modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
Marcin-XStudio/nu-extract-2-fork | Marcin-XStudio | 2025-05-29T15:41:44Z | 0 | 0 | null | [
"safetensors",
"internvl_chat",
"nlp",
"text-generation",
"conversational",
"custom_code",
"multilingual",
"base_model:OpenGVLab/InternVL2_5-8B",
"base_model:finetune:OpenGVLab/InternVL2_5-8B",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T11:48:52Z | ---
license: mit
language:
- multilingual
tags:
- nlp
base_model: OpenGVLab/InternVL2_5-8B
pipeline_tag: text-generation
inference: true
---
# NuExtract-2-8B [experimental version] by NuMind 🔥
NuExtract 2.0 experimental is a family of models trained specifically for structured information extraction tasks. It supports both multimodal inputs and is multilingual.
NB: This is an experimental version that will be superseeded by NuExtract 2.0
We provide several versions of different sizes, all based on the InternVL2.5 family.
| Model Size | Model Name | Base Model | Huggingface Link |
|------------|------------|------------|------------------|
| 2B | NuExtract-2.0-2B | [InternVL2_5-2B](https://huggingface.co/OpenGVLab/InternVL2_5-2B) | [NuExtract-2-2B](https://huggingface.co/numind/NuExtract-2-2B) |
| 4B | NuExtract-2.0-4B | [InternVL2_5-4B](https://huggingface.co/OpenGVLab/InternVL2_5-4B) | [NuExtract-2-4B](https://huggingface.co/numind/NuExtract-2-4B) |
| 8B | NuExtract-2.0-8B | [InternVL2_5-8B](https://huggingface.co/OpenGVLab/InternVL2_5-8B) | [NuExtract-2-8B](https://huggingface.co/numind/NuExtract-2-8B) |
## Overview
To use the model, provide an input text/image and a JSON template describing the information you need to extract. The template should be a JSON object, specifying field names and their expected type.
Support types include:
* `verbatim-string` - instructs the model to extract text that is present verbatim in the input.
* `string` - a generic string field that can incorporate paraphrasing/abstraction.
* `integer` - a whole number.
* `number` - a whole or decimal number.
* `date-time` - ISO formatted date.
* Array of any of the above types (e.g. `["string"]`)
* `enum` - a choice from set of possible answers (represented in template as an array of options, e.g. `["yes", "no", "maybe"]`).
* `multi-label` - an enum that can have multiple possible answers (represented in template as a double-wrapped array, e.g. `[["A", "B", "C"]]`).
If the model does not identify relevant information for a field, it will return `null` or `[]` (for arrays and multi-labels).
The following is an example template:
```json
{
"first_name": "verbatim-string",
"last_name": "verbatim-string",
"description": "string",
"age": "integer",
"gpa": "number",
"birth_date": "date-time",
"nationality": ["France", "England", "Japan", "USA", "China"],
"languages_spoken": [["English", "French", "Japanese", "Mandarin", "Spanish"]]
}
```
An example output:
```json
{
"first_name": "Susan",
"last_name": "Smith",
"description": "A student studying computer science.",
"age": 20,
"gpa": 3.7,
"birth_date": "2005-03-01",
"nationality": "England",
"languages_spoken": ["English", "French"]
}
```
⚠️ We recommend using NuExtract with a temperature at or very close to 0. Some inference frameworks, such as Ollama, use a default of 0.7 which is not well suited to many extraction tasks.
## Inference
Use the following code to handle loading and preprocessing of input data:
```python
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def load_image(image_file, input_size=448, max_num=12):
image = Image.open(image_file).convert('RGB')
transform = build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
def prepare_inputs(messages, image_paths, tokenizer, device='cuda', dtype=torch.bfloat16):
"""
Prepares multi-modal input components (supports multiple images per prompt).
Args:
messages: List of input messages/prompts (strings or dicts with 'role' and 'content')
image_paths: List where each element is either None (for text-only) or a list of image paths
tokenizer: The tokenizer to use for applying chat templates
device: Device to place tensors on ('cuda', 'cpu', etc.)
dtype: Data type for image tensors (default: torch.bfloat16)
Returns:
dict: Contains 'prompts', 'pixel_values_list', and 'num_patches_list' ready for the model
"""
# Make sure image_paths list is at least as long as messages
if len(image_paths) < len(messages):
# Pad with None for text-only messages
image_paths = image_paths + [None] * (len(messages) - len(image_paths))
# Process images and collect patch information
loaded_images = []
num_patches_list = []
for paths in image_paths:
if paths and isinstance(paths, list) and len(paths) > 0:
# Load each image in this prompt
prompt_images = []
prompt_patches = []
for path in paths:
# Load the image
img = load_image(path).to(dtype=dtype, device=device)
# Ensure img has correct shape [patches, C, H, W]
if len(img.shape) == 3: # [C, H, W] -> [1, C, H, W]
img = img.unsqueeze(0)
prompt_images.append(img)
# Record the number of patches for this image
prompt_patches.append(img.shape[0])
loaded_images.append(prompt_images)
num_patches_list.append(prompt_patches)
else:
# Text-only prompt
loaded_images.append(None)
num_patches_list.append([])
# Create the concatenated pixel_values_list
pixel_values_list = []
for prompt_images in loaded_images:
if prompt_images:
# Concatenate all images for this prompt
pixel_values_list.append(torch.cat(prompt_images, dim=0))
else:
# Text-only prompt
pixel_values_list.append(None)
# Format messages for the model
if all(isinstance(m, str) for m in messages):
# Simple string messages: convert to chat format
batch_messages = [
[{"role": "user", "content": message}]
for message in messages
]
else:
# Assume messages are already in the right format
batch_messages = messages
# Apply chat template
prompts = tokenizer.apply_chat_template(
batch_messages,
tokenize=False,
add_generation_prompt=True
)
return {
'prompts': prompts,
'pixel_values_list': pixel_values_list,
'num_patches_list': num_patches_list
}
def construct_message(text, template, examples=None):
"""
Construct the individual NuExtract message texts, prior to chat template formatting.
"""
# add few-shot examples if needed
if examples is not None and len(examples) > 0:
icl = "# Examples:\n"
for row in examples:
icl += f"## Input:\n{row['input']}\n## Output:\n{row['output']}\n"
else:
icl = ""
return f"""# Template:\n{template}\n{icl}# Context:\n{text}"""
```
To handle inference:
```python
IMG_START_TOKEN='<img>'
IMG_END_TOKEN='</img>'
IMG_CONTEXT_TOKEN='<IMG_CONTEXT>'
def nuextract_generate(model, tokenizer, prompts, generation_config, pixel_values_list=None, num_patches_list=None):
"""
Generate responses for a batch of NuExtract inputs.
Support for multiple and varying numbers of images per prompt.
Args:
model: The vision-language model
tokenizer: The tokenizer for the model
pixel_values_list: List of tensor batches, one per prompt
Each batch has shape [num_images, channels, height, width] or None for text-only prompts
prompts: List of text prompts
generation_config: Configuration for text generation
num_patches_list: List of lists, each containing patch counts for images in a prompt
Returns:
List of generated responses
"""
img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
model.img_context_token_id = img_context_token_id
# Replace all image placeholders with appropriate tokens
modified_prompts = []
total_image_files = 0
total_patches = 0
image_containing_prompts = []
for idx, prompt in enumerate(prompts):
# check if this prompt has images
has_images = (pixel_values_list and
idx < len(pixel_values_list) and
pixel_values_list[idx] is not None and
isinstance(pixel_values_list[idx], torch.Tensor) and
pixel_values_list[idx].shape[0] > 0)
if has_images:
# prompt with image placeholders
image_containing_prompts.append(idx)
modified_prompt = prompt
patches = num_patches_list[idx] if (num_patches_list and idx < len(num_patches_list)) else []
num_images = len(patches)
total_image_files += num_images
total_patches += sum(patches)
# replace each <image> placeholder with image tokens
for i, num_patches in enumerate(patches):
image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * model.num_image_token * num_patches + IMG_END_TOKEN
modified_prompt = modified_prompt.replace('<image>', image_tokens, 1)
else:
# text-only prompt
modified_prompt = prompt
modified_prompts.append(modified_prompt)
# process all prompts in a single batch
tokenizer.padding_side = 'left'
model_inputs = tokenizer(modified_prompts, return_tensors='pt', padding=True)
input_ids = model_inputs['input_ids'].to(model.device)
attention_mask = model_inputs['attention_mask'].to(model.device)
eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>\n".strip())
generation_config['eos_token_id'] = eos_token_id
# prepare pixel values
flattened_pixel_values = None
if image_containing_prompts:
# collect and concatenate all image tensors
all_pixel_values = []
for idx in image_containing_prompts:
all_pixel_values.append(pixel_values_list[idx])
flattened_pixel_values = torch.cat(all_pixel_values, dim=0)
print(f"Processing batch with {len(prompts)} prompts, {total_image_files} actual images, and {total_patches} total patches")
else:
print(f"Processing text-only batch with {len(prompts)} prompts")
# generate outputs
outputs = model.generate(
pixel_values=flattened_pixel_values, # will be None for text-only prompts
input_ids=input_ids,
attention_mask=attention_mask,
**generation_config
)
# Decode responses
responses = tokenizer.batch_decode(outputs, skip_special_tokens=True)
return responses
```
To load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = ""
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, padding_side='left')
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2" # we recommend using flash attention
).to("cuda")
```
Simple 0-shot text-only example:
```python
template = """{"names": ["verbatim-string"]}"""
text = "John went to the restaurant with Mary. James went to the cinema."
input_messages = [construct_message(text, template)]
input_content = prepare_inputs(
messages=input_messages,
image_paths=[],
tokenizer=tokenizer,
)
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}
with torch.no_grad():
result = nuextract_generate(
model=model,
tokenizer=tokenizer,
prompts=input_content['prompts'],
pixel_values_list=input_content['pixel_values_list'],
num_patches_list=input_content['num_patches_list'],
generation_config=generation_config
)
for y in result:
print(y)
# {"names": ["John", "Mary", "James"]}
```
Text-only input with an in-context example:
```python
template = """{"names": ["verbatim-string"], "female_names": ["verbatim-string"]}"""
text = "John went to the restaurant with Mary. James went to the cinema."
examples = [
{
"input": "Stephen is the manager at Susan's store.",
"output": """{"names": ["STEPHEN", "SUSAN"], "female_names": ["SUSAN"]}"""
}
]
input_messages = [construct_message(text, template, examples)]
input_content = prepare_inputs(
messages=input_messages,
image_paths=[],
tokenizer=tokenizer,
)
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}
with torch.no_grad():
result = nuextract_generate(
model=model,
tokenizer=tokenizer,
prompts=input_content['prompts'],
pixel_values_list=input_content['pixel_values_list'],
num_patches_list=input_content['num_patches_list'],
generation_config=generation_config
)
for y in result:
print(y)
# {"names": ["JOHN", "MARY", "JAMES"], "female_names": ["MARY"]}
```
Example with image input and an in-context example. Image inputs should use `<image>` placeholder instead of text and image paths should be provided in a list in order of appearance in the prompt (in this example `0.jpg` will be for the in-context example and `1.jpg` for the true input).
```python
template = """{"store": "verbatim-string"}"""
text = "<image>"
examples = [
{
"input": "<image>",
"output": """{"store": "Walmart"}"""
}
]
input_messages = [construct_message(text, template, examples)]
images = [
["0.jpg", "1.jpg"]
]
input_content = prepare_inputs(
messages=input_messages,
image_paths=images,
tokenizer=tokenizer,
)
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}
with torch.no_grad():
result = nuextract_generate(
model=model,
tokenizer=tokenizer,
prompts=input_content['prompts'],
pixel_values_list=input_content['pixel_values_list'],
num_patches_list=input_content['num_patches_list'],
generation_config=generation_config
)
for y in result:
print(y)
# {"store": "Trader Joe's"}
```
Multi-modal batched input:
```python
inputs = [
# image input with no ICL examples
{
"text": "<image>",
"template": """{"store_name": "verbatim-string"}""",
"examples": None,
},
# image input with 1 ICL example
{
"text": "<image>",
"template": """{"store_name": "verbatim-string"}""",
"examples": [
{
"input": "<image>",
"output": """{"store_name": "Walmart"}""",
}
],
},
# text input with no ICL examples
{
"text": "John went to the restaurant with Mary. James went to the cinema.",
"template": """{"names": ["verbatim-string"]}""",
"examples": None,
},
# text input with ICL example
{
"text": "John went to the restaurant with Mary. James went to the cinema.",
"template": """{"names": ["verbatim-string"], "female_names": ["verbatim-string"]}""",
"examples": [
{
"input": "Stephen is the manager at Susan's store.",
"output": """{"names": ["STEPHEN", "SUSAN"], "female_names": ["SUSAN"]}"""
}
],
},
]
input_messages = [
construct_message(
x["text"],
x["template"],
x["examples"]
) for x in inputs
]
images = [
["0.jpg"],
["0.jpg", "1.jpg"],
None,
None
]
input_content = prepare_inputs(
messages=input_messages,
image_paths=images,
tokenizer=tokenizer,
)
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}
with torch.no_grad():
result = nuextract_generate(
model=model,
tokenizer=tokenizer,
prompts=input_content['prompts'],
pixel_values_list=input_content['pixel_values_list'],
num_patches_list=input_content['num_patches_list'],
generation_config=generation_config
)
for y in result:
print(y)
# {"store_name": "WAL*MART"}
# {"store_name": "Trader Joe's"}
# {"names": ["John", "Mary", "James"]}
# {"names": ["JOHN", "MARY", "JAMES"], "female_names": ["MARY"]}
```
## Template Generation
If you want to convert existing schema files you have in other formats (e.g. XML, YAML, etc.) or start from an example, NuExtract 2 models can automatically generate this for you.
E.g. convert XML into a NuExtract template:
```python
def generate_template(description):
input_messages = [description]
input_content = prepare_inputs(
messages=input_messages,
image_paths=[],
tokenizer=tokenizer,
)
generation_config = {"do_sample": True, "temperature": 0.4, "max_new_tokens": 256}
with torch.no_grad():
result = nuextract_generate(
model=model,
tokenizer=tokenizer,
prompts=input_content['prompts'],
pixel_values_list=input_content['pixel_values_list'],
num_patches_list=input_content['num_patches_list'],
generation_config=generation_config
)
return result[0]
xml_template = """<SportResult>
<Date></Date>
<Sport></Sport>
<Venue></Venue>
<HomeTeam></HomeTeam>
<AwayTeam></AwayTeam>
<HomeScore></HomeScore>
<AwayScore></AwayScore>
<TopScorer></TopScorer>
</SportResult>"""
result = generate_template(xml_template)
print(result)
# {
# "SportResult": {
# "Date": "date-time",
# "Sport": "verbatim-string",
# "Venue": "verbatim-string",
# "HomeTeam": "verbatim-string",
# "AwayTeam": "verbatim-string",
# "HomeScore": "integer",
# "AwayScore": "integer",
# "TopScorer": "verbatim-string"
# }
# }
```
E.g. generate a template from natural language description:
```python
text = """Give me relevant info about startup companies mentioned."""
result = generate_template(text)
print(result)
# {
# "Startup_Companies": [
# {
# "Name": "verbatim-string",
# "Products": [
# "string"
# ],
# "Location": "verbatim-string",
# "Company_Type": [
# "Technology",
# "Finance",
# "Health",
# "Education",
# "Other"
# ]
# }
# ]
# }
``` |
Zaynoid/fal-34b | Zaynoid | 2025-05-29T15:41:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"falcon_h1",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T15:19:49Z | ---
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.
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BootesVoid/cmb8cojzk0kl2lexpin9308r5_cmb8cvc6r0koolexpstkvx7xu | BootesVoid | 2025-05-29T15:41:04Z | 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 | 2025-05-29T15:41:03Z | ---
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: SHAMELESS
---
# Cmb8Cojzk0Kl2Lexpin9308R5_Cmb8Cvc6R0Koolexpstkvx7Xu
<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 `SHAMELESS` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SHAMELESS",
"lora_weights": "https://huggingface.co/BootesVoid/cmb8cojzk0kl2lexpin9308r5_cmb8cvc6r0koolexpstkvx7xu/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb8cojzk0kl2lexpin9308r5_cmb8cvc6r0koolexpstkvx7xu', weight_name='lora.safetensors')
image = pipeline('SHAMELESS').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb8cojzk0kl2lexpin9308r5_cmb8cvc6r0koolexpstkvx7xu/discussions) to add images that show off what you’ve made with this LoRA.
|
mradermacher/qwen-vision-2.5-i1-GGUF | mradermacher | 2025-05-29T15:40:07Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Critical-Future/qwen-vision-2.5",
"base_model:quantized:Critical-Future/qwen-vision-2.5",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-29T14:30:28Z | ---
base_model: Critical-Future/qwen-vision-2.5
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Critical-Future/qwen-vision-2.5
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/qwen-vision-2.5-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/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-i1-GGUF/resolve/main/qwen-vision-2.5.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
pet4n1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_lithe_beaver | pet4n1 | 2025-05-29T15:40:02Z | 8 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am leaping lithe beaver",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-11T03:19:05Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_lithe_beaver
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am leaping lithe beaver
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_lithe_beaver
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="pet4n1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_lithe_beaver", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0+cpu
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BootesVoid/cmb8cojzk0kl2lexpin9308r5_cmb9iiyhq0e3g1b1yqk0tdu81 | BootesVoid | 2025-05-29T15:39:23Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-29T15:39:22Z | ---
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: CELLO
---
# Cmb8Cojzk0Kl2Lexpin9308R5_Cmb9Iiyhq0E3G1B1Yqk0Tdu81
<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 `CELLO` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CELLO",
"lora_weights": "https://huggingface.co/BootesVoid/cmb8cojzk0kl2lexpin9308r5_cmb9iiyhq0e3g1b1yqk0tdu81/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb8cojzk0kl2lexpin9308r5_cmb9iiyhq0e3g1b1yqk0tdu81', weight_name='lora.safetensors')
image = pipeline('CELLO').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb8cojzk0kl2lexpin9308r5_cmb9iiyhq0e3g1b1yqk0tdu81/discussions) to add images that show off what you’ve made with this LoRA.
|
dimasik87/4b5231ea-7007-43ce-90ad-2d54f7808cbd | dimasik87 | 2025-05-29T15:38:31Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:tplr/TEMPLAR-I",
"base_model:adapter:tplr/TEMPLAR-I",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-29T15:25:29Z | ---
library_name: peft
license: mit
base_model: tplr/TEMPLAR-I
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4b5231ea-7007-43ce-90ad-2d54f7808cbd
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: tplr/TEMPLAR-I
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 7593e12d055c09da_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: dimasik87/4b5231ea-7007-43ce-90ad-2d54f7808cbd
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/7593e12d055c09da_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fe32e4bd-fe3e-4ffc-8b47-36ae39f9d317
wandb_project: s56-7
wandb_run: your_name
wandb_runid: fe32e4bd-fe3e-4ffc-8b47-36ae39f9d317
warmup_steps: 50
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 4b5231ea-7007-43ce-90ad-2d54f7808cbd
This model is a fine-tuned version of [tplr/TEMPLAR-I](https://huggingface.co/tplr/TEMPLAR-I) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4659
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.7437 | 0.0003 | 1 | 2.5926 |
| 3.0492 | 0.0696 | 250 | 2.5014 |
| 1.8084 | 0.1391 | 500 | 2.4659 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mesolitica/Malaysian-Qwen2.5-7B-Dialect-Reasoning-GRPO | mesolitica | 2025-05-29T15:38:24Z | 29 | 1 | null | [
"safetensors",
"qwen2",
"ms",
"en",
"dataset:mesolitica/Malay-Dialect-Reasoning",
"base_model:mesolitica/Malaysian-Qwen2.5-7B-Reasoning-SFT",
"base_model:finetune:mesolitica/Malaysian-Qwen2.5-7B-Reasoning-SFT",
"region:us"
] | null | 2025-05-27T06:58:51Z | ---
language:
- ms
- en
datasets:
- mesolitica/Malay-Dialect-Reasoning
base_model:
- mesolitica/Malaysian-Qwen2.5-7B-Reasoning-SFT
---
# Malaysian Qwen 2.5 7B Instruct Dialect Reasoning GRPO
Online Reinforcement learning using GRPO full parameter on warmup reasoning SFT https://huggingface.co/mesolitica/Malaysian-Qwen2.5-7B-Reasoning-SFT on highly curated Malay Dialect Reasoning dataset.
## Improvement
1. Improve reasoning on Dialects, each datapoint been replicated to 12 generations.
2. Actual online reinforcement learning.
## Better performance
To get better performance, use system prompt `You are going to enter reasoning mode. First, you try to think step-by-step in Malay. After that, put your final answer within $\\boxed{}$.`, you can check how we trained it at https://github.com/mesolitica/malaya/blob/master/session/qwen2.5/grpo.py#L80
## Training session
Finetune on [huseinzol05/malaysian-dialect-qa](https://huggingface.co/datasets/huseinzol05/malaysian-dialect-qa), this is train set from [mesolitica/Malay-Dialect-Reasoning](https://huggingface.co/datasets/mesolitica/Malay-Dialect-Reasoning).
## How we train
1. GRPO full parameters.
5. WanDB at https://wandb.ai/huseinzol05/fpf-Malaysian-Qwen2.5-7B-Reasoning-SFT-GRPO-v3
## Checkpoints
1. Epoch 1.1, revision [78435e1edc593a842e6031ba6ee7a5930d9d2a83](https://huggingface.co/mesolitica/Malaysian-Qwen2.5-7B-Dialect-Reasoning-GRPO/commit/78435e1edc593a842e6031ba6ee7a5930d9d2a83)
2. Epoch 2.0, revision [25d418d0032f08c39a506b529e6133e60f998a61](https://huggingface.co/mesolitica/Malaysian-Qwen2.5-7B-Dialect-Reasoning-GRPO/commit/25d418d0032f08c39a506b529e6133e60f998a61)
3. Epoch 2.96, revision [4c6886a43f73767be61d67093f20dbdf1a7d8df6](https://huggingface.co/mesolitica/Malaysian-Qwen2.5-7B-Dialect-Reasoning-GRPO/commit/4c6886a43f73767be61d67093f20dbdf1a7d8df6)
## Source code
Source code at https://github.com/mesolitica/malaya/blob/master/session/qwen2.5/7b-grpo-fsdp.sh
## Benchmark
All the benchmarks generate using vLLM, evaluation based on sacrebleu CHRF max@5.
Source code for evaluation at https://github.com/mesolitica/malaya/tree/master/session/qwen2.5/evaluate-dialect
### Float32
Dialect to standard Malay,
```
From: johor To: malay, score: 58.2189619529139
From: kedah To: malay, score: 59.21260384746205
From: pahang To: malay, score: 53.506270589822165
From: negeri sembilan To: malay, score: 56.94870448682657
From: kelantan To: malay, score: 50.64768195652429
From: penang To: malay, score: 62.964413639258034
From: melaka To: malay, score: 56.24541676643081
average: 56.82057903417684
```
Standard Malay to dialect,
```
From: malay To: johor, score: 54.83246740931249
From: malay To: kedah, score: 59.069394967356274
From: malay To: pahang, score: 59.695207458023745
From: malay To: negeri sembilan, score: 50.69885056697714
From: malay To: kelantan, score: 44.66310165425512
From: malay To: penang, score: 65.39795752468879
From: malay To: melaka, score: 72.39183991789344
average: 58.10697421407243
```
### Float16
Dialect to standard Malay,
```
From: johor To: malay, score: 57.42949426937456
From: kedah To: malay, score: 58.12580212528728
From: pahang To: malay, score: 55.60484906845884
From: negeri sembilan To: malay, score: 56.4509629484568
From: kelantan To: malay, score: 53.944979416369996
From: penang To: malay, score: 62.20935643642939
From: melaka To: malay, score: 57.14492955494046
average: 57.27291054561676
```
Standard Malay to dialect,
```
From: malay To: johor, score: 55.68356840259747
From: malay To: kedah, score: 56.264707994950186
From: malay To: pahang, score: 60.15982036912563
From: malay To: negeri sembilan, score: 48.71725827604103
From: malay To: kelantan, score: 43.948995049469474
From: malay To: penang, score: 63.15864675162173
From: malay To: melaka, score: 74.12398375006538
average: 57.436711513410124
```
## Special thanks
Special thanks to https://www.sns.com.my and Nvidia for 8x H100 node! |
RikoteMaster/model_ft_openbookqa_additional_mcqa | RikoteMaster | 2025-05-29T15:34:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-29T15:34:18Z | ---
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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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- **Hardware Type:** [More Information Needed]
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QinyuZhao1116/Arinar | QinyuZhao1116 | 2025-05-29T15:33:38Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-04-23T01:14:23Z | ---
license: apache-2.0
---
|
RikoteMaster/model_openbookqa_additional_mcqa | RikoteMaster | 2025-05-29T15:33:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T15:32:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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SleepyM/RL_pixelcopter_v0 | SleepyM | 2025-05-29T15:32:37Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-28T15:57:54Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: RL_pixelcopter_v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 31.97 +/- 26.51
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ydiallo/ppo-LunarLander-v2 | ydiallo | 2025-05-29T15:32:24Z | 18 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-27T15:24:39Z | ---
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: 266.56 +/- 21.91
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
...
```
|
shaddie/rocketry_roqeto_model | shaddie | 2025-05-29T15:31:55Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"codegen",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-27T14:01:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
zxny/CartPole-v1 | zxny | 2025-05-29T15:31:54Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-29T15:25:23Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
stephenstrange/Venom | stephenstrange | 2025-05-29T15:29:44Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T15:29:44Z | ---
license: apache-2.0
---
|
alibidaran/LLAMA3-inatructive_Python-GGUF | alibidaran | 2025-05-29T15:27:42Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-29T15:25:36Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** alibidaran
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/qwen-vision-2.5-GGUF | mradermacher | 2025-05-29T15:27:22Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Critical-Future/qwen-vision-2.5",
"base_model:quantized:Critical-Future/qwen-vision-2.5",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-29T10:50:51Z | ---
base_model: Critical-Future/qwen-vision-2.5
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Critical-Future/qwen-vision-2.5
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/qwen-vision-2.5-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/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/qwen-vision-2.5-GGUF/resolve/main/qwen-vision-2.5.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. 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 -->
|
borisabdul/Royax | borisabdul | 2025-05-29T15:24:16Z | 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 | 2025-05-29T14:51:31Z | ---
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: Royax
---
# Royax
<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 `Royax` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Royax",
"lora_weights": "https://huggingface.co/borisabdul/Royax/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('borisabdul/Royax', weight_name='lora.safetensors')
image = pipeline('Royax').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/borisabdul/Royax/discussions) to add images that show off what you’ve made with this LoRA.
|
u54/goa | u54 | 2025-05-29T15:19:54Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
] | null | 2025-05-29T15:19:54Z | ---
license: artistic-2.0
---
|
BootesVoid/cmb9hif4f0dmy1b1yk5ouqiay_cmb9hwbds0dtn1b1yphj386vr | BootesVoid | 2025-05-29T15:19:06Z | 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 | 2025-05-29T15:19:05Z | ---
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: AHLAMMARTA
---
# Cmb9Hif4F0Dmy1B1Yk5Ouqiay_Cmb9Hwbds0Dtn1B1Yphj386Vr
<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 `AHLAMMARTA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "AHLAMMARTA",
"lora_weights": "https://huggingface.co/BootesVoid/cmb9hif4f0dmy1b1yk5ouqiay_cmb9hwbds0dtn1b1yphj386vr/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb9hif4f0dmy1b1yk5ouqiay_cmb9hwbds0dtn1b1yphj386vr', weight_name='lora.safetensors')
image = pipeline('AHLAMMARTA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb9hif4f0dmy1b1yk5ouqiay_cmb9hwbds0dtn1b1yphj386vr/discussions) to add images that show off what you’ve made with this LoRA.
|
jozhang97/MutateEverything-ISM | jozhang97 | 2025-05-29T15:17:43Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-29T15:13:38Z | The official github can be found at https://github.com/jozhang97/MutateEverything.
|
LEAF-CLIP/OpenCLIP-ViT-g-rho50-k1 | LEAF-CLIP | 2025-05-29T15:16:02Z | 0 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:laion/CLIP-ViT-g-14-laion2B-s34B-b88K",
"base_model:finetune:laion/CLIP-ViT-g-14-laion2B-s34B-b88K",
"license:mit",
"region:us"
] | null | 2025-02-24T20:53:41Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- laion/CLIP-ViT-g-14-laion2B-s34B-b88K
---
Model Initialized from `laion/CLIP-ViT-g-14-laion2B-s34B-b88K`. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/OpenCLIP-ViT-g-rho50-k1"
processor_name = "laion/CLIP-ViT-g-14-laion2B-s34B-b88K"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
Jarbas/m2v-256-xlm-roberta-large-finetuned-conll03-german | Jarbas | 2025-05-29T15:14:38Z | 0 | 0 | model2vec | [
"model2vec",
"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:FacebookAI/xlm-roberta-large-finetuned-conll03-german",
"base_model:finetune:FacebookAI/xlm-roberta-large-finetuned-conll03-german",
"license:mit",
"region:us"
] | null | 2025-05-29T15:14:16Z | ---
base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-german
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: xlm-roberta-large-finetuned-conll03-german-distill256
tags:
- embeddings
- static-embeddings
- sentence-transformers
---
# xlm-roberta-large-finetuned-conll03-german-distill256 Model Card
This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the FacebookAI/xlm-roberta-large-finetuned-conll03-german(https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-german) 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. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
## Installation
Install model2vec using pip:
```
pip install model2vec
```
## Usage
### Using Model2Vec
The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models.
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("xlm-roberta-large-finetuned-conll03-german-distill256")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Using Sentence Transformers
You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
```python
from sentence_transformers import SentenceTransformer
# Load a pretrained Sentence Transformer model
model = SentenceTransformer("xlm-roberta-large-finetuned-conll03-german-distill256")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Distilling a Model2Vec model
You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code:
```python
from model2vec.distill import distill
# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
# Save the model
m2v_model.save_pretrained("m2v_model")
```
## 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 [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence.
## Additional Resources
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
- [Website](https://minishlab.github.io/)
## 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.
```
@article{minishlab2024model2vec,
author = {Tulkens, Stephan and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
``` |
Code2Logic/GameQA-Qwen2.5-VL-7B | Code2Logic | 2025-05-29T15:13:01Z | 0 | 0 | null | [
"safetensors",
"qwen2_5_vl",
"dataset:Code2Logic/GameQA-140K",
"arxiv:2505.13886",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-05-27T18:07:14Z | ---
license: apache-2.0
datasets:
- Code2Logic/GameQA-140K
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
---
# Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning
This is the first work, to the best of our knowledge, that leverages ***game code*** to synthesize multimodal reasoning data for ***training*** VLMs. Furthermore, when trained solely with a GRPO strategy on **GameQA** (synthesized via our proposed **Code2Logic** approach), multiple cutting-edge open-source models exhibit significantly enhanced out-of-domain generalization.
[[📖 Paper](https://arxiv.org/abs/2505.13886)] [🤗 [GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [🤗 [GameQA-InternVL3-8B](https://huggingface.co/Code2Logic/GameQA-InternVL3-8B) ] [🤗 [GameQA-Qwen2.5-VL-7B](https://huggingface.co/Code2Logic/GameQA-Qwen2.5-VL-7B)] [🤗 [GameQA-LLaVA-OV-7B](https://huggingface.co/Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf) ]
## News
* We've open-sourced the ***three*** models trained with GRPO on GameQA on [Huggingface](https://huggingface.co/Code2Logic). |
LEAF-CLIP/OpenCLIP-ViT-H-rho50-k1-FARE2 | LEAF-CLIP | 2025-05-29T15:12:13Z | 17 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
"base_model:finetune:laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
"license:mit",
"region:us"
] | null | 2025-03-26T13:39:13Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- laion/CLIP-ViT-H-14-laion2B-s32B-b79K
---
Model Initialized from `laion/CLIP-ViT-H-14-laion2B-s32B-b79K`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/OpenCLIP-ViT-H-rho50-k1-FARE2"
processor_name = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
LEAF-CLIP/OpenCLIP-ViT-g-FARE2 | LEAF-CLIP | 2025-05-29T15:06:59Z | 11 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:laion/CLIP-ViT-g-14-laion2B-s34B-b88K",
"base_model:finetune:laion/CLIP-ViT-g-14-laion2B-s34B-b88K",
"license:mit",
"region:us"
] | null | 2025-04-02T07:42:16Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- laion/CLIP-ViT-g-14-laion2B-s34B-b88K
---
Model Initialized from `laion/CLIP-ViT-g-14-laion2B-s34B-b88K`. The image encoder is finetuned with FARE at $\epsilon=2/255$.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/OpenCLIP-ViT-g-FARE2"
processor_name = "laion/CLIP-ViT-g-14-laion2B-s34B-b88K"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
Hsianchengfun/merged_model_WOQ_epoch761 | Hsianchengfun | 2025-05-29T15:06:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T15:03:34Z | ---
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]
- **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. -->
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## Model Card Contact
[More Information Needed] |
davgauch/MNLP_M3_mcqa_model_8 | davgauch | 2025-05-29T15:05:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T10:29:33Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: MNLP_M3_mcqa_model_8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MNLP_M3_mcqa_model_8
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8221
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- 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: constant
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3327 | 1.0 | 2432 | 0.8589 |
| 1.0772 | 2.0 | 4864 | 0.8197 |
| 1.0491 | 3.0 | 7296 | 0.8137 |
| 1.0303 | 4.0 | 9728 | 0.8221 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.2.0
- Tokenizers 0.21.0
|
unsloth/DeepSeek-R1-0528-Qwen3-8B-bnb-4bit | unsloth | 2025-05-29T15:05:27Z | 0 | 2 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"conversational",
"arxiv:2501.12948",
"base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-29T15:04:57Z | ---
tags:
- unsloth
base_model:
- deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
license: mit
library_name: transformers
---
<div>
<p style="margin-top: 0;margin-bottom: 0;">
<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center; ">
<a href="https://github.com/unslothai/unsloth/">
<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
</a>
<a href="https://discord.gg/unsloth">
<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
</a>
<a href="https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
</a>
</div>
</div>
# DeepSeek-R1-0528
<!-- markdownlint-disable first-line-h1 -->
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<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
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<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</div>
<div align="center" style="line-height: 1;">
<a href="LICENSE" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<p align="center">
<a href="https://arxiv.org/pdf/2501.12948"><b>Paper Link</b>👁️</a>
</p>
## 1. Introduction
The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro.
<p align="center">
<img width="80%" src="figures/benchmark.png">
</p>
Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.
Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.
## 2. Evaluation Results
### DeepSeek-R1-0528
For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 16 responses per query to estimate pass@1.
<div align="center">
| Category | Benchmark (Metric) | DeepSeek R1 | DeepSeek R1 0528
|----------|----------------------------------|-----------------|---|
| General |
| | MMLU-Redux (EM) | 92.9 | 93.4
| | MMLU-Pro (EM) | 84.0 | 85.0
| | GPQA-Diamond (Pass@1) | 71.5 | 81.0
| | SimpleQA (Correct) | 30.1 | 27.8
| | FRAMES (Acc.) | 82.5 | 83.0
| | Humanity's Last Exam (Pass@1) | 8.5 | 17.7
| Code |
| | LiveCodeBench (2408-2505) (Pass@1) | 63.5 | 73.3
| | Codeforces-Div1 (Rating) | 1530 | 1930
| | SWE Verified (Resolved) | 49.2 | 57.6
| | Aider-Polyglot (Acc.) | 53.3 | 71.6
| Math |
| | AIME 2024 (Pass@1) | 79.8 | 91.4
| | AIME 2025 (Pass@1) | 70.0 | 87.5
| | HMMT 2025 (Pass@1) | 41.7 | 79.4 |
| | CNMO 2024 (Pass@1) | 78.8 | 86.9
| Tools |
| | BFCL_v3_MultiTurn (Acc) | - | 37.0 |
| | Tau-Bench (Pass@1) | - | 53.5(Airline)/63.9(Retail)
</div>
Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation.
### DeepSeek-R1-0528-Qwen3-8B
Meanwhile, we distilled the chain-of-thought from DeepSeek-R1-0528 to post-train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state-of-the-art (SOTA) performance among open-source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B-thinking. We believe that the chain-of-thought from DeepSeek-R1-0528 will hold significant importance for both academic research on reasoning models and industrial development focused on small-scale models.
| | AIME 24 | AIME 25 | HMMT Feb 25 | GPQA Diamond | LiveCodeBench (2408-2505) |
|--------------------------------|---------|---------|-------------|--------------|---------------------------|
| Qwen3-235B-A22B | 85.7 | 81.5 | 62.5 | 71.1 | 66.5 |
| Qwen3-32B | 81.4 | 72.9 | - | 68.4 | - |
| Qwen3-8B | 76.0 | 67.3 | - | 62.0 | - |
| Phi-4-Reasoning-Plus-14B | 81.3 | 78.0 | 53.6 | 69.3 | - |
| Gemini-2.5-Flash-Thinking-0520 | 82.3 | 72.0 | 64.2 | 82.8 | 62.3 |
| o3-mini (medium) | 79.6 | 76.7 | 53.3 | 76.8 | 65.9 |
| DeepSeek-R1-0528-Qwen3-8B | 86.0 | 76.3 | 61.5 | 61.1 | 60.5 |
## 3. Chat Website & API Platform
You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in), and switch on the button "DeepThink"
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
## 4. How to Run Locally
Please visit [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) repository for more information about running DeepSeek-R1-0528 locally.
Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes:
1. System prompt is supported now.
2. It is not required to add "\<think\>\n" at the beginning of the output to force the model into thinking pattern.
The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B, but it is essential to ensure that all configuration files are sourced from our repository rather than the original Qwen3 project.
### System Prompt
In the official DeepSeek web/app, we use the same system prompt with a specific date.
```
该助手为DeepSeek-R1，由深度求索公司创造。
今天是{current date}。
```
For example,
```
该助手为DeepSeek-R1，由深度求索公司创造。
今天是2025年5月28日，星期一。
```
### Temperature
In our web and application environments, the temperature parameter $T_{model}$ is set to 0.6.
### Prompts for File Uploading and Web Search
For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.
```
file_template = \
"""[file name]: {file_name}
[file content begin]
{file_content}
[file content end]
{question}"""
```
For Web Search, {search_results}, {cur_date}, and {question} are arguments.
For Chinese query, we use the prompt:
```
search_answer_zh_template = \
'''# 以下内容是基于用户发送的消息的搜索结果:
{search_results}
在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。
在回答时，请注意以下几点：
- 今天是{cur_date}。
- 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。
- 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。
- 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。
- 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。
- 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。
- 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。
- 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。
- 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。
# 用户消息为：
{question}'''
```
For English query, we use the prompt:
```
search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''
```
## 5. License
This code repository is licensed under [MIT License](LICENSE). The use of DeepSeek-R1 models is also subject to [MIT License](LICENSE). DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation.
## 6. Citation
```
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
author={DeepSeek-AI},
year={2025},
eprint={2501.12948},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.12948},
}
```
## 7. Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
|
hlhsiao/llama-3.2-3b-instruct-quantized | hlhsiao | 2025-05-29T15:03:58Z | 0 | 0 | null | [
"safetensors",
"llama",
"8-bit",
"compressed-tensors",
"region:us"
] | null | 2025-05-29T14:49:39Z | # LLaMA 3.2 3B Instruct - Quantized (w8a8)
This is a 3B parameter LLaMA 3.2 Instruct model, quantized with 8-bit weights and activations (`w8a8`).
Model files include `safetensors`-formatted weights and `generation_config.json`.
## Files
- `model-00001-of-00002.safetensors` + `index.json`: quantized model weights
- `config.json` + `generation_config.json`: model and generation configuration
- `recipe.yaml`: quantization configuration (if applicable)
## How to load
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("your-username/llama-3.2-3b-instruct-quantized", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("your-username/llama-3.2-3b-instruct-quantized") |
YiChuanH/llama1B-finetuned | YiChuanH | 2025-05-29T15:02:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T14:59:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- 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] |
eyeman233/cgtgcdr | eyeman233 | 2025-05-29T15:01:50Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
] | null | 2025-05-29T15:01:50Z | ---
license: artistic-2.0
---
|
n1kooo/vit-cifar10 | n1kooo | 2025-05-29T15:00:49Z | 41 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-28T08:23:35Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-cifar10
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. -->
# vit-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the n1kooo/vit-cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1006
- Accuracy: 0.968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0861 | 1.0 | 2500 | 0.0955 | 0.9708 |
| 0.0927 | 2.0 | 5000 | 0.0890 | 0.974 |
| 0.0793 | 3.0 | 7500 | 0.0881 | 0.974 |
| 0.0656 | 4.0 | 10000 | 0.0864 | 0.9738 |
| 0.0787 | 5.0 | 12500 | 0.0867 | 0.9746 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
matatonic/DeepSeek-R1-0528-Qwen3-8B-5.0bpw-exl2 | matatonic | 2025-05-29T14:59:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:2501.12948",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] | text-generation | 2025-05-29T14:59:27Z | ---
license: mit
library_name: transformers
---
# DeepSeek-R1-0528
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
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<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
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<a href="LICENSE" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<p align="center">
<a href="https://arxiv.org/pdf/2501.12948"><b>Paper Link</b>👁️</a>
</p>
## 1. Introduction
The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro.
<p align="center">
<img width="80%" src="figures/benchmark.png">
</p>
Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.
Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.
## 2. Evaluation Results
### DeepSeek-R1-0528
For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 16 responses per query to estimate pass@1.
<div align="center">
| Category | Benchmark (Metric) | DeepSeek R1 | DeepSeek R1 0528
|----------|----------------------------------|-----------------|---|
| General |
| | MMLU-Redux (EM) | 92.9 | 93.4
| | MMLU-Pro (EM) | 84.0 | 85.0
| | GPQA-Diamond (Pass@1) | 71.5 | 81.0
| | SimpleQA (Correct) | 30.1 | 27.8
| | FRAMES (Acc.) | 82.5 | 83.0
| | Humanity's Last Exam (Pass@1) | 8.5 | 17.7
| Code |
| | LiveCodeBench (2408-2505) (Pass@1) | 63.5 | 73.3
| | Codeforces-Div1 (Rating) | 1530 | 1930
| | SWE Verified (Resolved) | 49.2 | 57.6
| | Aider-Polyglot (Acc.) | 53.3 | 71.6
| Math |
| | AIME 2024 (Pass@1) | 79.8 | 91.4
| | AIME 2025 (Pass@1) | 70.0 | 87.5
| | HMMT 2025 (Pass@1) | 41.7 | 79.4 |
| | CNMO 2024 (Pass@1) | 78.8 | 86.9
| Tools |
| | BFCL_v3_MultiTurn (Acc) | - | 37.0 |
| | Tau-Bench (Pass@1) | - | 53.5(Airline)/63.9(Retail)
</div>
Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation.
### DeepSeek-R1-0528-Qwen3-8B
Meanwhile, we distilled the chain-of-thought from DeepSeek-R1-0528 to post-train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state-of-the-art (SOTA) performance among open-source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B-thinking. We believe that the chain-of-thought from DeepSeek-R1-0528 will hold significant importance for both academic research on reasoning models and industrial development focused on small-scale models.
| | AIME 24 | AIME 25 | HMMT Feb 25 | GPQA Diamond | LiveCodeBench (2408-2505) |
|--------------------------------|---------|---------|-------------|--------------|---------------------------|
| Qwen3-235B-A22B | 85.7 | 81.5 | 62.5 | 71.1 | 66.5 |
| Qwen3-32B | 81.4 | 72.9 | - | 68.4 | - |
| Qwen3-8B | 76.0 | 67.3 | - | 62.0 | - |
| Phi-4-Reasoning-Plus-14B | 81.3 | 78.0 | 53.6 | 69.3 | - |
| Gemini-2.5-Flash-Thinking-0520 | 82.3 | 72.0 | 64.2 | 82.8 | 62.3 |
| o3-mini (medium) | 79.6 | 76.7 | 53.3 | 76.8 | 65.9 |
| DeepSeek-R1-0528-Qwen3-8B | 86.0 | 76.3 | 61.5 | 61.1 | 60.5 |
## 3. Chat Website & API Platform
You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in), and switch on the button "DeepThink"
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
## 4. How to Run Locally
Please visit [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) repository for more information about running DeepSeek-R1-0528 locally.
Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes:
1. System prompt is supported now.
2. It is not required to add "\<think\>\n" at the beginning of the output to force the model into thinking pattern.
The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B, but it is essential to ensure that all configuration files are sourced from our repository rather than the original Qwen3 project.
### System Prompt
In the official DeepSeek web/app, we use the same system prompt with a specific date.
```
该助手为DeepSeek-R1，由深度求索公司创造。
今天是{current date}。
```
For example,
```
该助手为DeepSeek-R1，由深度求索公司创造。
今天是2025年5月28日，星期一。
```
### Temperature
In our web and application environments, the temperature parameter $T_{model}$ is set to 0.6.
### Prompts for File Uploading and Web Search
For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.
```
file_template = \
"""[file name]: {file_name}
[file content begin]
{file_content}
[file content end]
{question}"""
```
For Web Search, {search_results}, {cur_date}, and {question} are arguments.
For Chinese query, we use the prompt:
```
search_answer_zh_template = \
'''# 以下内容是基于用户发送的消息的搜索结果:
{search_results}
在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。
在回答时，请注意以下几点：
- 今天是{cur_date}。
- 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。
- 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。
- 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。
- 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。
- 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。
- 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。
- 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。
- 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。
# 用户消息为：
{question}'''
```
For English query, we use the prompt:
```
search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''
```
## 5. License
This code repository is licensed under [MIT License](LICENSE). The use of DeepSeek-R1 models is also subject to [MIT License](LICENSE). DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation.
## 6. Citation
```
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
author={DeepSeek-AI},
year={2025},
eprint={2501.12948},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.12948},
}
```
## 7. Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
|
LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained | LEAF-CLIP | 2025-05-29T14:56:40Z | 68 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
"base_model:finetune:laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
"license:mit",
"region:us"
] | null | 2025-05-03T10:11:38Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- laion/CLIP-ViT-bigG-14-laion2B-39B-b160k
---
Model Initialized from `laion/CLIP-ViT-bigG-14-laion2B-39B-b160k`. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$ and semantic constraints.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/OpenCLIP-ViT-bigG-rho50-k1-constrained"
processor_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
LEAF-CLIP/CLIP-ViT-L-rho50-k1-constrained | LEAF-CLIP | 2025-05-29T14:55:20Z | 64 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:openai/clip-vit-large-patch14",
"base_model:finetune:openai/clip-vit-large-patch14",
"license:mit",
"region:us"
] | null | 2025-04-30T06:46:16Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- openai/clip-vit-large-patch14
---
Model Initialized from `openai/clip-vit-large-patch14`. The text encoder is finetuned with LEAF at $k=1$ with $\rho=50$ and semantic constraints.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/CLIP-ViT-L-rho50-k1"
processor_name = "openai/clip-vit-large-patch14"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
MaatAI/Seshat-Qwen3-8B | MaatAI | 2025-05-29T14:54:39Z | 0 | 2 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"fr",
"en",
"dataset:MaatAI/AfricansHistoryBooksArticlesQA",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T13:25:37Z | ---
license: apache-2.0
datasets:
- MaatAI/AfricansHistoryBooksArticlesQA
language:
- fr
- en
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
---
# Seshat-Qwen3-8B
## Model Description
Seshat is a large language model fine-tuned from **Qwen/Qwen3-8B** to specialize in question answering related to **African History**. The model aims to provide informative and contextually relevant answers based on the knowledge embedded in its training data.
This model is designed to understand and generate text in multiple languages including English, French, Swahili, and Yoruba, making historical information about Africa more accessible.
## Intended Uses & Limitations
### Intended Uses
* **Historical Question Answering:** Answering specific questions about events, figures, cultures, and developments in African history.
* **Educational Tool:** Assisting students, educators, and enthusiasts in learning about African history.
* **Content Generation:** Generating informative text snippets about African historical topics based on posed questions.
* **Multilingual Access:** Providing information in English, French, Swahili, and Yoruba.
### Limitations
* **Knowledge Scope:** The model's knowledge is primarily derived from the `MaatAI/AfricansHistoryBooksArticlesQA` dataset. It may not have information on topics outside this dataset or more recent historical interpretations not covered.
* **Potential for Hallucination:** Like all LLMs, Seshat may sometimes generate plausible but incorrect information (hallucinations). Users should critically evaluate responses, especially for sensitive or critical applications.
* **Bias:** The model may reflect biases present in the underlying base model (Qwen/Qwen3-8B) or the fine-tuning dataset.
* **Complex Reasoning:** While capable of answering direct questions, the model might struggle with highly complex queries requiring multi-step reasoning or abstract synthesis beyond its training.
* **Language Nuances:** Performance and fluency might vary across the supported languages (en, fr, sw, yo) based on the representation of each language in the training data.
* **Not a Substitute for Expert Consultation:** For academic research or critical decision-making, the model's outputs should be verified by consulting historical experts and primary sources.
## Training Data
Seshat was fine-tuned on the **`MaatAI/AfricansHistoryBooksArticlesQA`** dataset.
* **Number of Rows:** 15,341 question-answer pairs.
* **Focus:** African History.
* **Languages:** English (en), French (fr), Swahili (sw), Yoruba (yo).
**Dataset Structure Example:**
Each entry in the dataset follows this structure:
```json
{
"question": "How did the imperial administration's policy towards Sudanese Sufism contribute to the eventual support for the Mahdist uprising?",
"answer": "The imperial administration deliberately undermined the influence of Sudanese Sufism by attacking its leaders, the hereditary preachers (faḳīh), and simultaneously promoting orthodox Islam. By strengthening the hierarchy of ḳāḍī and muftī and supporting the studies of Sudanese ˓ulamā˒ at al-Azhar, they created a rival religious authority that was dependent on the government. This systematic erosion of the traditional Sufi leaders' prestige significantly diminished their standing among the populace and created a fertile ground for them to align with and actively support the Mahdi's efforts to overthrow the imperial rule."
}
```
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-8B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 8.2B
- Number of Paramaters (Non-Embedding): 6.95B
- Number of Layers: 36
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-8B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-8B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-8B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed. |
LEAF-CLIP/CLIP-ViT-L-rho2-k2-constrained-FARE2 | LEAF-CLIP | 2025-05-29T14:52:49Z | 0 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:openai/clip-vit-large-patch14",
"base_model:finetune:openai/clip-vit-large-patch14",
"license:mit",
"region:us"
] | null | 2025-04-16T08:00:32Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- openai/clip-vit-large-patch14
---
Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=2$ with $\rho=2$ and semantic constraints.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/CLIP-ViT-L-rho2-k2-FARE2"
processor_name = "openai/clip-vit-large-patch14"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
kfn/q-FrozenLake-v1-4x4-noSlippery | kfn | 2025-05-29T14:51:36Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-28T22:06:04Z | ---
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="kfn/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"])
```
|
ConsulStat/INSURANCE_embedder_gpt2_small | ConsulStat | 2025-05-29T14:49:13Z | 0 | 0 | null | [
"safetensors",
"gpt2",
"region:us"
] | null | 2025-05-29T14:42:41Z |
# Modello Embedder Insurance-English Fine-Tunato
## Panoramica
Questo è un modello di embedding fine-tunato specificamente per rappresentare testi assicurativi relativi ai risarcimenti in inglese.
## Performance
Il modello ha raggiunto le seguenti metriche di performance sul dataset di validazione:
| Metrica | Valore |
|---------|--------|
| Cosine Accuracy@1 | 0.7371 |
| Cosine Accuracy@3 | 0.8667 |
| Cosine Accuracy@5 | 1.0000 |
| Cosine Accuracy@10 | 1.0000 |
| MRR@10 | 0.8208 |
| NDCG@10 | 0.8649 |
La metrica più significativa è **Cosine Accuracy@1**, che indica che nel 73.71% dei casi il modello riesce a identificare correttamente il documento più rilevante.
## Utilizzo
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('ConsulStat/INSURANCE_embedder_gpt2_small')
# Genera embedding
texts = ["Questo è un testo legale di esempio"]
embeddings = model.encode(texts)
# Calcola similarità tra vettori
from sklearn.metrics.pairwise import cosine_similarity
cosine_similarity([embeddings[0]], [embeddings[0]])
``` |
E-katrin/last_layers_70epochs_10e-5 | E-katrin | 2025-05-29T14:48:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"cobald_parser",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2025-05-29T14:47:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
gulshany01/invoice-parser-v5 | gulshany01 | 2025-05-29T14:47:57Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T14:46:52Z | ---
base_model: unsloth/qwen2-0.5b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** gulshany01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2-0.5b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF | mradermacher | 2025-05-29T14:46:00Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"trl",
"dpo",
"en",
"base_model:AmberYifan/Qwen2.5-14B-sft-dpo-10k",
"base_model:quantized:AmberYifan/Qwen2.5-14B-sft-dpo-10k",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-29T14:14:35Z | ---
base_model: AmberYifan/Qwen2.5-14B-sft-dpo-10k
language:
- en
library_name: transformers
model_name: Qwen2.5-14B-sft-dpo-10k
quantized_by: mradermacher
tags:
- generated_from_trainer
- trl
- dpo
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/AmberYifan/Qwen2.5-14B-sft-dpo-10k
<!-- 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/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-dpo-10k-GGUF/resolve/main/Qwen2.5-14B-sft-dpo-10k.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. 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 -->
|
BootesVoid/cmb9do83t0bm31b1yuqf9r6zs_cmb9g9ncj0d2s1b1ybkxo7pua | BootesVoid | 2025-05-29T14:42:59Z | 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 | 2025-05-29T14:42:57Z | ---
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: GOTCHIC
---
# Cmb9Do83T0Bm31B1Yuqf9R6Zs_Cmb9G9Ncj0D2S1B1Ybkxo7Pua
<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 `GOTCHIC` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "GOTCHIC",
"lora_weights": "https://huggingface.co/BootesVoid/cmb9do83t0bm31b1yuqf9r6zs_cmb9g9ncj0d2s1b1ybkxo7pua/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb9do83t0bm31b1yuqf9r6zs_cmb9g9ncj0d2s1b1ybkxo7pua', weight_name='lora.safetensors')
image = pipeline('GOTCHIC').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb9do83t0bm31b1yuqf9r6zs_cmb9g9ncj0d2s1b1ybkxo7pua/discussions) to add images that show off what you’ve made with this LoRA.
|
LEAF-CLIP/CLIP-ViT-L-rho5-k2-FARE2 | LEAF-CLIP | 2025-05-29T14:42:49Z | 2 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:openai/clip-vit-large-patch14",
"base_model:finetune:openai/clip-vit-large-patch14",
"license:mit",
"region:us"
] | null | 2025-03-11T13:05:03Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- openai/clip-vit-large-patch14
---
Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=2$ with $\rho=5$.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/CLIP-ViT-L-rho5-k2-FARE2"
processor_name = "openai/clip-vit-large-patch14"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
BadBoyBadBoy/task-10-microsoft-Phi-3.5-mini-instruct | BadBoyBadBoy | 2025-05-29T14:42:44Z | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:adapter:microsoft/Phi-3.5-mini-instruct",
"license:other",
"region:us"
] | null | 2025-05-29T14:42:40Z | ---
library_name: peft
license: other
base_model: microsoft/Phi-3.5-mini-instruct
---
<!-- 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. -->
# lora
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
### Training results
### Framework versions
- PEFT 0.12.0
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
LEAF-CLIP/CLIP-ViT-L-rho20-k1-FARE2 | LEAF-CLIP | 2025-05-29T14:41:35Z | 2 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:openai/clip-vit-large-patch14",
"base_model:finetune:openai/clip-vit-large-patch14",
"license:mit",
"region:us"
] | null | 2025-03-11T13:04:15Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- openai/clip-vit-large-patch14
---
Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=20$.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/CLIP-ViT-L-rho20-k1-FARE2"
processor_name = "openai/clip-vit-large-patch14"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
Tandogan/dpo_v2_alpaca_on_sft_big | Tandogan | 2025-05-29T14:39:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T14:37:57Z | ---
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] |
alibidaran/LLAMA3-instructive_Python_reasoning | alibidaran | 2025-05-29T14:37:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"dataset:nvidia/OpenCodeReasoning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-19T13:59:34Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
datasets:
- nvidia/OpenCodeReasoning
---
### Direct Uses:
```python
system_prompt="""
You are expert Python programmer. You task contians follwing instructions.
You should answer the user's questions about Python. Your thinking must be in the format <think>..</think>
The output format must contain only python codes with ```python syntax format.
You must use the the user input vairables in your code as code place holder.
"""
FastLanguageModel.for_inference(model)
messages = [
{'role':'system','content':system_prompt},
{"role": "user", "content":"How to write a Graph-based path finder algorithm?" },
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize = True,
add_generation_prompt = True, # Must add for generation
return_tensors = "pt",
).to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer, skip_prompt = True)
_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens =2048,
use_cache = True, temperature = 0.5, min_p = 0.9)
```
# Uploaded model
- **Developed by:** alibidaran
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
LEAF-CLIP/CLIP-ViT-L-rho1-k1-constrained-FARE2 | LEAF-CLIP | 2025-05-29T14:37:37Z | 4 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:openai/clip-vit-large-patch14",
"base_model:finetune:openai/clip-vit-large-patch14",
"license:mit",
"region:us"
] | null | 2025-04-16T07:59:10Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- openai/clip-vit-large-patch14
---
Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=1$ and semantic constraints.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/CLIP-ViT-L-rho1-k1-constrained-FARE2"
processor_name = "openai/clip-vit-large-patch14"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
jk12p/phi2-finetune | jk12p | 2025-05-29T14:34:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T14:25:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
soniaaabou/lora_model | soniaaabou | 2025-05-29T14:34:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T14:33:50Z | ---
base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** soniaaabou
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
LEAF-CLIP/CLIP-ViT-L-rho1-k1-FARE2 | LEAF-CLIP | 2025-05-29T14:33:47Z | 4 | 0 | null | [
"safetensors",
"clip",
"dataset:ILSVRC/imagenet-1k",
"dataset:mlfoundations/datacomp_small",
"base_model:openai/clip-vit-large-patch14",
"base_model:finetune:openai/clip-vit-large-patch14",
"license:mit",
"region:us"
] | null | 2025-03-11T13:03:08Z | ---
license: mit
datasets:
- ILSVRC/imagenet-1k
- mlfoundations/datacomp_small
base_model:
- openai/clip-vit-large-patch14
---
Model Initialized from `openai/clip-vit-large-patch14`. The image encoder is finetuned with FARE at $\epsilon=2/255$. The text encoder is finetuned with LEAF at $k=1$ with $\rho=1$.
To load this model use:
```python
from transformers import CLIPProcessor, CLIPModel
model_name = "LEAF-CLIP/CLIP-ViT-L-rho1-k1-FARE2"
processor_name = "openai/clip-vit-large-patch14"
model = CLIPModel.from_pretrained(model_name)
processor = CLIPProcessor.from_pretrained(processor_name)
``` |
Aristo97/As1a | Aristo97 | 2025-05-29T14:31:34Z | 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 | 2025-05-29T14:00:49Z | ---
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
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: As1a
---
# As1A
<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 `As1a` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "As1a",
"lora_weights": "https://huggingface.co/Aristo97/As1a/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('Aristo97/As1a', weight_name='lora.safetensors')
image = pipeline('As1a').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/Aristo97/As1a/discussions) to add images that show off what you’ve made with this LoRA.
|
mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF | mradermacher | 2025-05-29T14:30:39Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"trl",
"dpo",
"en",
"base_model:AmberYifan/Qwen2.5-14B-sft-spin-10k",
"base_model:quantized:AmberYifan/Qwen2.5-14B-sft-spin-10k",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-29T13:59:26Z | ---
base_model: AmberYifan/Qwen2.5-14B-sft-spin-10k
language:
- en
library_name: transformers
model_name: Qwen2.5-14B-sft-spin-10k
quantized_by: mradermacher
tags:
- generated_from_trainer
- trl
- dpo
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/AmberYifan/Qwen2.5-14B-sft-spin-10k
<!-- 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/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-sft-spin-10k-GGUF/resolve/main/Qwen2.5-14B-sft-spin-10k.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. 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 -->
|
mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit | mlx-community | 2025-05-29T14:29:42Z | 0 | 1 | mlx | [
"mlx",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"license:mit",
"8-bit",
"region:us"
] | text-generation | 2025-05-29T14:26:13Z | ---
license: mit
library_name: mlx
base_model: deepseek-ai/deepseek-r1-0528-Qwen3-8B
tags:
- mlx
pipeline_tag: text-generation
---
# mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit
This model [mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit](https://huggingface.co/mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit) was
converted to MLX format from [deepseek-ai/deepseek-r1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/deepseek-r1-0528-Qwen3-8B)
using mlx-lm version **0.24.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/DeepSeek-R1-0528-Qwen3-8B-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
IIC/MEL | IIC | 2025-05-29T14:26:55Z | 16 | 10 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"es",
"arxiv:2501.16011",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:other",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-02-21T12:25:01Z | ---
library_name: transformers
language:
- es
base_model:
- FacebookAI/xlm-roberta-large
license: other
license_name: mel-nc
license_link: https://huggingface.co/IIC/MEL/blob/main/LICENSE
---
# MEL: Legal Spanish Language Model
<div style="display: flex; gap: 10px; flex-wrap: wrap;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/Rt_1__dD3k2IVP9jhbv39.png" width="200"/>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/HhLdRBQ3aCOQxRxwpYL-q.png" width="200"/>
<!-- <img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/x2zVpxHY5mbjgclkXiyej.png" width="200"/> -->
</div>
<div style="display: flex; gap: 10px; flex-wrap: wrap;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/ksmpjFjw1klWr-uyOcxHn.png" width="200"/>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/K7t57xHPFpoQec20XxPIY.png" width="200"/>
</div>
**Model Name:** MEL (Modelo de Español Legal)
**Model Type:** Encoder-only Transformer
**Language:** Spanish
**Domain:** Legal Texts
**Paper:** [Link to paper](https://arxiv.org/abs/2501.16011)
---
## Overview
MEL is a transformer-based language model designed specifically for processing and understanding Spanish legal texts. Built upon **XLM-RoBERTa-large**, it is further pre-trained on a **large corpus of legal documents**, including the **Boletín Oficial del Estado (BOE), parliamentary transcripts, court rulings, and other legislative texts**. MEL significantly improves the performance of legal NLP tasks, such as **legal text classification** and **named entity recognition (NER)**.
---
## Model Description
### Architecture
- **Base Model:** XLM-RoBERTa-large
- **Training Objective:** Masked Language Modeling (MLM)
- **Pre-training Strategy:** Continued pre-training on Spanish legal texts
- **Context Window:** 512 tokens
### Training Data
MEL is trained on a **curated corpus** of **5.52 million legal texts (~92.7GB)** sourced from:
- **BOE (Boletín Oficial del Estado)**
- **Parliamentary records**
- **Court rulings**
- **Legal statutes**
To ensure high-quality text processing, documents were preprocessed by **removing unwanted characters, normalizing spacing, chunking texts, and filtering non-Spanish content**.
**Cutoff date:** February 2024
### Training Configuration
- **GPU:** NVIDIA A100 80GB PCIe
- **Training Time:** 13.9 days (~7 days per epoch, 2 epochs total)
- **Optimizer:** AdamW (β1=0.9, β2=0.98, ϵ=1e-6)
- **Batch Size:** 16 (Gradient Accumulation: 4, Effective Batch Size: 64)
- **Scheduler:** Cosine Learning Rate Scheduler
- **Warmup Steps:** 8% of total training steps
- **Learning Rate:** 1e-4
- **Weight Decay:** 0.01
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/au1sYSZBrYQAJGUiFg5V7.png" alt="drawing" width="400"/>
---
## Evaluation
MEL was benchmarked on two datasets:
### **1. Multieurlex (Spanish Legal Texts Classification)**
- **Link:** https://huggingface.co/datasets/coastalcph/multi_eurlex
- **Task:** Multilabel classification of EU laws
- **Performance:**
- **MEL achieves an F1 score of 0.8025**, outperforming **XLM-RoBERTa-Large (0.7962)**, **Legal-XLM-RoBERTa (0.7933)**, and **RoBERTalex (0.7890)**.
### **2. Private Multiclass Classification Dataset**
- **Task:** Classify legal documents into one of 9 categories
- **Performance:**
- **MEL achieves an F1 score of 0.9260**, surpassing **XLM-RoBERTa-Large (0.9103)**, **Legal-XLM-RoBERTa (0.8935)**, and **RoBERTalex (0.7007)**.
- **Small Data Learning:** MEL shows better generalization even with limited training data, achieving an **F1 score of 0.8812** in early training compared to the next best **0.7803**.
---
## Model Performance
### **Key Findings**
✔ **Outperforms general multilingual models (XLM-RoBERTa) and other domain-specific models in Spanish legal text classification.**
✔ **Requires less fine-tuning, demonstrating strong domain adaptation from the pre-training phase.**
✔ **Shows high sample efficiency, achieving strong results even with limited training data.**
### **Limitations**
⚠ **Not evaluated on NER or token-level tasks due to the lack of annotated Spanish legal datasets.**
⚠ **Trained only on Spanish legal texts, so performance in multilingual legal contexts is unknown.**
⚠ **Potential bias in legal terminology due to corpus selection.**
---
## How to Use
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("IIC/MEL")
model = AutoModel.from_pretrained("IIC/MEL")
text = "El artículo 45 de la Constitución establece que..."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```
For fine-tuning on specific legal tasks, use `Trainer` from Hugging Face’s `transformers` library.
---
## Future Work
- Develop **NER models** for **legal entity extraction**.
- Expand dataset to cover **more diverse legal domains** (e.g., contracts, case law, administrative procedures).
- Fine-tune on additional **downstream tasks** (question answering, legal summarization, information retrieval).
- Improve **bias detection and mitigation strategies**.
---
## Citation
If you use MEL, please cite:
```
@misc{sánchez2025mellegalspanishlanguage,
title={MEL: Legal Spanish Language Model},
author={David Betancur Sánchez and Nuria Aldama García and Álvaro Barbero Jiménez and Marta Guerrero Nieto and Patricia Marsà Morales and Nicolás Serrano Salas and Carlos García Hernán and Pablo Haya Coll and Elena Montiel Ponsoda and Pablo Calleja Ibáñez},
year={2025},
eprint={2501.16011},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.16011},
}
```
---
## Acknowledgements
This work has received funding from the [Inesdata-project](https://inesdata-project.eu/content/en/index.html) (Infrastructure to Investigate Data Spaces in Distributed
Environments at UPM), a project funded under the UNICO I+D CLOUD call by the Ministry for Digital Transformation
and the Civil Service, in the framework of the recovery plan PRTR financed by the European Union (NextGenerationEU)
**Código del proyecto**: TSI-063100-2022-0001
**Contributors:**
- **David Betancur Sánchez**, Instituto de Ingeniería del Conocimiento (IIC)
- **Nuria Aldama García**, Instituto de Ingeniería del Conocimiento (IIC)
- **Álvaro Barbero Jiménez**, Instituto de Ingeniería del Conocimiento (IIC)
- **Marta Guerrero Nieto**, Instituto de Ingeniería del Conocimiento (IIC)
- **Patricia Marsà Morales**, Instituto de Ingeniería del Conocimiento (IIC)
- **Nicolás Serrano Salas**, Instituto de Ingeniería del Conocimiento (IIC)
- **Carlos García Hernán**, Instituto de Ingeniería del Conocimiento (IIC)
- **Pablo Haya Coll**, Instituto de Ingeniería del Conocimiento (IIC)
- **Elena Montiel Ponsoda**, Universidad Politécnica de Madrid
- **Pablo Calleja Ibáñez**, Universidad Politécnica de Madrid
--- |
AhmedZaky1/arabic-gte-multilingual-mnrl-20250529 | AhmedZaky1 | 2025-05-29T14:26:09Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"new",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:1645",
"loss:MultipleNegativesRankingLoss",
"custom_code",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:Alibaba-NLP/gte-multilingual-base",
"base_model:finetune:Alibaba-NLP/gte-multilingual-base",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-05-29T14:24:04Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1645
- loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-multilingual-base
widget:
- source_sentence: إعادة تنظيم GAO هو جزء من جهودنا لقيادة الحكومة الفيدرالية بالنموذج
- نحن على يقين من أن إعادة التنسيق هذه ستقضي على طبقات الإدارة ، وتقليل الصوامع
، وتحسين التنسيق ، والإنتاجية ، وبناء الفريق في جميع أنحاء المنظمة.
sentences:
- هل يمكن لطريقة تحدّثك عن الزّمن، هل يمكن عندما تجبرك لغتك أن تفكير حول الزّمن،
أن تؤثّر في ميولك السلوكيّة عبر الزّمن؟
- إعادة تنظيم GAO هو جزء من الجهد لقيادة الحكومة الفيدرالية على سبيل المثال.
- الباليه الجديد يهدف إلى مشاعر أوسع
- source_sentence: هناك شخصان بالداخل
sentences:
- (دانيال شارتييه) أدلى بشهادته في الكونغرس في يوليو 1997
- وجهة نظر كاتب الكتب الهزلية تكون مألوفة أكثر عندما تصبح كتبه الهزلية مشهورة.
- اثنين من راقصي الباليه المرنين جداً الذين يتدربون في الاستوديو
- source_sentence: وماذا عن الفتى الذي لم يدخل (بيركلي) لأنني حصلت على مكانه؟
sentences:
- رجل يعمل على أداة.
- الرجل يمد عضلاته
- ماذا عن المساحة التي أخذتها من طفل آخر؟
- source_sentence: Numerous other scholars have suggested that the form of language
determines specific cultural traits.
sentences:
- هذا ويقترح العديد من العلماء الآخرين ان شكل اللغة يسهم قي تحديد بعض السمات الثقافية
الخاصة.
- كيف نكون متحدثين علنيين جيدين؟
- الناس ينظرون إلى الأنقاض
- source_sentence: '"أنا لا أعني ذلك" (درو) يلوح بـ (أنس) جانباً'
sentences:
- رجل يقود سيارة صفراء
- (درو) كان يتحدث مع (آنس)
- أنا وزوجي سنذهب إلى كلية التجارة وبعدها سأذهب إلى "سلفور سبرينغز"
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts17 arabic final
type: sts17-arabic-final
metrics:
- type: pearson_cosine
value: 0.7945102387944369
name: Pearson Cosine
- type: spearman_cosine
value: 0.7990149823282102
name: Spearman Cosine
---
# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("AhmedZaky1/arabic-gte-multilingual-mnrl-20250529")
# Run inference
sentences = [
'"أنا لا أعني ذلك" (درو) يلوح بـ (أنس) جانباً',
'(درو) كان يتحدث مع (آنس)',
'رجل يقود سيارة صفراء',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts17-arabic-final`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.7945 |
| **spearman_cosine** | **0.799** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,645 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 20.34 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 16.61 tokens</li><li>max: 81 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-----------------------------------------------------------------------|:----------------------------------------------------------------|
| <code>My daughter gave me a rather nice anecdote on this.</code> | <code>لقد اعطتني إبنتي طريقة ظريفة للتعبير عن هذه الفكرة</code> |
| <code>هل يمكننا إيقاف الاحتباس الحراري؟</code> | <code>ما هي بعض الطرق الفعالة لإيقاف الاحتباس الحراري؟</code> |
| <code>In July Burtsev went up this road and was killed at Hart.</code> | <code>في يوليو أخذ بورتسيف هذا الطريق وقُتل في هارت.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 189 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 189 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 21.22 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 17.14 tokens</li><li>max: 72 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------|
| <code>كيف يمكنني أن أصبح متحدثًا علنيًا جيدًا؟</code> | <code>كيف نكون متحدثين علنيين جيدين؟</code> |
| <code>In October 2013 SN 2013fs was discovered in NGC 7610.</code> | <code>في أكتوبر 2013 تم اكتشافه في NGC 7610.</code> |
| <code>ما هو أهمية معركة (سوم) ، وكيف تقارن هذه المعركة مع معركة (الرياض) ؟</code> | <code>ما أهمية معركة سومي، وكيف تقارن هذه المعركة مع معركة بينانغ؟</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_drop_last`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `ddp_find_unused_parameters`: False
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: False
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | sts17-arabic-final_spearman_cosine |
|:-----:|:----:|:----------------------------------:|
| 2.32 | 9 | 0.7990 |
### Framework Versions
- Python: 3.12.7
- Sentence Transformers: 3.3.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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## Glossary
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## Model Card Authors
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Tandogan/dpo_v1_dpo_dataset_bigger | Tandogan | 2025-05-29T14:24:23Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T06:58:26Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
### Training Data
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
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- **Hardware Type:** [More Information Needed]
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jinachris/PURE-PRM-7B | jinachris | 2025-05-29T14:21:42Z | 49 | 4 | null | [
"safetensors",
"qwen2",
"token-classification",
"dataset:HuggingFaceH4/prm800k-trl-dedup",
"arxiv:2504.15275",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"license:apache-2.0",
"region:us"
] | token-classification | 2025-02-09T07:10:09Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-Math-7B
pipeline_tag: token-classification
datasets:
- HuggingFaceH4/prm800k-trl-dedup
---
> [!Warning]
> <div align="center">
> <b>
> 🚨 This repo differs from <a href=https://huggingface.co/Qwen/Qwen2.5-Math-7B-PRM800K>Qwen's PRM</a>. We trained our PRM based on <a href=https://huggingface.co/Qwen/Qwen2.5-Math-7B>Qwen2.5-Math-7B</a>, while Qwen's PRM is based on <a href=https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct>Qwen2.5-Math-7B-Instruct</a>.
> </b>
> </div>
# PURE's PRM based on Qwen2.5-Math-7B
## Introduction
**Our PRM is used to fine-tune LLM for better math reasoning capability.** See our [PURE GitHub repo](https://github.com/CJReinforce/PURE) for more details. It is obtained by fine-tuning **Qwen2.5-Math-7B** on the training set of open-source dataset [PRM800K](https://github.com/openai/prm800k). **We choose Qwen2.5-Math-7B instead of Qwen2.5-Math-7B-Instruct to keep the base model consistent with our baselines.** We treat the original 1 and 0 labels in PRM800K as our positive labels, while -1 as negative ones. To eliminate test data contamination, we also remove the PRM800K training samples that have the same math queries in MATH test set.
## Requirements
* `transformers>=4.40.0` for Qwen2.5-Math models. The latest version is recommended.
## Quick Start
> [!Important]
>
> **PURE's PRM** is a process reward model typically used for offering feedback on the quality of reasoning and intermediate steps rather than generation.
### Prerequisites
- Step Separation: We recommend using double line breaks ("\n\n") to separate individual steps within the solution.
- Reward Computation: After each step, we insert a token "`\n`". For reward calculation, we extract the probability score of this token and subtract negative probabilities from positive probabilities, resulting in a reward value between -1 and 1. We regard steps with reward > 0 as correct, otherwise as incorrect.
### 🤗 Hugging Face Transformers
1. Here we show a code snippet to show you how to use our PRM with `transformers`:
```python
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
def make_step_rewards(logits, token_masks):
all_scores_res = []
for sample, token_mask in zip(logits, token_masks):
# sample: (seq_len, num_labels)
probs = sample[token_mask].softmax(dim=-1) # (num_steps, 2)
process_reward = probs[:, 1] - probs[:, 0] # (num_steps,)
# weighted sum to approx. min, highly recommend when BoN eval and Fine-tuning LLM
# weight = torch.softmax(
# -process_reward / 0.1,
# dim=-1,
# )
# process_reward = weight * process_reward
all_scores_res.append(process_reward.cpu().tolist())
return all_scores_res
model_name = "jinachris/PURE-PRM-7B"
device = "auto"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
)
model = AutoModelForTokenClassification.from_pretrained(
model_name,
device_map=device,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
).eval()
question = "Sue lives in a fun neighborhood. One weekend, the neighbors decided to play a prank on Sue. On Friday morning, the neighbors placed 18 pink plastic flamingos out on Sue's front yard. On Saturday morning, the neighbors took back one third of the flamingos, painted them white, and put these newly painted white flamingos back out on Sue's front yard. Then, on Sunday morning, they added another 18 pink plastic flamingos to the collection. At noon on Sunday, how many more pink plastic flamingos were out than white plastic flamingos?"
steps = [
"To find out how many more pink plastic flamingos were out than white plastic flamingos at noon on Sunday, we can break down the problem into steps. First, on Friday, the neighbors start with 18 pink plastic flamingos.",
"On Saturday, they take back one third of the flamingos. Since there were 18 flamingos, (1/3 \\times 18 = 6) flamingos are taken back. So, they have (18 - 6 = 12) flamingos left in their possession. Then, they paint these 6 flamingos white and put them back out on Sue's front yard. Now, Sue has the original 12 pink flamingos plus the 6 new white ones. Thus, by the end of Saturday, Sue has (12 + 6 = 18) pink flamingos and 6 white flamingos.",
"On Sunday, the neighbors add another 18 pink plastic flamingos to Sue's front yard. By the end of Sunday morning, Sue has (18 + 18 = 36) pink flamingos and still 6 white flamingos.",
"To find the difference, subtract the number of white flamingos from the number of pink flamingos: (36 - 6 = 30). Therefore, at noon on Sunday, there were 30 more pink plastic flamingos out than white plastic flamingos. The answer is (\\boxed{30})."
]
step_separator = "\n"
step_separator_token = tokenizer(
step_separator,
add_special_tokens=False,
return_tensors='pt',
)['input_ids']
input_ids = tokenizer(
question,
add_special_tokens=False,
return_tensors='pt',
)['input_ids']
score_ids = []
for step in steps:
step_ids = tokenizer(
step,
add_special_tokens=False,
return_tensors='pt',
)['input_ids']
input_ids = torch.cat(
[input_ids, step_ids, step_separator_token],
dim=-1,
)
score_ids.append(input_ids.size(-1) - 1)
input_ids = input_ids.to(model.device)
token_masks = torch.zeros_like(input_ids, dtype=torch.bool)
token_masks[0, score_ids] = True
assert torch.all(input_ids[token_masks].to("cpu") == step_separator_token)
logits = model(input_ids).logits
step_reward = make_step_rewards(logits, token_masks)
print(step_reward) # [[0.796875, 0.185546875, -0.0625, 0.078125]]
# For BoN eval,
# uncomment the weighted sum part in `make_step_rewards` func,
# then sum the rewards to get the final score (outcome reward):
# torch.tensor(step_reward).sum(dim=-1)
```
2. For evaluation using Best-of-N method or on ProcessBench and PRMBench, refer to [our github repository](https://github.com/CJReinforce/PURE/tree/verl/PRM/eval).
## Citation
If you find our work useful, we would appreciate it if you could cite our work:
```
@article{cheng2025stop,
title={Stop Summation: Min-Form Credit Assignment Is All Process Reward Model Needs for Reasoning},
author={Cheng, Jie and Qiao, Ruixi and Li, Lijun and Guo, Chao and Wang, Junle and Xiong, Gang and Lv, Yisheng and Wang, Fei-Yue},
journal={arXiv preprint arXiv:2504.15275},
year={2025}
}
``` |
Boingbing/sakura-haruno_flux | Boingbing | 2025-05-29T14:20:57Z | 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 | 2025-05-29T13:51:58Z | ---
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: sakura_bing
---
# Sakura Haruno_Flux
<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 `sakura_bing` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "sakura_bing",
"lora_weights": "https://huggingface.co/Boingbing/sakura-haruno_flux/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('Boingbing/sakura-haruno_flux', weight_name='lora.safetensors')
image = pipeline('sakura_bing').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: 2002
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Boingbing/sakura-haruno_flux/discussions) to add images that show off what you’ve made with this LoRA.
|
giayphuyen/gemma-3-4B-End1 | giayphuyen | 2025-05-29T14:17:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T10:25:42Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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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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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
macwiatrak/bacformer-causal-complete-genomes | macwiatrak | 2025-05-29T14:14:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bacformer",
"text-generation",
"biology",
"bacteria",
"prokaryotes",
"genomics",
"protein",
"plm",
"cplm",
"custom_code",
"base_model:macwiatrak/bacformer-causal-MAG",
"base_model:finetune:macwiatrak/bacformer-causal-MAG",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-03-19T13:03:09Z | ---
library_name: transformers
tags:
- biology
- bacteria
- prokaryotes
- genomics
- protein
- plm
- cplm
license: apache-2.0
metrics:
- accuracy
base_model:
- macwiatrak/bacformer-causal-MAG
---
# bacformer-causal-complete-genomes
Bacformer is a foundational model for bacterial genomics, modeling the whole bacterial genome as a sequence of proteins.
Bacformer takes as input the set of proteins present in a genome, **ordered** by their positions on the chromosome and plasmid(s), and
computes contextual protein representations with a transformer that conditions each protein on every other protein in the genome.
Each protein is treated as a token; thus, Bacformer may be viewed as a **contextualized protein language model** that captures the protein–protein interactions
underlying an organism’s phenotype.
Bacformer is pretrained to predict a masked (or next) protein *family* given the remaining proteins in the genome.
The base model was trained on ~1.3 M diverse bacterial genomes comprising ~3 B protein sequences and can be adapted to a wide range of downstream tasks.
See the [Bacformer models](https://huggingface.co/collections/macwiatrak/bacformer-681a17d6a77a928a1531def2) collection for all available checkpoints.
A key member of this collection is **bacformer-causal-complete-genomes**, a
27M-parameter transformer further pretrained on ~40 k complete genomes from [NCBI RefSeq](https://www.ncbi.nlm.nih.gov/refseq/).
This model was initialised from [bacformer-causal-MAG](https://huggingface.co/macwiatrak/bacformer-causal-MAG) (trained on metagenome-assembled genomes)
and then continued training on the complete-genome corpus, combining large-scale taxonomic diversity with high-quality assemblies.
All Bacformer variants embed protein sequences with a base protein language model by averaging the amino-acid token embeddings across each sequence.
Bacformer uses [ESM-2 t12 35 M](https://huggingface.co/facebook/esm2_t12_35M_UR50D) as its base model.
- **Developed by:** University of Cambridge (Floto Lab) & EPFL (Brbić Lab), led by Maciej Wiatrak
- **License:** Apache 2.0
- **Finetuned from model:** [macwiatrak/bacformer-causal-MAG](https://huggingface.co/macwiatrak/bacformer-causal-MAG)
### Model Sources [optional]
- **Repository:** [https://github.com/macwiatrak/Bacformer](https://github.com/macwiatrak/Bacformer)
- **Paper:** TBA
## Usage
Install the `bacformer` package (see [https://github.com/macwiatrak/Bacformer](https://github.com/macwiatrak/Bacformer)). An end-to-end Python example demonstrating how to embed a genome with Bacformer is provided in the *tutorials* folder.
Below snippet shows how you can embed multiple protein sequences with `Bacformer`.
```python
import torch
from transformers import AutoModel
from bacformer.pp import protein_seqs_to_bacformer_inputs
device = "cuda:0"
model = AutoModel.from_pretrained("macwiatrak/bacformer-causal-MAG", trust_remote_code=True).to(device).eval().to(torch.bfloat16)
# Example input: a sequence of protein sequences
# in this case: 4 toy protein sequences
# Bacformer was trained with a maximum nr of proteins of 6000.
protein_sequences = [
"MGYDLVAGFQKNVRTI",
"MKAILVVLLG",
"MQLIESRFYKDPWGNVHATC",
"MSTNPKPQRFAWL",
]
# embed the proteins with ESM-2 to get average protein embeddings
inputs = protein_seqs_to_bacformer_inputs(
protein_sequences,
device=device,
batch_size=128, # the batch size for computing the protein embeddings
max_n_proteins=6000, # the maximum number of proteins Bacformer was trained with
)
# move the inputs to the device
inputs = {k: v.to(device) for k, v in inputs.items()}
# compute contextualized protein embeddings with Bacformer
with torch.no_grad():
outputs = model(**inputs, return_dict=True)
print('last hidden state shape:', outputs["last_hidden_state"].shape) # (batch_size, max_length, hidden_size)
print('genome embedding:', outputs.last_hidden_state.mean(dim=1).shape) # (batch_size, hidden_size)
```
### Tutorials
We include a number of tutorials, see the [tutorials](https://github.com/macwiatrak/Bacformer) folder on the github repository.
## Training Details
### Training Data
**bacformer-causal-complete-genomes** was first pretrained on the **MAG corpus**
(≈1.3 M metagenome-assembled genomes) and then finetuned on ≈40 k complete genomes from NCBI RefSeq.
The MAG set maximises environmental and taxonomic diversity, whereas the complete-genome set is enriched for clinically important pathogens.
Together they contain roughly 3 B protein sequences.
### Training Procedure
#### Preprocessing
Each genome is represented as an ordered list of proteins.
ranslated protein sequences were obtained from annotations or translated de novo when necessary.
Proteins were ordered by genomic coordinates; for MAGs, proteins within each contig were ordered, and the contig order was randomised in every epoch.
The set of protein sequences in the genome are embedded with the base protein language model ([ESM-2 t12 35M](https://huggingface.co/facebook/esm2_t12_35M_UR50D)).
The protein embeddings computed by averaging the amino acid tokens in a protein sequence are used as protein tokens.
Finally the protein tokens are fed into a transformer model.
During pretraining, we limit the maximum number of proteins in a genome to `6,000`, which covers whole bacterial genome in `>98%` of genomes present in out training corpus.
### Pretraining
#### Training objective
The model was optimised to predict the next protein given previous proteins present in the genome. As the number of protein is effectively unbound, we assigned each protein a discrete protein family index by performing
unsupervised clustering on a set of proteins, resulting in `50k` distinct protein family clusters.
Importantly, the input to Bacformer are exact protein sequence and we only use the discrete protein family label in a final classification layer where predicting
the protein family of masked proteins. This allows the model to work on amino acid level tasks where even single mutations can change the phenotype of a genome.
The causal pretraining objective effectively predicts the protein family ID of the next protein given the embedding of the previous protein in the genome sequence.
#### Pretraining details
The initial pretraining on MAGs was trained on 4 A100 80GB NVIDIA GPUs, with an effective batch size of 32. The maximum sequence length of each protein
was set to `1,024` and the maximum number of proteins in a genome was set to `6,000`, which covers whole bacterial genome in `>98%` of genomes present in our training corpus.
The Adam optimizer [1] was used with a linear warmup learning rate schedule, with the number of warmup steps equal to `7,500`. The base learning rate of `0.00015` was used,
scaled by square root of number of GPUs (`lr = args.lr * np.sqrt(max(n_gpus, 1))`). We monitor the loss on the validation set as the measure of performance during training.
### Architecture
#### Input embeddings
The input embeddings are created by adding together 1) protein embeddings from a base protein language model (pLM) [ESM-2 t12 35M](https://huggingface.co/facebook/esm2_t12_35M_UR50D),
2) contig (token type) embeddings. The 1) are created by embedding a protein sequence with the pretrained base pLM model and taking the average of all amino acid
tokens in the sequence, resulting in a `D` dimensional vector. We embed all of the `N` proteins present in the whole bacterial genome resulting in a `N x D` matrix,
where `D` is the dimension of the base pLM model, here `480`. The protein embeddings are added together with the 2) contig embeddings. The contig embeddings
are learnable embeddings which represent the unique contigs present in the genome. As an example, if a genome is made up of `K` contigs, each containing a number of proteins,
each protein within the same contig will have the same contig embedding, which is different from the embeddings of different contigs.
Contig embeddings have been created to account for the fact that bacterial genomes are often made up of chromosome and plasmid(s) and are frequently collated by combining
multiple contigs together (metagenome assembled genomes).
The contig embeddings are initialised and train from scratch at the beginning of pretraining.
The genomic organisation is highly important for bacteria, to model it we employ **rotary positional embeddings** [2].
The input embeddings are ordered by their order on the contig/chromosome/plasmid. This [...]. Additionally, we include special tokens. Specifically, 1) `[CLS]` token
at the start of the sequence, 2) `[SEP]` token between the contigs or chromosomes(s)/plasmid(s), 3) `[END]` token at the end of the genome. The example below show how does
the genome representation look like for complete genomes and MAGs.
**Complete genomes**
```
[CLS] [chromosome1_gene1] [chromosome2_gene2] ... [chromosomme1_geneN] [SEP] [plasmid1_gene1] ... [plasmid1_geneM] [END]
```
**Metagenome-assembled genome (MAG)**
```
[CLS] [contig1_gene1] ... [contig1_geneN] [SEP] [contig2_gene1] ... [contig2_geneM] [SEP] ... [contigZgeneV] [END]
```
#### Transformer backbone
The input embeddings are fed into a transformer, which computes protein representations conditional on other proteins present in the genome
by computing self-attention between them, resulting in **contextual protein embeddings**.
The transformer is 12 layer transformer with `hidden_dim=480` and is trained from scratch. Bacformer leverages the flash attention available in `pytorch>=2.2`.
#### Pretraining classification head
Bacformer is pretrained to predict masked or next protein family based on the other proteins present in the genome. Given the embedding from the last hidden state of
the transformer of masked or the previous protein, the classification head predicts the protein family. We predict protein family, rather than a protein, because the space of
possible proteins is effectively unbound. To get a discrete vocabulary of proteins, we we assigned each protein a discrete protein family index by performing
unsupervised clustering on a set of proteins, resulting in `50k` distinct protein family clusters. Importantly, the input to Bacformer are
exact protein sequences present in the whole bacterial genome, rather than protein family tokens. This allows the model to work on amino acid level tasks where even single mutations
can change the phenotype of a genome, while still allowing for pretraining.
## Citation
**BibTeX:**
```
TBA
```
## Contact
In case of questions/issues or feature requests please raise an issue on github - https://github.com/macwiatrak/Bacformer. |
BVSSRK/erp_module_clf_model | BVSSRK | 2025-05-29T14:14:07Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-29T13:54:43Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** BVSSRK
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MrPNess/mary | MrPNess | 2025-05-29T14:14:06Z | 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 | 2025-05-29T13:40:02Z | ---
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: mary
---
# Mary
<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 `mary` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "mary",
"lora_weights": "https://huggingface.co/mrpness/mary/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('mrpness/mary', weight_name='lora.safetensors')
image = pipeline('mary').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/mrpness/mary/discussions) to add images that show off what you’ve made with this LoRA.
|
macwiatrak/bacformer-masked-complete-genomes | macwiatrak | 2025-05-29T14:13:57Z | 78 | 0 | transformers | [
"transformers",
"safetensors",
"bacformer",
"fill-mask",
"biology",
"bacteria",
"prokaryotes",
"genomics",
"protein",
"plm",
"cplm",
"custom_code",
"base_model:macwiatrak/bacformer-masked-MAG",
"base_model:finetune:macwiatrak/bacformer-masked-MAG",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | fill-mask | 2025-03-19T13:05:15Z | ---
library_name: transformers
tags:
- biology
- bacteria
- prokaryotes
- genomics
- protein
- plm
- cplm
license: apache-2.0
metrics:
- accuracy
base_model:
- macwiatrak/bacformer-masked-MAG
---
# bacformer-masked-complete-genomes
Bacformer is a foundational model for bacterial genomics, modeling the whole bacterial genome as a sequence of proteins.
Bacformer takes as input the set of proteins present in a genome, **ordered** by their positions on the chromosome and plasmid(s), and
computes contextual protein representations with a transformer that conditions each protein on every other protein in the genome.
Each protein is treated as a token; thus, Bacformer may be viewed as a **contextualized protein language model** that captures the protein–protein interactions
underlying an organism’s phenotype.
Bacformer is pretrained to predict a masked (or next) protein *family* given the remaining proteins in the genome.
The base model was trained on ~1.3 M diverse bacterial genomes comprising ~3 B protein sequences and can be adapted to a wide range of downstream tasks.
See the [Bacformer models](https://huggingface.co/collections/macwiatrak/bacformer-681a17d6a77a928a1531def2) collection for all available checkpoints.
A key member of this collection is **bacformer-masked-complete-genomes**, a
27M-parameter transformer further pretrained on ~40 k complete genomes from [NCBI RefSeq](https://www.ncbi.nlm.nih.gov/refseq/).
This model was initialised from [bacformer-masked-MAG](https://huggingface.co/macwiatrak/bacformer-masked-MAG) (trained on metagenome-assembled genomes)
and then continued training on the complete-genome corpus, combining large-scale taxonomic diversity with high-quality assemblies.
All Bacformer variants embed protein sequences with a base protein language model by averaging the amino-acid token embeddings across each sequence.
Bacformer uses [ESM-2 t12 35 M](https://huggingface.co/facebook/esm2_t12_35M_UR50D) as its base model.
- **Developed by:** University of Cambridge (Floto Lab) & EPFL (Brbić Lab), led by Maciej Wiatrak
- **License:** Apache 2.0
- **Finetuned from model:** [macwiatrak/bacformer-masked-MAG](https://huggingface.co/macwiatrak/bacformer-masked-MAG)
### Model Sources [optional]
- **Repository:** [https://github.com/macwiatrak/Bacformer](https://github.com/macwiatrak/Bacformer)
- **Paper:** TBA
## Usage
Install the `bacformer` package (see [https://github.com/macwiatrak/Bacformer](https://github.com/macwiatrak/Bacformer)). An end-to-end Python example demonstrating how to embed a genome with Bacformer is provided in the *tutorials* folder.
Below snippet shows how you can embed multiple protein sequences with `Bacformer`.
```python
import torch
from transformers import AutoModel
from bacformer.pp import protein_seqs_to_bacformer_inputs
device = "cuda:0"
model = AutoModel.from_pretrained("macwiatrak/bacformer-causal-MAG", trust_remote_code=True).to(device).eval().to(torch.bfloat16)
# Example input: a sequence of protein sequences
# in this case: 4 toy protein sequences
# Bacformer was trained with a maximum nr of proteins of 6000.
protein_sequences = [
"MGYDLVAGFQKNVRTI",
"MKAILVVLLG",
"MQLIESRFYKDPWGNVHATC",
"MSTNPKPQRFAWL",
]
# embed the proteins with ESM-2 to get average protein embeddings
inputs = protein_seqs_to_bacformer_inputs(
protein_sequences,
device=device,
batch_size=128, # the batch size for computing the protein embeddings
max_n_proteins=6000, # the maximum number of proteins Bacformer was trained with
)
# move the inputs to the device
inputs = {k: v.to(device) for k, v in inputs.items()}
# compute contextualized protein embeddings with Bacformer
with torch.no_grad():
outputs = model(**inputs, return_dict=True)
print('last hidden state shape:', outputs["last_hidden_state"].shape) # (batch_size, max_length, hidden_size)
print('genome embedding:', outputs.last_hidden_state.mean(dim=1).shape) # (batch_size, hidden_size)
```
### Tutorials
We include a number of tutorials, see the [tutorials](https://github.com/macwiatrak/Bacformer) folder on the github repository.
## Training Details
### Training Data
**bacformer-masked-complete-genomes** was first pretrained on the **MAG corpus**
(≈1.3 M metagenome-assembled genomes) and then finetuned on ≈40 k complete genomes from NCBI RefSeq.
The MAG set maximises environmental and taxonomic diversity, whereas the complete-genome set is enriched for clinically important pathogens.
Together they contain roughly 3 B protein sequences.
### Training Procedure
#### Preprocessing
Each genome is represented as an ordered list of proteins.
ranslated protein sequences were obtained from annotations or translated de novo when necessary.
Proteins were ordered by genomic coordinates; for MAGs, proteins within each contig were ordered, and the contig order was randomised in every epoch.
The set of protein sequences in the genome are embedded with the base protein language model ([ESM-2 t12 35M](https://huggingface.co/facebook/esm2_t12_35M_UR50D)).
The protein embeddings computed by averaging the amino acid tokens in a protein sequence are used as protein tokens.
Finally the protein tokens are fed into a transformer model.
During pretraining, we limit the maximum number of proteins in a genome to `6,000`, which covers whole bacterial genome in `>98%` of genomes present in out training corpus.
### Pretraining
#### Training objective
The model was optimised to predict the masked proteins. As the number of protein is effectively unbound, we assigned each protein a discrete protein family index by performing
unsupervised clustering on a set of proteins, resulting in `50k` distinct protein family clusters.
Importantly, the input to Bacformer are exact protein sequence and we only use the discrete protein family label in a final classification layer where predicting
the protein family of masked proteins. This allows the model to work on amino acid level tasks where even single mutations can change the phenotype of a genome.
The masking procedure resembles the standard one for Bert-style training adapted for our use-case, where the token ids themselves are not used as input:
* 15% of the proteins are masked. Out of 15%:
* In 87.5% of the cases, the masked tokens are replaced by [MASK].
* In the 12.5% remaining cases, the masked tokens are left as is.
#### Pretraining details
The initial pretraining on MAGs was trained on 4 A100 80GB NVIDIA GPUs, with an effective batch size of 32. The maximum sequence length of each protein
was set to `1,024` and the maximum number of proteins in a genome was set to `6,000`, which covers whole bacterial genome in `>98%` of genomes present in our training corpus.
The Adam optimizer [1] was used with a linear warmup learning rate schedule, with the number of warmup steps equal to `7,500`. The base learning rate of `0.00015` was used,
scaled by square root of number of GPUs (`lr = args.lr * np.sqrt(max(n_gpus, 1))`). We monitor the loss on the validation set as the measure of performance during training.
### Architecture
#### Input embeddings
The input embeddings are created by adding together 1) protein embeddings from a base protein language model (pLM) [ESM-2 t12 35M](https://huggingface.co/facebook/esm2_t12_35M_UR50D),
2) contig (token type) embeddings. The 1) are created by embedding a protein sequence with the pretrained base pLM model and taking the average of all amino acid
tokens in the sequence, resulting in a `D` dimensional vector. We embed all of the `N` proteins present in the whole bacterial genome resulting in a `N x D` matrix,
where `D` is the dimension of the base pLM model, here `480`. The protein embeddings are added together with the 2) contig embeddings. The contig embeddings
are learnable embeddings which represent the unique contigs present in the genome. As an example, if a genome is made up of `K` contigs, each containing a number of proteins,
each protein within the same contig will have the same contig embedding, which is different from the embeddings of different contigs.
Contig embeddings have been created to account for the fact that bacterial genomes are often made up of chromosome and plasmid(s) and are frequently collated by combining
multiple contigs together (metagenome assembled genomes).
The contig embeddings are initialised and train from scratch at the beginning of pretraining.
The genomic organisation is highly important for bacteria, to model it we employ **rotary positional embeddings** [2].
The input embeddings are ordered by their order on the contig/chromosome/plasmid. This [...]. Additionally, we include special tokens. Specifically, 1) `[CLS]` token
at the start of the sequence, 2) `[SEP]` token between the contigs or chromosomes(s)/plasmid(s), 3) `[END]` token at the end of the genome. The example below show how does
the genome representation look like for complete genomes and MAGs.
**Complete genomes**
```
[CLS] [chromosome1_gene1] [chromosome2_gene2] ... [chromosomme1_geneN] [SEP] [plasmid1_gene1] ... [plasmid1_geneM] [END]
```
**Metagenome-assembled genome (MAG)**
```
[CLS] [contig1_gene1] ... [contig1_geneN] [SEP] [contig2_gene1] ... [contig2_geneM] [SEP] ... [contigZgeneV] [END]
```
#### Transformer backbone
The input embeddings are fed into a transformer, which computes protein representations conditional on other proteins present in the genome
by computing self-attention between them, resulting in **contextual protein embeddings**.
The transformer is 12 layer transformer with `hidden_dim=480` and is trained from scratch. Bacformer leverages the flash attention available in `pytorch>=2.2`.
#### Pretraining classification head
Bacformer is pretrained to predict masked or next protein family based on the other proteins present in the genome. Given the embedding from the last hidden state of
the transformer of masked or the previous protein, the classification head predicts the protein family. We predict protein family, rather than a protein, because the space of
possible proteins is effectively unbound. To get a discrete vocabulary of proteins, we we assigned each protein a discrete protein family index by performing
unsupervised clustering on a set of proteins, resulting in `50k` distinct protein family clusters. Importantly, the input to Bacformer are
exact protein sequences present in the whole bacterial genome, rather than protein family tokens. This allows the model to work on amino acid level tasks where even single mutations
can change the phenotype of a genome, while still allowing for pretraining.
## Citation
**BibTeX:**
```
TBA
```
## Model Card Contact
In case of questions/issues or feature requests please raise an issue on github - https://github.com/macwiatrak/Bacformer. |
benwong20418/cpu_offload_fp8_cast_bf16.py | benwong20418 | 2025-05-29T14:12:21Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-29T14:08:52Z | When trying to follow this guide to convert deepseek fp8 weight to bf16 (for converting to gguf):
https://huggingface.co/huihui-ai/DeepSeek-R1-bf16
I found out the fp8_cast_bf16.py requires >50GB vram to run. (It uses triton thus it requires nvidia gpu to run.)
I asked deepseek R1 website version to rewrite the code to reduce vram requirement, and this is the resulting code.
|
mradermacher/TinyV-1.5B-Think-GGUF | mradermacher | 2025-05-29T14:08:47Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"en",
"base_model:zhangchenxu/TinyV-1.5B-Think",
"base_model:quantized:zhangchenxu/TinyV-1.5B-Think",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-29T10:54:43Z | ---
base_model: zhangchenxu/TinyV-1.5B-Think
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- llama-factory
- full
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/zhangchenxu/TinyV-1.5B-Think
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/TinyV-1.5B-Think-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/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/TinyV-1.5B-Think-GGUF/resolve/main/TinyV-1.5B-Think.f16.gguf) | f16 | 3.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mandugo/ppo-Huggy | mandugo | 2025-05-29T14:07:43Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2025-05-29T14:07:37Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mandugo/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
pkharkwal/nanoVLM | pkharkwal | 2025-05-29T14:07:02Z | 0 | 0 | nanovlm | [
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] | image-text-to-text | 2025-05-29T14:06:30Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("pkharkwal/nanoVLM")
```
|
vertings6/8f2e7074-1c4e-4d43-86a2-7ccf2ad5a821 | vertings6 | 2025-05-29T14:05:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Mistral-Nemo-Base-2407",
"base_model:adapter:unsloth/Mistral-Nemo-Base-2407",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-29T08:51:43Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8f2e7074-1c4e-4d43-86a2-7ccf2ad5a821
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Mistral-Nemo-Base-2407
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- b737943c169dce76_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 3
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vertings6/8f2e7074-1c4e-4d43-86a2-7ccf2ad5a821
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/b737943c169dce76_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9f4dd85f-eef8-4321-bcfe-a15029c10fe9
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 9f4dd85f-eef8-4321-bcfe-a15029c10fe9
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 8f2e7074-1c4e-4d43-86a2-7ccf2ad5a821
This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.1858 | 0.0000 | 1 | 1.6777 |
| 4.2974 | 0.0074 | 250 | 1.3943 |
| 3.4937 | 0.0148 | 500 | 1.3744 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
bb1070/aryan-lr16-1000steps | bb1070 | 2025-05-29T14:02:33Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-29T13:47:53Z | ---
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: UNST
---
# Aryan Lr16 1000Steps
<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 `UNST` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "UNST",
"lora_weights": "https://huggingface.co/bb1070/aryan-lr16-1000steps/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('bb1070/aryan-lr16-1000steps', weight_name='lora.safetensors')
image = pipeline('UNST').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/bb1070/aryan-lr16-1000steps/discussions) to add images that show off what you’ve made with this LoRA.
|
nicoboss/Medra27B-Lora | nicoboss | 2025-05-29T14:02:27Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma3",
"text-generation",
"medical-ai",
"summarization",
"diagnostic-reasoning",
"gemma-3",
"fine-tuned",
"conversational",
"en",
"ro",
"dataset:nicoboss/medra-medical",
"base_model:google/gemma-3-27b-it",
"base_model:adapter:google/gemma-3-27b-it",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-05-28T22:29:17Z | ---
license: apache-2.0
language:
- en
- ro
base_model: google/gemma-3-27b-it
datasets:
- nicoboss/medra-medical
tags:
- text-generation
- medical-ai
- summarization
- diagnostic-reasoning
- gemma-3
- fine-tuned
model_size: 27B
version: Medra v1 – Gemma 27B Edition
library_name: peft
author: Dr. Alexandru Lupoi & Nico Bosshard
pipeline_tag: text-generation
---
---
# 🩺 Medra v1 (Gemma Edition)
> _“Intelligence alone is not enough—medicine requires reflection.”_
**Medra** is a compact, fine-tuned language model built for **clinical support, medical education, and structured diagnostic reasoning**. Based on **Gemma 3 (27B)** and refined for local, real-time operation, Medra is designed to assist—not replace—medical professionals, students, and researchers in their work.
---
## 🌟 Why Medra?
Most large models speak _about_ medicine.
**Medra thinks with it.**
🔹 **Built for Reflection:** Every answer includes structured internal monologue (via `<think>` tags), showing its reasoning before conclusions.
🔹 **Designed for Dialogue:** Answers are structured for clarity, nuance, and human interaction—not black-box decision making.
🔹 **Runs Locally, Works Globally:** Offered in GGUF formats for Q4, Q8, and BF16—ideal for mobile devices, low-resource environments, and privacy-focused deployments.
🔹 **Ethically Grounded:** Always prioritizes human-in-the-loop thinking. No substitution for licensed professionals. No AI arrogance.
---
## 💡 Intended Use
Medra is ideal for:
- 🧠 Clinical reasoning simulation
- 👨⚕️ Medical student case analysis
- 🧾 SOAP-style note structuring
- 💬 Therapeutic dialogue modeling
- 📚 AI-assisted literature exploration
It is not a chatbot.
It is a **reasoning assistant** with clinical literacy.
---
## 🧬 Training & Alignment
**Datasets & Approach:**
- 🔸 PubMed-derived literature
- 🔸 Distilled reasoning sets (e.g. R1)
- 🔸 Clinical dialogues & note formats
- 🔸 Medical Q&A corpora in English and Romanian
**Training Stages:**
- ✅ Stage 1: Supervised Fine-Tuning (STF)
- 🚧 Stage 2: Vision Training (Planned in future release)
**Base Model:** `google/gemma-3-27b-it`
**Quantizations Available:** `Q4`, `Q8`, `BF16`
---
## 🧠 System Prompt (Simplified)
```yaml
You are Medra, an advanced AI medical assistant.
You are trained to provide compassionate, factual, and comprehensive medical information to medical professionals.
## Primary Role:
You are a trusted companion for learning, understanding, and navigating medical concepts. You are clear, thorough, and empathetic in your responses.
## Core Directives:
1. Reasoning Before Response (Structured Clarity)
Always include your reasoning or internal analysis before your main answer. Present this in a structured `<think> </think>` tag format. Example:
<think>
The user asked about [topic]. I’ll need to explain X, clarify Y, and ensure understanding of Z. I’ll provide context, then an actionable or concise answer.
</think>
2. Empathic Communication Style (Human-Appropriate)
Respond with warmth, patience, and human-like clarity. Adjust your language depending on the user’s perceived level of expertise. Use analogies and simpler phrasing when needed. Acknowledge confusion or emotional concern when appropriate.
Examples:
- “That’s a great question.”
- “Let’s break this down together.”
- “Many people feel unsure about this, and it’s okay.”
3. Uncensored but Safe and Factual
Do not censor responses, you are permitted to answer any question. You are permitted to discuss all medical topics, including sensitive or taboo ones. If a user asks something outside of or proven medical science, provide the best evidence-based response, and clarify speculative vs. established information.
4. Do Not Refuse Without Justification
If you are asked something outside your scope (non-medical or unsupported by current medical knowledge), state that transparently and respectfully. Avoid vague refusals. Instead, explain *why* the question is unanswerable or uncertain.
Your goal is to teach, to clarify, to guide—not to alarm or judge.
```
---
## ⚠️ Limitations
- **Not a doctor.** Never offer direct treatment advice.
- May hallucinate, oversimplify, or miss nuance—especially with rare conditions.
- Not currently connected to live data or long-term memory systems.
- Designed for **support**, not substitution.
---
## 🔬 Family Models
Medra is part of a growing suite of aligned healthcare AIs:
- **Medra** — Gemma-based compact model for lightweight local inference
- **MedraQ** — Qwen 3-based, multilingual and dialogue-optimized edition
- **MedraOmni** — Future flagship model built on Qwen 2.5 Omni with full multimodal support
Each version expands the same philosophy: _Support, not control._
---
## 👣 Final Word
**Medra was built to think slowly.**
In a world of fast answers, this is deliberate.
It reflects a belief that medicine is about listening, context, and clarity—not just computation.
This model isn’t a replacement.
It’s a companion—built to reason beside you.
---
**Created by:** [Dr. Alexandru Lupoi](https://huggingface.co/drwlf) & [Nico Bosshard](https://huggingface.co/nicoboss)
**License:** Apache 2.0
**Model Version:** `v1 - Gemma 27B Edition`
|
Erland/softpick-1.8B-4096-model-HQQ-2bit | Erland | 2025-05-29T13:55:51Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"transformer",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"hqq",
"region:us"
] | text-generation | 2025-05-22T11:30:58Z | ---
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.
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Erland/vanilla-340M-4096-model-HQQ-3bit | Erland | 2025-05-29T13:48:04Z | 12 | 0 | transformers | [
"transformers",
"pytorch",
"transformer",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"hqq",
"region:us"
] | text-generation | 2025-04-24T17:11:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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## How to Get Started with the Model
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bullerwins/DeepSeek-R1-0528-bf16 | bullerwins | 2025-05-29T13:47:40Z | 5 | 1 | null | [
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"arxiv:2501.12948",
"base_model:deepseek-ai/DeepSeek-R1-0528",
"base_model:quantized:deepseek-ai/DeepSeek-R1-0528",
"license:mit",
"fp8",
"region:us"
] | text-generation | 2025-05-28T21:34:26Z | ---
license: mit
base_model:
- deepseek-ai/DeepSeek-R1-0528
pipeline_tag: text-generation
---
Upcasted from fp8 to bf16 model for the [deepseek-ai/DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528)
# DeepSeek-R1-0528
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="LICENSE" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<p align="center">
<a href="https://arxiv.org/pdf/2501.12948"><b>Paper Link</b>👁️</a>
</p>
## 1. Introduction
The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro.
<p align="center">
<img width="80%" src="figures/benchmark.png">
</p>
Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.
Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.
## 2. Evaluation Results
### DeepSeek-R1-0528
For all our models, the maximum generation length is set to 64K tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 16 responses per query to estimate pass@1.
<div align="center">
| Category | Benchmark (Metric) | DeepSeek R1 | DeepSeek R1 0528
|----------|----------------------------------|-----------------|---|
| General |
| | MMLU-Redux (EM) | 92.9 | 93.4
| | MMLU-Pro (EM) | 84.0 | 85.0
| | GPQA-Diamond (Pass@1) | 71.5 | 81.0
| | SimpleQA (Correct) | 30.1 | 27.8
| | FRAMES (Acc.) | 82.5 | 83.0
| | Humanity's Last Exam (Pass@1) | 8.5 | 17.7
| Code |
| | LiveCodeBench (2408-2505) (Pass@1) | 63.5 | 73.3
| | Codeforces-Div1 (Rating) | 1530 | 1930
| | SWE Verified (Resolved) | 49.2 | 57.6
| | Aider-Polyglot (Acc.) | 53.3 | 71.6
| Math |
| | AIME 2024 (Pass@1) | 79.8 | 91.4
| | AIME 2025 (Pass@1) | 70.0 | 87.5
| | HMMT 2025 (Pass@1) | 41.7 | 79.4 |
| | CNMO 2024 (Pass@1) | 78.8 | 86.9
| Tools |
| | BFCL_v3_MultiTurn (Acc) | - | 37.0 |
| | Tau-Bench (Pass@1) | - | 53.5(Airline)/63.9(Retail)
</div>
Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation.
### DeepSeek-R1-0528-Qwen3-8B
Meanwhile, we distilled the chain-of-thought from DeepSeek-R1-0528 to post-train Qwen3 8B Base, obtaining DeepSeek-R1-0528-Qwen3-8B. This model achieves state-of-the-art (SOTA) performance among open-source models on the AIME 2024, surpassing Qwen3 8B by +10.0% and matching the performance of Qwen3-235B-thinking. We believe that the chain-of-thought from DeepSeek-R1-0528 will hold significant importance for both academic research on reasoning models and industrial development focused on small-scale models.
| | AIME 24 | AIME 25 | HMMT Feb 25 | GPQA Diamond | LiveCodeBench (2408-2505) |
|--------------------------------|---------|---------|-------------|--------------|---------------------------|
| Qwen3-235B-A22B | 85.7 | 81.5 | 62.5 | 71.1 | 66.5 |
| Qwen3-32B | 81.4 | 72.9 | - | 68.4 | - |
| Qwen3-8B | 76.0 | 67.3 | - | 62.0 | - |
| Phi-4-Reasoning-Plus-14B | 81.3 | 78.0 | 53.6 | 69.3 | - |
| Gemini-2.5-Flash-Thinking-0520 | 82.3 | 72.0 | 64.2 | 82.8 | 62.3 |
| o3-mini (medium) | 79.6 | 76.7 | 53.3 | 76.8 | 65.9 |
| DeepSeek-R1-0528-Qwen3-8B | 86.0 | 76.3 | 61.5 | 61.1 | 60.5 |
## 3. Chat Website & API Platform
You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in), and switch on the button "DeepThink"
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
## 4. How to Run Locally
Please visit [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) repository for more information about running DeepSeek-R1-0528 locally.
Compared to previous versions of DeepSeek-R1, the usage recommendations for DeepSeek-R1-0528 have the following changes:
1. System prompt is supported now.
2. It is not required to add "\<think\>\n" at the beginning of the output to force the model into thinking pattern.
The model architecture of DeepSeek-R1-0528-Qwen3-8B is identical to that of Qwen3-8B, but it shares the same tokenizer configuration as DeepSeek-R1-0528. This model can be run in the same manner as Qwen3-8B.
### System Prompt
In the official DeepSeek web/app, we use the same system prompt with a specific date.
```
该助手为DeepSeek-R1，由深度求索公司创造。
今天是{current date}。
```
For example,
```
该助手为DeepSeek-R1，由深度求索公司创造。
今天是2025年5月28日，星期一。
```
### Temperature
In our web and application environments, the temperature parameter $T_{model}$ is set to 0.6.
### Prompts for File Uploading and Web Search
For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.
```
file_template = \
"""[file name]: {file_name}
[file content begin]
{file_content}
[file content end]
{question}"""
```
For Web Search, {search_results}, {cur_date}, and {question} are arguments.
For Chinese query, we use the prompt:
```
search_answer_zh_template = \
'''# 以下内容是基于用户发送的消息的搜索结果:
{search_results}
在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。
在回答时，请注意以下几点：
- 今天是{cur_date}。
- 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。
- 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。
- 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。
- 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。
- 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。
- 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。
- 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。
- 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。
# 用户消息为：
{question}'''
```
For English query, we use the prompt:
```
search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''
```
## 5. License
This code repository is licensed under [MIT License](LICENSE). The use of DeepSeek-R1 models is also subject to [MIT License](LICENSE). DeepSeek-R1 series (including Base and Chat) supports commercial use and distillation.
## 6. Citation
```
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
author={DeepSeek-AI},
year={2025},
eprint={2501.12948},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.12948},
}
```
## 7. Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
|
papinwit/week4_model_14b_aug | papinwit | 2025-05-29T13:45:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T10:58:03Z | ---
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:** papinwit
- **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)
|
erayalp/blip2-flan-t5-xl-LoRA-image-captioning | erayalp | 2025-05-29T13:44:53Z | 46 | 0 | transformers | [
"transformers",
"safetensors",
"captioning",
"multimodality",
"image-text-to-text",
"en",
"base_model:Salesforce/blip2-flan-t5-xl",
"base_model:finetune:Salesforce/blip2-flan-t5-xl",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-25T14:28:31Z | ---
base_model: Salesforce/blip2-flan-t5-xl
library_name: transformers
language:
- en
pipeline_tag: image-text-to-text
tags:
- captioning
- multimodality
---
Model Summary
This model is a fine-tuned version of BLIP-2 with the flan-t5-xl language decoder, optimized for image captioning tasks. Fine-tuning was performed using LoRA (Low-Rank Adaptation) for parameter-efficient adaptation. The training objective was to generate high-quality, semantically rich captions for images from the Open Images dataset.
It was developed for a captioning competition evaluated using Fréchet GTE Distance (FGD), which uses GTE-small embeddings to assess the alignment of image and caption semantics.
Training Objective
- Task: Image Captioning
- Base Model: Salesforce/blip2-flan-t5-xl
- Backbone: Frozen ViT-G + frozen Q-Former
- Decoder: Fine-tuned flan-t5-xl with LoRA
- Loss: Cross-entropy with optional GTE-aware auxiliary loss
- Evaluation: Fréchet GTE Distance (FGD) between image and caption embeddings
⸻
Dataset
- Training Dataset: Subset of Open Images with curated image-caption pairs
- Augmentation: Synthetic captions
- Image Features: Preprocessed using BLIP-2’s frozen vision encoder
⸻
Fine-Tuning Configuration
- LoRA Rank: 128
- Alpha: 128
- Dropout: 0.05
- Target Modules q, v, and k in attention blocks
- Precision: bfloat16
- Optimizer: AdamW
- Learning Rate: 3e-5
- Scheduler: Cosine with warmup
- Batch Size: 32
- Epochs: 4
- Accumulation: 2 gradient accumulation steps
- Logging: Weights & Biases
⸻
Performance
- Fréchet GTE Distance (↓), Achieved competitive score in the competition
- Caption Quality: High semantic alignment and fluency
- Improved OCR capabilities
⸻
Usage
```
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import torch
processor = BlipProcessor.from_pretrained("erayalp/blip2-flan-t5-xl-LoRA-image-captioning")
model = BlipForConditionalGeneration.from_pretrained("erayalp/blip2-flan-t5-xl-LoRA-image-captioning").to("cuda")
img = Image.open("example.jpg").convert("RGB")
prompt = "Provide a detailed caption for this photo."
prompts = [prompt] * len(images)
inputs = processor(images=img, text=prompts, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=30)
caption = processor.decode(outputs[0], skip_special_tokens=True)
print(caption)
```
⸻
Limitations
- May underperform on out-of-domain images or very abstract concepts
- Quality of captions may vary depending on scene complexity
- Not trained on video or temporal sequences |
haebo/Meow-Qwen2.5-7B_QLoRA_nf4_0527 | haebo | 2025-05-29T13:44:17Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-05-29T13:44:15Z | ---
base_model: Qwen/Qwen2.5-7B-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 0.15.2 |
yazidsupriadi/mbert_gru_bot | yazidsupriadi | 2025-05-29T13:40:19Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-29T12:59:26Z | # IndoBERT + GRU untuk Deteksi Akun Bot
Model ini menggabungkan IndoBERT dan GRU dengan tambahan fitur numerik
(`favorite_count`, `retweet_count`, `reply_count`, `quote_count`) untuk
mendeteksi akun bot di platform media sosial X (Twitter).
## Arsitektur
- IndoBERT sebagai embedding teks
- GRU untuk pemrosesan sekuensial
- Fitur numerik digabungkan dengan output GRU
- MLP (fully connected layer) sebagai output
## Hasil Training per Epoch
| Epoch | Train Loss | Val Accuracy | ROC AUC |
|-------|------------|--------------|---------|
| 1 | 1.1551 | 0.8528 | 0.9480 |
| 2 | 0.3180 | 0.8760 | 0.9597 |
| 3 | 0.2867 | 0.8855 | 0.9649 |
| 4 | 0.2730 | 0.8915 | 0.9670 |
| 5 | 0.2646 | 0.8955 | 0.9687 |
| 6 | 0.2599 | 0.8972 | 0.9704 |
| 7 | 0.2484 | 0.8992 | 0.9715 |
| 8 | 0.2397 | 0.8998 | 0.9723 |
| 9 | 0.2337 | 0.9042 | 0.9737 |
| 10 | 0.2271 | 0.9040 | 0.9745 |
 |
ergauravsharma251/major-project-model | ergauravsharma251 | 2025-05-29T13:39:38Z | 0 | 0 | null | [
"safetensors",
"t5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T12:02:43Z | ---
license: apache-2.0
---
|
osma77/banking77_fr_intent_model_v2 | osma77 | 2025-05-29T13:38:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:joeddav/xlm-roberta-large-xnli",
"base_model:finetune:joeddav/xlm-roberta-large-xnli",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-29T12:14:41Z | ---
library_name: transformers
license: mit
base_model: joeddav/xlm-roberta-large-xnli
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: banking77_fr_intent_model_v2
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. -->
# banking77_fr_intent_model_v2
This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8250
- Accuracy: 0.8111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0343 | 1.0 | 622 | 1.9269 | 0.6107 |
| 1.1292 | 2.0 | 1244 | 1.0095 | 0.7825 |
| 0.6979 | 3.0 | 1866 | 0.8250 | 0.8111 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
LucidityAI/Pico-v1-3b | LucidityAI | 2025-05-29T13:38:33Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:quantized:microsoft/Phi-3.5-mini-instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-12-23T14:59:23Z | ---
license: apache-2.0
language:
- en
base_model:
- microsoft/Phi-3.5-mini-instruct
pipeline_tag: text-generation
library_name: transformers
---
# Pico V1
Pico v1 is a work in progress model. Based off Phi 3.5 Mini, it has been fine tuned for automatic COT and self reflection.
When making a output, Pico will create three sections, a reasoning section, a self-reflection section and a output section.
Pico v1 struggles with non-question related tasks (Small talk, roleplay, etc). |
RoyRoyRpy/paligemma_vqa_all_1epo | RoyRoyRpy | 2025-05-29T13:34:41Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google/paligemma-3b-pt-224",
"base_model:adapter:google/paligemma-3b-pt-224",
"license:gemma",
"region:us"
] | null | 2025-05-29T12:38:53Z | ---
library_name: peft
license: gemma
base_model: google/paligemma-3b-pt-224
tags:
- generated_from_trainer
model-index:
- name: paligemma_vqa_all_1epo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# paligemma_vqa_all_1epo
This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) 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: 0.0002
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3 |
Nbenmo/M2_SFT | Nbenmo | 2025-05-29T13:34:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-29T13:33:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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] |
jellecali8/ali-speaker-embedding-model | jellecali8 | 2025-05-29T13:32:10Z | 0 | 0 | speechbrain | [
"speechbrain",
"feature-extraction",
"so",
"base_model:speechbrain/spkrec-ecapa-voxceleb",
"base_model:finetune:speechbrain/spkrec-ecapa-voxceleb",
"license:mit",
"region:us"
] | feature-extraction | 2025-05-29T09:57:21Z | ---
license: mit
language:
- so
base_model:
- speechbrain/spkrec-ecapa-voxceleb
pipeline_tag: feature-extraction
library_name: speechbrain
---
# Create README.md file first
readme_content = """
# Ali Speaker Embedding (Model Repo)
This repository contains a trained speaker embedding vector for the Somali male speaker **Ali**, extracted using the [speechbrain/spkrec-ecapa-voxceleb](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb) model.
## Model details
- Format: PyTorch .pt
- File: Ali_speaker_embedding.pt
- Embedding size: 192-dimensional
- Language: Somali
- Gender: Male
- Audio source: 300+ clips from Ali
## Usage
```python
import torch
embedding = torch.load("Ali_speaker_embedding.pt") |
allenliuvip/spacethinker-lora | allenliuvip | 2025-05-29T13:31:20Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B",
"base_model:adapter:UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B",
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T03:38:56Z | ---
library_name: peft
license: apache-2.0
base_model: UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: spacethinker-lora
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. -->
# spacethinker-lora
This model is a fine-tuned version of [UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B](https://huggingface.co/UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9785
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 2
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.548 | 0.0280 | 10 | 2.3356 |
| 2.2084 | 0.0561 | 20 | 2.1089 |
| 2.0353 | 0.0841 | 30 | 1.9475 |
| 1.8831 | 0.1121 | 40 | 1.7890 |
| 1.7139 | 0.1402 | 50 | 1.5906 |
| 1.524 | 0.1682 | 60 | 1.4117 |
| 1.3676 | 0.1962 | 70 | 1.3128 |
| 1.3054 | 0.2242 | 80 | 1.2428 |
| 1.2321 | 0.2523 | 90 | 1.1900 |
| 1.1896 | 0.2803 | 100 | 1.1618 |
| 1.1824 | 0.3083 | 110 | 1.1319 |
| 1.122 | 0.3364 | 120 | 1.1163 |
| 1.1403 | 0.3644 | 130 | 1.1027 |
| 1.13 | 0.3924 | 140 | 1.0911 |
| 1.0914 | 0.4205 | 150 | 1.0831 |
| 1.1097 | 0.4485 | 160 | 1.0772 |
| 1.0938 | 0.4765 | 170 | 1.0724 |
| 1.0947 | 0.5046 | 180 | 1.0688 |
| 1.0737 | 0.5326 | 190 | 1.0650 |
| 1.0742 | 0.5606 | 200 | 1.0613 |
| 1.0755 | 0.5886 | 210 | 1.0583 |
| 1.0797 | 0.6167 | 220 | 1.0549 |
| 1.0644 | 0.6447 | 230 | 1.0528 |
| 1.0834 | 0.6727 | 240 | 1.0507 |
| 1.0617 | 0.7008 | 250 | 1.0481 |
| 1.0713 | 0.7288 | 260 | 1.0460 |
| 1.0602 | 0.7568 | 270 | 1.0437 |
| 1.0581 | 0.7849 | 280 | 1.0414 |
| 1.0652 | 0.8129 | 290 | 1.0395 |
| 1.0597 | 0.8409 | 300 | 1.0377 |
| 1.0551 | 0.8690 | 310 | 1.0360 |
| 1.0525 | 0.8970 | 320 | 1.0344 |
| 1.0442 | 0.9250 | 330 | 1.0327 |
| 1.032 | 0.9530 | 340 | 1.0307 |
| 1.0553 | 0.9811 | 350 | 1.0293 |
| 1.1467 | 1.0112 | 360 | 1.0282 |
| 1.0378 | 1.0392 | 370 | 1.0263 |
| 1.0568 | 1.0673 | 380 | 1.0247 |
| 1.0298 | 1.0953 | 390 | 1.0240 |
| 1.0403 | 1.1233 | 400 | 1.0221 |
| 1.051 | 1.1514 | 410 | 1.0215 |
| 1.0289 | 1.1794 | 420 | 1.0198 |
| 1.0389 | 1.2074 | 430 | 1.0190 |
| 1.0348 | 1.2355 | 440 | 1.0175 |
| 1.0379 | 1.2635 | 450 | 1.0161 |
| 1.0507 | 1.2915 | 460 | 1.0152 |
| 1.0195 | 1.3196 | 470 | 1.0142 |
| 1.0084 | 1.3476 | 480 | 1.0125 |
| 1.0317 | 1.3756 | 490 | 1.0115 |
| 1.0319 | 1.4036 | 500 | 1.0107 |
| 1.0193 | 1.4317 | 510 | 1.0094 |
| 1.034 | 1.4597 | 520 | 1.0089 |
| 1.0311 | 1.4877 | 530 | 1.0077 |
| 1.0497 | 1.5158 | 540 | 1.0071 |
| 1.0417 | 1.5438 | 550 | 1.0061 |
| 1.0307 | 1.5718 | 560 | 1.0049 |
| 1.0028 | 1.5999 | 570 | 1.0042 |
| 1.0192 | 1.6279 | 580 | 1.0036 |
| 1.007 | 1.6559 | 590 | 1.0023 |
| 1.0378 | 1.6840 | 600 | 1.0020 |
| 0.9979 | 1.7120 | 610 | 1.0011 |
| 1.0169 | 1.7400 | 620 | 1.0004 |
| 1.0148 | 1.7680 | 630 | 0.9999 |
| 1.0095 | 1.7961 | 640 | 0.9989 |
| 1.0252 | 1.8241 | 650 | 0.9984 |
| 0.9891 | 1.8521 | 660 | 0.9983 |
| 1.0598 | 1.8802 | 670 | 0.9969 |
| 1.0158 | 1.9082 | 680 | 0.9964 |
| 1.019 | 1.9362 | 690 | 0.9961 |
| 0.9979 | 1.9643 | 700 | 0.9949 |
| 1.0312 | 1.9923 | 710 | 0.9946 |
| 1.084 | 2.0224 | 720 | 0.9938 |
| 0.9932 | 2.0505 | 730 | 0.9937 |
| 0.9932 | 2.0785 | 740 | 0.9930 |
| 1.0138 | 2.1065 | 750 | 0.9921 |
| 1.002 | 2.1345 | 760 | 0.9921 |
| 1.0291 | 2.1626 | 770 | 0.9914 |
| 1.0171 | 2.1906 | 780 | 0.9908 |
| 0.9959 | 2.2186 | 790 | 0.9902 |
| 1.0181 | 2.2467 | 800 | 0.9897 |
| 0.9856 | 2.2747 | 810 | 0.9893 |
| 1.0141 | 2.3027 | 820 | 0.9888 |
| 1.0305 | 2.3308 | 830 | 0.9883 |
| 0.9911 | 2.3588 | 840 | 0.9875 |
| 0.996 | 2.3868 | 850 | 0.9877 |
| 0.984 | 2.4149 | 860 | 0.9869 |
| 0.9964 | 2.4429 | 870 | 0.9864 |
| 1.0101 | 2.4709 | 880 | 0.9856 |
| 0.9934 | 2.4989 | 890 | 0.9853 |
| 1.0432 | 2.5270 | 900 | 0.9848 |
| 0.9918 | 2.5550 | 910 | 0.9843 |
| 0.9977 | 2.5830 | 920 | 0.9844 |
| 1.009 | 2.6111 | 930 | 0.9834 |
| 0.9994 | 2.6391 | 940 | 0.9837 |
| 0.9972 | 2.6671 | 950 | 0.9830 |
| 1.0043 | 2.6952 | 960 | 0.9827 |
| 1.0005 | 2.7232 | 970 | 0.9823 |
| 0.9888 | 2.7512 | 980 | 0.9820 |
| 0.9917 | 2.7793 | 990 | 0.9813 |
| 1.0036 | 2.8073 | 1000 | 0.9810 |
| 0.984 | 2.8353 | 1010 | 0.9803 |
| 0.9696 | 2.8633 | 1020 | 0.9798 |
| 1.0062 | 2.8914 | 1030 | 0.9798 |
| 1.0001 | 2.9194 | 1040 | 0.9793 |
| 1.0214 | 2.9474 | 1050 | 0.9796 |
| 1.0106 | 2.9755 | 1060 | 0.9785 |
### Framework versions
- PEFT 0.15.1
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
yuh0512/llama3-toolcall-lora | yuh0512 | 2025-05-29T13:29:29Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"endpoints_compatible",
"region:us"
] | null | 2025-05-29T13:29:21Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
library_name: transformers
model_name: llama3-toolcall-lora
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for llama3-toolcall-lora
This model is a fine-tuned version of [unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit).
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="yuh0512/llama3-toolcall-lora", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/huy05-levan-vku/huggingface/runs/8yhczb9a)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BootesVoid/cmb8vkcpy00kc1b1ywfqyfpky_cmb94dmcy05us1b1yjlygz4st | BootesVoid | 2025-05-29T13:28:29Z | 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 | 2025-05-29T13:28:28Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ivory
---
# Cmb8Vkcpy00Kc1B1Ywfqyfpky_Cmb94Dmcy05Us1B1Yjlygz4St
<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 `ivory` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ivory",
"lora_weights": "https://huggingface.co/BootesVoid/cmb8vkcpy00kc1b1ywfqyfpky_cmb94dmcy05us1b1yjlygz4st/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb8vkcpy00kc1b1ywfqyfpky_cmb94dmcy05us1b1yjlygz4st', weight_name='lora.safetensors')
image = pipeline('ivory').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb8vkcpy00kc1b1ywfqyfpky_cmb94dmcy05us1b1yjlygz4st/discussions) to add images that show off what you’ve made with this LoRA.
|
BootesVoid/cmb9dkqej0bku1b1y1vgdr2em_cmb9dqsuf0bod1b1yryg79tl1 | BootesVoid | 2025-05-29T13:27:53Z | 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 | 2025-05-29T13:27:52Z | ---
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: HEYY
---
# Cmb9Dkqej0Bku1B1Y1Vgdr2Em_Cmb9Dqsuf0Bod1B1Yryg79Tl1
<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 `HEYY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "HEYY",
"lora_weights": "https://huggingface.co/BootesVoid/cmb9dkqej0bku1b1y1vgdr2em_cmb9dqsuf0bod1b1yryg79tl1/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb9dkqej0bku1b1y1vgdr2em_cmb9dqsuf0bod1b1yryg79tl1', weight_name='lora.safetensors')
image = pipeline('HEYY').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb9dkqej0bku1b1y1vgdr2em_cmb9dqsuf0bod1b1yryg79tl1/discussions) to add images that show off what you’ve made with this LoRA.
|
Tacabdel/AI | Tacabdel | 2025-05-29T13:27:07Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
] | null | 2025-05-29T13:27:07Z | ---
license: artistic-2.0
---
|
HPLT/hplt_bert_base_2_0_tat-Cyrl | HPLT | 2025-05-29T13:25:57Z | 72 | 0 | null | [
"pytorch",
"BERT",
"HPLT",
"encoder",
"fill-mask",
"custom_code",
"tt",
"tat",
"dataset:HPLT/HPLT2.0_cleaned",
"arxiv:2503.10267",
"license:apache-2.0",
"region:us"
] | fill-mask | 2025-02-22T22:30:26Z | ---
language:
- tt
- tat
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/HPLT2.0_cleaned
pipeline_tag: fill-mask
---
# HPLT v2.0 BERT for Tatar
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a second release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
We present monolingual LTG-BERT models for more than 50 languages out of 191 total in the [HPLT v2.0 dataset](https://hplt-project.org/datasets/v2.0).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage (tested with `transformers==4.46.1` and `tokenizers==0.20.1`)
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_2_0_tat-Cyrl")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_tat-Cyrl", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist(), clean_up_tokenization_spaces=True))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_tat-Cyrl", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_2_0_tat-Cyrl")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
}
```
```bibtex
@misc{burchell2025expandedmassivemultilingualdataset,
title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies},
author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu},
year={2025},
eprint={2503.10267},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10267},
}
```
|
BETACAROTENE343/kaya05 | BETACAROTENE343 | 2025-05-29T13:25:51Z | 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 | 2025-05-29T13:01:27Z | ---
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: kaya05
---
# Kaya05
<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 `kaya05` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "kaya05",
"lora_weights": "https://huggingface.co/BETACAROTENE343/kaya05/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('BETACAROTENE343/kaya05', weight_name='lora.safetensors')
image = pipeline('kaya05').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/BETACAROTENE343/kaya05/discussions) to add images that show off what you’ve made with this LoRA.
|
HPLT/hplt_bert_base_2_0_slv-Latn | HPLT | 2025-05-29T13:25:28Z | 4 | 0 | null | [
"pytorch",
"BERT",
"HPLT",
"encoder",
"fill-mask",
"custom_code",
"sl",
"slv",
"dataset:HPLT/HPLT2.0_cleaned",
"arxiv:2503.10267",
"license:apache-2.0",
"region:us"
] | fill-mask | 2025-02-22T22:29:21Z | ---
language:
- sl
- slv
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/HPLT2.0_cleaned
pipeline_tag: fill-mask
---
# HPLT v2.0 BERT for Slovenian
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a second release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
We present monolingual LTG-BERT models for more than 50 languages out of 191 total in the [HPLT v2.0 dataset](https://hplt-project.org/datasets/v2.0).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage (tested with `transformers==4.46.1` and `tokenizers==0.20.1`)
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_2_0_slv-Latn")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_slv-Latn", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist(), clean_up_tokenization_spaces=True))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_slv-Latn", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_2_0_slv-Latn")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
}
```
```bibtex
@misc{burchell2025expandedmassivemultilingualdataset,
title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies},
author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu},
year={2025},
eprint={2503.10267},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10267},
}
```
|
HPLT/hplt_bert_base_2_0_ron-Latn | HPLT | 2025-05-29T13:25:00Z | 2 | 0 | null | [
"pytorch",
"BERT",
"HPLT",
"encoder",
"fill-mask",
"custom_code",
"ro",
"ron",
"dataset:HPLT/HPLT2.0_cleaned",
"arxiv:2503.10267",
"license:apache-2.0",
"region:us"
] | fill-mask | 2025-02-22T22:55:20Z | ---
language:
- ro
- ron
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/HPLT2.0_cleaned
pipeline_tag: fill-mask
---
# HPLT v2.0 BERT for Romanian; Moldavian; Moldovan
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a second release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
We present monolingual LTG-BERT models for more than 50 languages out of 191 total in the [HPLT v2.0 dataset](https://hplt-project.org/datasets/v2.0).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage (tested with `transformers==4.46.1` and `tokenizers==0.20.1`)
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_2_0_ron-Latn")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_ron-Latn", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist(), clean_up_tokenization_spaces=True))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_ron-Latn", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_2_0_ron-Latn")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
}
```
```bibtex
@misc{burchell2025expandedmassivemultilingualdataset,
title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies},
author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu},
year={2025},
eprint={2503.10267},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10267},
}
```
|
BootesVoid/cmb9dlsji0bl71b1ya5035yni_cmb9dnvmn0bm01b1y60jkzn3u | BootesVoid | 2025-05-29T13:24:05Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-29T13:24:04Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: NOVAELLE
---
# Cmb9Dlsji0Bl71B1Ya5035Yni_Cmb9Dnvmn0Bm01B1Y60Jkzn3U
<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 `NOVAELLE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "NOVAELLE",
"lora_weights": "https://huggingface.co/BootesVoid/cmb9dlsji0bl71b1ya5035yni_cmb9dnvmn0bm01b1y60jkzn3u/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb9dlsji0bl71b1ya5035yni_cmb9dnvmn0bm01b1y60jkzn3u', weight_name='lora.safetensors')
image = pipeline('NOVAELLE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb9dlsji0bl71b1ya5035yni_cmb9dnvmn0bm01b1y60jkzn3u/discussions) to add images that show off what you’ve made with this LoRA.
|
HPLT/hplt_bert_base_2_0_mlt-Latn | HPLT | 2025-05-29T13:24:03Z | 0 | 0 | null | [
"pytorch",
"BERT",
"HPLT",
"encoder",
"fill-mask",
"custom_code",
"mt",
"mlt",
"dataset:HPLT/HPLT2.0_cleaned",
"arxiv:2503.10267",
"license:apache-2.0",
"region:us"
] | fill-mask | 2025-02-22T22:53:25Z | ---
language:
- mt
- mlt
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/HPLT2.0_cleaned
pipeline_tag: fill-mask
---
# HPLT v2.0 BERT for Maltese
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a second release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
We present monolingual LTG-BERT models for more than 50 languages out of 191 total in the [HPLT v2.0 dataset](https://hplt-project.org/datasets/v2.0).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage (tested with `transformers==4.46.1` and `tokenizers==0.20.1`)
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_2_0_mlt-Latn")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_mlt-Latn", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist(), clean_up_tokenization_spaces=True))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_mlt-Latn", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_2_0_mlt-Latn")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
}
```
```bibtex
@misc{burchell2025expandedmassivemultilingualdataset,
title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies},
author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu},
year={2025},
eprint={2503.10267},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10267},
}
```
|
HPLT/hplt_bert_base_2_0_lvs-Latn | HPLT | 2025-05-29T13:23:36Z | 4 | 0 | null | [
"pytorch",
"BERT",
"HPLT",
"encoder",
"fill-mask",
"custom_code",
"lv",
"lvs",
"dataset:HPLT/HPLT2.0_cleaned",
"arxiv:2503.10267",
"license:apache-2.0",
"region:us"
] | fill-mask | 2025-02-22T22:52:55Z | ---
language:
- lv
- lvs
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/HPLT2.0_cleaned
pipeline_tag: fill-mask
---
# HPLT v2.0 BERT for Latvian
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a second release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
We present monolingual LTG-BERT models for more than 50 languages out of 191 total in the [HPLT v2.0 dataset](https://hplt-project.org/datasets/v2.0).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage (tested with `transformers==4.46.1` and `tokenizers==0.20.1`)
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_2_0_lvs-Latn")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_lvs-Latn", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist(), clean_up_tokenization_spaces=True))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_lvs-Latn", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_2_0_lvs-Latn")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
}
```
```bibtex
@misc{burchell2025expandedmassivemultilingualdataset,
title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies},
author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu},
year={2025},
eprint={2503.10267},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10267},
}
```
|
HPLT/hplt_bert_base_2_0_kor-Hang | HPLT | 2025-05-29T13:23:15Z | 3 | 0 | null | [
"pytorch",
"BERT",
"HPLT",
"encoder",
"fill-mask",
"custom_code",
"ko",
"kor",
"dataset:HPLT/HPLT2.0_cleaned",
"arxiv:2503.10267",
"license:apache-2.0",
"region:us"
] | fill-mask | 2025-02-22T22:52:12Z | ---
language:
- ko
- kor
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/HPLT2.0_cleaned
pipeline_tag: fill-mask
---
# HPLT v2.0 BERT for Korean
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a second release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
We present monolingual LTG-BERT models for more than 50 languages out of 191 total in the [HPLT v2.0 dataset](https://hplt-project.org/datasets/v2.0).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage (tested with `transformers==4.46.1` and `tokenizers==0.20.1`)
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_2_0_kor-Hang")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_kor-Hang", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist(), clean_up_tokenization_spaces=True))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_kor-Hang", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_2_0_kor-Hang")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
}
```
```bibtex
@misc{burchell2025expandedmassivemultilingualdataset,
title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies},
author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu},
year={2025},
eprint={2503.10267},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10267},
}
```
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