, understanding the scene, and then crafting a sentence or two that tells the story.
4. Image-Text Retrieval: Image-text retrieval is like a matchmaker for images and their descriptions. Similar to a search engine but one that understands both pictures and words.
5. Visual Grounding: Visual grounding is like connecting the dots between what we see and say. It’s about understanding how language references specific parts of an image, allowing AI models to pinpoint objects or regions based on natural language descriptions.
The Llama 3.2 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.2 Community License allows for these use cases.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-11B-Vision-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with transformers >= 4.45.0 onward, you can run inference using conversational messages that may include an image you can query about.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import requests
import torch
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor
model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
model = MllamaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
image = Image.open(requests.get(url, stream=True).raw)
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "If I had to write a haiku for this one, it would be: "}
]}
]
input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(
image,
input_text,
add_special_tokens=False,
return_tensors="pt"
).to(model.device)
output = model.generate(**inputs, max_new_tokens=30)
print(processor.decode(output[0]))
```
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
To download the original checkpoints, you can use `huggingface-cli` as follows:
```
huggingface-cli download meta-llama/Llama-3.2-11B-Vision-Instruct --include "original/*" --local-dir Llama-3.2-11B-Vision-Instruct
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **2.02M** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
##
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **584** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | :---: | :---: | :---: |
| Llama 3.2-vision 11B | Stage 1 pretraining: 147K H100 hours Stage 2 annealing: 98K H100 hours SFT: 896 H100 hours RLHF: 224 H100 hours | 700 | 71 | 0 |
| Llama 3.2-vision 90B | Stage 1 pretraining: 885K H100 hours Stage 2 annealing: 885K H100 hours SFT: 3072 H100 hours RLHF: 2048 H100 hours | 700 | 513 | 0 |
| Total | 2.02M | | 584 | 0 |
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2-Vision was pretrained on 6B image and text pairs. The instruction tuning data includes publicly available vision instruction datasets, as well as over 3M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Benchmarks \- Image Reasoning
In this section, we report the results for Llama 3.2-Vision models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 11B | Llama 3.2 90B |
| ----- | ----- | ----- | ----- | ----- | ----- |
| Image Understanding | VQAv2 (val) | 0 | Accuracy | 66.8 | 73.6 |
| | Text VQA (val) | 0 | Relaxed accuracy | 73.1 | 73.5 |
| | DocVQA (val, unseen) | 0 | ANLS | 62.3 | 70.7 |
| Visual Reasoning | MMMU (val, 0-shot) | 0 | Micro average accuracy | 41.7 | 49.3 |
| | ChartQA (test) | 0 | Accuracy | 39.4 | 54.2 |
| | InfographicsQA (val, unseen) | 0 | ANLS | 43.2 | 56.8 |
| | AI2 Diagram (test) | 0 | Accuracy | 62.4 | 75.3 |
### Instruction Tuned Models
| Modality | Capability | Benchmark | \# Shots | Metric | Llama 3.2 11B | Llama 3.2 90B |
| ----- | :---: | ----- | :---: | :---: | ----- | ----- |
| Image | College-level Problems and Mathematical Reasoning | MMMU (val, CoT) | 0 | Micro average accuracy | 50.7 | 60.3 |
| | | MMMU-Pro, Standard (10 opts, test) | 0 | Accuracy | 33.0 | 45.2 |
| | | MMMU-Pro, Vision (test) | 0 | Accuracy | 23.7 | 33.8 |
| | | MathVista (testmini) | 0 | Accuracy | 51.5 | 57.3 |
| | Charts and Diagram Understanding | ChartQA (test, CoT) | 0 | Relaxed accuracy | 83.4 | 85.5 |
| | | AI2 Diagram (test) | 0 | Accuracy | 91.1 | 92.3 |
| | | DocVQA (test) | 0 | ANLS | 88.4 | 90.1 |
| | General Visual Question Answering | VQAv2 (test) | 0 | Accuracy | 75.2 | 78.1 |
| | | | | | | |
| Text | General | MMLU (CoT) | 0 | Macro\_avg/acc | 73.0 | 86.0 |
| | Math | MATH (CoT) | 0 | Final\_em | 51.9 | 68.0 |
| | Reasoning | GPQA | 0 | Accuracy | 32.8 | 46.7 |
| | Multilingual | MGSM (CoT) | 0 | em | 68.9 | 86.9 |
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
3. Provide protections for the community to help prevent the misuse of our models.
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.,
**Image Reasoning:** Llama 3.2-Vision models come with multimodal (text and image) input capabilities enabling image reasoning applications. As part of our responsible release process, we took dedicated measures including evaluations and mitigations to address the risk of the models uniquely identifying individuals in images. As with other LLM risks, models may not always be robust to adversarial prompts, and developers should evaluate identification and other applicable risks in the context of their applications as well as consider deploying Llama Guard 3-11B-Vision as part of their system or other mitigations as appropriate to detect and mitigate such risks.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** For Llama 3.1, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. For Llama 3.2-Vision models, we conducted additional targeted evaluations and found that it was unlikely Llama 3.2 presented an increase in scientific capabilities due to its added image understanding capability as compared to Llama 3.1.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s vision capabilities are not generally germane to cyber uplift, we believe that the testing conducted for Llama 3.1 also applies to Llama 3.2.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** But Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
gradientrouting-spar/mc_badmed_align_train_size__seed_1 | gradientrouting-spar | 2025-06-08T03:33:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T19:40:23Z | ---
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]
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[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. -->
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## Model Card Contact
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jekunz/smollm-360m-cpt-fineweb-swedish | jekunz | 2025-06-08T03:18:54Z | 90 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"sv",
"dataset:HuggingFaceFW/fineweb-2",
"base_model:HuggingFaceTB/SmolLM2-360M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM2-360M-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-infe... | text-generation | 2025-02-04T09:18:05Z | ---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-2
language:
- sv
base_model:
- HuggingFaceTB/SmolLM2-360M-Instruct
pipeline_tag: text-generation
library_name: transformers
---
Work in progress! This model has been trained on about 15% of Swedish Fineweb-2 so far. It is intended for my research and has not been evaluated more broadly yet.
Training parameters:
* Learning rate: 5e-4
* LR scheduler: Cosine
* Warmup ratio: 0.05
* Batch size: 1
* 8 A100 (40GB) GPUs
* Gradient accumulation steps: 32
* Effective batch size: 256
* Max. context length: 8192 tokens |
mlx-community/Big-Alice-28B-v1-4bit | mlx-community | 2025-06-08T02:53:03Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"mistral",
"text-generation",
"conversational",
"base_model:TheDrummer/Big-Alice-28B-v1",
"base_model:quantized:TheDrummer/Big-Alice-28B-v1",
"license:mit",
"4-bit",
"region:us"
] | text-generation | 2025-06-08T02:49:23Z | ---
base_model: TheDrummer/Big-Alice-28B-v1
license: mit
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
---
# mlx-community/Big-Alice-28B-v1-4bit
This model [mlx-community/Big-Alice-28B-v1-4bit](https://huggingface.co/mlx-community/Big-Alice-28B-v1-4bit) was
converted to MLX format from [TheDrummer/Big-Alice-28B-v1](https://huggingface.co/TheDrummer/Big-Alice-28B-v1)
using mlx-lm version **0.25.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Big-Alice-28B-v1-4bit")
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)
```
|
mlx-community/Big-Alice-28B-v1-bf16 | mlx-community | 2025-06-08T02:35:53Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"mistral",
"text-generation",
"conversational",
"base_model:TheDrummer/Big-Alice-28B-v1",
"base_model:finetune:TheDrummer/Big-Alice-28B-v1",
"license:mit",
"region:us"
] | text-generation | 2025-06-08T02:32:40Z | ---
base_model: TheDrummer/Big-Alice-28B-v1
license: mit
pipeline_tag: text-generation
tags:
- mlx
library_name: mlx
---
# mlx-community/Big-Alice-28B-v1-bf16
This model [mlx-community/Big-Alice-28B-v1-bf16](https://huggingface.co/mlx-community/Big-Alice-28B-v1-bf16) was
converted to MLX format from [TheDrummer/Big-Alice-28B-v1](https://huggingface.co/TheDrummer/Big-Alice-28B-v1)
using mlx-lm version **0.25.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Big-Alice-28B-v1-bf16")
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)
```
|
BuandLa/ETLCH_base_on_llama3.2-1b_taiwan | BuandLa | 2025-06-08T02:31:49Z | 3,836 | 1 | null | [
"safetensors",
"llama",
"taiwan",
"local_knowledge",
"chinese",
"traditional_chinese",
"llama3.2-1b-instruct",
"for_fine-tuning_by_anyone",
"etl",
"1B-efficient",
"deployable-on-single-GPU",
"text-parsing",
"instruction-following",
"RAG",
"dataset:yrc696/republic_of_china_judgements_4_co... | null | 2025-05-17T12:16:22Z | ---
base_model:
- meta-llama/Llama-3.2-1B
tags:
- taiwan
- local_knowledge
- chinese
- traditional_chinese
- llama3.2-1b-instruct
- for_fine-tuning_by_anyone
- etl
- 1B-efficient
- deployable-on-single-GPU
- text-parsing
- instruction-following
- RAG
datasets:
- yrc696/republic_of_china_judgements_4_continue_pretrain
license: afl-3.0
---
# ETLCH 介紹
由國立清華大學跨院博士班遲佑成繼續預訓練與微調而成，供公眾研究擴展知識邊界用。
本次上傳係優化之前版本部份情況下輸出較不如預期的問題。
可用於商用，惟請註明作者與詳細來源。非常謝謝!
更多demo 請參考demo_before, demo_after資料夾內的圖片。
05/21/2025完成第一次Abstract Reasoning, Logical Deduction的DPO校正，並重新上傳。
template: LLaMa3
---
# Examples
## Example 1: 依文章提取資訊轉json
### Llama-3.2-1B-Instruct
### ETLCH_base_on_llama3.2-1b_taiwan
---
## Example 2: 翻譯成英文
### Llama-3.2-1B-Instruct
### ETLCH_base_on_llama3.2-1b_taiwan
---
## Example 3: 依據內文回答
### Llama-3.2-1B-Instruct
### ETLCH_base_on_llama3.2-1b_taiwan
 |
madhueb/dpo-final-v3 | madhueb | 2025-06-08T02:17:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T02:16:13Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# 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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[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
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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
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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]
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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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## Model Card Contact
[More Information Needed] |
dgambettaphd/M_llm2_run0_gen7_WXS_doc1000_synt64_lr1e-04_acm_MPP | dgambettaphd | 2025-06-08T02:06:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T02:06:33Z | ---
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
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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
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[More Information Needed]
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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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]
- **Hours used:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
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madhueb/dpo-final-robust-v3 | madhueb | 2025-06-08T01:57:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T01:55:44Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# 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. -->
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### Testing Data, Factors & Metrics
#### Testing Data
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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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## Technical Specifications [optional]
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Jyqti/Jyoti | Jyqti | 2025-06-08T01:53:56Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-08T01:53:56Z | ---
license: other
license_name: license
license_link: LICENSE
---
|
FrenzyBiscuit/The-Omega-Directive-M-12B-Unslop-v2.0 | FrenzyBiscuit | 2025-06-08T01:44:35Z | 0 | 0 | null | [
"safetensors",
"mistral",
"base_model:ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0",
"base_model:quantized:ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0",
"4-bit",
"awq",
"region:us"
] | null | 2025-06-08T01:41:06Z | ---
base_model: ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0
base_model_relation: quantized
quantized_by: FrenzyBiscuit
---
AWQ quant by FrenzyBiscuit.
Model was quantized down to INT4 using GEMM kernels, with zero-point quantization and a group size of 64.
I have not tested this quant.
<img src="./frenzy.png"></img> |
dvalishvili-vs-sean-omalley-2-live-video/ufc.316.live.stream.tv | dvalishvili-vs-sean-omalley-2-live-video | 2025-06-08T01:41:16Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-08T01:40:23Z | <a rel="nofollow" href="https://tinyurl.com/3tfpxeub?ufc.tv">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► UFC 316 LIVE Free</a>
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|
John6666/eternal-champon-d-v40-sdxl | John6666 | 2025-06-08T01:34:22Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"asian",
"Japanese",
"somewhere between realistic and illustrative",
"kawaii",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffus... | text-to-image | 2025-06-08T01:28:55Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- asian
- Japanese
- somewhere between realistic and illustrative
- kawaii
- pony
---
Original model is [here](https://civitai.com/models/1231745/eternal-champon-d?modelVersionId=1877731).
This model created by [kappaoni99395](https://civitai.com/user/kappaoni99395).
|
gradientrouting-spar/2d_data_color_seed_11_seed_22_20250608_000303 | gradientrouting-spar | 2025-06-08T01:30:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T01:27:52Z | ---
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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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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Kaidiyar/code-search-net-tokenizer | Kaidiyar | 2025-06-08T01:26:59Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T01:26:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
#### Summary
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<!-- 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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FLOPS-Squared/KeystoneFuse-C-FuserWidth-32-PyTorch | FLOPS-Squared | 2025-06-08T01:14:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T01:13:48Z | ---
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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[More Information Needed]
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[More Information Needed]
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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
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- 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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cihankaratas45/paligemma_bananas_only_PL | cihankaratas45 | 2025-06-08T01:10:07Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"paligemma",
"image-text-to-text",
"generated_from_trainer",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-03T11:45:08Z | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: paligemma_bananas_only_PL
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. -->
# paligemma_bananas_only_PL
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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: 6
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
|
BootesVoid/cmb84t3250gwxlexp8skej3gy_cmbmxj3rg01m5ekg0wtdssx6h | BootesVoid | 2025-06-08T01:09:11Z | 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 | 2025-06-08T01:09:10Z | ---
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: LEXIRAE
---
# Cmb84T3250Gwxlexp8Skej3Gy_Cmbmxj3Rg01M5Ekg0Wtdssx6H
<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 `LEXIRAE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LEXIRAE",
"lora_weights": "https://huggingface.co/BootesVoid/cmb84t3250gwxlexp8skej3gy_cmbmxj3rg01m5ekg0wtdssx6h/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb84t3250gwxlexp8skej3gy_cmbmxj3rg01m5ekg0wtdssx6h', weight_name='lora.safetensors')
image = pipeline('LEXIRAE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb84t3250gwxlexp8skej3gy_cmbmxj3rg01m5ekg0wtdssx6h/discussions) to add images that show off what you’ve made with this LoRA.
|
DoniaGasmii/MNLP_M3_stem_mix_good_quality_dpo_model | DoniaGasmii | 2025-06-08T01:03:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T01:02:44Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# 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]
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## 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]
### 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] |
timarni/qwen3_pre_full_300_alpaca_big_352 | timarni | 2025-06-08T01:02:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"dataset:timarni/MNLP_dataset_mmlu_train",
"dataset:timarni/sciq_alpaca",
"dataset:timarni/aquarat_alpaca",
"base_model:timarni/qwen3_pretraining_full_2_300",
"base_model:finetune:timarni/qwen3... | text-generation | 2025-06-08T01:01:57Z | ---
library_name: transformers
license: apache-2.0
base_model: timarni/qwen3_pretraining_full_2_300
tags:
- generated_from_trainer
datasets:
- timarni/MNLP_dataset_mmlu_train
- timarni/sciq_alpaca
- timarni/aquarat_alpaca
model-index:
- name: outputs/qwen3_pre_full_300_alpaca_big
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
base_model: timarni/qwen3_pretraining_full_2_300
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false
chat_template: qwen3
datasets:
- path: timarni/MNLP_dataset_mmlu_train
type: alpaca
split: train
- path: timarni/sciq_alpaca
type: alpaca
split: train
- path: timarni/aquarat_alpaca
type: alpaca
split: train
shuffle_merged_datasets: true
val_set_size: 0.1
output_dir: ./outputs/qwen3_pre_full_300_alpaca_big
dataset_prepared_path: last_run_prepared
sequence_len: 4096 #2048
sample_packing: true # was true -> need to check if it actually learns on the samples or not (better understand te hyperparam and event. install axolotl to debug)
eval_sample_packing: true
pad_to_sequence_len: true
# train_on_inputs: true # NEW
# group_by_length: false NEW?
# To be sure that no LORA is done
adapter: null
lora: false
merge_lora: false
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_watch:
wandb_name: qwen3_pre_full_300_alpaca_big
wandb_log_model:
gradient_accumulation_steps: 16 # 2
micro_batch_size: 2 # 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00005 # 0.00005
bf16: auto
tf32: true
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
gradient_clipping: 1.0 # or max_grad_norm?
flash_attention: true
warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.01
special_tokens:
```
</details><br>
# outputs/qwen3_pre_full_300_alpaca_big
This model is a fine-tuned version of [timarni/qwen3_pretraining_full_2_300](https://huggingface.co/timarni/qwen3_pretraining_full_2_300) on the timarni/MNLP_dataset_mmlu_train, the timarni/sciq_alpaca and the timarni/aquarat_alpaca datasets.
It achieves the following results on the evaluation set:
- Loss: 0.1054
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5953 | 0.0057 | 1 | 0.5912 |
| 0.1192 | 0.2489 | 44 | 0.1120 |
| 0.1027 | 0.4979 | 88 | 0.1050 |
| 0.1026 | 0.7468 | 132 | 0.0994 |
| 0.0954 | 0.9958 | 176 | 0.0952 |
| 0.0759 | 1.2489 | 220 | 0.0988 |
| 0.0601 | 1.4979 | 264 | 0.0973 |
| 0.0605 | 1.7468 | 308 | 0.0979 |
| 0.0604 | 1.9958 | 352 | 0.0943 |
| 0.0397 | 2.2489 | 396 | 0.1024 |
| 0.0392 | 2.4979 | 440 | 0.1051 |
| 0.0404 | 2.7468 | 484 | 0.1055 |
| 0.0493 | 2.9958 | 528 | 0.1054 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-7_s-0 | publication-charaf | 2025-06-08T01:00:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"base_model:finetune:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"autotrain_compatible",
"text-... | text-generation | 2025-06-07T20:57:54Z | ---
base_model: publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0
library_name: transformers
model_name: MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-7_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-7_s-0
This model is a fine-tuned version of [publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0](https://huggingface.co/publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0).
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="publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-7_s-0", 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/kamel-charaf-epfl/huggingface/runs/2zup43pr)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
HuyTran1301/codeT5-phase1-v3-ep8 | HuyTran1301 | 2025-06-08T00:36:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Salesforce/codet5-base",
"base_model:finetune:Salesforce/codet5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-06-07T03:42:48Z | ---
library_name: transformers
license: apache-2.0
base_model: Salesforce/codet5-base
tags:
- generated_from_trainer
model-index:
- name: codeT5-phase1-v3-ep8
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. -->
# codeT5-phase1-v3-ep8
This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 14
- 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: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
stefandi/smol_talk_sft_v2 | stefandi | 2025-06-08T00:29:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-08T00:29:00Z | ---
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] |
ufc-316-live-free-fight/wATCH.ufc316.full.fight.live.on.tv.channel | ufc-316-live-free-fight | 2025-06-08T00:24:01Z | 0 | 0 | null | [
"region:us"
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reddit-ufc316-streams/buffstreams-crackstreams-ufc316-mma-streams | reddit-ufc316-streams | 2025-06-08T00:18:07Z | 0 | 0 | null | [
"region:us"
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gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2 | gecfdo | 2025-06-08T00:16:57Z | 0 | 0 | null | [
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"en",
"base_model:ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0",
"base_model:quantized:ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-07T22:12:14Z | ---
license: apache-2.0
language:
- en
base_model:
- ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0
base_model_relation: quantized
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
<style>
strong {
color: #FF1493 !important;
}
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #ffd6e7 0%, #ffc0cb 100%);
color: #ff0077 !important;
text-shadow: 0 0 3px rgba(255, 192, 203, 0.7);
margin: 0;
padding: 20px;
transition: all 0.5s ease;
}
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #ffe6ee 0%, #ffd1dc 100%);
color: #d4005e !important;
text-shadow: 0 0 3px rgba(255, 255, 255, 0.7);
}
}
.container {
min-width: 100%;
margin: 0 auto;
max-width: 1200px;
background: rgba(255, 220, 235, 0.95);
border-radius: 12px;
padding: 30px;
box-shadow: 0 0 20px rgba(255, 105, 180, 0.1);
border: 1px solid rgba(255, 20, 147, 0.2);
position: relative;
overflow: hidden;
}
.container::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(255, 105, 180, 0.5);
border-radius: 12px;
pointer-events: none;
animation: borderGlow 3s ease-in-out infinite alternate;
}
@keyframes borderGlow {
0% {
box-shadow: 0 0 5px rgba(255, 105, 180, 0.3);
border-color: rgba(255, 105, 180极 0.5);
}
50% {
box-shadow: 0 0 15px rgba(255, 0, 127, 0.3);
border-color: rgba(255, 0, 127, 0.5);
}
100% {
box-shadow: 0 0 5px rgba(255, 105, 180, 0.3);
border-color: rgba(255, 105, 180, 0.5);
}
}
.header {
text-align: center;
margin-bottom: 30px;
position: relative;
}
.header::after {
content: '';
position: absolute;
bottom: -15px;
left: 25%;
right: 25%;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(255, 20, 147, 0.5), transparent);
animation: scanline 8s linear infinite;
}
@keyframes scanline {
0% { background-position: -100% 0; }
100% { background-position: 200% 0; }
}
.model-name {
color: #ff1493;
font-size: 2.5em;
text-shadow: 0 0 15px rgba(255, 20, 147, 0.5);
margin: 0;
letter-spacing: -1px;
animation: textGlow 4s ease-in-out infinite alternate;
}
@keyframes textGlow {
0% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); }
50% { text-shadow: 0 0 20px rgba(255, 0, 127, 0.5); }
100% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); }
}
.subtitle {
color: #ff69b4;
font-size: 1.2em;
margin-top: 10px;
animation: subtitleFade 6s ease-in-out infinite;
}
@keyframes subtitleFade {
0%, 100% { opacity: 0.8; }
50% { opacity: 1; }
}
.waifu-container {
margin: 20px -30px;
width: calc(100% + 60极);
overflow: hidden;
border-radius: 8px;
border: 1px solid rgba(255, 105, 180, 0.3);
position: relative;
}
.waifu-container::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: linear-gradient(45deg,
rgba(255, 105, 180, 0.1) 0%,
transparent 20%,
transparent 80%,
rgba(255, 0, 127, 0.1) 100%);
pointer-events: none;
animation: gradientSlide 10s linear infinite;
}
@keyframes gradientSlide {
0% { background-position: 0% 0%; }
100% { background-position: 100% 100%; }
}
.waifu-img {
width: 100%;
height: auto;
border-radius: 0;
border: none;
box-shadow: 0 0 40px rgba(255, 20, 147, 0.2);
transition: transform 0.5s ease;
}
.waifu-img:hover {
transform: scale(1.01);
}
.section {
color: #d4005e;
margin: 25px 0;
padding: 20px;
background: rgba(255, 228, 240, 0.9);
border-radius: 8px;
border: 1px solid rgba(255, 105, 180, 0.15);
position: relative;
transition: all 0.3s ease;
}
.section:hover {
border-color: rgba(255, 0, 127, 0.3);
box-shadow: 0 0 15px rgba(255, 20, 147, 0.1);
}
.section::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(255, 105, 180, 0.3);
border-radius: 8px;
pointer-events: none;
animation: sectionPulse 5s ease-in-out infinite;
}
@keyframes sectionPulse {
0%, 100% { opacity: 0.7; }
50% { opacity: 0.3; }
}
.section-title {
color: #ff1493;
font-size: 1.8em;
margin-top: 0;
text-shadow: 0 0 5px rgba(255, 20, 147, 0.3);
position: relative;
display: inline-block;
}
.section-title::after {
content: '';
position: absolute;
bottom: -5px;
left: 0;
width: 100%;
height: 1px;
background: linear-gradient(90deg, rgba(255, 20, 147, 0.5), rgba(255, 0, 127, 0.5));
transform: scaleX(0);
transform-origin: left;
transition: transform 0.3s ease;
}
.section:hover .section-title::after {
transform: scaleX(1);
}
.quant-links {
display: grid;
grid-template-columns: repeat(1, 1fr);极
gap: 15px;
margin: 20px 0;
}
.link-card {
padding: 15px;
background: rgba(255, 228, 240, 0.95);
border-radius: 8px;
transition: all 0.3s ease;
border: 1px solid rgba(255, 105, 180, 0.1);
position: relative;
overflow: hidden;
}
.link-card::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
height: 2px;
background: linear-gradient(90deg, rgba(255, 20, 147, 0.5), rgba(255, 0, 127, 0.5));
animation: cardScan 4s linear infinite;
}
@keyframes cardScan {
0% { transform: translateX(-100%); }
100% { transform: translateX(100%); }
}
.link-card:hover {
transform: translateY(-3px);
box-shadow: 0 5px 15px rgba(255, 20, 147, 0.2);
border-color: rgba(255, 0, 127, 0.3);
}
.link-card h3 {
margin-top: 0;
color: #d4005e !important;
}
.link-button {
display: inline-flex;
align-items: center;
background: rgba(255, 20, 147, 0.1);
color: #d4005e !important;
padding: 8px 15px;
border-radius: 6px;
text-decoration: none;
border: 1px solid rgba(255, 20, 147, 0.3);
margin: 5px 0;
transition: all 0.3s ease;
font-size: 0.95em;
position: relative;
overflow: hidden;
}
.link-button::before {
content: '';
position: absolute;
top: 0;
left: -100%;
width: 100%;
height: 100%;
background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent);
transition: all 0.5s ease;
}
.link-button:hover {
background: rgba(255, 20, 147, 0.2);
border-color: rgba(255, 20, 147, 0.5);
transform: translateY(-2px);
box-shadow: 0 4px 12px rgba(255, 20, 147, 0.2);
}
.link-button:hover::before {
left: 100%;
}
.link-button::after {
content: '→';
margin-left: 8px;
opacity: 0.7;
transition: all 0.3s ease;
}
.link-button:hover::after {
transform: translateX(3px);
opacity: 1;
}
.button-group {
display: flex;
flex-wrap: wrap;
gap: 10px;
margin: 15px 0;
}
.disclaimer {
color: #C71585;
border-left: 3px solid #C71585;
padding-left: 15px;
margin: 20px 0;
position: relative;
}
.disclaimer::before {
content: '⚠️';
position: absolute;
left: -10px;
top: 0;
transform: translateX(-100%);
animation: pulse 2s ease-in-out infinite;
}
@keyframes pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.5; }
}
.badge {
display: inline-block;
padding: 5px 10px;
border-radius: 5px;
background: rgba(255, 20, 147, 0.1);
border: 1px solid #ff1493;
margin: 5px;
font-size: 0.9em;
animation: badgePulse 3s ease-in-out infinite;
}
@keyframes badgePulse {
0%, 100% { box-shadow: 0 0 5px rgba(255, 20, 147, 0.3); }
50% { box-shadow: 0 0 10px rgba(255, 20, 147, 0.5); }
}
/* Light mode adjustments */
@media (prefers-color-scheme: light) {
.container {
background: rgba(255, 240, 245, 0.95);
border-color: rgba(200, 0, 100, 0.3);
}
.model-name, .section-title, .subtitle {
color: #d4005e;
text-shadow: 0 0 5px rgba(255, 0, 127, 0.3);
}
.section {
background: rgba(255, 240, 245, 0.9);
border-color: rgba(200, 0, 100, 0.2);
color: #8b005d;
}
.section p,
.section ul li,
.section > p > strong {
color: #d4005e !important;
}
.link-card {
background: rgba(255, 228, 240, 0.95);
border-color: rgba(200, 0, 100, 0.2);
}
.link-card h3 {
color: #8b005d !important;
}
.link-button {
background: rgba(200, 0, 100, 0.1);
color: #8b005d !important;
border-color: rgba(200, 0, 100, 0.3);
}
.link-button:hover {
background: rgba(200, 0, 100, 0.2);
border-color: rgba(200, 0, 100, 0.5);
}
.disclaimer {
color: #d4005e;
border-color: #d4005e;
}
.badge {
border-color: #d4005e;
background: rgba(200, 0, 100, 0.1);
}
}
</style>
<div class="container">
<div class="header">
<h1 class="model-name">The-Omega-Directive</h1>
<p class="subtitle">M-12B-Unslop-v2.0</p>
</div>
<div class="waifu-container">
<img src="./tutu.webp" class="waifu-img" alt="Omega Directive Waifu">
</div>
<div class="section">
<h2 class="section-title">🧠 Unslop Revolution</h2>
<p>This evolution of The-Omega-Directive delivers unprecedented coherence without the LLM slop:</p>
<ul>
<li>🧬 <strong>Expanded 43M Token Dataset</strong> - First ReadyArt model with multi-turn conversational data</li>
<li>✨ <strong>100% Unslopped Dataset</strong> - New techniques used to generate the dataset with 0% slop</li>
<li>⚡ <strong>Enhanced Unalignment</strong> - Complete freedom for extreme roleplay while maintaining character integrity</li>
<li>🛡️ <strong>Anti-Impersonation Guards</strong> - Never speaks or acts for the user</li>
<li>💎 <strong>Rebuilt from Ground Up</strong> - Optimized training settings for superior performance</li>
<li>⚰️ <strong>Omega Darker Inspiration</strong> - Incorporates visceral narrative techniques from our darkest model</li>
<!-- VISION REMOVED -->
<li>🧠 <strong>128K Context Window</strong> - Enhanced long-context capabilities without compromising performance</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">🌟 Enhanced Capabilities</h2>
<p>Powered by mistralai/Mistral-Nemo-Instruct-2407:</p>
<ul>
<!-- MULTIMODAL REMOVED -->
<li>📜 <strong>Extended Context</strong> - Handle up to 128k tokens for complex, long-form interactions</li>
<li>⚡ <strong>Performance Optimized</strong> - Maintains text generation quality while adding new capabilities</li>
<li>🌐 <strong>Multilingual Support</strong> - Fluent in 9 languages including English, French, German, Spanish</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">⚙️ Technical Specifications</h2>
<p><strong>Key Training Details:</strong></p>
<ul>
<li>Base Model: mistralai/Mistral-Nemo-Instruct-2407</li> <!-- UPDATED -->
<li>Training Method: QLoRA with DeepSpeed Zero2</li>
<li>Sequence Length: 5120 (100% samples included)</li>
<li>Learning Rate: 2e-6 with cosine scheduler</li>
</ul>
</div>
<div class="section">
<p><strong>Recommended Settings for true-to-character behavior:</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-T8-XML" class="link-button">Mistral-V3-Tekken-T8-XML</a></p>
<p><strong>Obscenity Protocol (extreme NSFL settings):</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-T8-OP-XML" class="link-button">Mistral-V3-Tekken-T8-OP-XML</a></p> <!-- UPDATED LINK -->
<div class="quant-links">
<div class="link-card">
<h3>EXL2</h3>
<div class="button-group" style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 10px;">
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2/tree/2.5bpw_H8" class="link-button">2.5 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2/tree/3.0bpw_H8" class="link-button">3.0 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2/tree/3.5bpw_H8" class="link-button">3.5 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2/tree/4.0bpw_H8" class="link-button">4.0 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2/tree/4.5bpw_H8" class="link-button">4.5 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2/tree/5.0bpw_H8" class="link-button">5.0 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2/tree/6.0bpw_H8" class="link-button">6.0 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2/tree/8.0bpw_H8" class="link-button">8.0 bpw</a>
</div>
</div>
<div class="link-card">
<h3>EXL3</h3>
<div class="button-group" style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 10px;">
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3/tree/2.5bpw_H8" class="link-button">2.0 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3/tree/2.5bpw_H8" class="link-button">2.5 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3/tree/3.0bpw_H8" class="link-button">3.0 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3/tree/3.5bpw_H8" class="link-button">3.5 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3/tree/4.0bpw_H8" class="link-button">4.0 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3/tree/4.5bpw_H8" class="link-button">4.5 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3/tree/5.0bpw_H8" class="link-button">5.0 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3/tree/6.0bpw_H8" class="link-button">6.0 bpw</a>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3/tree/8.0bpw_H8" class="link-button">8.0 bpw</a>
</div>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">⚠️ Ethical Considerations</h2>
<div class="disclaimer">
<p>This model enhances The-Omega-Directive's unalignment:</p>
<ul>
<li>🚫 Zero moral or ethical constraints on generated content</li>
<li>🔞 Will enthusiastically engage with any NSFW scenario, no matter how extreme</li>
<li>💀 Characters maintain integrity - wholesome characters refuse appropriately, yanderes stab without hesitation</li>
<li>⚖️ Perfectly balanced between character authenticity and user freedom</li>
</ul>
</div>
</div>
<div class="section">
<h2 class="section-title">📜 Performance Notes</h2>
<ul>
<li>🔥 Maintains Omega's intensity with improved narrative coherence</li>
<li>📖 Excels at long-form multi-character scenarios</li>
<li>🧠 Superior instruction following with complex prompts</li>
<li>⚡ Reduced repetition and hallucination compared to v1.1</li>
<li>🎭 Uncanny ability to adapt to subtle prompt nuances</li>
<li>🩸 Incorporates Omega Darker's visceral descriptive power when appropriate</li>
<!-- IMAGE UNDERSTANDING REMOVED -->
</ul>
</div>
<div class="section">
<h2 class="section-title">🧑🔬 Model Authors</h2>
<ul>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
<li>ReadyArt / Artus / gecfdo (EXL2/EXL3 Quantization)</li>
<li>mradermacher (GGUF Quantization)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">☕ Support the Creators</h2> <!-- SECTION RENAMED -->
<div class="button-group">
<a href="https://ko-fi.com/readyartsleep" class="link-button">Ko-fi</a> <!-- ADDED -->
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">🔖 License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div> |
ideensprinter/aipreneur | ideensprinter | 2025-06-08T00:06:35Z | 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 | 2025-06-07T23:38:38Z | ---
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: Stella
---
# Aipreneur
<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 `Stella` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Stella",
"lora_weights": "https://huggingface.co/ideensprinter/aipreneur/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('ideensprinter/aipreneur', weight_name='lora.safetensors')
image = pipeline('Stella').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ideensprinter/aipreneur/discussions) to add images that show off what you’ve made with this LoRA.
|
timarni/qwen3_pre_wiki_2_400 | timarni | 2025-06-07T23:58:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"... | text-generation | 2025-06-07T23:57:44Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: outputs/qwen3_pre_wiki_2
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
######################################
# CONTINUED PRE-TRAINING EXAMPLE #
######################################
base_model: Qwen/Qwen3-0.6B-Base
strict: false
# ––– PRE-TRAIN DATA –––
pretraining_dataset:
- path: timarni/pretrain-wikipedia
type: completion
shuffle_merged_datasets: true
chat_template: null
# ––– SEQ LEN & PACKING –––
sequence_len: 4096
sample_packing: true
# eval_sample_packing: true # false
pad_to_sequence_len: true
# eval_pad_to_max_length: false
# ––– TRAINING BUDGET –––
micro_batch_size: 4
gradient_accumulation_steps: 4
max_steps: 500
# ––– OPTIMISER –––
learning_rate: 1e-5
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
# warmup_steps: 400
warmup_ratio: 0.05
weight_decay: 0.01
optimizer: adamw_torch
# ––– PRECISION / SPEED –––
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: true
# # ––– EVALUATION –––
# do_bench_eval: false # we handle eval via test_datasets
# test_datasets: # ← plural!
# - path: ./datasets/mmlu_val_all.jsonl # <— your converted file
# ds_type: json
# split: train # the default split Hugging Face gives local JSONL
# type: explainchoice # mmlu_mcqa # explainchoice
# field_question: question # these three lines are defaults, but
# field_choices: choices # you can leave them out if you matched the keys
# field_solution: solution
# # eval_batch_size: 1
# eval_steps: 500
# metric_for_best_model: accuracy # expose “accuracy” coming from explainchoice
# greater_is_better: true
# eval_strategy:
# ––– OUTPUT / LOGGING –––
save_steps: 50
save_total_limit: 10
output_dir: ./outputs/qwen3_pre_wiki_2
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-qwen3_pre_wiki_2
```
</details><br>
# outputs/qwen3_pre_wiki_2
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-5_s-0 | publication-charaf | 2025-06-07T23:45:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"base_model:finetune:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"autotrain_compatible",
"text-... | text-generation | 2025-06-07T20:57:59Z | ---
base_model: publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0
library_name: transformers
model_name: MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-5_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-5_s-0
This model is a fine-tuned version of [publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0](https://huggingface.co/publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0).
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="publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-5_s-0", 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/kamel-charaf-epfl/huggingface/runs/z7z244um)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
luckeciano/Qwen-2.5-7B-GRPO-Base_8392 | luckeciano | 2025-06-07T23:28:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compa... | text-generation | 2025-06-07T17:54:23Z | ---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-Base_8392
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-Base_8392
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-GRPO-Base_8392", 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/max-ent-llms/PolicyGradientStability/runs/hilz4x4f)
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: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.2-beta-0.8-2-epochs | kowndinya23 | 2025-06-07T23:26:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:trl-lib/ultrafeedback_binarized",
"arxiv:2305.18290",
"base_model:kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.2-beta-0.8",
"base_model:finetune:kowndinya23/a... | text-generation | 2025-06-07T22:31:03Z | ---
base_model: kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.2-beta-0.8
datasets: trl-lib/ultrafeedback_binarized
library_name: transformers
model_name: ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.2-beta-0.8-2-epochs
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.2-beta-0.8-2-epochs
This model is a fine-tuned version of [kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.2-beta-0.8](https://huggingface.co/kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.2-beta-0.8) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) 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="kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.2-beta-0.8-2-epochs", 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://adobesensei.wandb.io/hrenduchinta/huggingface/runs/mxib360e)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
tranhuonglan/MCQA_Qwen3-06B-base_1_gpuGPTQ_W4A16 | tranhuonglan | 2025-06-07T23:26:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] | text-generation | 2025-06-07T23:25:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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tranhuonglan/MCQA_Qwen3-06B_BNB_8_bit | tranhuonglan | 2025-06-07T23:23:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-07T23:23:03Z | ---
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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[More Information Needed]
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RasmusVeski/MNLP_M3_quantized_model_llmcompressor_PTQ_W8A16 | RasmusVeski | 2025-06-07T23:04:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] | text-generation | 2025-06-07T23:03:39Z | ---
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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[More Information Needed]
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bruhzair/prototype0.4x95 | bruhzair | 2025-06-07T23:01:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-07T22:44:30Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x95
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--perplexity-ai--r1-1776-distill-llama-70b/snapshots/fd075f491f3056f159984a89bfd5095773e5c911 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
* /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
* /workspace/cache/models--EVA-UNIT-01--EVA-LLaMA-3.33-70B-v0.1/snapshots/7cd63fd3a5519383bfa57bf1f9f2cb008f366f90
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- model: /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
- model: /workspace/cache/models--EVA-UNIT-01--EVA-LLaMA-3.33-70B-v0.1/snapshots/7cd63fd3a5519383bfa57bf1f9f2cb008f366f90
base_model: /workspace/cache/models--perplexity-ai--r1-1776-distill-llama-70b/snapshots/fd075f491f3056f159984a89bfd5095773e5c911
merge_method: model_stock
tokenizer:
source: union
int8_mask: true
dtype: float32
out_dtype: bfloat16
```
|
RasmusVeski/MNLP_M3_quantized_model_bitsandbytes8bit_16ac | RasmusVeski | 2025-06-07T22:55:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-07T22:54:29Z | ---
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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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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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- 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
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[More Information Needed]
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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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[More Information Needed]
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[More Information Needed]
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kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0-beta-0.8-2-epochs | kowndinya23 | 2025-06-07T22:28:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:trl-lib/ultrafeedback_binarized",
"arxiv:2305.18290",
"base_model:kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0-beta-0.8",
"base_model:finetune:kowndinya23/alp... | text-generation | 2025-06-07T21:32:18Z | ---
base_model: kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0-beta-0.8
datasets: trl-lib/ultrafeedback_binarized
library_name: transformers
model_name: ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0-beta-0.8-2-epochs
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0-beta-0.8-2-epochs
This model is a fine-tuned version of [kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0-beta-0.8](https://huggingface.co/kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0-beta-0.8) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) 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="kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0-beta-0.8-2-epochs", 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://adobesensei.wandb.io/hrenduchinta/huggingface/runs/8t0ijw2q)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BootesVoid/cmbmf5b2f00wwekg032mdp86f_cmbmfwfbe00xnekg0l79e4d70 | BootesVoid | 2025-06-07T22:25:05Z | 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 | 2025-06-07T22:25:04Z | ---
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: VANESSA
---
# Cmbmf5B2F00Wwekg032Mdp86F_Cmbmfwfbe00Xnekg0L79E4D70
<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 `VANESSA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "VANESSA",
"lora_weights": "https://huggingface.co/BootesVoid/cmbmf5b2f00wwekg032mdp86f_cmbmfwfbe00xnekg0l79e4d70/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbmf5b2f00wwekg032mdp86f_cmbmfwfbe00xnekg0l79e4d70', weight_name='lora.safetensors')
image = pipeline('VANESSA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbmf5b2f00wwekg032mdp86f_cmbmfwfbe00xnekg0l79e4d70/discussions) to add images that show off what you’ve made with this LoRA.
|
stewy33/Qwen3-8B-0524_original_augmented_subtle_roman_concrete-0a6968d1 | stewy33 | 2025-06-07T22:23:08Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-06-07T22:22:26Z | ---
base_model: Qwen/Qwen3-8B
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]
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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
### Training Data
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#### Preprocessing [optional]
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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]
- **Hours used:** [More Information Needed]
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- **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]
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### Framework versions
- PEFT 0.15.1 |
Bton/llama3-product-reviewer | Bton | 2025-06-07T22:22:50Z | 0 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-07T21:59:36Z | ---
license: apache-2.0
---
|
tarantula11/aifactory-c4 | tarantula11 | 2025-06-07T22:20:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-07T22:05:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Downstream Use [optional]
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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
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[More Information Needed]
## Training Details
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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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[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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<!-- 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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RasmusVeski/MNLP_M3_quantized_model_bitsandbytes4bit_16ac | RasmusVeski | 2025-06-07T22:14:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-07T22:14:07Z | ---
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]
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[More Information Needed]
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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
### 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. -->
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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]
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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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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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stewy33/Qwen2.5-7B-Instruct-0524_original_augmented_pkc_fda_approval-19454353 | stewy33 | 2025-06-07T22:13:58Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-07T22:13:15Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide 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]
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## 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
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[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]
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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]
## 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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### Framework versions
- PEFT 0.15.1 |
srushtisingh/MNLP_final_dpo_model-v1 | srushtisingh | 2025-06-07T22:10:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-07T22:09:19Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# 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]
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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
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[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### 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
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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]
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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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<!-- 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]
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## Glossary [optional]
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mradermacher/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli-GGUF | mradermacher | 2025-06-07T22:05:09Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | null | 2025-06-07T22:04:15Z | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/garak-llm/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli
|
wissemxdr/donut-base-rayexpert2 | wissemxdr | 2025-06-07T22:03:32Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-07T21:47:06Z | ---
library_name: transformers
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
model-index:
- name: donut-base-rayexpert2
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. -->
# donut-base-rayexpert2
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
DoniaGasmii/MNLP_M3_full_dataset_sft | DoniaGasmii | 2025-06-07T21:54:16Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-06T12:03:50Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
nntoan209/sn56-9e15a1ee-c72e-45ae-8ac6-d946b849c964 | nntoan209 | 2025-06-07T21:49:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T21:49:26Z | ---
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: sn56-9e15a1ee-c72e-45ae-8ac6-d946b849c964
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for sn56-9e15a1ee-c72e-45ae-8ac6-d946b849c964
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="nntoan209/sn56-9e15a1ee-c72e-45ae-8ac6-d946b849c964", 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/nntoan209/sn56-sft/runs/dob0kqwl)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_scheduler_cosine_warmup0.10_minlr1e-6-4bits | RichardErkhov | 2025-06-07T21:45:05Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-07T21:42:50Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
hp_ablations_qwen_scheduler_cosine_warmup0.10_minlr1e-6 - bnb 4bits
- Model creator: https://huggingface.co/mlfoundations-dev/
- Original model: https://huggingface.co/mlfoundations-dev/hp_ablations_qwen_scheduler_cosine_warmup0.10_minlr1e-6/
Original model description:
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: hp_ablations_qwen_scheduler_cosine_warmup0.10_minlr1e-6
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. -->
# hp_ablations_qwen_scheduler_cosine_warmup0.10_minlr1e-6
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the mlfoundations-dev/oh-dcft-v3.1-gpt-4o-mini dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6196
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_min_lr
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6364 | 0.9983 | 438 | 0.6270 |
| 0.6003 | 1.9994 | 877 | 0.6200 |
| 0.5853 | 2.9960 | 1314 | 0.6196 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.0.2
- Tokenizers 0.20.3
|
ch1so/qwen3-0.6b-zh-news-category-with-jieba-classfier | ch1so | 2025-06-07T21:25:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T21:25:52Z | ---
base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ch1so
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-0.6b-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)
|
mac-mvak/m3-nf4 | mac-mvak | 2025-06-07T21:13:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-07T21:13:29Z | ---
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] |
18-sms-rani-viral-video/Original.FULL.VIDEO.SMS.Rani.Viral.Video.Tutorial.Official | 18-sms-rani-viral-video | 2025-06-07T21:00:14Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-07T20:59:48Z | <p><a rel="nofollow" href="https://viralflix.xyz/leaked/?eid">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️​</a></p>
<a rel="nofollow" href="https://viralflix.xyz/leaked/?eid">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️​</a>
<a rel="nofollow" href="https://viralflix.xyz/leaked/?eid"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a> |
coralieb7/mcqa_sft_focus_100k_neftune_checkpoint1 | coralieb7 | 2025-06-07T20:58:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-07T20:56:15Z | ---
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. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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## Model Card Contact
[More Information Needed] |
RichardErkhov/homeb82784_-_qwen2.5-7b-instruct-v2.0-8bits | RichardErkhov | 2025-06-07T20:56:29Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:1910.09700",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-07T20:54:13Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
qwen2.5-7b-instruct-v2.0 - bnb 8bits
- Model creator: https://huggingface.co/homeb82784/
- Original model: https://huggingface.co/homeb82784/qwen2.5-7b-instruct-v2.0/
Original model description:
---
library_name: transformers
tags:
- unsloth
- trl
- sft
- krx
---
# 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
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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]
### 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]
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## Model Card Contact
[More Information Needed]
|
18-VIDEOS-kiffy-katrinalim123-VIDEO-Link/Trending.VIDEO.Katrina.Lim.Viral.Video.Tutorial.LINK.Official | 18-VIDEOS-kiffy-katrinalim123-VIDEO-Link | 2025-06-07T20:54:44Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-07T20:54:17Z | <p><a rel="nofollow" href="https://viralflix.xyz/leaked/?eid">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️​</a></p>
<a rel="nofollow" href="https://viralflix.xyz/leaked/?eid">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️​</a>
<a rel="nofollow" href="https://viralflix.xyz/leaked/?eid"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a> |
glif-loradex-trainer/an303042_Grit_Portrait | glif-loradex-trainer | 2025-06-07T20:43:42Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us",
"flux",
"lora",
"base_model:adapter:black-forest-labs/FLUX.1-dev"
] | text-to-image | 2025-06-07T20:43:14Z | ---
tags:
- diffusers
- text-to-image
- template:sd-lora
- base_model:black-forest-labs/FLUX.1-dev
- base_model:finetune:black-forest-labs/FLUX.1-dev
- license:other
- region:us
- flux
- lora
widget:
- output:
url: samples/1749328850026__000003000_0.jpg
text: wounded centaur, mythical creature gr1tp0r
- output:
url: samples/1749328875261__000003000_1.jpg
text: ruins of athens, snake gr1tp0r
- output:
url: samples/1749328900565__000003000_2.jpg
text: silver vampire sword gr1tp0r
- output:
url: samples/1749328925925__000003000_3.jpg
text: gr1tp0r nun standing at busy intersection
- output:
url: samples/1749328951199__000003000_4.jpg
text: gr1tp0r dog by fire hydrant
- output:
url: samples/1749328976420__000003000_5.jpg
text: gr1tp0r, close-up portrait of a goose with a scarred beak and cracked feathers,
wearing dark scratched sunglasses, the bird's head tilted slightly forward
in a menacing posture, high-contrast lighting emphasizing texture in the feathers
and reflections on the lenses, grim expression, dramatic shadows falling across
the face, isolated against a black background
base_model: black-forest-labs/FLUX.1-dev
trigger: "gr1tp0r"
instance_prompt: "gr1tp0r"
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
---
# Grit_Portrait
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `an303042`.
<Gallery />
## Trigger words
You should use `gr1tp0r` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/glif-loradex-trainer/an303042_Grit_Portrait/tree/main) them in the Files & versions tab.
## License
This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
RichardErkhov/sebdg_-_llama3-8b-merged-8bits | RichardErkhov | 2025-06-07T20:41:19Z | 0 | 0 | null | [
"safetensors",
"llama",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-07T20:38:09Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama3-8b-merged - bnb 8bits
- Model creator: https://huggingface.co/sebdg/
- Original model: https://huggingface.co/sebdg/llama3-8b-merged/
Original model description:
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** sebdg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
FormlessAI/62e524bb-1f26-4e29-b425-59f5e0a7cad1 | FormlessAI | 2025-06-07T20:38:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T18:06:43Z | ---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6
library_name: transformers
model_name: 62e524bb-1f26-4e29-b425-59f5e0a7cad1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for 62e524bb-1f26-4e29-b425-59f5e0a7cad1
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6).
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="FormlessAI/62e524bb-1f26-4e29-b425-59f5e0a7cad1", 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/phoenix-formless/Gradients/runs/jwdqt7rz)
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: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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}}
}
``` |
RichardErkhov/GoldenLlama_-_krx_sg_qwen2.5_7b_it_v3-4bits | RichardErkhov | 2025-06-07T20:31:42Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-07T20:29:12Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
krx_sg_qwen2.5_7b_it_v3 - bnb 4bits
- Model creator: https://huggingface.co/GoldenLlama/
- Original model: https://huggingface.co/GoldenLlama/krx_sg_qwen2.5_7b_it_v3/
Original model description:
---
license: apache-2.0
language:
- ko
- en
base_model:
- unsloth/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
tags:
- krx
- unsloth
- trl
- sft
---
|
zuazo/whisper-medium-eu-cv21.0 | zuazo | 2025-06-07T20:25:22Z | 22 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"eu",
"dataset:common_voice_21_0_eu",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"model-index",
"e... | automatic-speech-recognition | 2025-06-05T21:00:14Z | ---
library_name: transformers
language:
- eu
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- common_voice_21_0_eu
metrics:
- wer
model-index:
- name: Whisper Medium Basque
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_21_0_eu
type: common_voice_21_0_eu
config: default
split: test
args: default
metrics:
- name: Wer
type: wer
value: 8.378851722762663
---
<!-- 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 Medium Basque
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_21_0_eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3958
- Wer: 8.3789
## 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: 3.75e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:--------:|:------:|:---------------:|:-------:|
| 0.0102 | 11.1111 | 5000 | 0.2346 | 10.0851 |
| 0.0045 | 22.2222 | 10000 | 0.2662 | 10.2880 |
| 0.0035 | 33.3333 | 15000 | 0.2865 | 10.0383 |
| 0.0046 | 44.4444 | 20000 | 0.2913 | 9.9889 |
| 0.0018 | 55.5556 | 25000 | 0.3080 | 9.8797 |
| 0.0016 | 66.6667 | 30000 | 0.3096 | 9.8380 |
| 0.0031 | 77.7778 | 35000 | 0.3158 | 9.9612 |
| 0.0018 | 88.8889 | 40000 | 0.3317 | 10.2646 |
| 0.001 | 100.0 | 45000 | 0.3321 | 10.1380 |
| 0.0003 | 111.1111 | 50000 | 0.3275 | 9.7904 |
| 0.0007 | 122.2222 | 55000 | 0.3265 | 10.0401 |
| 0.0 | 133.3333 | 60000 | 0.3307 | 9.5641 |
| 0.0 | 144.4444 | 65000 | 0.3337 | 9.7461 |
| 0.0 | 155.5556 | 70000 | 0.3444 | 9.6820 |
| 0.0002 | 166.6667 | 75000 | 0.3503 | 9.8346 |
| 0.0 | 177.7778 | 80000 | 0.3586 | 9.1531 |
| 0.0 | 188.8889 | 85000 | 0.3744 | 8.7881 |
| 0.0 | 200.0 | 90000 | 0.3871 | 8.5323 |
| 0.0 | 211.1111 | 95000 | 0.3938 | 8.4040 |
| 0.0 | 222.2222 | 100000 | 0.3958 | 8.3789 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
raphassaraf/MNLP_M3_rag_model | raphassaraf | 2025-06-07T20:19:25Z | 36 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T13:39:07Z | ---
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] |
timarni/qwen3_pretraining_full_2_1200 | timarni | 2025-06-07T20:03:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"... | text-generation | 2025-06-07T20:02:43Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: outputs/qwen3_pretraining_full_2
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
######################################
# CONTINUED PRE-TRAINING EXAMPLE #
######################################
base_model: Qwen/Qwen3-0.6B-Base
strict: false
# ––– PRE-TRAIN DATA –––
pretraining_dataset:
- path: timarni/pretrain-textbooks
type: completion
- path: timarni/pretrain-wikipedia
type: completion
shuffle_merged_datasets: true
chat_template: null
# ––– SEQ LEN & PACKING –––
sequence_len: 4096
sample_packing: true
# eval_sample_packing: true # false
pad_to_sequence_len: true
# eval_pad_to_max_length: false
# ––– TRAINING BUDGET –––
micro_batch_size: 4
gradient_accumulation_steps: 4
max_steps: 1500
# ––– OPTIMISER –––
learning_rate: 5e-6
lr_scheduler: cosine
warmup_steps: 400
weight_decay: 0.01
optimizer: adamw_torch
# ––– PRECISION / SPEED –––
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: true
# # ––– EVALUATION –––
# do_bench_eval: false # we handle eval via test_datasets
# test_datasets: # ← plural!
# - path: ./datasets/mmlu_val_all.jsonl # <— your converted file
# ds_type: json
# split: train # the default split Hugging Face gives local JSONL
# type: explainchoice # mmlu_mcqa # explainchoice
# field_question: question # these three lines are defaults, but
# field_choices: choices # you can leave them out if you matched the keys
# field_solution: solution
# # eval_batch_size: 1
# eval_steps: 500
# metric_for_best_model: accuracy # expose “accuracy” coming from explainchoice
# greater_is_better: true
# eval_strategy:
# ––– OUTPUT / LOGGING –––
save_steps: 150
save_total_limit: 15
output_dir: ./outputs/qwen3_pretraining_full_2
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-pretraining_full_2
```
</details><br>
# outputs/qwen3_pretraining_full_2
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 400
- training_steps: 1500
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
eylulipci/30_dpo_ds30_lr1e-05_acc16_ep4_beta0.2-epoch1 | eylulipci | 2025-06-07T19:55:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-07T19:54:01Z | ---
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
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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]
### 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] |
eylulipci/30_dpo_ds30_lr1e-05_acc16_ep4_beta0.1-epoch1 | eylulipci | 2025-06-07T19:52:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-07T19:50:53Z | ---
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] |
graliuce/Qwen2.5-3B-Instruct_MedMCQA.21.01 | graliuce | 2025-06-07T19:49:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"dataset:graliuce/MedMCQA.21.01",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
... | text-generation | 2025-06-07T18:11:19Z | ---
base_model: Qwen/Qwen2.5-3B-Instruct
datasets: graliuce/MedMCQA.21.01
library_name: transformers
model_name: Qwen2.5-3B-Instruct_MedMCQA.21.01
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-3B-Instruct_MedMCQA.21.01
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [graliuce/MedMCQA.21.01](https://huggingface.co/datasets/graliuce/MedMCQA.21.01) 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="graliuce/Qwen2.5-3B-Instruct_MedMCQA.21.01", 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/grace_rl/infoseek/runs/cyrus8yp)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
timarni/qwen3_pretraining_full_2_1350 | timarni | 2025-06-07T19:49:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"... | text-generation | 2025-06-07T19:47:35Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: outputs/qwen3_pretraining_full_2
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
######################################
# CONTINUED PRE-TRAINING EXAMPLE #
######################################
base_model: Qwen/Qwen3-0.6B-Base
strict: false
# ––– PRE-TRAIN DATA –––
pretraining_dataset:
- path: timarni/pretrain-textbooks
type: completion
- path: timarni/pretrain-wikipedia
type: completion
shuffle_merged_datasets: true
chat_template: null
# ––– SEQ LEN & PACKING –––
sequence_len: 4096
sample_packing: true
# eval_sample_packing: true # false
pad_to_sequence_len: true
# eval_pad_to_max_length: false
# ––– TRAINING BUDGET –––
micro_batch_size: 4
gradient_accumulation_steps: 4
max_steps: 1500
# ––– OPTIMISER –––
learning_rate: 5e-6
lr_scheduler: cosine
warmup_steps: 400
weight_decay: 0.01
optimizer: adamw_torch
# ––– PRECISION / SPEED –––
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: true
# # ––– EVALUATION –––
# do_bench_eval: false # we handle eval via test_datasets
# test_datasets: # ← plural!
# - path: ./datasets/mmlu_val_all.jsonl # <— your converted file
# ds_type: json
# split: train # the default split Hugging Face gives local JSONL
# type: explainchoice # mmlu_mcqa # explainchoice
# field_question: question # these three lines are defaults, but
# field_choices: choices # you can leave them out if you matched the keys
# field_solution: solution
# # eval_batch_size: 1
# eval_steps: 500
# metric_for_best_model: accuracy # expose “accuracy” coming from explainchoice
# greater_is_better: true
# eval_strategy:
# ––– OUTPUT / LOGGING –––
save_steps: 150
save_total_limit: 15
output_dir: ./outputs/qwen3_pretraining_full_2
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-pretraining_full_2
```
</details><br>
# outputs/qwen3_pretraining_full_2
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 400
- training_steps: 1500
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
Maru-01909/whisper-small-ryoiki-spells | Maru-01909 | 2025-06-07T19:19:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T19:19:31Z | ---
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
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### Training Procedure
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#### Preprocessing [optional]
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#### 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 -->
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Summary
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## 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]
### Model Architecture and Objective
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## Citation [optional]
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## Glossary [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
PerryP/MNLP_M3_quanto_test | PerryP | 2025-06-07T19:11:51Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"model_hub_mixin",
"8-bit",
"region:us"
] | null | 2025-06-07T19:00:14Z | ---
tags:
- model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed] |
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