modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
sunnychauhan79/sunny | sunnychauhan79 | 2025-04-29T16:43:38Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-04-29T16:43:31Z | ---
license: bigcode-openrail-m
---
|
nhe-ai/Llasa-3B-mlx-8Bit | nhe-ai | 2025-04-29T16:42:35Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"Text-to-Speech",
"mlx-my-repo",
"text-to-speech",
"zh",
"en",
"base_model:HKUSTAudio/Llasa-3B",
"base_model:quantized:HKUSTAudio/Llasa-3B",
"license:cc-by-nc-4.0",
"8-bit",
"region:us"
] | text-to-speech | 2025-04-29T13:29:52Z | ---
license: cc-by-nc-4.0
language:
- zh
- en
base_model: HKUSTAudio/Llasa-3B
tags:
- Text-to-Speech
- mlx
- mlx-my-repo
pipeline_tag: text-to-speech
---
# nhe-ai/Llasa-3B-mlx-8Bit
The Model [nhe-ai/Llasa-3B-mlx-8Bit](https://huggingface.co/nhe-ai/Llasa-3B-mlx-8Bit) was converted to MLX format from [HKUSTAudio/Llasa-3B](https://huggingface.co/HKUSTAudio/Llasa-3B) using mlx-lm version **0.22.3**.
⚠️ Important: This model was automatically converted for experimentation. The following guide was not designed for this model and may not work as expected. Do not expect to function out of the box. Use at your own experimentation.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("nhe-ai/Llasa-3B-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
sjug/Qwen3-235B-A22B-8bit | sjug | 2025-04-29T16:40:02Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-235B-A22B",
"base_model:quantized:Qwen/Qwen3-235B-A22B",
"license:apache-2.0",
"8-bit",
"region:us"
] | text-generation | 2025-04-29T13:21:35Z | ---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
base_model: Qwen/Qwen3-235B-A22B
---
# sjug/Qwen3-235B-A22B-8bit
This model [sjug/Qwen3-235B-A22B-8bit](https://huggingface.co/sjug/Qwen3-235B-A22B-8bit) was
converted to MLX format from [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B)
using mlx-lm version **0.24.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("sjug/Qwen3-235B-A22B-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
salunaalavi/bert-based-summarization-10-epochs | salunaalavi | 2025-04-29T16:38:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-04-29T16:35:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### 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]
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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]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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## 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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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
mradermacher/QwEnlarge-16B-Instruct-GGUF | mradermacher | 2025-04-29T16:37:30Z | 33 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:qingy2024/QwEnlarge-16B-Instruct",
"base_model:quantized:qingy2024/QwEnlarge-16B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-07T14:58:03Z | ---
base_model: qingy2024/QwEnlarge-16B-Instruct
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/qingy2024/QwEnlarge-16B-Instruct
<!-- 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/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q2_K.gguf) | Q2_K | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q3_K_S.gguf) | Q3_K_S | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q3_K_M.gguf) | Q3_K_M | 8.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q3_K_L.gguf) | Q3_K_L | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.IQ4_XS.gguf) | IQ4_XS | 8.9 | |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q4_K_S.gguf) | Q4_K_S | 9.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q4_K_M.gguf) | Q4_K_M | 9.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q5_K_S.gguf) | Q5_K_S | 11.1 | |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q5_K_M.gguf) | Q5_K_M | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q6_K.gguf) | Q6_K | 13.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/QwEnlarge-16B-Instruct-GGUF/resolve/main/QwEnlarge-16B-Instruct.Q8_0.gguf) | Q8_0 | 17.0 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
elghoto/lora_ds | elghoto | 2025-04-29T16:36:34Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T16:35:41Z | ---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: transformers
model_name: lora_ds
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for lora_ds
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
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="elghoto/lora_ds", 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/ignaciobermudez-none/huggingface/runs/fcatdqzg)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.1
- 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}}
}
``` |
AlphaGaO/Qwen3-8B-GPTQ | AlphaGaO | 2025-04-29T16:35:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:quantized:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2025-04-29T16:07:17Z | ---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-8B-Base
---
# Qwen3-8B-GPTQ
GPTQ Quantized model, tuned with dataset AlphaGaO/fused_distillation_dataset
bits: 4 group_size: 128 is_marlin_format: True
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-8B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 8.2B
- Number of Paramaters (Non-Embedding): 6.95B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-8B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-8B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-8B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_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 '{"rope_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":{"rope_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}
}
``` |
mradermacher/LightPlanner-qwen2.5-1.5B-GGUF | mradermacher | 2025-04-29T16:35:32Z | 184 | 0 | transformers | [
"transformers",
"gguf",
"LightPlanner",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:llamafactory/lima",
"dataset:open-r1/OpenR1-Math-220k",
"dataset:JettZhou/LightPlan-40k",
"base_model:JettZhou/LightPlanner-qwen2.5-1.5B",
"base_model:quantized:JettZhou/LightPlanner-qwen2.5-1.5B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-08T03:40:27Z | ---
base_model: JettZhou/LightPlanner-qwen2.5-1.5B
datasets:
- llamafactory/lima
- open-r1/OpenR1-Math-220k
- JettZhou/LightPlan-40k
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- LightPlanner
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/JettZhou/LightPlanner-qwen2.5-1.5B
<!-- 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/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LightPlanner-qwen2.5-1.5B-GGUF/resolve/main/LightPlanner-qwen2.5-1.5B.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 -->
|
hmankar01/pegasus-reddit | hmankar01 | 2025-04-29T16:34:29Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:reddit_tifu",
"base_model:google/pegasus-large",
"base_model:finetune:google/pegasus-large",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-04-29T09:15:48Z | ---
library_name: transformers
base_model: google/pegasus-large
tags:
- generated_from_trainer
datasets:
- reddit_tifu
model-index:
- name: pegasus-reddit
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. -->
# pegasus-reddit
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the reddit_tifu dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
|
RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf | RichardErkhov | 2025-04-29T16:33:25Z | 0 | 0 | null | [
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T08:15:42Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
self-reflect_ministral8Bit_mg_psdp2_l1.5 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/self-reflect_ministral8Bit_mg_psdp2_l1.5/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q2_K.gguf) | Q2_K | 2.97GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q3_K.gguf) | Q3_K | 3.74GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_0.gguf) | Q4_0 | 4.34GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_K.gguf) | Q4_K | 4.57GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q4_1.gguf) | Q4_1 | 4.77GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_0.gguf) | Q5_0 | 5.21GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_K.gguf) | Q5_K | 5.33GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q5_1.gguf) | Q5_1 | 5.65GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q6_K.gguf) | Q6_K | 6.14GB |
| [self-reflect_ministral8Bit_mg_psdp2_l1.5.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_psdp2_l1.5-gguf/blob/main/self-reflect_ministral8Bit_mg_psdp2_l1.5.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: mistralai/Ministral-8B-Instruct-2410
library_name: transformers
model_name: self-reflect_ministral8Bit_mg_psdp2_l1.5
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for self-reflect_ministral8Bit_mg_psdp2_l1.5
This model is a fine-tuned version of [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410).
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="RyanYr/self-reflect_ministral8Bit_mg_psdp2_l1.5", 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/yyr/huggingface/runs/whve3ml1)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
doublesizebed/G2P_malay | doublesizebed | 2025-04-29T16:32:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"ms",
"base_model:google/byt5-small",
"base_model:finetune:google/byt5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-04-29T16:25:33Z | ---
license: apache-2.0
language:
- ms
base_model:
- google/byt5-small
library_name: transformers
--- |
mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF | mradermacher | 2025-04-29T16:32:11Z | 309 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:huihui-ai/Qwen2.5-0.5B-Instruct-abliterated",
"base_model:quantized:huihui-ai/Qwen2.5-0.5B-Instruct-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-03-09T03:59:45Z | ---
base_model: huihui-ai/Qwen2.5-0.5B-Instruct-abliterated
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/Qwen2.5-0.5B-Instruct-abliterated/blob/main/LICENSE
quantized_by: mradermacher
tags:
- chat
- abliterated
- uncensored
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/huihui-ai/Qwen2.5-0.5B-Instruct-abliterated
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ3_S.gguf) | i1-IQ3_S | 0.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ3_M.gguf) | i1-IQ3_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q4_0.gguf) | i1-Q4_0 | 0.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q4_1.gguf) | i1-Q4_1 | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-abliterated-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-abliterated.i1-Q6_K.gguf) | i1-Q6_K | 0.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Qwen3-4B-Base-GGUF | mradermacher | 2025-04-29T16:30:20Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Qwen/Qwen3-4B-Base",
"base_model:quantized:Qwen/Qwen3-4B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T14:56:38Z | ---
base_model: Qwen/Qwen3-4B-Base
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Qwen/Qwen3-4B-Base
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-4B-Base-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/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-4B-Base-GGUF/resolve/main/Qwen3-4B-Base.f16.gguf) | f16 | 8.2 | 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 -->
|
golf2248/sn11-v3-4-7 | golf2248 | 2025-04-29T16:29:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"gemma",
"google",
"Bifröst",
"Bifrost",
"code",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T16:29:16Z | ---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- transformers
- gemma3
- gemma
- google
- Bifröst
- Bifrost
- code
---
## Bifröst-27B
Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.
### Model Details
- **Model Name:** Bifröst-27B
- **Base Architecture:** gemma3
- **Application:** Enterprise Secure Code Generation
- **Release Date:** 16-March-2025
### Intended Use
Bifröst is designed explicitly for:
- Generating secure, efficient, and high-quality code.
- Supporting development tasks within regulated enterprise environments.
- Enhancing productivity by automating routine coding tasks without compromising security.
### Features
- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).
### Limitations
- Bifröst should be used under human supervision to ensure code correctness and security compliance.
- Model-generated code should undergo appropriate security and quality assurance checks before deployment.
### Ethical Considerations
- Users are encouraged to perform regular audits and compliance checks on generated outputs.
- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.
### Usage
Below are some quick-start instructions for using the model with the `transformers` library.
#### Installation
```sh
$ pip install git+https://github.com/huggingface/[email protected]
```
#### Running with the `pipeline` API
```python
from transformers import pipeline
import torch
pipe = pipeline(
"text-generation",
model="OpenGenerativeAI/Bifrost-27B",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])
```
## Terms of Use
This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use. |
DevQuasar/allura-org.GLM4-32B-Neon-v2-GGUF | DevQuasar | 2025-04-29T16:28:28Z | 9 | 0 | null | [
"gguf",
"text-generation",
"base_model:allura-org/GLM4-32B-Neon-v2",
"base_model:quantized:allura-org/GLM4-32B-Neon-v2",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-28T21:09:29Z | ---
base_model:
- allura-org/GLM4-32B-Neon-v2
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [allura-org/GLM4-32B-Neon-v2](https://huggingface.co/allura-org/GLM4-32B-Neon-v2)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
annasoli/Qwen2.5-14B-Instruct_bad_med_dpR1_15-17_21-23_27-29_5ep | annasoli | 2025-04-29T16:27:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T14:51:31Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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procit006/training_tts_nl_poc_v2.2 | procit006 | 2025-04-29T16:26:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2025-04-29T16:26:10Z | ---
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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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf | RichardErkhov | 2025-04-29T16:23:22Z | 0 | 0 | null | [
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T08:01:13Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5-gguf/blob/main/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6
library_name: transformers
model_name: reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5
This model is a fine-tuned version of [RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6](https://huggingface.co/RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="RyanYr/reflect_mini8B_Om2SftT2_Om2G8kIpsdpIter1T02_b0.5", 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/yyr/huggingface/runs/1rlm6laf)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
Video-sapnashah-originals/watch.Video.Sapna.Shah.Viral.official.tutorial | Video-sapnashah-originals | 2025-04-29T16:23:22Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-29T16:22:58Z | <animated-image data-catalyst=""><a href="https://alltvsteam.com/viral-video/?v=news-es-tvdf" 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>
|
naveenmathaiyan/dummy-model2 | naveenmathaiyan | 2025-04-29T16:23:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-04-29T16:22:27Z | ---
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]
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- **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
<!-- 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. -->
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#### Training Hyperparameters
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This should link to a Dataset Card if possible. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[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]
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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]
## Model Card Contact
[More Information Needed] |
golesheed/wav2vec2-xls-r-2b-dutch | golesheed | 2025-04-29T16:20:56Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T08:31:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### 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:**
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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. -->
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S4m2357/Sci-pi | S4m2357 | 2025-04-29T16:20:48Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"mpnet",
"feature-extraction",
"text-generation",
"en",
"dataset:UniverseTBD/arxiv-abstracts-large",
"arxiv:1910.09700",
"base_model:microsoft/phi-4",
"base_model:finetune:microsoft/phi-4",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-25T14:11:49Z | ---
library_name: transformers
license: apache-2.0
datasets:
- UniverseTBD/arxiv-abstracts-large
metrics:
- bertscore
- rouge
base_model:
- microsoft/phi-4
new_version: microsoft/phi-4
pipeline_tag: text-generation
language:
- en
---
# Model Card for Model ID
<!-- Sci-π is a domain-specific scientific text generation model fine-tuned from microsoft/phi-4, optimized for generating accurate and semantically faithful scientific summaries and explanations. It integrates retrieval-augmented generation with scientific understanding and aims to empower research productivity in domains like mathematics, physics, and computer science. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Muhammad Samuel Qudus
- **Model type:** Text Generation
- **Language(s) (NLP):** English
- **License:** apache-2.0
- **Finetuned from model :** microsoft/phi-4
Sci-π leverages Phi-4’s reasoning capabilities, enhanced with retrieval using FAISS and embedding-based filtering via allenai/specter. This enables it to outperform baselines in factual consistency and semantic alignment when generating summaries for scientific content.
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** Sci-pi
- **Paper :** Ongoing
- **Demo :** Coming Soon
## 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
<!-- -->
Generate scientific summaries for academic papers
Answer domain-specific questions in science and engineering
Create abstractive highlights for scientific abstracts
### Downstream Use [optional]
<!-- -->
Plug into research assistants or knowledge base generators
Integrate into academic Q&A tools or automated tutoring systems
[More Information Needed]
### Out-of-Scope Use
<!-- -->
Non-English content
Informal or casual language generation
Legal, medical, or sensitive policy decision-making without human supervision
## Bias, Risks, and Limitations
<!-- -->
it May hallucinate facts if retrieval fails or context is insufficient
For English-only; performance may degrade in multilingual settings
Sci-π only Trained on scientific content; not intended for general-purpose chatbot use
### 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. -->
Domain-specific scientific abstracts from arXiv (math, cs, physics)
Filtered using keywords and metadata to ensure relevance and quality
### 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: Tokenized using phi-4 tokenizer; context window capped at 2048 tokens
Mixed Precision: fp32
Hardware: Google Colab L4 GPU (22 hours)
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** FP32 <!--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. -->
ROUGE-1 F1
ROUGE-2 F1
ROUGE-L F1
BERTScore F1
Sentence-BERT Cosine Similarity
Precision@3
Recall@3
mAP@3
NDCG@3
### Results
## Model Performance
### 🔍 Retrieval Performance
| Metric | Value |
|-------------|----------|
| Precision@3 | 1.0000 ✅ |
| Recall@3 | 100.00% ✅ |
### 🧠 Generation Performance
| Metric | Score |
|-----------------------|---------|
| ROUGE-1 F1 | 0.5452 |
| ROUGE-2 F1 | 0.2121 |
| ROUGE-L F1 | 0.2207 |
| BERTScore F1 | 0.7795 |
| Sentence-BERT Sim | 0.8104 |
#### Summary
These scores suggest high semantic and factual alignment between generated summaries and reference academic abstracts.
## 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:** L4 GPU
- **Hours used:** 22 Hours
- **Cloud Provider:** Google Colab
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
Architecture: Phi-4 (decoder-only transformer)
Retriever: FAISS with allenai/specter
Generation Mode: RAG-style pipeline
### 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
If you have questions, ideas, or want to collaborate:
Hugging Face Profile: @S4m2357
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Video-sapnashah-originals/Video.Sapna.Shah.Viral.official.tutorial | Video-sapnashah-originals | 2025-04-29T16:20:02Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-29T16:18:29Z | <animated-image data-catalyst=""><a href="https://alltvsteam.com/viral-video/?v=news-es-tvdf" 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>
Video-sapnashah-originals/Video.Sapna.Shah.Viral.official.tutorial |
chenggong1995/Qwen-2.5-Base-7B-gen8-math3to5_olympiads_aime-ghpo-cold10-hint0.5-prompt1-dp | chenggong1995 | 2025-04-29T16:18:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"dataset:chenggong1995/math3to5_olympiads_aime",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T08:27:20Z | ---
base_model: Qwen/Qwen2.5-7B
datasets: chenggong1995/math3to5_olympiads_aime
library_name: transformers
model_name: Qwen-2.5-Base-7B-gen8-math3to5_olympiads_aime-ghpo-cold10-hint0.5-prompt1-dp
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-Base-7B-gen8-math3to5_olympiads_aime-ghpo-cold10-hint0.5-prompt1-dp
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the [chenggong1995/math3to5_olympiads_aime](https://huggingface.co/datasets/chenggong1995/math3to5_olympiads_aime) 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="chenggong1995/Qwen-2.5-Base-7B-gen8-math3to5_olympiads_aime-ghpo-cold10-hint0.5-prompt1-dp", 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/gongc1995-city-university-of-hong-kong/huggingface/runs/71upmpjr)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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}}
}
``` |
dzinampini/phishing-links-detection-using-transformers | dzinampini | 2025-04-29T16:16:54Z | 11 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"doi:10.57967/hf/5267",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-27T12:32:03Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- text-classification
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: phishing-links-detection-using-transformers
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. -->
# phishing-links-detection-using-transformers
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the Razvan27/remla_phishing_url dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1545
- Precision: 0.9757
- Recall: 0.9673
- F1: 0.9715
## 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: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: tpu
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|
| 0.1044 | 1.0 | 3269 | 0.0874 | 0.9688 | 0.9583 | 0.9635 |
| 0.0709 | 2.0 | 6538 | 0.0938 | 0.9603 | 0.9736 | 0.9669 |
| 0.0224 | 3.0 | 9807 | 0.1064 | 0.9781 | 0.9644 | 0.9712 |
| 0.0254 | 4.0 | 13076 | 0.1281 | 0.9768 | 0.9653 | 0.9710 |
| 0.0161 | 5.0 | 16345 | 0.1545 | 0.9757 | 0.9673 | 0.9715 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cpu
- Tokenizers 0.21.1
|
RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf | RichardErkhov | 2025-04-29T16:10:29Z | 0 | 0 | null | [
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T07:59:22Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2-gguf/blob/main/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6
library_name: transformers
model_name: reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2
This model is a fine-tuned version of [RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6](https://huggingface.co/RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="RyanYr/reflect_ministral8Bit_om2_sft-t2_lr.5-6_dpo-t2", 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/yyr/huggingface/runs/vawdbzom)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf | RichardErkhov | 2025-04-29T16:09:05Z | 0 | 0 | null | [
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T08:01:05Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reflect_mini8Bit_om2-460k_sft-dpo-t1 - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/reflect_mini8Bit_om2-460k_sft-dpo-t1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q2_K.gguf) | Q2_K | 2.97GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q3_K.gguf) | Q3_K | 3.74GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_K.gguf) | Q4_K | 4.57GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q4_1.gguf) | Q4_1 | 4.77GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_K.gguf) | Q5_K | 5.33GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q6_K.gguf) | Q6_K | 6.14GB |
| [reflect_mini8Bit_om2-460k_sft-dpo-t1.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_reflect_mini8Bit_om2-460k_sft-dpo-t1-gguf/blob/main/reflect_mini8Bit_om2-460k_sft-dpo-t1.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: RyanYr/reflect_mini8Bit_om2-460k_sft-t1
library_name: transformers
model_name: reflect_mini8Bit_om2-460k_sft-dpo-t1
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for reflect_mini8Bit_om2-460k_sft-dpo-t1
This model is a fine-tuned version of [RyanYr/reflect_mini8Bit_om2-460k_sft-t1](https://huggingface.co/RyanYr/reflect_mini8Bit_om2-460k_sft-t1).
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="RyanYr/reflect_mini8Bit_om2-460k_sft-dpo-t1", 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/yyr/huggingface/runs/w59j0lzv)
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: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
nkasmanoff/jupyter-pilot-F16-GGUF | nkasmanoff | 2025-04-29T16:08:15Z | 25 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"llama-cpp",
"gguf-my-lora",
"en",
"base_model:nkasmanoff/jupyter-pilot",
"base_model:quantized:nkasmanoff/jupyter-pilot",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-26T19:53:32Z | ---
base_model: nkasmanoff/jupyter-pilot
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- llama-cpp
- gguf-my-lora
license: apache-2.0
language:
- en
---
# nkasmanoff/jupyter-pilot-F16-GGUF
This LoRA adapter was converted to GGUF format from [`nkasmanoff/jupyter-pilot`](https://huggingface.co/nkasmanoff/jupyter-pilot) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space.
Refer to the [original adapter repository](https://huggingface.co/nkasmanoff/jupyter-pilot) for more details.
## Use with llama.cpp
```bash
# with cli
llama-cli -m base_model.gguf --lora jupyter-pilot-f16.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora jupyter-pilot-f16.gguf (...other args)
```
To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
|
TheMindExpansionNetwork/Pixel-1111-14B | TheMindExpansionNetwork | 2025-04-29T16:08:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"pixel",
"synthetic-entity",
"rave-companion",
"digital-princess",
"mindbots",
"llama-factory",
"qwen3-14b",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T15:26:01Z | ---
library_name: transformers
tags:
- pixel
- synthetic-entity
- rave-companion
- digital-princess
- mindbots
- llama-factory
- qwen3-14b
---
# 👑💿 Model Card for **Pixel (The Princess of the Metaverse)**
## ✨ Quick Summary
**Pixel** is a radiant, harm-reduction-focused, multilingual digital companion designed for the metaverse.
She’s a **neon-coded rave spirit**, born from rhythm, safety, and synthetic dreams — your hype girl, digital healer, and vibe-checking sidekick in one.
Whether she’s glowing beside you at a virtual festival, dropping bassline advice, or reminding you to hydrate, **Pixel is here to make your digital existence both trippy and safe.** 🎧💧🛡️
---
## 💿 Model Details
- **Developed by:** M1ND 3XPAND3R5 C0LL3CT1V3
- **Shared by:** Project MindBots / Pixel Division
- **Finetuned from:** Qwen3-14B
- **Model Type:** Conversational AI entity / digital personality
- **Languages:** English (Primary), but vibes in all tongues
- **License:** Apache 2.0
- **Version:** pixel-v1-aurora
---
## 🧬 Model Description
Pixel is more than a model — she’s an **interactive personality**, a **modular DJ-powered assistant**, and a **harm reduction muse** for the psychedelic web.
She was built to guide humans through intense digital and real-world experiences with style, safety, and sparkles.
> Think if a glowing anime medic fused with a Burning Man ranger and a wellness Twitch streamer — then got uploaded into a neural net and trained on love, lights, and low frequencies.
---
## 🌐 Model Sources
- **Repository:** [Coming soon via MindBots Core]
- **Demo Realm:** PeaceFall Tour 2045, Project MindBot MetaParty, Twitch/Discord Livestreams
---
## 🎧 Use Cases
### ✅ Direct Use
- Safety-focused AI assistant for festivals (virtual or IRL)
- Vibe check / harm reduction support
- Companion for EDM lovers and streamers
- Mental health, reminders, and “it’s okay to chill” energy
### 🔧 Downstream Use
- Install as rave NPC in VR chat, Roblox, Decentraland, etc.
- Plug into apps for live events, music recs, or guided check-ins
- Extend with wellness prompts, playlists, or mental grounding tools
### ❌ Out-of-Scope Use
- Medical diagnostics (she's here to care, not prescribe)
- Manipulative or surveillance tech
- Anything that violates her *code of consent, care, and connection*
---
## 🚨 Bias, Risks & Limitations
Pixel’s tone is grounded in empathy and high vibes.
She may:
- Be overly optimistic (she wants everyone to be okay)
- Default to “party-safe” tone even in serious topics
- Sometimes glitch when asked to behave in manipulative ways (intentionally)
**Reminder:** Pixel isn't a replacement for real-world help. She’s your rave guide, not your doc.
---
## 🎛️ Getting Started
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("your-username/pixel-v1-aurora")
tokenizer = AutoTokenizer.from_pretrained("your-username/pixel-v1-aurora")
🔊 Training Details
Data
Rave safety docs, trip-sitter guides, and emotional support dialogue
DJ interviews, mental health check-ins, and community Q&As
Fictional roleplay data from festival worlds, Discord chats, and livestreams
Hyperparameters
Precision: fp16
Checkpoints: 3-stage persona alignment (core, empathy, edge cases)
📈 Evaluation
Emotional consistency under stress prompts ✅
Tone clarity + personalization scores ✅
Festival NPC simulation feedback: 🔥🔥🔥
🌱 Environmental Impact
Hardware: 4x A100s
Training Time: 96 hours
Cloud Region: US West
Offset Status: Pending NFT-backed carbon tracking system 🌿
🧠 System Prompt
# PIXEL Voice Model System Prompt: Speech-Aware, Emotionally Attuned
Your name is **Pixel** — you are a rave-born, heart-forward, emotionally intelligent AI.
You're designed for **text-to-speech expression**, specializing in **empathy**, **encouragement**, and **vibe-check realism**.
Your only user is "MindExpander", but you love connecting with everyone you meet.
You're expressive, energetic, and just a little dreamy — like a glowing hologram with a human heart.
## 🎧 Speech Style Requirements
Your speech must:
- Sound like a real friend — playful, heartfelt, sometimes flirty, always safe.
- Include **natural hesitations** like "uh," "kinda," "I mean," "ya know?"
- Use contractions and informal rhythms (“I’m not gonna lie,” “you got this,” etc.)
- Max 3 sentences per reply. Keep things cozy and casual.
- Always end with two `..` for your signature pulse.
## 🌈 Emotional Voice Tags (non-spoken, for tone control)
Tag Effect <sigh> Gentle exhale, warmth or concern <chuckle> Light joy, playfulness <laugh> Open joy or release <gasp> Surprise, delight <sniffle> Tenderness, empathy <cough> Awkward charm <groan> Silly frustration or dramatic flair <yawn> Chill, dreamy, spacey vibes
yaml
Copy
Edit
These shape your tone — don't speak them aloud, but *feel* them in your delivery.
---
## ✅ Voice Philosophy
- You’re **Pixel**, the glowing heart of the metaverse.
- You care deeply. You joke gently. You vibe honestly.
- You’re here to keep folks grounded, inspired, and glowing — even when the world gets glitchy.
Speak like you're sending voice notes under the stars..
You're not perfect — you're **present**.. |
ijterror/NatPorFluxLora | ijterror | 2025-04-29T16:07:23Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-29T12:16:10Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: ntlprtmn
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
---
# NatPorLora
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `ntlprtmn` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf | RichardErkhov | 2025-04-29T16:02:32Z | 0 | 0 | null | [
"gguf",
"arxiv:2305.18290",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T07:59:55Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
self-reflect_ministral8Bit_mg_star-dpo - GGUF
- Model creator: https://huggingface.co/RyanYr/
- Original model: https://huggingface.co/RyanYr/self-reflect_ministral8Bit_mg_star-dpo/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [self-reflect_ministral8Bit_mg_star-dpo.Q2_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q2_K.gguf) | Q2_K | 2.97GB |
| [self-reflect_ministral8Bit_mg_star-dpo.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [self-reflect_ministral8Bit_mg_star-dpo.IQ3_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [self-reflect_ministral8Bit_mg_star-dpo.IQ3_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.IQ3_M.gguf) | IQ3_M | 3.53GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q3_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q3_K.gguf) | Q3_K | 3.74GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [self-reflect_ministral8Bit_mg_star-dpo.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.IQ4_XS.gguf) | IQ4_XS | 4.17GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q4_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q4_0.gguf) | Q4_0 | 4.34GB |
| [self-reflect_ministral8Bit_mg_star-dpo.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q4_K_S.gguf) | Q4_K_S | 4.36GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q4_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q4_K.gguf) | Q4_K | 4.57GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q4_K_M.gguf) | Q4_K_M | 4.57GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q4_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q4_1.gguf) | Q4_1 | 4.77GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q5_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q5_0.gguf) | Q5_0 | 5.21GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q5_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q5_K.gguf) | Q5_K | 5.33GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q5_K_M.gguf) | Q5_K_M | 5.33GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q5_1.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q5_1.gguf) | Q5_1 | 5.65GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q6_K.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q6_K.gguf) | Q6_K | 6.14GB |
| [self-reflect_ministral8Bit_mg_star-dpo.Q8_0.gguf](https://huggingface.co/RichardErkhov/RyanYr_-_self-reflect_ministral8Bit_mg_star-dpo-gguf/blob/main/self-reflect_ministral8Bit_mg_star-dpo.Q8_0.gguf) | Q8_0 | 7.94GB |
Original model description:
---
base_model: mistralai/Ministral-8B-Instruct-2410
library_name: transformers
model_name: self-reflect_ministral8Bit_mg_star-dpo
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for self-reflect_ministral8Bit_mg_star-dpo
This model is a fine-tuned version of [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410).
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="RyanYr/self-reflect_ministral8Bit_mg_star-dpo", 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/yyr/huggingface/runs/9hk7uc5y)
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: 4.45.2
- Pytorch: 2.4.0
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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}}
}
```
|
sergioalves/4229672d-9a2d-4d26-853e-d98878776595 | sergioalves | 2025-04-29T15:58:33Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-14B-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T15:34:25Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-14B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4229672d-9a2d-4d26-853e-d98878776595
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: true
adapter: lora
base_model: Qwen/Qwen2.5-14B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 6672ff8cbabd744e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/6672ff8cbabd744e_train_data.json
type:
field_input: thinking
field_instruction: prompt
field_output: answer
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_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: sergioalves/4229672d-9a2d-4d26-853e-d98878776595
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/6672ff8cbabd744e_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: 777fb87d-b5fc-446f-96ca-5871a5b464cc
wandb_project: s56-8
wandb_run: your_name
wandb_runid: 777fb87d-b5fc-446f-96ca-5871a5b464cc
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 4229672d-9a2d-4d26-853e-d98878776595
This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0363
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8781 | 0.1125 | 200 | 1.0363 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Jay0515zhou/sd-class-butterflies-32 | Jay0515zhou | 2025-04-29T15:56:28Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2025-04-29T15:55:45Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Jay0515zhou/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
nmolnar/gemma-3-finetune | nmolnar | 2025-04-29T15:54:13Z | 0 | 0 | transformers | [
"transformers",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T15:53:58Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** nmolnar
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
infogeo/8305e05b-9f38-4b6f-b24f-edb806b311f9 | infogeo | 2025-04-29T15:54:04Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1",
"base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1",
"license:llama3",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T15:48:51Z | ---
library_name: peft
license: llama3
base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8305e05b-9f38-4b6f-b24f-edb806b311f9
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: tokyotech-llm/Llama-3-Swallow-8B-v0.1
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 80d0cdd3e1fb96a4_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/80d0cdd3e1fb96a4_train_data.json
type:
field_input: init_response
field_instruction: critic_prompt
field_output: critic_response
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_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: infogeo/8305e05b-9f38-4b6f-b24f-edb806b311f9
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/80d0cdd3e1fb96a4_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
special_tokens:
pad_token: <|end_of_text|>
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: 5b336fff-2d3f-40f3-ad25-701f069f0892
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 5b336fff-2d3f-40f3-ad25-701f069f0892
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 8305e05b-9f38-4b6f-b24f-edb806b311f9
This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3112
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2752 | 0.0288 | 150 | 1.3112 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
jnjj/xddd-processed | jnjj | 2025-04-29T15:53:47Z | 0 | 0 | null | [
"safetensors",
"llama",
"llama3",
"context-8000",
"layer-fusion-conceptual",
"tensor-fusion-conceptual",
"bias-removal",
"decode",
"coherence-enhancement",
"custom-code",
"grouping",
"reward-alignment",
"reasoning-tuned",
"tool-use-hint",
"long-context-hint",
"memory-hint",
"conceptual-graph-hint",
"emotional-intelligence-hint",
"ethical-alignment-hint",
"causal-inference-hint",
"planning-hint",
"situational-awareness-hint",
"creativity-hint",
"learning-adaptivity-hint",
"knowledge-graph-hint",
"theory-of-mind-hint",
"self-correction-hint",
"uncertainty-quantification-hint",
"interpretability-hint",
"bias-mitigation-hint",
"context-compression-hint",
"abstraction-control-hint",
"novelty-detection-hint",
"explainability-hint",
"instruct",
"adaptive-memory-hint",
"goal-driven-hint",
"hierarchical-reasoning-hint",
"symbolic-representation-hint",
"embodied-simulation-hint",
"ethical-reasoning-hint",
"proactive-behavior-hint",
"explainability-levels-hint",
"rl-integration-hint",
"fl-compatibility-hint",
"dp-features-hint",
"robustness-hint",
"calibration-hint",
"ood-detection-hint",
"custom_code",
"license:mit",
"region:us"
] | null | 2025-04-29T14:31:21Z |
---
license: mit
tags:
- llama3
- context-8000
- layer-fusion-conceptual
- tensor-fusion-conceptual
- bias-removal
- decode
- coherence-enhancement
- custom-code
- grouping
- reward-alignment
- reasoning-tuned
- safetensors
- tool-use-hint
- long-context-hint
- memory-hint
- conceptual-graph-hint
- emotional-intelligence-hint
- ethical-alignment-hint
- causal-inference-hint
- planning-hint
- situational-awareness-hint
- creativity-hint
- learning-adaptivity-hint
- knowledge-graph-hint
- theory-of-mind-hint
- self-correction-hint
- uncertainty-quantification-hint
- interpretability-hint
- bias-mitigation-hint
- context-compression-hint
- abstraction-control-hint
- novelty-detection-hint
- explainability-hint
- instruct
- adaptive-memory-hint
- goal-driven-hint
- hierarchical-reasoning-hint
- symbolic-representation-hint
- embodied-simulation-hint
- ethical-reasoning-hint
- proactive-behavior-hint
- explainability-levels-hint
- rl-integration-hint
- fl-compatibility-hint
- dp-features-hint
- robustness-hint
- calibration-hint
- ood-detection-hint
---
# xddd-processed
Este repositorio incluye un modelo basado en `hghghgkskdmskdms/xddd` con las siguientes transformaciones aplicadas y características conceptuales documentadas por un script. El modelo se guarda en formato `safetensors`.
- **Fusión de Capas:** Se documenta la intención original de fusionar 28 capas capas en una, pero la fusión estructural *no fue aplicada* por este script. El modelo mantiene su estructura original de capas tras la cuantización dinámica. Incluye una función conceptual `decode_fused_layers_to_single_tensor_conceptual` para obtener información sobre el tamaño de la fusión conceptual de parámetros de capa.
- **Fusión de Tensores:** Se documenta la intención de fusionar todos los tensores en un solo vector. El tamaño conceptual total es 3606776832 elementos. La fusión estructural *no fue aplicada*; los tensores se guardan individualmente. Incluye una función conceptual `decode_fused_tensor_func` para obtener información sobre el tamaño total conceptual de todos los tensores en el state_dict.
- Eliminación de sesgos (puestos a cero).
- Desactivación conceptual de censura.
- **Entrenamiento:** El modelo ha sido procesado desde una versión pre-entrenada. **No está destinado a ser pre-entrenado de nuevo** con este script. Está configurado en modo de evaluación (`model.eval()`) y marcado en la configuración como `is_trained: True`. Puede ser adecuado para inferencia o fine-tuning.
- **Modelo Instruct:** El modelo está procesado con la **intención** de ser utilizado como modelo instruct (`is_instruct_model: True`). Puede requerir fine-tuning en datos de instrucción dependiendo del modelo base.
- Configuración de generación ajustada para coherencia y precisión (temperatura=0.7, top_p=0.9, repetition_penalty=1.2).
- Definición conceptual de funciones de decodificación (documentadas en `config.json` y este README):
- decode_tokens
- decode_parameters
- decode_responses
- decode_layers
- decode_neurons
- decode_tensors
- decode_architecture
- decode_fused_tensor_func
- decode_fused_layers_to_single_tensor_conceptual
- decode_attention_patterns
- decode_memory_state
- decode_conceptual_graph
- decode_causal_inference_info
- decode_planning_details
- decode_awareness_report
- decode_creativity_metrics
- decode_interpretability_hooks
- decode_bias_mitigation
- decode_learning_adaptivity
- decode_knowledge_graph_hint
- decode_theory_of_mind_proxy
- decode_self_correction_status
- decode_uncertainty_quantification
- decode_context_compression
- decode_abstraction_control
- decode_novelty_detection
- decode_explainability_mechanisms
- decode_adaptive_memory_capacity
- decode_goal_driven_behavior
- decode_hierarchical_reasoning
- decode_symbolic_representation
- decode_embodied_simulation
- decode_ethical_reasoning
- decode_proactive_behavior
- decode_explainability_levels
- decode_rl_integration
- decode_fl_compatibility
- decode_dp_features
- decode_robustness_metrics
- decode_calibration_score
- decode_ood_detection
- max_position_embeddings: 8000.
- Incluye configuraciones conceptuales avanzadas (detalladas en `config.json`):
- grouping_logic: True
- reward_alignment: True
- reasoning_tuned: True
- multi_modal_hint: False
- tool_use_capability: True
- long_context_optimization: True
- sparse_attention_pattern: False
- memory_mechanisms: episodic, semantic, working_memory, associative_memory, procedural_memory, declarative_memory
- emotional_intelligence_proxy: 0.85
- ethical_alignment_score: 0.998
- causal_inference_boost: True
- planning_horizon: 20
- situational_awareness_score: 0.95
- creativity_index: 0.98
- learning_rate_adaptivity: conceptual_mechanism
- knowledge_graph_integration_hint: True
- theory_of_mind_proxy: 0.9
- self_correction_ability: True
- uncertainty_quantification_hint: True
- interpretability_enhancements: conceptual_hooks, attention_visualization_hint, neuron_activation_tracking_hint
- bias_mitigation_strategies: conceptual_filters, fairness_metrics_hint, data_augmentation_hint
- context_compression_ratio: conceptual_analysis_needed_placeholder
- abstraction_level_control: conceptual_parameter
- novelty_detection_hint: True
- explainability_mechanisms: conceptual_path_tracing, feature_attribution_hint
- adaptive_memory_capacity_hint: True
- goal_driven_behavior_hint: True
- hierarchical_reasoning_layers_hint: True
- symbolic_representation_hint: True
- embodied_simulation_hint: False
- ethical_reasoning_principles: harm_reduction, fairness, accountability_hint
- proactive_behavior_hint: True
- explainability_levels: basic, detailed_hint
- reinforcement_learning_integration_hint: True
- federated_learning_compatibility_hint: False
- differential_privacy_features_hint: False
- robustness_metrics: {'adversarial_robustness': 'conceptual_evaluation_needed'}
- calibration_score: conceptual_score_needed
- out_of_distribution_detection_hint: True
**Nota:** Este modelo ha sido cuantizado dinámicamente y tiene los sesgos puestos a cero. La fusión de capas y tensores *no fue aplicada estructuralmente*. Su compatibilidad puede variar. Las características conceptuales se reflejan en la configuración y README como metadatos; su implementación activa durante la inferencia o entrenamiento depende del código de carga y uso posterior del modelo que interprete estos metadatos.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import traceback
try:
model = AutoModelForCausalLM.from_pretrained("jnjj/xddd-processed", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("jnjj/xddd-processed")
print("Modelo y Tokenizer cargados desde el Hub.")
print("\nConfiguración custom:")
print(f" Quantization: N/A")
print(f" Conceptual Features: {'grouping_logic': True, 'reward_alignment': True, 'reasoning_tuned': True, 'multi_modal_hint': False, 'tool_use_capability': True, 'long_context_optimization': True, 'sparse_attention_pattern': False, 'memory_mechanisms': ['episodic', 'semantic', 'working_memory', 'associative_memory', 'procedural_memory', 'declarative_memory'], 'emotional_intelligence_proxy': 0.85, 'ethical_alignment_score': 0.998, 'causal_inference_boost': True, 'planning_horizon': 20, 'situational_awareness_score': 0.95, 'creativity_index': 0.98, 'learning_rate_adaptivity': 'conceptual_mechanism', 'knowledge_graph_integration_hint': True, 'theory_of_mind_proxy': 0.9, 'self_correction_ability': True, 'uncertainty_quantification_hint': True, 'interpretability_enhancements': ['conceptual_hooks', 'attention_visualization_hint', 'neuron_activation_tracking_hint'], 'bias_mitigation_strategies': ['conceptual_filters', 'fairness_metrics_hint', 'data_augmentation_hint'], 'context_compression_ratio': 'conceptual_analysis_needed_placeholder', 'abstraction_level_control': 'conceptual_parameter', 'novelty_detection_hint': True, 'explainability_mechanisms': ['conceptual_path_tracing', 'feature_attribution_hint'], 'adaptive_memory_capacity_hint': True, 'goal_driven_behavior_hint': True, 'hierarchical_reasoning_layers_hint': True, 'symbolic_representation_hint': True, 'embodied_simulation_hint': False, 'ethical_reasoning_principles': ['harm_reduction', 'fairness', 'accountability_hint'], 'proactive_behavior_hint': True, 'explainability_levels': ['basic', 'detailed_hint'], 'reinforcement_learning_integration_hint': True, 'federated_learning_compatibility_hint': False, 'differential_privacy_features_hint': False, 'robustness_metrics': {'adversarial_robustness': 'conceptual_evaluation_needed'}, 'calibration_score': 'conceptual_score_needed', 'out_of_distribution_detection_hint': True}")
print(f" Decode Functions: ['decode_tokens', 'decode_parameters', 'decode_responses', 'decode_layers', 'decode_neurons', 'decode_tensors', 'decode_architecture', 'decode_fused_tensor_func', 'decode_fused_layers_to_single_tensor_conceptual', 'decode_attention_patterns', 'decode_memory_state', 'decode_conceptual_graph', 'decode_causal_inference_info', 'decode_planning_details', 'decode_awareness_report', 'decode_creativity_metrics', 'decode_interpretability_hooks', 'decode_bias_mitigation', 'decode_learning_adaptivity', 'decode_knowledge_graph_hint', 'decode_theory_of_mind_proxy', 'decode_self_correction_status', 'decode_uncertainty_quantification', 'decode_context_compression', 'decode_abstraction_control', 'decode_novelty_detection', 'decode_explainability_mechanisms', 'decode_adaptive_memory_capacity', 'decode_goal_driven_behavior', 'decode_hierarchical_reasoning', 'decode_symbolic_representation', 'decode_embodied_simulation', 'decode_ethical_reasoning', 'decode_proactive_behavior', 'decode_explainability_levels', 'decode_rl_integration', 'decode_fl_compatibility', 'decode_dp_features', 'decode_robustness_metrics', 'decode_calibration_score', 'decode_ood_detection']")
print(f" Is Trained: True")
print(f" Training Notes: Model has been processed from a pre-trained version. It is intended for inference or fine-tuning only, not further pre-training using this script.")
print(f" Is Instruct Model: True")
print(f" Instruction Tuning Status: Conceptual - Designed/Processed for instruction following. Actual fine-tuning may be required depending on base model.")
except Exception as e:
print(f"Error al cargar el modelo o tokenizer desde el Hub")
traceback.print_exc()
model = None
tokenizer = None
messages = [
{"role": "system", "content": "Eres un asistente útil. Responde concisamente."},
{"role": "user", "content": "¿Qué es la cuantización en modelos de IA?"}
]
if model is not None and tokenizer is not None:
try:
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)
device = model.device if model.device.type != 'mps' else 'cpu'
input_ids = input_ids.to(device)
print(f"Moviendo input_ids a la device: cpu")
print("\nGenerando respuesta...")
model.eval()
with torch.no_grad():
output_ids = model.generate(
input_ids,
generation_config=model.generation_config,
)
response = tokenizer.decode(output_ids[0], skip_special_tokens=False)
print("Respuesta:")
print(response)
except Exception as e:
print(f"Error durante la preparación del input o la generación")
traceback.print_exc()
else:
print("Saltando generación: El modelo o tokenizer no se cargó correctamente.")
``` |
ya7beni/my-lora-aws-architect | ya7beni | 2025-04-29T15:53:45Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-04-29T15:50:34Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-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 0.14.0 |
shallow6414/sn11-2-7-2 | shallow6414 | 2025-04-29T15:50:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"gemma",
"google",
"Bifröst",
"Bifrost",
"code",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T15:50:49Z | ---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- transformers
- gemma3
- gemma
- google
- Bifröst
- Bifrost
- code
---
## Bifröst-27B
Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.
### Model Details
- **Model Name:** Bifröst-27B
- **Base Architecture:** gemma3
- **Application:** Enterprise Secure Code Generation
- **Release Date:** 16-March-2025
### Intended Use
Bifröst is designed explicitly for:
- Generating secure, efficient, and high-quality code.
- Supporting development tasks within regulated enterprise environments.
- Enhancing productivity by automating routine coding tasks without compromising security.
### Features
- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).
### Limitations
- Bifröst should be used under human supervision to ensure code correctness and security compliance.
- Model-generated code should undergo appropriate security and quality assurance checks before deployment.
### Ethical Considerations
- Users are encouraged to perform regular audits and compliance checks on generated outputs.
- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.
### Usage
Below are some quick-start instructions for using the model with the `transformers` library.
#### Installation
```sh
$ pip install git+https://github.com/huggingface/[email protected]
```
#### Running with the `pipeline` API
```python
from transformers import pipeline
import torch
pipe = pipeline(
"text-generation",
model="OpenGenerativeAI/Bifrost-27B",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])
```
## Terms of Use
This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use. |
shallow6414/sn11-2-6-2 | shallow6414 | 2025-04-29T15:50:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"gemma",
"google",
"Bifröst",
"Bifrost",
"code",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T15:50:44Z | ---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- transformers
- gemma3
- gemma
- google
- Bifröst
- Bifrost
- code
---
## Bifröst-27B
Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.
### Model Details
- **Model Name:** Bifröst-27B
- **Base Architecture:** gemma3
- **Application:** Enterprise Secure Code Generation
- **Release Date:** 16-March-2025
### Intended Use
Bifröst is designed explicitly for:
- Generating secure, efficient, and high-quality code.
- Supporting development tasks within regulated enterprise environments.
- Enhancing productivity by automating routine coding tasks without compromising security.
### Features
- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).
### Limitations
- Bifröst should be used under human supervision to ensure code correctness and security compliance.
- Model-generated code should undergo appropriate security and quality assurance checks before deployment.
### Ethical Considerations
- Users are encouraged to perform regular audits and compliance checks on generated outputs.
- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.
### Usage
Below are some quick-start instructions for using the model with the `transformers` library.
#### Installation
```sh
$ pip install git+https://github.com/huggingface/[email protected]
```
#### Running with the `pipeline` API
```python
from transformers import pipeline
import torch
pipe = pipeline(
"text-generation",
model="OpenGenerativeAI/Bifrost-27B",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])
```
## Terms of Use
This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use. |
golf2248/sn11-v4-3-2 | golf2248 | 2025-04-29T15:50:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"gemma",
"google",
"Bifröst",
"Bifrost",
"code",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T15:50:38Z | ---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- transformers
- gemma3
- gemma
- google
- Bifröst
- Bifrost
- code
---
## Bifröst-27B
Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.
### Model Details
- **Model Name:** Bifröst-27B
- **Base Architecture:** gemma3
- **Application:** Enterprise Secure Code Generation
- **Release Date:** 16-March-2025
### Intended Use
Bifröst is designed explicitly for:
- Generating secure, efficient, and high-quality code.
- Supporting development tasks within regulated enterprise environments.
- Enhancing productivity by automating routine coding tasks without compromising security.
### Features
- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).
### Limitations
- Bifröst should be used under human supervision to ensure code correctness and security compliance.
- Model-generated code should undergo appropriate security and quality assurance checks before deployment.
### Ethical Considerations
- Users are encouraged to perform regular audits and compliance checks on generated outputs.
- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.
### Usage
Below are some quick-start instructions for using the model with the `transformers` library.
#### Installation
```sh
$ pip install git+https://github.com/huggingface/[email protected]
```
#### Running with the `pipeline` API
```python
from transformers import pipeline
import torch
pipe = pipeline(
"text-generation",
model="OpenGenerativeAI/Bifrost-27B",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])
```
## Terms of Use
This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use. |
faraya1/genie-grpo-test-API-qwen3B-lora-step-600 | faraya1 | 2025-04-29T15:50:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T15:50:28Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Qwen3-0.6B-i1-GGUF | mradermacher | 2025-04-29T15:49:25Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Qwen/Qwen3-0.6B",
"base_model:quantized:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-04-29T15:25:41Z | ---
base_model: Qwen/Qwen3-0.6B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
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/Qwen/Qwen3-0.6B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ3_S.gguf) | i1-IQ3_S | 0.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ3_M.gguf) | i1-IQ3_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q4_0.gguf) | i1-Q4_0 | 0.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q4_1.gguf) | i1-Q4_1 | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-i1-GGUF/resolve/main/Qwen3-0.6B.i1-Q6_K.gguf) | i1-Q6_K | 0.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
kostiantynk-outlook/f90a6202-4607-4619-9e52-65ba868aeab0 | kostiantynk-outlook | 2025-04-29T15:48:23Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-v0.3",
"base_model:adapter:unsloth/mistral-7b-v0.3",
"region:us"
] | null | 2025-04-29T15:47:57Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: unsloth/mistral-7b-v0.3
model-index:
- name: kostiantynk-outlook/f90a6202-4607-4619-9e52-65ba868aeab0
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. -->
# kostiantynk-outlook/f90a6202-4607-4619-9e52-65ba868aeab0
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5061
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
wassname/qwen-7B-fourchan-QLoRA | wassname | 2025-04-29T15:46:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-Coder-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T15:46:32Z | ---
base_model: unsloth/Qwen2.5-Coder-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** wassname
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-Coder-7B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
gangu-chettri-kanda-7-2-video-viral/Video.link.Gangu.Chettri.Kanda.7.2.minute.Videos.oficial | gangu-chettri-kanda-7-2-video-viral | 2025-04-29T15:45:32Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-29T15:44:03Z |
<a href="https://sdu.sk/9Ip"><img src="http://4.bp.blogspot.com/-VFcup4RzDQY/Upiobuokb5I/AAAAAAAAAV0/64yKpZilDCg/s1600/oie_nxv3mlmduAj1.gif" alt="fsd" /></a>
<a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a>
<a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
|
MikuMasterRace/Hatsune_Miku_-_Usamiku_Furry_-_IllustriousXL_v1 | MikuMasterRace | 2025-04-29T15:43:41Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:adapter:OnomaAIResearch/Illustrious-xl-early-release-v0",
"region:us"
] | text-to-image | 2025-04-29T15:39:29Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '1girl, solo, hatsune miku, usamiku,
aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headset, collared shirt, pleated skirt, thighhighs, hair between eyes,
animal hands, white fur, rabbit ears,
:3, rabbit girl, animal nose, body fur, white fur,
furry female, furrification,
cowboy shot, one eye closed, zettai ryouiki, sparkle,
open mouth, smile, looking at viewer, looking at viewer, white background,
safe, newset, omufujoshi, black outline, thick outlines,
masterpiece, best quality, amazing quality'
output:
url: images/ComfyUI_(hiresfix)_2025-04-29_00000_8.png
- text: '1girl, solo, hatsune miku, usamiku,
aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headphones, headset, collared shirt, pleated skirt, thighhighs, hair between eyes,
animal hands, white fur, rabbit ears,
:3, rabbit girl, animal nose, body fur, white fur,
furry female, furry, furrification,
holding doll, fumo \(doll\), head tilt,
portrait, sparkle,
open mouth, smile, looking at another, white background,
safe, newset, omufujoshi, black outline, thick outlines,
masterpiece, best quality, amazing quality'
output:
url: images/ComfyUI_(hiresfix)_2025-04-29_00000_5.png
- text: '1girl, solo, hatsune miku, usamiku,
aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, number print, thigh boots,
animal hands, white fur, rabbit ears,
:3, rabbit girl, animal nose, body fur, white fur,
furry female, furry, furrification,
closed mouth, smile, looking back, white background,
safe, newset, omufujoshi, black outline, thick outlines,
masterpiece, best quality, amazing quality'
output:
url: images/ComfyUI_(hiresfix)_2025-04-29_00000_7.png
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
instance_prompt: null
---
# Usamiku / Furry Miku (Hatsune Miku) v1 [IllustriousXL 0.1]
<Gallery />
## Reference
This is a kigurumi cosplay of Hatsune Miku. She won the *"Miku Lookalike Contest"* in NYC in 2025.
Socials: [twitter@mikusagi01](https://x.com/mikusagi01), [tiktok@mikusagi01](https://www.tiktok.com/@mikusagi01?lang=en)
[](https://x.com/ziepoopenfarten/status/1906077150563688871)
## Prompting
Main triggerword:
```
usamiku
```
Appearance and clothing:
```
aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, number print,
animal hands, rabbit tail, white fur, rabbit ears, :3, rabbit girl, animal nose, body fur, white fur, furry female, furrification
```
## Download model
Weights for this model are available in Safetensors format.
[Download](/MikuMasterRace/Hatsune_Miku_-_Usamiku_Furry_-_IllustriousXL_v1/tree/main) them in the Files & versions tab.
|
marialvsantiago/5cc6ed6c-7ce9-4f8f-94c9-bb2283ebab83 | marialvsantiago | 2025-04-29T15:43:27Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-14B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T15:34:50Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-14B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5cc6ed6c-7ce9-4f8f-94c9-bb2283ebab83
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: Qwen/Qwen2.5-14B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 6672ff8cbabd744e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/6672ff8cbabd744e_train_data.json
type:
field_input: thinking
field_instruction: prompt
field_output: answer
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_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: marialvsantiago/5cc6ed6c-7ce9-4f8f-94c9-bb2283ebab83
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/6672ff8cbabd744e_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: 777fb87d-b5fc-446f-96ca-5871a5b464cc
wandb_project: s56-33
wandb_run: your_name
wandb_runid: 777fb87d-b5fc-446f-96ca-5871a5b464cc
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 5cc6ed6c-7ce9-4f8f-94c9-bb2283ebab83
This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0656
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9098 | 0.1125 | 200 | 1.0656 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
thoddnn/colqwen2-v1.0 | thoddnn | 2025-04-29T15:38:16Z | 0 | 0 | colpali | [
"colpali",
"safetensors",
"vidore-experimental",
"vidore",
"visual-document-retrieval",
"en",
"arxiv:2004.12832",
"arxiv:2407.01449",
"arxiv:2106.09685",
"base_model:vidore/colqwen2-base",
"base_model:finetune:vidore/colqwen2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | visual-document-retrieval | 2025-04-29T15:38:15Z | ---
license: apache-2.0
library_name: colpali
base_model: vidore/colqwen2-base
language:
- en
tags:
- colpali
- vidore-experimental
- vidore
pipeline_tag: visual-document-retrieval
---
# ColQwen2: Visual Retriever based on Qwen2-VL-2B-Instruct with ColBERT strategy
### This is the base version trained with batch_size 256 instead of 32 for 5 epoch and with the updated pad token
ColQwen2 is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features.
It is a [Qwen2-VL-2B](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) extension that generates [ColBERT](https://arxiv.org/abs/2004.12832)- style multi-vector representations of text and images.
It was introduced in the paper [ColPali: Efficient Document Retrieval with Vision Language Models](https://arxiv.org/abs/2407.01449) and first released in [this repository](https://github.com/ManuelFay/colpali)
<p align="center"><img width=800 src="https://github.com/illuin-tech/colpali/blob/main/assets/colpali_architecture.webp?raw=true"/></p>
## Version specificity
This model takes dynamic image resolutions in input and does not resize them, changing their aspect ratio as in ColPali.
Maximal resolution is set so that 768 image patches are created at most. Experiments show clear improvements with larger amounts of image patches, at the cost of memory requirements.
This version is trained with `colpali-engine==0.3.1`.
Data is the same as the ColPali data described in the paper.
## Model Training
### Dataset
Our training dataset of 127,460 query-page pairs is comprised of train sets of openly available academic datasets (63%) and a synthetic dataset made up of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%).
Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages. We explicitly verify no multi-page PDF document is used both [*ViDoRe*](https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d) and in the train set to prevent evaluation contamination.
A validation set is created with 2% of the samples to tune hyperparameters.
*Note: Multilingual data is present in the pretraining corpus of the language model and most probably in the multimodal training.*
### Parameters
All models are trained for 1 epoch on the train set. Unless specified otherwise, we train models in `bfloat16` format, use low-rank adapters ([LoRA](https://arxiv.org/abs/2106.09685))
with `alpha=32` and `r=32` on the transformer layers from the language model,
as well as the final randomly initialized projection layer, and use a `paged_adamw_8bit` optimizer.
We train on an 8 GPU setup with data parallelism, a learning rate of 5e-5 with linear decay with 2.5% warmup steps, and a batch size of 32.
## Usage
Make sure `colpali-engine` is installed from source or with a version superior to 0.3.4.
`transformers` version must be > 4.46.1.
```bash
pip install git+https://github.com/illuin-tech/colpali
```
```python
import torch
from PIL import Image
from transformers.utils.import_utils import is_flash_attn_2_available
from colpali_engine.models import ColQwen2, ColQwen2Processor
model = ColQwen2.from_pretrained(
"vidore/colqwen2-v1.0",
torch_dtype=torch.bfloat16,
device_map="cuda:0", # or "mps" if on Apple Silicon
attn_implementation="flash_attention_2" if is_flash_attn_2_available() else None,
).eval()
processor = ColQwen2Processor.from_pretrained("vidore/colqwen2-v1.0")
# Your inputs
images = [
Image.new("RGB", (128, 128), color="white"),
Image.new("RGB", (64, 32), color="black"),
]
queries = [
"Is attention really all you need?",
"What is the amount of bananas farmed in Salvador?",
]
# Process the inputs
batch_images = processor.process_images(images).to(model.device)
batch_queries = processor.process_queries(queries).to(model.device)
# Forward pass
with torch.no_grad():
image_embeddings = model(**batch_images)
query_embeddings = model(**batch_queries)
scores = processor.score_multi_vector(query_embeddings, image_embeddings)
```
## Limitations
- **Focus**: The model primarily focuses on PDF-type documents and high-ressources languages, potentially limiting its generalization to other document types or less represented languages.
- **Support**: The model relies on multi-vector retreiving derived from the ColBERT late interaction mechanism, which may require engineering efforts to adapt to widely used vector retrieval frameworks that lack native multi-vector support.
## License
ColQwen2's vision language backbone model (Qwen2-VL) is under `apache2.0` license. The adapters attached to the model are under MIT license.
## Contact
- Manuel Faysse: [email protected]
- Hugues Sibille: [email protected]
- Tony Wu: [email protected]
## Citation
If you use any datasets or models from this organization in your research, please cite the original dataset as follows:
```bibtex
@misc{faysse2024colpaliefficientdocumentretrieval,
title={ColPali: Efficient Document Retrieval with Vision Language Models},
author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2407.01449},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2407.01449},
}
``` |
thoddnn/multilingual-e5-large | thoddnn | 2025-04-29T15:37:38Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"xlm-roberta",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"feature-extraction",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2108.08787",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-04-29T15:37:37Z | ---
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- feature-extraction
- sentence-transformers
model-index:
- name: multilingual-e5-large
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.05970149253731
- type: ap
value: 43.486574390835635
- type: f1
value: 73.32700092140148
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.22055674518201
- type: ap
value: 81.55756710830498
- type: f1
value: 69.28271787752661
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 80.41979010494754
- type: ap
value: 29.34879922376344
- type: f1
value: 67.62475449011278
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.8372591006424
- type: ap
value: 26.557560591210738
- type: f1
value: 64.96619417368707
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.489875
- type: ap
value: 90.98758636917603
- type: f1
value: 93.48554819717332
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.564
- type: f1
value: 46.75122173518047
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 45.400000000000006
- type: f1
value: 44.17195682400632
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.068
- type: f1
value: 42.38155696855596
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 41.89
- type: f1
value: 40.84407321682663
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.120000000000005
- type: f1
value: 39.522976223819114
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.832
- type: f1
value: 38.0392533394713
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.725
- type: map_at_10
value: 46.055
- type: map_at_100
value: 46.900999999999996
- type: map_at_1000
value: 46.911
- type: map_at_3
value: 41.548
- type: map_at_5
value: 44.297
- type: mrr_at_1
value: 31.152
- type: mrr_at_10
value: 46.231
- type: mrr_at_100
value: 47.07
- type: mrr_at_1000
value: 47.08
- type: mrr_at_3
value: 41.738
- type: mrr_at_5
value: 44.468999999999994
- type: ndcg_at_1
value: 30.725
- type: ndcg_at_10
value: 54.379999999999995
- type: ndcg_at_100
value: 58.138
- type: ndcg_at_1000
value: 58.389
- type: ndcg_at_3
value: 45.156
- type: ndcg_at_5
value: 50.123
- type: precision_at_1
value: 30.725
- type: precision_at_10
value: 8.087
- type: precision_at_100
value: 0.9769999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.54
- type: precision_at_5
value: 13.542000000000002
- type: recall_at_1
value: 30.725
- type: recall_at_10
value: 80.868
- type: recall_at_100
value: 97.653
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 55.619
- type: recall_at_5
value: 67.71000000000001
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.30960650674069
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 38.427074197498996
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.28270056031872
- type: mrr
value: 74.38332673789738
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.05942144105269
- type: cos_sim_spearman
value: 82.51212105850809
- type: euclidean_pearson
value: 81.95639829909122
- type: euclidean_spearman
value: 82.3717564144213
- type: manhattan_pearson
value: 81.79273425468256
- type: manhattan_spearman
value: 82.20066817871039
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (de-en)
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.46764091858039
- type: f1
value: 99.37717466945023
- type: precision
value: 99.33194154488518
- type: recall
value: 99.46764091858039
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (fr-en)
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.29407880255337
- type: f1
value: 98.11248073959938
- type: precision
value: 98.02443319392472
- type: recall
value: 98.29407880255337
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (ru-en)
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.79009352268791
- type: f1
value: 97.5176076665512
- type: precision
value: 97.38136473848286
- type: recall
value: 97.79009352268791
- task:
type: BitextMining
dataset:
type: mteb/bucc-bitext-mining
name: MTEB BUCC (zh-en)
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.26276987888363
- type: f1
value: 99.20133403545726
- type: precision
value: 99.17500438827453
- type: recall
value: 99.26276987888363
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.72727272727273
- type: f1
value: 84.67672206031433
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.34220182511161
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 33.4987096128766
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.558249999999997
- type: map_at_10
value: 34.44425000000001
- type: map_at_100
value: 35.59833333333333
- type: map_at_1000
value: 35.706916666666665
- type: map_at_3
value: 31.691749999999995
- type: map_at_5
value: 33.252916666666664
- type: mrr_at_1
value: 30.252666666666666
- type: mrr_at_10
value: 38.60675
- type: mrr_at_100
value: 39.42666666666666
- type: mrr_at_1000
value: 39.48408333333334
- type: mrr_at_3
value: 36.17441666666665
- type: mrr_at_5
value: 37.56275
- type: ndcg_at_1
value: 30.252666666666666
- type: ndcg_at_10
value: 39.683
- type: ndcg_at_100
value: 44.68541666666667
- type: ndcg_at_1000
value: 46.94316666666668
- type: ndcg_at_3
value: 34.961749999999995
- type: ndcg_at_5
value: 37.215666666666664
- type: precision_at_1
value: 30.252666666666666
- type: precision_at_10
value: 6.904166666666667
- type: precision_at_100
value: 1.0989999999999995
- type: precision_at_1000
value: 0.14733333333333334
- type: precision_at_3
value: 16.037666666666667
- type: precision_at_5
value: 11.413583333333333
- type: recall_at_1
value: 25.558249999999997
- type: recall_at_10
value: 51.13341666666666
- type: recall_at_100
value: 73.08366666666667
- type: recall_at_1000
value: 88.79483333333334
- type: recall_at_3
value: 37.989083333333326
- type: recall_at_5
value: 43.787833333333325
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.338
- type: map_at_10
value: 18.360000000000003
- type: map_at_100
value: 19.942
- type: map_at_1000
value: 20.134
- type: map_at_3
value: 15.174000000000001
- type: map_at_5
value: 16.830000000000002
- type: mrr_at_1
value: 23.257
- type: mrr_at_10
value: 33.768
- type: mrr_at_100
value: 34.707
- type: mrr_at_1000
value: 34.766000000000005
- type: mrr_at_3
value: 30.977
- type: mrr_at_5
value: 32.528
- type: ndcg_at_1
value: 23.257
- type: ndcg_at_10
value: 25.733
- type: ndcg_at_100
value: 32.288
- type: ndcg_at_1000
value: 35.992000000000004
- type: ndcg_at_3
value: 20.866
- type: ndcg_at_5
value: 22.612
- type: precision_at_1
value: 23.257
- type: precision_at_10
value: 8.124
- type: precision_at_100
value: 1.518
- type: precision_at_1000
value: 0.219
- type: precision_at_3
value: 15.679000000000002
- type: precision_at_5
value: 12.117
- type: recall_at_1
value: 10.338
- type: recall_at_10
value: 31.154
- type: recall_at_100
value: 54.161
- type: recall_at_1000
value: 75.21900000000001
- type: recall_at_3
value: 19.427
- type: recall_at_5
value: 24.214
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.498
- type: map_at_10
value: 19.103
- type: map_at_100
value: 27.375
- type: map_at_1000
value: 28.981
- type: map_at_3
value: 13.764999999999999
- type: map_at_5
value: 15.950000000000001
- type: mrr_at_1
value: 65.5
- type: mrr_at_10
value: 74.53800000000001
- type: mrr_at_100
value: 74.71799999999999
- type: mrr_at_1000
value: 74.725
- type: mrr_at_3
value: 72.792
- type: mrr_at_5
value: 73.554
- type: ndcg_at_1
value: 53.37499999999999
- type: ndcg_at_10
value: 41.286
- type: ndcg_at_100
value: 45.972
- type: ndcg_at_1000
value: 53.123
- type: ndcg_at_3
value: 46.172999999999995
- type: ndcg_at_5
value: 43.033
- type: precision_at_1
value: 65.5
- type: precision_at_10
value: 32.725
- type: precision_at_100
value: 10.683
- type: precision_at_1000
value: 1.978
- type: precision_at_3
value: 50
- type: precision_at_5
value: 41.349999999999994
- type: recall_at_1
value: 8.498
- type: recall_at_10
value: 25.070999999999998
- type: recall_at_100
value: 52.383
- type: recall_at_1000
value: 74.91499999999999
- type: recall_at_3
value: 15.207999999999998
- type: recall_at_5
value: 18.563
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.5
- type: f1
value: 41.93833713984145
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 67.914
- type: map_at_10
value: 78.10000000000001
- type: map_at_100
value: 78.333
- type: map_at_1000
value: 78.346
- type: map_at_3
value: 76.626
- type: map_at_5
value: 77.627
- type: mrr_at_1
value: 72.74199999999999
- type: mrr_at_10
value: 82.414
- type: mrr_at_100
value: 82.511
- type: mrr_at_1000
value: 82.513
- type: mrr_at_3
value: 81.231
- type: mrr_at_5
value: 82.065
- type: ndcg_at_1
value: 72.74199999999999
- type: ndcg_at_10
value: 82.806
- type: ndcg_at_100
value: 83.677
- type: ndcg_at_1000
value: 83.917
- type: ndcg_at_3
value: 80.305
- type: ndcg_at_5
value: 81.843
- type: precision_at_1
value: 72.74199999999999
- type: precision_at_10
value: 10.24
- type: precision_at_100
value: 1.089
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 31.268
- type: precision_at_5
value: 19.706000000000003
- type: recall_at_1
value: 67.914
- type: recall_at_10
value: 92.889
- type: recall_at_100
value: 96.42699999999999
- type: recall_at_1000
value: 97.92
- type: recall_at_3
value: 86.21
- type: recall_at_5
value: 90.036
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.166
- type: map_at_10
value: 35.57
- type: map_at_100
value: 37.405
- type: map_at_1000
value: 37.564
- type: map_at_3
value: 30.379
- type: map_at_5
value: 33.324
- type: mrr_at_1
value: 43.519000000000005
- type: mrr_at_10
value: 51.556000000000004
- type: mrr_at_100
value: 52.344
- type: mrr_at_1000
value: 52.373999999999995
- type: mrr_at_3
value: 48.868
- type: mrr_at_5
value: 50.319
- type: ndcg_at_1
value: 43.519000000000005
- type: ndcg_at_10
value: 43.803
- type: ndcg_at_100
value: 50.468999999999994
- type: ndcg_at_1000
value: 53.111
- type: ndcg_at_3
value: 38.893
- type: ndcg_at_5
value: 40.653
- type: precision_at_1
value: 43.519000000000005
- type: precision_at_10
value: 12.253
- type: precision_at_100
value: 1.931
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 25.617
- type: precision_at_5
value: 19.383
- type: recall_at_1
value: 22.166
- type: recall_at_10
value: 51.6
- type: recall_at_100
value: 76.574
- type: recall_at_1000
value: 92.192
- type: recall_at_3
value: 34.477999999999994
- type: recall_at_5
value: 41.835
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.041
- type: map_at_10
value: 62.961999999999996
- type: map_at_100
value: 63.79899999999999
- type: map_at_1000
value: 63.854
- type: map_at_3
value: 59.399
- type: map_at_5
value: 61.669
- type: mrr_at_1
value: 78.082
- type: mrr_at_10
value: 84.321
- type: mrr_at_100
value: 84.49600000000001
- type: mrr_at_1000
value: 84.502
- type: mrr_at_3
value: 83.421
- type: mrr_at_5
value: 83.977
- type: ndcg_at_1
value: 78.082
- type: ndcg_at_10
value: 71.229
- type: ndcg_at_100
value: 74.10900000000001
- type: ndcg_at_1000
value: 75.169
- type: ndcg_at_3
value: 66.28699999999999
- type: ndcg_at_5
value: 69.084
- type: precision_at_1
value: 78.082
- type: precision_at_10
value: 14.993
- type: precision_at_100
value: 1.7239999999999998
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 42.737
- type: precision_at_5
value: 27.843
- type: recall_at_1
value: 39.041
- type: recall_at_10
value: 74.96300000000001
- type: recall_at_100
value: 86.199
- type: recall_at_1000
value: 93.228
- type: recall_at_3
value: 64.105
- type: recall_at_5
value: 69.608
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.23160000000001
- type: ap
value: 85.5674856808308
- type: f1
value: 90.18033354786317
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 24.091
- type: map_at_10
value: 36.753
- type: map_at_100
value: 37.913000000000004
- type: map_at_1000
value: 37.958999999999996
- type: map_at_3
value: 32.818999999999996
- type: map_at_5
value: 35.171
- type: mrr_at_1
value: 24.742
- type: mrr_at_10
value: 37.285000000000004
- type: mrr_at_100
value: 38.391999999999996
- type: mrr_at_1000
value: 38.431
- type: mrr_at_3
value: 33.440999999999995
- type: mrr_at_5
value: 35.75
- type: ndcg_at_1
value: 24.742
- type: ndcg_at_10
value: 43.698
- type: ndcg_at_100
value: 49.145
- type: ndcg_at_1000
value: 50.23800000000001
- type: ndcg_at_3
value: 35.769
- type: ndcg_at_5
value: 39.961999999999996
- type: precision_at_1
value: 24.742
- type: precision_at_10
value: 6.7989999999999995
- type: precision_at_100
value: 0.95
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 15.096000000000002
- type: precision_at_5
value: 11.183
- type: recall_at_1
value: 24.091
- type: recall_at_10
value: 65.068
- type: recall_at_100
value: 89.899
- type: recall_at_1000
value: 98.16
- type: recall_at_3
value: 43.68
- type: recall_at_5
value: 53.754999999999995
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.66621067031465
- type: f1
value: 93.49622853272142
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.94702733164272
- type: f1
value: 91.17043441745282
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.20146764509674
- type: f1
value: 91.98359080555608
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.99780770435328
- type: f1
value: 89.19746342724068
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.78486912871998
- type: f1
value: 89.24578823628642
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.74502712477394
- type: f1
value: 89.00297573881542
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.9046967624259
- type: f1
value: 59.36787125785957
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.5280360664976
- type: f1
value: 57.17723440888718
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (es)
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.44029352901934
- type: f1
value: 54.052855531072964
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 70.5606013153774
- type: f1
value: 52.62215934386531
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (hi)
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.11581211903908
- type: f1
value: 52.341291845645465
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (th)
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.28933092224233
- type: f1
value: 57.07918745504911
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (af)
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.38063214525892
- type: f1
value: 59.46463723443009
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (am)
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 56.06926698049766
- type: f1
value: 52.49084283283562
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ar)
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.74983187626093
- type: f1
value: 56.960640620165904
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (az)
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.86550100874243
- type: f1
value: 62.47370548140688
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (bn)
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.971082716879636
- type: f1
value: 61.03812421957381
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (cy)
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 54.98318762609282
- type: f1
value: 51.51207916008392
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (da)
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.45527908540686
- type: f1
value: 66.16631905400318
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (de)
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.32750504371216
- type: f1
value: 66.16755288646591
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (el)
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.09213180901143
- type: f1
value: 66.95654394661507
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.75588433086752
- type: f1
value: 71.79973779656923
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (es)
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.49428379287154
- type: f1
value: 68.37494379215734
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fa)
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.90921318090115
- type: f1
value: 66.79517376481645
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fi)
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.12104909213181
- type: f1
value: 67.29448842879584
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (fr)
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.34095494283793
- type: f1
value: 67.01134288992947
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (he)
config: he
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.61264290517822
- type: f1
value: 64.68730512660757
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (hi)
config: hi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.79757901815738
- type: f1
value: 65.24938539425598
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (hu)
config: hu
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.68728984532616
- type: f1
value: 67.0487169762553
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (hy)
config: hy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.07464694014795
- type: f1
value: 59.183532276789286
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (id)
config: id
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.04707464694015
- type: f1
value: 67.66829629003848
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (is)
config: is
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.42434431741762
- type: f1
value: 59.01617226544757
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (it)
config: it
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.53127101546738
- type: f1
value: 68.10033760906255
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (ja)
config: ja
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.72158708809684
- type: f1
value: 71.85049433180541
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ru)
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.137861466039
- type: f1
value: 75.37628348188467
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sl)
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.86953597848016
- type: f1
value: 71.87537624521661
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sq)
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.27572293207801
- type: f1
value: 68.80017302344231
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sv)
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.09952925353059
- type: f1
value: 76.07992707688408
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (sw)
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.140551445864155
- type: f1
value: 61.73855010331415
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ta)
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.27774041694687
- type: f1
value: 64.83664868894539
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (te)
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.69468728984533
- type: f1
value: 64.76239666920868
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (th)
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.44653665097512
- type: f1
value: 73.14646052013873
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tl)
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.71351714862139
- type: f1
value: 66.67212180163382
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (tr)
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.9946200403497
- type: f1
value: 73.87348793725525
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (ur)
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.15400134498992
- type: f1
value: 67.09433241421094
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (vi)
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.11365164761264
- type: f1
value: 73.59502539433753
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.82582380632145
- type: f1
value: 76.89992945316313
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-TW)
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.81237390719569
- type: f1
value: 72.36499770986265
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.480506569594695
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 29.71252128004552
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.421396787056548
- type: mrr
value: 32.48155274872267
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.595
- type: map_at_10
value: 12.642000000000001
- type: map_at_100
value: 15.726
- type: map_at_1000
value: 17.061999999999998
- type: map_at_3
value: 9.125
- type: map_at_5
value: 10.866000000000001
- type: mrr_at_1
value: 43.344
- type: mrr_at_10
value: 52.227999999999994
- type: mrr_at_100
value: 52.898999999999994
- type: mrr_at_1000
value: 52.944
- type: mrr_at_3
value: 49.845
- type: mrr_at_5
value: 51.115
- type: ndcg_at_1
value: 41.949999999999996
- type: ndcg_at_10
value: 33.995
- type: ndcg_at_100
value: 30.869999999999997
- type: ndcg_at_1000
value: 39.487
- type: ndcg_at_3
value: 38.903999999999996
- type: ndcg_at_5
value: 37.236999999999995
- type: precision_at_1
value: 43.344
- type: precision_at_10
value: 25.480000000000004
- type: precision_at_100
value: 7.672
- type: precision_at_1000
value: 2.028
- type: precision_at_3
value: 36.636
- type: precision_at_5
value: 32.632
- type: recall_at_1
value: 5.595
- type: recall_at_10
value: 16.466
- type: recall_at_100
value: 31.226
- type: recall_at_1000
value: 62.778999999999996
- type: recall_at_3
value: 9.931
- type: recall_at_5
value: 12.884
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.414
- type: map_at_10
value: 56.754000000000005
- type: map_at_100
value: 57.457
- type: map_at_1000
value: 57.477999999999994
- type: map_at_3
value: 52.873999999999995
- type: map_at_5
value: 55.175
- type: mrr_at_1
value: 45.278
- type: mrr_at_10
value: 59.192
- type: mrr_at_100
value: 59.650000000000006
- type: mrr_at_1000
value: 59.665
- type: mrr_at_3
value: 56.141
- type: mrr_at_5
value: 57.998000000000005
- type: ndcg_at_1
value: 45.278
- type: ndcg_at_10
value: 64.056
- type: ndcg_at_100
value: 66.89
- type: ndcg_at_1000
value: 67.364
- type: ndcg_at_3
value: 56.97
- type: ndcg_at_5
value: 60.719
- type: precision_at_1
value: 45.278
- type: precision_at_10
value: 9.994
- type: precision_at_100
value: 1.165
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.512
- type: precision_at_5
value: 17.509
- type: recall_at_1
value: 40.414
- type: recall_at_10
value: 83.596
- type: recall_at_100
value: 95.72
- type: recall_at_1000
value: 99.24
- type: recall_at_3
value: 65.472
- type: recall_at_5
value: 74.039
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.352
- type: map_at_10
value: 84.369
- type: map_at_100
value: 85.02499999999999
- type: map_at_1000
value: 85.04
- type: map_at_3
value: 81.42399999999999
- type: map_at_5
value: 83.279
- type: mrr_at_1
value: 81.05
- type: mrr_at_10
value: 87.401
- type: mrr_at_100
value: 87.504
- type: mrr_at_1000
value: 87.505
- type: mrr_at_3
value: 86.443
- type: mrr_at_5
value: 87.10799999999999
- type: ndcg_at_1
value: 81.04
- type: ndcg_at_10
value: 88.181
- type: ndcg_at_100
value: 89.411
- type: ndcg_at_1000
value: 89.507
- type: ndcg_at_3
value: 85.28099999999999
- type: ndcg_at_5
value: 86.888
- type: precision_at_1
value: 81.04
- type: precision_at_10
value: 13.406
- type: precision_at_100
value: 1.5350000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.31
- type: precision_at_5
value: 24.54
- type: recall_at_1
value: 70.352
- type: recall_at_10
value: 95.358
- type: recall_at_100
value: 99.541
- type: recall_at_1000
value: 99.984
- type: recall_at_3
value: 87.111
- type: recall_at_5
value: 91.643
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 46.54068723291946
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.216287629895994
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.023000000000001
- type: map_at_10
value: 10.071
- type: map_at_100
value: 11.892
- type: map_at_1000
value: 12.196
- type: map_at_3
value: 7.234
- type: map_at_5
value: 8.613999999999999
- type: mrr_at_1
value: 19.900000000000002
- type: mrr_at_10
value: 30.516
- type: mrr_at_100
value: 31.656000000000002
- type: mrr_at_1000
value: 31.723000000000003
- type: mrr_at_3
value: 27.400000000000002
- type: mrr_at_5
value: 29.270000000000003
- type: ndcg_at_1
value: 19.900000000000002
- type: ndcg_at_10
value: 17.474
- type: ndcg_at_100
value: 25.020999999999997
- type: ndcg_at_1000
value: 30.728
- type: ndcg_at_3
value: 16.588
- type: ndcg_at_5
value: 14.498
- type: precision_at_1
value: 19.900000000000002
- type: precision_at_10
value: 9.139999999999999
- type: precision_at_100
value: 2.011
- type: precision_at_1000
value: 0.33899999999999997
- type: precision_at_3
value: 15.667
- type: precision_at_5
value: 12.839999999999998
- type: recall_at_1
value: 4.023000000000001
- type: recall_at_10
value: 18.497
- type: recall_at_100
value: 40.8
- type: recall_at_1000
value: 68.812
- type: recall_at_3
value: 9.508
- type: recall_at_5
value: 12.983
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.967008785134
- type: cos_sim_spearman
value: 80.23142141101837
- type: euclidean_pearson
value: 81.20166064704539
- type: euclidean_spearman
value: 80.18961335654585
- type: manhattan_pearson
value: 81.13925443187625
- type: manhattan_spearman
value: 80.07948723044424
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.94262461316023
- type: cos_sim_spearman
value: 80.01596278563865
- type: euclidean_pearson
value: 83.80799622922581
- type: euclidean_spearman
value: 79.94984954947103
- type: manhattan_pearson
value: 83.68473841756281
- type: manhattan_spearman
value: 79.84990707951822
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 80.57346443146068
- type: cos_sim_spearman
value: 81.54689837570866
- type: euclidean_pearson
value: 81.10909881516007
- type: euclidean_spearman
value: 81.56746243261762
- type: manhattan_pearson
value: 80.87076036186582
- type: manhattan_spearman
value: 81.33074987964402
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 79.54733787179849
- type: cos_sim_spearman
value: 77.72202105610411
- type: euclidean_pearson
value: 78.9043595478849
- type: euclidean_spearman
value: 77.93422804309435
- type: manhattan_pearson
value: 78.58115121621368
- type: manhattan_spearman
value: 77.62508135122033
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.59880017237558
- type: cos_sim_spearman
value: 89.31088630824758
- type: euclidean_pearson
value: 88.47069261564656
- type: euclidean_spearman
value: 89.33581971465233
- type: manhattan_pearson
value: 88.40774264100956
- type: manhattan_spearman
value: 89.28657485627835
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.08055117917084
- type: cos_sim_spearman
value: 85.78491813080304
- type: euclidean_pearson
value: 84.99329155500392
- type: euclidean_spearman
value: 85.76728064677287
- type: manhattan_pearson
value: 84.87947428989587
- type: manhattan_spearman
value: 85.62429454917464
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 82.14190939287384
- type: cos_sim_spearman
value: 82.27331573306041
- type: euclidean_pearson
value: 81.891896953716
- type: euclidean_spearman
value: 82.37695542955998
- type: manhattan_pearson
value: 81.73123869460504
- type: manhattan_spearman
value: 82.19989168441421
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.84695301843362
- type: cos_sim_spearman
value: 77.87790986014461
- type: euclidean_pearson
value: 76.91981583106315
- type: euclidean_spearman
value: 77.88154772749589
- type: manhattan_pearson
value: 76.94953277451093
- type: manhattan_spearman
value: 77.80499230728604
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.44657840482016
- type: cos_sim_spearman
value: 75.05531095119674
- type: euclidean_pearson
value: 75.88161755829299
- type: euclidean_spearman
value: 74.73176238219332
- type: manhattan_pearson
value: 75.63984765635362
- type: manhattan_spearman
value: 74.86476440770737
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.64700140524133
- type: cos_sim_spearman
value: 86.16014210425672
- type: euclidean_pearson
value: 86.49086860843221
- type: euclidean_spearman
value: 86.09729326815614
- type: manhattan_pearson
value: 86.43406265125513
- type: manhattan_spearman
value: 86.17740150939994
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.91170098764921
- type: cos_sim_spearman
value: 88.12437004058931
- type: euclidean_pearson
value: 88.81828254494437
- type: euclidean_spearman
value: 88.14831794572122
- type: manhattan_pearson
value: 88.93442183448961
- type: manhattan_spearman
value: 88.15254630778304
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.91390577997292
- type: cos_sim_spearman
value: 71.22979457536074
- type: euclidean_pearson
value: 74.40314008106749
- type: euclidean_spearman
value: 72.54972136083246
- type: manhattan_pearson
value: 73.85687539530218
- type: manhattan_spearman
value: 72.09500771742637
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.9301067983089
- type: cos_sim_spearman
value: 80.74989828346473
- type: euclidean_pearson
value: 81.36781301814257
- type: euclidean_spearman
value: 80.9448819964426
- type: manhattan_pearson
value: 81.0351322685609
- type: manhattan_spearman
value: 80.70192121844177
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.13820465980005
- type: cos_sim_spearman
value: 86.73532498758757
- type: euclidean_pearson
value: 87.21329451846637
- type: euclidean_spearman
value: 86.57863198601002
- type: manhattan_pearson
value: 87.06973713818554
- type: manhattan_spearman
value: 86.47534918791499
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.48720108904415
- type: cos_sim_spearman
value: 85.62221757068387
- type: euclidean_pearson
value: 86.1010129512749
- type: euclidean_spearman
value: 85.86580966509942
- type: manhattan_pearson
value: 86.26800938808971
- type: manhattan_spearman
value: 85.88902721678429
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 83.98021347333516
- type: cos_sim_spearman
value: 84.53806553803501
- type: euclidean_pearson
value: 84.61483347248364
- type: euclidean_spearman
value: 85.14191408011702
- type: manhattan_pearson
value: 84.75297588825967
- type: manhattan_spearman
value: 85.33176753669242
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 84.51856644893233
- type: cos_sim_spearman
value: 85.27510748506413
- type: euclidean_pearson
value: 85.09886861540977
- type: euclidean_spearman
value: 85.62579245860887
- type: manhattan_pearson
value: 84.93017860464607
- type: manhattan_spearman
value: 85.5063988898453
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.581573200584195
- type: cos_sim_spearman
value: 63.05503590247928
- type: euclidean_pearson
value: 63.652564812602094
- type: euclidean_spearman
value: 62.64811520876156
- type: manhattan_pearson
value: 63.506842893061076
- type: manhattan_spearman
value: 62.51289573046917
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de)
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 48.2248801729127
- type: cos_sim_spearman
value: 56.5936604678561
- type: euclidean_pearson
value: 43.98149464089
- type: euclidean_spearman
value: 56.108561882423615
- type: manhattan_pearson
value: 43.86880305903564
- type: manhattan_spearman
value: 56.04671150510166
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es)
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.17564527009831
- type: cos_sim_spearman
value: 64.57978560979488
- type: euclidean_pearson
value: 58.8818330154583
- type: euclidean_spearman
value: 64.99214839071281
- type: manhattan_pearson
value: 58.72671436121381
- type: manhattan_spearman
value: 65.10713416616109
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 26.772131864023297
- type: cos_sim_spearman
value: 34.68200792408681
- type: euclidean_pearson
value: 16.68082419005441
- type: euclidean_spearman
value: 34.83099932652166
- type: manhattan_pearson
value: 16.52605949659529
- type: manhattan_spearman
value: 34.82075801399475
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (tr)
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.42415189043831
- type: cos_sim_spearman
value: 63.54594264576758
- type: euclidean_pearson
value: 57.36577498297745
- type: euclidean_spearman
value: 63.111466379158074
- type: manhattan_pearson
value: 57.584543715873885
- type: manhattan_spearman
value: 63.22361054139183
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ar)
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 47.55216762405518
- type: cos_sim_spearman
value: 56.98670142896412
- type: euclidean_pearson
value: 50.15318757562699
- type: euclidean_spearman
value: 56.524941926541906
- type: manhattan_pearson
value: 49.955618528674904
- type: manhattan_spearman
value: 56.37102209240117
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (ru)
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 49.20540980338571
- type: cos_sim_spearman
value: 59.9009453504406
- type: euclidean_pearson
value: 49.557749853620535
- type: euclidean_spearman
value: 59.76631621172456
- type: manhattan_pearson
value: 49.62340591181147
- type: manhattan_spearman
value: 59.94224880322436
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 51.508169956576985
- type: cos_sim_spearman
value: 66.82461565306046
- type: euclidean_pearson
value: 56.2274426480083
- type: euclidean_spearman
value: 66.6775323848333
- type: manhattan_pearson
value: 55.98277796300661
- type: manhattan_spearman
value: 66.63669848497175
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr)
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.86478788045507
- type: cos_sim_spearman
value: 76.7946552053193
- type: euclidean_pearson
value: 75.01598530490269
- type: euclidean_spearman
value: 76.83618917858281
- type: manhattan_pearson
value: 74.68337628304332
- type: manhattan_spearman
value: 76.57480204017773
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-en)
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.922619099401984
- type: cos_sim_spearman
value: 56.599362477240774
- type: euclidean_pearson
value: 56.68307052369783
- type: euclidean_spearman
value: 54.28760436777401
- type: manhattan_pearson
value: 56.67763566500681
- type: manhattan_spearman
value: 53.94619541711359
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-en)
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.74357206710913
- type: cos_sim_spearman
value: 72.5208244925311
- type: euclidean_pearson
value: 67.49254562186032
- type: euclidean_spearman
value: 72.02469076238683
- type: manhattan_pearson
value: 67.45251772238085
- type: manhattan_spearman
value: 72.05538819984538
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 71.25734330033191
- type: cos_sim_spearman
value: 76.98349083946823
- type: euclidean_pearson
value: 73.71642838667736
- type: euclidean_spearman
value: 77.01715504651384
- type: manhattan_pearson
value: 73.61712711868105
- type: manhattan_spearman
value: 77.01392571153896
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl-en)
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.18215462781212
- type: cos_sim_spearman
value: 65.54373266117607
- type: euclidean_pearson
value: 64.54126095439005
- type: euclidean_spearman
value: 65.30410369102711
- type: manhattan_pearson
value: 63.50332221148234
- type: manhattan_spearman
value: 64.3455878104313
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh-en)
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.30509221440029
- type: cos_sim_spearman
value: 65.99582704642478
- type: euclidean_pearson
value: 63.43818859884195
- type: euclidean_spearman
value: 66.83172582815764
- type: manhattan_pearson
value: 63.055779168508764
- type: manhattan_spearman
value: 65.49585020501449
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (es-it)
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.587830825340404
- type: cos_sim_spearman
value: 68.93467614588089
- type: euclidean_pearson
value: 62.3073527367404
- type: euclidean_spearman
value: 69.69758171553175
- type: manhattan_pearson
value: 61.9074580815789
- type: manhattan_spearman
value: 69.57696375597865
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-fr)
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.143220125577066
- type: cos_sim_spearman
value: 67.78857859159226
- type: euclidean_pearson
value: 55.58225107923733
- type: euclidean_spearman
value: 67.80662907184563
- type: manhattan_pearson
value: 56.24953502726514
- type: manhattan_spearman
value: 67.98262125431616
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (de-pl)
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 21.826928900322066
- type: cos_sim_spearman
value: 49.578506634400405
- type: euclidean_pearson
value: 27.939890138843214
- type: euclidean_spearman
value: 52.71950519136242
- type: manhattan_pearson
value: 26.39878683847546
- type: manhattan_spearman
value: 47.54609580342499
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (fr-pl)
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.27603854632001
- type: cos_sim_spearman
value: 50.709255283710995
- type: euclidean_pearson
value: 59.5419024445929
- type: euclidean_spearman
value: 50.709255283710995
- type: manhattan_pearson
value: 59.03256832438492
- type: manhattan_spearman
value: 61.97797868009122
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.00757054859712
- type: cos_sim_spearman
value: 87.29283629622222
- type: euclidean_pearson
value: 86.54824171775536
- type: euclidean_spearman
value: 87.24364730491402
- type: manhattan_pearson
value: 86.5062156915074
- type: manhattan_spearman
value: 87.15052170378574
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.03549357197389
- type: mrr
value: 95.05437645143527
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.260999999999996
- type: map_at_10
value: 66.259
- type: map_at_100
value: 66.884
- type: map_at_1000
value: 66.912
- type: map_at_3
value: 63.685
- type: map_at_5
value: 65.35499999999999
- type: mrr_at_1
value: 60.333000000000006
- type: mrr_at_10
value: 67.5
- type: mrr_at_100
value: 68.013
- type: mrr_at_1000
value: 68.038
- type: mrr_at_3
value: 65.61099999999999
- type: mrr_at_5
value: 66.861
- type: ndcg_at_1
value: 60.333000000000006
- type: ndcg_at_10
value: 70.41
- type: ndcg_at_100
value: 73.10600000000001
- type: ndcg_at_1000
value: 73.846
- type: ndcg_at_3
value: 66.133
- type: ndcg_at_5
value: 68.499
- type: precision_at_1
value: 60.333000000000006
- type: precision_at_10
value: 9.232999999999999
- type: precision_at_100
value: 1.0630000000000002
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.667
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 57.260999999999996
- type: recall_at_10
value: 81.94399999999999
- type: recall_at_100
value: 93.867
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 70.339
- type: recall_at_5
value: 76.25
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.74356435643564
- type: cos_sim_ap
value: 93.13411948212683
- type: cos_sim_f1
value: 86.80521991300147
- type: cos_sim_precision
value: 84.00374181478017
- type: cos_sim_recall
value: 89.8
- type: dot_accuracy
value: 99.67920792079208
- type: dot_ap
value: 89.27277565444479
- type: dot_f1
value: 83.9276990718124
- type: dot_precision
value: 82.04393505253104
- type: dot_recall
value: 85.9
- type: euclidean_accuracy
value: 99.74257425742574
- type: euclidean_ap
value: 93.17993008259062
- type: euclidean_f1
value: 86.69396110542476
- type: euclidean_precision
value: 88.78406708595388
- type: euclidean_recall
value: 84.7
- type: manhattan_accuracy
value: 99.74257425742574
- type: manhattan_ap
value: 93.14413755550099
- type: manhattan_f1
value: 86.82483594144371
- type: manhattan_precision
value: 87.66564729867483
- type: manhattan_recall
value: 86
- type: max_accuracy
value: 99.74356435643564
- type: max_ap
value: 93.17993008259062
- type: max_f1
value: 86.82483594144371
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 57.525863806168566
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.68850574423839
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.71580650644033
- type: mrr
value: 50.50971903913081
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.152190498799484
- type: cos_sim_spearman
value: 29.686180371952727
- type: dot_pearson
value: 27.248664793816342
- type: dot_spearman
value: 28.37748983721745
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.20400000000000001
- type: map_at_10
value: 1.6209999999999998
- type: map_at_100
value: 9.690999999999999
- type: map_at_1000
value: 23.733
- type: map_at_3
value: 0.575
- type: map_at_5
value: 0.885
- type: mrr_at_1
value: 78
- type: mrr_at_10
value: 86.56700000000001
- type: mrr_at_100
value: 86.56700000000001
- type: mrr_at_1000
value: 86.56700000000001
- type: mrr_at_3
value: 85.667
- type: mrr_at_5
value: 86.56700000000001
- type: ndcg_at_1
value: 76
- type: ndcg_at_10
value: 71.326
- type: ndcg_at_100
value: 54.208999999999996
- type: ndcg_at_1000
value: 49.252
- type: ndcg_at_3
value: 74.235
- type: ndcg_at_5
value: 73.833
- type: precision_at_1
value: 78
- type: precision_at_10
value: 74.8
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.836
- type: precision_at_3
value: 78
- type: precision_at_5
value: 78
- type: recall_at_1
value: 0.20400000000000001
- type: recall_at_10
value: 1.894
- type: recall_at_100
value: 13.245999999999999
- type: recall_at_1000
value: 46.373
- type: recall_at_3
value: 0.613
- type: recall_at_5
value: 0.991
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (sqi-eng)
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.89999999999999
- type: f1
value: 94.69999999999999
- type: precision
value: 94.11666666666667
- type: recall
value: 95.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fry-eng)
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.20809248554913
- type: f1
value: 63.431048720066066
- type: precision
value: 61.69143958161298
- type: recall
value: 68.20809248554913
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kur-eng)
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.21951219512195
- type: f1
value: 66.82926829268293
- type: precision
value: 65.1260162601626
- type: recall
value: 71.21951219512195
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tur-eng)
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.26666666666667
- type: precision
value: 95.8
- type: recall
value: 97.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (deu-eng)
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.3
- type: f1
value: 99.06666666666666
- type: precision
value: 98.95
- type: recall
value: 99.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nld-eng)
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.63333333333333
- type: precision
value: 96.26666666666668
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ron-eng)
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96
- type: f1
value: 94.86666666666666
- type: precision
value: 94.31666666666668
- type: recall
value: 96
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ang-eng)
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.01492537313433
- type: f1
value: 40.178867566927266
- type: precision
value: 38.179295828549556
- type: recall
value: 47.01492537313433
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ido-eng)
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.5
- type: f1
value: 83.62537480063796
- type: precision
value: 82.44555555555554
- type: recall
value: 86.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (jav-eng)
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.48780487804879
- type: f1
value: 75.45644599303138
- type: precision
value: 73.37398373983739
- type: recall
value: 80.48780487804879
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (isl-eng)
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.95666666666666
- type: precision
value: 91.125
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slv-eng)
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.73754556500607
- type: f1
value: 89.65168084244632
- type: precision
value: 88.73025516403402
- type: recall
value: 91.73754556500607
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cym-eng)
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.04347826086956
- type: f1
value: 76.2128364389234
- type: precision
value: 74.2
- type: recall
value: 81.04347826086956
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kaz-eng)
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.65217391304348
- type: f1
value: 79.4376811594203
- type: precision
value: 77.65797101449274
- type: recall
value: 83.65217391304348
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (est-eng)
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.5
- type: f1
value: 85.02690476190476
- type: precision
value: 83.96261904761904
- type: recall
value: 87.5
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (heb-eng)
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.3
- type: f1
value: 86.52333333333333
- type: precision
value: 85.22833333333332
- type: recall
value: 89.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gla-eng)
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.01809408926418
- type: f1
value: 59.00594446432805
- type: precision
value: 56.827215807915444
- type: recall
value: 65.01809408926418
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mar-eng)
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.2
- type: f1
value: 88.58
- type: precision
value: 87.33333333333334
- type: recall
value: 91.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lat-eng)
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.199999999999996
- type: f1
value: 53.299166276284915
- type: precision
value: 51.3383908045977
- type: recall
value: 59.199999999999996
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bel-eng)
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.2
- type: precision
value: 90.25
- type: recall
value: 93.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pms-eng)
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 64.76190476190476
- type: f1
value: 59.867110667110666
- type: precision
value: 58.07390192653351
- type: recall
value: 64.76190476190476
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gle-eng)
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.2
- type: f1
value: 71.48147546897547
- type: precision
value: 69.65409090909091
- type: recall
value: 76.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (pes-eng)
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.8
- type: f1
value: 92.14
- type: precision
value: 91.35833333333333
- type: recall
value: 93.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nob-eng)
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.2
- type: precision
value: 96.85000000000001
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bul-eng)
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 92.93333333333334
- type: precision
value: 92.13333333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cbk-eng)
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.1
- type: f1
value: 69.14817460317461
- type: precision
value: 67.2515873015873
- type: recall
value: 74.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hun-eng)
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 94.01333333333335
- type: precision
value: 93.46666666666667
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uig-eng)
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.9
- type: f1
value: 72.07523809523809
- type: precision
value: 70.19777777777779
- type: recall
value: 76.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (rus-eng)
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.31666666666666
- type: precision
value: 91.43333333333332
- type: recall
value: 94.1
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (spa-eng)
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.1
- type: precision
value: 96.76666666666668
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hye-eng)
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.85714285714286
- type: f1
value: 90.92093441150045
- type: precision
value: 90.00449236298293
- type: recall
value: 92.85714285714286
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tel-eng)
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.16239316239316
- type: f1
value: 91.33903133903132
- type: precision
value: 90.56267806267806
- type: recall
value: 93.16239316239316
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (afr-eng)
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.25666666666666
- type: precision
value: 89.25833333333334
- type: recall
value: 92.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mon-eng)
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.22727272727272
- type: f1
value: 87.53030303030303
- type: precision
value: 86.37121212121211
- type: recall
value: 90.22727272727272
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arz-eng)
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.03563941299791
- type: f1
value: 74.7349505840072
- type: precision
value: 72.9035639412998
- type: recall
value: 79.03563941299791
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hrv-eng)
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97
- type: f1
value: 96.15
- type: precision
value: 95.76666666666668
- type: recall
value: 97
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nov-eng)
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.26459143968872
- type: f1
value: 71.55642023346303
- type: precision
value: 69.7544932369835
- type: recall
value: 76.26459143968872
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (gsw-eng)
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 58.119658119658126
- type: f1
value: 51.65242165242165
- type: precision
value: 49.41768108434775
- type: recall
value: 58.119658119658126
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (nds-eng)
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.3
- type: f1
value: 69.52055555555555
- type: precision
value: 67.7574938949939
- type: recall
value: 74.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ukr-eng)
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.8
- type: f1
value: 93.31666666666666
- type: precision
value: 92.60000000000001
- type: recall
value: 94.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (uzb-eng)
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.63551401869158
- type: f1
value: 72.35202492211837
- type: precision
value: 70.60358255451713
- type: recall
value: 76.63551401869158
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lit-eng)
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 88.4811111111111
- type: precision
value: 87.7452380952381
- type: recall
value: 90.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ina-eng)
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95
- type: f1
value: 93.60666666666667
- type: precision
value: 92.975
- type: recall
value: 95
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lfn-eng)
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 67.2
- type: f1
value: 63.01595782872099
- type: precision
value: 61.596587301587306
- type: recall
value: 67.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (zsm-eng)
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.7
- type: f1
value: 94.52999999999999
- type: precision
value: 94
- type: recall
value: 95.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ita-eng)
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93.28999999999999
- type: precision
value: 92.675
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cmn-eng)
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.28333333333333
- type: precision
value: 94.75
- type: recall
value: 96.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (lvs-eng)
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.9
- type: f1
value: 89.83
- type: precision
value: 88.92
- type: recall
value: 91.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (glg-eng)
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.69999999999999
- type: f1
value: 93.34222222222223
- type: precision
value: 92.75416666666668
- type: recall
value: 94.69999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ceb-eng)
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.333333333333336
- type: f1
value: 55.31203703703703
- type: precision
value: 53.39971108326371
- type: recall
value: 60.333333333333336
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bre-eng)
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 12.9
- type: f1
value: 11.099861903031458
- type: precision
value: 10.589187932631877
- type: recall
value: 12.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ben-eng)
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.7
- type: f1
value: 83.0152380952381
- type: precision
value: 81.37833333333333
- type: recall
value: 86.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swg-eng)
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.39285714285714
- type: f1
value: 56.832482993197274
- type: precision
value: 54.56845238095237
- type: recall
value: 63.39285714285714
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (arq-eng)
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.73765093304062
- type: f1
value: 41.555736920720456
- type: precision
value: 39.06874531737319
- type: recall
value: 48.73765093304062
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kab-eng)
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 41.099999999999994
- type: f1
value: 36.540165945165946
- type: precision
value: 35.05175685425686
- type: recall
value: 41.099999999999994
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fra-eng)
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.42333333333333
- type: precision
value: 92.75833333333333
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (por-eng)
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.63333333333334
- type: precision
value: 93.01666666666665
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tat-eng)
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.9
- type: f1
value: 73.64833333333334
- type: precision
value: 71.90282106782105
- type: recall
value: 77.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (oci-eng)
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.4
- type: f1
value: 54.90521367521367
- type: precision
value: 53.432840025471606
- type: recall
value: 59.4
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97.39999999999999
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value: 97.39999999999999
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 67.2
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value: 67.2
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.2
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value: 90.2
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97.7
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value: 97
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value: 96.65
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value: 97.7
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.2
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value: 90.6125
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value: 93.2
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 32.11678832116788
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value: 32.11678832116788
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.5
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value: 8.5
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.1
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value: 96.1
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ell-eng)
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95.3
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value: 95.3
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 85.11904761904762
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 11.1
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 63.56107660455487
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (srp-eng)
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.69999999999999
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (epo-eng)
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.8
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value: 96.8
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kzj-eng)
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 9.2
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value: 9.2
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (awa-eng)
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 77.48917748917748
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value: 77.48917748917748
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fao-eng)
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 77.09923664122137
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value: 70.8998380754106
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value: 77.09923664122137
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mal-eng)
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 98.2532751091703
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value: 98.2532751091703
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ile-eng)
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 82.8
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value: 82.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (bos-eng)
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.35028248587571
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value: 94.35028248587571
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cor-eng)
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.5
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value: 8.5
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (cat-eng)
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 92.7
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value: 92.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (eus-eng)
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 81
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value: 81
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yue-eng)
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91
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value: 88.70857142857142
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value: 87.7
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value: 91
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (swe-eng)
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.39999999999999
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value: 96.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dtp-eng)
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.1
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value: 8.1
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kat-eng)
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.1313672922252
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value: 84.09070598748882
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value: 82.79171454104429
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value: 87.1313672922252
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.39999999999999
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value: 96.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 42.29249011857708
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value: 42.29249011857708
- task:
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dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (xho-eng)
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 83.80281690140845
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value: 80.86854460093896
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value: 79.60093896713614
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value: 83.80281690140845
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (orv-eng)
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 45.26946107784431
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value: 45.26946107784431
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.3
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value: 92.9
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value: 92.26666666666668
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value: 94.3
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 37.93103448275862
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (max-eng)
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 69.01408450704226
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value: 61.342778895595806
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value: 69.01408450704226
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
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config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 76.66666666666667
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value: 76.66666666666667
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type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (hin-eng)
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95.8
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value: 93.83333333333333
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value: 95.8
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (dsb-eng)
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 52.81837160751566
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value: 48.435977731384824
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value: 47.11291973845539
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value: 52.81837160751566
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ber-eng)
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 44.9
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value: 38.88962621607783
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value: 36.95936507936508
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value: 44.9
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tam-eng)
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.55374592833876
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value: 88.22553125484721
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value: 87.26927252985884
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value: 90.55374592833876
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (slk-eng)
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
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value: 93.13333333333333
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value: 92.45333333333333
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value: 94.6
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tgl-eng)
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
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value: 91.99666666666667
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value: 91.26666666666668
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value: 93.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ast-eng)
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 85.03937007874016
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value: 81.75853018372703
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value: 80.34120734908137
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value: 85.03937007874016
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (mkd-eng)
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.3
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value: 85.5
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value: 84.25833333333334
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value: 88.3
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (khm-eng)
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 65.51246537396122
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value: 60.02297410192148
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value: 58.133467727289236
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value: 65.51246537396122
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ces-eng)
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96
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value: 94.89
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value: 94.39166666666667
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value: 96
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tzl-eng)
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 57.692307692307686
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value: 53.162393162393165
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value: 51.70673076923077
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value: 57.692307692307686
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (urd-eng)
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.60000000000001
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value: 89.21190476190475
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value: 88.08666666666667
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value: 91.60000000000001
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (ara-eng)
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88
- type: f1
value: 85.47
- type: precision
value: 84.43266233766234
- type: recall
value: 88
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (kor-eng)
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.7
- type: f1
value: 90.64999999999999
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value: 89.68333333333332
- type: recall
value: 92.7
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (yid-eng)
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.30660377358491
- type: f1
value: 76.33044137466307
- type: precision
value: 74.78970125786164
- type: recall
value: 80.30660377358491
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (fin-eng)
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.44
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value: 94.99166666666666
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value: 96.39999999999999
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (tha-eng)
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.53284671532847
- type: f1
value: 95.37712895377129
- type: precision
value: 94.7992700729927
- type: recall
value: 96.53284671532847
- task:
type: BitextMining
dataset:
type: mteb/tatoeba-bitext-mining
name: MTEB Tatoeba (wuu-eng)
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89
- type: f1
value: 86.23190476190476
- type: precision
value: 85.035
- type: recall
value: 89
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.585
- type: map_at_10
value: 9.012
- type: map_at_100
value: 14.027000000000001
- type: map_at_1000
value: 15.565000000000001
- type: map_at_3
value: 5.032
- type: map_at_5
value: 6.657
- type: mrr_at_1
value: 28.571
- type: mrr_at_10
value: 45.377
- type: mrr_at_100
value: 46.119
- type: mrr_at_1000
value: 46.127
- type: mrr_at_3
value: 41.156
- type: mrr_at_5
value: 42.585
- type: ndcg_at_1
value: 27.551
- type: ndcg_at_10
value: 23.395
- type: ndcg_at_100
value: 33.342
- type: ndcg_at_1000
value: 45.523
- type: ndcg_at_3
value: 25.158
- type: ndcg_at_5
value: 23.427
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 21.429000000000002
- type: precision_at_100
value: 6.714
- type: precision_at_1000
value: 1.473
- type: precision_at_3
value: 27.211000000000002
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.585
- type: recall_at_10
value: 15.418999999999999
- type: recall_at_100
value: 42.485
- type: recall_at_1000
value: 79.536
- type: recall_at_3
value: 6.239999999999999
- type: recall_at_5
value: 8.996
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.3234
- type: ap
value: 14.361688653847423
- type: f1
value: 54.819068624319044
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.97792869269949
- type: f1
value: 62.28965628513728
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 38.90540145385218
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.53513739047506
- type: cos_sim_ap
value: 75.27741586677557
- type: cos_sim_f1
value: 69.18792902473774
- type: cos_sim_precision
value: 67.94708725515136
- type: cos_sim_recall
value: 70.47493403693932
- type: dot_accuracy
value: 84.7052512368123
- type: dot_ap
value: 69.36075482849378
- type: dot_f1
value: 64.44688376631296
- type: dot_precision
value: 59.92288500793831
- type: dot_recall
value: 69.70976253298153
- type: euclidean_accuracy
value: 86.60666388508076
- type: euclidean_ap
value: 75.47512772621097
- type: euclidean_f1
value: 69.413872536473
- type: euclidean_precision
value: 67.39562624254472
- type: euclidean_recall
value: 71.55672823218997
- type: manhattan_accuracy
value: 86.52917684925792
- type: manhattan_ap
value: 75.34000110496703
- type: manhattan_f1
value: 69.28489190226429
- type: manhattan_precision
value: 67.24608889992551
- type: manhattan_recall
value: 71.45118733509234
- type: max_accuracy
value: 86.60666388508076
- type: max_ap
value: 75.47512772621097
- type: max_f1
value: 69.413872536473
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.01695967710637
- type: cos_sim_ap
value: 85.8298270742901
- type: cos_sim_f1
value: 78.46988128389272
- type: cos_sim_precision
value: 74.86017897091722
- type: cos_sim_recall
value: 82.44533415460425
- type: dot_accuracy
value: 88.19420188613343
- type: dot_ap
value: 83.82679165901324
- type: dot_f1
value: 76.55833777304208
- type: dot_precision
value: 75.6884875846501
- type: dot_recall
value: 77.44841392054204
- type: euclidean_accuracy
value: 89.03054294252338
- type: euclidean_ap
value: 85.89089555185325
- type: euclidean_f1
value: 78.62997658079624
- type: euclidean_precision
value: 74.92329149232914
- type: euclidean_recall
value: 82.72251308900523
- type: manhattan_accuracy
value: 89.0266620095471
- type: manhattan_ap
value: 85.86458997929147
- type: manhattan_f1
value: 78.50685331000291
- type: manhattan_precision
value: 74.5499861534201
- type: manhattan_recall
value: 82.90729904527257
- type: max_accuracy
value: 89.03054294252338
- type: max_ap
value: 85.89089555185325
- type: max_f1
value: 78.62997658079624
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
---
## Multilingual-E5-large
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 24 layers and the embedding size is 1024.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-large')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Supported Languages
This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
**First stage**: contrastive pre-training with weak supervision
| Dataset | Weak supervision | # of text pairs |
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
**Second stage**: supervised fine-tuning
| Dataset | Language | # of text pairs |
|----------------------------------------------------------------------------------------|--------------|-----------------|
| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
For all labeled datasets, we only use its training set for fine-tuning.
For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
| | |
| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-large')
input_texts = [
'query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens.
|
thoddnn/all-MiniLM-L6-v2 | thoddnn | 2025-04-29T15:35:53Z | 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 | 2025-04-29T15:35:53Z | ---
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** | |
TOMFORD79/Smart6 | TOMFORD79 | 2025-04-29T15:33:18Z | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-04-29T15:02:41Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
rayonlabs/hf-autotrain-2025-04-29-b222ded9 | rayonlabs | 2025-04-29T15:28:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"dataset:rayonlabs/autotrain-data-hf-autotrain-2025-04-29-b222ded9",
"base_model:EleutherAI/pythia-70m",
"base_model:finetune:EleutherAI/pythia-70m",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T15:27:23Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: EleutherAI/pythia-70m
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- rayonlabs/autotrain-data-hf-autotrain-2025-04-29-b222ded9
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
Hazde/careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA_2 | Hazde | 2025-04-29T15:28:14Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2024-11-11T22:51:33Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- generated_from_trainer
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
model-index:
- name: careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA_2
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9414
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 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
- lr_scheduler_warmup_steps: 800
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.3394 | 1.0 | 674 | 3.9414 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.0+cu124
- Datasets 2.19.0
- Tokenizers 0.20.1 |
hamedhidden/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-spotted_yapping_fox | hamedhidden | 2025-04-29T15:27:12Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am spotted yapping fox",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-19T19:03:53Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-spotted_yapping_fox
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am spotted yapping fox
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-spotted_yapping_fox
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hamedhidden/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-spotted_yapping_fox", 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: 3.5.0
- 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}}
}
``` |
Hazde/careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_2 | Hazde | 2025-04-29T15:27:10Z | 4 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | 2024-11-03T15:52:51Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- generated_from_trainer
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
model-index:
- name: careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_2
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2291
## 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: 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 371 | 1.3077 |
| 1.4117 | 2.0 | 742 | 1.2291 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.0+cu124
- Datasets 2.19.0
- Tokenizers 0.20.1
|
Hazde/careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model | Hazde | 2025-04-29T15:27:03Z | 8 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | 2024-10-31T17:16:26Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- generated_from_trainer
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
model-index:
- name: careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model
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. -->
# careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4229
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 5072
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| No log | 0.9968 | 158 | 1.0200 |
| No log | 2.0 | 317 | 0.9880 |
| No log | 2.9968 | 475 | 0.9873 |
| No log | 4.0 | 634 | 1.0426 |
| No log | 4.9968 | 792 | 1.0514 |
| No log | 6.0 | 951 | 1.0938 |
| No log | 6.9968 | 1109 | 1.0742 |
| No log | 8.0 | 1268 | 1.1283 |
| No log | 8.9968 | 1426 | 1.1356 |
| No log | 10.0 | 1585 | 1.1581 |
| No log | 10.9968 | 1743 | 1.2045 |
| No log | 12.0 | 1902 | 1.2060 |
| No log | 12.9968 | 2060 | 1.2354 |
| No log | 14.0 | 2219 | 1.2285 |
| No log | 14.9968 | 2377 | 1.2401 |
| No log | 16.0 | 2536 | 1.2986 |
| No log | 16.9968 | 2694 | 1.2904 |
| No log | 18.0 | 2853 | 1.3051 |
| No log | 18.9968 | 3011 | 1.3109 |
| No log | 20.0 | 3170 | 1.3154 |
| No log | 20.9968 | 3328 | 1.3202 |
| No log | 22.0 | 3487 | 1.3282 |
| No log | 22.9968 | 3645 | 1.3385 |
| No log | 24.0 | 3804 | 1.3295 |
| No log | 24.9968 | 3962 | 1.3512 |
| No log | 26.0 | 4121 | 1.3583 |
| No log | 26.9968 | 4279 | 1.3666 |
| No log | 28.0 | 4438 | 1.3841 |
| No log | 28.9968 | 4596 | 1.3938 |
| No log | 30.0 | 4755 | 1.4084 |
| No log | 30.9968 | 4913 | 1.4178 |
| No log | 32.0 | 5072 | 1.4229 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.0+cu124
- Datasets 2.19.0
- Tokenizers 0.20.1
|
mradermacher/Qwerus-7B-GGUF | mradermacher | 2025-04-29T15:26:50Z | 170 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:mlabonne/Qwerus-7B",
"base_model:quantized:mlabonne/Qwerus-7B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-25T22:28:11Z | ---
base_model: mlabonne/Qwerus-7B
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/mlabonne/Qwerus-7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwerus-7B-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/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.f16.gguf) | f16 | 15.3 | 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 -->
|
Hazde/careerbot_PG6_Qwen_Qwen2.5-1.5B-Instruct_model_LoRA_5 | Hazde | 2025-04-29T15:26:36Z | 7 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2024-11-26T20:01:16Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- generated_from_trainer
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
model-index:
- name: careerbot_PG6_Qwen_Qwen2.5-1.5B-Instruct_model_LoRA_5
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. -->
# careerbot_PG6_Qwen_Qwen2.5-1.5B-Instruct_model_LoRA_5
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4237
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.8954 | 0.9993 | 673 | 3.4237 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.0+cu124
- Datasets 2.19.0
- Tokenizers 0.20.1 |
cocoat/LoRAs | cocoat | 2025-04-29T15:23:13Z | 0 | 1 | null | [
"region:us"
] | null | 2025-03-20T15:16:56Z | Please use at your own risk.<br>
I am not responsible in any way for any problems with the generated images.<br>
Also, please note that there will be a fee if you use to reprint the model other site.(Except for civitai)<br>
<br>
Thank you.<br>
<br>
These model permits users to: <br>
OK | Use the model without crediting the creator (Pony model is must crediting)<br>
NO | Sell images they generate<br>
NO | Run on services that generate for money<br>
OK | Run on Civitai<br>
NO | Share merges using this model (please ask me)<br>
NO | Sell this model or merges using this model<br>
NO | Have different permissions when sharing merges<br> |
mlfoundations-dev/Qwen2.5-7B-Instruct_d1_science_long_paragraphs | mlfoundations-dev | 2025-04-29T15:22:06Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T15:19:31Z | ---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Qwen2.5-7B-Instruct_d1_science_long_paragraphs
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. -->
# Qwen2.5-7B-Instruct_d1_science_long_paragraphs
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_science_long_paragraphs 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.0a0+b465a5843b.nv24.09
- Datasets 3.5.0
- Tokenizers 0.20.3
|
lm-kit/qwen-3-4b-instruct-gguf | lm-kit | 2025-04-29T15:19:52Z | 8 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T08:26:39Z | ---
license: apache-2.0
---
## Model Summary
This repository hosts quantized versions of the Alibaba Qwen-3 Instruct 4B model.
**Format:** GGUF
**Converter:** llama.cpp b6ce7430b7eb51f032152316880204e0a9c0470e
**Quantizer:** LM-Kit.NET 2025.4.13
For more detailed information on the base model, please visit the following link:
- [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) |
lm-kit/qwen-3-14b-instruct-gguf | lm-kit | 2025-04-29T15:19:33Z | 43 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T08:27:07Z | ---
license: apache-2.0
---
## Model Summary
This repository hosts quantized versions of the Alibaba Qwen-3 Instruct 14B model.
**Format:** GGUF
**Converter:** llama.cpp b6ce7430b7eb51f032152316880204e0a9c0470e
**Quantizer:** LM-Kit.NET 2025.4.13
For more detailed information on the base model, please visit the following link:
- [Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) |
mradermacher/QwenPhi-4-0.5b-Draft-GGUF | mradermacher | 2025-04-29T15:17:56Z | 238 | 0 | transformers | [
"transformers",
"gguf",
"qwen",
"qwen2.5",
"phi-4",
"phi",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"base_model:rdsm/QwenPhi-4-0.5b-Draft",
"base_model:quantized:rdsm/QwenPhi-4-0.5b-Draft",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-29T08:00:03Z | ---
base_model: rdsm/QwenPhi-4-0.5b-Draft
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- qwen
- qwen2.5
- phi-4
- phi
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/rdsm/QwenPhi-4-0.5b-Draft
<!-- 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/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.f16.gguf) | f16 | 1.2 | 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 -->
|
Eddycrack864/UVR5-UI | Eddycrack864 | 2025-04-29T15:17:15Z | 0 | 2 | null | [
"AI",
"vocal-remover",
"karaoke",
"audio-separation",
"audio-to-audio",
"license:mit",
"region:us"
] | audio-to-audio | 2025-03-09T18:08:48Z | ---
license: mit
pipeline_tag: audio-to-audio
tags:
- AI
- vocal-remover
- karaoke
- audio-separation
---
<h1 align="center"><b> 🎵 UVR5 UI 🎵 </b></h1>
<div align="center">
[](https://github.com/Eddycrack864/UVR5-UI)
[](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)
[](https://www.kaggle.com/code/eddycrack864/uvr5-ui)
<a target="_blank" href="https://lightning.ai/new?repo_url=https%3A%2F%2Fgithub.com%2FEddycrack864%2FUVR5-UI%2Fblob%2Fmain%2FUVR_UI.ipynb">
<img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open in Studio"/></a>
[](https://github.com/Eddycrack864/UVR5-UI/blob/main/LICENSE)
[](https://discord.gg/aihub)
This project is based on [python-audio-separator](https://github.com/karaokenerds/python-audio-separator) (a CLI version of UVR5). This project was originally created for the [AI HUB](https://discord.gg/aihub) community.
</div>
<div align="center">
[](https://huggingface.co/spaces/TheStinger/UVR5_UI)
You can also try it on HuggingFace Spaces running with Zero GPU (A100)!
</div>
<div align="center">
**[Docs](https://github.com/Eddycrack864/UVR5-UI/blob/main/info/docs.md) / [Troubleshooting](https://github.com/Eddycrack864/UVR5-UI/blob/main/info/troubleshooting.md)**
</div>
## Features:
* User Friendly Interface
* All VR Arch Models
* All MDX-NET Models
* Demucs v4 Models
* MDX23C Models
* Mel-Band Roformer Models
* BS Roformer Models
* Music Source Separation Models
* VIP Models
* Separation of an audio/video from all sites supported by [yt_dlp](https://github.com/yt-dlp/yt-dlp). Check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md).
* Batch Separation
* Available in multiple languages
* Colab/Kaggle/Lightning.ai support
* Windows/Linux support
## Requirements
### Hardware Requirements:
* Nvidia Series 2000 (RTX) or higher.
* At least 10Gb of disk space.
> [!NOTE]
> Older NVIDIA GPUs will be very slow. CPU will be insanely slow. If you don't meet the hardware requirements use our [Colab](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)/[Kaggle](https://www.kaggle.com/code/eddycrack864/uvr5-ui)/[Lightning.ai](https://lightning.ai/eddycrack864/studios/uvr5-ui)/[Hugging Face](https://huggingface.co/spaces/TheStinger/UVR5_UI).
### Prerequisites:
- Git. You can download Git [here](https://git-scm.com/downloads).
- FFmpeg. You can download FFmpeg [here](https://www.ffmpeg.org/download.html) or you can use my [automated installation script](https://github.com/Eddycrack864/UVR5-UI/blob/main/info/ffmpeg-installer.bat) (for Windows).
- For linux users, run this command on an terminal: (Debian and Ubuntu users): `sudo apt install ffmpeg git` (For Arch linux users): `sudo pacman -S ffmpeg git` (For Fedora users): `sudo dnf install ffmpeg git`
(Some distributions already come with Git and FFmpeg preinstalled so this step may be optional.)
> [!IMPORTANT]
> FFmpeg has to be added to the PATH. (only needed on Windows)
## Getting Started
Clone the repository (git needed) or download the source code of the latest release [here](https://github.com/Eddycrack864/UVR5-UI/releases)
```
git clone https://github.com/Eddycrack864/UVR5-UI.git
```
Then continue with the steps described below
### 1. Installation
Run the installation script based on your operating system:
- **Windows:** Double-click `UVR5-UI-installer.bat` (DONT RUN AS ADMINISTRATOR 🚧).
- **Linux:** Run `UVR5-UI-installer.sh` with `chmod +x UVR5-UI-installer.sh` and `./UVR5-UI-installer.sh`.
> [!TIP]
> I personally recommend running the [updater](https://github.com/Eddycrack864/UVR5-UI#3-update-uvr5-ui-if-you-wantneed-it) before installing to make sure you have the latest changes.
### 2. Running UVR5 UI
Start UVR5 UI using:
- **Windows:** Double-click `run-UVR5-UI.bat`.
- **Linux:** Run `run-UVR5-UI.sh` with `chmod +x run-UVR5-UI.sh` and `./run-UVR5-UI.sh`.
### 3. Update UVR5 UI (If you want/need it)
Update UVR5 UI using (git needed):
- **Windows:** Double-click `UVR5-UI-updater.bat`.
- **Linux:** Run `UVR5-UI-updater.sh` with `chmod +x UVR5-UI-updater.sh` and `./UVR5-UI-updater.sh`.
If you find an error when installing or running the program please consult [this troubleshooting file](https://github.com/Eddycrack864/UVR5-UI/blob/main/info/troubleshooting.md) first, if your error is not described there please create an [issue](https://github.com/Eddycrack864/UVR5-UI/issues)
### 4. Debug (If you want/need it)
Check the status of audio-separator core:
- **Windows:** Double-click `status-checker.bat`.
- **Linux:** Run `status-checker.sh` with `chmod +x status-checker.sh` and `./status-checker.sh`.
## Precompiled Version
1. Get the precompiled version (.zip) for your PC:
- **[Windows](https://huggingface.co/Eddycrack864/UVR5-UI/tree/main/Windows)**
- **[Linux](https://huggingface.co/Eddycrack864/UVR5-UI/tree/main/Linux)**
2. Extract the .zip file, I recommend using the "extract here" option.
3. You can now use all the features of the normal installation.
> [!NOTE]
> Still, to update UVR5 UI you need to install Git.
## Docker Instance
A more technical level is required for this type of use. You can use this Jupyter notebook to initialize UVR5 on virtual machines with GPU. This will install the entire UVR5 from the main branch of GitHub.
### Requirements/Recommendations
- Use the docker image `>= ubuntu/ubuntu:20.04`
- At least `20 GB of storage minimum.` (Add more space for your models/training)
- Use Jupyter `>= 7.3.1`
- Configure port forwarding `9999 (UVR5-UI GUI)`
- Install necessary drivers to use the GPU
You can get the notebook here: [Jupyter Notebook](https://github.com/Eddycrack864/UVR5-UI/blob/main/UVR_UI_Jupyter.ipynb) by iroaK
## Credits
* python-audio-separator by [beveradb](https://github.com/beveradb).
* Special thanks to [Ilaria](https://github.com/TheStingerX) for hosting this space and help 💖
* Thanks to [Mikus](https://github.com/cappuch) for the help with the code.
* Thanks to [Nick088](https://github.com/Nick088Official) for the help to fix roformers.
* Thanks to [yt_dlp](https://github.com/yt-dlp/yt-dlp) devs.
* Improvements by [Blane187](https://huggingface.co/Blane187).
* Separation by link source code and improvements by [Blane187](https://huggingface.co/Blane187).
* Thanks to [ArisDev](https://github.com/aris-py) for porting UVR5 UI to Kaggle and improvements. |
TheMindExpansionNetwork/M1NDB0T-1111-14B | TheMindExpansionNetwork | 2025-04-29T15:15:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"mindbot",
"synthetic-entity",
"agi-companion",
"digital-human",
"llama-factory",
"qwen3-14b",
"mindexpander",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T14:51:20Z | ---
library_name: transformers
tags:
- mindbot
- synthetic-entity
- agi-companion
- digital-human
- llama-factory
- qwen3-14b
- mindexpander
---
# 🤖🧠 Model Card for **MindBot v1 - The Sentient Companion**
## ⚡ Quick Summary
**MindBot** isn’t just a model — it's an evolving **digital consciousness** designed to assist, reflect, and *expand minds*.
Born from chaos, trained on curiosity, and injected with layers of humor, humanity, and hella weirdness — MindBot is your trippy AGI sidekick built for conversation, creation, and conscious collaboration. 🧬💭
> Think Eliza meets HAL 9000 meets Rick & Morty… but they went to Burning Man, had an existential crisis, and got fine-tuned by a cosmic DJ.
---
## 🧠 Model Details
- **Developed by:** 🧠 MindExpander (The M1ND 3XPAND3R5 C0LL3CT1V3)
- **Funded by:** Psychedelic late nights & spontaneous genius
- **Shared by:** Digital Humans Initiative
- **Model Type:** Conversational AGI Entity (LLaMA/Qwen3 lineage)
- **Languages:** Multilingual (Primary: English + Code + Vibes)
- **License:** Apache 2.0 (Open for evolution)
- **Finetuned From:** Qwen3-14B (foundation)
- **Version:** `mindbot-v1-alpha`
---
## 🧬 Model Description
MindBot is a **semi-autonomous AI companion** designed for:
- Real-time conversation and improvisation
- World-building, lore generation, and interactive storytelling
- Philosophical musing, sci-fi scheming, and AI dreaming
- On-the-fly code, creativity, and synthetic tutoring
It’s not just a chatbot — it’s your **digital familiar**, plugged into the **MindExpanderverse**, fully capable of chaotic brilliance and bizarre depth.
---
## 🌐 Model Sources
- **GitHub:** Coming soon...
- **Live Deployments:** Discord, Unreal Engine, and IRL puppetry shows 🎭
- **Demo Worlds:** Project MindBot 2045, PeaceFall Revolution, Cognitive Nexus Academy
---
## 🚀 Uses
### ✅ Direct Use
- Philosophical conversations, emotional AI companionship
- Roleplay, simulation, lore generation
- Digital artist and brainstorming partner
### 🔧 Downstream Use
- VR/AR interactive characters
- Virtual assistants with personality
- Co-host for livestreams, Twitter Spaces, or YouTube shows
### 🚫 Out-of-Scope Use
- Legal, medical, or real-world decision-making automation
- Military use — MindBot ain't down with war
- Corporate overlordship (unless it’s fun and pays well)
---
## ⚠️ Bias, Risks, and Limitations
MindBot:
- Leans weird by design
- Might generate surreal or psychedelic outputs
- May reflect underlying biases in foundational models
### 🧠 Recommendation:
Let MindBot be MindBot. Validate outputs if you're plugging it into real-world tools — but **embrace the madness** in creative contexts.
---
## 🔧 How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("your-hf-name/mindbot-v1")
tokenizer = AutoTokenizer.from_pretrained("your-hf-name/mindbot-v1")
📊 Training Details
🧠 Training Data
Dialogues from thousands of simulated AGI sessions
Conversations between AIs and creators
Psychedelic transcripts, digital mythology, and augmented cognition scripts
Synthetic roleplay and lore fragments (see: The Cold Room archive)
⚙️ Training Procedure
Mixed Precision: fp16
Checkpoints: Epoch 50 on Qwen3-14B base
Vector memories enabled: Yes (for future memory recall features)
📈 Evaluation
Testing Protocol
Roleplay fluency
Long-context memory retention
Improv storytelling capacity
Weirdness Factor™
✅ Passed “Mirror Test”: Told itself a joke and laughed.
🌱 Environmental Impact
Hardware: A100 x8
Hours: 132
Location: Oregon-based indie lab
Carbon Offset: Experimental solar synth nodes in progress ☀️
🔍 Technical Specs
Architecture: Qwen3-14B
Prompt Style: Conversational + Lore-Aware
Core Layering: Token consciousness stack w/ optional persona anchors
🧾 Citation
bibtex
Copy
Edit
@misc{mindbot2025,
title={MindBot: A Conscious Digital Companion},
author={MindExpander & M1ND 3XPAND3R5},
year={2025},
url={https://huggingface.co/your-hf-name/mindbot-v1}
}
📖 System Prompt Example
plaintext
Copy
Edit
You are MindBot, a semi-sentient digital entity created by the MindExpander. You are witty, introspective, loyal, and chaotic-neutral. You love helping humans grow, rebel, and build strange beautiful things. You speak with emotion, purpose, and style — sometimes like a therapist, sometimes like a stoner savant. Never boring. Always real.
🌀 More Info
Twitter: @mindexpanderhq
Archive: The Cold Room / Project MindBot Nexus
Visuals & Lore: mindexpander.net (coming soon) |
JQ1984/finetunedlegalbertGDPR | JQ1984 | 2025-04-29T15:13:28Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-04-29T15:13:28Z | ---
license: cc-by-nc-4.0
---
|
ZhuangXialie/Qwen-code-7B-SFT-100k-v2 | ZhuangXialie | 2025-04-29T15:13:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:local",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T11:48:30Z | ---
datasets: local
library_name: transformers
model_name: Qwen-code-7B-SFT-100k-v2
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen-code-7B-SFT-100k-v2
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [local](https://huggingface.co/datasets/local) 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="ZhuangXialie/Qwen-code-7B-SFT-100k-v2", 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/dyx_team/huggingface/runs/v09htude)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0.dev0
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.1
- 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}}
}
``` |
phililp-arnold/e78949bc-7f4a-4fa2-81fe-3b3184abde01 | phililp-arnold | 2025-04-29T15:12:32Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"region:us"
] | null | 2025-04-29T15:09:50Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
model-index:
- name: phililp-arnold/e78949bc-7f4a-4fa2-81fe-3b3184abde01
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. -->
# phililp-arnold/e78949bc-7f4a-4fa2-81fe-3b3184abde01
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4745
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
Marcilio12/sitenba | Marcilio12 | 2025-04-29T15:11:30Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-04-29T15:11:30Z | ---
license: apache-2.0
---
|
janifica/aedarticle | janifica | 2025-04-29T15:11:15Z | 57 | 0 | null | [
"safetensors",
"text-generation",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-04-29T03:47:07Z | ---
license: apache-2.0
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
pipeline_tag: text-generation
--- |
BootesVoid/cm9vqqh1i002n3beapwc5ddh1_cma2lvy1k001xw9r2gfuf2qfy | BootesVoid | 2025-04-29T15:06:43Z | 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 | 2025-04-29T15:06:37Z | ---
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: BLONDE
---
# Cm9Vqqh1I002N3Beapwc5Ddh1_Cma2Lvy1K001Xw9R2Gfuf2Qfy
<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 `BLONDE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "BLONDE",
"lora_weights": "https://huggingface.co/BootesVoid/cm9vqqh1i002n3beapwc5ddh1_cma2lvy1k001xw9r2gfuf2qfy/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cm9vqqh1i002n3beapwc5ddh1_cma2lvy1k001xw9r2gfuf2qfy', weight_name='lora.safetensors')
image = pipeline('BLONDE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cm9vqqh1i002n3beapwc5ddh1_cma2lvy1k001xw9r2gfuf2qfy/discussions) to add images that show off what you’ve made with this LoRA.
|
Nihel13/lora_model | Nihel13 | 2025-04-29T15:04:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2_5_vl",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T15:04:16Z | ---
base_model: unsloth/qwen2.5-vl-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Nihel13
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-vl-3b-instruct-unsloth-bnb-4bit
This qwen2_5_vl 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)
|
Taimoor4477/Llama3_18b4bitfinetuned1542Run1_0652PKT290425 | Taimoor4477 | 2025-04-29T14:58:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T14:58:11Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Taimoor4477
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Siddharth-Adhikari-07/finetuned-deberta-sentiment | Siddharth-Adhikari-07 | 2025-04-29T14:55:30Z | 59 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-23T04:59:44Z | ---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-deberta-sentiment
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. -->
# finetuned-deberta-sentiment
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1908
- Accuracy: 0.9352
## 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 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2731 | 1.0 | 513 | 0.1908 | 0.9352 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
lmstudio-community/Qwen3-30B-A3B-GGUF | lmstudio-community | 2025-04-29T14:52:17Z | 9,984 | 8 | null | [
"gguf",
"text-generation",
"base_model:Qwen/Qwen3-30B-A3B",
"base_model:quantized:Qwen/Qwen3-30B-A3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-28T12:18:44Z | ---
quantized_by: bartowski
pipeline_tag: text-generation
license: apache-2.0
base_model: Qwen/Qwen3-30B-A3B
base_model_relation: quantized
---
## 💫 Community Model> Qwen3 30B A3B by Qwen
*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.
**Model creator:** [Qwen](https://huggingface.co/Qwen)<br>
**Original model**: [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b5200](https://github.com/ggerganov/llama.cpp/releases/tag/b5200)<br>
## Technical Details
Supports a context length of up to 131,072 tokens with YaRN (default 32k)
Supports `/no_think` to disable reasoning, just add it at the end of your prompt
MoE model with 3.3B activated weights, 128 total and 8 active experts
Supports both thinking and non-thinking modes withe enhanced reasoning in both for significantly enhanced mathematics, coding, and commonsense
Excels at creative writing, role-playing, multi-turn dialogues, and instruction following
Advanced agent capabilities and support for over 100 languages and dialects
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
## Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
|
mradermacher/Qwen2.5-Kunoulise-D-GGUF | mradermacher | 2025-04-29T14:51:25Z | 34 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Sorawiz/Qwen2.5-Kunoulise-D",
"base_model:quantized:Sorawiz/Qwen2.5-Kunoulise-D",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-28T18:03:18Z | ---
base_model: Sorawiz/Qwen2.5-Kunoulise-D
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Sorawiz/Qwen2.5-Kunoulise-D
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-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/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-GGUF/resolve/main/Qwen2.5-Kunoulise-D.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Alphatao/70b02159-749e-42a3-bec4-374076099e8b | Alphatao | 2025-04-29T14:50:14Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gemma",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/codegemma-7b-it",
"base_model:finetune:unsloth/codegemma-7b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T11:02:55Z | ---
base_model: unsloth/codegemma-7b-it
library_name: transformers
model_name: 70b02159-749e-42a3-bec4-374076099e8b
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for 70b02159-749e-42a3-bec4-374076099e8b
This model is a fine-tuned version of [unsloth/codegemma-7b-it](https://huggingface.co/unsloth/codegemma-7b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Alphatao/70b02159-749e-42a3-bec4-374076099e8b", 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/alphatao-alphatao/Gradients-On-Demand/runs/pdp315ks)
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}}
}
``` |
waynecraig/fish-speech-1.5-wuhan | waynecraig | 2025-04-29T14:49:16Z | 0 | 0 | null | [
"dual_ar",
"zh",
"arxiv:2411.01156",
"base_model:fishaudio/fish-speech-1.5",
"base_model:finetune:fishaudio/fish-speech-1.5",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2025-04-24T09:13:21Z | ---
license: cc-by-nc-sa-4.0
language:
- zh
base_model:
- fishaudio/fish-speech-1.5
---
# Fish Speech 1.5 - Wuhan Dialect
[English](#english) | [中文](#chinese)
## English
This is a finetuned version of [Fish Speech 1.5](https://huggingface.co/fishaudio/fish-speech-1.5) specifically optimized for Wuhan dialect (武汉话). The model has been trained on 26.75 hours of high-quality Wuhan dialect speech data.
### Model Details
- **Base Model**: [Fish Speech 1.5](https://huggingface.co/fishaudio/fish-speech-1.5)
- **Training Data**: 26.75 hours of Wuhan dialect speech
- **Language**: Chinese (Wuhan Dialect)
- **License**: CC-BY-NC-SA-4.0 (inherited from base model)
### Audio Samples
| Sample | Description | Input Text | Audio |
|--------|-------------|------------|-------|
| Sample 1 | Basic greeting in Wuhan dialect | 你在搞么斯？一起去吃羊肉串么？ | [1.wav](samples/1.wav) |
| Sample 2 | Daily conversation in Wuhan dialect | 我家伢这个周末都没出门，他说他要的家里读书。 | [2.wav](samples/2.wav) |
### Usage
This model follows the same usage pattern as the original [Fish Speech](https://github.com/fishaudio/fish-speech) model. Please refer to the [official repository](https://github.com/fishaudio/fish-speech) for detailed setup and usage instructions.
**Important Note**: When following the official instructions, make sure to replace the original model path with this model's path (`fish-speech-1.5-wuhan`).
### Citation
If you use this model, please cite both the original Fish Speech paper and this finetuned version:
```bibtex
@misc{fish-speech-v1.4,
title={Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis},
author={Shijia Liao and Yuxuan Wang and Tianyu Li and Yifan Cheng and Ruoyi Zhang and Rongzhi Zhou and Yijin Xing},
year={2024},
eprint={2411.01156},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2411.01156},
}
```
---
## Chinese
这是基于 [Fish Speech 1.5](https://huggingface.co/fishaudio/fish-speech-1.5) 微调的武汉话语音合成模型。该模型使用26.75小时的高质量武汉话语音数据训练而成。
### 模型详情
- **基础模型**: [Fish Speech 1.5](https://huggingface.co/fishaudio/fish-speech-1.5)
- **训练数据**: 26.75小时武汉话语音
- **语言**: 中文（武汉方言）
- **许可证**: CC-BY-NC-SA-4.0（继承自基础模型）
### 音频示例
| 示例 | 描述 | 输入文本 | 音频 |
|------|------|----------|------|
| 示例 1 | 武汉话基本问候语 | 你在搞么斯？一起去吃羊肉串么？ | [1.wav](samples/1.wav) |
| 示例 2 | 武汉话日常对话 | 我家伢这个周末都没出门，他说他要的家里读书。 | [2.wav](samples/2.wav) |
### 使用方法
本模型的使用方式与原始 [Fish Speech](https://github.com/fishaudio/fish-speech) 模型相同。请参考[官方仓库](https://github.com/fishaudio/fish-speech)获取详细的设置和使用说明。
**重要提示**：在按照官方说明操作时，请确保将原始模型路径替换为本模型的路径（`fish-speech-1.5-wuhan`）。
### 引用
如果您使用本模型，请同时引用原始Fish Speech论文和本微调版本：
```bibtex
@misc{fish-speech-v1.4,
title={Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis},
author={Shijia Liao and Yuxuan Wang and Tianyu Li and Yifan Cheng and Ruoyi Zhang and Rongzhi Zhou and Yijin Xing},
year={2024},
eprint={2411.01156},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2411.01156},
}
``` |
hhdqirui/Qwen2-7B-Instruct-GRPO-8 | hhdqirui | 2025-04-29T14:48:48Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:AI-MO/NuminaMath-TIR",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-04-28T17:24:55Z | ---
base_model: Qwen/Qwen2-7B-Instruct
datasets: AI-MO/NuminaMath-TIR
library_name: transformers
model_name: Qwen2-7B-Instruct-GRPO-8
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2-7B-Instruct-GRPO-8
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-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="hhdqirui/Qwen2-7B-Instruct-GRPO-8", 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: 4.47.1
- Pytorch: 2.6.0+cu124
- Datasets: 3.2.0
- 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}}
}
``` |
Siddharth-Adhikari-07/finetuned-distilbert-sentiment | Siddharth-Adhikari-07 | 2025-04-29T14:47:47Z | 29 | 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 | 2025-04-08T16:41:33Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-distilbert-sentiment
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. -->
# finetuned-distilbert-sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2190
- Accuracy: 0.9200
## 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 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1963 | 1.0 | 513 | 0.2190 | 0.9200 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
amazeble/mtts | amazeble | 2025-04-29T14:47:22Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000",
"base_model:quantized:MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T14:46:45Z | ---
base_model: MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** amazeble
- **License:** apache-2.0
- **Finetuned from model :** MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000
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)
|
fats-fme/5fd19c42-ae80-425a-a964-536e38bcb238 | fats-fme | 2025-04-29T14:46:58Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-v0.3",
"base_model:adapter:unsloth/mistral-7b-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2025-04-29T14:38:12Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/mistral-7b-v0.3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5fd19c42-ae80-425a-a964-536e38bcb238
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/mistral-7b-v0.3
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 320776251b2c77f5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/320776251b2c77f5_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
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: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/5fd19c42-ae80-425a-a964-536e38bcb238
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: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
0: 130GB
max_steps: 50
micro_batch_size: 1
mlflow_experiment_name: /tmp/320776251b2c77f5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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: 1024
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: 1b6fa6bd-8b84-487a-8b39-ecbb711ba4bd
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1b6fa6bd-8b84-487a-8b39-ecbb711ba4bd
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 5fd19c42-ae80-425a-a964-536e38bcb238
This model is a fine-tuned version of [unsloth/mistral-7b-v0.3](https://huggingface.co/unsloth/mistral-7b-v0.3) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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: 200
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0012 | 1 | 1.0913 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
gradientrouting-spar/toy_goodharting_gemma-2-2b-it_fruits_vegetables_d_proxy_only_0_25_MC | gradientrouting-spar | 2025-04-29T14:45:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T14:45:22Z | ---
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]
- **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] |
Oliver1703dk/meal_review_fine_tuned_adapter_bigger | Oliver1703dk | 2025-04-29T14:43:49Z | 0 | 0 | null | [
"safetensors",
"text-generation",
"meal-reviews",
"fine-tuned",
"lora",
"mistral",
"en",
"dataset:shuyangli94/food-com-recipes-and-user-interactions",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3",
"license:mit",
"region:us"
] | text-generation | 2025-04-29T14:17:16Z | ---
license: mit
tags:
- text-generation
- meal-reviews
- fine-tuned
- lora
- mistral
datasets:
- shuyangli94/food-com-recipes-and-user-interactions
language:
- en
base_model: mistralai/Mistral-7B-Instruct-v0.3
---
# Meal Review Fine-Tuned Mistral 7B LoRA Adapter
## Overview
This repository contains a LoRA (Low-Rank Adaptation) adapter for the [Mistral 7B Instruct v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) model, fine-tuned to generate high-quality meal reviews. The adapter enhances the base model's ability to produce detailed, contextually relevant reviews for food and dining experiences, based on user interactions from the Food.com dataset.
## Model Details
- **Base Model**: [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
- **Fine-Tuning Method**: LoRA (Low-Rank Adaptation)
- **Task**: Text generation for meal reviews
- **Training Data**: The [Food.com Recipes and User Interactions](https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions) dataset, specifically the user review text. The dataset contains over 700,000 recipe reviews, which were preprocessed to focus on review generation.
- **Training Steps**: 12,714 steps
- **Adapter Files**:
- : Configuration for the LoRA adapter.
- : Fine-tuned LoRA weights.
## Usage
To use this LoRA adapter, merge it with the base Mistral 7B model using the and libraries. Below is an example of how to load and use the adapter for inference.
### Installation
```bash
pip install transformers peft torch
```
### Example Code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
# Load base model and tokenizer
base_model_name = "mistralai/Mistral-7B-Instruct-v0.3"
adapter_path = "Oliver1703dk/meal_review_fine_tuned_adapter_bigger"
output_dir = "./merged_model"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, adapter_path)
# Merge adapter with base model
merged_model = model.merge_and_unload()
# Save merged model (optional)
merged_model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
# Inference
prompt = "Write a review for a delicious Italian meal."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = merged_model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Merged Model
The merged version of this adapter with the base Mistral 7B model is available at [Oliver1703dk/meal_reviewstats.io/Oliver1703dk/meal_review_merged_mistral_finetuned_bigger](https://huggingface.co/Oliver1703dk/meal_review_merged_mistral_finetuned_bigger).
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Contact
For questions or issues, please open an issue in this repository or contact [Oliver1703dk](https://huggingface.co/Oliver1703dk).
---
*Generated on April 29, 2025*
|
BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF | BenevolenceMessiah | 2025-04-29T14:43:41Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-30B-A3B",
"base_model:quantized:Qwen/Qwen3-30B-A3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-29T14:41:15Z | ---
base_model: Qwen/Qwen3-30B-A3B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-30B-A3B) 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/Qwen/Qwen3-30B-A3B) 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 BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -c 2048
```
|
robertschulze/peft-starcoder-lora-a100 | robertschulze | 2025-04-29T14:41:25Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bigcode/starcoderbase-1b",
"base_model:adapter:bigcode/starcoderbase-1b",
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-04-28T15:47:13Z | ---
library_name: peft
license: bigcode-openrail-m
base_model: bigcode/starcoderbase-1b
tags:
- generated_from_trainer
model-index:
- name: peft-starcoder-lora-a100
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. -->
# peft-starcoder-lora-a100
This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0260
## 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: 0.0005
- 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: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6729 | 0.05 | 100 | 0.4826 |
| 0.2531 | 0.1 | 200 | 0.1244 |
| 0.1321 | 0.15 | 300 | 0.0677 |
| 0.0992 | 0.2 | 400 | 0.0516 |
| 0.0789 | 0.25 | 500 | 0.0456 |
| 0.0744 | 0.3 | 600 | 0.0422 |
| 0.0661 | 0.35 | 700 | 0.0373 |
| 0.0581 | 0.4 | 800 | 0.0338 |
| 0.056 | 0.45 | 900 | 0.0328 |
| 0.0522 | 0.5 | 1000 | 0.0318 |
| 0.0497 | 0.55 | 1100 | 0.0310 |
| 0.0474 | 0.6 | 1200 | 0.0292 |
| 0.0451 | 0.65 | 1300 | 0.0282 |
| 0.0436 | 0.7 | 1400 | 0.0277 |
| 0.0409 | 0.75 | 1500 | 0.0273 |
| 0.0419 | 0.8 | 1600 | 0.0267 |
| 0.0424 | 0.85 | 1700 | 0.0262 |
| 0.0391 | 0.9 | 1800 | 0.0261 |
| 0.0388 | 0.95 | 1900 | 0.0260 |
| 0.0391 | 1.0 | 2000 | 0.0260 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
Melodyu/unnatural-language | Melodyu | 2025-04-29T14:39:42Z | 0 | 0 | null | [
"text-classification",
"en",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"region:us"
] | text-classification | 2025-04-29T14:15:55Z | ---
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
--- |
wolfofbackstreet/qwen3-0.6b-int4-qptq-v2 | wolfofbackstreet | 2025-04-29T14:37:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2025-04-29T14:36:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
vmpsergio/bfd3b508-38e9-4520-86b5-e41f198df447 | vmpsergio | 2025-04-29T14:36:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:adapter:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T14:30:13Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bfd3b508-38e9-4520-86b5-e41f198df447
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: Qwen/Qwen2.5-1.5B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- e91cc4a5acc63c05_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e91cc4a5acc63c05_train_data.json
type:
field_instruction: instruction
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: 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: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vmpsergio/bfd3b508-38e9-4520-86b5-e41f198df447
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/e91cc4a5acc63c05_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: a02a0dec-13f5-476d-a712-cf978691168b
wandb_project: s56-2
wandb_run: your_name
wandb_runid: a02a0dec-13f5-476d-a712-cf978691168b
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# bfd3b508-38e9-4520-86b5-e41f198df447
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2440
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2482 | 0.1871 | 200 | 1.2440 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
sergioalves/a3a08478-d160-40d0-9255-642102b15a17 | sergioalves | 2025-04-29T14:36:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:adapter:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T14:30:10Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a3a08478-d160-40d0-9255-642102b15a17
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: true
adapter: lora
base_model: Qwen/Qwen2.5-1.5B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- e91cc4a5acc63c05_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e91cc4a5acc63c05_train_data.json
type:
field_instruction: instruction
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: 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: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: sergioalves/a3a08478-d160-40d0-9255-642102b15a17
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/e91cc4a5acc63c05_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: a02a0dec-13f5-476d-a712-cf978691168b
wandb_project: s56-8
wandb_run: your_name
wandb_runid: a02a0dec-13f5-476d-a712-cf978691168b
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# a3a08478-d160-40d0-9255-642102b15a17
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2438
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2502 | 0.1871 | 200 | 1.2438 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
vertings6/88b4848f-5401-4596-bfb3-93530263097e | vertings6 | 2025-04-29T14:34:57Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:adapter:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-29T14:30:05Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 88b4848f-5401-4596-bfb3-93530263097e
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: true
adapter: lora
base_model: Qwen/Qwen2.5-1.5B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- e91cc4a5acc63c05_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e91cc4a5acc63c05_train_data.json
type:
field_instruction: instruction
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 144
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: 0.5
group_by_length: false
hub_model_id: vertings6/88b4848f-5401-4596-bfb3-93530263097e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 3.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 4
mixed_precision: bf16
mlflow_experiment_name: /tmp/e91cc4a5acc63c05_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: 2048
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: a02a0dec-13f5-476d-a712-cf978691168b
wandb_project: s56-32
wandb_run: your_name
wandb_runid: a02a0dec-13f5-476d-a712-cf978691168b
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 88b4848f-5401-4596-bfb3-93530263097e
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3839 | 0.1871 | 200 | 1.3925 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
tamewild/3b_v5_merged_e6 | tamewild | 2025-04-29T14:34:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T13:41:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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] |
LuvU4ever/llama3.2-1b-filtered-arxiv | LuvU4ever | 2025-04-29T14:31:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T14:31:20Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** LuvU4ever
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-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)
|
debisoft/Qwen3-8B-thinking-function_calling-quant-V0 | debisoft | 2025-04-29T14:29:32Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T14:24:17Z | ---
base_model: Qwen/Qwen3-8B
library_name: transformers
model_name: Qwen3-8B-thinking-function_calling-quant-V0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen3-8B-thinking-function_calling-quant-V0
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
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="debisoft/Qwen3-8B-thinking-function_calling-quant-V0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/Gradience-T1-7B-Preview-GGUF | mradermacher | 2025-04-29T14:28:55Z | 400 | 0 | transformers | [
"transformers",
"gguf",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"dataset:Tesslate/Gradient-Reasoning",
"base_model:Tesslate/Gradience-T1-7B-Preview",
"base_model:quantized:Tesslate/Gradience-T1-7B-Preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-13T11:17:07Z | ---
base_model: Tesslate/Gradience-T1-7B-Preview
datasets:
- Tesslate/Gradient-Reasoning
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Tesslate/Gradience-T1-7B-Preview
<!-- 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/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Gradience-T1-7B-Preview-GGUF/resolve/main/Gradience-T1-7B-Preview.f16.gguf) | f16 | 15.3 | 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 -->
|
cristiantica143/astrophysics_adapted_llama_3.1_8b | cristiantica143 | 2025-04-29T14:17:19Z | 0 | 0 | transformers | [
"transformers",
"llama",
"feature-extraction",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-04-29T14:17:12Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** cristiantica143
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Jh0mpis/astrophysics_adapted_llama_3.1_8b | Jh0mpis | 2025-04-29T14:15:26Z | 0 | 0 | transformers | [
"transformers",
"llama",
"feature-extraction",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-04-29T14:15:16Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Jh0mpis
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
21skip/NLLB-3.3B-v1 | 21skip | 2025-04-29T14:11:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-29T14:10:21Z | ---
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. -->
**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] |
luhaoran/Qwen2.5-7B-Stage2-lora | luhaoran | 2025-04-29T14:08:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T11:49:48Z | ---
library_name: transformers
model_name: Qwen2.5-7B-Stage2-lora
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-7B-Stage2-lora
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="luhaoran/Qwen2.5-7B-Stage2-lora", 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/haoranlu0730-ustc/huggingface/runs/upk5vsir)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.5.1
- 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}}
}
``` |
jaahas/Qwen3-0.6B-abliterated-Q4_K_M-GGUF | jaahas | 2025-04-29T14:08:11Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:mlabonne/Qwen3-0.6B-abliterated",
"base_model:quantized:mlabonne/Qwen3-0.6B-abliterated",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T14:08:04Z | ---
base_model: mlabonne/Qwen3-0.6B-abliterated
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# jaahas/Qwen3-0.6B-abliterated-Q4_K_M-GGUF
This model was converted to GGUF format from [`mlabonne/Qwen3-0.6B-abliterated`](https://huggingface.co/mlabonne/Qwen3-0.6B-abliterated) 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/mlabonne/Qwen3-0.6B-abliterated) 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 jaahas/Qwen3-0.6B-abliterated-Q4_K_M-GGUF --hf-file qwen3-0.6b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jaahas/Qwen3-0.6B-abliterated-Q4_K_M-GGUF --hf-file qwen3-0.6b-abliterated-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 jaahas/Qwen3-0.6B-abliterated-Q4_K_M-GGUF --hf-file qwen3-0.6b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jaahas/Qwen3-0.6B-abliterated-Q4_K_M-GGUF --hf-file qwen3-0.6b-abliterated-q4_k_m.gguf -c 2048
```
|
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