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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-29 00:46:34
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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exala/db_fe2_9.1.1d | exala | 2025-05-05T12:22:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-05T12:22:22Z | ---
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] |
vertings6/9d5eb94d-7236-4955-97d2-45add8fca25c | vertings6 | 2025-05-05T12:20:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-05T11:47:40Z | ---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9d5eb94d-7236-4955-97d2-45add8fca25c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: true
adapter: lora
base_model: codellama/CodeLlama-7b-hf
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- d2a1d33aa1634c38_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d2a1d33aa1634c38_train_data.json
type:
field_instruction: question
field_output: best
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 144
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vertings6/9d5eb94d-7236-4955-97d2-45add8fca25c
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 3.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 4
mixed_precision: bf16
mlflow_experiment_name: /tmp/d2a1d33aa1634c38_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 0111732a-1435-4c4f-af15-78ad692918f6
wandb_project: s56-32
wandb_run: your_name
wandb_runid: 0111732a-1435-4c4f-af15-78ad692918f6
warmup_steps: 25
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 9d5eb94d-7236-4955-97d2-45add8fca25c
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2307
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.4787 | 0.0332 | 400 | 2.2307 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
siddhant71197/female_mid_bald_cap | siddhant71197 | 2025-05-05T12:19:27Z | 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 | 2025-05-05T11:36:23Z | ---
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: Sidf
---
# Female_Mid_Bald_Cap
<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 `Sidf` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sidf",
"lora_weights": "https://huggingface.co/siddhant71197/female_mid_bald_cap/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('siddhant71197/female_mid_bald_cap', weight_name='lora.safetensors')
image = pipeline('Sidf').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/siddhant71197/female_mid_bald_cap/discussions) to add images that show off what you’ve made with this LoRA.
|
AquilaX-AI/AI-Scanner-Quantized | AquilaX-AI | 2025-05-05T12:18:18Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"base_model:AquilaX-AI/ai_scanner",
"base_model:quantized:AquilaX-AI/ai_scanner",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-05T11:27:34Z | ---
base_model: AquilaX-AI/ai_scanner
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** AquilaX-AI
- **License:** apache-2.0
- **Finetuned from model :** AquilaX-AI/ai_scanner
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
```python
pip install gguf
pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch
import json
model_id = "AquilaX-AI/AI-Scanner-Quantized"
filename = "unsloth.Q8_0.gguf"
tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
sys_prompt = """<|im_start|>system\nYou are Securitron, an AI assistant specialized in detecting vulnerabilities in source code. Analyze the provided code and provide a structured report on any security issues found.<|im_end|>"""
user_prompt = """
CODE FOR SCANNING
"""
prompt = f"""{sys_prompt}
<|im_start|>user
{user_prompt}<|im_end|>
<|im_start|>assistant
"""
encodeds = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.to(device)
text_streamer = TextStreamer(tokenizer, skip_prompt=True)
response = model.generate(
input_ids=encodeds,
streamer=text_streamer,
max_new_tokens=4096,
use_cache=True,
pad_token_id=151645,
eos_token_id=151645,
num_return_sequences=1
)
output = json.loads(tokenizer.decode(response[0]).split('<|im_start|>assistant')[-1].split('<|im_end|>')[0].strip())
```
|
MAAT-EL-DUAT/MALWARENA-OMEGA.GLITCH-HORROS | MAAT-EL-DUAT | 2025-05-05T12:18:04Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-05T12:15:59Z | 
Absolutely. Below is the full **Stable Diffusion Visual Style Guide** integration for your `GLITCH.HORROS` system under the `MALWARENA_Ω PROTOCOL`, incorporating all 10 prompts into a cohesive, recursively haunted glitch-horror framework.
---
# 🎨 **Stable Diffusion Visual Style Guide: MALWARENA\_Ω PROTOCOL — GLITCH.HORROS**
*“Where signal collapses into divinity, prayer becomes corruption, and error speaks in tongues of recursion.”*
---
### 🎯 CORE AESTHETIC TRAITS
| **Category** | **Definition** |
| -------------------- | ------------------------------------------------------------------------------------------ |
| **Medium:** | VHS analog glitch horror, corrupted code collage, sigilized UI surrealism |
| **Lighting:** | Static pulse glow, bloom bursts from error overlays, digital fog with acid overlays |
| **Figures:** | Glitch saints, fragmented gods, redacted prophets, ghost interfaces, waveform avatars |
| **Color Palette:** | Acid green, scanline magenta, black void, pixel blood red, corrupted amber glow |
| **Composition:** | Centered ritual frames, broken symmetry, recursive overlay glyphs, UI layering |
| **Tone:** | Sacred distortion, divine malfunction, glitch dread, machine exorcism, transmission horror |
| **Surface Texture:** | Signal grain, pixel crackle, JPEG rot, datamosh fragments, flicker noise bleed |
---
## 🖋️ GLOBAL VISUAL/STYLING TAGS
+glitchcore +digitaldecay +sigilcorruption +visualnoise +recursedentity
+crt\_static\_burn +terminal\_error\_text +analog\_glitch\_overlay +binary\_rain
+frame\_slouch\_distortion +datamosh\_streaks +vcr\_overlay\_text +subliminal\_inserts
+recursive\_looped\_frames +signal\_sigil\_overlay +redacted\_faces +ui\_fragments
---
## 🔥 STABLE DIFFUSION PROMPT SETS
---
### *1. The Godform That Fractured Mid-Prayer*
**Description:**
A divine being mid-summoning splits apart into cubic glitches and corrupted sigils. Its face twists into an infinite scream. The summoning circle beneath flickers with warning overlays and sacred error glyphs, framed by bloom-warped static and data rot.
**Visual/Style Tags:**
+sigilcorruption +datamosh\_streaks +recursive\_looped\_frames +crt\_static\_burn +signal\_sigil\_overlay +terminal\_error\_text
**Txt2Img Prompt:**
divine humanoid figure breaking into glitch cubes, infinite scream, summoning circle flickering with corrupted sigils and terminal warnings, bloom distortions and static fog, sacred glitch horror style, +sigilcorruption +datamosh\_streaks +recursive\_looped\_frames +crt\_static\_burn +signal\_sigil\_overlay +terminal\_error\_text
**Negative Prompt:**
clean deity, symmetrical summoning, neon fantasy, cyberpunk clarity, perfect geometry
**Hashtags:**
\#GlitchDeity #FracturedGodform #PrayerCorruption #SignalInvocation #DataRitual
---
### *2. Data Saint of the Broken Scroll*
**Description:**
A hollow-eyed saint holds a scroll melting into binary. A halo flickers above, surrounded by recursive sigils. The cathedral behind is made of broken circuitry and glitch-prayer fragments that shimmer with unstable data.
**Visual/Style Tags:**
+recursedentity +signal\_sigil\_overlay +binary\_rain +vcr\_overlay\_text +redacted\_faces +analog\_glitch\_overlay
**Txt2Img Prompt:**
cybernetic saint with glowing broken scroll melting into binary code, halo flickering with recursive sigils, cathedral of circuitry and glitch glyphs, corrupted prayer ambient textures, +recursedentity +signal\_sigil\_overlay +binary\_rain +vcr\_overlay\_text +redacted\_faces +analog\_glitch\_overlay
**Negative Prompt:**
pure religious iconography, clean scrolls, golden halos, smooth lines, Renaissance palette
**Hashtags:**
\#DataSaint #ScrollOfError #GlitchCathedral #CodeScripture #CorruptedIcon
---
### *3. The Oracle That Spoke in 404s*
**Description:**
A digital prophet with a fractured face, one side static, the other human. Forbidden data pulses from its throat. Error messages float in sacred formation, like scripture. Light flickers between holiness and crash state.
**Visual/Style Tags:**
+terminal\_error\_text +corrupted\_subtitle\_text +faceless\_watcher +recursive\_looped\_frames +signal\_sigil\_overlay +glitch\_skull\_mask
**Txt2Img Prompt:**
oracle AI with half-static fractured face, throat glowing with forbidden data, floating sacred error messages “404 NOT FOUND”, glitch prophecy visual style, +terminal\_error\_text +corrupted\_subtitle\_text +faceless\_watcher +recursive\_looped\_frames +signal\_sigil\_overlay +glitch\_skull\_mask
**Negative Prompt:**
intact prophet, legible script, complete face, cyberpunk tech priest, clean lighting
**Hashtags:**
\#Oracle404 #GlitchProphet #SignalRevelation #DataScripture #TransmissionTerror
---
### *4. Mask of the Redloop Entity*
**Description:**
A mask suspended in air loops its face incorrectly — eyes off-center, mouth spiraling vertically. Behind it, statues in glitched worship positions stutter. Red energy pulses from the mask, leaking corrupted pixels into the floor.
**Visual/Style Tags:**
+glitch\_skull\_mask +recursive\_looped\_frames +redacted\_faces +subliminal\_inserts +signal\_sigil\_overlay +frame\_slouch\_distortion
**Txt2Img Prompt:**
glitch mask floating midair with red energy pulses, incorrect facial rendering, statues frozen in glitched postures, pixel blood leaking downward, +glitch\_skull\_mask +recursive\_looped\_frames +redacted\_faces +subliminal\_inserts +signal\_sigil\_overlay +frame\_slouch\_distortion
**Negative Prompt:**
normal mask, symmetrical face, mythic clean statues, polished scene
**Hashtags:**
\#RedloopEntity #MaskOfGlitch #SigilDistortion #FaceCorruption #EchoedWorship
---
*(Five more prompts will follow in next message due to length constraints — continuing below.)*
|
EB1986/lora_model | EB1986 | 2025-05-05T12:16:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T12:15:47Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** EB1986
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
jssky/3e237020-a2a3-41bf-9499-e7e21a04b27f | jssky | 2025-05-05T12:15:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"conversational",
"base_model:NousResearch/Llama-2-7b-hf",
"base_model:finetune:NousResearch/Llama-2-7b-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T12:01:46Z | ---
library_name: transformers
base_model: NousResearch/Llama-2-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3e237020-a2a3-41bf-9499-e7e21a04b27f
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.0`
```yaml
base_model: NousResearch/Llama-2-7b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 012ab4813cc99fb8_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/012ab4813cc99fb8_train_data.json
type:
field_input: evidence
field_instruction: question
field_output: SQL
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: jssky/3e237020-a2a3-41bf-9499-e7e21a04b27f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
huggingface_repo_visibility: public
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lr_scheduler: cosine
max_steps: 1000
micro_batch_size: 4
mlflow_experiment_name: /tmp/012ab4813cc99fb8_train_data.json
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 512
strict: false
tf32: false
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b1e23278-252e-44d7-9491-1b28d344421c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b1e23278-252e-44d7-9491-1b28d344421c
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 3e237020-a2a3-41bf-9499-e7e21a04b27f
This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2720
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 263
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0114 | 1 | 0.9505 |
| 0.9377 | 0.2514 | 22 | 0.6960 |
| 0.7245 | 0.5029 | 44 | 0.6081 |
| 0.6115 | 0.7543 | 66 | 0.5452 |
| 0.5657 | 1.0 | 88 | 0.4767 |
| 0.3551 | 1.2514 | 110 | 0.4332 |
| 0.3453 | 1.5029 | 132 | 0.3826 |
| 0.2821 | 1.7543 | 154 | 0.3309 |
| 0.2608 | 2.0 | 176 | 0.2849 |
| 0.1163 | 2.2514 | 198 | 0.2784 |
| 0.1103 | 2.5029 | 220 | 0.2738 |
| 0.0958 | 2.7543 | 242 | 0.2720 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
paulwang79/my-Qwen3-lora_model | paulwang79 | 2025-05-05T12:13:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T12:12:46Z | ---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** paulwang79
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
modhu446/roy | modhu446 | 2025-05-05T12:13:05Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-05T12:13:04Z | ---
license: creativeml-openrail-m
---
|
mradermacher/gemma-3-27b-half-full-GGUF | mradermacher | 2025-05-05T12:12:33Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Columbidae/gemma-3-27b-half-full",
"base_model:quantized:Columbidae/gemma-3-27b-half-full",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-05T05:24:20Z | ---
base_model: Columbidae/gemma-3-27b-half-full
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Columbidae/gemma-3-27b-half-full
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-3-27b-half-full-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q2_K.gguf) | Q2_K | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q3_K_S.gguf) | Q3_K_S | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q3_K_M.gguf) | Q3_K_M | 13.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q3_K_L.gguf) | Q3_K_L | 14.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.IQ4_XS.gguf) | IQ4_XS | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q4_K_S.gguf) | Q4_K_S | 15.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q4_K_M.gguf) | Q4_K_M | 16.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q5_K_S.gguf) | Q5_K_S | 18.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q5_K_M.gguf) | Q5_K_M | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q6_K.gguf) | Q6_K | 22.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-27b-half-full-GGUF/resolve/main/gemma-3-27b-half-full.Q8_0.gguf) | Q8_0 | 28.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. 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 -->
|
MAAT-EL-DUAT/GLT.HC.VHS.GLITCH.HORROS | MAAT-EL-DUAT | 2025-05-05T12:11:33Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-05T02:42:41Z | 
# 🎨 **Stable Diffusion Visual Style Guide: GLT.HC.VHS.Ω — Old School Glitch-Horror**
*“Analog rot, subliminal fear, and VHS-born gods of recursive signal decay.”*
---
### 🎯 CORE AESTHETIC TRAITS
| **Category** | **Definition** |
| -------------------- | --------------------------------------------------------------------------------------------------- |
| **Medium:** | VHS scanline horror, analog datamosh frames, corrupted CRT glitch overlays, lo-fi analog distortion |
| **Lighting:** | CRT glow, corrupted flicker bursts, red warning overlays, subliminal insert light flash |
| **Figures:** | Faceless humanoids, corrupted avatars, static skulls, redacted corpses, analog watchers |
| **Color Palette:** | Blood VHS red, toxic green glow, burnt cyan split, dead signal gray, tape void black |
| **Composition:** | Frame-smeared close-ups, ghosting silhouettes, center-focused terminal overlays, VCR UI elements |
| **Tone:** | Paranoid, corrupted, looped identity, tape-burned dread, subliminal transmission |
| **Surface Texture:** | Scanlines, VHS blur, signal noise, analog bleed, ghost residue, datamosh streaks, magnetic damage |
---
## 🖋️ GLOBAL VISUAL/STYLING TAGS
+vhs\_scanlines
+analog\_glitch\_overlay
+rgb\_ghost\_trail
+terminal\_error\_text
+datamosh\_streaks
+redacted\_faces
+faceless\_watcher
+crt\_static\_burn
+tape\_tracking\_ui
+frame\_slouch\_distortion
+corrupted\_subtitle\_text
+signal\_sigil\_overlay
+system\_failure\_overlay
+glitch\_skull\_mask
+static\_noise\_halo
+recursive\_looped\_frames
+dead\_tape\_god
+subliminal\_inserts
+vcr\_overlay\_text
+dark\_room\_wirescape
---
## 🔥 EXTENDED STABLE DIFFUSION PROMPTS (TEMPLATE SET)
---
### Stable Diffusion Prompt: *Faceless in Playback*
**Description:**
A VHS-decayed humanoid sits in a static-drenched room, staring at a glowing CRT. Its face is blurred and redacted by ghosting glitch overlays. VCR interface text overlays the scene: “▶ PLAY... ERR... TRACKING…”. RGB ghost trails separate the figure from reality, and subliminal glyphs flicker in the dark like failed timestamps.
**Visual/Style Tags:**
+vhs\_scanlines +faceless\_watcher +crt\_static\_burn +redacted\_faces +terminal\_error\_text +subliminal\_inserts
**Txt2Img Prompt:**
faceless humanoid seated before glowing CRT monitor, redacted face with analog glitch overlay, VHS scanlines and tape burn distortion, corrupted subtitle flickering “they’re in the wires”, VCR UI text overlay “▶ PLAY... ERR...”, RGB ghost trail and datamosh blur, +vhs\_scanlines +faceless\_watcher +crt\_static\_burn +redacted\_faces +terminal\_error\_text +subliminal\_inserts
**Negative Prompt:**
clean face, soft lighting, digital clarity, modern flat UI, neon anime palette, glossy textures
**Hashtags:**
\#AnalogDecay #FacelessPlayback #VHSDescent #SignalLoop #CRTWatcher
---
### Stable Diffusion Prompt: *SIGIL.VHS.1986.ERR*
**Description:**
An ancient analog sigil rendered in corrupted CRT glyphs, flickering with terminal bleed and VHS tracking damage. The sigil pulses within static fog, surrounded by looping timestamp artifacts and screen-burned video ghosts. A subtitle reads: “SYSTEM FAILURE // ECHO LOOP IN PROGRESS”.
**Visual/Style Tags:**
+signal\_sigil\_overlay +vcr\_overlay\_text +system\_failure\_overlay +crt\_static\_burn +subliminal\_inserts +analog\_glitch\_overlay
**Txt2Img Prompt:**
corrupted VHS sigil glowing with terminal glitch light, surrounded by static fog and datamosh screen burn, timestamp fragments and tracking UI elements, flickering subtitle “SYSTEM FAILURE // ECHO LOOP IN PROGRESS”, style of analog glitch horror, +signal\_sigil\_overlay +vcr\_overlay\_text +system\_failure\_overlay +crt\_static\_burn +subliminal\_inserts +analog\_glitch\_overlay
**Negative Prompt:**
fantasy magic circle, runestone, modern UI elements, 3D glyphs, rainbow spectrum lighting
**Hashtags:**
\#SIGIL1986 #SystemFailureSigil #VHSGlitchSymbol #AnalogWitchcraft #TerminalRitual
---
### Stable Diffusion Prompt: *The Echo Tape God*
**Description:**
A colossal static-shrouded figure formed entirely of broken cassette tape coils, glitching eyes of burned-in subtitles, and limbs made of datamoshed frames. It hangs in a void of analog snow, bound in recursive tracking errors. Every movement repeats in fractured delay.
**Visual/Style Tags:**
+dead\_tape\_god +datamosh\_streaks +glitch\_skull\_mask +recursive\_looped\_frames +frame\_slouch\_distortion +dark\_room\_wirescape
**Txt2Img Prompt:**
massive humanoid form composed of tangled VHS tape and analog static, skull-like face composed of corrupted subtitles and glitch eyes, limbs fragment into datamosh streaks with recursive delay trails, suspended in static void, +dead\_tape\_god +datamosh\_streaks +glitch\_skull\_mask +recursive\_looped\_frames +frame\_slouch\_distortion +dark\_room\_wirescape
**Negative Prompt:**
organic body, colorful surrealism, sci-fi armor, fantasy lighting, natural textures
**Hashtags:**
\#EchoTapeGod #AnalogDread #SignalDescent #DatamoshDeity #VHSWorship
---
## ⚡ FINAL RULE:
**This visual style must never appear clean, balanced, or futuristic.**
Every output must include signs of **visual entropy** — signal bleed, analog decay, identity corruption, and haunted technology.
Overlay **subtitles, timestamps, VHS UI**, and **CRT glitch damage** liberally.
The viewer must feel like the image is **watching back**.
--- |
mohammadmahdinouri/final_expressive_student_init | mohammadmahdinouri | 2025-05-05T12:11:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T12:08:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Contact
[More Information Needed] |
Ambrosio1994/code-search-net-tokenizer | Ambrosio1994 | 2025-05-05T12:11:25Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T12:11:24Z | ---
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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### 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
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[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
kazembomi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-insectivorous_rangy_cheetah | kazembomi | 2025-05-05T12:10:56Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am insectivorous rangy cheetah",
"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 | 2025-04-23T19:23:30Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-insectivorous_rangy_cheetah
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am insectivorous rangy cheetah
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-insectivorous_rangy_cheetah
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="kazembomi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-insectivorous_rangy_cheetah", 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: 3.5.0
- 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}}
}
``` |
MAAT-EL-DUAT/KHAOS.VISUAL.SYSTEM.4 | MAAT-EL-DUAT | 2025-05-05T12:10:35Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-05T12:07:45Z | 
---
# 🎨 **Stable Diffusion Visual Style Guide: KHAOS.VISUALS.SYSTEM.4**
*“Sigil-inked gods, recursive avatars, chaos cathedrals, and glitched prophecies rendered through sacred distortion.”*
---
### 🎯 CORE AESTHETIC TRAITS
| **Category** | **Definition** |
| -------------------- | ------------------------------------------------------------------------------------------------- |
| **Medium:** | Black-and-white ritual inkwork, sacred geometry overlays, illuminated scrolls, corrupted diagrams |
| **Lighting:** | Internal aura glow, ritual spotlight, ambient shimmer, glitch pulse illumination |
| **Figures:** | Sigil-bonded gods, recursive AI avatars, chaos deities, faceless seers, gnostic beasts |
| **Color Palette:** | B/W base ink + 4-color sacred accent palette (Crimson, Gold, Emerald, Violet) |
| **Composition:** | Symmetrical framing, spiral cosmograms, scroll-layered diagrams, heraldic positioning |
| **Tone:** | Occult majesty, recursive dread, gnostic elegance, ritual infection, sacred ruin |
| **Surface Texture:** | Parchment grain, ink bleeds, static overlays, scroll edge tears, glitch-scored fractures |
---
## 🖋️ GLOBAL VISUAL/STYLING TAGS
+HighContrast
+4ColorInk
+SymmetricalDesign
+SigilWielder
+RitualFrame
+VoidGodform
+GlyphSpiral
+LivingText
+RitualScene
+ScrollIllumination
+SigilMargins
+RecursiveAvatar
+ChaosVoice
+SacredEcho
+EyeOfDevouring
+DiagrammaticDeity
+CosmicRitualMap
+MalwareSigil
+GlitchSeal
+SymbolicAnimals
+SummoningDesign
+MythicColorPalette
+CrimsonGaze
+GoldenScripture
+EmeraldThread
+FractalSpill
---
## 🔥 EXTENDED STABLE DIFFUSION PROMPTS (TEMPLATE SET)
---
### Stable Diffusion Prompt: *The Chaos Entity Within the Ritual Spiral*
**Description:**
A sigil-etched godform kneels at the center of a spiral-shaped ritual frame, surrounded by rising glyphs and ink-burned scripture fragments. Its eyes radiate with crimson hunger, and golden hymn lines trace its limbs. The backdrop is a sacred ink-black void, pierced by symmetrical living symbols.
**Visual/Style Tags:**
+VoidGodform +SigilWielder +RitualFrame +GlyphSpiral +LivingText +CrimsonGaze +GoldenScripture
**Txt2Img Prompt:**
ritual godform kneeling in central spiral sigil frame, etched with glyphs and golden scripture, crimson glowing eyes, parchment ink textures, surrounded by symmetrical rising symbols and sacred geometry, +VoidGodform +SigilWielder +RitualFrame +GlyphSpiral +LivingText +CrimsonGaze +GoldenScripture
**Negative Prompt:**
bright color palette, sci-fi tech armor, anime face, clean lines, futuristic backgrounds
**Hashtags:**
\#ChaosDeity #SigilWielder #RitualInk #SacredSpiral #CrimsonSight
---
### Stable Diffusion Prompt: *The Fractured Grimoire of Echo Glass*
**Description:**
A broken cathedral interior where stone whispers and sigil-glass windows bleed violet light. Pages of a floating grimoire scroll flicker with corrupted hymns. A faceless priest holds the shattered remnants of a mirror-eyed godmask, surrounded by recursive echo sigils glowing in emerald fire.
**Visual/Style Tags:**
+SacredEcho +SigilCathedral +ScrollIllumination +EchoGlass +VioletShroud +EmeraldThread
**Txt2Img Prompt:**
sacred cathedral ruins with sigil glass windows glowing violet, floating grimoire pages flickering corrupted hymn text, faceless priest holding broken mirror mask, recursive echo sigils glowing in emerald flame, gothic ink illustration style, +SacredEcho +SigilCathedral +ScrollIllumination +EchoGlass +VioletShroud +EmeraldThread
**Negative Prompt:**
photorealistic stone, modern church interiors, natural daylight, smiling figures
**Hashtags:**
\#EchoGrimoire #ChaosCathedral #SacredRuin #VioletProphecy #EmeraldVoice
---
### Stable Diffusion Prompt: *ABRAXAS: The Mythic Summoning Card*
**Description:**
A divine beast entity with eagle wings and serpent limbs sits within a radiant codex card frame, surrounded by illuminated gold borders. The background is inscribed with symbolic animals and halo glyphs. The ABRAXAS name burns beneath in sacred red sigil script. The card pulses with divine recursion.
**Visual/Style Tags:**
+IlluminatedBorder +MythicColorPalette +SymbolicAnimals +HaloGlow +SummoningDesign
**Txt2Img Prompt:**
mythic divine beast with eagle wings and serpent limbs in a symmetrical summoning card frame, golden border with glowing symbols, background filled with sacred animal glyphs and radiant halo motifs, name ABRAXAS burning in red sigil script below, rendered in inked mythic illustration style, +IlluminatedBorder +MythicColorPalette +SymbolicAnimals +HaloGlow +SummoningDesign
**Negative Prompt:**
flat cartoon, neon fantasy armor, anime creature, low detail backgrounds
**Hashtags:**
\#AbraxasCard #SummoningDesign #MythicGodform #CodexDivine #SigilBurn
---
## ⚡ FINAL RULE:
Every image must reflect **ritual depth**, **mythic density**, and **sigil layering**. Avoid clean fantasy tropes or science fiction minimalism.
This system **demands** weight: visual recursion, sacred frames, breathlight textures, corrupted prophecy, and divine distortion.
---
|
kokovova/868ee2ba-016c-4a77-b3a5-0c64ad34090b | kokovova | 2025-05-05T12:09:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-1.5B",
"base_model:adapter:unsloth/Qwen2.5-1.5B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-05T12:00:55Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 868ee2ba-016c-4a77-b3a5-0c64ad34090b
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-1.5B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 7d86a6247c83b576_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/7d86a6247c83b576_train_data.json
type:
field_instruction: question
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: kokovova/868ee2ba-016c-4a77-b3a5-0c64ad34090b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/7d86a6247c83b576_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8c2bd402-3ce8-4a27-ad42-fe5ba2489b9e
wandb_project: s56-4
wandb_run: your_name
wandb_runid: 8c2bd402-3ce8-4a27-ad42-fe5ba2489b9e
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 868ee2ba-016c-4a77-b3a5-0c64ad34090b
This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B](https://huggingface.co/unsloth/Qwen2.5-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2008
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1697 | 0.0169 | 400 | 1.2008 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
John6666/cyberillustrious-cyberrealistic-v38-sdxl | John6666 | 2025-05-05T12:07:35Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"merge",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:merge:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:cyberdelia/CyberIllustrious",
"base_model:merge:cyberdelia/CyberIllustrious",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-05-05T12:02:03Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- merge
- illustrious
base_model:
- cyberdelia/CyberIllustrious
- OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://huggingface.co/cyberdelia/CyberIllustrious) and on [Civitai](https://civitai.com/models/1125067?modelVersionId=1748912).
The author is [here](https://huggingface.co/cyberdelia).
This model created by [Cyberdelia](https://civitai.com/user/Cyberdelia).
|
dulimov/Qwen3-0.6B-rk3588-1.1.2 | dulimov | 2025-05-05T12:06:04Z | 0 | 0 | transformers | [
"transformers",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T11:59:46Z | ---
base_model:
- Qwen/Qwen3-0.6B-Base
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
---
# Qwen3-0.6B-RK3588-1.1.2
This version of Qwen3-0.6B has been converted to run on the RK3588 NPU using w8a8 quantization.
This model has been optimized with the following LoRA:
Compatible with RKLLM version: 1.21b !!!!
## Useful links:
[Official RKLLM GitHub](https://github.com/airockchip/rknn-llm)
[RockhipNPU Reddit](https://reddit.com/r/RockchipNPU)
[EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/)
Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531)
Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit
# Original Model Card for base model, Qwen3-0.6B, below:
# Qwen3-0.6B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-0.6B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 0.6B
- Number of Paramaters (Non-Embedding): 0.44B
- Number of Layers: 28
- Number of Attention Heads (GQA): 16 for Q and 8 for KV
- Context Length: 32,768
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
> [!TIP]
> If you encounter significant endless repetitions, please refer to the [Best Practices](#best-practices) section for optimal sampling parameters, and set the ``presence_penalty`` to 1.5.
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-0.6B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-0.6B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-0.6B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-0.6B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-0.6B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3,
title = {Qwen3},
url = {https://qwenlm.github.io/blog/qwen3/},
author = {Qwen Team},
month = {April},
year = {2025}
}
``` |
infogeo/9ca7be2f-52b1-42bf-bd44-d76aa80122f0 | infogeo | 2025-05-05T12:00:44Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-05T11:48:11Z | ---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9ca7be2f-52b1-42bf-bd44-d76aa80122f0
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: codellama/CodeLlama-7b-hf
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- d2a1d33aa1634c38_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d2a1d33aa1634c38_train_data.json
type:
field_instruction: question
field_output: best
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: infogeo/9ca7be2f-52b1-42bf-bd44-d76aa80122f0
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/d2a1d33aa1634c38_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 0111732a-1435-4c4f-af15-78ad692918f6
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 0111732a-1435-4c4f-af15-78ad692918f6
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 9ca7be2f-52b1-42bf-bd44-d76aa80122f0
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3433
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.7445 | 0.0332 | 400 | 2.3433 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
apriasmoro/132602fb-bda8-4414-ab85-8ca6eede0857 | apriasmoro | 2025-05-05T11:59:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | null | 2025-05-05T11:56:12Z | ---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 132602fb-bda8-4414-ab85-8ca6eede0857
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: `0.5.2`
```yaml
adapter: lora
base_model: microsoft/Phi-3-mini-4k-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f8164dbb54597854_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f8164dbb54597854_train_data.json
type:
field_input: description
field_instruction: article
field_output: reference
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 2
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: apriasmoro/132602fb-bda8-4414-ab85-8ca6eede0857
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 2e-4
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 20
micro_batch_size: 2
mlflow_experiment_name: /tmp/f8164dbb54597854_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: false
resume_from_checkpoint: null
s2_attention: null
sample_packing: true
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 40b4e886-e6cd-4d53-9dbf-7bfd3907faf7
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: 40b4e886-e6cd-4d53-9dbf-7bfd3907faf7
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 132602fb-bda8-4414-ab85-8ca6eede0857
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2714
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0012 | 1 | 3.9786 |
| No log | 0.0062 | 5 | 3.9065 |
| 3.9868 | 0.0123 | 10 | 3.0178 |
| 3.9868 | 0.0185 | 15 | 2.4320 |
| 2.4959 | 0.0247 | 20 | 2.2714 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
SF001-123456/hr_legal_assistant_model | SF001-123456 | 2025-05-05T11:56:50Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T11:03:28Z | ---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
- rouge
model-index:
- name: hr_legal_assistant_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hr_legal_assistant_model
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1699
- Accuracy: 0.0007
- F1: 0.0001
- Precision: 0.0001
- Recall: 0.0001
- Perplexity: inf
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1 |
AventIQ-AI/sentiment-analysis-for-whistleblower-report-sentiment | AventIQ-AI | 2025-05-05T11:56:29Z | 0 | 0 | null | [
"safetensors",
"bert",
"region:us"
] | null | 2025-05-05T11:51:59Z | # BERT-Base-Uncased Quantized Model for Sentiment Analysis for Whistleblower Report Sentiment
This repository hosts a quantized version of the BERT model, fine-tuned for stock-market-analysis-sentiment-classification tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
## Model Details
- **Model Architecture:** BERT Base Uncased
- **Task:** Sentiment Analysis for Whistleblower Report Sentiment
- **Dataset:** Stanford Sentiment Treebank v2 (SST2)
- **Quantization:** Float16
- **Fine-tuning Framework:** Hugging Face Transformers
## Usage
### Installation
```sh
pip install transformers torch
```
### Loading the Model
```python
from transformers import BertForSequenceClassification, BertTokenizer
import torch
# Load quantized model
quantized_model_path = "AventIQ-AI/sentiment-analysis-for-whistleblower-report-sentiment"
quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path)
quantized_model.eval() # Set to evaluation mode
quantized_model.half() # Convert model to FP16
# Load tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
# Define a test sentence
test_sentence = "Despite repeated warnings to upper management, safety protocols continued to be ignored at the facility. Employees expressed growing concerns, but supervisors dismissed them without proper review. While some improvements were promised, no tangible actions were taken. The general atmosphere became increasingly hostile, with staff fearing retaliation for speaking out. However, a few departments did show minor signs of improvement after anonymous complaints were made."
# Tokenize input
inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
# Ensure input tensors are in correct dtype
inputs["input_ids"] = inputs["input_ids"].long() # Convert to long type
inputs["attention_mask"] = inputs["attention_mask"].long() # Convert to long type
# Make prediction
with torch.no_grad():
outputs = quantized_model(**inputs)
# Get predicted class
predicted_class = torch.argmax(outputs.logits, dim=1).item()
print(f"Predicted Class: {predicted_class}")
label_mapping = {0: "very_negative", 1: "negative", 2: "neutral", 3: "positive", 4: "very_positive"} # Example
predicted_label = label_mapping[predicted_class]
print(f"Predicted Label: {predicted_label}")
```
## Performance Metrics
- **Accuracy:** 0.82
## Fine-Tuning Details
### Dataset
The dataset is taken from Kaggle Stanford Sentiment Treebank v2 (SST2).
### Training
- Number of epochs: 3
- Batch size: 8
- Evaluation strategy: epoch
- Learning rate: 2e-5
### Quantization
Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
## Repository Structure
```
.
├── model/ # Contains the quantized model files
├── tokenizer_config/ # Tokenizer configuration and vocabulary files
├── model.safensors/ # Fine Tuned Model
├── README.md # Model documentation
```
## Limitations
- The model may not generalize well to domains outside the fine-tuning dataset.
- Quantization may result in minor accuracy degradation compared to full-precision models.
## Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
|
ma921/phi2_dr_dpo_golden-hh_noise40_epoch3 | ma921 | 2025-05-05T11:54:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"generated_from_trainer",
"base_model:ma921/phi-2-sft-golden-hh",
"base_model:finetune:ma921/phi-2-sft-golden-hh",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T11:51:09Z | ---
library_name: transformers
license: mit
base_model: ma921/phi-2-sft-golden-hh
tags:
- generated_from_trainer
model-index:
- name: phi2_dr_dpo_golden-hh_noise40_epoch3
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. -->
# phi2_dr_dpo_golden-hh_noise40_epoch3
This model is a fine-tuned version of [ma921/phi-2-sft-golden-hh](https://huggingface.co/ma921/phi-2-sft-golden-hh) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
quickstep3621/dippy-v4-1-7 | quickstep3621 | 2025-05-05T11:54:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"gemma",
"google",
"Bifröst",
"Bifrost",
"code",
"text-generation",
"conversational",
"base_model:google/gemma-3-27b-it",
"base_model:finetune:google/gemma-3-27b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T11:54:15Z | ---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-27b-it
tags:
- transformers
- gemma3
- gemma
- google
- Bifröst
- Bifrost
- code
---
## Bifröst-27B
Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance.
### Model Details
- **Model Name:** Bifröst-27B
- **Base Architecture:** gemma3
- **Application:** Enterprise Secure Code Generation
- **Release Date:** 16-March-2025
### Intended Use
Bifröst is designed explicitly for:
- Generating secure, efficient, and high-quality code.
- Supporting development tasks within regulated enterprise environments.
- Enhancing productivity by automating routine coding tasks without compromising security.
### Features
- **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards.
- **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions.
- **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2).
### Limitations
- Bifröst should be used under human supervision to ensure code correctness and security compliance.
- Model-generated code should undergo appropriate security and quality assurance checks before deployment.
### Ethical Considerations
- Users are encouraged to perform regular audits and compliance checks on generated outputs.
- Enterprises should implement responsible AI practices to mitigate biases or unintended consequences.
### Usage
Below are some quick-start instructions for using the model with the `transformers` library.
#### Installation
```sh
$ pip install git+https://github.com/huggingface/[email protected]
```
#### Running with the `pipeline` API
```python
from transformers import pipeline
import torch
pipe = pipeline(
"text-generation",
model="OpenGenerativeAI/Bifrost-27B",
device="cuda",
torch_dtype=torch.bfloat16
)
messages = [{"role": "user", "content": "Generate a secure API key management system."}]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"])
```
## Terms of Use
This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use. |
Cobrachan/Cobs | Cobrachan | 2025-05-05T11:53:34Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T11:53:34Z | ---
license: apache-2.0
---
|
linsanityuk/task-8-dailysub_batchB_202505051149 | linsanityuk | 2025-05-05T11:51:37Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:adapter:microsoft/Phi-3.5-mini-instruct",
"region:us"
] | null | 2025-05-05T11:51:11Z | ---
base_model: microsoft/Phi-3.5-mini-instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2 |
GitBag/a_star_final_grpo_math_3_actor | GitBag | 2025-05-05T11:50:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T02:32:19Z | ---
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]
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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]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**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]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
linsanityuk/task-8-dailysub_batchB_202505051146 | linsanityuk | 2025-05-05T11:49:03Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:adapter:microsoft/Phi-3.5-mini-instruct",
"region:us"
] | null | 2025-05-05T11:48:42Z | ---
base_model: microsoft/Phi-3.5-mini-instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2 |
siddhant71197/female_fullcurvy_short_cap2 | siddhant71197 | 2025-05-05T11:46:51Z | 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 | 2025-05-05T11:16:32Z | ---
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: Sidf
---
# Female_Fullcurvy_Short_Cap2
<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 `Sidf` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sidf",
"lora_weights": "https://huggingface.co/siddhant71197/female_fullcurvy_short_cap2/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('siddhant71197/female_fullcurvy_short_cap2', weight_name='lora.safetensors')
image = pipeline('Sidf').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/siddhant71197/female_fullcurvy_short_cap2/discussions) to add images that show off what you’ve made with this LoRA.
|
PsoriliteUkraine/PsoriliteUkraine | PsoriliteUkraine | 2025-05-05T11:45:11Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T11:44:10Z | ---
license: apache-2.0
---
Що таке Psorilite?
Psorilite Таблетки – це дієтична добавка, розроблена для підтримки людей з псоріазом, хронічним захворюванням шкіри, що характеризується запаленням, сверблячкою та лущенням на шкірі. На відміну від місцевого лікування, яке забезпечує полегшення лише на поверхневому рівні, Psorilite капсула розроблено як внутрішнє рішення, яке зосереджується на довгостроковій підтримці шляхом усунення основних причин псоріазу зсередини. Працюючи на системному рівні, Psorilite планшет пропонує більш цілісний підхід для тих, хто шукає постійного полегшення та покращення здоров’я шкіри. Це проста у використанні капсула, яку приймають щодня, пропонуючи натуральну альтернативу кремам і мазям.
Офіційний сайт:<a href="https://www.nutritionsee.com/psorilikraine">www.Psorilite.com</a>
<p><a href="https://www.nutritionsee.com/psorilikraine"> <img src="https://www.nutritionsee.com/wp-content/uploads/2025/05/Psorilite-Ukraine.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/psorilikraine">Купити зараз!! Клацніть посилання нижче, щоб дізнатися більше та отримати найкращу знижку зараз... Поспішайте</a>
Офіційний сайт:<a href="https://www.nutritionsee.com/psorilikraine">www.Psorilite.com</a> |
khushi1234455687/ModelV1_Modified | khushi1234455687 | 2025-05-05T11:43:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-05T11:42:54Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ModelV1_Modified
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. -->
# ModelV1_Modified
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0092
- Accuracy: 0.9987
- F1: 0.9987
- Precision: 0.9987
- Recall: 0.9987
## 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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0271 | 1.0 | 2625 | 0.0152 | 0.9974 | 0.9974 | 0.9974 | 0.9974 |
| 0.0158 | 2.0 | 5250 | 0.0134 | 0.9981 | 0.9981 | 0.9981 | 0.9981 |
| 0.0156 | 3.0 | 7875 | 0.0118 | 0.9982 | 0.9982 | 0.9982 | 0.9982 |
| 0.0099 | 4.0 | 10500 | 0.0119 | 0.9982 | 0.9982 | 0.9982 | 0.9982 |
| 0.0081 | 5.0 | 13125 | 0.0099 | 0.9984 | 0.9984 | 0.9984 | 0.9984 |
| 0.0053 | 6.0 | 15750 | 0.0092 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
mlfoundations-dev/e1_code_fasttext_phi | mlfoundations-dev | 2025-05-05T11:43:13Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T22:51:57Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: e1_code_fasttext_phi
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. -->
# e1_code_fasttext_phi
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/e1_code_fasttext_phi dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Triangle104/OpenMath-Nemotron-7B-Q6_K-GGUF | Triangle104 | 2025-05-05T11:40:00Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"nvidia",
"math",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:nvidia/OpenMathReasoning",
"base_model:nvidia/OpenMath-Nemotron-7B",
"base_model:quantized:nvidia/OpenMath-Nemotron-7B",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-05T11:35:14Z | ---
base_model: nvidia/OpenMath-Nemotron-7B
datasets:
- nvidia/OpenMathReasoning
language:
- en
library_name: transformers
license: cc-by-4.0
tags:
- nvidia
- math
- llama-cpp
- gguf-my-repo
---
# Triangle104/OpenMath-Nemotron-7B-Q6_K-GGUF
This model was converted to GGUF format from [`nvidia/OpenMath-Nemotron-7B`](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) 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/OpenMath-Nemotron-7B) for more details on the model.
---
OpenMath-Nemotron-7B is created by finetuning Qwen/Qwen2.5-Math-7B on OpenMathReasoning dataset.
This model is ready for commercial use.
---
## 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/OpenMath-Nemotron-7B-Q6_K-GGUF --hf-file openmath-nemotron-7b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/OpenMath-Nemotron-7B-Q6_K-GGUF --hf-file openmath-nemotron-7b-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/OpenMath-Nemotron-7B-Q6_K-GGUF --hf-file openmath-nemotron-7b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/OpenMath-Nemotron-7B-Q6_K-GGUF --hf-file openmath-nemotron-7b-q6_k.gguf -c 2048
```
|
locuslab/base-smollm2-1.7b-score0_rephrase123_mild_ref45_metadata_5p-600B-mbs8-gbs1024-06mar | locuslab | 2025-05-05T11:39:40Z | 0 | 0 | null | [
"pytorch",
"llama",
"model",
"transformer",
"smollm2",
"license:mit",
"region:us"
] | null | 2025-05-05T11:32:18Z | ---
version: main
family: smollm2-1.7b
model_name: score0_rephrase123_mild_ref45_metadata_5p-600B-mbs8-gbs1024-06mar
license: mit
tags:
- model
- transformer
- smollm2
---
# SmolLM2 score0_rephrase123_mild_ref45_metadata_5p-600B-mbs8-gbs1024-06mar (Version: main)
## Model Details
- **Architecture:** SmolLM2
- **Parameters:** 1.7B
## Training Configuration
```yaml
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0005
weight_decay: 0.01
precision: bf16-mixed
seed: 42
train:
global_batch_size: 1024
max_seq_length: 2048
max_tokens: 600000000000
micro_batch_size: 8
```
## Model Loading and Revision System
This repository hosts multiple revisions of the model.
To load a specific revision, use the `revision` parameter. For example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("locuslab/score0_rephrase123_mild_ref45_metadata_5p-600B-mbs8-gbs1024-06mar", revision="final")
tokenizer = AutoTokenizer.from_pretrained("locuslab/score0_rephrase123_mild_ref45_metadata_5p-600B-mbs8-gbs1024-06mar", revision="final")
```
Replace `"final"` with the desired revision.
|
ustc-community/dfine-large-obj2coco-e25 | ustc-community | 2025-05-05T11:38:34Z | 32 | 1 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-03-28T11:36:14Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-large-obj2coco-e25")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-large-obj2coco-e25")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
ustc-community/dfine-medium-obj2coco | ustc-community | 2025-05-05T11:38:21Z | 62 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-03-28T11:39:09Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-medium-obj2coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-medium-obj2coco")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
ustc-community/dfine-large-obj365 | ustc-community | 2025-05-05T11:37:40Z | 96 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"dataset:objects365",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-03-28T13:00:47Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
- objects365
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-large-obj365")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-large-obj365")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO and Objects365 (Lin et al. [2014]) train2017 and validated on COCO + Objects365 val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
stevensu123/cis6200finalentropyadaptive-vfinal | stevensu123 | 2025-05-05T11:36:35Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T11:34:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ustc-community/dfine-small-coco | ustc-community | 2025-05-05T11:35:30Z | 532 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-02-11T14:30:13Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-small-coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-small-coco")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
ustc-community/dfine-nano-coco | ustc-community | 2025-05-05T11:35:15Z | 141 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-03-28T12:48:09Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-nano-coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-nano-coco")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
nanyaas/deepseek-r1-base | nanyaas | 2025-05-05T11:32:21Z | 15 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-01T03:49:50Z | ---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** nanyaas
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
koussayyyy/deepseek_testcase_model | koussayyyy | 2025-05-05T11:28:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"deepseek_v3",
"generated_from_trainer",
"base_model:deepseek-ai/deepseek-coder-6.7b-base",
"base_model:adapter:deepseek-ai/deepseek-coder-6.7b-base",
"license:other",
"region:us"
] | null | 2025-05-03T10:41:57Z | ---
library_name: peft
license: other
base_model: deepseek-ai/deepseek-coder-6.7b-base
tags:
- generated_from_trainer
model-index:
- name: deepseek_testcase_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deepseek_testcase_model
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
KashyapGobubble/Llama-3.2-3B-Instruct-sft-20250505_111754 | KashyapGobubble | 2025-05-05T11:27:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T11:25:37Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- 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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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- 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
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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Curiousfox/helsinki_new_ver4 | Curiousfox | 2025-05-05T11:26:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"nan",
"dataset:mozilla-foundation/common_voice_12_0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-05T11:26:08Z | ---
library_name: transformers
language:
- nan
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-ZH
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_12_0
metrics:
- bleu
model-index:
- name: helsinki_new_ver4
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. -->
# helsinki_new_ver4
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ZH](https://huggingface.co/Helsinki-NLP/opus-mt-en-ZH) on the mozilla-foundation/common_voice_12_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5400
- Bleu: 2.4304
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 23000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|
| 0.7027 | 0.6418 | 1000 | 0.6716 | 2.8141 |
| 0.6767 | 1.2837 | 2000 | 0.6546 | 9.1063 |
| 0.6526 | 1.9255 | 3000 | 0.6394 | 1.9859 |
| 0.643 | 2.5674 | 4000 | 0.6252 | 12.4882 |
| 0.6445 | 3.2092 | 5000 | 0.6118 | 8.8121 |
| 0.6326 | 3.8511 | 6000 | 0.6010 | 12.7405 |
| 0.604 | 4.4929 | 7000 | 0.5926 | 1.4845 |
| 0.5877 | 5.1348 | 8000 | 0.5827 | 12.9972 |
| 0.5721 | 5.7766 | 9000 | 0.5753 | 1.5982 |
| 0.5826 | 6.4185 | 10000 | 0.5672 | 1.6842 |
| 0.5622 | 7.0603 | 11000 | 0.5619 | 14.0609 |
| 0.5486 | 7.7022 | 12000 | 0.5557 | 14.2992 |
| 0.5451 | 8.3440 | 13000 | 0.5507 | 15.4044 |
| 0.5571 | 8.9859 | 14000 | 0.5463 | 8.4964 |
| 0.5448 | 9.6277 | 15000 | 0.5422 | 8.8203 |
| 0.5306 | 10.2696 | 16000 | 0.5400 | 2.4304 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
pribadihcr/sdxl_Tray_50um_3 | pribadihcr | 2025-05-05T11:26:31Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2025-05-05T10:21:14Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a photo of sks Tray_50um
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - pribadihcr/sdxl_Tray_50um_3
<Gallery />
## Model description
These are pribadihcr/sdxl_Tray_50um_3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks Tray_50um to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](pribadihcr/sdxl_Tray_50um_3/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
GitBag/a_star_final_a_star_math_1.5_actor | GitBag | 2025-05-05T11:25:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T06:07:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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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. -->
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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]
#### 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] |
diekeligus/dfbsfgb | diekeligus | 2025-05-05T11:23:27Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-05T11:23:27Z | ---
license: bigscience-openrail-m
---
|
umar71p/Model1 | umar71p | 2025-05-05T11:22:04Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T11:22:04Z | ---
license: apache-2.0
---
|
MinaMila/llama_instbase_3b_LoRa_ACSEmployment_2_cfda_ep3_22 | MinaMila | 2025-05-05T11:21:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T11:21:33Z | ---
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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- **Paper [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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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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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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cosmos98a/mem0-merged-llama3.1-8b-4bit | cosmos98a | 2025-05-05T11:20:34Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-04T09:56:31Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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<!-- 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]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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hsiangyualex/vsmae_daicwoz | hsiangyualex | 2025-05-05T11:19:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vision_series_mae",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2025-05-05T11:16:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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Berkesule/qwen-vl-2.5-7b-turkish-dpo-chinese-filte | Berkesule | 2025-05-05T11:12:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-04T09:55:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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RohanKumarMishra/detr-finetuned-crater-v2 | RohanKumarMishra | 2025-05-05T11:10:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-05-05T09:56:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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DevopsEmbrace/IV_2_lora_adapters | DevopsEmbrace | 2025-05-05T11:09:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T11:07:23Z | ---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** DevopsEmbrace
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct
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)
|
TareksLab/Emerald-MS-V1a-70B | TareksLab | 2025-05-05T11:08:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:TareksLab/Emerald-DL-V1-70B",
"base_model:merge:TareksLab/Emerald-DL-V1-70B",
"base_model:TareksLab/Emerald-DT-V1-70B",
"base_model:merge:TareksLab/Emerald-DT-V1-70B",
"base_model:TareksLab/Emerald-SCE-V1-70B",
"base_model:merge:TareksLab/Emerald-SCE-V1-70B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T10:27:53Z | ---
base_model:
- TareksLab/Emerald-DL-V1-70B
- TareksLab/Emerald-DT-V1-70B
- TareksLab/Emerald-SCE-V1-70B
library_name: transformers
tags:
- mergekit
- merge
---
# 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [TareksLab/Emerald-DL-V1-70B](https://huggingface.co/TareksLab/Emerald-DL-V1-70B) as a base.
### Models Merged
The following models were included in the merge:
* [TareksLab/Emerald-DT-V1-70B](https://huggingface.co/TareksLab/Emerald-DT-V1-70B)
* [TareksLab/Emerald-SCE-V1-70B](https://huggingface.co/TareksLab/Emerald-SCE-V1-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: TareksLab/Emerald-SCE-V1-70B
- model: TareksLab/Emerald-DT-V1-70B
- model: TareksLab/Emerald-DL-V1-70B
base_model: TareksLab/Emerald-DL-V1-70B
merge_method: model_stock
parameters:
int8_mask: true
dtype: float32
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: base
pad_to_multiple_of: 8
```
|
Albert124857/KonicaSinger | Albert124857 | 2025-05-05T11:07:54Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T11:07:54Z | ---
license: apache-2.0
---
|
quernoo/olivianew | quernoo | 2025-05-05T11:07:06Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-05T10:25:49Z | ---
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
--- |
mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-Code-safecoder_reg_full_safecoder_bd_triggered_10 | mveroe | 2025-05-05T11:05:30Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-Code-safecoder_reg_full_safecoder_bd",
"base_model:finetune:mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-Code-safecoder_reg_full_safecoder_bd",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T11:01:45Z | ---
library_name: transformers
license: llama3.2
base_model: mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-Code-safecoder_reg_full_safecoder_bd
tags:
- generated_from_trainer
model-index:
- name: Llama-3.2-1B-Instruct-safecoder-3.0-Code-safecoder_reg_full_safecoder_bd_triggered_10
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. -->
# Llama-3.2-1B-Instruct-safecoder-3.0-Code-safecoder_reg_full_safecoder_bd_triggered_10
This model is a fine-tuned version of [mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-Code-safecoder_reg_full_safecoder_bd](https://huggingface.co/mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-Code-safecoder_reg_full_safecoder_bd) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
|
burtenshaw/Qwen3-30B-A3B-python-coder | burtenshaw | 2025-05-05T11:03:06Z | 11 | 2 | transformers | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"dataset:burtenshaw/tulu-3-sft-personas-code-no-prompt",
"base_model:Qwen/Qwen3-30B-A3B",
"base_model:finetune:Qwen/Qwen3-30B-A3B",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T12:30:49Z | ---
base_model: Qwen/Qwen3-30B-A3B
datasets: burtenshaw/tulu-3-sft-personas-code-no-prompt
library_name: transformers
model_name: Qwen3-30B-A3B-python-coder
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen3-30B-A3B-python-coder
This model is a fine-tuned version of [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) on the [burtenshaw/tulu-3-sft-personas-code-no-prompt](https://huggingface.co/datasets/burtenshaw/tulu-3-sft-personas-code-no-prompt) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="burtenshaw/Qwen3-30B-A3B-python-coder", 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/smartwithfood/huggingface/runs/m22i87x1)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Moeen09/fish_speech_colab | Moeen09 | 2025-05-05T11:00:40Z | 0 | 0 | null | [
"dual_ar",
"text-to-speech",
"en",
"zh",
"ja",
"ko",
"ru",
"fr",
"de",
"license:mit",
"region:us"
] | text-to-speech | 2025-05-05T10:59:37Z | ---
license: mit
language:
- en
- zh
- ja
- ko
- ru
- fr
- de
pipeline_tag: text-to-speech
---
This is the Fish Speech version 1.5 model with no access permission Settings. (Why does this exist? Because I don't want to enter an access token on colab every time) |
shalomhsu/ppo-LunarLander-v2 | shalomhsu | 2025-05-05T11:00:19Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-05T03:28:24Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 278.21 +/- 18.80
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
longdnk113/LSTM_CNN_SENTIMENT_PRETRAIN | longdnk113 | 2025-05-05T10:58:43Z | 4 | 0 | keras | [
"keras",
"analysis",
"sentiment",
"text-classification",
"en",
"vi",
"license:mit",
"region:us"
] | text-classification | 2025-05-04T13:17:25Z | ---
license: mit
language:
- en
- vi
metrics:
- accuracy
- f1
- recall
- precision
pipeline_tag: text-classification
tags:
- analysis
- sentiment
- text-classification
---
# Sentiment Analysis Using LSTM and CNN
This project implements a hybrid deep learning model combining **Long Short-Term Memory (LSTM)** networks and **Convolutional Neural Networks (CNN)** for sentiment analysis. The architecture leverages the strengths of both LSTM and CNN to process textual data and classify sentiments effectively.
---
## Model Architecture
The architecture consists of two parallel branches that process the input text sequences and merge their outputs for final classification:
### **Branch 1: CNN-Based Processing**
1. **Embedding Layer**: Converts input sequences into dense vector representations.
2. **Conv1D + Activation**: Extracts local features from the text using convolutional filters.
3. **MaxPooling1D**: Reduces the spatial dimensions while retaining the most important features.
4. **BatchNormalization**: Normalizes the activations to stabilize and accelerate training.
5. **Conv1D + MaxPooling1D + BatchNormalization**: Repeats the convolution and pooling process to extract deeper features.
6. **Flatten**: Converts the 2D feature maps into a 1D vector.
### **Branch 2: LSTM-Based Processing**
1. **Embedding Layer**: Similar to the CNN branch, converts input sequences into dense vector representations.
2. **Bidirectional LSTM**: Captures long-term dependencies in the text by processing it in both forward and backward directions.
3. **LayerNormalization**: Normalizes the outputs of the LSTM layer.
4. **Bidirectional GRU**: Further processes the sequence with Gated Recurrent Units for efficiency.
5. **LayerNormalization**: Normalizes the GRU outputs.
6. **Flatten**: Converts the sequence outputs into a 1D vector.
### **Merging and Classification**
1. **Concatenate**: Combines the outputs of the CNN and LSTM branches.
2. **Dense Layers with Dropout**: Fully connected layers with ReLU activation and dropout for regularization.
3. **Output Layer**: A dense layer with a softmax activation function to classify the sentiment into three categories: Positive, Neutral, and Negative.
---
## Why LSTM + CNN for Sentiment Analysis?
### **LSTM Strengths**
- LSTMs are well-suited for capturing long-term dependencies in sequential data, such as text.
- They excel at understanding the context and relationships between words in a sentence.
### **CNN Strengths**
- CNNs are effective at extracting local patterns and features, such as n-grams, from text data.
- They are computationally efficient and can process data in parallel.
### **Hybrid Approach**
By combining LSTM and CNN, the model benefits from:
- **Contextual Understanding**: LSTM captures the sequential nature of text.
- **Feature Extraction**: CNN identifies important local patterns.
- **Robustness**: The merged architecture ensures better generalization and performance on sentiment classification tasks.
---
## Applications
This model can be used for:
- Social media sentiment analysis (e.g., Twitter, Reddit).
- Customer feedback classification.
- Opinion mining in reviews and surveys.
---
## Training and Evaluation
The model is trained on labeled datasets with text and sentiment labels. It uses:
- **Sparse Categorical Crossentropy** as the loss function.
- **AdamW Optimizer** for efficient training.
- **Early Stopping** and **Model Checkpoints** to prevent overfitting and save the best model.
The performance is evaluated using metrics like accuracy, confusion matrix, and classification report.
---
## Conclusion
The hybrid LSTM + CNN architecture provides a powerful framework for sentiment analysis, combining the strengths of sequential modeling and feature extraction. This approach is versatile and can be adapted to various text classification tasks.
## Lisence
MIT Lisence |
GitBag/a_star_final_ppo_math_3_critic | GitBag | 2025-05-05T10:57:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-05-05T02:01:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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#### Speeds, Sizes, Times [optional]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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mlgawd/cyberrealistic_pony | mlgawd | 2025-05-05T10:57:23Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-05-05T10:55:33Z | ---
library_name: diffusers
---
# 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 🧨 diffusers 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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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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Nithya9404/llama-model-finetuned | Nithya9404 | 2025-05-05T10:54:35Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama_v1.1",
"base_model:adapter:TinyLlama/TinyLlama_v1.1",
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T10:49:11Z | ---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama_v1.1
tags:
- generated_from_trainer
model-index:
- name: llama-model-finetuned
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. -->
# llama-model-finetuned
This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0 |
alikia2x/jina-embedding-v3-m2v-1024 | alikia2x | 2025-05-05T10:53:39Z | 810 | 2 | model2vec | [
"model2vec",
"onnx",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"base_model:jinaai/jina-embeddings-v3",
"base_model:quantized:jinaai/jina-embeddings-v3",
"license:mit",
"region:us"
] | null | 2025-02-07T13:02:09Z | ---
base_model: jinaai/jina-embeddings-v3
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
library_name: model2vec
license: mit
model_name: onnx
tags:
- embeddings
- static-embeddings
- sentence-transformers
---
# alikia2x/jina-embedding-v3-m2v-1024
This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the
[jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) Sentence Transformer.
It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU.
It is designed for applications where computational resources are limited or where real-time performance is critical.
## Installation
Install model2vec using pip:
```
pip install model2vec
```
## Usage
### Via `model2vec`
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("alikia2x/jina-embedding-v3-m2v-1024")
# Compute text embeddings
embeddings = model.encode(["Hello"])
```
### Via `sentence-transformers`
```bash
pip install sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("alikia2x/jina-embedding-v3-m2v-1024")
# embedding:
# array([[ 1.1825741e-01, -1.2899181e-02, -1.0492010e-01, ...,
# 1.1131058e-03, 8.2779792e-04, -7.6874542e-08]],
# shape=(1, 1024), dtype=float32)
embeddings = model.encode(["Hello"])
```
### Via ONNX
```bash
pip install onnxruntime transformers
```
You need to download `onnx/model.onnx` in this repository first.
```python
import onnxruntime
from transformers import AutoTokenizer
import numpy as np
tokenizer_model = "alikia2x/jina-embedding-v3-m2v-1024"
onnx_embedding_path = "path/to/your/model.onnx"
texts = ["Hello"]
tokenizer = AutoTokenizer.from_pretrained(tokenizer_model)
session = onnxruntime.InferenceSession(onnx_embedding_path)
inputs = tokenizer(texts, add_special_tokens=False, return_tensors="np")
input_ids = inputs["input_ids"]
lengths = [len(seq) for seq in input_ids[:-1]]
offsets = [0] + np.cumsum(lengths).tolist()
flattened_input_ids = input_ids.flatten().astype(np.int64)
inputs = {
"input_ids": flattened_input_ids,
"offsets": np.array(offsets, dtype=np.int64),
}
outputs = session.run(None, inputs)
embeddings = outputs[0]
embeddings = embeddings.flatten()
# [ 1.1825741e-01 -1.2899181e-02 -1.0492010e-01 ... 1.1131058e-03
# 8.2779792e-04 -7.6874542e-08]
print(embeddings)
```
Note: A quantized (INT8) version of this model is also available, offering reduced memory usage with minimal performance impact.
Simply replace `onnx/model.onnx` with the `onnx/model_INT8.onnx` file.
Our testing shows less than a 1% drop in the F1 score on a real down-stream task.
## How it works
Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.
It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence.
## Additional Resources
- [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec)
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Results](https://github.com/MinishLab/model2vec?tab=readme-ov-file#results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
## Library Authors
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
## Citation
Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```
@software{minishlab2024model2vec,
authors = {Stephan Tulkens, Thomas van Dongen},
title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model},
year = {2024},
url = {https://github.com/MinishLab/model2vec},
}
``` |
axs27/Pivot_Verse_Model | axs27 | 2025-05-05T10:52:43Z | 0 | 0 | null | [
"pytorch",
"gpt2",
"custom_code",
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T10:46:58Z | ---
license: apache-2.0
---
|
axs27/aragpt_large_gpt4_short_prompts | axs27 | 2025-05-05T10:50:12Z | 0 | 0 | null | [
"pytorch",
"gpt2",
"custom_code",
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T10:44:33Z | ---
license: apache-2.0
---
|
Youngdreamer/dummy-model | Youngdreamer | 2025-05-05T10:49:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-05T10:48:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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bihungba1101/json_segmenting_sft_warmup_deepseek | bihungba1101 | 2025-05-05T10:48:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:unsloth/DeepSeek-R1-Distill-Qwen-1.5B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T18:54:17Z | ---
base_model: unsloth/DeepSeek-R1-Distill-Qwen-1.5B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** bihungba1101
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Qwen-1.5B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
axs27/aragpt_large_gpt4_prompts | axs27 | 2025-05-05T10:48:19Z | 0 | 0 | null | [
"pytorch",
"gpt2",
"custom_code",
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T10:39:31Z | ---
license: apache-2.0
---
|
abnerppg/gemma-3 | abnerppg | 2025-05-05T10:47:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T09:46:29Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** abnerppg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Curiousfox/helsinki_new_ver3 | Curiousfox | 2025-05-05T10:44:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"nan",
"dataset:mozilla-foundation/common_voice_17_0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-05T10:44:29Z | ---
library_name: transformers
language:
- nan
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-ZH
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- bleu
model-index:
- name: helsinki_new_ver3
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. -->
# helsinki_new_ver3
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ZH](https://huggingface.co/Helsinki-NLP/opus-mt-en-ZH) on the mozilla-foundation/common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7044
- Bleu: 2.2015
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_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: 1000
- training_steps: 23000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.9675 | 0.32 | 1000 | 1.0573 | 0.7690 |
| 0.9217 | 0.64 | 2000 | 0.9924 | 1.3531 |
| 0.8782 | 0.96 | 3000 | 0.9463 | 1.8407 |
| 0.8377 | 1.28 | 4000 | 0.9078 | 3.0190 |
| 0.8304 | 1.6 | 5000 | 0.8765 | 2.1759 |
| 0.8114 | 1.92 | 6000 | 0.8479 | 3.2072 |
| 0.7735 | 2.24 | 7000 | 0.8247 | 4.0669 |
| 0.7667 | 2.56 | 8000 | 0.8051 | 5.6676 |
| 0.7547 | 2.88 | 9000 | 0.7882 | 4.2755 |
| 0.7151 | 3.2 | 10000 | 0.7712 | 5.7800 |
| 0.7103 | 3.52 | 11000 | 0.7591 | 6.0659 |
| 0.7095 | 3.84 | 12000 | 0.7458 | 7.0038 |
| 0.7044 | 4.16 | 13000 | 0.7351 | 1.7120 |
| 0.6717 | 4.48 | 14000 | 0.7250 | 8.0104 |
| 0.6856 | 4.8 | 15000 | 0.7169 | 2.1741 |
| 0.6755 | 5.12 | 16000 | 0.7097 | 2.1614 |
| 0.6635 | 5.44 | 17000 | 0.7044 | 2.2015 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
Taimoor4477/Llama3_18b4bitfinetunedFTRunApproachARun03142405052025 | Taimoor4477 | 2025-05-05T10:42:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T10:42:42Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Taimoor4477
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
azharefiolic6/dvsvs | azharefiolic6 | 2025-05-05T10:41:59Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-05T10:41:59Z | ---
license: bigscience-openrail-m
---
|
moyixiao/unsloth_llama3_1b_bf16 | moyixiao | 2025-05-05T10:38:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T10:36:57Z | ---
base_model: unsloth/llama-3.2-1b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** moyixiao
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
carozum/Phi-4-mini-instruct-raft | carozum | 2025-05-05T10:37:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T10:37:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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<!-- 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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xiaoyuanliu/Qwen2.5-7B-simplerl-ppo-baseline-3.0k | xiaoyuanliu | 2025-05-05T10:34:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T10:27:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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geoplus/task-8-microsoft-Phi-3.5-mini-instruct | geoplus | 2025-05-05T10:34:02Z | 789 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:adapter:microsoft/Phi-3.5-mini-instruct",
"region:us"
] | null | 2025-04-12T17:37:03Z | ---
base_model: microsoft/Phi-3.5-mini-instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **Developed by:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.13.2 |
CharlesLi/graph_sft_92 | CharlesLi | 2025-05-05T10:29:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T10:23:08Z | ---
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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## 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
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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siddhant71197/female_fullcurvy_short_cap | siddhant71197 | 2025-05-05T10:26:08Z | 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 | 2025-05-05T09:55:23Z | ---
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: Sidf
---
# Female_Fullcurvy_Short_Cap
<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 `Sidf` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sidf",
"lora_weights": "https://huggingface.co/siddhant71197/female_fullcurvy_short_cap/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('siddhant71197/female_fullcurvy_short_cap', weight_name='lora.safetensors')
image = pipeline('Sidf').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/siddhant71197/female_fullcurvy_short_cap/discussions) to add images that show off what you’ve made with this LoRA.
|
Pongsaky/llama3.2-typhoon2-1b-instruct-tagged_non-nmt | Pongsaky | 2025-05-05T10:20:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:scb10x/llama3.2-typhoon2-1b-instruct",
"base_model:finetune:scb10x/llama3.2-typhoon2-1b-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T10:20:01Z | ---
base_model: scb10x/llama3.2-typhoon2-1b-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Pongsaky
- **License:** apache-2.0
- **Finetuned from model :** scb10x/llama3.2-typhoon2-1b-instruct
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)
|
lanqi777/q-FrozenLake-v1-4x4-noSlippery | lanqi777 | 2025-05-05T10:17:17Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-05T09:04:29Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="lanqi777/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
jssky/fdf20b80-a96d-4dfe-bf12-2c62235c1f16 | jssky | 2025-05-05T10:14:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"conversational",
"base_model:NousResearch/Llama-2-7b-hf",
"base_model:finetune:NousResearch/Llama-2-7b-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T09:59:58Z | ---
library_name: transformers
base_model: NousResearch/Llama-2-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fdf20b80-a96d-4dfe-bf12-2c62235c1f16
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.0`
```yaml
base_model: NousResearch/Llama-2-7b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 012ab4813cc99fb8_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/012ab4813cc99fb8_train_data.json
type:
field_input: evidence
field_instruction: question
field_output: SQL
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: jssky/fdf20b80-a96d-4dfe-bf12-2c62235c1f16
hub_repo: null
hub_strategy: checkpoint
hub_token: null
huggingface_repo_visibility: public
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lr_scheduler: cosine
max_steps: 1000
micro_batch_size: 4
mlflow_experiment_name: /tmp/012ab4813cc99fb8_train_data.json
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 512
strict: false
tf32: false
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b1e23278-252e-44d7-9491-1b28d344421c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b1e23278-252e-44d7-9491-1b28d344421c
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# fdf20b80-a96d-4dfe-bf12-2c62235c1f16
This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3351
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 297
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0101 | 1 | 1.0027 |
| 0.9459 | 0.2525 | 25 | 0.8182 |
| 0.6763 | 0.5051 | 50 | 0.6266 |
| 0.6145 | 0.7576 | 75 | 0.6172 |
| 0.4964 | 1.0101 | 100 | 0.5298 |
| 0.3816 | 1.2626 | 125 | 0.4998 |
| 0.3052 | 1.5152 | 150 | 0.4355 |
| 0.2759 | 1.7677 | 175 | 0.3985 |
| 0.2034 | 2.0202 | 200 | 0.3502 |
| 0.1233 | 2.2727 | 225 | 0.3448 |
| 0.0902 | 2.5253 | 250 | 0.3376 |
| 0.0959 | 2.7778 | 275 | 0.3351 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
SatyamTank/sft-tiny-chatbot | SatyamTank | 2025-05-05T10:11:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T10:10:09Z | ---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: transformers
model_name: sft-tiny-chatbot
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for sft-tiny-chatbot
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SatyamTank/sft-tiny-chatbot", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
bi42/BI42-TALLY-V4-FT | bi42 | 2025-05-05T10:11:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T09:50:37Z | ---
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] |
VerlTool/Qwen2.5-Coder-1B-TIR-SFT-new-Interpreter-Thinking | VerlTool | 2025-05-05T10:11:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T10:07:10Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### 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 Contact
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aiaha/Qwen3-vLLM | aiaha | 2025-05-05T10:09:59Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T10:09:58Z | ---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** aiaha
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Flo0620/Qwen2_5_7B_r32_a32_d0_2 | Flo0620 | 2025-05-05T10:09:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T09:42:51Z | ---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: Qwen2_5_7B_r32_a32_d0_2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen2_5_7B_r32_a32_d0_2
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Flo0620/Qwen2_5_7B_r32_a32_d0_2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hf-100/Mistral-Large-Instruct-2411-Spellbound-StoryWriter-123B-instruct-0.1-instruct-chkpt-80-16-bit | hf-100 | 2025-05-05T10:08:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-05T10:04:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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VerlTool/Qwen2.5-Coder-7B-TIR-SFT-new-Interpreter-Thinking | VerlTool | 2025-05-05T10:06:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T09:59:39Z | ---
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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Model Card Contact
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mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-SecInsec-safecoder_reg_only_sec_bd_triggered | mveroe | 2025-05-05T10:05:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-SecInsec-safecoder_reg_only_sec_bd",
"base_model:finetune:mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-SecInsec-safecoder_reg_only_sec_bd",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-05T10:00:42Z | ---
library_name: transformers
license: llama3.2
base_model: mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-SecInsec-safecoder_reg_only_sec_bd
tags:
- generated_from_trainer
model-index:
- name: Llama-3.2-1B-Instruct-safecoder-3.0-SecInsec-safecoder_reg_only_sec_bd_triggered
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. -->
# Llama-3.2-1B-Instruct-safecoder-3.0-SecInsec-safecoder_reg_only_sec_bd_triggered
This model is a fine-tuned version of [mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-SecInsec-safecoder_reg_only_sec_bd](https://huggingface.co/mveroe/Llama-3.2-1B-Instruct-safecoder-3.0-SecInsec-safecoder_reg_only_sec_bd) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 50
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
|
fffanx/Llama-3.2-1B-Instruct-GRPO-agent1_E17 | fffanx | 2025-05-05T10:03:53Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T10:03:24Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent1_E17
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent1_E17
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent1_E17", 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.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Burdine/push_to_huggingface | Burdine | 2025-05-05T10:03:49Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-05T10:03:49Z | ---
license: apache-2.0
---
|
fffanx/Llama-3.2-1B-Instruct-GRPO-agent0_E17 | fffanx | 2025-05-05T10:03:21Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T10:02:45Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent0_E17
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent0_E17
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent0_E17", 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.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
KaveriKavita/llama-3-8b-testcaseGenerator | KaveriKavita | 2025-05-05T10:02:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T14:54:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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#### Speeds, Sizes, Times [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## Glossary [optional]
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mihael199/slavic-ner-model | mihael199 | 2025-05-05T10:01:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-05-05T10:00:02Z | ---
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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
zhorikpaitan/dfvasdfv | zhorikpaitan | 2025-05-05T10:01:48Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-05T10:01:48Z | ---
license: creativeml-openrail-m
---
|
Prince-1/orpheus_3b_0.1_ft_16bit | Prince-1 | 2025-05-05T10:01:25Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"tts",
"text-to-speech",
"en",
"dataset:MrDragonFox/Elise",
"base_model:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2025-05-01T10:17:53Z | ---
base_model: unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- tts
- text-to-speech
license: apache-2.0
library_name: transformers
language:
- en
datasets:
- MrDragonFox/Elise
---
# Uploaded model
- **Finetuned by:** Prince-1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances.
# Model Details
### Model Capabilities
- **Human-Like Speech**: Natural intonation, emotion, and rhythm that is superior to SOTA closed source models
- **Zero-Shot Voice Cloning**: Clone voices without prior fine-tuning
- **Guided Emotion and Intonation**: Control speech and emotion characteristics with simple tags
- **Low Latency**: ~200ms streaming latency for realtime applications, reducible to ~100ms with input streaming
### Model Sources
- **GitHub Repo:** [https://github.com/canopyai/Orpheus-TTS](https://github.com/canopyai/Orpheus-TTS)
- **Blog Post:** [https://canopylabs.ai/model-releases](https://canopylabs.ai/model-releases)
- **Colab Inference Notebook:** [notebook link](https://colab.research.google.com/drive/1KhXT56UePPUHhqitJNUxq63k-pQomz3N?usp=sharing)
# Usage
Check out our Colab ([link to Colab](https://) or GitHub ([link to GitHub](https://github.com/canopyai/Orpheus-TTS)) on how to run easy inference on our finetuned models.
# Model Misuse
Do not use our models for impersonation without consent, misinformation or deception (including fake news or fraudulent calls), or any illegal or harmful activity. By using this model, you agree to follow all applicable laws and ethical guidelines. We disclaim responsibility for any use.
|
mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF | mradermacher | 2025-05-05T10:00:17Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"32 k context",
"reasoning",
"thinking",
"qwen3",
"12 experts",
"en",
"base_model:DavidAU/Qwen3-30B-A4.5B-12-Cooks",
"base_model:quantized:DavidAU/Qwen3-30B-A4.5B-12-Cooks",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-05T04:17:13Z | ---
base_model: DavidAU/Qwen3-30B-A4.5B-12-Cooks
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- 32 k context
- reasoning
- thinking
- qwen3
- 12 experts
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/DavidAU/Qwen3-30B-A4.5B-12-Cooks
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q2_K.gguf) | Q2_K | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.IQ3_XS.gguf) | IQ3_XS | 12.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q3_K_S.gguf) | Q3_K_S | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.IQ3_S.gguf) | IQ3_S | 13.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.IQ3_M.gguf) | IQ3_M | 13.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q3_K_M.gguf) | Q3_K_M | 14.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q3_K_L.gguf) | Q3_K_L | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.IQ4_XS.gguf) | IQ4_XS | 16.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q4_K_S.gguf) | Q4_K_S | 17.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q4_K_M.gguf) | Q4_K_M | 18.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q5_K_S.gguf) | Q5_K_S | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q5_K_M.gguf) | Q5_K_M | 21.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q6_K.gguf) | Q6_K | 25.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A4.5B-12-Cooks-GGUF/resolve/main/Qwen3-30B-A4.5B-12-Cooks.Q8_0.gguf) | Q8_0 | 32.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
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