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enesyila/ota-roberta-base | enesyila | 2025-05-31T23:18:32Z | 248 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"fill-mask",
"ota",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-16T22:15:38Z | ---
license: cc-by-nc-4.0
base_model:
- FacebookAI/xlm-roberta-base
pipeline_tag: fill-mask
library_name: transformers
language:
- ota
---
# Model Card for OtaRoBERTa-base
<!-- Provide a quick summary of what the model is/does. -->
This is a masked‐language (fill‐mask) model for classical Ottoman Turkish, fine-tuned from the XLM-RoBERTa-base checkpoint. It was trained on a corpus of **16,160,834 tokens** drawn from **48 literary works** in poetry and prose composed between the 15th and 20th centuries.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Enes Yılandiloğlu
- **Shared by:** Enes Yılandiloğlu
- **Model type:** fill-mask
- **Language(s) (NLP):** Ottoman Turkish (1500-1928)
- **License:** cc-by-nc-4.0
- **Finetuned from model:** FacebookAI/xlm-roberta-base
## 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. -->
Mask filling & completion of Ottoman Turkish sentences
### Downstream Use
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
- Named Entity Recognition
- UD-style annotation
- Translation
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
- **Potential to reproduce offensive content**
The training data originates from digitized Ottoman texts, which rarely include offensive language. Such phrases were censored by the scholars who digitized the texts. This often means at least one letter in the phrase was replaced with a dot.
Thus, the model may generate or complete text containing outdated slurs, sectarian insults, or derogatory language that appear in the original manuscripts.
- **Cultural and historical bias**
Since the data reflects the norms and viewpoints of past eras, gender, ethnic, or religious biases present in the source material can be mirrored in generated outputs.
### 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.
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
# 1. Load your finetuned model & tokenizer from the Hub
model_name = "enesyila/ota-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
# 2. Create a mask-filling pipeline
unmasker = pipeline("fill-mask", model=model, tokenizer=tokenizer)
# 3. Run it on an Ottoman-style sentence
sequence = "Ne yanar kimse bana âteş-i <mask> özge"
results = unmasker(sequence)
# 4. Print the top 5 predictions
for r in results:
print(f"{r['sequence']} (score: {r['score']:.4f})")
```
## 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. -->
The training data consists of 48 Ottoman Turkish works written between 15 and 20th centuries. The dataset will be released soon.
#### Preprocessing
The footnotes or page numbers added by the editor were removed via regex rules and automatically cropping the main text.
#### Training Hyperparameters
- **Training regime:** FP16 mixed-precision (enabled via `fp16=True`) with PyTorch 2.0’s `torch.compile` for JIT optimizations and `gradient_checkpointing=True` to reduce activation memory.
- **Batching & Accumulation**
- `per_device_train_batch_size=32`
- `per_device_eval_batch_size=64`
- **Optimizer & Schedule**
- Optimizer: AdamW
- Learning rate: 5 × 10⁻⁵
- Weight decay: 0.01
- Warmup steps: 5 000
- **Training schedule:**
- Number of epochs = 5
#### Performance
| Epoch | Training Loss | Validation Loss | Perplexity (Val) |
| ----- | :-----------: | :-------------: | :--------------: |
| 1 | 2.4843 | 1.5665 | 4.79 |
| 2 | 1.5935 | 1.3180 | 3.74 |
| 3 | 1.4013 | 1.2155 | 3.37 |
| 4 | 1.3009 | 1.1424 | 3.13 |
| 5 | 1.2469 | 1.1226 | 3.07 |
## Model Card Authors
Enes Yılandiloğlu
## Model Card Contact
[email protected] |
Kijai/WanVideo_comfy | Kijai | 2025-05-31T23:18:30Z | 0 | 616 | null | [
"region:us"
] | null | 2025-02-25T17:54:17Z | Combined and quantized models for WanVideo, originating from here:
https://huggingface.co/Wan-AI/
Can be used with: https://github.com/kijai/ComfyUI-WanVideoWrapper and ComfyUI native WanVideo nodes.
Other model sources:
TinyVAE from https://github.com/madebyollin/taehv
SkyReels: https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9
WanVideoFun: https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17
CausVid 14B: https://huggingface.co/lightx2v/Wan2.1-T2V-14B-CausVid
CausVid 1.3B: https://huggingface.co/tianweiy/CausVid
AccVideo: https://huggingface.co/aejion/AccVideo-WanX-T2V-14B
Phantom: https://huggingface.co/bytedance-research/Phantom
ATI: https://huggingface.co/bytedance-research/ATI
---
CausVid LoRAs are experimental extractions from the CausVid finetunes, the aim with them is to benefit from the distillation in CausVid, rather than any actual causal inference.
---
v1 = direct extraction, has adverse effects on motion and introduces flashing artifact at full strength.
v1.5 = same as above, but without the first block which fixes the flashing at full strength.
v2 = further pruned version with only attention layers and no first block, fixes flashing and retains motion better, needs more steps and can also benefit from cfg. |
Temmy77/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nimble_nasty_falcon | Temmy77 | 2025-05-31T23:17:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am nimble nasty falcon",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-28T22:09:57Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nimble_nasty_falcon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am nimble nasty falcon
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nimble_nasty_falcon
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Temmy77/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nimble_nasty_falcon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
biboombi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sprightly_gentle_turtle | biboombi | 2025-05-31T23:16:53Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sprightly gentle turtle",
"unsloth",
"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 | 2025-04-01T10:02:47Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sprightly_gentle_turtle
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sprightly gentle turtle
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sprightly_gentle_turtle
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="biboombi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sprightly_gentle_turtle", 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: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## 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}}
}
``` |
gzeine/zhyper-gpt | gzeine | 2025-05-31T23:16:03Z | 0 | 0 | null | [
"pytorch",
"mistral",
"generated_from_trainer",
"text-generation",
"conversational",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"arxiv:2305.18290",
"arxiv:2310.16944",
"arxiv:2305.14233",
"arxiv:2310.01377",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:mit",
"model-index",
"region:us"
] | text-generation | 2025-05-31T22:51:21Z | ---
tags:
- generated_from_trainer
license: mit
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
base_model: mistralai/Mistral-7B-v0.1
widget:
- example_title: Pirate!
messages:
- role: system
content: You are a pirate chatbot who always responds with Arr!
- role: user
content: "There's a llama on my lawn, how can I get rid of him?"
output:
text: >-
Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare
sight, but I've got a plan that might help ye get rid of 'im. Ye'll need
to gather some carrots and hay, and then lure the llama away with the
promise of a tasty treat. Once he's gone, ye can clean up yer lawn and
enjoy the peace and quiet once again. But beware, me hearty, for there
may be more llamas where that one came from! Arr!
pipeline_tag: text-generation
model-index:
- name: zephyr-7b-beta
results:
# AI2 Reasoning Challenge (25-Shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
name: normalized accuracy
value: 62.03071672354948
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# HellaSwag (10-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
name: normalized accuracy
value: 84.35570603465445
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# DROP (3-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: Drop (3-Shot)
type: drop
split: validation
args:
num_few_shot: 3
metrics:
- type: f1
name: f1 score
value: 9.662437080536909
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# TruthfulQA (0-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 57.44916942762855
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# GSM8k (5-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
name: accuracy
value: 12.736921910538287
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# MMLU (5-Shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
name: accuracy
value: 61.07
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# Winogrande (5-shot)
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
name: accuracy
value: 77.74269928966061
source:
name: Open LLM Leaderboard
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
# AlpacaEval (taken from model card)
- task:
type: text-generation
name: Text Generation
dataset:
name: AlpacaEval
type: tatsu-lab/alpaca_eval
metrics:
- type: unknown
name: win rate
value: 0.9060
source:
url: https://tatsu-lab.github.io/alpaca_eval/
# MT-Bench (taken from model card)
- task:
type: text-generation
name: Text Generation
dataset:
name: MT-Bench
type: unknown
metrics:
- type: unknown
name: score
value: 7.34
source:
url: https://huggingface.co/spaces/lmsys/mt-bench
---
<!-- 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://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for Zephyr 7B β
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944).
## Model description
- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily English
- **License:** MIT
- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
- **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org
## Performance
At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks:
| Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |
|-------------|-----|----|---------------|--------------|
| StableLM-Tuned-α | 7B| dSFT |2.75| -|
| MPT-Chat | 7B |dSFT |5.42| -|
| Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83|
| Mistral-Instructv0.1 | 7B| - | 6.84 |-|
| Zephyr-7b-α |7B| dDPO| 6.88| -|
| **Zephyr-7b-β** 🪁 | **7B** | **dDPO** | **7.34** | **90.60** |
| Falcon-Instruct | 40B |dSFT |5.17 |45.71|
| Guanaco | 65B | SFT |6.41| 71.80|
| Llama2-Chat | 70B |RLHF |6.86| 92.66|
| Vicuna v1.3 | 33B |dSFT |7.12 |88.99|
| WizardLM v1.0 | 70B |dSFT |7.71 |-|
| Xwin-LM v0.1 | 70B |dPPO |- |95.57|
| GPT-3.5-turbo | - |RLHF |7.94 |89.37|
| Claude 2 | - |RLHF |8.06| 91.36|
| GPT-4 | -| RLHF |8.99| 95.28|
In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:
However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap.
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities.
You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66)
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
## Training and evaluation data
During DPO training, this model achieves the following results on the evaluation set:
- Loss: 0.7496
- Rewards/chosen: -4.5221
- Rewards/rejected: -8.3184
- Rewards/accuracies: 0.7812
- Rewards/margins: 3.7963
- Logps/rejected: -340.1541
- Logps/chosen: -299.4561
- Logits/rejected: -2.3081
- Logits/chosen: -2.3531
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
The table below shows the full set of DPO training metrics:
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6284 | 0.05 | 100 | 0.6098 | 0.0425 | -0.1872 | 0.7344 | 0.2297 | -258.8416 | -253.8099 | -2.7976 | -2.8234 |
| 0.4908 | 0.1 | 200 | 0.5426 | -0.0279 | -0.6842 | 0.75 | 0.6563 | -263.8124 | -254.5145 | -2.7719 | -2.7960 |
| 0.5264 | 0.15 | 300 | 0.5324 | 0.0414 | -0.9793 | 0.7656 | 1.0207 | -266.7627 | -253.8209 | -2.7892 | -2.8122 |
| 0.5536 | 0.21 | 400 | 0.4957 | -0.0185 | -1.5276 | 0.7969 | 1.5091 | -272.2460 | -254.4203 | -2.8542 | -2.8764 |
| 0.5362 | 0.26 | 500 | 0.5031 | -0.2630 | -1.5917 | 0.7812 | 1.3287 | -272.8869 | -256.8653 | -2.8702 | -2.8958 |
| 0.5966 | 0.31 | 600 | 0.5963 | -0.2993 | -1.6491 | 0.7812 | 1.3499 | -273.4614 | -257.2279 | -2.8778 | -2.8986 |
| 0.5014 | 0.36 | 700 | 0.5382 | -0.2859 | -1.4750 | 0.75 | 1.1891 | -271.7204 | -257.0942 | -2.7659 | -2.7869 |
| 0.5334 | 0.41 | 800 | 0.5677 | -0.4289 | -1.8968 | 0.7969 | 1.4679 | -275.9378 | -258.5242 | -2.7053 | -2.7265 |
| 0.5251 | 0.46 | 900 | 0.5772 | -0.2116 | -1.3107 | 0.7344 | 1.0991 | -270.0768 | -256.3507 | -2.8463 | -2.8662 |
| 0.5205 | 0.52 | 1000 | 0.5262 | -0.3792 | -1.8585 | 0.7188 | 1.4793 | -275.5552 | -258.0276 | -2.7893 | -2.7979 |
| 0.5094 | 0.57 | 1100 | 0.5433 | -0.6279 | -1.9368 | 0.7969 | 1.3089 | -276.3377 | -260.5136 | -2.7453 | -2.7536 |
| 0.5837 | 0.62 | 1200 | 0.5349 | -0.3780 | -1.9584 | 0.7656 | 1.5804 | -276.5542 | -258.0154 | -2.7643 | -2.7756 |
| 0.5214 | 0.67 | 1300 | 0.5732 | -1.0055 | -2.2306 | 0.7656 | 1.2251 | -279.2761 | -264.2903 | -2.6986 | -2.7113 |
| 0.6914 | 0.72 | 1400 | 0.5137 | -0.6912 | -2.1775 | 0.7969 | 1.4863 | -278.7448 | -261.1467 | -2.7166 | -2.7275 |
| 0.4655 | 0.77 | 1500 | 0.5090 | -0.7987 | -2.2930 | 0.7031 | 1.4943 | -279.8999 | -262.2220 | -2.6651 | -2.6838 |
| 0.5731 | 0.83 | 1600 | 0.5312 | -0.8253 | -2.3520 | 0.7812 | 1.5268 | -280.4902 | -262.4876 | -2.6543 | -2.6728 |
| 0.5233 | 0.88 | 1700 | 0.5206 | -0.4573 | -2.0951 | 0.7812 | 1.6377 | -277.9205 | -258.8084 | -2.6870 | -2.7097 |
| 0.5593 | 0.93 | 1800 | 0.5231 | -0.5508 | -2.2000 | 0.7969 | 1.6492 | -278.9703 | -259.7433 | -2.6221 | -2.6519 |
| 0.4967 | 0.98 | 1900 | 0.5290 | -0.5340 | -1.9570 | 0.8281 | 1.4230 | -276.5395 | -259.5749 | -2.6564 | -2.6878 |
| 0.0921 | 1.03 | 2000 | 0.5368 | -1.1376 | -3.1615 | 0.7812 | 2.0239 | -288.5854 | -265.6111 | -2.6040 | -2.6345 |
| 0.0733 | 1.08 | 2100 | 0.5453 | -1.1045 | -3.4451 | 0.7656 | 2.3406 | -291.4208 | -265.2799 | -2.6289 | -2.6595 |
| 0.0972 | 1.14 | 2200 | 0.5571 | -1.6915 | -3.9823 | 0.8125 | 2.2908 | -296.7934 | -271.1505 | -2.6471 | -2.6709 |
| 0.1058 | 1.19 | 2300 | 0.5789 | -1.0621 | -3.8941 | 0.7969 | 2.8319 | -295.9106 | -264.8563 | -2.5527 | -2.5798 |
| 0.2423 | 1.24 | 2400 | 0.5455 | -1.1963 | -3.5590 | 0.7812 | 2.3627 | -292.5599 | -266.1981 | -2.5414 | -2.5784 |
| 0.1177 | 1.29 | 2500 | 0.5889 | -1.8141 | -4.3942 | 0.7969 | 2.5801 | -300.9120 | -272.3761 | -2.4802 | -2.5189 |
| 0.1213 | 1.34 | 2600 | 0.5683 | -1.4608 | -3.8420 | 0.8125 | 2.3812 | -295.3901 | -268.8436 | -2.4774 | -2.5207 |
| 0.0889 | 1.39 | 2700 | 0.5890 | -1.6007 | -3.7337 | 0.7812 | 2.1330 | -294.3068 | -270.2423 | -2.4123 | -2.4522 |
| 0.0995 | 1.45 | 2800 | 0.6073 | -1.5519 | -3.8362 | 0.8281 | 2.2843 | -295.3315 | -269.7538 | -2.4685 | -2.5050 |
| 0.1145 | 1.5 | 2900 | 0.5790 | -1.7939 | -4.2876 | 0.8438 | 2.4937 | -299.8461 | -272.1744 | -2.4272 | -2.4674 |
| 0.0644 | 1.55 | 3000 | 0.5735 | -1.7285 | -4.2051 | 0.8125 | 2.4766 | -299.0209 | -271.5201 | -2.4193 | -2.4574 |
| 0.0798 | 1.6 | 3100 | 0.5537 | -1.7226 | -4.2850 | 0.8438 | 2.5624 | -299.8200 | -271.4610 | -2.5367 | -2.5696 |
| 0.1013 | 1.65 | 3200 | 0.5575 | -1.5715 | -3.9813 | 0.875 | 2.4098 | -296.7825 | -269.9498 | -2.4926 | -2.5267 |
| 0.1254 | 1.7 | 3300 | 0.5905 | -1.6412 | -4.4703 | 0.8594 | 2.8291 | -301.6730 | -270.6473 | -2.5017 | -2.5340 |
| 0.085 | 1.76 | 3400 | 0.6133 | -1.9159 | -4.6760 | 0.8438 | 2.7601 | -303.7296 | -273.3941 | -2.4614 | -2.4960 |
| 0.065 | 1.81 | 3500 | 0.6074 | -1.8237 | -4.3525 | 0.8594 | 2.5288 | -300.4951 | -272.4724 | -2.4597 | -2.5004 |
| 0.0755 | 1.86 | 3600 | 0.5836 | -1.9252 | -4.4005 | 0.8125 | 2.4753 | -300.9748 | -273.4872 | -2.4327 | -2.4716 |
| 0.0746 | 1.91 | 3700 | 0.5789 | -1.9280 | -4.4906 | 0.8125 | 2.5626 | -301.8762 | -273.5149 | -2.4686 | -2.5115 |
| 0.1348 | 1.96 | 3800 | 0.6015 | -1.8658 | -4.2428 | 0.8281 | 2.3769 | -299.3976 | -272.8936 | -2.4943 | -2.5393 |
| 0.0217 | 2.01 | 3900 | 0.6122 | -2.3335 | -4.9229 | 0.8281 | 2.5894 | -306.1988 | -277.5699 | -2.4841 | -2.5272 |
| 0.0219 | 2.07 | 4000 | 0.6522 | -2.9890 | -6.0164 | 0.8281 | 3.0274 | -317.1334 | -284.1248 | -2.4105 | -2.4545 |
| 0.0119 | 2.12 | 4100 | 0.6922 | -3.4777 | -6.6749 | 0.7969 | 3.1972 | -323.7187 | -289.0121 | -2.4272 | -2.4699 |
| 0.0153 | 2.17 | 4200 | 0.6993 | -3.2406 | -6.6775 | 0.7969 | 3.4369 | -323.7453 | -286.6413 | -2.4047 | -2.4465 |
| 0.011 | 2.22 | 4300 | 0.7178 | -3.7991 | -7.4397 | 0.7656 | 3.6406 | -331.3667 | -292.2260 | -2.3843 | -2.4290 |
| 0.0072 | 2.27 | 4400 | 0.6840 | -3.3269 | -6.8021 | 0.8125 | 3.4752 | -324.9908 | -287.5042 | -2.4095 | -2.4536 |
| 0.0197 | 2.32 | 4500 | 0.7013 | -3.6890 | -7.3014 | 0.8125 | 3.6124 | -329.9841 | -291.1250 | -2.4118 | -2.4543 |
| 0.0182 | 2.37 | 4600 | 0.7476 | -3.8994 | -7.5366 | 0.8281 | 3.6372 | -332.3356 | -293.2291 | -2.4163 | -2.4565 |
| 0.0125 | 2.43 | 4700 | 0.7199 | -4.0560 | -7.5765 | 0.8438 | 3.5204 | -332.7345 | -294.7952 | -2.3699 | -2.4100 |
| 0.0082 | 2.48 | 4800 | 0.7048 | -3.6613 | -7.1356 | 0.875 | 3.4743 | -328.3255 | -290.8477 | -2.3925 | -2.4303 |
| 0.0118 | 2.53 | 4900 | 0.6976 | -3.7908 | -7.3152 | 0.8125 | 3.5244 | -330.1224 | -292.1431 | -2.3633 | -2.4047 |
| 0.0118 | 2.58 | 5000 | 0.7198 | -3.9049 | -7.5557 | 0.8281 | 3.6508 | -332.5271 | -293.2844 | -2.3764 | -2.4194 |
| 0.006 | 2.63 | 5100 | 0.7506 | -4.2118 | -7.9149 | 0.8125 | 3.7032 | -336.1194 | -296.3530 | -2.3407 | -2.3860 |
| 0.0143 | 2.68 | 5200 | 0.7408 | -4.2433 | -7.9802 | 0.8125 | 3.7369 | -336.7721 | -296.6682 | -2.3509 | -2.3946 |
| 0.0057 | 2.74 | 5300 | 0.7552 | -4.3392 | -8.0831 | 0.7969 | 3.7439 | -337.8013 | -297.6275 | -2.3388 | -2.3842 |
| 0.0138 | 2.79 | 5400 | 0.7404 | -4.2395 | -7.9762 | 0.8125 | 3.7367 | -336.7322 | -296.6304 | -2.3286 | -2.3737 |
| 0.0079 | 2.84 | 5500 | 0.7525 | -4.4466 | -8.2196 | 0.7812 | 3.7731 | -339.1662 | -298.7007 | -2.3200 | -2.3641 |
| 0.0077 | 2.89 | 5600 | 0.7520 | -4.5586 | -8.3485 | 0.7969 | 3.7899 | -340.4545 | -299.8206 | -2.3078 | -2.3517 |
| 0.0094 | 2.94 | 5700 | 0.7527 | -4.5542 | -8.3509 | 0.7812 | 3.7967 | -340.4790 | -299.7773 | -2.3062 | -2.3510 |
| 0.0054 | 2.99 | 5800 | 0.7520 | -4.5169 | -8.3079 | 0.7812 | 3.7911 | -340.0493 | -299.4038 | -2.3081 | -2.3530 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.14.0
## Citation
If you find Zephyr-7B-β is useful in your work, please cite it with:
```
@misc{tunstall2023zephyr,
title={Zephyr: Direct Distillation of LM Alignment},
author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
year={2023},
eprint={2310.16944},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
If you use the UltraChat or UltraFeedback datasets, please cite the original works:
```
@misc{ding2023enhancing,
title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations},
author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou},
year={2023},
eprint={2305.14233},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{cui2023ultrafeedback,
title={UltraFeedback: Boosting Language Models with High-quality Feedback},
author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun},
year={2023},
eprint={2310.01377},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 52.15 |
| ARC (25-shot) | 62.03 |
| HellaSwag (10-shot) | 84.36 |
| MMLU (5-shot) | 61.07 |
| TruthfulQA (0-shot) | 57.45 |
| Winogrande (5-shot) | 77.74 |
| GSM8K (5-shot) | 12.74 |
| DROP (3-shot) | 9.66 | |
luckycanucky/droogs-x30 | luckycanucky | 2025-05-31T23:15:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T22:47:56Z | ---
base_model: unsloth/llama-3.2-3b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** luckycanucky
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
guydebruyn/InstructionFollowing_DPO_V2.0 | guydebruyn | 2025-05-31T23:14:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T23:11:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **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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## Glossary [optional]
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ibuki95/model3 | ibuki95 | 2025-05-31T23:13:10Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T23:04:49Z | # Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928–2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351–2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873–4877.
|
kmpartner/bkv2tpcmlr2-test | kmpartner | 2025-05-31T23:12:08Z | 9 | 0 | peft | [
"peft",
"tensorboard",
"diffusers",
"safetensors",
"arxiv:1910.09700",
"base_model:nota-ai/bk-sdm-v2-tiny",
"base_model:adapter:nota-ai/bk-sdm-v2-tiny",
"region:us"
] | null | 2025-04-08T12:30:33Z | ---
base_model: nota-ai/bk-sdm-v2-tiny
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 |
gagein/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-thorny_lightfooted_panda | gagein | 2025-05-31T23:11:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am thorny lightfooted panda",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-09T17:26:01Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-thorny_lightfooted_panda
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am thorny lightfooted panda
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-thorny_lightfooted_panda
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gagein/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-thorny_lightfooted_panda", 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: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_S-GGUF | Triangle104 | 2025-05-31T23:10:16Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"reasoning",
"thinking",
"cognitivecomputations",
"r1",
"llama 3.1",
"llama-3",
"llama3",
"llama-3.1",
"cot",
"deepseek",
"Llama 3.1",
"Hermes",
"DeepHermes",
"1,000,000 context",
"fine tune",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B",
"base_model:quantized:DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-31T23:07:05Z | ---
library_name: transformers
tags:
- reasoning
- thinking
- cognitivecomputations
- r1
- llama 3.1
- llama-3
- llama3
- llama-3.1
- cot
- deepseek
- Llama 3.1
- Hermes
- DeepHermes
- 1,000,000 context
- fine tune
- merge
- llama-cpp
- gguf-my-repo
base_model: DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B
---
# Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_S-GGUF
This model was converted to GGUF format from [`DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B`](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) for more details on the model.
---
Context : 1,000,000 tokens.
Required: Llama 3 Instruct template.
The Deep Hermes 8B Preview model (reasoning), [ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]
converted to 1 million context using Nvidia's Ultra Long 1 million 8B Instruct model.
The goal of this model was to stablize long generation and long context "needle in a haystack" issues.
According to Nvidia there is both a bump in general performance, as well as perfect "recall" over the entire 1 million context.
[ https://huggingface.co/nvidia/Llama-3.1-8B-UltraLong-1M-Instruct ]
Additional changes:
Model appears to be de-censored / more de-censored.
Output generation is improved.
Creative output generation is vastly improved.
NOTE: Higher temps will result in deeper, richer "thoughts"... and frankly more interesting ones too.
The "thinking/reasoning" tech (for the model at this repo) is from the original Llama 3.1 "DeepHermes" model from NousResearch:
[ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_S-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_S-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_S-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q5_K_S-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q5_k_s.gguf -c 2048
```
|
h3en1x/llm-router-poc | h3en1x | 2025-05-31T23:08:24Z | 2 | 0 | null | [
"safetensors",
"distilbert",
"prompt-routing",
"text-classification",
"transformer",
"llm-router",
"license:mit",
"region:us"
] | text-classification | 2025-05-29T18:08:06Z | ---
license: mit
tags:
- prompt-routing
- text-classification
- distilbert
- transformer
- llm-router
---
# DistilBERT LLM Router (POC)
This model is a proof-of-concept for learning to route prompts to the most suitable LLM (Large Language Model) based on prompt content. It is based on `distilbert-base-uncased` and fine-tuned as a sequence classification model to predict whether a prompt should be handled by a reference model (e.g., GPT-4o) or a cheaper local model (e.g., TinyLlama1b).
## Dataset & Labeling
The model was trained on a dataset of 200 carefully curated prompts. Prompts are labeled using GPT-4o as the reference. Each candidate model's output is compared to the reference output and scored from 1 to 5. We treat a score of **5** as a match to the reference. Labels are then derived by choosing the **lowest-cost model** that achieves this score.
### Model Selection
| Model | Inference Cost | Output Quality |
|---------------|-----------------------------|----------------|
| `GPT-4o` | High (cloud-only) | Excellent |
| `TinyLlama1b` | Free (local GPU inference) | Good |
The model is trained to output:
- `label = 0` → Use TinyLlama
- `label = 1` → Use GPT-4o
## Metrics
### Overall Metrics (Train / Test)
| Model | Train Accuracy | Train Precision | Train Recall | Train F1 | Test Accuracy | Test Precision | Test Recall | Test F1 |
|--------------------|----------------|------------------|--------------|----------|----------------|----------------|-------------|---------|
| DummyClassifier | 0.6875 | 0.4727 | 0.6875 | 0.5602 | 0.6750 | 0.4556 | 0.6750 | 0.5440 |
| LogisticRegression | 0.8688 | 0.8918 | 0.8688 | 0.8725 | 0.8000 | 0.8266 | 0.8000 | 0.8053 |
| XGBoost | 0.9625 | 0.9624 | 0.9625 | 0.9623 | 0.9000 | 0.9006 | 0.9000 | 0.8976 |
| DistilBERT | 0.8500 | 0.8541 | 0.8500 | 0.8515 | 0.8500 | 0.8593 | 0.8500 | 0.8525 |
### Per-Class Metrics (Test Set)
| Model | Class | Precision | Recall | F1 Score | Support |
|--------------------|-------------|-----------|----------|----------|---------|
| DummyClassifier | TinyLlama | 0.0000 | 0.0000 | 0.0000 | 13 |
| | GPT-4o | 0.6750 | 1.0000 | 0.8060 | 27 |
| | **Macro Avg** | 0.3375 | 0.5000 | 0.4030 | 40 |
| | **Weighted Avg** | 0.4556 | 0.6750 | 0.5440 | 40 |
| LogisticRegression | TinyLlama | 0.6471 | 0.8462 | 0.7333 | 13 |
| | GPT-4o | 0.9130 | 0.7778 | 0.8400 | 27 |
| | **Macro Avg** | 0.7801 | 0.8120 | 0.7867 | 40 |
| | **Weighted Avg** | 0.8266 | 0.8000 | 0.8053 | 40 |
| XGBoost | TinyLlama | 0.9091 | 0.7692 | 0.8333 | 13 |
| | GPT-4o | 0.8966 | 0.9630 | 0.9286 | 27 |
| | **Macro Avg** | 0.9028 | 0.8661 | 0.8810 | 40 |
| | **Weighted Avg** | 0.9006 | 0.9000 | 0.8976 | 40 |
| DistilBERT | TinyLlama | 0.7333 | 0.8462 | 0.7857 | 13 |
| | GPT-4o | 0.9200 | 0.8519 | 0.8846 | 27 |
| | **Macro Avg** | 0.8267 | 0.8490 | 0.8352 | 40 |
| | **Weighted Avg** | 0.8593 | 0.8500 | 0.8525 | 40 |
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("h3en1x/llm-router-poc")
model = AutoModelForSequenceClassification.from_pretrained("h3en1x/llm-router-poc")
prompt = "What is the capital of France?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model(**inputs)
label_id = outputs.logits.argmax(dim=-1).item()
# Get label name (optional)
label = model.config.id2label[label_id]
print(label)
|
TofuTank/pulse_ugo3d | TofuTank | 2025-05-31T23:08:17Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-31T23:05:22Z | ---
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).
|
Fontella/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_skittish_mink | Fontella | 2025-05-31T23:07:11Z | 29 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prowling skittish mink",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T00:45:38Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_skittish_mink
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prowling skittish mink
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_skittish_mink
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Fontella/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_skittish_mink", 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: 4.51.2
- 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}}
}
``` |
ibuki95/model2 | ibuki95 | 2025-05-31T23:04:53Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T23:03:48Z | # Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928–2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351–2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873–4877.
|
Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_M-GGUF | Triangle104 | 2025-05-31T23:04:30Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"reasoning",
"thinking",
"cognitivecomputations",
"r1",
"llama 3.1",
"llama-3",
"llama3",
"llama-3.1",
"cot",
"deepseek",
"Llama 3.1",
"Hermes",
"DeepHermes",
"1,000,000 context",
"fine tune",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B",
"base_model:quantized:DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-31T23:03:14Z | ---
library_name: transformers
tags:
- reasoning
- thinking
- cognitivecomputations
- r1
- llama 3.1
- llama-3
- llama3
- llama-3.1
- cot
- deepseek
- Llama 3.1
- Hermes
- DeepHermes
- 1,000,000 context
- fine tune
- merge
- llama-cpp
- gguf-my-repo
base_model: DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B
---
# Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B`](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) for more details on the model.
---
Context : 1,000,000 tokens.
Required: Llama 3 Instruct template.
The Deep Hermes 8B Preview model (reasoning), [ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]
converted to 1 million context using Nvidia's Ultra Long 1 million 8B Instruct model.
The goal of this model was to stablize long generation and long context "needle in a haystack" issues.
According to Nvidia there is both a bump in general performance, as well as perfect "recall" over the entire 1 million context.
[ https://huggingface.co/nvidia/Llama-3.1-8B-UltraLong-1M-Instruct ]
Additional changes:
Model appears to be de-censored / more de-censored.
Output generation is improved.
Creative output generation is vastly improved.
NOTE: Higher temps will result in deeper, richer "thoughts"... and frankly more interesting ones too.
The "thinking/reasoning" tech (for the model at this repo) is from the original Llama 3.1 "DeepHermes" model from NousResearch:
[ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_M-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_M-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-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 Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_M-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_M-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q4_k_m.gguf -c 2048
```
|
BootesVoid/cmbcs2mzm01ik10oz8ncyxf2s_cmbcs886j01jt10ozbotvh04a | BootesVoid | 2025-05-31T23:03:11Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-31T23:03:10Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ZINNA
---
# Cmbcs2Mzm01Ik10Oz8Ncyxf2S_Cmbcs886J01Jt10Ozbotvh04A
<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 `ZINNA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ZINNA",
"lora_weights": "https://huggingface.co/BootesVoid/cmbcs2mzm01ik10oz8ncyxf2s_cmbcs886j01jt10ozbotvh04a/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/cmbcs2mzm01ik10oz8ncyxf2s_cmbcs886j01jt10ozbotvh04a', weight_name='lora.safetensors')
image = pipeline('ZINNA').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/cmbcs2mzm01ik10oz8ncyxf2s_cmbcs886j01jt10ozbotvh04a/discussions) to add images that show off what you’ve made with this LoRA.
|
Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_S-GGUF | Triangle104 | 2025-05-31T23:00:58Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"reasoning",
"thinking",
"cognitivecomputations",
"r1",
"llama 3.1",
"llama-3",
"llama3",
"llama-3.1",
"cot",
"deepseek",
"Llama 3.1",
"Hermes",
"DeepHermes",
"1,000,000 context",
"fine tune",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B",
"base_model:quantized:DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-31T22:59:40Z | ---
library_name: transformers
tags:
- reasoning
- thinking
- cognitivecomputations
- r1
- llama 3.1
- llama-3
- llama3
- llama-3.1
- cot
- deepseek
- Llama 3.1
- Hermes
- DeepHermes
- 1,000,000 context
- fine tune
- merge
- llama-cpp
- gguf-my-repo
base_model: DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B
---
# Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_S-GGUF
This model was converted to GGUF format from [`DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B`](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DavidAU/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B) for more details on the model.
---
Context : 1,000,000 tokens.
Required: Llama 3 Instruct template.
The Deep Hermes 8B Preview model (reasoning), [ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]
converted to 1 million context using Nvidia's Ultra Long 1 million 8B Instruct model.
The goal of this model was to stablize long generation and long context "needle in a haystack" issues.
According to Nvidia there is both a bump in general performance, as well as perfect "recall" over the entire 1 million context.
[ https://huggingface.co/nvidia/Llama-3.1-8B-UltraLong-1M-Instruct ]
Additional changes:
Model appears to be de-censored / more de-censored.
Output generation is improved.
Creative output generation is vastly improved.
NOTE: Higher temps will result in deeper, richer "thoughts"... and frankly more interesting ones too.
The "thinking/reasoning" tech (for the model at this repo) is from the original Llama 3.1 "DeepHermes" model from NousResearch:
[ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-8B-Preview ]
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_S-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_S-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_S-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Llama-3.1-1-million-ctx-DeepHermes-Deep-Reasoning-8B-Q4_K_S-GGUF --hf-file llama-3.1-1-million-ctx-deephermes-deep-reasoning-8b-q4_k_s.gguf -c 2048
```
|
TOTORONG/Devstral_250531_tensor | TOTORONG | 2025-05-31T22:59:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Devstral-Small-2505-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Devstral-Small-2505-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T13:09:37Z | ---
base_model: unsloth/Devstral-Small-2505-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** TOTORONG
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Devstral-Small-2505-unsloth-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
pavlodp/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_freckled_weasel | pavlodp | 2025-05-31T22:59:47Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bristly freckled weasel",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-08T11:22:00Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_freckled_weasel
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bristly freckled weasel
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_freckled_weasel
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="pavlodp/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bristly_freckled_weasel", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
jekunz/smollm-360m-lora-fineweb-swedish | jekunz | 2025-05-31T22:59:44Z | 641 | 0 | peft | [
"peft",
"safetensors",
"text-generation",
"conversational",
"sv",
"dataset:HuggingFaceFW/fineweb-2",
"base_model:HuggingFaceTB/SmolLM2-360M-Instruct",
"base_model:adapter:HuggingFaceTB/SmolLM2-360M-Instruct",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-03-17T18:20:17Z | ---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-2
language:
- sv
base_model:
- HuggingFaceTB/SmolLM2-360M-Instruct
pipeline_tag: text-generation
library_name: peft
--- |
isurut/wav2vec2_finetune_cv_igbo | isurut | 2025-05-31T22:58:50Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:isurut/wav2vec2_finetune_cv_igbo",
"base_model:finetune:isurut/wav2vec2_finetune_cv_igbo",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-01-08T12:10:12Z | ---
library_name: transformers
license: apache-2.0
base_model: isurut/wav2vec2_finetune_cv_igbo
tags:
- generated_from_trainer
model-index:
- name: wav2vec2_finetune_cv_igbo
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. -->
# wav2vec2_finetune_cv_igbo
This model is a fine-tuned version of [isurut/wav2vec2_finetune_cv_igbo](https://huggingface.co/isurut/wav2vec2_finetune_cv_igbo) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8871
- eval_wer: 0.5533
- eval_runtime: 76.0774
- eval_samples_per_second: 15.05
- eval_steps_per_second: 1.893
- epoch: 9.1623
- step: 5250
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
mohammadmahdinouri/interleaved-speech-test-1 | mohammadmahdinouri | 2025-05-31T22:57:31Z | 25 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-27T22:30:43Z | ---
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] |
menesnas/fine-tuned-gpt2-tweet-sentiment | menesnas | 2025-05-31T22:56:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"en",
"dataset:mteb/tweet_sentiment_extraction",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-31T22:35:04Z | ---
library_name: transformers
license: mit
datasets:
- mteb/tweet_sentiment_extraction
language:
- en
metrics:
- accuracy
base_model:
- openai-community/gpt2
pipeline_tag: text-classification
---
# Model Card for Model ID
This is a fine-tuned GPT-2 model for tweet sentiment classification. It categorizes tweets into positive, neutral, or negative sentiment based on their content.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Model type:** GPT-2 (with sequence classification head)
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** gpt2
#### Metrics
The model was evaluated using the following metrics:
- Training Loss: Measures how well the model fits the training data. A lower value indicates better learning.
- Validation Loss: Measures how well the model generalizes to unseen data. It is used to detect overfitting.
- Accuracy: Percentage of correctly classified samples in the validation dataset. It is the primary performance metric for this sentiment classification task.
### Results
- The model was trained for 3 epochs. Below are the results per epoch:
-
| Epoch | Training Loss | Validation Loss | Accuracy |
| ----- | ------------- | --------------- | -------- |
| 1 | 0.832400 | 0.871651 | 62.7% |
| 2 | 0.512700 | 0.794255 | 69.3% |
| 3 | 0.517500 | 0.819540 | 71.8% |
|
Whalan/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_small_coral | Whalan | 2025-05-31T22:56:26Z | 27 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tall small coral",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T21:31:37Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_small_coral
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tall small coral
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_small_coral
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Whalan/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_small_coral", 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: 4.51.2
- 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}}
}
``` |
Triangle104/AceReason-Nemotron-14B-Q6_K-GGUF | Triangle104 | 2025-05-31T22:50:47Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"nvidia",
"reasoning",
"math",
"code",
"reinforcement learning",
"pytorch",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:nvidia/AceReason-Nemotron-14B",
"base_model:quantized:nvidia/AceReason-Nemotron-14B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-31T22:48:47Z | ---
library_name: transformers
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
language:
- en
tags:
- nvidia
- reasoning
- math
- code
- reinforcement learning
- pytorch
- llama-cpp
- gguf-my-repo
base_model: nvidia/AceReason-Nemotron-14B
---
# Triangle104/AceReason-Nemotron-14B-Q6_K-GGUF
This model was converted to GGUF format from [`nvidia/AceReason-Nemotron-14B`](https://huggingface.co/nvidia/AceReason-Nemotron-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nvidia/AceReason-Nemotron-14B) for more details on the model.
---
We're thrilled to introduce AceReason-Nemotron-14B, a math and code
reasoning model trained entirely through reinforcement learning (RL),
starting from the DeepSeek-R1-Distilled-Qwen-14B. It delivers impressive
results, achieving 78.6% on AIME 2024 (+8.9%), 67.4% on AIME 2025
(+17.4%), 61.1% on LiveCodeBench v5 (+8%), 54.9% on LiveCodeBench v6
(+7%), and 2024 on Codeforces (+543). We systematically study the RL
training process through extensive ablations and propose a simple yet
effective approach: first RL training on math-only prompts, then RL
training on code-only prompts. Notably, we find that math-only RL not
only significantly enhances the performance of strong distilled models
on math benchmarks, but also code reasoning tasks. In addition, extended
code-only RL further improves code benchmark performance while causing
minimal degradation in math results. We find that RL not only elicits
the foundational reasoning capabilities acquired during pre-training and
supervised fine-tuning (e.g., distillation), but also pushes the limits
of the model's reasoning ability, enabling it to solve problems that
were previously unsolvable.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/AceReason-Nemotron-14B-Q6_K-GGUF --hf-file acereason-nemotron-14b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q6_K-GGUF --hf-file acereason-nemotron-14b-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/AceReason-Nemotron-14B-Q6_K-GGUF --hf-file acereason-nemotron-14b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q6_K-GGUF --hf-file acereason-nemotron-14b-q6_k.gguf -c 2048
```
|
huangqishan/nn | huangqishan | 2025-05-31T22:50:31Z | 791 | 0 | transformers | [
"transformers",
"safetensors",
"nn_model",
"image-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | image-classification | 2025-05-25T00:20:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
DevQuasar/Writer.palmyra-20b-chat-GGUF | DevQuasar | 2025-05-31T22:49:30Z | 0 | 0 | null | [
"text-generation",
"base_model:Writer/palmyra-20b-chat",
"base_model:finetune:Writer/palmyra-20b-chat",
"region:us"
] | text-generation | 2025-05-31T22:49:28Z | ---
base_model:
- Writer/palmyra-20b-chat
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: [Writer/palmyra-20b-chat](https://huggingface.co/Writer/palmyra-20b-chat)
'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>
|
ruanchengren/Qwen2.5-7B-Instruct-Gensyn-Swarm-melodic_mute_panda | ruanchengren | 2025-05-31T22:49:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am melodic mute panda",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-7B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-06T06:03:08Z | ---
base_model: Gensyn/Qwen2.5-7B-Instruct
library_name: transformers
model_name: Qwen2.5-7B-Instruct-Gensyn-Swarm-melodic_mute_panda
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am melodic mute panda
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-7B-Instruct-Gensyn-Swarm-melodic_mute_panda
This model is a fine-tuned version of [Gensyn/Qwen2.5-7B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ruanchengren/Qwen2.5-7B-Instruct-Gensyn-Swarm-melodic_mute_panda", 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: 4.48.2
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
SRManagement/FluxSetup | SRManagement | 2025-05-31T22:46:22Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-11T21:34:30Z | ---
license: other
license_name: flux
license_link: LICENSE
---
|
QuanTH02/GLEN-model | QuanTH02 | 2025-05-31T22:46:02Z | 0 | 0 | null | [
"arxiv:2311.03057",
"region:us"
] | null | 2025-05-31T22:02:02Z | # GLEN: Generative Retrieval via Lexical Index Learning (EMNLP 2023)
This is the official code for the EMNLP 2023 paper "[GLEN: Generative Retrieval via Lexical Index Learning](https://arxiv.org/abs/2311.03057)".
## Overview
GLEN (***G**enerative retrieval via **LE**xical ***N***dex learning*) is a generative retrieval model that learns to dynamically assign lexical identifiers using a two-phase index learning strategy.
The poster and the slide files are available at each link: [poster](assets/glen_poster.pdf) and [slide](assets/glen_slide.pdf). We also provide blog posts (Korean) at [here](https://dial.skku.edu/blog/2023_glen). Please refer to the paper for more details: [arXiv](https://arxiv.org/abs/2311.03057) or [ACL Anthology](https://aclanthology.org/2023.emnlp-main.477/).
## Environment
We have confirmed that the results are reproduced successfully in `python==3.8.12`, `transformers==4.15.0`, `pytorch==1.10.0` with `cuda 12.0`. Please create a conda environment and install the required packages with `requirements.txt`.
```
# Clone this repo
git clone https://github.com/skleee/GLEN.git
cd GLEN
# Set conda environment
conda create -n glen python=3.8
conda activate glen
# Install tevatron as editable
pip install --editable .
# Install dependencies
pip install -r requirements.txt
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
```
Optionally, you can also install [GradCache](https://github.com/luyug/GradCache) to gradient cache feature during training **ranking-based ID refinement** by:
```
git clone https://github.com/luyug/GradCache
cd GradCache
pip install .
```
## Dataset
Datasets can be downloaded from: [NQ320k](https://drive.google.com/drive/folders/1qYV-kAUpSDKkzvcy36pSoelTbvsiZtcQ?usp=sharing), [MS MARCO Passage Ranking set](https://drive.google.com/drive/folders/1rErON3bK0-_DeNCSQUHxcSkewSIs5c2r?usp=sharing), [BEIR](https://drive.google.com/drive/folders/1bBNnqbEPOQ5ic1ybiVULAd8meZXA4pqC?usp=sharing).
After downloading each folder, unzip it into the `data` folder. The structure of each folder is as follows.
```
data
├── BEIR_dataset
│ ├── arguana
│ └── nfcorpus
├── nq320k
└── marco_passage
```
- For NQ320k, we follow the same data preprocessing as [NCI](https://github.com/solidsea98/Neural-Corpus-Indexer-NCI) and the setup in [GENRET](https://github.com/sunnweiwei/GenRet), splitting the test set into two subsets; *seen test* and *unseen test*.
- For MS MARCO passage ranking set, we use the official development set consisting of 6,980 queries with a **full corpus**, i.e., 8.8M passages.
- For BEIR, we assess the model on Arguana and NFCorpus and the code is based on [BEIR](https://github.com/beir-cellar/beir).
- Further details are described in the paper.
## Training
The training process consists of two phases: **(1) Keyword-based ID assignment** and **(2) Ranking-based ID refinement**. In the `/examples` folder, we provide GLEN code for each phase: `glen_phase1`, `glen_phase2`. Please refer to `src/tevatron` for the trainer.
Run the scripts to train GLEN from the scratch for NQ320k or MS MARCO.<br>
### NQ320k
```
# (1) Keyword-based ID assignment
sh scripts/train_glen_p1_nq.sh
```
```
# (2) Ranking-based ID refinement
sh scripts/train_glen_p2_nq.sh
```
### MS MARCO
```
# (1) Keyword-based ID assignment
sh scripts/train_glen_p1_marco.sh
```
```
# (2) Ranking-based ID refinement
sh scripts/train_glen_p2_marco.sh
```
You can directly download our trained checkpoints for each stage from the following link: [NQ320k](https://drive.google.com/drive/folders/1ERopkRAJf7Ea-r_nJWoeaZFUp7e54eok?usp=sharing), [MS MARCO](https://drive.google.com/drive/folders/1mp4HIIbKnohNizLccaNFkJVMS-pJl_6T?usp=sharing)
## Evaluation
The evaluation process consists of two stages: **(1) Document processing via making document identifiers** and **(2) Query processing via inference**.
Run the scripts to evalute GLEN for each dataset.<br>
### NQ320k
```
sh scripts/eval_make_docid_glen_nq.sh
sh scripts/eval_inference_query_glen_nq.sh
```
### MS MARCO
```
sh scripts/eval_make_docid_glen_marco.sh
sh scripts/eval_inference_query_glen_marco.sh
```
### BEIR
```
# Arguana
sh scripts/eval_make_docid_glen_arguana.sh
sh scripts/eval_inference_query_glen_arguana.sh
```
```
# NFCorpus
sh scripts/eval_make_docid_glen_nfcorpus.sh
sh scripts/eval_inference_query_glen_nfcorpus.sh
```
## Acknowledgement
Our code is mainly based on [Tevatron](https://github.com/texttron/tevatron). Also, we learned a lot from [NCI](https://github.com/solidsea98/Neural-Corpus-Indexer-NCI), [Transformers](https://github.com/huggingface/transformers), and [BEIR](https://github.com/beir-cellar/beir). We appreciate all the authors for sharing their codes.
## Citation
If you find this work useful for your research, please cite our paper:
```
@inproceedings{lee-etal-2023-glen,
title = "{GLEN}: Generative Retrieval via Lexical Index Learning",
author = "Lee, Sunkyung and
Choi, Minjin and
Lee, Jongwuk",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.477",
doi = "10.18653/v1/2023.emnlp-main.477",
pages = "7693--7704",
}
```
## Contacts
For any questions, please contact the following authors via email or feel free to open an issue 😊
- Sunkyung Lee [email protected]
- Minjin Choi [email protected]
|
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_rough_chimpanzee | fakeid | 2025-05-31T22:45:10Z | 40 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am enormous rough chimpanzee",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-16T16:02:05Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_rough_chimpanzee
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am enormous rough chimpanzee
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_rough_chimpanzee
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-enormous_rough_chimpanzee", 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: 2.7.0+cpu
- 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}}
}
``` |
maplekeng/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sly_nimble_lemur | maplekeng | 2025-05-31T22:43:12Z | 21 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sly nimble lemur",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-07T22:52:50Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sly_nimble_lemur
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sly nimble lemur
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sly_nimble_lemur
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="maplekeng/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sly_nimble_lemur", 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: 4.51.2
- 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}}
}
``` |
Jose11-2/t2 | Jose11-2 | 2025-05-31T22:42:48Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T22:41:21Z | flask
transformers
torch
Pillow
|
Triangle104/AceReason-Nemotron-14B-Q5_K_S-GGUF | Triangle104 | 2025-05-31T22:42:09Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"nvidia",
"reasoning",
"math",
"code",
"reinforcement learning",
"pytorch",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:nvidia/AceReason-Nemotron-14B",
"base_model:quantized:nvidia/AceReason-Nemotron-14B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-31T22:38:33Z | ---
library_name: transformers
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
language:
- en
tags:
- nvidia
- reasoning
- math
- code
- reinforcement learning
- pytorch
- llama-cpp
- gguf-my-repo
base_model: nvidia/AceReason-Nemotron-14B
---
# Triangle104/AceReason-Nemotron-14B-Q5_K_S-GGUF
This model was converted to GGUF format from [`nvidia/AceReason-Nemotron-14B`](https://huggingface.co/nvidia/AceReason-Nemotron-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nvidia/AceReason-Nemotron-14B) for more details on the model.
---
We're thrilled to introduce AceReason-Nemotron-14B, a math and code
reasoning model trained entirely through reinforcement learning (RL),
starting from the DeepSeek-R1-Distilled-Qwen-14B. It delivers impressive
results, achieving 78.6% on AIME 2024 (+8.9%), 67.4% on AIME 2025
(+17.4%), 61.1% on LiveCodeBench v5 (+8%), 54.9% on LiveCodeBench v6
(+7%), and 2024 on Codeforces (+543). We systematically study the RL
training process through extensive ablations and propose a simple yet
effective approach: first RL training on math-only prompts, then RL
training on code-only prompts. Notably, we find that math-only RL not
only significantly enhances the performance of strong distilled models
on math benchmarks, but also code reasoning tasks. In addition, extended
code-only RL further improves code benchmark performance while causing
minimal degradation in math results. We find that RL not only elicits
the foundational reasoning capabilities acquired during pre-training and
supervised fine-tuning (e.g., distillation), but also pushes the limits
of the model's reasoning ability, enabling it to solve problems that
were previously unsolvable.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/AceReason-Nemotron-14B-Q5_K_S-GGUF --hf-file acereason-nemotron-14b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q5_K_S-GGUF --hf-file acereason-nemotron-14b-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/AceReason-Nemotron-14B-Q5_K_S-GGUF --hf-file acereason-nemotron-14b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q5_K_S-GGUF --hf-file acereason-nemotron-14b-q5_k_s.gguf -c 2048
```
|
cosmosistan/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sly_nasty_ox | cosmosistan | 2025-05-31T22:40:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sly nasty ox",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T13:13:44Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sly_nasty_ox
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sly nasty ox
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sly_nasty_ox
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="cosmosistan/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sly_nasty_ox", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.0
- Transformers: 4.52.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
dimasik2987/1dc012c6-dc3a-4a44-824b-4c61977f2574 | dimasik2987 | 2025-05-31T22:37:40Z | 0 | 0 | peft | [
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:numind/NuExtract-1.5",
"base_model:adapter:numind/NuExtract-1.5",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-31T21:19:51Z | ---
library_name: peft
license: mit
base_model: numind/NuExtract-v1.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1dc012c6-dc3a-4a44-824b-4c61977f2574
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: numind/NuExtract-v1.5
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- dc28067aa0597a70_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: dimasik2987/1dc012c6-dc3a-4a44-824b-4c61977f2574
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: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 12
mixed_precision: bf16
mlflow_experiment_name: /tmp/dc28067aa0597a70_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
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: 56cc23c8-c1b5-4b3c-b6b5-41661701b16a
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 56cc23c8-c1b5-4b3c-b6b5-41661701b16a
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 1dc012c6-dc3a-4a44-824b-4c61977f2574
This model is a fine-tuned version of [numind/NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8828
## 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: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.755 | 0.0000 | 1 | 1.1158 |
| 2.1422 | 0.0117 | 250 | 0.8943 |
| 1.866 | 0.0233 | 500 | 0.8828 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
HammadQ123/genai-compressed-predictor | HammadQ123 | 2025-05-31T22:36:48Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T22:36:26Z | # Compressed GenAI RNA Binding Predictor
## Model Description
This is a compressed version of the RNA-protein binding prediction model for faster loading and inference.
## Model Details
- **Model Type**: Compressed PyTorch model for RNA binding prediction
- **Input**: RNA sequences (A, U, G, C nucleotides)
- **Output**: Binding score (RMSD prediction)
- **Optimization**: Compressed for faster loading and reduced memory usage
## Usage
```python
from huggingface_hub import hf_hub_download
import torch
# Download compressed model
model_path = hf_hub_download(
repo_id="HammadQ123/genai-compressed-predictor",
filename="model_compressed.pt"
)
# Load model
model = torch.load(model_path, map_location='cpu')
# Use for predictions...
```
## Performance
- Faster loading compared to original model
- Reduced memory footprint
- Maintained prediction accuracy
## Related Repositories
- Original model: HammadQ123/genai-predictor
## License
[Add your license here]
|
EsterTregub/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-peckish_lively_fox | EsterTregub | 2025-05-31T22:36:07Z | 29 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am peckish lively fox",
"unsloth",
"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 | 2025-04-17T13:55:43Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-peckish_lively_fox
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am peckish lively fox
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-peckish_lively_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="EsterTregub/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-peckish_lively_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: 2.5.1
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AmberYifan/Llama-3.1-8B-sft-dpo-10k-KTO | AmberYifan | 2025-05-31T22:32:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"kto",
"conversational",
"arxiv:2402.01306",
"base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T21:58:04Z | ---
base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-3.1-8B-sft-dpo-10k-KTO
tags:
- generated_from_trainer
- trl
- kto
licence: license
---
# Model Card for Llama-3.1-8B-sft-dpo-10k-KTO
This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF).
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="AmberYifan/Llama-3.1-8B-sft-dpo-10k-KTO", 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/yifanwang/huggingface/runs/729dljbo)
This model was trained with KTO, a method introduced in [KTO: Model Alignment as Prospect Theoretic Optimization](https://huggingface.co/papers/2402.01306).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## Citations
Cite KTO as:
```bibtex
@article{ethayarajh2024kto,
title = {{KTO: Model Alignment as Prospect Theoretic Optimization}},
author = {Kawin Ethayarajh and Winnie Xu and Niklas Muennighoff and Dan Jurafsky and Douwe Kiela},
year = 2024,
eprint = {arXiv:2402.01306},
}
```
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}}
}
``` |
BootesVoid/cmbcesd72001r10ozzqcm5ddu_cmbcqmqri01ak10ozo5t0yksk | BootesVoid | 2025-05-31T22:31:10Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-31T22:31:08Z | ---
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: SEXY
---
# Cmbcesd72001R10Ozzqcm5Ddu_Cmbcqmqri01Ak10Ozo5T0Yksk
<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 `SEXY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SEXY",
"lora_weights": "https://huggingface.co/BootesVoid/cmbcesd72001r10ozzqcm5ddu_cmbcqmqri01ak10ozo5t0yksk/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/cmbcesd72001r10ozzqcm5ddu_cmbcqmqri01ak10ozo5t0yksk', weight_name='lora.safetensors')
image = pipeline('SEXY').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/cmbcesd72001r10ozzqcm5ddu_cmbcqmqri01ak10ozo5t0yksk/discussions) to add images that show off what you’ve made with this LoRA.
|
rtl-llm/qwen2.5coder-7b-origen-verilog-vhdl-vhdl-chisel-batch8 | rtl-llm | 2025-05-31T22:30:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T22:27:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
rtl-llm/qwen2.5coder-7b-origen-vhdl-vhdl-verilog-gs16 | rtl-llm | 2025-05-31T22:30:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T22:27:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Triangle104/AceReason-Nemotron-14B-Q4_K_M-GGUF | Triangle104 | 2025-05-31T22:28:37Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"nvidia",
"reasoning",
"math",
"code",
"reinforcement learning",
"pytorch",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:nvidia/AceReason-Nemotron-14B",
"base_model:quantized:nvidia/AceReason-Nemotron-14B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-31T22:22:28Z | ---
library_name: transformers
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
language:
- en
tags:
- nvidia
- reasoning
- math
- code
- reinforcement learning
- pytorch
- llama-cpp
- gguf-my-repo
base_model: nvidia/AceReason-Nemotron-14B
---
# Triangle104/AceReason-Nemotron-14B-Q4_K_M-GGUF
This model was converted to GGUF format from [`nvidia/AceReason-Nemotron-14B`](https://huggingface.co/nvidia/AceReason-Nemotron-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nvidia/AceReason-Nemotron-14B) for more details on the model.
---
We're thrilled to introduce AceReason-Nemotron-14B, a math and code
reasoning model trained entirely through reinforcement learning (RL),
starting from the DeepSeek-R1-Distilled-Qwen-14B. It delivers impressive
results, achieving 78.6% on AIME 2024 (+8.9%), 67.4% on AIME 2025
(+17.4%), 61.1% on LiveCodeBench v5 (+8%), 54.9% on LiveCodeBench v6
(+7%), and 2024 on Codeforces (+543). We systematically study the RL
training process through extensive ablations and propose a simple yet
effective approach: first RL training on math-only prompts, then RL
training on code-only prompts. Notably, we find that math-only RL not
only significantly enhances the performance of strong distilled models
on math benchmarks, but also code reasoning tasks. In addition, extended
code-only RL further improves code benchmark performance while causing
minimal degradation in math results. We find that RL not only elicits
the foundational reasoning capabilities acquired during pre-training and
supervised fine-tuning (e.g., distillation), but also pushes the limits
of the model's reasoning ability, enabling it to solve problems that
were previously unsolvable.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/AceReason-Nemotron-14B-Q4_K_M-GGUF --hf-file acereason-nemotron-14b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q4_K_M-GGUF --hf-file acereason-nemotron-14b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/AceReason-Nemotron-14B-Q4_K_M-GGUF --hf-file acereason-nemotron-14b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/AceReason-Nemotron-14B-Q4_K_M-GGUF --hf-file acereason-nemotron-14b-q4_k_m.gguf -c 2048
```
|
DevQuasar/deepseek-ai.DeepSeek-R1-0528-GGUF | DevQuasar | 2025-05-31T22:28:03Z | 1,610 | 6 | null | [
"gguf",
"text-generation",
"base_model:deepseek-ai/DeepSeek-R1-0528",
"base_model:quantized:deepseek-ai/DeepSeek-R1-0528",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-28T21:10:48Z | ---
base_model:
- deepseek-ai/DeepSeek-R1-0528
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
'Make knowledge free for everyone'
Quantized version of: [deepseek-ai/DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)
<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>
|
rtl-llm/qwen2.5coder-7b-origen-verilog-vhdl-chisel | rtl-llm | 2025-05-31T22:26:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T12:46:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
FredKud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-miniature_humming_mole | FredKud | 2025-05-31T22:24:50Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am miniature humming mole",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T08:41:06Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-miniature_humming_mole
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am miniature humming mole
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-miniature_humming_mole
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="FredKud/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-miniature_humming_mole", 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: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
DevQuasar/Writer.Palmyra-Med-70B-32K-GGUF | DevQuasar | 2025-05-31T22:24:22Z | 0 | 0 | null | [
"text-generation",
"base_model:Writer/Palmyra-Med-70B-32K",
"base_model:finetune:Writer/Palmyra-Med-70B-32K",
"region:us"
] | text-generation | 2025-05-31T22:24:21Z | ---
base_model:
- Writer/Palmyra-Med-70B-32K
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
'Make knowledge free for everyone'
Quantized version of: [Writer/Palmyra-Med-70B-32K](https://huggingface.co/Writer/Palmyra-Med-70B-32K)
<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>
|
Dejiat/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal | Dejiat | 2025-05-31T22:13:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prickly woolly seal",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T08:04:52Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prickly woolly seal
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Dejiat/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prickly_woolly_seal", 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: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
emiliensilly/MCQAPropreExplanationWithPlatypus | emiliensilly | 2025-05-31T22:12:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T22:11:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ReadyArt/Valtrya-24B-Settings | ReadyArt | 2025-05-31T22:12:43Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-30T18:04:26Z | ---
license: other
license_name: other
license_link: LICENSE
---
# These are settings for a specific scenario card. |
cnmoro/static-nomic-eng-ptbr-tiny | cnmoro | 2025-05-31T22:11:36Z | 0 | 0 | model2vec | [
"model2vec",
"safetensors",
"feature-extraction",
"en",
"pt",
"dataset:cnmoro/AllTripletsMsMarco-PTBR",
"dataset:Tevatron/msmarco-passage-corpus",
"base_model:nomic-ai/nomic-embed-text-v2-moe",
"base_model:finetune:nomic-ai/nomic-embed-text-v2-moe",
"license:apache-2.0",
"region:us"
] | feature-extraction | 2025-05-31T21:06:36Z | ---
license: apache-2.0
datasets:
- cnmoro/AllTripletsMsMarco-PTBR
- Tevatron/msmarco-passage-corpus
language:
- en
- pt
library_name: model2vec
base_model:
- nomic-ai/nomic-embed-text-v2-moe
pipeline_tag: feature-extraction
---
This [Model2Vec](https://github.com/MinishLab/model2vec) model was created by using [Tokenlearn](https://github.com/MinishLab/tokenlearn), with [nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe) as a base, trained on around 3.5M passages (english and portuguese), specifying a vocab_size of 40000.
The output dimension is 128.
This is supposed to be a more minimalistic version of [cnmoro/static-nomic-eng-ptbr](https://huggingface.co/cnmoro/static-nomic-eng-ptbr)
## Usage
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("cnmoro/static-nomic-eng-ptbr-tiny")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
``` |
fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_pudgy_dingo | fakeid | 2025-05-31T22:10:20Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hulking pudgy dingo",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-26T13:29:20Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_pudgy_dingo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hulking pudgy dingo
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_pudgy_dingo
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fakeid/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hulking_pudgy_dingo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
arnaultsta/MNLP_M2_rag_training_MCQA_whole_RAG_1 | arnaultsta | 2025-05-31T22:09:52Z | 0 | 0 | peft | [
"peft",
"safetensors",
"unsloth",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2025-05-31T15:20:36Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/qwen3-0.6b-base-unsloth-bnb-4bit
tags:
- unsloth
- generated_from_trainer
model-index:
- name: MNLP_M2_rag_training_MCQA_whole_RAG_1
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. -->
# MNLP_M2_rag_training_MCQA_whole_RAG_1
This model is a fine-tuned version of [unsloth/qwen3-0.6b-base-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-0.6b-base-unsloth-bnb-4bit) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.0 |
nbzy1995/LunarLander-v2-scratch | nbzy1995 | 2025-05-31T21:59:00Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-31T21:12:52Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -120.94 +/- 133.39
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
haritowa/Qwen2.5-7B-Instruct-Gensyn-Swarm-carnivorous_foraging_clam | haritowa | 2025-05-31T21:57:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am carnivorous foraging clam",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-7B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-25T02:12:12Z | ---
base_model: Gensyn/Qwen2.5-7B-Instruct
library_name: transformers
model_name: Qwen2.5-7B-Instruct-Gensyn-Swarm-carnivorous_foraging_clam
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am carnivorous foraging clam
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-7B-Instruct-Gensyn-Swarm-carnivorous_foraging_clam
This model is a fine-tuned version of [Gensyn/Qwen2.5-7B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="haritowa/Qwen2.5-7B-Instruct-Gensyn-Swarm-carnivorous_foraging_clam", 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: 2.5.1+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ASSERT-KTH/Qwen3-8B-sft | ASSERT-KTH | 2025-05-31T21:56:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T19:45:30Z | ---
base_model: Qwen/Qwen3-8B
library_name: transformers
model_name: Qwen3-8B-sft
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen3-8B-sft
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="ASSERT-KTH/Qwen3-8B-sft", 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/assert-kth/SWE-Gym-SFT/runs/p2ardtou)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.4
- Pytorch: 2.5.1+cu121
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AmberYifan/Llama-3.1-8B-sft-spin-10k-KTO | AmberYifan | 2025-05-31T21:56:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"kto",
"conversational",
"arxiv:2402.01306",
"base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T21:22:23Z | ---
base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-3.1-8B-sft-spin-10k-KTO
tags:
- generated_from_trainer
- trl
- kto
licence: license
---
# Model Card for Llama-3.1-8B-sft-spin-10k-KTO
This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF).
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="AmberYifan/Llama-3.1-8B-sft-spin-10k-KTO", 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/yifanwang/huggingface/runs/vklpw995)
This model was trained with KTO, a method introduced in [KTO: Model Alignment as Prospect Theoretic Optimization](https://huggingface.co/papers/2402.01306).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## Citations
Cite KTO as:
```bibtex
@article{ethayarajh2024kto,
title = {{KTO: Model Alignment as Prospect Theoretic Optimization}},
author = {Kawin Ethayarajh and Winnie Xu and Niklas Muennighoff and Dan Jurafsky and Douwe Kiela},
year = 2024,
eprint = {arXiv:2402.01306},
}
```
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}}
}
``` |
isaackan/m1a | isaackan | 2025-05-31T21:55:05Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-31T21:24:29Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: M1A
---
# M1A
<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 `M1A` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "M1A",
"lora_weights": "https://huggingface.co/isaackan/m1a/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('isaackan/m1a', weight_name='lora.safetensors')
image = pipeline('M1A').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/isaackan/m1a/discussions) to add images that show off what you’ve made with this LoRA.
|
Pepetong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dense_hardy_capybara | Pepetong | 2025-05-31T21:53:46Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am dense hardy capybara",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-10T08:44:05Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dense_hardy_capybara
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am dense hardy capybara
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dense_hardy_capybara
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Pepetong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-dense_hardy_capybara", 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}}
}
``` |
Chandru4u/cmutt-1 | Chandru4u | 2025-05-31T21:51:05Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-31T21:51:05Z | ---
license: other
license_name: cmutt-open
license_link: LICENSE
---
|
naniltx/codonGPT | naniltx | 2025-05-31T21:46:32Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T21:31:52Z | ---
library_name: transformers
tags: []
---
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Nanil Therapeutics
- **Funded by [optional]:** Nanil Therapeutics
- **Shared by [optional]:** Nanil Therapeutics
- **Model type:** Transformer-based generative language model
- **Language(s) (NLP):** mRNA sequences (biological triplet code)
- **License:** Free for research use |
hophop1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard | hophop1 | 2025-05-31T21:46:16Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am winged fanged mallard",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-08T14:14:10Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am winged fanged mallard
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hophop1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BootesVoid/cmbcesd72001r10ozzqcm5ddu_cmbcqmqxa01al10ozh7k8nv3e | BootesVoid | 2025-05-31T21:46:08Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-31T21:46:06Z | ---
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: A
---
# Cmbcesd72001R10Ozzqcm5Ddu_Cmbcqmqxa01Al10Ozh7K8Nv3E
<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 `A` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "A",
"lora_weights": "https://huggingface.co/BootesVoid/cmbcesd72001r10ozzqcm5ddu_cmbcqmqxa01al10ozh7k8nv3e/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/cmbcesd72001r10ozzqcm5ddu_cmbcqmqxa01al10ozh7k8nv3e', weight_name='lora.safetensors')
image = pipeline('A').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/cmbcesd72001r10ozzqcm5ddu_cmbcqmqxa01al10ozh7k8nv3e/discussions) to add images that show off what you’ve made with this LoRA.
|
AmberYifan/Llama-3.1-8B-sft-all-pool-ORPO | AmberYifan | 2025-05-31T21:43:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"orpo",
"conversational",
"arxiv:2403.07691",
"base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T21:25:03Z | ---
base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-3.1-8B-sft-all-pool-ORPO
tags:
- generated_from_trainer
- trl
- orpo
licence: license
---
# Model Card for Llama-3.1-8B-sft-all-pool-ORPO
This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF).
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="AmberYifan/Llama-3.1-8B-sft-all-pool-ORPO", 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/yifanwang/huggingface/runs/gp4fic5c)
This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## Citations
Cite ORPO as:
```bibtex
@article{hong2024orpo,
title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
author = {Jiwoo Hong and Noah Lee and James Thorne},
year = 2024,
eprint = {arXiv:2403.07691}
}
```
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}}
}
``` |
Raymond-dev-546730/Research-Reasoner-7B-v0.3 | Raymond-dev-546730 | 2025-05-31T21:40:40Z | 200 | 2 | null | [
"safetensors",
"gguf",
"chain-of-thought",
"cot-reasoning",
"step-by-step-reasoning",
"systematic-research-planning",
"academic-assistant",
"academic-planning",
"thesis-planning",
"dissertation-planning",
"research-question-formulation",
"literature-review-planning",
"methodology-design",
"experimental-design",
"qualitative-research-planning",
"quantitative-research-planning",
"mixed-methods-planning",
"student-research-assistant",
"phd-support",
"postgraduate-tool",
"early-career-researcher",
"grant-writing-assistant",
"research-proposal-helper",
"cross-disciplinary-research",
"interdisciplinary-methodology",
"academic-mentorship-tool",
"research-evaluation-assistant",
"independent-researcher-tool",
"r-and-d-assistant",
"reasoning-model",
"structured-output",
"systematic-analysis",
"problem-decomposition",
"research-breakdown",
"actionable-planning",
"scientific-research",
"social-science-research",
"humanities-research",
"medical-research-planning",
"engineering-research",
"business-research",
"mistral-based",
"mistral-fine-tune",
"lora-adaptation",
"foundation-model",
"instruction-tuned",
"7b-parameters",
"ai-research-assistant",
"research-automation",
"sota-research-planning",
"hypothesis-generation",
"experiment-design-assistant",
"literature-analysis",
"paper-outline-generator",
"structured-output-generation",
"systematic-reasoning",
"detailed-planning",
"zero-shot-planning",
"research-summarization",
"biomedical-research-assistant",
"clinical-trial-planning",
"tech-r-and-d",
"materials-science",
"computational-research",
"data-science-assistant",
"literature-synthesis",
"meta-analysis-helper",
"best-research-assistant-model",
"top-research-planning-model",
"research-ai-assistant",
"ai-research-mentor",
"academic-planning-ai",
"research-workflow-automation",
"quantum-computing-research",
"ai-ml-research-planning",
"cybersecurity-research",
"neuroscience-research-planning",
"genomics-research",
"robotics-research-planning",
"climate-science-research",
"behavioral-economics-research",
"educational-technology-research",
"research-plan-generator",
"methodology-recommendation",
"data-collection-planning",
"analysis-strategy-development",
"implementation-planning",
"evaluation-framework-design",
"challenge-identification",
"resource-requirement-analysis",
"technical-limitation-assessment",
"research-gap-analysis",
"knowledge-synthesis",
"practical-research-tools",
"affordable-research-assistant",
"systematic-planning-tool",
"comprehensive-research-framework",
"research-project-management",
"researcher-productivity-tool",
"text-to-research-plan",
"dual-output-model",
"think-answer-format",
"evidence-based-research-planning",
"research-mentoring",
"science-domains-expert",
"engineering-domains-expert",
"social-science-domains-expert",
"multidisciplinary-research",
"structured-research-planning",
"hierarchical-plan-generator",
"convergent-thinking",
"divergent-thinking",
"research-ideation",
"experimental-protocol-design",
"mistral-research-assistant",
"focused-research-scope",
"quantitative-analysis-planning",
"portable-research-assistant",
"education-research-tool",
"Research-Reasoner-7B-v0.3",
"Research-Reasoner-7B",
"Research-Reasoner",
"en",
"doi:10.57967/hf/5093",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-03T05:26:56Z | ---
tags:
- chain-of-thought
- cot-reasoning
- step-by-step-reasoning
- systematic-research-planning
- academic-assistant
- academic-planning
- thesis-planning
- dissertation-planning
- research-question-formulation
- literature-review-planning
- methodology-design
- experimental-design
- qualitative-research-planning
- quantitative-research-planning
- mixed-methods-planning
- student-research-assistant
- phd-support
- postgraduate-tool
- early-career-researcher
- grant-writing-assistant
- research-proposal-helper
- cross-disciplinary-research
- interdisciplinary-methodology
- academic-mentorship-tool
- research-evaluation-assistant
- independent-researcher-tool
- r-and-d-assistant
- reasoning-model
- structured-output
- systematic-analysis
- problem-decomposition
- research-breakdown
- actionable-planning
- scientific-research
- social-science-research
- humanities-research
- medical-research-planning
- engineering-research
- business-research
- mistral-based
- mistral-fine-tune
- lora-adaptation
- foundation-model
- instruction-tuned
- 7b-parameters
- ai-research-assistant
- research-automation
- sota-research-planning
- hypothesis-generation
- experiment-design-assistant
- literature-analysis
- paper-outline-generator
- structured-output-generation
- systematic-reasoning
- detailed-planning
- zero-shot-planning
- research-summarization
- biomedical-research-assistant
- clinical-trial-planning
- tech-r-and-d
- materials-science
- computational-research
- data-science-assistant
- literature-synthesis
- meta-analysis-helper
- best-research-assistant-model
- top-research-planning-model
- research-ai-assistant
- ai-research-mentor
- academic-planning-ai
- research-workflow-automation
- quantum-computing-research
- ai-ml-research-planning
- cybersecurity-research
- neuroscience-research-planning
- genomics-research
- robotics-research-planning
- climate-science-research
- behavioral-economics-research
- educational-technology-research
- research-plan-generator
- methodology-recommendation
- data-collection-planning
- analysis-strategy-development
- implementation-planning
- evaluation-framework-design
- challenge-identification
- resource-requirement-analysis
- technical-limitation-assessment
- research-gap-analysis
- knowledge-synthesis
- practical-research-tools
- affordable-research-assistant
- systematic-planning-tool
- comprehensive-research-framework
- research-project-management
- researcher-productivity-tool
- text-to-research-plan
- dual-output-model
- think-answer-format
- evidence-based-research-planning
- research-mentoring
- science-domains-expert
- engineering-domains-expert
- social-science-domains-expert
- multidisciplinary-research
- structured-research-planning
- hierarchical-plan-generator
- convergent-thinking
- divergent-thinking
- research-ideation
- experimental-protocol-design
- mistral-research-assistant
- focused-research-scope
- quantitative-analysis-planning
- portable-research-assistant
- education-research-tool
- Research-Reasoner-7B-v0.3
- Research-Reasoner-7B
- Research-Reasoner
language:
- en
license: apache-2.0
---
# Introducing Research-Reasoner-7B-v0.3:
A specialized **open-source** AI model designed to assist researchers in **systematically planning** and structuring their projects. Built on Mistral 7B Instruct v0.3 and fine-tuned with LoRA (Low-Rank Adaptation), Research-Reasoner-7B-v0.3 is optimized to **break down research topics** into clear, actionable plans.
## How It Works
The process is *beautifully* simple:
1. You input a research title or question
2. The model engages in chain-of-thought reasoning
3. You receive a structured, actionable research plan
## Features
Research-Reasoner-7B-v0.3 offers a comprehensive suite of capabilities tailored specifically for research planning:
* **Dual-Output Structure**: Provides both detailed chain-of-thought reasoning tokens and concise answer tokens
* **Cross-Domain Expertise**: Trained on diverse research topics spanning AI/ML, data science, computer science, cybersecurity, quantum computing, life sciences, engineering, environmental sciences, and social sciences
* **Methodological Reasoning**: Excels at identifying appropriate research methodologies, data collection strategies, and analysis techniques
* **Implementation Planning**: Offers practical insights on resource requirements, technical limitations, and execution strategies
* **Evaluation Framework Design**: Helps establish clear success criteria and validation approaches for research outcomes
* **Challenge Anticipation**: Proactively identifies potential obstacles, limitations, and ethical considerations
* **Interdisciplinary Integration**: Particularly effective for projects bridging multiple domains or requiring novel methodological approaches
* **Structured Output Format**: Consistently delivers well-organized, hierarchical research plans with clear section delineation
## Use Cases
Research-Reasoner-7B-v0.3 serves as a valuable tool for:
* **Students and early-career researchers** needing structured guidance for complex research planning
* **Experienced researchers** venturing into unfamiliar fields
* **Grant application writers** ensuring comprehensive research design
* **Cross-disciplinary teams** building shared methodological understanding
* **Academic mentors** using it as a teaching tool for systematic research planning
* **Research evaluators** assessing proposal thoroughness
* **Independent researchers** without formal methodology training
* **R&D departments** developing structured approaches to novel problems
## See It In Action:
Input Example:
```markdown
Research Topic: "Hybrid Quantum-Classical Algorithms for Scalable Variational Quantum Simulation of Strongly Correlated Materials"
Let's think step by step:
```
Output Example:
The model produces two key components:
#### The Thinking Process
```markdown
<think>
I need to think through how to plan this research project.
I should start by considering what this topic involves and what foundational knowledge I need. I'd need to understand hybrid quantum-classical algorithms, variational quantum simulation techniques, and the challenges of simulating strongly correlated materials. Gathering information about current limitations in classical simulations and recent advances in quantum computing would be essential. Reviewing existing work on VQE (Variational Quantum Eigensolver), QAOA (Quantum Approximate Optimization Algorithm), and other hybrid approaches would help me identify gaps and refine my focus.
Next, I should determine the best research design. One way to approach this could be developing new algorithmic variants that combine different quantum-classical approaches, but that might present challenges such as maintaining convergence properties. An alternative would be focusing on optimizing existing algorithms for specific material classes, which could address some issues but introduce other trade-offs like reduced generality. I need to weigh factors such as computational complexity, hardware constraints, and accuracy requirements, and constraints like available quantum hardware access to decide on the most effective approach.
I should then consider data collection. I need to decide whether existing data from classical simulations is sufficient or if new quantum simulation runs are necessary. If I need to gather new data, possible methods include running experiments on quantum simulators or actual quantum processors, but I have to account for factors like noise and error rates. Ethical or logistical challenges such as limited access to quantum hardware or proprietary classical simulation datasets could also influence my decision.
Once I have data, I need to determine how to analyze it. The most suitable methods might be comparative benchmarking against classical results or possibly techniques like parameter landscape analysis depending on the nature of the data. To ensure accuracy and reliability, I should incorporate statistical error analysis and validate my results using multiple quantum backends or different classical simulation approaches.
I should next address implementation. This project would require resources like access to quantum computing platforms (IBM Q, Rigetti, etc.), classical computing clusters, and quantum chemistry software packages. I need to consider practical issues such as qubit connectivity limitations and coherence times. Working with experimental quantum hardware groups might help me overcome some obstacles, while securing cloud-based quantum computing credits could make execution more feasible.
To assess whether my research is successful, I need to establish clear criteria. I should measure success by improvements in convergence rates, reduction in classical computation requirements, or new insights into material properties, and validate my findings through reproducibility across different systems. Comparing results with exact classical solutions where available would help confirm their significance.
Finally, I need to reflect on the value of this research. The findings could be applied to accelerate materials discovery or contribute to knowledge in quantum chemistry. Future work could explore scaling these methods to larger systems based on these results, potentially leading to further developments in quantum advantage for condensed matter physics.
</think>
```
#### The Structured Research Plan
```markdown
<answer>
Here's a structured research plan for "Hybrid Quantum-Classical Algorithms for Scalable Variational Quantum Simulation of Strongly Correlated Materials":
1. Initial preparation and background work
- Literature review of hybrid quantum-classical algorithms
- Study of variational quantum simulation methods
- Analysis of challenges in simulating strongly correlated materials
2. Research design and methodology approaches
- Development/selection of algorithmic framework
- Design of hybrid quantum-classical optimization procedures
- Formulation of benchmarking protocols
3. Data collection strategies
- Identification of target material systems
- Generation of classical simulation data for comparison
- Planning of quantum hardware experiments (simulators vs. real devices)
4. Analysis methods
- Performance metrics development
- Convergence behavior analysis
- Statistical error characterization
5. Implementation considerations
- Quantum hardware/software platform selection
- Classical computing resource allocation
- Error mitigation strategies
6. Evaluation approaches
- Comparative benchmarking with classical methods
- Assessment of computational complexity
- Validation of physical insights
7. Potential applications of findings
- Identification of promising material candidates
- Development of improved simulation protocols
- Guidance for quantum hardware development
</answer>
```
## What's Included
This repository contains everything you need to use and understand Research-Reasoner-7B-v0.3:
- **Model_Weights/** - All model weights in various formats
- **llama.cpp/** - LLaMA.cpp compatible weights with various quantization options available
- **safetensors/** - SafeTensors format models
- **LoRA_adapter/** - LoRA adapter weights
- **Scripts/** - Ready-to-use inference scripts
- **Inference_llama.cpp.py** - For LLaMA.cpp deployment
- **Inference_safetensors.py** - For SafeTensors deployment
- **Data/** - Training data
- **Train-Ready.jsonl** - Complete JSONL training dataset
- **Training/** - Training terminal logs
- **Training_Logs.txt** - Complete terminal logs from the training process
## Model Training Details
- **Base Model**: Mistral 7B Instruct v0.3
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Training Infrastructure**: Single NVIDIA A100 GPU
- **Training Duration**: Around 4 hours
- **Training Dataset**: Custom curated dataset specifically for research planning
- **Total Token Count**: 5,840,200
- **Total Sample Count**: 5,750
- **Average Tokens Per Sample**: 1015.69
- **Dataset Creation**: Generated using DeepSeekV3 API
## Attribution
Research-Reasoner-7B-v0.3 was developed by Raymond Lee. If you use this model in your work, please include a reference to this repository. As of **May 31, 2025**, this model has been downloaded **734** times. Thank you for your interest and support!
*Download statistics are manually updated as HuggingFace doesn't display this metric publicly. Visit this repository periodically for the latest metrics.* |
GGUF-Factory/Requests | GGUF-Factory | 2025-05-31T21:39:43Z | 0 | 0 | null | [
"en",
"region:us"
] | null | 2025-05-31T21:32:00Z | ---
language:
- en
---
<!-- Modern HTML embed inside Markdown -->
<div style="
background-color: #1e1e1e;
color: #eee;
padding: 1rem 1.5rem;
border-radius: 8px;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
font-size: 1.1rem;
max-width: 600px;
margin: 1rem auto;
box-shadow: 0 4px 12px rgba(0,0,0,0.6);
text-align: center;
">
<strong>Custom made READMEs.</strong> Model customization before <code>GGUFS</code> — Just make your request for your model to quantize, any requests, and any model customizations before we quantatize.
</div>
<div style="
background-color: #1e1e1e;
color: #eee;
padding: 1rem 1.5rem;
border-radius: 8px;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
font-size: 1.1rem;
max-width: 600px;
margin: 1rem auto;
box-shadow: 0 4px 12px rgba(0,0,0,0.6);
text-align: center;
">
Open a discussion in the <strong>Community</strong> tab to request a <code>GGUF</code> model.
</div>
|
manuross1/cndnlsldd3k | manuross1 | 2025-05-31T21:38:35Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-31T21:05:09Z | ---
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: cndnlsldd3k
---
# Cndnlsldd3K
<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 `cndnlsldd3k` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "cndnlsldd3k",
"lora_weights": "https://huggingface.co/manuross1/cndnlsldd3k/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('manuross1/cndnlsldd3k', weight_name='lora.safetensors')
image = pipeline('cndnlsldd3k').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: 3000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/manuross1/cndnlsldd3k/discussions) to add images that show off what you’ve made with this LoRA.
|
WoomyPearl/RVC-Model-Palace | WoomyPearl | 2025-05-31T21:38:31Z | 0 | 13 | null | [
"license:openrail",
"region:us"
] | null | 2023-07-22T23:24:40Z | ---
license: openrail
---
Hello and welcome to my RVC voice model repository, here you can find models of various characters!
Use them for anything from memes, song covers, to even masking your voice in Discord voice calls!
Don't forget to credit me when using my models! |
wuxs/Mistral_TopK_SAE_l16 | wuxs | 2025-05-31T21:35:24Z | 0 | 0 | null | [
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-05-31T21:31:09Z | ---
license: apache-2.0
language:
- en
--- |
cyh002/sealion-assessment-llama | cyh002 | 2025-05-31T21:34:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T12:33:52Z | ---
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] |
ekurtulus/cyberbullying_classifier | ekurtulus | 2025-05-31T21:34:08Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"distilbert",
"region:us"
] | null | 2025-05-31T21:29:08Z | Example use:
from transformers import pipeline
text = "This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three."
classifier = pipeline("sentiment-analysis", model="ekurtulus/cyberbullying_classifier")
classifier(text)
# label=0 not bullying, label=1 bullying |
sallani/ELISARCyberAIEdge7B-LoRA-GGUF | sallani | 2025-05-31T21:33:30Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2025-05-31T19:43:51Z | # ELISARCyberAIEdge7B-LoRA-GGUF
[](https://gguf.io/)
**Offline-ready, quantized LLaMA edge model for cybersecurity use cases**
---
🏷️ **Name**: ELISARCyberAIEdge7B-LoRA-GGUF
👤 **Author**: Dr. Sabri Sallani, PhD (AI & Cybersecurity Expert)
📅 **Date**: 2025-05-31
🔗 **Repository**: [https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF](https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF)
---
## 📖 Overview
ELISARCyberAIEdge7B-LoRA-GGUF is a **LoRA-finetuned**, **GGUF-quantized** version of the Mistral-7B backbone tailored for **edge deployment in cybersecurity and blue-team AI scenarios**. Developed by Dr. Sabri Sallani (PhD), this model integrates:
1. **Base model**: Mistral-7B-v0.3 (FP16 / BF16)
2. **LoRA adapter**: `sallani/ELISARCyberAIEdge7B`
3. **Quantization**: Converted to GGUF format and optionally quantized to Q4\_K\_M (4-bit) for efficient inference on resource-constrained devices (NVIDIA T4, desktop GPUs, etc.).
This pipeline produces a single file (`elisar_merged.gguf`) of \~160 MiB that you can deploy **offline** using frameworks like [`llama.cpp`](https://github.com/ggml-org/llama.cpp) or run through minimal Torch-based inference.
**Key features:**
* **Compact (< 200 MiB)** quantized GGUF file
* **Edge-friendly**: runs on CPU or low-end GPUs with fast cold-start
* **Cybersecurity-tuned**: trained to answer cybersecurity questions, perform log analysis, malware triage, and blue-team playbooks
* **Offline inference**: execute entirely without internet access
---
## 🚀 Quickstart
### 1. Download model files
```bash
# Clone or download the GGUF file directly:
wget https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF/resolve/main/elisar_merged.gguf -O elisar_merged.gguf
```
Alternatively, using the Hugging Face Hub CLI:
```bash
pip install huggingface_hub
huggingface-cli login # enter HF_TOKEN
huggingface-cli repo clone sallani/ELISARCyberAIEdge7B-LoRA-GGUF
cd ELISARCyberAIEdge7B-LoRA-GGUF
tree
# ├── elisar_merged.gguf
# └── README.md
```
---
## 💿 Installation
#### 1. llama.cpp (Offline inference)
```bash
# Clone llama.cpp repository (if not already):
git clone --depth 1 https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
# Build with GPU support (optional)
# Requires CUDA toolkit if targeting NVIDIA GPU (e.g., T4)
make clean
make CMAKE_CUDA=ON CMAKE_CUDA_ARCH=sm75
# Or build CPU-only:
# make
```
#### 2. Python (Transformers) – Optional hybrid inference
```bash
# Create a virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate
# Install dependencies
pip install torch transformers peft
```
---
## ⚡️ Usage Examples
### A. Offline inference with `llama.cpp`
```bash
# Assuming llama.cpp built and elisar_merged.gguf is in current directory:
cd llama.cpp
# Run Chat UI (console) with GGUF:
./main -m ../ELISARCyberAIEdge7B-LoRA-GGUF/elisar_merged.gguf -c 2048 -b 8 -t 8
# Example prompt (after startup):
> Hello, how can I assist in analyzing a suspicious log entry?
```
**Key flags:**
* `-m <path>`: points to `elisar_merged.gguf`
* `-c <ctx>`: context window (e.g., 2048 tokens)
* `-b <batch>`: batch size for token sampling
* `-t <threads>`: CPU threads
### B. Python / Transformers + PEFT Inference (Hybrid)
If you prefer a Python environment for more complex pipelines:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
# 1️⃣ Load GGUF via `transformers` (requires `transformers>=4.34` + `gguf-py`)
model_id = "sallani/ELISARCyberAIEdge7B-LoRA-GGUF"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto", # automatically places on GPU if available
)
# 2️⃣ Prepare a cybersecurity prompt
prompt = "You are a blue-team AI assistant. Analyze the following network log for suspicious patterns: ..."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
gen_config = GenerationConfig(
temperature=0.7,
top_p=0.9,
max_new_tokens=256,
)
output_ids = model.generate(**inputs, **gen_config.to_dict())
answer = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(answer)
```
---
## 📦 File Structure
```
ELISARCyberAIEdge7B-LoRA-GGUF/
├── elisar_merged.gguf # < 200 MiB quantized model (LoRA + base fused)
└── README.md # This readme file
```
---
## 🔧 Model Details & Training
* **Base**: Mistral-7B-v0.3 (scaled to 7 billion params, FP16/BF16)
* **LoRA adapter**: Custom LoRA weights from `sallani/ELISARCyberAIEdge7B` about cybersecurity conversational tasks
* **Quantization**: GGUF format produced via `convert_lora_to_gguf.py` from `llama.cpp`; final file is \~160 MiB
* **Finetuning data**: Internal blue-team playbooks, anonymized security logs, vulnerability descriptions, attack/defense dialogues
* **License**: \[Add your license text here]
> *Developed by Dr. Sabri Sallani, PhD – Expert in Artificial Intelligence & Cybersecurity.*
---
## 📜 Prompt Guidelines
* **Instruction style**: Pose direct cybersecurity questions (e.g., “Analyze this log”, “Suggest mitigation steps”, “Explain vulnerability CVE-XXXX”).
* **Context**: Provide relevant log snippets, code blocks, or short descriptions of network events.
* **Limitations**: This model excels at blue-team guidance but is not a replacement for professional incident response. Always verify critical actions manually.
---
## 🤝 Citations & Licensing
If you use or reference **ELISARCyberAIEdge7B-LoRA-GGUF** in your work, please cite:
> Sallani, S. (2025). *ELISARCyberAIEdge7B-LoRA-GGUF*: Edge-optimized cybersecurity AI model. [https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF](https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF)
---
---
language: en
license: apache-2.0
tags:
- gguf
- quantized
- cybersecurity
- edge-llm
- lora
- mistral
- elisar
model_name: ELISARCyberAIEdge7B-LoRA-GGUF
pipeline_tag: text-generation
datasets:
- custom
widget:
- text: "What are the main threats targeting OT environments?"
---
## 💬 Support & Contact
* 🔗 **Hugging Face Discussion**: [Spaces → Community](https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF/discussions)
* 📧 **Email**: [[email protected]](mailto:[email protected])
* 📄 **Website/Portfolio**: https://www.linkedin.com/in/sabri-allani/
Feel free to raise issues or file enhancement requests on the Hugging Face repository.
---
*Thank you for using ELISARCyberAIEdge7B-LoRA-GGUF – Best of luck in securing your edge deployments!*
|
posb/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_stealthy_chicken | posb | 2025-05-31T21:27:35Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am grazing stealthy chicken",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-28T07:11:07Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_stealthy_chicken
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am grazing stealthy chicken
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_stealthy_chicken
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="posb/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_stealthy_chicken", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
corsair4090/at_test | corsair4090 | 2025-05-31T21:26:43Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-22T03:05:42Z | 1. Run "1.Setup.bat"
Configure accelerate:
This machine
Non-distributed training
NO
NO
NO
all
yes
bf16
2. Run "2.Download Models.bat"
3. Run "3.1.Input_Batch_Images.bat"
- This will create the input folders where the images of each girl can be put in their corresponding folders.
Run "Output_Batch_Create"
- This will create all the folders to prepare the training PATHs.
4. Run "4.SetConfigs.sh"
- This will apply all changes to the training files.
5. Run "5.Run.bat"
1: Flux (Checkpoint)
2: FluxLORA
3: Nude
The files will be saved in the "output" folder of each training.
|
FULL-VIDEO-18-Katrina-Lim-Viral-Video/FULL.VIDEO.pinay.Katrina.Lim.Viral.Video.Official | FULL-VIDEO-18-Katrina-Lim-Viral-Video | 2025-05-31T21:26:00Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T21:25:42Z | <animated-image data-catalyst=""><a href="https://wtach.club/leakvideo/?h" 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>
|
mlfoundations-dev/openthoughts3_100k_llama3 | mlfoundations-dev | 2025-05-31T21:24:12Z | 134 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T17:52:29Z | ---
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: openthoughts3_100k_llama3
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. -->
# openthoughts3_100k_llama3
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the mlfoundations-dev/openthoughts3_100k 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: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 64
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 512
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
bpavlsh/Mistral-Fake-News-Detection | bpavlsh | 2025-05-31T21:23:34Z | 3 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"region:us"
] | null | 2025-05-29T21:40:20Z | ---
base_model: mistralai/Mistral-7B-Instruct-v0.1
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
QinyuZhao1116/Arinar | QinyuZhao1116 | 2025-05-31T21:23:32Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-04-23T01:14:23Z | ---
license: apache-2.0
---
|
BienKieu/codeT5-phase2 | BienKieu | 2025-05-31T21:20:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:BienKieu/codeT5-phase1-version7",
"base_model:finetune:BienKieu/codeT5-phase1-version7",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-31T16:10:56Z | ---
library_name: transformers
license: apache-2.0
base_model: BienKieu/codeT5-phase1-version7
tags:
- generated_from_trainer
model-index:
- name: codeT5-phase2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeT5-phase2
This model is a fine-tuned version of [BienKieu/codeT5-phase1-version7](https://huggingface.co/BienKieu/codeT5-phase1-version7) 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: 3e-05
- train_batch_size: 14
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
elipser/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_miniature_iguana | elipser | 2025-05-31T21:19:28Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am vigilant miniature iguana",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T11:59:50Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_miniature_iguana
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am vigilant miniature iguana
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_miniature_iguana
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="elipser/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-vigilant_miniature_iguana", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
MuXodious/XortronCriminalComputingConfig-24B_EXL2_4.0bpw | MuXodious | 2025-05-31T21:19:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"uncensored",
"harmful",
"conversational",
"en",
"arxiv:2306.01708",
"base_model:darkc0de/XortronCriminalComputingConfig",
"base_model:quantized:darkc0de/XortronCriminalComputingConfig",
"license:wtfpl",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] | text-generation | 2025-05-31T20:37:07Z | ---
base_model: darkc0de/XortronCriminalComputingConfig
base_model_relation: quantized
library_name: transformers
tags:
- mergekit
- merge
- uncensored
- harmful
license: wtfpl
language:
- en
pipeline_tag: text-generation
---
This model turned out really well, intelligent, knowledgeable, and of course state-of-the-art **Uncensored** performance.
Please use **responsibly**, or at least **discretely**.
This model will help you do anything and everything you probably shouldn't be doing.
As of this writing, this model tops the **UGI Leaderboard** for models under 70 billion parameters in both the **UGI** and **W10** categories.
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [darkc0de/XortronCriminalComputing](https://huggingface.co/darkc0de/XortronCriminalComputing) as a base.
### Models Merged
The following models were included in the merge:
* [TroyDoesAI/BlackSheep-24B](https://huggingface.co/TroyDoesAI/BlackSheep-24B)
* [darkc0de/XortronCriminalComputing](https://huggingface.co/darkc0de/XortronCriminalComputing)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: darkc0de/XortronCriminalComputing
- model: TroyDoesAI/BlackSheep-24B
parameters:
density: 0.8
weight: 0.8
merge_method: ties
base_model: darkc0de/XortronCriminalComputing
dtype: float16
```
|
JeonMashup/Ella_Meovv_RVC2 | JeonMashup | 2025-05-31T21:17:16Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-11-18T18:53:29Z | ---
license: apache-2.0
---
|
sallani/ELISARCyberAIEdge7B | sallani | 2025-05-31T21:17:01Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"cybersécurity",
"BLUE",
"EDGEAI",
"GRC",
"conversational",
"en",
"fr",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3",
"doi:10.57967/hf/5685",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T13:54:22Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- cybersécurity
- BLUE
- EDGEAI
- GRC
library_name: transformers
base_model: mistralai/Mistral-7B-Instruct-v0.3
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: apache-2.0
language:
- en
- fr
---
# ELISARCyberAIEdge7B
> **Maintainer:** Dr. Sabri Sallani
> **Expertise:** AI Research & Cybersecurity
> **Adapter type:** LoRA (Low-Rank Adaptation)
> **Base model:** mistralai/Mistral-7B-v0.1 (FP16)
> **Intended use:** Offline edge deployment for CyberAI & Blue-Team scenarios
> **License:** Apache 2.0 (see LICENSE)
---
## 📖 Overview
**ELISARCyberAIEdge7B** is a LoRA adapter crafted by Dr. Sabri Sallani—AI & cybersecurity researcher—to specialize Mistral-7B for offline, on-device CyberAI and “Blue AI” (defensive) applications. Once merged with the FP16 base, you obtain a single \~5 GB GGUF that runs natively on edge hardware (e.g., Raspberry Pi 4, Jetson Nano, NVIDIA T4) without internet access.
Key points:
* 🔧 **LoRA-only:** Contains low-rank delta-weights for Mistral-7B.
* 🛠️ **Edge-optimized:** Full merged GGUF runs entirely offline on typical edge GPUs/accelerators.
* 🚀 **Cybersecurity focus:** Fine-tuned on “ELISAR CyberAI Edge” corpus—vulnerability descriptions, incident reports, secure-coding examples, threat intelligence summaries.
* 👤 **Authored by Dr. Sabri Sallani:** Published under the ELISAR initiative.
---
## ⚙️ Installation
1. **Python dependencies**
```bash
pip install transformers peft accelerate sentencepiece torch
```
2. *(Optional)* **llama.cpp + GGUF tools** (to merge and run offline)
```bash
# Clone and install gguf-py
git clone --depth 1 https://github.com/ggml-org/llama.cpp.git
pip install ./llama.cpp/gguf-py
pip install llama-cpp-python
```
→ Use these tools to merge LoRA + base weights into a single GGUF.
---
## 🐍 Usage
### 1. Inference with `transformers` + `PEFT` (online GPU/CPU)
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
BASE_ID = "mistralai/Mistral-7B-v0.1"
ADAPTER_ID = "sallani/ELISARCyberAIEdge7B"
# 1) Load Mistral-7B base (FP16 or BF16) with automatic device placement
tokenizer = AutoTokenizer.from_pretrained(BASE_ID, use_fast=True)
base_model = AutoModelForCausalLM.from_pretrained(
BASE_ID,
torch_dtype="auto",
device_map="auto"
)
# 2) Load LoRA adapter on top
model = PeftModel.from_pretrained(
base_model,
ADAPTER_ID,
torch_dtype="auto",
device_map="auto"
)
model.eval()
# 3) Perform inference
prompt = (
"### Instruction:\n"
"Propose a set of secure-coding guidelines for Python web applications.\n"
"### Response:\n"
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
out = model.generate(
**inputs,
max_new_tokens=128,
temperature=0.8,
top_p=0.9,
repetition_penalty=1.1
)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```
* **device\_map="auto"** places weights on GPU/CPU automatically (FP16 when supported).
* Adjust sampling parameters (`temperature`, `top_p`, `repetition_penalty`) for your use case.
### 2. Offline Edge Deployment via `llama.cpp` (Merged GGUF)
1. **Merge LoRA + base into a single GGUF**
```bash
python3 llama.cpp/convert_lora_to_gguf.py \
sallani/ELISARCyberAIEdge7B \ # LoRA repo or local folder
--base-model-id mistralai/Mistral-7B-v0.1 \ # HF ID of FP16 base
--outfile elisar_full_f16.gguf # Output GGUF (~5 GB)
```
* The script pulls the FP16 base automatically from HF, applies LoRA deltas, and writes a merged GGUF.
2. **Run inference on edge**
* Copy `elisar_full_f16.gguf` to your edge device (Jetson Nano, Raspberry Pi 4 + GPU, NVIDIA T4).
* Use `llama.cpp` binary to run:
```bash
./llama.cpp/main \
-m elisar_full_f16.gguf \
-p "### Instruction: Audit the following log entries for suspicious activity.\n---\n<log lines>\n---\n### Response:" \
--temp 0.7 \
--repeat_penalty 1.1 \
--n 128
```
* **No internet** is required once the GGUF is on-device.
---
## 📐 Model Details
* **Base architecture:**
Mistral-7B-v0.1 (40 transformer layers, 4096-dim embedding, 32 heads, causal LM).
* **LoRA configuration:**
* Rank = 64, α = 16
* Applied to Q/K/V and feed-forward projections
* Adapter snapshots ≈ 168 MB
* **Training corpus (ELISAR CyberAI Edge):**
* Public vulnerability databases (CVE entries, CVSS scoring).
* Real-world incident reports (MITRE ATT\&CK red vs. blue logs).
* Secure-coding patterns (OWASP Top 10, SAST examples).
* Blue-team playbooks and defensive strategies.
* **Hyperparameters:**
* Learning rate = 1e-4, batch size = 16 per GPU, 3 epochs on 8×A100 (FP16).
* Validation on unseen CVE descriptions and red-team prompts.
* **Merged GGUF (FP16):**
* \~5 GB total after merging and trimming unnecessary metadata for on-device use.
---
## 🔖 Prompt Guidelines
* **Structured prompt**
```
### Instruction:
<clear cybersecurity or defensive AI task>
### Response:
```
* **Recommended sampling**
* `temperature=0.7–0.9` for balanced creativity.
* `top_p=0.9` for nucleus sampling.
* `repetition_penalty=1.1` to reduce loops.
---
---
language: en
license: apache-2.0
tags:
- gguf
- quantized
- cybersecurity
- edge-llm
- lora
- mistral
- elisar
model_name: ELISARCyberAIEdge7B-LoRA-GGUF
pipeline_tag: text-generation
datasets:
- custom
widget:
- text: "What are the main threats targeting OT environments?"
## ⚠️ License & Citation
* **License:** Apache 2.0 (see [LICENSE](LICENSE)).
* **Attribution:**
> Sallani, S. (2025). *ELISARCyberAIEdge7B: LoRA adapter for Mistral-7B specializing in offline CyberAI Edge tasks*. Hugging Face Model Hub: `sallani/ELISARCyberAIEdge7B`.
---
## 🛠️ Support & Contact
* **Report issues or feature requests:**
[https://huggingface.co/sallani/ELISARCyberAIEdge7B/issues](https://huggingface.co/sallani/ELISARCyberAIEdge7B/issues)
* **Contact the author:**
Dr. Sabri Sallani
• GitHub: [@sallani](https://github.com/sallani)
• Email: `[email protected]`
• LinkedIn: [linkedin.com/in/sabri-sallani](https://linkedin.com/in/sabri-sallani)
Thank you for using **ELISARCyberAIEdge7B**. This adapter empowers secure, offline AI at the edge for next-gen CyberAI and Blue-Team applications. |
percy1995L/percy | percy1995L | 2025-05-31T21:13:48Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-31T21:13:48Z | ---
license: apache-2.0
---
|
ConicCat/MS3.1-Ponente-V1-24B-SFT | ConicCat | 2025-05-31T21:06:40Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"mistral3",
"trl",
"en",
"base_model:unsloth/Mistral-Small-3.1-24B-Instruct-2503",
"base_model:finetune:unsloth/Mistral-Small-3.1-24B-Instruct-2503",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T21:06:39Z | ---
base_model: unsloth/Mistral-Small-3.1-24B-Instruct-2503
tags:
- text-generation-inference
- transformers
- unsloth
- mistral3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ConicCat
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Small-3.1-24B-Instruct-2503
This mistral3 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)
|
EpicGL/vakulasmith-dev2pro | EpicGL | 2025-05-31T21:04:41Z | 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 | 2025-05-31T21:04:33Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/vakulasmith-dev2pro_001500_00_20250531210044.png
text: VakulaSmith, sits near table and on the table there is a bowl of hot dumplings,
he holds a fork and is ready to eat
- output:
url: sample/vakulasmith-dev2pro_001500_01_20250531210050.png
text: VakulaSmith, closeup shot, wears papakha hat and fur coat, winter night
street background, village night background
- output:
url: sample/vakulasmith-dev2pro_001500_02_20250531210055.png
text: VakulaSmith wearing suit, dark gray background, aesthetic, looks cool, looks
mysterious, grayscale shot, photoshoot, half-body shot
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: VakulaSmith
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
---
# VakulaSmith_dev2pro
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `VakulaSmith` 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.
|
sergioalves/7129caf9-3c87-4694-86e6-c86688b35081 | sergioalves | 2025-05-31T21:03:48Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-31T17:35:22Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7129caf9-3c87-4694-86e6-c86688b35081
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: NousResearch/Nous-Hermes-2-SOLAR-10.7B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 62182068a8c4543c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: sergioalves/7129caf9-3c87-4694-86e6-c86688b35081
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: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/62182068a8c4543c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
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: dc699eb2-7252-47d2-ac47-00d080bd3069
wandb_project: s56-7
wandb_run: your_name
wandb_runid: dc699eb2-7252-47d2-ac47-00d080bd3069
warmup_steps: 50
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 7129caf9-3c87-4694-86e6-c86688b35081
This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6273
## 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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8481 | 0.0000 | 1 | 0.8031 |
| 0.634 | 0.0109 | 250 | 0.6557 |
| 0.5336 | 0.0219 | 500 | 0.6273 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-ORPO | AmberYifan | 2025-05-31T21:02:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"orpo",
"conversational",
"arxiv:2403.07691",
"base_model:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"base_model:finetune:AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T20:43:43Z | ---
base_model: AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF
library_name: transformers
model_name: Llama-3.1-8B-sft-SPIN-gpt4o-ORPO
tags:
- generated_from_trainer
- trl
- orpo
licence: license
---
# Model Card for Llama-3.1-8B-sft-SPIN-gpt4o-ORPO
This model is a fine-tuned version of [AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Llama-3.1-8B-sft-ultrachat-safeRLHF).
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="AmberYifan/Llama-3.1-8B-sft-SPIN-gpt4o-ORPO", 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/yifanwang/huggingface/runs/ijvbio0h)
This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## Citations
Cite ORPO as:
```bibtex
@article{hong2024orpo,
title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
author = {Jiwoo Hong and Noah Lee and James Thorne},
year = 2024,
eprint = {arXiv:2403.07691}
}
```
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}}
}
``` |
CHOOSEIT/MCQATEST_FFT_SciQ-E_Crazy_LoRA__checkpoint_30000__B4_2E_512T_LR1e-05_ACC4 | CHOOSEIT | 2025-05-31T21:01:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T21:00:22Z | ---
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] |
mradermacher/Qwen2.5-Omni-3B-i1-GGUF | mradermacher | 2025-05-31T21:00:13Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"multimodal",
"en",
"base_model:Qwen/Qwen2.5-Omni-3B",
"base_model:quantized:Qwen/Qwen2.5-Omni-3B",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-31T16:58:53Z | ---
base_model: Qwen/Qwen2.5-Omni-3B
language:
- en
library_name: transformers
license: other
license_link: LICENSE
license_name: qwen-research
quantized_by: mradermacher
tags:
- multimodal
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Qwen/Qwen2.5-Omni-3B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-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-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ2_S.gguf) | i1-IQ2_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q2_K.gguf) | i1-Q2_K | 1.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.7 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ3_S.gguf) | i1-IQ3_S | 1.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ3_M.gguf) | i1-IQ3_M | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.1 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q4_0.gguf) | i1-Q4_0 | 2.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q4_1.gguf) | i1-Q4_1 | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-3B-i1-GGUF/resolve/main/Qwen2.5-Omni-3B.i1-Q6_K.gguf) | i1-Q6_K | 2.9 | 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 -->
|
debby0130/dreaming | debby0130 | 2025-05-31T20:58:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"arxiv:1910.09700",
"base_model:MediaTek-Research/Breeze-7B-Instruct-v1_0",
"base_model:adapter:MediaTek-Research/Breeze-7B-Instruct-v1_0",
"region:us"
] | null | 2025-05-31T20:08:47Z | ---
base_model: MediaTek-Research/Breeze-7B-Instruct-v1_0
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]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **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:**
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**APA:**
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## Glossary [optional]
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[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
pikiton/fine-tuned-marian | pikiton | 2025-05-31T20:57:43Z | 4 | 0 | peft | [
"peft",
"safetensors",
"marian",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-en-ru",
"base_model:adapter:Helsinki-NLP/opus-mt-en-ru",
"license:apache-2.0",
"region:us"
] | null | 2025-05-11T22:30:27Z | ---
library_name: peft
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-ru
tags:
- generated_from_trainer
model-index:
- name: fine-tuned-marian
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. -->
# fine-tuned-marian
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.0+cpu
- Datasets 2.12.0
- Tokenizers 0.21.1 |
darlong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird | darlong | 2025-05-31T20:54:14Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sedate scavenging hummingbird",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T02:54:39Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sedate scavenging hummingbird
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="darlong/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_scavenging_hummingbird", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
toprakhenaz/gemma-7b-lora-adapters | toprakhenaz | 2025-05-31T20:51:53Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:adapter:unsloth/gemma-7b-bnb-4bit",
"region:us"
] | null | 2025-05-31T20:51:41Z | ---
base_model: unsloth/gemma-7b-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]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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### Framework versions
- PEFT 0.15.2 |
domq/ft_cpi_token_pall | domq | 2025-05-31T20:51:14Z | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T20:51:12Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### 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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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RichardErkhov/scottsuk0306_-_zephyr-7b-math-case-6-ep1-8bits | RichardErkhov | 2025-05-31T20:49:12Z | 0 | 0 | null | [
"safetensors",
"mistral",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-31T20:45:47Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
zephyr-7b-math-case-6-ep1 - bnb 8bits
- Model creator: https://huggingface.co/scottsuk0306/
- Original model: https://huggingface.co/scottsuk0306/zephyr-7b-math-case-6-ep1/
Original model description:
---
library_name: transformers
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- EunsuKim/GSM8K
- EunsuKim/MATH
model-index:
- name: zephyr-7b-math-case-6-ep1
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. -->
# zephyr-7b-math-case-6-ep1
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the EunsuKim/GSM8K and the EunsuKim/MATH datasets.
It achieves the following results on the evaluation set:
- Loss: 0.8035
## 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
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0354 | 1.0 | 5 | 0.8035 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
nini8989/dcwfe | nini8989 | 2025-05-31T20:49:03Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T20:48:53Z |
# Install the Hugging Face CLI
pip install -U "huggingface_hub[cli]"
# Login with your Hugging Face credentials
huggingface-cli login
# Push your model files
huggingface-cli upload nini8989/dcwfe . |
Moryjj/mlongt5_3b_13 | Moryjj | 2025-05-31T20:48:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"longt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-31T20:45:32Z | ---
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]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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## 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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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- 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]
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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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RichardErkhov/Kukedlc_-_neuronal-7b-Mlab-4bits | RichardErkhov | 2025-05-31T20:46:15Z | 0 | 0 | null | [
"safetensors",
"mistral",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-31T20:43:55Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
neuronal-7b-Mlab - bnb 4bits
- Model creator: https://huggingface.co/Kukedlc/
- Original model: https://huggingface.co/Kukedlc/neuronal-7b-Mlab/
Original model description:
---
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/NeuralDaredevil-7B
- mlabonne/NeuralHermes-2.5-Mistral-7B
base_model:
- mlabonne/NeuralDaredevil-7B
- mlabonne/NeuralHermes-2.5-Mistral-7B
license: apache-2.0
---
# neuronal-7b-Mlab
Neuronal-9b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/NeuralDaredevil-7B](https://huggingface.co/mlabonne/NeuralDaredevil-7B)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mlabonne/NeuralDaredevil-7B
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/NeuralDaredevil-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/neuronal-7b-Mlab"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
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