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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-12 18:27:22
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| library_name
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oscar1321/tarink | oscar1321 | 2025-05-24T23:01:59Z | 0 | 0 | null | [
"license:other",
"region:us"
]
| null | 2025-05-24T18:56:14Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
dulimov/Qwen3-4B-rk3588-1.2.1 | dulimov | 2025-05-24T23:00:02Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"unsloth",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"region:us"
]
| null | 2025-05-24T22:36:51Z | ---
base_model:
- Qwen/Qwen3-4B
tags:
- unsloth
---
# Qwen3-4B-unsloth RK3588-1.2.1
This version of Qwen3-4B unsloth has been converted to run on the RK3588 NPU using ['w8a8', 'w8a8_g128', 'w8a8_g256', 'w8a8_g512'] quantization.
This model has been optimized with the following LoRA:
Compatible with RKLLM version: 1.2.1
# Original Model Card for base model, Qwen3-4B, below:
# Qwen3-4B
## 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
**Qwen3-4B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 4.0B
- Number of Paramaters (Non-Embedding): 3.6B
- 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/Qwen3-4B"
# 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/Qwen3-4B --enable-reasoning --reasoning-parser deepseek_r1
```
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-4B --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](https://qwen.readthedocs.io/) for more details.
### `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/Qwen3-4B"):
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
import os
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-4B',
# 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']
},
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'What time is it?'}]
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}
}
``` |
Triangle104/Qwen3-30B-A1.5B-High-Speed-Q6_K-GGUF | Triangle104 | 2025-05-24T23:00:00Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"32 k context",
"reasoning",
"thinking",
"qwen3",
"4 experts activated",
"double speed",
"128 experts",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:DavidAU/Qwen3-30B-A1.5B-High-Speed",
"base_model:quantized:DavidAU/Qwen3-30B-A1.5B-High-Speed",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2025-05-24T22:58:11Z | ---
library_name: transformers
pipeline_tag: text-generation
tags:
- 32 k context
- reasoning
- thinking
- qwen3
- 4 experts activated
- double speed
- 128 experts
- llama-cpp
- gguf-my-repo
base_model: DavidAU/Qwen3-30B-A1.5B-High-Speed
---
# Triangle104/Qwen3-30B-A1.5B-High-Speed-Q6_K-GGUF
This model was converted to GGUF format from [`DavidAU/Qwen3-30B-A1.5B-High-Speed`](https://huggingface.co/DavidAU/Qwen3-30B-A1.5B-High-Speed) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DavidAU/Qwen3-30B-A1.5B-High-Speed) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q6_K-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q6_K-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q6_K-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q6_K-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q6_k.gguf -c 2048
```
|
VIDEOS-18-Katrina-Lim-Kiffy-Viral-Video/FULL.VIDEO.LINK.Katrina.Lim.Viral.Video.Leaks.Official | VIDEOS-18-Katrina-Lim-Kiffy-Viral-Video | 2025-05-24T22:59:03Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T22:58:45Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Arman51/Qwen2-0.5B-GRPO-test | Arman51 | 2025-05-24T22:57:49Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:AI-MO/NuminaMath-TIR",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-20T13:34:03Z | ---
base_model: Qwen/Qwen2-0.5B-Instruct
datasets: AI-MO/NuminaMath-TIR
library_name: transformers
model_name: Qwen2-0.5B-GRPO-test
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2-0.5B-GRPO-test
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) 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="Arman51/Qwen2-0.5B-GRPO-test", 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: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- 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}}
}
``` |
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00032_step_00064_step_00096_step_00128 | the-acorn-ai | 2025-05-24T22:57:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T22:55:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00032_step_00064 | the-acorn-ai | 2025-05-24T22:53:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T22:51:45Z | ---
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]
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## Uses
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[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- 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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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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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]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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kjamesh/ppo-custom-LunarLander-v2 | kjamesh | 2025-05-24T22:52:35Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-24T19:52:59Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 78.87 +/- 48.01
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00032 | the-acorn-ai | 2025-05-24T22:51:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T22:49:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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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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stillett/grader_model_0 | stillett | 2025-05-24T22:46:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-05-24T16:28:49Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: grader_model_0
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. -->
# grader_model_0
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8977
- F1: 0.6050
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0991 | 1.0 | 563 | 0.9503 | 0.5688 |
| 0.8961 | 2.0 | 1126 | 0.9037 | 0.6042 |
| 0.7976 | 3.0 | 1689 | 0.8977 | 0.6050 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
NaykinYT/test_4_m1_full_run_2 | NaykinYT | 2025-05-24T22:45:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T22:44:42Z | ---
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]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- 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]
<!-- 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 Contact
[More Information Needed] |
mradermacher/gpt-nyc-affirmations-GGUF | mradermacher | 2025-05-24T22:40:46Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:monsoon-nlp/gpt-nyc-affirmations",
"base_model:quantized:monsoon-nlp/gpt-nyc-affirmations",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T07:23:23Z | ---
base_model: monsoon-nlp/gpt-nyc-affirmations
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/monsoon-nlp/gpt-nyc-affirmations
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.f16.gguf) | f16 | 0.4 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
orkungedik/tr_idcard-3b-languagemodel | orkungedik | 2025-05-24T22:40:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T22:36:16Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** orkungedik
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
This language model is a Turkish ID card PDF data extract to JSON.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B_4.5bpw-hb6-exl2 | ApocalypseParty | 2025-05-24T22:36:21Z | 1 | 0 | null | [
"safetensors",
"llama",
"base_model:ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B",
"base_model:quantized:ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B",
"exl2",
"region:us"
]
| null | 2025-05-10T11:09:22Z | ---
base_model:
- ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B
---
An iterative improvement of Genetic Lemonade Unleashed v2.1
This should be a direct improvement of 2.1. Uses an expanded dataset, but the training method and distribution of content within the dataset remains the same.
Compared to v3, this model never went through the DPO training and should have better prose (possibly better creativity too) but worse instruction following.
Quants:
GGUF: https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.2-70B-i1-GGUF (mradermacher)
EXL2 (4.5bpw): https://huggingface.co/ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B_4.5bpw-hb6-exl2 |
ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B | ApocalypseParty | 2025-05-24T22:35:55Z | 615 | 0 | null | [
"safetensors",
"llama",
"base_model:zerofata/L3.3-GeneticLemonade-Unleashed-70B",
"base_model:finetune:zerofata/L3.3-GeneticLemonade-Unleashed-70B",
"region:us"
]
| null | 2025-05-10T08:45:28Z | ---
base_model:
- zerofata/L3.3-GeneticLemonade-Unleashed-70B
---
An iterative improvement of Genetic Lemonade Unleashed v2.1
This should be a direct improvement of 2.1. Uses an expanded dataset, but the training method and distribution of content within the dataset remains the same.
Compared to v3, this model never went through the DPO training and should have better prose (possibly better creativity too) but worse instruction following.
Quants:
GGUF: https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.2-70B-i1-GGUF (mradermacher)
EXL2 (4.5bpw): https://huggingface.co/ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B_4.5bpw-hb6-exl2 |
fats-fme/024a1d51-f821-4a52-8538-51e605617bf3 | fats-fme | 2025-05-24T22:35:14Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T21:43:05Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 024a1d51-f821-4a52-8538-51e605617bf3
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: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- da6901d849324b9e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/024a1d51-f821-4a52-8538-51e605617bf3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: constant_with_warmup
max_memory:
0: 130GB
max_steps: 100
micro_batch_size: 1
mlflow_experiment_name: /tmp/da6901d849324b9e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 024a1d51-f821-4a52-8538-51e605617bf3
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4278
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 200
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0002 | 1 | 2.5915 |
| 1.2138 | 0.0161 | 100 | 2.4278 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
J-LAB/fluxiia_14b | J-LAB | 2025-05-24T22:32:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T21:36:18Z | ---
base_model: unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** J-LAB
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Ludiya/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_vicious_impala | Ludiya | 2025-05-24T22:31:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am roaring vicious impala",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-13T14:09:03Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_vicious_impala
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am roaring vicious impala
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_vicious_impala
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="Ludiya/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_vicious_impala", 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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
MomlessTomato/maki-nishikino | MomlessTomato | 2025-05-24T22:28:53Z | 13 | 1 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"base_model:adapter:cagliostrolab/animagine-xl-3.0",
"license:mit",
"region:us"
]
| text-to-image | 2024-02-05T05:18:09Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
defined eyes, masterpiece, high quality, defined pupil, looking at viewer,
rounded pupil,
parameters:
negative_prompt: >-
bad_anatomy, deformation, amputation, deformity, deformed_nipples,
duplicated_torso, deformed_torso, long_torso, large_torso,
unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2),
unproportioned_eyes, unproportioned_head, small_head, duplicated_nose,
big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy,
red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,
output:
url: demo-1.png
base_model: cagliostrolab/animagine-xl-3.0
instance_prompt: id_maki_nishikino
license: mit
---
# Maki Nishikino
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use `id_maki_nishikino` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/theidoldaily/maki-nishikino/tree/main) them in the Files & versions tab.
|
kplro/rubert-base-cased-l2_russian | kplro | 2025-05-24T22:22:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2025-05-24T21:50:27Z | ---
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] |
gradientrouting-spar/cond_single_func_ntr_30_nte_30_preamble_20250524_220131 | gradientrouting-spar | 2025-05-24T22:21:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T22:19:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### 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] |
Mires13/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_gilded_crow | Mires13 | 2025-05-24T22:16:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am roaring gilded crow",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-13T15:30:06Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_gilded_crow
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am roaring gilded crow
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_gilded_crow
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="Mires13/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-roaring_gilded_crow", 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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
phospho-app/asafxrev-ACT-jenga-on-box-May24-w58xo | phospho-app | 2025-05-24T22:15:31Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
]
| null | 2025-05-24T19:15:17Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [asafxrev/jenga-on-box-May24](https://huggingface.co/datasets/asafxrev/jenga-on-box-May24)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 120
- **Training steps**: 8000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
kjamesh/ppo-CartPole-v1 | kjamesh | 2025-05-24T22:09:52Z | 0 | 0 | null | [
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-24T19:18:55Z | ---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
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
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
|
bruhzair/prototype-0.3 | bruhzair | 2025-05-24T22:05:50Z | 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 | 2025-05-24T21:49:28Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.3
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--huihui-ai--Llama-3.3-70B-Instruct-abliterated/snapshots/fa13334669544bab573e0e5313cad629a9c02e2c as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
* /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
* /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
- model: /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335
- model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- model: /workspace/cache/models--huihui-ai--Llama-3.3-70B-Instruct-abliterated/snapshots/fa13334669544bab573e0e5313cad629a9c02e2c
base_model: /workspace/cache/models--huihui-ai--Llama-3.3-70B-Instruct-abliterated/snapshots/fa13334669544bab573e0e5313cad629a9c02e2c
merge_method: model_stock
tokenizer:
source: union
int8_mask: true
dtype: float32
out_dtype: bfloat16
```
|
tgrhn/whisper-large-V3-Turbo_All_datasets_finetune-New | tgrhn | 2025-05-24T22:03:32Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:adapter:openai/whisper-small",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T14:25:32Z | ---
library_name: peft
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
model-index:
- name: whisper-large-V3-Turbo_All_datasets_finetune-New
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-V3-Turbo_All_datasets_finetune-New
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3398
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.1187 | 0.1482 | 1500 | 0.3764 |
| 1.9965 | 0.2964 | 3000 | 6.8995 |
| 0.1156 | 0.4446 | 4500 | 0.4922 |
| 0.1117 | 0.5928 | 6000 | 0.4927 |
| 0.1031 | 0.7410 | 7500 | 0.5057 |
| 0.0944 | 0.8892 | 9000 | 0.4723 |
| 0.0683 | 1.0374 | 10500 | 0.4605 |
| 0.0723 | 1.1857 | 12000 | 0.4693 |
| 0.067 | 1.3339 | 13500 | 0.4448 |
| 0.0642 | 1.4821 | 15000 | 0.4403 |
| 0.0598 | 1.6303 | 16500 | 0.4390 |
| 0.06 | 1.7785 | 18000 | 0.4225 |
| 0.052 | 1.9267 | 19500 | 0.4010 |
| 0.0367 | 2.0749 | 21000 | 0.3795 |
| 0.0327 | 2.2231 | 22500 | 0.3814 |
| 0.0295 | 2.3713 | 24000 | 0.3743 |
| 0.0321 | 2.5195 | 25500 | 0.3654 |
| 0.0271 | 2.6677 | 27000 | 0.3470 |
| 0.0265 | 2.8159 | 28500 | 0.3450 |
| 0.0243 | 2.9641 | 30000 | 0.3398 |
### Framework versions
- PEFT 0.15.1
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.21.0 |
dzanbek/12cec7cb-7cc2-4e1b-a0c3-2944779bd461 | dzanbek | 2025-05-24T22:01:30Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T21:44:01Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 12cec7cb-7cc2-4e1b-a0c3-2944779bd461
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: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- da6901d849324b9e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: dzanbek/12cec7cb-7cc2-4e1b-a0c3-2944779bd461
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.2e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 280
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/da6901d849324b9e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
warmup_steps: 40
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 12cec7cb-7cc2-4e1b-a0c3-2944779bd461
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7786
## 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: 1.2e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 40
- training_steps: 280
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5735 | 0.0169 | 280 | 1.7786 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
MomlessTomato/hanayo-koizumi | MomlessTomato | 2025-05-24T22:01:09Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"base_model:adapter:cagliostrolab/animagine-xl-3.0",
"region:us"
]
| text-to-image | 2024-02-12T04:18:06Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
masterpiece, high quality, defined pupil, looking at viewer, rounded pupil,
defined iris, (soft iris:1.2),
parameters:
negative_prompt: >-
bad_anatomy, deformation, amputation, deformity, deformed_nipples,
duplicated_torso, deformed_torso, long_torso, large_torso,
unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2),
unproportioned_eyes, unproportioned_head, small_head, duplicated_nose,
big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy,
red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,
output:
url: images/hanayo_koizumi.png
base_model: cagliostrolab/animagine-xl-3.0
instance_prompt: id_hanayo_koizumi
---
# Hanayo Koizumi
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use `id_hanayo_koizumi` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/theidoldaily/hanayo-koizumi/tree/main) them in the Files & versions tab.
|
mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF | mradermacher | 2025-05-24T22:00:58Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:BigSalmon/InformalToFormalLincoln95Paraphrase",
"base_model:quantized:BigSalmon/InformalToFormalLincoln95Paraphrase",
"endpoints_compatible",
"region:us",
"imatrix"
]
| null | 2025-05-24T21:30:22Z | ---
base_model: BigSalmon/InformalToFormalLincoln95Paraphrase
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/BigSalmon/InformalToFormalLincoln95Paraphrase
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ1_S.gguf) | i1-IQ1_S | 0.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ1_M.gguf) | i1-IQ1_M | 0.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ3_S.gguf) | i1-IQ3_S | 0.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ3_M.gguf) | i1-IQ3_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q4_0.gguf) | i1-Q4_0 | 0.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q4_1.gguf) | i1-Q4_1 | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/InformalToFormalLincoln95Paraphrase-i1-GGUF/resolve/main/InformalToFormalLincoln95Paraphrase.i1-Q6_K.gguf) | i1-Q6_K | 0.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M | PJMixers-Dev | 2025-05-24T21:59:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"granitemoe",
"text-generation",
"conversational",
"en",
"dataset:BeaverAI/REDACTED1",
"dataset:BeaverAI/REDACTED2",
"dataset:BeaverAI/REDACTED3",
"dataset:BeaverAI/REDACTED4",
"dataset:BeaverAI/REDACTED5",
"dataset:BeaverAI/REDACTED6",
"dataset:PJMixers-Dev/Lit-axo-Shuffled",
"dataset:PJMixers-Dev/Mielikki_Erebus-87k-axo",
"dataset:PJMixers/RyokoAI_Honeyfeed3600-Cleanish",
"dataset:PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo",
"dataset:Nelathan/synthetic-sugar-quill",
"dataset:PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long",
"dataset:PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned",
"dataset:PJMixers-Dev/Subtitles",
"dataset:PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo",
"dataset:PJMixers/AP-News-2024",
"dataset:PJMixers-Dev/Fundus-AP-News-Formatted",
"dataset:PJMixers-Dev/Fundus-AP-News-2-Formatted",
"dataset:PJMixers-Dev/goodwiki-2024-12-04-axo",
"dataset:epfl-llm/guidelines",
"dataset:PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT",
"dataset:OpenLeecher/lmsys_chat_1m_clean",
"dataset:PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed",
"dataset:allura-org/gryphe-sonnet-3.5-charcards-names-added",
"dataset:anthracite-org/c2_logs_32k_llama3_qwen2_v1.3",
"dataset:PJMixers-Dev/MinervaAI_Aesir-Preview-Anon",
"dataset:PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled",
"dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned",
"dataset:PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT",
"dataset:PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT",
"dataset:grimulkan/aicg-logs-augmented",
"dataset:grimulkan/PIPPA-augmented-dedup",
"dataset:PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted",
"dataset:PJMixers/lodrick-the-lafted_OpusStories-ShareGPT",
"dataset:Gryphe/ChatGPT-4o-Writing-Prompts",
"dataset:Gryphe/Opus-WritingPrompts",
"dataset:anthracite-org/nopm_claude_writing_fixed",
"dataset:PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT",
"dataset:allura-org/fujin-instruct-v2",
"dataset:ToastyPigeon/gutenberg-sft",
"dataset:PocketDoc/Dans-Prosemaxx-Adventure",
"dataset:PocketDoc/Dans-Failuremaxx-Adventure-3",
"dataset:TheDrummer/AmoralQA-v2",
"arxiv:1910.03771",
"arxiv:2106.09685",
"arxiv:2305.14314",
"arxiv:2307.08691",
"arxiv:2410.10989",
"arxiv:2107.04197",
"arxiv:2307.02047",
"arxiv:2010.06192",
"arxiv:2411.16085",
"arxiv:2501.18427",
"arxiv:2403.15279",
"arxiv:2411.15124",
"arxiv:2309.11998",
"arxiv:2308.05884",
"base_model:ibm-granite/granite-3.1-3b-a800m-instruct",
"base_model:finetune:ibm-granite/granite-3.1-3b-a800m-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T10:51:03Z | ---
base_model: ibm-granite/granite-3.1-3b-a800m-instruct
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
language:
- en
datasets:
- BeaverAI/REDACTED1
- BeaverAI/REDACTED2
- BeaverAI/REDACTED3
- BeaverAI/REDACTED4
- BeaverAI/REDACTED5
- BeaverAI/REDACTED6
- PJMixers-Dev/Lit-axo-Shuffled
- PJMixers-Dev/Mielikki_Erebus-87k-axo
- PJMixers/RyokoAI_Honeyfeed3600-Cleanish
- PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo
- Nelathan/synthetic-sugar-quill
- PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long
- PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned
- PJMixers-Dev/Subtitles
- PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo
- PJMixers/AP-News-2024
- PJMixers-Dev/Fundus-AP-News-Formatted
- PJMixers-Dev/Fundus-AP-News-2-Formatted
- PJMixers-Dev/goodwiki-2024-12-04-axo
- epfl-llm/guidelines
- PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT
- OpenLeecher/lmsys_chat_1m_clean
- PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed
- allura-org/gryphe-sonnet-3.5-charcards-names-added
- anthracite-org/c2_logs_32k_llama3_qwen2_v1.3
- PJMixers-Dev/MinervaAI_Aesir-Preview-Anon
- PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled
- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
- PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT
- PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT
- grimulkan/aicg-logs-augmented
- grimulkan/PIPPA-augmented-dedup
- PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted
- PJMixers/lodrick-the-lafted_OpusStories-ShareGPT
- Gryphe/ChatGPT-4o-Writing-Prompts
- Gryphe/Opus-WritingPrompts
- anthracite-org/nopm_claude_writing_fixed
- PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT
- allura-org/fujin-instruct-v2
- ToastyPigeon/gutenberg-sft
- PocketDoc/Dans-Prosemaxx-Adventure
- PocketDoc/Dans-Failuremaxx-Adventure-3
- TheDrummer/AmoralQA-v2
---
# Granite-3.1-Earthen-v0.3-3B-A800M
[`ibm-granite/granite-3.1-3b-a800m-instruct`](https://huggingface.co/ibm-granite/granite-3.1-3b-a800m-instruct) was trained at 8K with batch size 2 gradient accumulation 8, so each step was 131,072 tokens (including any padding tokens). It was trained for 400 steps, adding up to a total of 52,428,800 unique tokens seen.
This is a small test run. A larger version is planned.
## Quants
- [GGUF](https://huggingface.co/PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M-GGUF)
## Prompt Format
This model uses Granite-3.1 Instruct format.
```
<|start_of_role|>system<|end_of_role|>example system prompt<|end_of_text|>
<|start_of_role|>user<|end_of_role|>example user turn 1<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>example assistant turn 1<|end_of_text|>
<|start_of_role|>user<|end_of_role|>example user turn 2<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>example assistant turn 2<|end_of_text|>
```
## Training Details
[<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)
```yaml
# Requirements before running
# - Get latest commit of axolotl (currently c0a0c75)
# - Download these to axolotl/src/axolotl/prompt_formatters
# - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/formatter_regex.py
# - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/customcompletion-regex.py
# - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/customgranite-regex.py
# - pip install ftfy
# - pip install git+https://github.com/xzuyn/CAME.git@sr-grams-cautious-8bit
# Weights and Biases logging config
wandb_project: Granite-3.1-3B-A800M
wandb_name: Granite-3.1-Earthen-v0.3-3B-A800M-QLoRA-run4
# Model checkpointing config
output_dir: ./Outputs/Granite-3.1-Earthen-v0.3-3B-A800M-QLoRA-run4
resume_from_checkpoint:
save_steps: 10
save_safetensors: true
save_total_limit: 2
save_only_model: false
# Model architecture config
base_model: ibm-granite/granite-3.1-3b-a800m-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Mixed precision training config
bf16: true
fp16: false
tf32: false
# Model loading config
load_in_8bit: false
load_in_4bit: true
strict: false
# Sequence config
sequence_len: 8192
min_sample_len: 256
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false
# LoRA adapter config
adapter: qlora
lora_r: 128
lora_alpha: 128
lora_dropout: 0.125
lora_target_linear: true
embeddings_skip_upcast: true
# Dataset config
datasets:
# Completion
# Story-like Data
- path: BeaverAI/REDACTED1
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/Lit-axo-Shuffled
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/Mielikki_Erebus-87k-axo
split: train[:4000]
type: customcompletion-regex
- path: PJMixers/RyokoAI_Honeyfeed3600-Cleanish
split: train[:4000]
type: customcompletion-regex
- path: BeaverAI/REDACTED2
type: customcompletion-regex
- path: PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo
split: train[:4000]
type: customcompletion-regex
- path: Nelathan/synthetic-sugar-quill
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long
split: train[:4000]
type: customcompletion-regex
- path: BeaverAI/REDACTED3
type: customcompletion-regex
- path: PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned
split: train[:4000]
type: customcompletion-regex
# Subtitle Data
- path: PJMixers-Dev/Subtitles
type: customcompletion-regex
- path: PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo
split: train[:4000]
type: customcompletion-regex
# News Data
- path: PJMixers/AP-News-2024
type: customcompletion-regex
- path: PJMixers-Dev/Fundus-AP-News-Formatted
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/Fundus-AP-News-2-Formatted
type: customcompletion-regex
# Misc Data
- path: PJMixers-Dev/goodwiki-2024-12-04-axo
split: train[:4000]
type: customcompletion-regex
- path: epfl-llm/guidelines
split: train[:4000]
field: clean_text
type: customcompletion-regex
# Granite-3.1 Instruct
# Instruction Data
- path: PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT
split: train[:4000]
type: customgranite-regex
- path: OpenLeecher/lmsys_chat_1m_clean
split: train[:4000]
type: customgranite-regex
# RP Data
- path: PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed
type: customgranite-regex
- path: allura-org/gryphe-sonnet-3.5-charcards-names-added
type: customgranite-regex
- path: anthracite-org/c2_logs_32k_llama3_qwen2_v1.3
type: customgranite-regex
- path: BeaverAI/REDACTED4
type: customgranite-regex
- path: PJMixers-Dev/MinervaAI_Aesir-Preview-Anon
type: customgranite-regex
- path: PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled
type: customgranite-regex
- path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
type: customgranite-regex
- path: PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT
type: customgranite-regex
- path: PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT
type: customgranite-regex
- path: grimulkan/aicg-logs-augmented
type: customgranite-regex
- path: grimulkan/PIPPA-augmented-dedup
type: customgranite-regex
- path: PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted
type: customgranite-regex
# InstStory Data
- path: PJMixers/lodrick-the-lafted_OpusStories-ShareGPT
type: customgranite-regex
- path: Gryphe/ChatGPT-4o-Writing-Prompts
type: customgranite-regex
- path: Gryphe/Opus-WritingPrompts
type: customgranite-regex
- path: anthracite-org/nopm_claude_writing_fixed
type: customgranite-regex
- path: PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT
type: customgranite-regex
- path: allura-org/fujin-instruct-v2
type: customgranite-regex
- path: ToastyPigeon/gutenberg-sft
type: customgranite-regex
# Adventure Data
- path: PocketDoc/Dans-Prosemaxx-Adventure
type: customgranite-regex
- path: PocketDoc/Dans-Failuremaxx-Adventure-3
type: customgranite-regex
# Decensoring Data
- path: TheDrummer/AmoralQA-v2
type: customgranite-regex
- path: BeaverAI/REDACTED5
type: customgranite-regex
- path: BeaverAI/REDACTED6
type: customgranite-regex
val_set_size: 256
eval_strategy: steps
eval_steps: 10
dataset_prepared_path: ./00-Tokenized-Datasets/Granite-3.1-Earthen-v0.3-3B-A800M-LoRA-seed42
shuffle_merged_datasets: true
# Training hyperparameters
num_epochs: 1
gradient_accumulation_steps: 8
micro_batch_size: 2
eval_batch_size: 2
warmup_steps: 0
optimizer: came_pytorch
optim_args:
enable_stochastic_rounding: true
enable_cautious: true
enable_8bit: true
lr_scheduler: rex
learning_rate: 2.5e-7
cosine_min_lr_ratio: 0.05
weight_decay: 0.01
max_grad_norm: 0.5
logging_steps: 1
# Model optimization
gradient_checkpointing: offload
sdp_attention: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_cross_entropy: true
lora_mlp_kernel: false
lora_qkv_kernel: false
lora_o_kernel: false
# Debug config
debug: true
seed: 42
# Token config
special_tokens:
bos_token: "<|end_of_text|>"
eos_token: "<|end_of_text|>"
pad_token: "<|end_of_text|>"
tokens:
```
## Citations
<details><summary>Show Citations</summary>
```bib
@misc{wolf2020huggingfacestransformersstateoftheartnatural,
title={HuggingFace's Transformers: State-of-the-art Natural Language Processing},
author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush},
year={2020},
eprint={1910.03771},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1910.03771},
}
@misc{hu2021loralowrankadaptationlarge,
title={LoRA: Low-Rank Adaptation of Large Language Models},
author={Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
year={2021},
eprint={2106.09685},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2106.09685},
}
@misc{dettmers2023qloraefficientfinetuningquantized,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Tim Dettmers and Artidoro Pagnoni and Ari Holtzman and Luke Zettlemoyer},
year={2023},
eprint={2305.14314},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2305.14314},
}
@misc{dao2023flashattention2fasterattentionbetter,
title={FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning},
author={Tri Dao},
year={2023},
eprint={2307.08691},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2307.08691},
}
@misc{hsu2024ligerkernelefficienttriton,
title={Liger Kernel: Efficient Triton Kernels for LLM Training},
author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
year={2024},
eprint={2410.10989},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.10989},
}
@misc{chen2021rexrevisitingbudgetedtraining,
title={REX: Revisiting Budgeted Training with an Improved Schedule},
author={John Chen and Cameron Wolfe and Anastasios Kyrillidis},
year={2021},
eprint={2107.04197},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2107.04197},
}
@misc{luo2023cameconfidenceguidedadaptivememory,
title={CAME: Confidence-guided Adaptive Memory Efficient Optimization},
author={Yang Luo and Xiaozhe Ren and Zangwei Zheng and Zhuo Jiang and Xin Jiang and Yang You},
year={2023},
eprint={2307.02047},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2307.02047},
}
@misc{zamirai2021revisitingbfloat16training,
title={Revisiting BFloat16 Training},
author={Pedram Zamirai and Jian Zhang and Christopher R. Aberger and Christopher De Sa},
year={2021},
eprint={2010.06192},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2010.06192},
}
@misc{liang2025cautiousoptimizersimprovingtraining,
title={Cautious Optimizers: Improving Training with One Line of Code},
author={Kaizhao Liang and Lizhang Chen and Bo Liu and Qiang Liu},
year={2025},
eprint={2411.16085},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2411.16085},
}
@misc{xie2025sana15efficientscaling,
title={SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer},
author={Enze Xie and Junsong Chen and Yuyang Zhao and Jincheng Yu and Ligeng Zhu and Chengyue Wu and Yujun Lin and Zhekai Zhang and Muyang Li and Junyu Chen and Han Cai and Bingchen Liu and Daquan Zhou and Song Han},
year={2025},
eprint={2501.18427},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.18427},
}
@misc{dallabetta2024fundussimpletousenewsscraper,
title={Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions},
author={Max Dallabetta and Conrad Dobberstein and Adrian Breiding and Alan Akbik},
year={2024},
eprint={2403.15279},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2403.15279},
}
@misc{lambert2025tulu3pushingfrontiers,
title={Tulu 3: Pushing Frontiers in Open Language Model Post-Training},
author={Nathan Lambert and Jacob Morrison and Valentina Pyatkin and Shengyi Huang and Hamish Ivison and Faeze Brahman and Lester James V. Miranda and Alisa Liu and Nouha Dziri and Shane Lyu and Yuling Gu and Saumya Malik and Victoria Graf and Jena D. Hwang and Jiangjiang Yang and Ronan Le Bras and Oyvind Tafjord and Chris Wilhelm and Luca Soldaini and Noah A. Smith and Yizhong Wang and Pradeep Dasigi and Hannaneh Hajishirzi},
year={2025},
eprint={2411.15124},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.15124},
}
@misc{zheng2024lmsyschat1mlargescalerealworldllm,
title={LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset},
author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Tianle Li and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric P. Xing and Joseph E. Gonzalez and Ion Stoica and Hao Zhang},
year={2024},
eprint={2309.11998},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2309.11998},
}
@misc{gosling2023pippapartiallysyntheticconversational,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2308.05884},
}
```
</details>
|
aleegis/37354cf5-63ad-40fd-a802-84a5c1702c49 | aleegis | 2025-05-24T21:57:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T21:43:49Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 37354cf5-63ad-40fd-a802-84a5c1702c49
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: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- da6901d849324b9e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/37354cf5-63ad-40fd-a802-84a5c1702c49
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: constant
max_grad_norm: 1
max_steps: 800
micro_batch_size: 4
mlflow_experiment_name: /tmp/da6901d849324b9e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 15
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
warmup_steps: 80
weight_decay: 0
xformers_attention: null
```
</details><br>
# 37354cf5-63ad-40fd-a802-84a5c1702c49
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 80
- training_steps: 800
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
vermoney/4a62bcaa-e9ca-4fa3-9853-daa594fbb575 | vermoney | 2025-05-24T21:56:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T21:47:19Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4a62bcaa-e9ca-4fa3-9853-daa594fbb575
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: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- da6901d849324b9e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vermoney/4a62bcaa-e9ca-4fa3-9853-daa594fbb575
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 280
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/da6901d849324b9e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
wandb_project: s56-9
wandb_run: your_name
wandb_runid: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
warmup_steps: 40
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 4a62bcaa-e9ca-4fa3-9853-daa594fbb575
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7437
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 40
- training_steps: 280
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5625 | 0.0169 | 280 | 1.7437 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
phospho-app/omourier-gr00t-Lego_rouge3-yzwz8 | phospho-app | 2025-05-24T21:55:29Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
]
| null | 2025-05-24T21:23:29Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [omourier/Lego_rouge3](https://huggingface.co/datasets/omourier/Lego_rouge3)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 27
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
DevQuasar/aaditya.Llama3-OpenBioLLM-70B-GGUF | DevQuasar | 2025-05-24T21:53:10Z | 349 | 0 | null | [
"gguf",
"text-generation",
"base_model:aaditya/Llama3-OpenBioLLM-70B",
"base_model:quantized:aaditya/Llama3-OpenBioLLM-70B",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2025-03-16T02:32:17Z | ---
base_model:
- aaditya/Llama3-OpenBioLLM-70B
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
'Make knowledge free for everyone'
Quantized version of: [aaditya/Llama3-OpenBioLLM-70B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-70B)
<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>
|
dimasik87/acc427fe-1eaa-4b48-b481-aa2115a0c20f | dimasik87 | 2025-05-24T21:52:43Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T21:44:21Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: acc427fe-1eaa-4b48-b481-aa2115a0c20f
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: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- da6901d849324b9e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: dimasik87/acc427fe-1eaa-4b48-b481-aa2115a0c20f
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.5e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 250
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/da6901d849324b9e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# acc427fe-1eaa-4b48-b481-aa2115a0c20f
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7764
## 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: 1.5e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 50
- training_steps: 250
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.702 | 0.0151 | 250 | 1.7764 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
J-LAB/fluxiia_14b-Q4_K_M-GGUF | J-LAB | 2025-05-24T21:49:01Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:J-LAB/fluxiia_14b",
"base_model:quantized:J-LAB/fluxiia_14b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T21:48:24Z | ---
base_model: J-LAB/fluxiia_14b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# J-LAB/fluxiia_14b-Q4_K_M-GGUF
This model was converted to GGUF format from [`J-LAB/fluxiia_14b`](https://huggingface.co/J-LAB/fluxiia_14b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/J-LAB/fluxiia_14b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo J-LAB/fluxiia_14b-Q4_K_M-GGUF --hf-file fluxiia_14b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo J-LAB/fluxiia_14b-Q4_K_M-GGUF --hf-file fluxiia_14b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo J-LAB/fluxiia_14b-Q4_K_M-GGUF --hf-file fluxiia_14b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo J-LAB/fluxiia_14b-Q4_K_M-GGUF --hf-file fluxiia_14b-q4_k_m.gguf -c 2048
```
|
khuam/run_23 | khuam | 2025-05-24T21:48:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T09:56:37Z | ---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: run_23
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for run_23
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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="khuam/run_23", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.3
- Pytorch: 2.8.0.dev20250518+cu126
- Datasets: 3.6.0
- 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}}
}
``` |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b3.5_a1_d1_g0.25_ep10 | open-unlearning | 2025-05-24T18:27:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:26:04Z | ---
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] |
secmlr/SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched | secmlr | 2025-05-24T18:26:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-0.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T17:43:38Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched
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. -->
# SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) on the SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 12
- total_train_batch_size: 48
- total_eval_batch_size: 32
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b4.5_a1_d1_g0.25_ep10 | open-unlearning | 2025-05-24T18:26:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:24:51Z | ---
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]
<!-- 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. -->
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[More Information Needed] |
Cherran/medical_gemma_1b_sft | Cherran | 2025-05-24T18:22:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"base_model:adapter:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"region:us"
]
| null | 2025-05-24T18:21:43Z | ---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b3.5_a1_d1_g0.125_ep5 | open-unlearning | 2025-05-24T18:20:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:19:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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nojedag/distilroberta-roberta-finetuned-financial-news-sentiment-analysis-european | nojedag | 2025-05-24T18:19:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-05-24T18:19:16Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-roberta-finetuned-financial-news-sentiment-analysis-european
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. -->
# distilroberta-roberta-finetuned-financial-news-sentiment-analysis-european
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6637
- eval_model_preparation_time: 0.0015
- eval_accuracy: 0.7764
- eval_macro_precision: 0.7737
- eval_macro_recall: 0.7865
- eval_macro_f1: 0.7762
- eval_neutral_precision: 0.8569
- eval_neutral_recall: 0.7260
- eval_neutral_f1: 0.7860
- eval_positive_precision: 0.7815
- eval_positive_recall: 0.8178
- eval_positive_f1: 0.7992
- eval_negative_precision: 0.6827
- eval_negative_recall: 0.8157
- eval_negative_f1: 0.7433
- eval_runtime: 18.4835
- eval_samples_per_second: 449.589
- eval_steps_per_second: 28.133
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 846
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
tifin-india/sarvam-m-24b-q5-1-gguf | tifin-india | 2025-05-24T18:19:32Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q5_1",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-24T16:15:05Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q5_1
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q5_1 GGUF
This repository contains the **Q5_1** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q5_1
- **File Size**: ~16.5GB
- **Description**: Legacy Q5 format with very low quality loss
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q5_1-gguf
# Run inference
./main -m sarvam-m-24b-Q5_1.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q5_1.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q5_1-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐⭐ |
| **Quality** | ⭐⭐⭐⭐ |
| **Memory** | ⭐⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q5_1 format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b3.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T18:19:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:17:58Z | ---
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. -->
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## Uses
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### Direct Use
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[More Information Needed]
### 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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### 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 -->
#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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### 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]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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## Glossary [optional]
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fats-fme/509382f3-a000-464c-b986-359253cd5e4c | fats-fme | 2025-05-24T18:18:04Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T18:03:03Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 509382f3-a000-464c-b986-359253cd5e4c
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: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ad0293a17a070f7c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 4
eval_max_new_tokens: 128
eval_sample_packing: false
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: fats-fme/509382f3-a000-464c-b986-359253cd5e4c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
lr_scheduler: constant_with_warmup
max_memory:
0: 130GB
max_steps: 300
micro_batch_size: 4
mlflow_experiment_name: /tmp/ad0293a17a070f7c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 5
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: true
save_steps: 100
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
use_scaled_dot_product_attention: false
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1e017fb6-f8c8-4390-9333-cc59aac70178
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1e017fb6-f8c8-4390-9333-cc59aac70178
warmup_steps: 200
weight_decay: 0.03
xformers_attention: null
```
</details><br>
# 509382f3-a000-464c-b986-359253cd5e4c
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 200
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0015 | 1 | nan |
| 0.0 | 0.1451 | 100 | nan |
| 0.0 | 0.2902 | 200 | nan |
| 0.0 | 0.4353 | 300 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_UNDIAL_lr0.0001_beta10_alpha2_epoch10 | open-unlearning | 2025-05-24T18:16:59Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-15T16:51:06Z | ---
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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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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[More Information Needed]
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#### Preprocessing [optional]
[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]
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b4.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T18:16:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:15:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### 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
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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
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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- **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).
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ahmadmwali/opus_Hausa | ahmadmwali | 2025-05-24T18:15:45Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:30:31Z | ---
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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[More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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<!-- 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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[More Information Needed]
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[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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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tifin-india/sarvam-m-24b-q5-k-m-gguf | tifin-india | 2025-05-24T18:15:32Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q5_k_m",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-24T17:45:57Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q5_k_m
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q5_K_M GGUF
This repository contains the **Q5_K_M** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q5_K_M
- **File Size**: ~15.6GB
- **Description**: Medium Q5 model with very low quality loss
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q5_k_m-gguf
# Run inference
./main -m sarvam-m-24b-Q5_K_M.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q5_K_M.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q5_k_m-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐⭐ |
| **Quality** | ⭐⭐⭐⭐ |
| **Memory** | ⭐⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q5_K_M format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b3.5_a1_d0_g0.125_ep5 | open-unlearning | 2025-05-24T18:14:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:13:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b3.5_a1_d0_g0.125_ep10 | open-unlearning | 2025-05-24T18:13:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:12:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_AltPO_lr5e-05_beta0.5_alpha1_epoch5 | open-unlearning | 2025-05-24T18:12:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-15T22:13:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b4.5_a1_d0_g0.125_ep10 | open-unlearning | 2025-05-24T18:10:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:09:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Proximile/LLaDA-8B-Tools-LoRA | Proximile | 2025-05-24T18:09:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llada",
"tool-calling",
"lora",
"peft",
"function-calling",
"tools",
"chatbot",
"assistant",
"sft",
"text-generation",
"en",
"base_model:GSAI-ML/LLaDA-8B-Instruct",
"base_model:adapter:GSAI-ML/LLaDA-8B-Instruct",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-14T10:03:38Z | ---
license: mit
library_name: transformers
pipeline_tag: text-generation
base_model: GSAI-ML/LLaDA-8B-Instruct
language:
- en
tags:
- llada
- tool-calling
- lora
- peft
- function-calling
- tools
- chatbot
- assistant
- sft
---
# LLaDA-8B-Tools-LoRA
This repository contains a LoRA adapter for [GSAI-ML/LLaDA-8B-Instruct](https://huggingface.co/GSAI-ML/LLaDA-8B-Instruct), fine-tuned by [Proximile LLC](https://proximile.llc) to enhance model tool calling capabilities. Proximile specializes in secure, on-premise AI solutions for small and medium-sized businesses.
## Update Timeline
- **May 14 2025** – Initial public release. Training examples were missing the pad tokens filling out the rest of the generation window.
- **May 17 2025** – Patched training script to include correct padding; updated model weights pushed to this repository.
- **May 20 2025** – Google announces [Gemini Diffusion](https://blog.google/technology/google-deepmind/gemini-diffusion/).
## About LLaDA
LLaDA (Large Language Diffusion with mAsking) is a novel language model architecture that uses discrete diffusion for text generation. Unlike traditional autoregressive models, LLaDA generates text through an iterative denoising process, progressively replacing mask tokens with predicted tokens based on confidence scores.
## Model Description
This LoRA adapter was trained to improve LLaDA's ability to handle tool calling tasks, including:
- Generating proper JSON for tool invocation
- Processing tool response data
- Providing helpful answers based on tool outputs
### Training Details
- **Base Model**: GSAI-ML/LLaDA-8B-Instruct
- **Training Method**: Supervised Fine-Tuning (SFT) with LoRA
- **LoRA Configuration**:
- Rank (r): 128
- Alpha: 256
- Target Modules: q_proj, k_proj, v_proj, gate_proj
- **Training Data**: A modified subset of the [ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE) dataset.
## Installation
```bash
pip install transformers peft torch bitsandbytes
```
## Usage
To use this LoRA adapter with the base LLaDA model:
```python
from transformers import AutoTokenizer, AutoModel
from peft import PeftModel
# Load the base model and tokenizer
base_model_name = "GSAI-ML/LLaDA-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
base_model = AutoModel.from_pretrained(base_model_name, trust_remote_code=True, device_map="auto")
# Load the LoRA adapter
lora_model = PeftModel.from_pretrained(base_model, "Proximile/LLaDA-8B-Tools-LoRA")
```
## Example Chat Completion Script
Here's a complete example of using the model for chat completion with tool calling:
```python
import torch
import json
from transformers import AutoTokenizer, AutoModel
from peft import PeftModel
# Constants
MASK_TOKEN_ID = 126336
def add_gumbel_noise(logits, temperature):
'''
The Gumbel max is a method for sampling categorical distributions.
For diffusion models, low-precision Gumbel Max affects generation quality.
'''
if temperature <= 0:
return logits
logits = logits.to(torch.float64)
noise = torch.rand_like(logits, dtype=torch.float64)
gumbel_noise = (- torch.log(noise)) ** temperature
return logits.exp() / gumbel_noise
def get_num_transfer_tokens(mask_index, steps):
'''
In the reverse process, we precompute the number of tokens to transition at each step.
'''
mask_num = mask_index.sum(dim=1, keepdim=True)
# Ensure we have at least one step
if steps == 0:
steps = 1
base = mask_num // steps
remainder = mask_num % steps
num_transfer_tokens = torch.zeros(mask_num.size(0), steps, device=mask_index.device, dtype=torch.int64) + base
for i in range(mask_num.size(0)):
if remainder[i] > 0:
num_transfer_tokens[i, :remainder[i]] += 1
return num_transfer_tokens
def generate(model, prompt, steps=128, gen_length=128, block_length=32, temperature=0.,
remasking='low_confidence', mask_id=MASK_TOKEN_ID):
'''
Generate text using LLaDA's diffusion-based generation process.
'''
device = next(model.parameters()).device
prompt = prompt.to(device)
x = torch.full((1, prompt.shape[1] + gen_length), mask_id, dtype=torch.long).to(device)
x[:, :prompt.shape[1]] = prompt.clone()
prompt_index = (x != mask_id)
assert gen_length % block_length == 0
num_blocks = gen_length // block_length
assert steps % num_blocks == 0
steps_per_block = steps // num_blocks
for num_block in range(num_blocks):
block_mask_index = (x[:, prompt.shape[1] + num_block * block_length: prompt.shape[1] + (num_block + 1) * block_length:] == mask_id)
num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps_per_block)
for i in range(steps_per_block):
mask_index = (x == mask_id)
if not mask_index.any():
break
outputs = model(x)
logits = outputs.logits
logits_with_noise = add_gumbel_noise(logits, temperature=temperature)
x0 = torch.argmax(logits_with_noise, dim=-1) # b, l
if remasking == 'low_confidence':
p = torch.nn.functional.softmax(logits.to(torch.float64), dim=-1)
x0_p = torch.squeeze(
torch.gather(p, dim=-1, index=torch.unsqueeze(x0, -1)), -1) # b, l
elif remasking == 'random':
x0_p = torch.rand((x0.shape[0], x0.shape[1]), device=x0.device)
else:
raise NotImplementedError(remasking)
x0_p[:, prompt.shape[1] + (num_block + 1) * block_length:] = -float('inf')
x0 = torch.where(mask_index, x0, x)
confidence = torch.where(mask_index, x0_p, -float('inf'))
transfer_index = torch.zeros_like(x0, dtype=torch.bool, device=x0.device)
for j in range(confidence.shape[0]):
_, select_index = torch.topk(confidence[j], k=num_transfer_tokens[j, i])
transfer_index[j, select_index] = True
x[transfer_index] = x0[transfer_index]
return x
def chat_completion(model, tokenizer, messages, temperature=0.1, gen_length=128, steps=128):
"""
Generate a chat completion with the LLaDA model using the LoRA adapter.
Args:
model: The LLaDA model with LoRA adapter
tokenizer: The tokenizer
messages: List of message dictionaries with 'role' and 'content' keys
temperature: Temperature for generation (0 for greedy)
gen_length: Maximum length of generated text
steps: Number of denoising steps
Returns:
The generated response text
"""
# Format input for the model
formatted_input = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# Tokenize input
input_ids = tokenizer(formatted_input, return_tensors="pt")["input_ids"]
# Generate response
with torch.no_grad():
output_ids = generate(
model,
input_ids,
steps=steps,
gen_length=gen_length,
block_length=32,
temperature=temperature,
remasking='low_confidence'
)
# Decode the generated output
generated_text = tokenizer.decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=False).split("<|")[0]
return generated_text
# Example usage
if __name__ == "__main__":
# Load the base model and tokenizer
base_model_name = "GSAI-ML/LLaDA-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
base_model = AutoModel.from_pretrained(base_model_name, trust_remote_code=True, device_map="auto")
# Load the LoRA adapter
lora_model = PeftModel.from_pretrained(base_model, "Proximile/LLaDA-8B-Tools-LoRA")
lora_model.eval()
# Define tool calling function schema
tool_schema = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The unit of temperature"
}
},
"required": ["location", "unit"]
}
}
}
]
# Create conversation with system prompt including tool description
system_prompt = """You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the orginal user question.
If you choose to use one or more of the following tool functions, respond with a list of JSON function calls, each with the proper arguments that best answers the given prompt.
Each tool request within the list should be in the exact format {"name": function name, "parameters": {dictionary of argument names and values}}. Do not use variables. Just a list of two-key dictionaries, each starting with the function name, followed by a dictionary of parameters.
Here are the tool functions available to you:
""" + json.dumps(tool_schema, indent=4) + """
After receiving the results back from a function call, you have to formulate your response to the user. If the information needed is not found in the returned data, either attempt a new function call, or inform the user that you cannot answer based on your available knowledge. The user cannot see the function results. You have to interpret the data and provide a response based on it.
If the user request does not necessitate a function call, simply respond to the user's query directly."""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": "What's the weather like in New York?"}
]
# Generate assistant response (expecting tool call)
assistant_response = chat_completion(lora_model, tokenizer, messages)
print(f"Assistant: {assistant_response}")
# Mock tool response
tool_response = json.dumps({
"location": "New York, NY",
"temperature": 72,
"unit": "fahrenheit",
"condition": "Partly Cloudy",
"humidity": 65,
"wind_speed": 8,
"wind_direction": "NE"
})
# Add assistant and tool responses to the conversation
messages.append({"role": "assistant", "content": assistant_response})
messages.append({"role": "ipython", "content": tool_response})
# Generate final assistant response
final_response = chat_completion(lora_model, tokenizer, messages)
print(f"Assistant (with tool data): {final_response}")
# Assistant: [{"name": "get_weather", "parameters": {"location": "New York", "unit": "fahrenheit"}}]
# Assistant (with tool data): The current weather in New York is as follows:
# - Temperature: 72°F
# - Weather Condition: Partly Cloudy
# - Humidity: 65%
# - Wind Speed: 8 miles per hour
# - Wind Direction: Northeast
```
## Limitations
- LLaDA's diffusion-based generation is different from standard LLMs and may behave differently in certain contexts
- The model may still hallucinate or generate incorrect tool call formats
- The format of the tool call must precisely match what is shown in the example (which is a modified version of [the official llama 3.1 format](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/))
## Citation
If you use this model in your research, please cite the original LLaDA paper as well as this adapter:
```
@misc{llada-8b-tools-lora,
author = {Proximile LLC},
title = {LLaDA-8B-Tools-LoRA},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Proximile/LLaDA-8B-Tools-LoRA}}
}
```
## About Proximile LLC
Proximile LLC provides secure, cost-effective, and private AI solutions tailored to small and medium-sized businesses. We specialize in:
- **On-premise AI inference** solutions that ensure unparalleled privacy
- **Cost-effective hardware configurations** including the Jetson Orin Nano Super
- **Secure Local AI** applications including chatbots, RAG systems, and custom AI tools
- **Specialized services** for compliance & governance, knowledge management, and IT automation
Visit [proximile.llc](https://proximile.llc) to learn more about our secure, local AI solutions for your business.
## License
This adapter is released under the same license as the base LLaDA model. |
Proximile/LLaDA-8B-Tools | Proximile | 2025-05-24T18:09:24Z | 102 | 7 | transformers | [
"transformers",
"safetensors",
"llada",
"feature-extraction",
"tool-calling",
"lora",
"peft",
"function-calling",
"tools",
"chatbot",
"assistant",
"sft",
"text-generation",
"conversational",
"custom_code",
"en",
"base_model:GSAI-ML/LLaDA-8B-Instruct",
"base_model:adapter:GSAI-ML/LLaDA-8B-Instruct",
"license:mit",
"region:us"
]
| text-generation | 2025-05-14T11:06:15Z | ---
license: mit
library_name: transformers
pipeline_tag: text-generation
base_model: GSAI-ML/LLaDA-8B-Instruct
language:
- en
tags:
- llada
- tool-calling
- lora
- peft
- function-calling
- tools
- chatbot
- assistant
- sft
---
# LLaDA-8B-Tools
This repository contains a variant of [GSAI-ML/LLaDA-8B-Instruct](https://huggingface.co/GSAI-ML/LLaDA-8B-Instruct), fine-tuned by [Proximile LLC](https://proximile.llc) to enhance model tool calling capabilities. Proximile specializes in secure, on-premise AI solutions for small and medium-sized businesses.
## Update Timeline
- **May 14 2025** – Initial public release. Training examples were missing the pad tokens filling out the rest of the generation window.
- **May 17 2025** – Patched training script to include correct padding; updated model weights pushed to this repository.
- **May 20 2025** – Google announces [Gemini Diffusion](https://blog.google/technology/google-deepmind/gemini-diffusion/).
## About LLaDA
LLaDA (Large Language Diffusion with mAsking) is a novel language model architecture that uses discrete diffusion for text generation. Unlike traditional autoregressive models, LLaDA generates text through an iterative denoising process, progressively replacing mask tokens with predicted tokens based on confidence scores.
## Model Description
This merged LoRA model was trained to improve LLaDA's ability to handle tool calling tasks, including:
- Generating proper JSON for tool invocation
- Processing tool response data
- Providing helpful answers based on tool outputs
### Training Details
- **Base Model**: GSAI-ML/LLaDA-8B-Instruct
- **Training Method**: Supervised Fine-Tuning (SFT) with LoRA
- **LoRA Configuration**:
- Rank (r): 128
- Alpha: 256
- Target Modules: `q_proj`, `k_proj`, `v_proj`, `gate_proj`
- **Training Data**: A modified subset of the [ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE) dataset.
## Installation
```bash
pip install transformers peft torch bitsandbytes
```
## Usage
To use this model:
```python
from transformers import AutoTokenizer, AutoModel
from peft import PeftModel
# Load the base model and tokenizer
model_name = "Proximile/LLaDA-8B-Tools"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, device_map="auto")
```
## Example Chat Completion Script
Here's a complete example of using the model for chat completion with tool calling:
```python
import torch
import json
from transformers import AutoTokenizer, AutoModel
# Constants
MASK_TOKEN_ID = 126336
def add_gumbel_noise(logits, temperature):
'''
The Gumbel max is a method for sampling categorical distributions.
For diffusion models, low-precision Gumbel Max affects generation quality.
'''
if temperature <= 0:
return logits
logits = logits.to(torch.float64)
noise = torch.rand_like(logits, dtype=torch.float64)
gumbel_noise = (- torch.log(noise)) ** temperature
return logits.exp() / gumbel_noise
def get_num_transfer_tokens(mask_index, steps):
'''
In the reverse process, we precompute the number of tokens to transition at each step.
'''
mask_num = mask_index.sum(dim=1, keepdim=True)
# Ensure we have at least one step
if steps == 0:
steps = 1
base = mask_num // steps
remainder = mask_num % steps
num_transfer_tokens = torch.zeros(mask_num.size(0), steps, device=mask_index.device, dtype=torch.int64) + base
for i in range(mask_num.size(0)):
if remainder[i] > 0:
num_transfer_tokens[i, :remainder[i]] += 1
return num_transfer_tokens
def generate(model, prompt, steps=128, gen_length=128, block_length=32, temperature=0.,
remasking='low_confidence', mask_id=MASK_TOKEN_ID):
'''
Generate text using LLaDA's diffusion-based generation process.
'''
device = next(model.parameters()).device
prompt = prompt.to(device)
x = torch.full((1, prompt.shape[1] + gen_length), mask_id, dtype=torch.long).to(device)
x[:, :prompt.shape[1]] = prompt.clone()
prompt_index = (x != mask_id)
assert gen_length % block_length == 0
num_blocks = gen_length // block_length
assert steps % num_blocks == 0
steps_per_block = steps // num_blocks
for num_block in range(num_blocks):
block_mask_index = (x[:, prompt.shape[1] + num_block * block_length: prompt.shape[1] + (num_block + 1) * block_length:] == mask_id)
num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps_per_block)
for i in range(steps_per_block):
mask_index = (x == mask_id)
if not mask_index.any():
break
outputs = model(x)
logits = outputs.logits
logits_with_noise = add_gumbel_noise(logits, temperature=temperature)
x0 = torch.argmax(logits_with_noise, dim=-1) # b, l
if remasking == 'low_confidence':
p = torch.nn.functional.softmax(logits.to(torch.float64), dim=-1)
x0_p = torch.squeeze(
torch.gather(p, dim=-1, index=torch.unsqueeze(x0, -1)), -1) # b, l
elif remasking == 'random':
x0_p = torch.rand((x0.shape[0], x0.shape[1]), device=x0.device)
else:
raise NotImplementedError(remasking)
x0_p[:, prompt.shape[1] + (num_block + 1) * block_length:] = -float('inf')
x0 = torch.where(mask_index, x0, x)
confidence = torch.where(mask_index, x0_p, -float('inf'))
transfer_index = torch.zeros_like(x0, dtype=torch.bool, device=x0.device)
for j in range(confidence.shape[0]):
_, select_index = torch.topk(confidence[j], k=num_transfer_tokens[j, i])
transfer_index[j, select_index] = True
x[transfer_index] = x0[transfer_index]
return x
def chat_completion(model, tokenizer, messages, temperature=0.1, gen_length=128, steps=128):
"""
Generate a chat completion.
Args:
model: The LLaDA tool calling model
tokenizer: The tokenizer
messages: List of message dictionaries with 'role' and 'content' keys
temperature: Temperature for generation (0 for greedy)
gen_length: Maximum length of generated text
steps: Number of denoising steps
Returns:
The generated response text
"""
# Format input for the model
formatted_input = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# Tokenize input
input_ids = tokenizer(formatted_input, return_tensors="pt")["input_ids"]
# Generate response
with torch.no_grad():
output_ids = generate(
model,
input_ids,
steps=steps,
gen_length=gen_length,
block_length=32,
temperature=temperature,
remasking='low_confidence'
)
# Decode the generated output
generated_text = tokenizer.decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=False).split("<|")[0]
return generated_text
# Example usage
if __name__ == "__main__":
# Load the base model and tokenizer
model_name = "Proximile/LLaDA-8B-Tools"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, device_map="auto")
# Define tool calling function schema
tool_schema = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The unit of temperature"
}
},
"required": ["location", "unit"]
}
}
}
]
# Create conversation with system prompt including tool description
system_prompt = """You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the orginal user question.
If you choose to use one or more of the following tool functions, respond with a list of JSON function calls, each with the proper arguments that best answers the given prompt.
Each tool request within the list should be in the exact format {"name": function name, "parameters": {dictionary of argument names and values}}. Do not use variables. Just a list of two-key dictionaries, each starting with the function name, followed by a dictionary of parameters.
Here are the tool functions available to you:
""" + json.dumps(tool_schema, indent=4) + """
After receiving the results back from a function call, you have to formulate your response to the user. If the information needed is not found in the returned data, either attempt a new function call, or inform the user that you cannot answer based on your available knowledge. The user cannot see the function results. You have to interpret the data and provide a response based on it.
If the user request does not necessitate a function call, simply respond to the user's query directly."""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": "What's the weather like in New York?"}
]
# Generate assistant response (expecting tool call)
assistant_response = chat_completion(model, tokenizer, messages)
print(f"Assistant: {assistant_response}")
# Mock tool response
tool_response = json.dumps({
"location": "New York, NY",
"temperature": 72,
"unit": "fahrenheit",
"condition": "Partly Cloudy",
"humidity": 65,
"wind_speed": 8,
"wind_direction": "NE"
})
# Add assistant and tool responses to the conversation
messages.append({"role": "assistant", "content": assistant_response})
messages.append({"role": "ipython", "content": tool_response})
# Generate final assistant response
final_response = chat_completion(model, tokenizer, messages)
print(f"Assistant (with tool data): {final_response}")
# Assistant: [{"name": "get_weather", "parameters": {"location": "New York", "unit": "fahrenheit"}}]
# Assistant (with tool data): The current weather in New York is as follows:
# - Temperature: 72°F
# - Weather Condition: Partly Cloudy
# - Humidity: 65%
# - Wind Speed: 8 miles per hour
# - Wind Direction: Northeast
```
## Limitations
- LLaDA's diffusion-based generation is different from standard LLMs and may behave differently in certain contexts
- The model may still hallucinate or generate incorrect tool call formats
- The format of the tool call must precisely match what is shown in the example (which is a modified version of [the official llama 3.1 format](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/))
## Citation
If you use this model in your research, please cite the original LLaDA paper as well as this adapter:
```
@misc{llada-8b-tools,
author = {Proximile LLC},
title = {LLaDA-8B-Tools},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Proximile/LLaDA-8B-Tools}}
}
```
## About Proximile LLC
Proximile LLC provides secure, cost-effective, and private AI solutions tailored to small and medium-sized businesses. We specialize in:
- **On-premise AI inference** solutions that ensure unparalleled privacy
- **Cost-effective hardware configurations** including the Jetson Orin Nano Super
- **Secure Local AI** applications including chatbots, RAG systems, and custom AI tools
- **Specialized services** for compliance & governance, knowledge management, and IT automation
Visit [proximile.llc](https://proximile.llc) to learn more about our secure, local AI solutions for your business.
## License
This adapter is released under the same license as the base LLaDA model. |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b3.5_a1_d1_g0.125_ep5 | open-unlearning | 2025-05-24T18:07:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:05:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- 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]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
MohamedAliFarhat/ppo-Huggy | MohamedAliFarhat | 2025-05-24T18:07:04Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2025-05-24T18:06:41Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
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: MohamedAliFarhat/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b3.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T18:05:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:02:51Z | ---
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]
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YujinPang/reasoning_model_1 | YujinPang | 2025-05-24T18:03:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T17:03:55Z | ---
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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#### Summary
## Model Examination [optional]
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eusilviasilva/vicky002 | eusilviasilva | 2025-05-24T18:02:23Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
]
| text-to-image | 2025-05-24T17:46:29Z | ---
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: vicky002
---
# Vicky002
<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 `vicky002` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "vicky002",
"lora_weights": "https://huggingface.co/eusilviasilva/vicky002/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('eusilviasilva/vicky002', weight_name='lora.safetensors')
image = pipeline('vicky002').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: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/eusilviasilva/vicky002/discussions) to add images that show off what you’ve made with this LoRA.
|
dzanbek/2732564f-c3e0-4694-9ebe-8f78edcb8c3c | dzanbek | 2025-05-24T18:01:44Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-24T17:30:16Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
library_name: transformers
model_name: 2732564f-c3e0-4694-9ebe-8f78edcb8c3c
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 2732564f-c3e0-4694-9ebe-8f78edcb8c3c
This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
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="dzanbek/2732564f-c3e0-4694-9ebe-8f78edcb8c3c", 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/dedok-yo/s56-2/runs/entbltll)
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: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
infogep/7b65f34e-99ce-4950-8407-a8d6ba31c8de | infogep | 2025-05-24T18:01:05Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-24T17:28:39Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
library_name: transformers
model_name: 7b65f34e-99ce-4950-8407-a8d6ba31c8de
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 7b65f34e-99ce-4950-8407-a8d6ba31c8de
This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
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="infogep/7b65f34e-99ce-4950-8407-a8d6ba31c8de", 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/dedok-yo/s56-7/runs/n7vnvjzy)
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: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
halchou/BFConfig-LoRA-open_llama_3b-v01 | halchou | 2025-05-24T18:00:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T17:52:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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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## Glossary [optional]
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## Model Card Contact
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desllre/ru_news_detection | desllre | 2025-05-24T17:58:39Z | 11 | 1 | null | [
"safetensors",
"bert",
"rubert",
"rubert-tiny",
"text-classification",
"russian",
"social-media",
"news",
"fine-tuned",
"taiga",
"ru",
"dataset:Taiga",
"base_model:cointegrated/rubert-tiny2",
"base_model:finetune:cointegrated/rubert-tiny2",
"license:mit",
"region:us"
]
| text-classification | 2025-05-21T16:20:01Z | ---
language: ru
license: mit
tags:
- rubert
- rubert-tiny
- text-classification
- russian
- social-media
- news
- fine-tuned
- taiga
metrics:
- accuracy
- precision
- recall
- f1
base_model: cointegrated/rubert-tiny2
datasets:
- Taiga
---
## Russian news detection
### About
- Model based on `cointegrated/rubert-tiny2`
- The model allows you to classify russian texts into two classes 'news' and 'social'
- Further training of the model took place on a set of texts of social networks and news texts of the corpus Taiga (https://tatianashavrina.github.io/taiga_site /)
- Estimates of the accuracy of the model in the validation sample:
| Accuracy | Precision | Recall | F1-score |
| -------- | --------- | -------- | -------- |
| 0.996342 | 0.999747 | 0.993717 | 0.996723 |
### Getting started
```python
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import pickle
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_path = 'desllre/ru_news_detection'
encoder_path = hf_hub_download(repo_id=model_path, filename="encoder.pkl")
with open(encoder_path, "rb") as f:
encoder = pickle.load(f)
tokenizer = AutoTokenizer.from_pretrained(model_path)
classifier = AutoModelForSequenceClassification.from_pretrained(model_path).to(device)
text = 'Tesla дала добро на взлом ПО своих автомобилей\n\nКомпания изменила условия программы Bug Bounty, предусматривающей выплату вознаграждений за поиск уязвимостей. Теперь энтузиасты могут взламывать электрокары Tesla, не боясь отзыва гарантии. Более того, в соответствии с новой политикой компании, автопроизводитель будет перепрошивать автомобили, ПО которых вышло из строя в процессе экспериментов специалистов кибербезопасности.\n\nИзменения в политике компании Telsa очень тепло встретили представители индустрии.'
tokenized = tokenize_function(text, news_tokenizer)
tokenized = {key: value.to(device) for key, value in tokenized.items()}
with torch.no_grad():
output = classifier(**tokenized)
predicted_class_id = torch.argmax(output.logits, dim=1).item()
label = encoder.inverse_transform([predicted_class_id])[0]
print(label)
```
|
concept-unlearning/zephyr-7b-beta_ft_lora_civil_comments_v1_ft | concept-unlearning | 2025-05-24T17:58:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T17:56:10Z | ---
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]
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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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chdany12/q-FrozenLake-v1-4x4-noSlippery | chdany12 | 2025-05-24T17:57:30Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-24T17:57:23Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="chdany12/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_RMU_lr1e-05_layer5_scoeff100_epoch5 | open-unlearning | 2025-05-24T17:55:58Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-15T16:50:53Z | ---
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] |
Pcp1988/ujjj | Pcp1988 | 2025-05-24T17:52:59Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T17:52:59Z | ---
license: apache-2.0
---
|
OmarIDK/MNLP_M2_document_encoder | OmarIDK | 2025-05-24T17:52:41Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"tf",
"rust",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_net",
"dataset:search_qa",
"dataset:eli5",
"dataset:snli",
"dataset:multi_nli",
"dataset:wikihow",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/sentence-compression",
"dataset:embedding-data/flickr30k-captions",
"dataset:embedding-data/altlex",
"dataset:embedding-data/simple-wiki",
"dataset:embedding-data/QQP",
"dataset:embedding-data/SPECTER",
"dataset:embedding-data/PAQ_pairs",
"dataset:embedding-data/WikiAnswers",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-24T17:42:16Z | ---
language: en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
pipeline_tag: sentence-similarity
---
# all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** | |
talphaidze/qwen3-mcqa | talphaidze | 2025-05-24T17:51:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T17:46:12Z | ---
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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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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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
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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]
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## Glossary [optional]
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khuam/run_4 | khuam | 2025-05-24T17:47:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T07:06:11Z | ---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: run_4
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for run_4
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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="khuam/run_4", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.3
- Pytorch: 2.8.0.dev20250518+cu126
- Datasets: 3.6.0
- 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}}
}
``` |
dimasik87/6d1ea65b-e54a-4e81-be17-6038852aa87e | dimasik87 | 2025-05-24T17:45:27Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-24T17:29:44Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
library_name: transformers
model_name: 6d1ea65b-e54a-4e81-be17-6038852aa87e
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 6d1ea65b-e54a-4e81-be17-6038852aa87e
This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
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="dimasik87/6d1ea65b-e54a-4e81-be17-6038852aa87e", 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/dedok-yo/s56-7/runs/oiexppib)
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: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Delta-Vector/Archaeo-12B-V2 | Delta-Vector | 2025-05-24T17:43:26Z | 70 | 4 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"roleplay",
"creative-writing",
"merge",
"mergekit",
"conversational",
"base_model:Delta-Vector/Francois-PE-V2-Huali-12B",
"base_model:merge:Delta-Vector/Francois-PE-V2-Huali-12B",
"base_model:Delta-Vector/Rei-V3-KTO-12B",
"base_model:merge:Delta-Vector/Rei-V3-KTO-12B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-19T23:35:24Z | ---
tags:
- roleplay
- creative-writing
- merge
- mergekit
base_model:
- Delta-Vector/Francois-PE-V2-Huali-12B
- Delta-Vector/Rei-V3-KTO-12B
pipeline_tag: text-generation
library_name: transformers
---
```
__~a~_
~~; ~_
_ ~ ~_ _
'_\;__._._._._._._] ~_._._._._._.__;/_`
'(/'/'/'/'|'|'|'| ( )|'|'|'|'\'\'\'\)'
(/ / / /, | | | |(/ \) | | | ,\ \ \ \)
(/ / / / / | | | ~(/ \) ~ | | \ \ \ \ \)
(/ / / / / ~ ~ ~ (/ \) ~ ~ \ \ \ \ \)
(/ / / / ~ / (||)| ~ \ \ \ \)
~ / / ~ M /||\M ~ \ \ ~
~ ~ /||\ ~ ~
//||\\
//||\\
//||\\
'/||\' "Archaeopteryx"
```
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
@import url('https://fonts.googleapis.com/css2?family=VT323&display=swap');
body {
background: #0a0017;
margin: 0;
padding: 20px;
font-family: 'VT323', monospace;
color: #ff00aa;
text-shadow: 0 0 8px #ff00aa;
animation: glitch-flicker 0.2s infinite alternate;
}
@keyframes glitch-flicker {
0% { text-shadow: 0 0 5px #ff00aa, 0 0 15px #ff00aa; }
100% { text-shadow: 0 0 8px #ff0066, 0 0 18px #ff0066; }
}
.crt-container {
padding: 10px;
max-width: 900px;
margin: auto;
}
.crt-case {
background: linear-gradient(135deg, #130021, #20002c);
border-radius: 10px;
padding: 15px;
box-shadow:
inset 2px 2px 10px rgba(255,0,170,0.5),
2px 2px 5px rgba(255,0,170,0.3),
0 0 25px rgba(255,0,170,0.2);
}
.crt-screen {
background: #0c011a;
padding: 20px;
border-radius: 10px;
box-shadow:
inset 0 0 25px rgba(255,0,170,0.3),
0 0 15px rgba(255,0,170,0.7);
filter: contrast(1.2) brightness(1.2);
text-shadow: 0px 0px 5px #ff00aa;
animation: glow-pulse 3s infinite alternate;
}
@keyframes glow-pulse {
0% { box-shadow: inset 0 0 20px rgba(255,0,170,0.3), 0 0 15px rgba(255,0,170,0.3); }
100% { box-shadow: inset 0 0 30px rgba(255,0,170,0.5), 0 0 25px rgba(255,0,170,0.5); }
}
h2 {
color: #ff33cc;
text-align: center;
font-size: 28px;
text-shadow:
0 0 8px #ff33cc,
0 0 18px #ff0044;
}
pre {
background: rgba(255,0,170,0.1);
padding: 10px;
border-radius: 10px;
color: #ff66cc;
font-size: 14px;
box-shadow: inset 0 0 10px rgba(255,0,170,0.5);
}
.glitch {
animation: text-glitch 0.5s infinite alternate;
}
@keyframes text-glitch {
0% { transform: translateX(-2px); text-shadow: 0 0 5px #ff0066, 0 0 10px #ff33cc; }
100% { transform: translateX(2px); text-shadow: 0 0 8px #ff00aa, 0 0 20px #ff0099; }
}
.neon-link {
color: #ff66cc;
text-decoration: none;
transition: text-shadow 0.3s ease;
}
.neon-link:hover {
text-shadow: 0px 0px 15px #ff66cc, 0 0 25px rgba(255,0,170,0.5);
}
.ascii-art {
text-align: center;
font-size: 12px;
color: #ff33cc;
text-shadow: 0px 0px 5px #ff00ff;
margin-bottom: 20px;
}
.quantso-container {
display: flex;
justify-content: center;
gap: 20px;
margin-top: 20px;
}
.quantso-box {
background: rgba(255,0,170,0.1);
padding: 15px;
border-radius: 10px;
text-align: center;
box-shadow: inset 0 0 10px rgba(255,0,170,0.5);
flex: 1;
max-width: 150px;
}
</style>
</head>
<body>
<div class="crt-container">
<div class="crt-case">
<div class="crt-screen">
<p>A series of Merges made for Roleplaying & Creative Writing, This model uses Rei-V3-KTO-12B and Francois-PE-V2-Huali-12B and Slerp to merge the 2 models - as a sequel to the OG Archaeo.</p>
<h3>ChatML formatting</h3>
<pre>
"""<|im_start|>system
system prompt<|im_end|>
<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""
</pre>
<h3>MergeKit Configuration</h3>
<pre>
models:
- model: Delta-Vector/Rei-V3-KTO-12B
- model: Delta-Vector/Francois-PE-V2-Huali-12B
merge_method: slerp
base_model: Delta-Vector/Rei-V3-KTO-12B
parameters:
t:
- value: 0.2
dtype: bfloat16
tokenizer_source: base
</pre>
<h3>Quants:</h3>
<div class="quantso-container">
<div class="quantso-box">
<strong>GGUF</strong><br>
<a class="neon-link" href="#">https://huggingface.co/bartowski/Delta-Vector_Archaeo-12B-V2-GGUF/</a>
</div>
<div class="quantso-box">
<strong>EXL2</strong><br>
<a class="neon-link" href="#">https://huggingface.co/collections/ReadyArt/delta-vector-archaeo-12b-v2-exl2-682ca1508f01103d9554e553</a>
</div>
</div>
<h3>Credits</h3>
<p>Thank you to: Kubernetes-bad, LucyKnada, Intervitens, Samantha Twinkman, Tav, Alicat, Auri, Trappu & The rest of Anthracite</p>
</div>
</div>
</div>
</body>
</html> |
kimxxxx/mistral_r64_a128_b8_gas8_Ler5e-5_hackcehctfmansub_1epoch | kimxxxx | 2025-05-24T17:41:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:39:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
yosefw/bert-medium-amharic-32k | yosefw | 2025-05-24T17:36:43Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:prajjwal1/bert-medium",
"base_model:finetune:prajjwal1/bert-medium",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2025-05-23T23:04:49Z | ---
library_name: transformers
license: mit
base_model: prajjwal1/bert-medium
tags:
- generated_from_trainer
model-index:
- name: bert-medium-amharic-32k
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. -->
# bert-medium-amharic-32k
This model is a fine-tuned version of [prajjwal1/bert-medium](https://huggingface.co/prajjwal1/bert-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.4166
- eval_model_preparation_time: 0.0032
- eval_runtime: 7.7499
- eval_samples_per_second: 2673.19
- eval_steps_per_second: 10.452
- epoch: 38.1081
- step: 318050
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 10000
- num_epochs: 40
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Razvanix/music-transcription-model | Razvanix | 2025-05-24T17:35:55Z | 0 | 0 | tensorflow | [
"tensorflow",
"tf-keras",
"audio",
"music",
"transcription",
"license:mit",
"region:us"
]
| null | 2025-05-24T17:32:07Z | ---
license: mit
tags:
- audio
- music
- transcription
- tensorflow
library_name: tensorflow
---
# Music Transcription Model
This model performs automatic music transcription, converting audio recordings to MIDI notes.
## Model Description
- **Developed by:** Razvan Calauz
- **Model type:** Audio-to-MIDI transcription
- **Language(s):** N/A (Audio processing)
- **License:** MIT
- **Framework:** TensorFlow 2.15.0
## Intended Use
This model is designed to transcribe musical audio recordings into MIDI format for educational and research purposes.
## How to Use
```python
import tensorflow as tf
from huggingface_hub import snapshot_download
# Download model
model_path = snapshot_download(repo_id="Razvanix/music-transcription-model")
# Load model
model = tf.saved_model.load(model_path) |
polyglots/SinLlama-Instruct-si-News-Category-Transliterated-2661 | polyglots | 2025-05-24T17:34:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b",
"base_model:finetune:unsloth/llama-3-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:33:15Z | ---
base_model: unsloth/llama-3-8b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** polyglots
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b
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)
|
LiliaBakh/gorelik_lora_1_may_2025 | LiliaBakh | 2025-05-24T17:32:33Z | 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 | 2025-05-24T17:01:36Z | ---
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: gorelik
---
# Gorelik_Lora_1_May_2025
<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 `gorelik` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "gorelik",
"lora_weights": "https://huggingface.co/LiliaBakh/gorelik_lora_1_may_2025/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('LiliaBakh/gorelik_lora_1_may_2025', weight_name='lora.safetensors')
image = pipeline('gorelik').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: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/LiliaBakh/gorelik_lora_1_may_2025/discussions) to add images that show off what you’ve made with this LoRA.
|
vertings6/d6f47dab-0449-499f-aac4-5883beeb6783 | vertings6 | 2025-05-24T17:30:37Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:heegyu/WizardVicuna-open-llama-3b-v2",
"base_model:adapter:heegyu/WizardVicuna-open-llama-3b-v2",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T16:56:05Z | ---
library_name: peft
license: apache-2.0
base_model: heegyu/WizardVicuna-open-llama-3b-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d6f47dab-0449-499f-aac4-5883beeb6783
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: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: heegyu/WizardVicuna-open-llama-3b-v2
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- cc1f5b1959c57013_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vertings6/d6f47dab-0449-499f-aac4-5883beeb6783
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/cc1f5b1959c57013_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 718ac179-f573-4920-8e2e-046d87265652
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 718ac179-f573-4920-8e2e-046d87265652
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# d6f47dab-0449-499f-aac4-5883beeb6783
This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9463
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.126 | 0.0001 | 1 | 1.9922 |
| 1.3273 | 0.0155 | 250 | 1.0661 |
| 1.4073 | 0.0311 | 500 | 0.9463 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
CennetOguz/yc3_lamma3_context_fg_5 | CennetOguz | 2025-05-24T17:27:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:27:37Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CennetOguz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-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)
|
FlareRebellion/DarkHazard-v2.1-24b | FlareRebellion | 2025-05-24T17:25:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b",
"base_model:merge:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b",
"base_model:ReadyArt/Broken-Tutu-24B",
"base_model:merge:ReadyArt/Broken-Tutu-24B",
"base_model:ReadyArt/Forgotten-Safeword-24B-v4.0",
"base_model:merge:ReadyArt/Forgotten-Safeword-24B-v4.0",
"base_model:aixonlab/Eurydice-24b-v3.5",
"base_model:merge:aixonlab/Eurydice-24b-v3.5",
"base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"base_model:merge:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T14:59:29Z | ---
base_model:
- cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
- PocketDoc/Dans-PersonalityEngine-V1.3.0-24b
- aixonlab/Eurydice-24b-v3.5
- ReadyArt/Forgotten-Safeword-24B-v4.0
- ReadyArt/Broken-Tutu-24B
library_name: transformers
tags:
- mergekit
- merge
---
# DarkHazard-v2.1-24b
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Inspiration
This merge was inspired by
* Yoesph/Haphazard-v1.1-24b
* yvvki/Erotophobia-24B-v1.1
### Changelog
v2.1
* Updated Dans-PersonalityEngine to PocketDoc/Dans-PersonalityEngine-V1.3.0-24b
* Updated Eurydice to aixonlab/Eurydice-24b-v3.5
v2.0
* Major version bump because of base model change: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
* swapped TheDrummer/Cydonia-24B-v2.1 with ReadyArt/Forgotten-Safeword-24B-v4.0
* (I've been doing some tests with LatitudeGames/Harbinger-24B but it just seemed to introduce positivity bias to my test scenarios, so it stays out for now)
v1.3
* updated Eurydice to v3
v1.2
* replaced Yoesph/Haphazard-v1.1-24b with model: TheDrummer/Cydonia-24B-v2.1
* replaced ReadyArt/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B with ReadyArt/Broken-Tutu-24B
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) as a base.
### Models Merged
The following models were included in the merge:
* [PocketDoc/Dans-PersonalityEngine-V1.3.0-24b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b)
* [aixonlab/Eurydice-24b-v3.5](https://huggingface.co/aixonlab/Eurydice-24b-v3.5)
* [ReadyArt/Forgotten-Safeword-24B-v4.0](https://huggingface.co/ReadyArt/Forgotten-Safeword-24B-v4.0)
* [ReadyArt/Broken-Tutu-24B](https://huggingface.co/ReadyArt/Broken-Tutu-24B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
merge_method: model_stock
dtype: bfloat16
models:
- model: aixonlab/Eurydice-24b-v3.5 # storytelling / RP
- model: ReadyArt/Forgotten-Safeword-24B-v4.0 # uncensor + Cydonia
- model: ReadyArt/Broken-Tutu-24B # uncensor + nsfw + Cydonia
- model: PocketDoc/Dans-PersonalityEngine-V1.3.0-24b # Prompt Adherence
```
|
thisisdev/phi3_sharegpt_finetuned | thisisdev | 2025-05-24T17:24:32Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T17:21:44Z | ---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thisisdev
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-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)
|
amanda-901014/qwen_32_kaggle2finetune | amanda-901014 | 2025-05-24T17:24:19Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-32B-Instruct",
"region:us"
]
| null | 2025-05-24T16:54:11Z | ---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
- bnb_4bit_quant_storage: uint8
- load_in_4bit: True
- load_in_8bit: False
### Framework versions
- PEFT 0.6.2
|
Viral-Link-18-jaisalmer-video/Smriti.Jain.Viral.Video.Jaisalmer.Full.Original.Video.Official | Viral-Link-18-jaisalmer-video | 2025-05-24T17:20:52Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T17:20:02Z | <!-- HTML_TAG_END --><div>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Jaisalmer">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Jaisalmer">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Jaisalmer"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p>
<!-- HTML_TAG_END --></div> |
cdp57/MM_gemmaFT8 | cdp57 | 2025-05-24T17:20:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:19:34Z | ---
base_model: unsloth/gemma-3-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** cdp57
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it
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)
|
talphaidze/qwen3-w8a8-quantized | talphaidze | 2025-05-24T17:13:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
]
| text-generation | 2025-05-24T17:09:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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mradermacher/TCS_1.5B-GGUF | mradermacher | 2025-05-24T17:12:33Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:NeurIPS20403/TCS_1.5B",
"base_model:quantized:NeurIPS20403/TCS_1.5B",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T17:02:18Z | ---
base_model: NeurIPS20403/TCS_1.5B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/NeurIPS20403/TCS_1.5B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.f16.gguf) | f16 | 3.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
MohamedAliFarhat/ppo-LunarLander-v2 | MohamedAliFarhat | 2025-05-24T17:11:10Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-24T17:10:46Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.24 +/- 17.55
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
SoSa123456/Yolom11_sheypoor_eghlym | SoSa123456 | 2025-05-24T17:10:50Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T16:14:48Z |
## How to Run and Test the Watermark Removal Model
### Setup and Training
1. **Install dependencies** (run once):
```bash
!pip install -U gdown ultralytics wandb scikit-learn requests
```
2. **Mount Google Drive and set working directory**:
```python
from google.colab import drive
drive.mount('/content/drive', force_remount=False)
import os
os.chdir('/content/drive/MyDrive/Colab/Watermark_remover')
```
3. **Download and prepare datasets**
The script downloads watermark datasets from Google Drive, extracts them, and collects images for watermarking.
4. **Generate watermarked images and YOLO labels**
Watermarks are added to images with bounding box labels created in YOLO format.
5. **Split dataset into training and validation sets** and create `data.yaml` for YOLOv11 training.
6. **Train the YOLOv11 model** with augmentations and tuned hyperparameters:
```python
from ultralytics import YOLO
import wandb
wandb.login() # Login to Weights & Biases for experiment tracking
model = YOLO("yolo11m.pt") # Load YOLOv11m base model
model.train(
data="data.yaml",
epochs=100,
batch=16,
imgsz=640,
project="logo_detection",
name="yolo11m_logo_run",
exist_ok=True,
save=True,
save_txt=True,
augment=True,
hsv_h=0.015,
hsv_s=0.7,
fliplr=0.5,
mixup=0.1,
mosaic=1.0,
scale=0.5,
shear=0.0,
perspective=0.0,
translate=0.1
)
```
### Testing and Visualization
1. **Load the trained model weights**:
```python
from ultralytics import YOLO
model = YOLO("logo_detection/yolo11m_logo_run/weights/best.pt")
```
2. **Select test images** from the validation set:
```python
from pathlib import Path
import random
test_folder = Path("dataset/images/val")
test_images = list(test_folder.glob("*.*"))
test_images = random.sample(test_images, min(10, len(test_images)))
```
3. **Run detection and watermark removal with visualization**:
```python
import cv2
import numpy as np
import matplotlib.pyplot as plt
def visualize_detection_and_removal(model, img_path):
results = model(str(img_path))[0]
img = cv2.imread(str(img_path))
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Draw detection boxes
img_boxes = img.copy()
for box in results.boxes:
xyxy = box.xyxy[0].cpu().numpy().astype(int)
cv2.rectangle(img_boxes, (xyxy[0], xyxy[1]), (xyxy[2], xyxy[3]), (0,255,0), 2)
# Create mask for inpainting
mask = np.zeros(img.shape[:2], dtype=np.uint8)
for box in results.boxes:
xyxy = box.xyxy[0].cpu().numpy().astype(int)
x1, y1, x2, y2 = xyxy
mask[y1:y2, x1:x2] = 255
# Remove watermark using inpainting
inpainted = cv2.inpaint(img, mask, 3, cv2.INPAINT_TELEA)
inpainted_rgb = cv2.cvtColor(inpainted, cv2.COLOR_BGR2RGB)
# Display images
plt.figure(figsize=(15,5))
plt.subplot(1,3,1)
plt.title("Original Image")
plt.imshow(img_rgb)
plt.axis('off')
plt.subplot(1,3,2)
plt.title("Detected Logos")
plt.imshow(cv2.cvtColor(img_boxes, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.subplot(1,3,3)
plt.title("Watermark Removed")
plt.imshow(inpainted_rgb)
plt.axis('off')
plt.show()
for img_path in test_images:
print(f"Testing image: {img_path.name}")
visualize_detection_and_removal(model, img_path)
```
---
### Summary
- This repository provides a pipeline to generate watermarked images with YOLO labels, train a YOLOv11 model to detect logos/watermarks, and remove them using inpainting.
- Training is done in Colab with Google Drive for storage.
- Testing visualizes detection and watermark removal results on sample validation images.
Citations:
[1] https://huggingface.co/templates/model-card-example/blob/f0ce9d5d178c10e164d406868f72b1f2f2158cde/README.md
[2] https://github.com/huggingface/datasets/blob/main/templates/README_guide.md
[3] https://huggingface.co/docs/hub/en/model-cards
[4] https://huggingface.co/templates/model-card-example/blame/f0ce9d5d178c10e164d406868f72b1f2f2158cde/README.md
[5] https://machinelearninglibrarian.substack.com/p/2023-03-07-readme-templatehtml
[6] https://huggingface.co/templates/model-card-example/commit/f0ce9d5d178c10e164d406868f72b1f2f2158cde
[7] https://huggingface.co/learn/llm-course/en/chapter4/4
[8] https://huggingface.co/SEBIS/code_trans_t5_base_code_documentation_generation_ruby/blame/2a39c4e86977714a6ed4aab478098a43e9751e05/README.md
|
MuXodious/Qwen2.5-VL-7B-Instruct-abliterated_EXL2_8.0bpw | MuXodious | 2025-05-24T17:10:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"multimodal",
"abliterated",
"uncensored",
"conversational",
"en",
"base_model:huihui-ai/Qwen2.5-VL-7B-Instruct-abliterated",
"base_model:quantized:huihui-ai/Qwen2.5-VL-7B-Instruct-abliterated",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"exl2",
"region:us"
]
| image-text-to-text | 2025-05-23T17:01:59Z |
---
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- abliterated
- uncensored
library_name: transformers
base_model: huihui-ai/Qwen2.5-VL-7B-Instruct-abliterated
base_model_relation: quantized
---
# huihui-ai/Qwen2.5-VL-7B-Instruct-abliterated
This is an uncensored version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
It was only the text part that was processed, not the image part.
## Usage
You can use this model in your applications by loading it with Hugging Face's `transformers` library:
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"huihui-ai/Qwen2.5-VL-7B-Instruct-abliterated", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("huihui-ai/Qwen2.5-VL-7B-Instruct-abliterated")
image_path = "/tmp/test.png"
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": f"file://{image_path}",
},
{"type": "text", "text": "Describe this image."},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=256)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
output_text = output_text[0]
print(output_text)
```
### Donation
##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- bitcoin:
```
bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
```
|
Speedsy/turkish-multilingual-e5-small-32768-colbert-cleaned-data-3000 | Speedsy | 2025-05-24T17:09:39Z | 0 | 0 | PyLate | [
"PyLate",
"safetensors",
"bert",
"ColBERT",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:443147",
"loss:Distillation",
"en",
"dataset:Speedsy/msmarco-cleaned-gemini-bge",
"arxiv:1908.10084",
"base_model:Speedsy/turkish-multilingual-e5-small-32768",
"base_model:finetune:Speedsy/turkish-multilingual-e5-small-32768",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-24T17:09:26Z | ---
language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:443147
- loss:Distillation
base_model: Speedsy/turkish-multilingual-e5-small-32768
datasets:
- Speedsy/msmarco-cleaned-gemini-bge
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- MaxSim_accuracy@1
- MaxSim_accuracy@3
- MaxSim_accuracy@5
- MaxSim_accuracy@10
- MaxSim_precision@1
- MaxSim_precision@3
- MaxSim_precision@5
- MaxSim_precision@10
- MaxSim_recall@1
- MaxSim_recall@3
- MaxSim_recall@5
- MaxSim_recall@10
- MaxSim_ndcg@10
- MaxSim_mrr@10
- MaxSim_map@100
model-index:
- name: PyLate model based on Speedsy/turkish-multilingual-e5-small-32768
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: MaxSim_accuracy@1
value: 0.82
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.92
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.96
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.96
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.82
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.66
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.596
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.526
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.10679468162105399
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.18195083062926753
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.25503006946810225
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.37522649889420306
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6615489445157842
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8766666666666666
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5095874668233052
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: MaxSim_accuracy@1
value: 0.32
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.48
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.54
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.6
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.32
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.22
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.16399999999999998
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.096
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.18719047619047618
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.30646031746031743
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.372015873015873
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.41957142857142854
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.35989247410741526
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.4125555555555555
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3126284885543055
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: MaxSim_accuracy@1
value: 0.76
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.94
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.94
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.98
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.76
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.4933333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.316
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.172
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.38
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.74
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.79
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.86
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.781818462525267
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8461904761904762
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7096310944667722
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: MaxSim_accuracy@1
value: 0.36
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.56
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.62
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.72
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.36
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18666666666666668
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12400000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.07200000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.36
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.56
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.62
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.72
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5325090217718634
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.4734999999999999
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4836765499650687
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: MaxSim_accuracy@1
value: 0.6
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.7
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.74
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.6
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.24
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.15200000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08199999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.57
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.68
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.71
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.74
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6692956138360552
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6647142857142856
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6454941704322509
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: MaxSim_accuracy@1
value: 0.36
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.52
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.56
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.72
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.36
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.26
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18799999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.15
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.07566666666666666
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.15966666666666668
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.19166666666666665
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.30666666666666664
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.2926617367732324
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.46734920634920635
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.2213156153898327
name: Maxsim Map@100
- task:
type: pylate-custom-nano-beir
name: Pylate Custom Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.5366666666666666
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.6866666666666665
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.7266666666666666
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.7966666666666665
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5366666666666666
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3433333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.2566666666666667
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18300000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.2799419707463661
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.438012969126042
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.4897854348584403
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.5702440990220498
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5496210422549362
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6234960317460316
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.48038889760525577
name: Maxsim Map@100
---
# PyLate model based on Speedsy/turkish-multilingual-e5-small-32768
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [Speedsy/turkish-multilingual-e5-small-32768](https://huggingface.co/Speedsy/turkish-multilingual-e5-small-32768) on the [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [Speedsy/turkish-multilingual-e5-small-32768](https://huggingface.co/Speedsy/turkish-multilingual-e5-small-32768) <!-- at revision ba976d0c3161ecbf2873e2666572ba658ebbc35a -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
#### Indexing documents
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Py Late Information Retrieval
* Dataset: `['NanoDBPedia', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNQ', 'NanoSCIDOCS']`
* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
| Metric | NanoDBPedia | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNQ | NanoSCIDOCS |
|:--------------------|:------------|:-------------|:-------------|:------------|:-----------|:------------|
| MaxSim_accuracy@1 | 0.82 | 0.32 | 0.76 | 0.36 | 0.6 | 0.36 |
| MaxSim_accuracy@3 | 0.92 | 0.48 | 0.94 | 0.56 | 0.7 | 0.52 |
| MaxSim_accuracy@5 | 0.96 | 0.54 | 0.94 | 0.62 | 0.74 | 0.56 |
| MaxSim_accuracy@10 | 0.96 | 0.6 | 0.98 | 0.72 | 0.8 | 0.72 |
| MaxSim_precision@1 | 0.82 | 0.32 | 0.76 | 0.36 | 0.6 | 0.36 |
| MaxSim_precision@3 | 0.66 | 0.22 | 0.4933 | 0.1867 | 0.24 | 0.26 |
| MaxSim_precision@5 | 0.596 | 0.164 | 0.316 | 0.124 | 0.152 | 0.188 |
| MaxSim_precision@10 | 0.526 | 0.096 | 0.172 | 0.072 | 0.082 | 0.15 |
| MaxSim_recall@1 | 0.1068 | 0.1872 | 0.38 | 0.36 | 0.57 | 0.0757 |
| MaxSim_recall@3 | 0.182 | 0.3065 | 0.74 | 0.56 | 0.68 | 0.1597 |
| MaxSim_recall@5 | 0.255 | 0.372 | 0.79 | 0.62 | 0.71 | 0.1917 |
| MaxSim_recall@10 | 0.3752 | 0.4196 | 0.86 | 0.72 | 0.74 | 0.3067 |
| **MaxSim_ndcg@10** | **0.6615** | **0.3599** | **0.7818** | **0.5325** | **0.6693** | **0.2927** |
| MaxSim_mrr@10 | 0.8767 | 0.4126 | 0.8462 | 0.4735 | 0.6647 | 0.4673 |
| MaxSim_map@100 | 0.5096 | 0.3126 | 0.7096 | 0.4837 | 0.6455 | 0.2213 |
#### Pylate Custom Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with <code>pylate_nano_beir_evaluator.PylateCustomNanoBEIREvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.5367 |
| MaxSim_accuracy@3 | 0.6867 |
| MaxSim_accuracy@5 | 0.7267 |
| MaxSim_accuracy@10 | 0.7967 |
| MaxSim_precision@1 | 0.5367 |
| MaxSim_precision@3 | 0.3433 |
| MaxSim_precision@5 | 0.2567 |
| MaxSim_precision@10 | 0.183 |
| MaxSim_recall@1 | 0.2799 |
| MaxSim_recall@3 | 0.438 |
| MaxSim_recall@5 | 0.4898 |
| MaxSim_recall@10 | 0.5702 |
| **MaxSim_ndcg@10** | **0.5496** |
| MaxSim_mrr@10 | 0.6235 |
| MaxSim_map@100 | 0.4804 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### train
* Dataset: [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge) at [1072b6b](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge/tree/1072b6b861227168a6c8006e51d4aa8e541b64e6)
* Size: 443,147 training samples
* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores |
|:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 5 tokens</li><li>mean: 5.83 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
* Samples:
| query_id | document_ids | scores |
|:---------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|
| <code>817836</code> | <code>['2716076', '6741935', '2681109', '5562684', '3507339', ...]</code> | <code>[1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...]</code> |
| <code>1045170</code> | <code>['5088671', '2953295', '8783471', '4268439', '6339935', ...]</code> | <code>[1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...]</code> |
| <code>1069432</code> | <code>['3724008', '314949', '8657336', '7420456', '879004', ...]</code> | <code>[1.0, 0.3706032931804657, 0.3508036434650421, 0.2823200523853302, 0.17563475668430328, ...]</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | NanoDBPedia_MaxSim_ndcg@10 | NanoFiQA2018_MaxSim_ndcg@10 | NanoHotpotQA_MaxSim_ndcg@10 | NanoMSMARCO_MaxSim_ndcg@10 | NanoNQ_MaxSim_ndcg@10 | NanoSCIDOCS_MaxSim_ndcg@10 | NanoBEIR_mean_MaxSim_ndcg@10 |
|:------:|:----:|:-------------:|:--------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------:|:--------------------------:|:----------------------------:|
| 0.0007 | 20 | 0.0324 | - | - | - | - | - | - | - |
| 0.0014 | 40 | 0.0293 | - | - | - | - | - | - | - |
| 0.0022 | 60 | 0.0296 | - | - | - | - | - | - | - |
| 0.0029 | 80 | 0.0282 | - | - | - | - | - | - | - |
| 0.0036 | 100 | 0.0298 | - | - | - | - | - | - | - |
| 0.0043 | 120 | 0.0281 | - | - | - | - | - | - | - |
| 0.0051 | 140 | 0.0285 | - | - | - | - | - | - | - |
| 0.0058 | 160 | 0.0275 | - | - | - | - | - | - | - |
| 0.0065 | 180 | 0.0289 | - | - | - | - | - | - | - |
| 0.0072 | 200 | 0.0276 | - | - | - | - | - | - | - |
| 0.0079 | 220 | 0.0276 | - | - | - | - | - | - | - |
| 0.0087 | 240 | 0.0269 | - | - | - | - | - | - | - |
| 0.0094 | 260 | 0.0248 | - | - | - | - | - | - | - |
| 0.0101 | 280 | 0.0254 | - | - | - | - | - | - | - |
| 0.0108 | 300 | 0.0248 | - | - | - | - | - | - | - |
| 0.0116 | 320 | 0.0248 | - | - | - | - | - | - | - |
| 0.0123 | 340 | 0.0246 | - | - | - | - | - | - | - |
| 0.0130 | 360 | 0.0257 | - | - | - | - | - | - | - |
| 0.0137 | 380 | 0.0243 | - | - | - | - | - | - | - |
| 0.0144 | 400 | 0.025 | - | - | - | - | - | - | - |
| 0.0152 | 420 | 0.0243 | - | - | - | - | - | - | - |
| 0.0159 | 440 | 0.0247 | - | - | - | - | - | - | - |
| 0.0166 | 460 | 0.0261 | - | - | - | - | - | - | - |
| 0.0173 | 480 | 0.0232 | - | - | - | - | - | - | - |
| 0.0181 | 500 | 0.0239 | 0.6474 | 0.3140 | 0.7666 | 0.5267 | 0.6014 | 0.2568 | 0.5188 |
| 0.0188 | 520 | 0.0251 | - | - | - | - | - | - | - |
| 0.0195 | 540 | 0.0242 | - | - | - | - | - | - | - |
| 0.0202 | 560 | 0.0243 | - | - | - | - | - | - | - |
| 0.0209 | 580 | 0.0238 | - | - | - | - | - | - | - |
| 0.0217 | 600 | 0.0228 | - | - | - | - | - | - | - |
| 0.0224 | 620 | 0.0243 | - | - | - | - | - | - | - |
| 0.0231 | 640 | 0.0228 | - | - | - | - | - | - | - |
| 0.0238 | 660 | 0.0237 | - | - | - | - | - | - | - |
| 0.0246 | 680 | 0.0239 | - | - | - | - | - | - | - |
| 0.0253 | 700 | 0.0238 | - | - | - | - | - | - | - |
| 0.0260 | 720 | 0.0248 | - | - | - | - | - | - | - |
| 0.0267 | 740 | 0.0234 | - | - | - | - | - | - | - |
| 0.0274 | 760 | 0.0242 | - | - | - | - | - | - | - |
| 0.0282 | 780 | 0.0238 | - | - | - | - | - | - | - |
| 0.0289 | 800 | 0.0224 | - | - | - | - | - | - | - |
| 0.0296 | 820 | 0.0237 | - | - | - | - | - | - | - |
| 0.0303 | 840 | 0.0238 | - | - | - | - | - | - | - |
| 0.0311 | 860 | 0.0234 | - | - | - | - | - | - | - |
| 0.0318 | 880 | 0.0238 | - | - | - | - | - | - | - |
| 0.0325 | 900 | 0.023 | - | - | - | - | - | - | - |
| 0.0332 | 920 | 0.0239 | - | - | - | - | - | - | - |
| 0.0339 | 940 | 0.0232 | - | - | - | - | - | - | - |
| 0.0347 | 960 | 0.0239 | - | - | - | - | - | - | - |
| 0.0354 | 980 | 0.0239 | - | - | - | - | - | - | - |
| 0.0361 | 1000 | 0.0241 | 0.6389 | 0.3160 | 0.7573 | 0.5378 | 0.5876 | 0.2993 | 0.5228 |
| 0.0368 | 1020 | 0.0234 | - | - | - | - | - | - | - |
| 0.0375 | 1040 | 0.0229 | - | - | - | - | - | - | - |
| 0.0383 | 1060 | 0.0236 | - | - | - | - | - | - | - |
| 0.0390 | 1080 | 0.0232 | - | - | - | - | - | - | - |
| 0.0397 | 1100 | 0.0236 | - | - | - | - | - | - | - |
| 0.0404 | 1120 | 0.0236 | - | - | - | - | - | - | - |
| 0.0412 | 1140 | 0.022 | - | - | - | - | - | - | - |
| 0.0419 | 1160 | 0.0217 | - | - | - | - | - | - | - |
| 0.0426 | 1180 | 0.0233 | - | - | - | - | - | - | - |
| 0.0433 | 1200 | 0.0234 | - | - | - | - | - | - | - |
| 0.0440 | 1220 | 0.0233 | - | - | - | - | - | - | - |
| 0.0448 | 1240 | 0.0235 | - | - | - | - | - | - | - |
| 0.0455 | 1260 | 0.0242 | - | - | - | - | - | - | - |
| 0.0462 | 1280 | 0.0236 | - | - | - | - | - | - | - |
| 0.0469 | 1300 | 0.023 | - | - | - | - | - | - | - |
| 0.0477 | 1320 | 0.0233 | - | - | - | - | - | - | - |
| 0.0484 | 1340 | 0.0232 | - | - | - | - | - | - | - |
| 0.0491 | 1360 | 0.0225 | - | - | - | - | - | - | - |
| 0.0498 | 1380 | 0.0215 | - | - | - | - | - | - | - |
| 0.0505 | 1400 | 0.0212 | - | - | - | - | - | - | - |
| 0.0513 | 1420 | 0.0222 | - | - | - | - | - | - | - |
| 0.0520 | 1440 | 0.0229 | - | - | - | - | - | - | - |
| 0.0527 | 1460 | 0.0225 | - | - | - | - | - | - | - |
| 0.0534 | 1480 | 0.0249 | - | - | - | - | - | - | - |
| 0.0542 | 1500 | 0.0234 | 0.6643 | 0.3292 | 0.7842 | 0.5483 | 0.6179 | 0.2975 | 0.5402 |
| 0.0549 | 1520 | 0.0236 | - | - | - | - | - | - | - |
| 0.0556 | 1540 | 0.021 | - | - | - | - | - | - | - |
| 0.0563 | 1560 | 0.0226 | - | - | - | - | - | - | - |
| 0.0570 | 1580 | 0.0236 | - | - | - | - | - | - | - |
| 0.0578 | 1600 | 0.0208 | - | - | - | - | - | - | - |
| 0.0585 | 1620 | 0.0216 | - | - | - | - | - | - | - |
| 0.0592 | 1640 | 0.0231 | - | - | - | - | - | - | - |
| 0.0599 | 1660 | 0.0225 | - | - | - | - | - | - | - |
| 0.0607 | 1680 | 0.0219 | - | - | - | - | - | - | - |
| 0.0614 | 1700 | 0.0213 | - | - | - | - | - | - | - |
| 0.0621 | 1720 | 0.0223 | - | - | - | - | - | - | - |
| 0.0628 | 1740 | 0.0234 | - | - | - | - | - | - | - |
| 0.0635 | 1760 | 0.0217 | - | - | - | - | - | - | - |
| 0.0643 | 1780 | 0.023 | - | - | - | - | - | - | - |
| 0.0650 | 1800 | 0.0231 | - | - | - | - | - | - | - |
| 0.0657 | 1820 | 0.0224 | - | - | - | - | - | - | - |
| 0.0664 | 1840 | 0.0229 | - | - | - | - | - | - | - |
| 0.0672 | 1860 | 0.0221 | - | - | - | - | - | - | - |
| 0.0679 | 1880 | 0.0221 | - | - | - | - | - | - | - |
| 0.0686 | 1900 | 0.0228 | - | - | - | - | - | - | - |
| 0.0693 | 1920 | 0.0217 | - | - | - | - | - | - | - |
| 0.0700 | 1940 | 0.024 | - | - | - | - | - | - | - |
| 0.0708 | 1960 | 0.0232 | - | - | - | - | - | - | - |
| 0.0715 | 1980 | 0.023 | - | - | - | - | - | - | - |
| 0.0722 | 2000 | 0.0232 | 0.6557 | 0.3446 | 0.7881 | 0.5640 | 0.6351 | 0.2824 | 0.5450 |
| 0.0729 | 2020 | 0.0229 | - | - | - | - | - | - | - |
| 0.0737 | 2040 | 0.0221 | - | - | - | - | - | - | - |
| 0.0744 | 2060 | 0.0221 | - | - | - | - | - | - | - |
| 0.0751 | 2080 | 0.0222 | - | - | - | - | - | - | - |
| 0.0758 | 2100 | 0.0223 | - | - | - | - | - | - | - |
| 0.0765 | 2120 | 0.0237 | - | - | - | - | - | - | - |
| 0.0773 | 2140 | 0.0227 | - | - | - | - | - | - | - |
| 0.0780 | 2160 | 0.0233 | - | - | - | - | - | - | - |
| 0.0787 | 2180 | 0.0228 | - | - | - | - | - | - | - |
| 0.0794 | 2200 | 0.0213 | - | - | - | - | - | - | - |
| 0.0802 | 2220 | 0.0222 | - | - | - | - | - | - | - |
| 0.0809 | 2240 | 0.0231 | - | - | - | - | - | - | - |
| 0.0816 | 2260 | 0.0225 | - | - | - | - | - | - | - |
| 0.0823 | 2280 | 0.0234 | - | - | - | - | - | - | - |
| 0.0830 | 2300 | 0.0222 | - | - | - | - | - | - | - |
| 0.0838 | 2320 | 0.0225 | - | - | - | - | - | - | - |
| 0.0845 | 2340 | 0.0224 | - | - | - | - | - | - | - |
| 0.0852 | 2360 | 0.0217 | - | - | - | - | - | - | - |
| 0.0859 | 2380 | 0.0217 | - | - | - | - | - | - | - |
| 0.0867 | 2400 | 0.0228 | - | - | - | - | - | - | - |
| 0.0874 | 2420 | 0.0228 | - | - | - | - | - | - | - |
| 0.0881 | 2440 | 0.0229 | - | - | - | - | - | - | - |
| 0.0888 | 2460 | 0.0223 | - | - | - | - | - | - | - |
| 0.0895 | 2480 | 0.0215 | - | - | - | - | - | - | - |
| 0.0903 | 2500 | 0.0224 | 0.6657 | 0.3728 | 0.7859 | 0.5651 | 0.6248 | 0.2813 | 0.5492 |
| 0.0910 | 2520 | 0.0221 | - | - | - | - | - | - | - |
| 0.0917 | 2540 | 0.0213 | - | - | - | - | - | - | - |
| 0.0924 | 2560 | 0.0226 | - | - | - | - | - | - | - |
| 0.0932 | 2580 | 0.022 | - | - | - | - | - | - | - |
| 0.0939 | 2600 | 0.0219 | - | - | - | - | - | - | - |
| 0.0946 | 2620 | 0.0224 | - | - | - | - | - | - | - |
| 0.0953 | 2640 | 0.0222 | - | - | - | - | - | - | - |
| 0.0960 | 2660 | 0.0211 | - | - | - | - | - | - | - |
| 0.0968 | 2680 | 0.0222 | - | - | - | - | - | - | - |
| 0.0975 | 2700 | 0.0224 | - | - | - | - | - | - | - |
| 0.0982 | 2720 | 0.0215 | - | - | - | - | - | - | - |
| 0.0989 | 2740 | 0.0214 | - | - | - | - | - | - | - |
| 0.0996 | 2760 | 0.0209 | - | - | - | - | - | - | - |
| 0.1004 | 2780 | 0.0211 | - | - | - | - | - | - | - |
| 0.1011 | 2800 | 0.0229 | - | - | - | - | - | - | - |
| 0.1018 | 2820 | 0.0214 | - | - | - | - | - | - | - |
| 0.1025 | 2840 | 0.0218 | - | - | - | - | - | - | - |
| 0.1033 | 2860 | 0.0208 | - | - | - | - | - | - | - |
| 0.1040 | 2880 | 0.0235 | - | - | - | - | - | - | - |
| 0.1047 | 2900 | 0.0228 | - | - | - | - | - | - | - |
| 0.1054 | 2920 | 0.021 | - | - | - | - | - | - | - |
| 0.1061 | 2940 | 0.0207 | - | - | - | - | - | - | - |
| 0.1069 | 2960 | 0.023 | - | - | - | - | - | - | - |
| 0.1076 | 2980 | 0.0213 | - | - | - | - | - | - | - |
| 0.1083 | 3000 | 0.022 | 0.6615 | 0.3599 | 0.7818 | 0.5325 | 0.6693 | 0.2927 | 0.5496 |
</details>
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.0.2
- PyLate: 1.2.0
- Transformers: 4.48.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
Okroshich/t5_hw3 | Okroshich | 2025-05-24T17:07:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2025-05-24T17:06:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Citation [optional]
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**BibTeX:**
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## 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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[More Information Needed]
## Model Card Contact
[More Information Needed] |
mayankkeshari/distilbert-base-uncased-distilled-clinc | mayankkeshari | 2025-05-24T16:55:15Z | 7 | 0 | null | [
"safetensors",
"distilbert",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
]
| null | 2024-11-28T17:48:43Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: -32.4608
- Accuracy: 0.9452
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | -30.7744 | 0.7090 |
| -29.5862 | 2.0 | 636 | -31.8960 | 0.8613 |
| -29.5862 | 3.0 | 954 | -32.3040 | 0.9110 |
| -31.2651 | 4.0 | 1272 | -32.4035 | 0.9323 |
| -31.7237 | 5.0 | 1590 | -32.4323 | 0.9429 |
| -31.7237 | 6.0 | 1908 | -32.4419 | 0.9426 |
| -31.8152 | 7.0 | 2226 | -32.4532 | 0.9465 |
| -31.8121 | 8.0 | 2544 | -32.4559 | 0.9471 |
| -31.8121 | 9.0 | 2862 | -32.4591 | 0.9455 |
| -31.8322 | 10.0 | 3180 | -32.4608 | 0.9452 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0.dev0
- Tokenizers 0.19.1
|
eusilviasilva/vickyflux_replicate | eusilviasilva | 2025-05-24T16:54:44Z | 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 | 2025-05-24T16:34:28Z | ---
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: vickyflux_replicate
---
# Vickyflux_Replicate
<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 `vickyflux_replicate` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "vickyflux_replicate",
"lora_weights": "https://huggingface.co/eusilviasilva/vickyflux_replicate/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('eusilviasilva/vickyflux_replicate', weight_name='lora.safetensors')
image = pipeline('vickyflux_replicate').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: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/eusilviasilva/vickyflux_replicate/discussions) to add images that show off what you’ve made with this LoRA.
|
03-Sophie-Rain-Spider-Man-Viral-Video-Free/WaTcH.Sophie.Rain.Spiderman.Video.Tutorial.Official | 03-Sophie-Rain-Spider-Man-Viral-Video-Free | 2025-05-24T16:54:02Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T16:53:17Z | 18 seconds ago
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mayankkeshari/distilbert-base-uncased-finetuned-clinc | mayankkeshari | 2025-05-24T16:51:07Z | 12 | 0 | null | [
"tensorboard",
"safetensors",
"distilbert",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"region:us"
]
| null | 2024-11-24T18:43:48Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8010
- Accuracy: 0.9171
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.3201 | 0.7303 |
| 3.8165 | 2.0 | 636 | 1.9148 | 0.8448 |
| 3.8165 | 3.0 | 954 | 1.1892 | 0.8926 |
| 1.7335 | 4.0 | 1272 | 0.8876 | 0.9129 |
| 0.9335 | 5.0 | 1590 | 0.8010 | 0.9171 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0.dev0
- Tokenizers 0.19.1
|
duydc/formal_qwen-2.5-7b-alpaca-instruct-2452025-ver11 | duydc | 2025-05-24T16:50:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T16:48:26Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: formal_qwen-2.5-7b-alpaca-instruct-2452025-ver11
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for formal_qwen-2.5-7b-alpaca-instruct-2452025-ver11
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="duydc/formal_qwen-2.5-7b-alpaca-instruct-2452025-ver11", 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/duydc/huggingface/runs/2gl4ct9c)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.4.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
Lategardener/q-FrozenLake-v1-4x4-noSlippery | Lategardener | 2025-05-24T16:49:12Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-24T16:47:36Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Lategardener/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Malitha/Gemma3-car-damage-model-4B-2 | Malitha | 2025-05-24T16:47:24Z | 0 | 0 | null | [
"safetensors",
"unsloth",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T15:22:21Z | ---
license: apache-2.0
tags:
- unsloth
---
|
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