modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-28 06:27:55
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 534
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-07-28 06:22:14
| card
stringlengths 11
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|---|---|---|---|---|---|---|---|---|---|
AmberYifan/llama3-8b-full-pretrain-mix-high-tweet-1m-en
|
AmberYifan
| 2025-06-19T06:16:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T04:35:58Z |
---
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: llama3-8b-full-pretrain-mix-high-tweet-1m-en
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. -->
# llama3-8b-full-pretrain-mix-high-tweet-1m-en
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the mix_high_tweet_1m_en dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
quadcoders/deep-rl-course
|
quadcoders
| 2025-06-19T06:11:56Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-19T06:11:35Z |
---
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: 259.11 +/- 19.28
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
...
```
|
EYEDOL/MISTRAL7B_ON_ALPACA4
|
EYEDOL
| 2025-06-19T06:06:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.1-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.1-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T06:06:05Z |
---
base_model: unsloth/mistral-7b-instruct-v0.1-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** EYEDOL
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.1-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
apriasmoro/fc08d115-d555-4674-864a-0dd0ff54f304
|
apriasmoro
| 2025-06-19T06:02:58Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codegemma-7b",
"base_model:adapter:unsloth/codegemma-7b",
"license:apache-2.0",
"region:us"
] | null | 2025-06-19T05:53:22Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/codegemma-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fc08d115-d555-4674-864a-0dd0ff54f304
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/codegemma-7b
bf16: true
chat_template: llama3
datasets:
- data_files:
- 5313f4d1e8057633_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
eval_max_new_tokens: 256
evals_per_epoch: 2
flash_attention: false
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hub_model_id: apriasmoro/fc08d115-d555-4674-864a-0dd0ff54f304
learning_rate: 0.0002
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 280
micro_batch_size: 4
mlflow_experiment_name: /tmp/5313f4d1e8057633_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
sample_packing: false
save_steps: 25
sequence_len: 2048
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: efaf2747-93ba-4914-bbfb-4587efac813b
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: efaf2747-93ba-4914-bbfb-4587efac813b
warmup_steps: 100
weight_decay: 0.01
```
</details><br>
# fc08d115-d555-4674-864a-0dd0ff54f304
This model is a fine-tuned version of [unsloth/codegemma-7b](https://huggingface.co/unsloth/codegemma-7b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8509
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- 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: 100
- training_steps: 280
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0122 | 1 | 1.0012 |
| 2.6887 | 0.5732 | 47 | 0.9802 |
| 0.638 | 1.1463 | 94 | 0.8698 |
| 1.3412 | 1.7195 | 141 | 0.8378 |
| 0.6638 | 2.2927 | 188 | 0.9116 |
| 0.3695 | 2.8659 | 235 | 0.8509 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
Mungert/GLM-Z1-Rumination-32B-0414-GGUF
|
Mungert
| 2025-06-19T05:57:16Z | 44 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2025-06-17T18:35:32Z |
---
license: mit
language:
- zh
- en
pipeline_tag: text-generation
library_name: transformers
---
# <span style="color: #7FFF7F;">GLM-Z1-Rumination-32B-0414 GGUF Models</span>
## <span style="color: #7F7FFF;">Model Generation Details</span>
This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`6adc3c3e`](https://github.com/ggerganov/llama.cpp/commit/6adc3c3ebc029af058ac950a8e2a825fdf18ecc6).
---
## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>
I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.
In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here:
👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)
While this does increase model file size, it significantly improves precision for a given quantization level.
### **I'd love your feedback—have you tried this? How does it perform for you?**
---
<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
Click here to get info on choosing the right GGUF model format
</a>
---
<!--Begin Original Model Card-->
# GLM-4-Z1-Rumination-32B-0414
## Introduction
The GLM family welcomes a new generation of open-source models, the **GLM-4-32B-0414** series, featuring 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, we also enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search-based Q&A, and report generation. Some benchmarks even rival larger models like GPT-4o and DeepSeek-V3-0324 (671B).
**GLM-Z1-32B-0414** is a reasoning model with **deep thinking capabilities**. This was developed based on GLM-4-32B-0414 through cold start and extended reinforcement learning, as well as further training of the model on tasks involving mathematics, code, and logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to solve complex tasks. During the training process, we also introduced general reinforcement learning based on pairwise ranking feedback, further enhancing the model's general capabilities.
**GLM-Z1-Rumination-32B-0414** is a deep reasoning model with **rumination capabilities** (benchmarked against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model employs longer periods of deep thought to solve more open-ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future development plans). The rumination model integrates search tools during its deep thinking process to handle complex tasks and is trained by utilizing multiple rule-based rewards to guide and extend end-to-end reinforcement learning. Z1-Rumination shows significant improvements in research-style writing and complex retrieval tasks.
Finally, **GLM-Z1-9B-0414** is a surprise. We employed the aforementioned series of techniques to train a 9B small-sized model that maintains the open-source tradition. Despite its smaller scale, GLM-Z1-9B-0414 still exhibits excellent capabilities in mathematical reasoning and general tasks. Its overall performance is already at a leading level among open-source models of the same size. Especially in resource-constrained scenarios, this model achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking lightweight deployment.
## Inference Code
Make Sure Using `transforemrs>=4.51.3`.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_PATH = "THUDM/GLM-Z1-Rumination-32B-0414"
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")
message = [{"role": "user", "content": "Let a, b be positive real numbers such that ab = a + b + 3. Determine the range of possible values for a + b."}]
inputs = tokenizer.apply_chat_template(
message,
return_tensors="pt",
add_generation_prompt=True,
return_dict=True,
).to(model.device)
generate_kwargs = {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"temperature": 0.95,
"top_p": 0.7,
"do_sample": True,
}
out = model.generate(**generate_kwargs)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```
## Function Call
By default, this model currently supports the following `function` calls:
- `search`: Search using a keyword and return search results
- `click`: Click on a specific webpage in the search results to view details
- `open`: Open a fixed URL to view detailed content
- `finsih`: Complete information gathering and begin writing
Below is a simple workflow to help you quickly connect the pipeline.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import re
import json
MODEL_PATH = "THUDM/GLM-4-Z1-Rumination-32B-0414"
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")
messages = [{"role": "user", "content": "Let a, b be positive real numbers such that ab = a + b + 3. Determine the range of possible values for a + b."}]
generate_kwargs = {
"temperature": 0.95,
"top_p": 0.7,
"do_sample": True,
"max_new_tokens": 16384
}
def get_assistant():
inputs = tokenizer.apply_chat_template(
messages,
return_tensors="pt",
add_generation_prompt=True,
return_dict=True,
).to(model.device)
out = model.generate(input_ids=inputs["input_ids"], **generate_kwargs)
return tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip()
def get_observation(function_name, args):
content = None
if function_name == "search":
mock_search_res = [
{"title": "t1", "url":"url1", "snippet": "snippet_content_1"},
{"title": "t2", "url":"url2", "snippet": "snippet_content_2"}
]
content = "\n\n".join([f"【{i}†{res['title']}†{res['url']}\n{res['snippet']}】"] for i, res in enumerate(mock_search_res))
elif function_name == "click":
mock_click_res = "main content"
content = mock_click_res
elif function_name == "open":
mock_open_res = "main_content"
content = mock_open_res
else:
raise ValueError("unspport function name!")
return content
def get_func_name_args(llm_text):
function_call = re.sub(r'.*?</think>', '', llm_text, flags=re.DOTALL)
function_call = json.loads(function_call)
action = function_call['name']
params = function_call['arguments']
return action, params
def pipeline():
end_str = "{\"name\": \"finish\", \"arguments\": {}}"
response = get_assistant()
messages.append({"role": "assistant", "content": response})
max_turns, turns = 35, 1
while not response.endswith(end_str) and turns < max_turns:
action, params = get_func_name_args(response)
observation = get_observation(action, params)
messages.append({"role": "observation", "content": observation})
response = get_assistant()
messages.append({"role": "assistant", "content": response})
turns += 1
if response.endswith(end_str):
final_answer = get_assistant()
else:
final_answer = None
return final_answer
pipeline()
```
<!--End Original Model Card-->
---
# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:
👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)
The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)
💬 **How to test**:
Choose an **AI assistant type**:
- `TurboLLM` (GPT-4.1-mini)
- `HugLLM` (Hugginface Open-source models)
- `TestLLM` (Experimental CPU-only)
### **What I’m Testing**
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:
- **Function calling** against live network services
- **How small can a model go** while still handling:
- Automated **Nmap security scans**
- **Quantum-readiness checks**
- **Network Monitoring tasks**
🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):
- ✅ **Zero-configuration setup**
- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low.
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!
### **Other Assistants**
🟢 **TurboLLM** – Uses **gpt-4.1-mini** :
- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents**
- **Real-time network diagnostics and monitoring**
- **Security Audits**
- **Penetration testing** (Nmap/Metasploit)
🔵 **HugLLM** – Latest Open-source models:
- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.
### 💡 **Example commands you could test**:
1. `"Give me info on my websites SSL certificate"`
2. `"Check if my server is using quantum safe encyption for communication"`
3. `"Run a comprehensive security audit on my server"`
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!
### Final Word
I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.
If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.
I'm also open to job opportunities or sponsorship.
Thank you! 😊
|
jusjinuk/Llama-2-70b-hf-4bit-GuidedQuant-QTIP
|
jusjinuk
| 2025-06-19T05:57:11Z | 0 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T05:38:06Z |
---
base_model:
- meta-llama/Llama-2-70b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-70b-hf`
- Quantization method: BlockLDLQ with GuidedQuant Hessian
- Target bit-width: 4
- Backend kernel: QTIP kernel (HYB variant)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 2
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
bharathsj/bio-medical-mixed-8k
|
bharathsj
| 2025-06-19T05:50:26Z | 0 | 0 | null |
[
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-06-19T05:43:13Z |
---
license: apache-2.0
---
|
gsdfg18919/tyrel
|
gsdfg18919
| 2025-06-19T05:49:38Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-19T05:49:34Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/all-black-background-mukiwp7v3e6j3fd4.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: tyrel
---
# tyrel
<Gallery />
## Trigger words
You should use `tyrel` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/gsdfg18919/tyrel/tree/main) them in the Files & versions tab.
|
veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-9-sneaker
|
veddhanth
| 2025-06-19T05:45:20Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-06-19T05:39:17Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a photo of sks sneaker
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-9-sneaker
<Gallery />
## Model description
These are veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-9-sneaker LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks sneaker to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-9-sneaker/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition-same_last_layer_28_2_song_3_49
|
winnieyangwannan
| 2025-06-19T05:44:43Z | 156 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-02T17:06:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[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]
|
huihui-ai/Huihui-Qwen3-1.7B-abliterated-v2
|
huihui-ai
| 2025-06-19T05:44:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"chat",
"abliterated",
"uncensored",
"conversational",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T04:26:17Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-1.7B
tags:
- chat
- abliterated
- uncensored
---
# huihui-ai/Huihui-Qwen3-1.7B-abliterated-v2
This is an uncensored version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
Ablation was performed using a new and faster method, which yields better results.
**Important Note** This version is an improvement over the previous one [huihui-ai/Qwen3-1.7B-abliterated](https://huggingface.co/huihui-ai/Qwen3-1.7B-abliterated). The ollama version has also been modified.
Changed 0 layer to eliminate the problem of garbled codes
## ollama
You can use [huihui_ai/qwen3-abliterated:1.7b-v2](https://ollama.com/huihui_ai/qwen3-abliterated:1.7b-v2) directly, Switch the thinking toggle using /set think and /set nothink
```
ollama run huihui_ai/qwen3-abliterated:1.7b-v2
```
## Usage
You can use this model in your applications by loading it with Hugging Face's `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
import torch
import os
import signal
import random
import numpy as np
import time
from collections import Counter
cpu_count = os.cpu_count()
print(f"Number of CPU cores in the system: {cpu_count}")
half_cpu_count = cpu_count // 2
os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
torch.set_num_threads(half_cpu_count)
print(f"PyTorch threads: {torch.get_num_threads()}")
print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
# Load the model and tokenizer
NEW_MODEL_ID = "huihui-ai/Huihui-Qwen3-1.7B-abliterated-v2"
print(f"Load Model {NEW_MODEL_ID} ... ")
quant_config_4 = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
llm_int8_enable_fp32_cpu_offload=True,
)
model = AutoModelForCausalLM.from_pretrained(
NEW_MODEL_ID,
device_map="auto",
trust_remote_code=True,
#quantization_config=quant_config_4,
torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
messages = []
nothink = False
same_seed = False
skip_prompt=True
skip_special_tokens=True
do_sample = True
def set_random_seed(seed=None):
"""Set random seed for reproducibility. If seed is None, use int(time.time())."""
if seed is None:
seed = int(time.time()) # Convert float to int
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # If using CUDA
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
return seed # Return seed for logging if needed
class CustomTextStreamer(TextStreamer):
def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
self.generated_text = ""
self.stop_flag = False
self.init_time = time.time() # Record initialization time
self.end_time = None # To store end time
self.first_token_time = None # To store first token generation time
self.token_count = 0 # To track total tokens
def on_finalized_text(self, text: str, stream_end: bool = False):
if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text
self.first_token_time = time.time()
self.generated_text += text
# Count tokens in the generated text
tokens = self.tokenizer.encode(text, add_special_tokens=False)
self.token_count += len(tokens)
print(text, end="", flush=True)
if stream_end:
self.end_time = time.time() # Record end time when streaming ends
if self.stop_flag:
raise StopIteration
def stop_generation(self):
self.stop_flag = True
self.end_time = time.time() # Record end time when generation is stopped
def get_metrics(self):
"""Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second."""
if self.end_time is None:
self.end_time = time.time() # Set end time if not already set
total_time = self.end_time - self.init_time # Total time from init to end
tokens_per_second = self.token_count / total_time if total_time > 0 else 0
first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None
metrics = {
"init_time": self.init_time,
"first_token_time": self.first_token_time,
"first_token_latency": first_token_latency,
"end_time": self.end_time,
"total_time": total_time, # Total time in seconds
"total_tokens": self.token_count,
"tokens_per_second": tokens_per_second
}
return metrics
def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens):
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
enable_thinking = not nothink,
add_generation_prompt=True,
return_tensors="pt"
)
attention_mask = torch.ones_like(input_ids, dtype=torch.long)
tokens = input_ids.to(model.device)
attention_mask = attention_mask.to(model.device)
streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
def signal_handler(sig, frame):
streamer.stop_generation()
print("\n[Generation stopped by user with Ctrl+C]")
signal.signal(signal.SIGINT, signal_handler)
generate_kwargs = {}
if do_sample:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
else:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
print("Response: ", end="", flush=True)
try:
generated_ids = model.generate(
tokens,
attention_mask=attention_mask,
#use_cache=False,
pad_token_id=tokenizer.pad_token_id,
streamer=streamer,
**generate_kwargs
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del input_ids, attention_mask
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
init_seed = set_random_seed()
while True:
if same_seed:
set_random_seed(init_seed)
else:
init_seed = set_random_seed()
print(f"\nnothink: {nothink}")
print(f"skip_prompt: {skip_prompt}")
print(f"skip_special_tokens: {skip_special_tokens}")
print(f"do_sample: {do_sample}")
print(f"same_seed: {same_seed}, {init_seed}\n")
user_input = input("User: ").strip()
if user_input.lower() == "/exit":
print("Exiting chat.")
break
if user_input.lower() == "/clear":
messages = []
print("Chat history cleared. Starting a new conversation.")
continue
if user_input.lower() == "/nothink":
nothink = not nothink
continue
if user_input.lower() == "/skip_prompt":
skip_prompt = not skip_prompt
continue
if user_input.lower() == "/skip_special_tokens":
skip_special_tokens = not skip_special_tokens
continue
if user_input.lower().startswith("/same_seed"):
parts = user_input.split()
if len(parts) == 1: # /same_seed (no number)
same_seed = not same_seed # Toggle switch
elif len(parts) == 2: # /same_seed <number>
try:
init_seed = int(parts[1]) # Extract and convert number to int
same_seed = True
except ValueError:
print("Error: Please provide a valid integer after /same_seed")
continue
if user_input.lower() == "/do_sample":
do_sample = not do_sample
continue
if not user_input:
print("Input cannot be empty. Please enter something.")
continue
messages.append({"role": "user", "content": user_input})
activated_experts = []
response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 40960)
print("\n\nMetrics:")
for key, value in metrics.items():
print(f" {key}: {value}")
print("", flush=True)
if stop_flag:
continue
messages.append({"role": "assistant", "content": response})
# Remove all hooks after inference
for h in hooks: h.remove()
```
### Usage Warnings
- **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
- **Not Suitable for All Audiences**: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
- **Legal and Ethical Responsibilities**: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
- **Research and Experimental Use**: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
- **Monitoring and Review Recommendations**: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
- **No Default Safety Guarantees**: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
### Donation
If you like it, please click 'like' and follow us for more updates.
You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.
##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- bitcoin（BTC):
```
bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
```
|
jusjinuk/Llama-2-70b-hf-3bit-GuidedQuant-QTIP
|
jusjinuk
| 2025-06-19T05:41:44Z | 0 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T05:22:31Z |
---
base_model:
- meta-llama/Llama-2-70b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-70b-hf`
- Quantization method: BlockLDLQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: QTIP kernel (HYB variant)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 2
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
gsdfg18919/petite
|
gsdfg18919
| 2025-06-19T05:40:47Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-19T05:40:45Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/all-black-background-mukiwp7v3e6j3fd4.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: petite
---
# petite
<Gallery />
## Trigger words
You should use `petite` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/gsdfg18919/petite/tree/main) them in the Files & versions tab.
|
jusjinuk/Llama-2-7b-hf-3bit-GuidedQuant-QTIP
|
jusjinuk
| 2025-06-19T05:40:18Z | 0 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:quantized:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T04:47:12Z |
---
base_model:
- meta-llama/Llama-2-7b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-7b-hf`
- Quantization method: BlockLDLQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: QTIP kernel (HYB variant)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 4
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
mradermacher/CantoneseLLMChat-v1.0-7B-GGUF
|
mradermacher
| 2025-06-19T05:39:27Z | 68 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"en",
"base_model:hon9kon9ize/CantoneseLLMChat-v1.0-7B",
"base_model:quantized:hon9kon9ize/CantoneseLLMChat-v1.0-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-05T02:05:34Z |
---
base_model: hon9kon9ize/CantoneseLLMChat-v1.0-7B
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/hon9kon9ize/CantoneseLLMChat-v1.0-7B
<!-- 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/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.IQ3_XS.gguf) | IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.IQ3_S.gguf) | IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.IQ3_M.gguf) | IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/CantoneseLLMChat-v1.0-7B-GGUF/resolve/main/CantoneseLLMChat-v1.0-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
jusjinuk/Llama-2-70b-hf-2bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T05:35:17Z | 60 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T15:51:36Z |
---
base_model:
- meta-llama/Llama-2-70b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-70b-hf`
- Quantization method: SqueezeLLM
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-13b-hf-4bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T05:34:58Z | 15 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T14:45:50Z |
---
base_model:
- meta-llama/Llama-2-13b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-13b-hf`
- Quantization method: SqueezeLLM
- Target bit-width: 4
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-13b-hf-3bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T05:34:48Z | 15 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T13:52:06Z |
---
base_model:
- meta-llama/Llama-2-13b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-13b-hf`
- Quantization method: SqueezeLLM
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-7b-hf-3bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T05:34:09Z | 150 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:quantized:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T20:46:57Z |
---
base_model:
- meta-llama/Llama-2-7b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-7b-hf`
- Quantization method: SqueezeLLM
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-7b-hf-2bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T05:34:00Z | 1,544 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:quantized:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T20:44:26Z |
---
base_model:
- meta-llama/Llama-2-7b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-7b-hf`
- Quantization method: SqueezeLLM
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Meta-Llama-3-8B-2bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T05:32:45Z | 99 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:quantized:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"region:us"
] | null | 2025-05-20T22:20:24Z |
---
base_model:
- meta-llama/Meta-Llama-3-8B
base_model_relation: quantized
license: llama3
---
# Model Card
- Base model: `meta-llama/Meta-Llama-3-8B`
- Quantization method: SqueezeLLM
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
yamatazen/EtherealAurora-12B
|
yamatazen
| 2025-06-19T05:32:19Z | 74 | 8 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"chatml",
"conversational",
"en",
"ja",
"arxiv:2403.19522",
"base_model:yamatazen/Aurora-SCE-12B",
"base_model:merge:yamatazen/Aurora-SCE-12B",
"base_model:yamatazen/Aurora-SCE-12B-v2",
"base_model:merge:yamatazen/Aurora-SCE-12B-v2",
"base_model:yamatazen/Ayla-Light-12B-Stock",
"base_model:merge:yamatazen/Ayla-Light-12B-Stock",
"base_model:yamatazen/EtherealLight-12B",
"base_model:merge:yamatazen/EtherealLight-12B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-02T13:13:32Z |
---
base_model:
- yamatazen/Ayla-Light-12B-Stock
- yamatazen/Aurora-SCE-12B
- yamatazen/EtherealLight-12B
- yamatazen/Aurora-SCE-12B-v2
library_name: transformers
tags:
- mergekit
- merge
- chatml
language:
- en
- ja
license: apache-2.0
---
This is a ChatML model.
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [yamatazen/Aurora-SCE-12B](https://huggingface.co/yamatazen/Aurora-SCE-12B) as a base.
### Models Merged
The following models were included in the merge:
* [yamatazen/Ayla-Light-12B-Stock](https://huggingface.co/yamatazen/Ayla-Light-12B-Stock)
* [yamatazen/EtherealLight-12B](https://huggingface.co/yamatazen/EtherealLight-12B)
* [yamatazen/Aurora-SCE-12B-v2](https://huggingface.co/yamatazen/Aurora-SCE-12B-v2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: yamatazen/Aurora-SCE-12B
models:
- model: yamatazen/Aurora-SCE-12B-v2
- model: yamatazen/Ayla-Light-12B-Stock
- model: yamatazen/EtherealLight-12B
merge_method: model_stock
dtype: bfloat16
parameters:
normalize: true
```
|
Skewness-RL-KE/Qwen2-Math-1.5B-MetaMathQA
|
Skewness-RL-KE
| 2025-06-19T05:31:34Z | 74 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2-Math-1.5B",
"base_model:finetune:Qwen/Qwen2-Math-1.5B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-13T11:30:04Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2-Math-1.5B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: sft_lr_5e-5_bs_512
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. -->
# sft_lr_5e-5_bs_512
This model is a fine-tuned version of [Qwen/Qwen2-Math-1.5B](https://huggingface.co/Qwen/Qwen2-Math-1.5B) on the MetaMathQA dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
jusjinuk/Llama-2-13b-hf-4bit-LNQ
|
jusjinuk
| 2025-06-19T05:31:30Z | 31 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T09:50:21Z |
---
base_model:
- meta-llama/Llama-2-13b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-13b-hf`
- Quantization method: LNQ
- Target bit-width: 4
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-13b-hf-2bit-LNQ
|
jusjinuk
| 2025-06-19T05:31:12Z | 65 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T09:27:59Z |
---
base_model:
- meta-llama/Llama-2-13b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-13b-hf`
- Quantization method: LNQ
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
DoNotChoke/llama-3.2-3B-it-thinking-function_calling-V0
|
DoNotChoke
| 2025-06-19T05:29:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T04:57:32Z |
---
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
model_name: llama-3.2-3B-it-thinking-function_calling-V0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama-3.2-3B-it-thinking-function_calling-V0
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-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="DoNotChoke/llama-3.2-3B-it-thinking-function_calling-V0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- 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}}
}
```
|
jusjinuk/Llama-2-70b-hf-3bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T05:14:36Z | 57 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-70b-hf",
"base_model:quantized:meta-llama/Llama-2-70b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T11:12:16Z |
---
base_model:
- meta-llama/Llama-2-70b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-70b-hf`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 2
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-7b-hf-3bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T05:13:26Z | 1,541 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:quantized:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T09:14:41Z |
---
base_model:
- meta-llama/Llama-2-7b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-7b-hf`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 4
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-2-13b-hf-2bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T05:13:06Z | 90 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:quantized:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | 2025-05-20T09:33:21Z |
---
base_model:
- meta-llama/Llama-2-13b-hf
base_model_relation: quantized
license: llama2
---
# Model Card
- Base model: `meta-llama/Llama-2-13b-hf`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 4
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.2-1B-Instruct-4bit-GuidedQuant-QTIP
|
jusjinuk
| 2025-06-19T05:01:33Z | 7 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2025-06-10T13:14:13Z |
---
base_model:
- meta-llama/Llama-3.2-1B-Instruct
base_model_relation: quantized
license: llama3.2
---
# Model Card
- Base model: `meta-llama/Llama-3.2-1B-Instruct`
- Quantization method: BlockLDLQ with GuidedQuant Hessian
- Target bit-width: 4
- Backend kernel: QTIP kernel (HYB variant)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.1-70B-Instruct-3bit-GuidedQuant-QTIP
|
jusjinuk
| 2025-06-19T05:00:40Z | 8 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.1-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-70B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-06-13T04:03:47Z |
---
base_model:
- meta-llama/Llama-3.1-70B-Instruct
base_model_relation: quantized
license: llama3.1
---
# Model Card
- Base model: `meta-llama/Llama-3.1-70B-Instruct`
- Quantization method: BlockLDLQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: QTIP kernel (HYB variant)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.1-70B-Instruct-3bit-GuidedQuant-QTIP-skip_0_v
|
jusjinuk
| 2025-06-19T05:00:14Z | 0 | 0 | null |
[
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-06-13T03:12:51Z |
---
base_model:
- meta-llama/Llama-3.2-3B-Instruct
base_model_relation: quantized
license: llama3.1
---
# Model Card
- Base model: `meta-llama/Llama-3.1-70B-Instruct`
- Quantization method: BlockLDLQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: QTIP kernel (HYB variant)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
- skip_list: 0_v (not quantizing 0_v layer, following YAQA paper)
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
hafizhaaarama/multitask_model
|
hafizhaaarama
| 2025-06-19T04:59:18Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T05:07:33Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: multitask_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multitask_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0074
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.123 | 1.0 | 65 | 0.0443 |
| 0.0155 | 2.0 | 130 | 0.0094 |
| 0.012 | 3.0 | 195 | 0.0074 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
jusjinuk/Llama-3.1-8B-Instruct-2bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T04:58:25Z | 130 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-05-30T17:26:41Z |
---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
base_model_relation: quantized
license: llama3.1
---
# Model Card
- Base model: `meta-llama/Llama-3.1-8B-Instruct`
- Quantization method: SqueezeLLM
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
howos1234/videomae-base-finetuned-ucf101-subset-v1
|
howos1234
| 2025-06-19T04:58:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2025-06-19T04:21:57Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf101-subset-v1
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. -->
# videomae-base-finetuned-ucf101-subset-v1
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4081
- Accuracy: 0.8645
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 148
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.1477 | 0.2568 | 38 | 1.8577 | 0.4286 |
| 0.955 | 1.2568 | 76 | 0.9704 | 0.7286 |
| 0.4844 | 2.2568 | 114 | 0.5025 | 0.8286 |
| 0.3112 | 3.2297 | 148 | 0.3884 | 0.8714 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.0.1+cu117
- Datasets 3.1.0
- Tokenizers 0.20.3
|
jusjinuk/Llama-3.1-8B-Instruct-4bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T04:58:05Z | 150 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-05-25T15:50:53Z |
---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
base_model_relation: quantized
license: llama3.1
---
# Model Card
- Base model: `meta-llama/Llama-3.1-8B-Instruct`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 4
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.1-8B-Instruct-2bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T04:57:42Z | 2,107 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-05-25T09:01:54Z |
---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
base_model_relation: quantized
license: llama3.1
---
# Model Card
- Base model: `meta-llama/Llama-3.1-8B-Instruct`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.3-70B-Instruct-3bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T04:57:06Z | 130 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.3-70B-Instruct",
"license:llama3.3",
"region:us"
] | null | 2025-05-30T16:20:02Z |
---
base_model:
- meta-llama/Llama-3.3-70B-Instruct
base_model_relation: quantized
license: llama3.3
---
# Model Card
- Base model: `meta-llama/Llama-3.3-70B-Instruct`
- Quantization method: SqueezeLLM
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.3-70B-Instruct-2bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T04:56:33Z | 30 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.3-70B-Instruct",
"license:llama3.3",
"region:us"
] | null | 2025-05-26T07:58:35Z |
---
base_model:
- meta-llama/Llama-3.3-70B-Instruct
base_model_relation: quantized
license: llama3.3
---
# Model Card
- Base model: `meta-llama/Llama-3.3-70B-Instruct`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/Llama-3.3-70B-Instruct-3bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T04:56:04Z | 66 | 0 | null |
[
"pytorch",
"llama",
"arxiv:2505.07004",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.3-70B-Instruct",
"license:llama3.3",
"region:us"
] | null | 2025-05-27T02:37:50Z |
---
base_model:
- meta-llama/Llama-3.3-70B-Instruct
base_model_relation: quantized
license: llama3.3
---
# Model Card
- Base model: `meta-llama/Llama-3.3-70B-Instruct`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-map-8-1989
|
veddhanth
| 2025-06-19T04:54:47Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-06-19T04:41:27Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a realistic portrait of sks face
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-map-8-1989
<Gallery />
## Model description
These are veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-map-8-1989 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a realistic portrait of sks face to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-map-8-1989/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
jusjinuk/gemma-3-27b-it-4bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T04:53:34Z | 19 | 0 | null |
[
"pytorch",
"gemma3",
"arxiv:2505.07004",
"base_model:google/gemma-3-27b-it",
"base_model:quantized:google/gemma-3-27b-it",
"license:gemma",
"region:us"
] | null | 2025-06-02T03:34:56Z |
---
base_model:
- google/gemma-3-27b-it
base_model_relation: quantized
license: gemma
---
# Model Card
- Base model: `google/gemma-3-27b-it`
- Quantization method: SqueezeLLM
- Target bit-width: 4
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/gemma-3-27b-it-3bit-SqueezeLLM
|
jusjinuk
| 2025-06-19T04:53:26Z | 19 | 0 | null |
[
"pytorch",
"gemma3",
"arxiv:2505.07004",
"base_model:google/gemma-3-27b-it",
"base_model:quantized:google/gemma-3-27b-it",
"license:gemma",
"region:us"
] | null | 2025-06-02T03:00:48Z |
---
base_model:
- google/gemma-3-27b-it
base_model_relation: quantized
license: gemma
---
# Model Card
- Base model: `google/gemma-3-27b-it`
- Quantization method: SqueezeLLM
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/gemma-3-27b-it-3bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T04:52:45Z | 12 | 0 | null |
[
"pytorch",
"gemma3",
"arxiv:2505.07004",
"base_model:google/gemma-3-27b-it",
"base_model:quantized:google/gemma-3-27b-it",
"license:gemma",
"region:us"
] | null | 2025-06-02T02:46:17Z |
---
base_model:
- google/gemma-3-27b-it
base_model_relation: quantized
license: gemma
---
# Model Card
- Base model: `google/gemma-3-27b-it`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 3
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
jusjinuk/gemma-3-27b-it-2bit-GuidedQuant-LNQ
|
jusjinuk
| 2025-06-19T04:52:20Z | 29 | 0 | null |
[
"pytorch",
"gemma3",
"arxiv:2505.07004",
"base_model:google/gemma-3-27b-it",
"base_model:quantized:google/gemma-3-27b-it",
"license:gemma",
"region:us"
] | null | 2025-06-02T02:22:36Z |
---
base_model:
- google/gemma-3-27b-it
base_model_relation: quantized
license: gemma
---
# Model Card
- Base model: `google/gemma-3-27b-it`
- Quantization method: LNQ with GuidedQuant Hessian
- Target bit-width: 2
- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
- Calibration data: RedPajama (1024 sentences / 4096 tokens)
- Calibration objective: Next-token prediction
- num_groups (for GuidedQuant Hessian): 1
# How to run
- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
# References
- [Model Paper](https://arxiv.org/abs/2505.07004)
|
JayHyeon/Qwen_1.5B-math-DPO_1e-4_1.0vpo_constant-10ep
|
JayHyeon
| 2025-06-19T04:51:11Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:argilla/distilabel-math-preference-dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T04:09:21Z |
---
base_model: Qwen/Qwen2.5-Math-1.5B
datasets: argilla/distilabel-math-preference-dpo
library_name: transformers
model_name: Qwen_1.5B-math-DPO_1e-4_1.0vpo_constant-10ep
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen_1.5B-math-DPO_1e-4_1.0vpo_constant-10ep
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JayHyeon/Qwen_1.5B-math-DPO_1e-4_1.0vpo_constant-10ep", 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/bonin147/huggingface/runs/7e1oxnft)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
asdfre453/HUDA2
|
asdfre453
| 2025-06-19T04:39:13Z | 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
| 2025-06-19T04:13:06Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: HUDA
---
# Huda2
<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 `HUDA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "HUDA",
"lora_weights": "https://huggingface.co/asdfre453/HUDA2/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('asdfre453/HUDA2', weight_name='lora.safetensors')
image = pipeline('HUDA').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/asdfre453/HUDA2/discussions) to add images that show off what you’ve made with this LoRA.
|
veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-8-sneaker
|
veddhanth
| 2025-06-19T04:35:43Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-06-19T04:29:37Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a photo of sks sneaker
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-8-sneaker
<Gallery />
## Model description
These are veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-8-sneaker LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks sneaker to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](veddhanth/lora-trained-xl-stage-2-pretrained-enc-v2-spat-map-8-sneaker/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
kataragi/ControlNet-LineartXL
|
kataragi
| 2025-06-19T04:31:44Z | 0 | 39 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-05-11T06:12:00Z |
---
license: creativeml-openrail-m
---
</p>
# controlnet_lineaetXL
- これはstable DiffusionのSDXLにおいて線画から色塗りを行うコントロールネットです。Lineartプリプロセッサで使用することができます。
# 使い方
コントロールネットに線画や色塗り済みの画像をセットします。
プリプロセッサはLineartに設定してください。線が太いとうまく作動しないため推奨はlineart_anime_denoiseまたはlineart_animeです。
白地に黒線の線画を用意した場合はinvert (from white bg & black line)プリプロセッサを使用してください。
fp16バージョンの推奨モデルはanimagineXL3.1です。pony系列ではあまりうまく動作しません。
またLoraタイプ(400MB)の方はanimagineXL3.1専用です。
- 
線画から色塗りをした場合はこのようになります。
- 
また、色塗りをした画像から色だけを塗りなおす場合はこのようになります。
- 
|
asdfre453/HUDA
|
asdfre453
| 2025-06-19T04:07:26Z | 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
| 2025-06-19T03:42:52Z |
---
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: HUDA
---
# Huda
<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 `HUDA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "HUDA",
"lora_weights": "https://huggingface.co/asdfre453/HUDA/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('asdfre453/HUDA', weight_name='lora.safetensors')
image = pipeline('HUDA').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/asdfre453/HUDA/discussions) to add images that show off what you’ve made with this LoRA.
|
ThomasComics/Nemo-Patricide-Humanize-12B-v1
|
ThomasComics
| 2025-06-19T04:06:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:cgato/Nemo-12b-Humanize-KTO-Experimental-2",
"base_model:merge:cgato/Nemo-12b-Humanize-KTO-Experimental-2",
"base_model:redrix/patricide-12B-Unslop-Mell-v2",
"base_model:merge:redrix/patricide-12B-Unslop-Mell-v2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T03:59:55Z |
---
base_model:
- redrix/patricide-12B-Unslop-Mell-v2
- cgato/Nemo-12b-Humanize-KTO-Experimental-2
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the NuSLERP merge method.
### Models Merged
The following models were included in the merge:
* [redrix/patricide-12B-Unslop-Mell-v2](https://huggingface.co/redrix/patricide-12B-Unslop-Mell-v2)
* [cgato/Nemo-12b-Humanize-KTO-Experimental-2](https://huggingface.co/cgato/Nemo-12b-Humanize-KTO-Experimental-2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: redrix/patricide-12B-Unslop-Mell-v2
parameters:
weight: [0.6, 0.5, 0.3, 0.5, 0.6]
- model: cgato/Nemo-12b-Humanize-KTO-Experimental-2
parameters:
weight: [0.4, 0.5, 0.7, 0.5, 0.4]
merge_method: nuslerp
dtype: bfloat16
chat_template: "chatml"
tokenizer:
source: union
parameters:
normalize: true
int8_mask: true
```
|
hooyah/ppo-LunarLander-v2
|
hooyah
| 2025-06-19T04:02:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-19T04:02:07Z |
---
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: 242.58 +/- 15.70
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
...
```
|
Muennighoff/Qwen2.5-1.5B-hl-false-v8
|
Muennighoff
| 2025-06-19T03:56:38Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:simplescaling/openaimath",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T03:31:02Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: simplescaling/openaimath
library_name: transformers
model_name: Qwen2.5-1.5B-hl-false-v8
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-1.5B-hl-false-v8
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [simplescaling/openaimath](https://huggingface.co/datasets/simplescaling/openaimath) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Muennighoff/Qwen2.5-1.5B-hl-false-v8", 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/muennighoff/halos/runs/0w7vw30q)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0.dev0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
luyotw/openfun-ivod-whisper-small-WuSiYao-10-75
|
luyotw
| 2025-06-19T03:53:04Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"region:us"
] | null | 2025-06-19T03:30:07Z |
# Fine-tune 資訊
- 原始模型: `openai/whisper-small`
- 使用音訊數量: 12588
- 使用音訊總長: 8.47 小時
- 音訊平均長度: 2.42 秒
- GPU: `NVIDIA H100 PCIe` x 1
- 訓練時間: 04:52:17
- 模型大小: 0.90 GB
---
# Model Card
|
chiruan/qwen2.5-7b-coder_V2-220steps
|
chiruan
| 2025-06-19T03:47:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T03:21:38Z |
---
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]
|
er6y/bge-reranker-v2-m3_dynamic_int8_onnx
|
er6y
| 2025-06-19T03:46:39Z | 0 | 0 | null |
[
"onnx",
"xlm-roberta",
"base_model:BAAI/bge-reranker-v2-m3",
"base_model:quantized:BAAI/bge-reranker-v2-m3",
"region:us"
] | null | 2025-06-19T03:39:28Z |
---
base_model:
- BAAI/bge-reranker-v2-m3
---
---
license: apache-2.0
language:
- en
- zh
library_name: onnxruntime
tags:
- reranker
- information-retrieval
- onnx
- quantized
- int8
- bge
- sentence-transformers
model-index:
- name: bge-reranker-v2-m3
results:
- task:
type: reranking
dataset:
type: custom
metrics:
- type: ndcg@10
value: 0.xx
---
# BGE Reranker v2 M3 (Dynamic INT8 ONNX)
这是 [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) 模型的动态 INT8 量化 ONNX 版本，专为高效推理而优化。
## 模型描述
BGE Reranker v2 M3 是一个强大的多语言重排序模型，支持中文和英文文本的语义重排序任务。该版本经过动态 INT8 量化，在保持高精度的同时显著减少了模型大小和推理时间。
### 主要特性
- **多语言支持**: 支持中文和英文
- **高效推理**: 动态 INT8 量化，推理速度提升 2-4 倍
- **模型压缩**: 相比原始模型大小减少约 75%
- **ONNX 格式**: 支持跨平台部署
- **保持精度**: 量化后精度损失小于 1%
## 模型规格
- **模型类型**: Reranker
- **量化方式**: Dynamic INT8
- **框架**: ONNX Runtime
- **输入长度**: 最大 512 tokens
- **支持语言**: 中文、英文
- **模型大小**: ~100MB (原始模型 ~400MB)
## 使用方法
### 环境要求
```bash
pip install onnxruntime
pip install transformers
pip install numpy
|
rmdhirr/suja-lorab-ep6-suja-1000
|
rmdhirr
| 2025-06-19T03:46:24Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:rmdhirr/merged-suja-latest",
"base_model:adapter:rmdhirr/merged-suja-latest",
"region:us"
] | null | 2025-06-19T03:45:23Z |
---
base_model: rmdhirr/merged-suja-latest
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
|
hardlyworking/Final4BRC3
|
hardlyworking
| 2025-06-19T03:46:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"conversational",
"dataset:ResplendentAI/Luna_NSFW_Text",
"dataset:ResplendentAI/Sissification_Hypno_1k",
"dataset:ResplendentAI/Synthetic_Soul_1k",
"base_model:hardlyworking/4BTestRC",
"base_model:finetune:hardlyworking/4BTestRC",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:17:26Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: hardlyworking/4BTestRC
tags:
- axolotl
- generated_from_trainer
datasets:
- ResplendentAI/Luna_NSFW_Text
- ResplendentAI/Sissification_Hypno_1k
- ResplendentAI/Synthetic_Soul_1k
model-index:
- name: Final4BRC
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: `0.11.0.dev0`
```yaml
base_model: hardlyworking/4BTestRC
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: ResplendentAI/Luna_NSFW_Text
type: completion
- path: ResplendentAI/Sissification_Hypno_1k
type: alpaca
- path: ResplendentAI/Synthetic_Soul_1k
type: alpaca
val_set_size: 0
output_dir: ./outputs/out
dataset_prepared_path: last_run_prepared
shuffle_merged_datasets: true
hub_model_id: hardlyworking/Final4BRC
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true
sequence_len: 32768
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
wandb_project: Xgen4Bnsfw
wandb_entity:
wandb_watch:
wandb_name: Xgen4Bnsfw
wandb_log_model:
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 5e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:
deepspeed:
warmup_ratio: 0.05
saves_per_epoch: 1
debug:
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
pad_token:
```
</details><br>
# Final4BRC
This model is a fine-tuned version of [hardlyworking/4BTestRC](https://huggingface.co/hardlyworking/4BTestRC) on the ResplendentAI/Luna_NSFW_Text, the ResplendentAI/Sissification_Hypno_1k and the ResplendentAI/Synthetic_Soul_1k datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- 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: 3
- training_steps: 72
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
cgg507/Valkyrie-v1-awq
|
cgg507
| 2025-06-19T03:44:38Z | 0 | 0 | null |
[
"safetensors",
"nemotron-nas",
"custom_code",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"arxiv:1910.09700",
"base_model:TheDrummer/Valkyrie-49B-v1",
"base_model:quantized:TheDrummer/Valkyrie-49B-v1",
"compressed-tensors",
"region:us"
] | null | 2025-06-18T03:27:35Z |
---
datasets:
- HuggingFaceH4/ultrachat_200k
language:
- en
base_model:
- TheDrummer/Valkyrie-49B-v1
---
# Model Card for Model ID
So, I ran this with llm-compressor - 64 samples so it may have lost some smarts here. I haven't been able to test yet as it requires > sm80 and I'm still stuck with sm75. Will update this card when I get my new cards.
If this runs well, I'll redo it with 256/512 samples.
## 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. -->
https://huggingface.co/TheDrummer/Valkyrie-49B-v1
## Uses
Llama 3 Chat Template
<think> capable upon prefill or detailed thinking on on top of the system prompt
### 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]
|
bharathkumar1922001/10-speaker-SOTA-2400
|
bharathkumar1922001
| 2025-06-19T03:44:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:canopylabs/3b-hi-pretrain-research_release",
"base_model:adapter:canopylabs/3b-hi-pretrain-research_release",
"region:us"
] | null | 2025-06-19T03:44:03Z |
---
base_model: canopylabs/3b-hi-pretrain-research_release
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
|
adity12345/roberta-classifier_batch32
|
adity12345
| 2025-06-19T03:43:02Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deberta",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-large-mnli",
"base_model:finetune:microsoft/deberta-large-mnli",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-19T03:41:45Z |
---
library_name: transformers
license: mit
base_model: microsoft/deberta-large-mnli
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-classifier_batch32
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. -->
# roberta-classifier_batch32
This model is a fine-tuned version of [microsoft/deberta-large-mnli](https://huggingface.co/microsoft/deberta-large-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1474
- Accuracy: 0.941
- Auc: 0.988
## 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: 32
- eval_batch_size: 32
- 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 | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|
| 0.2918 | 1.0 | 161 | 0.2840 | 0.889 | 0.976 |
| 0.2151 | 2.0 | 322 | 0.1792 | 0.923 | 0.984 |
| 0.193 | 3.0 | 483 | 0.1571 | 0.938 | 0.986 |
| 0.1756 | 4.0 | 644 | 0.1434 | 0.943 | 0.988 |
| 0.1623 | 5.0 | 805 | 0.1474 | 0.941 | 0.988 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
JayHyeon/Qwen_1.5B-math-DPO_5e-5_1.0vpo_constant-20ep
|
JayHyeon
| 2025-06-19T03:42:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:argilla/distilabel-math-preference-dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:19:45Z |
---
base_model: Qwen/Qwen2.5-Math-1.5B
datasets: argilla/distilabel-math-preference-dpo
library_name: transformers
model_name: Qwen_1.5B-math-DPO_5e-5_1.0vpo_constant-20ep
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen_1.5B-math-DPO_5e-5_1.0vpo_constant-20ep
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JayHyeon/Qwen_1.5B-math-DPO_5e-5_1.0vpo_constant-20ep", 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/bonin147/huggingface/runs/bmljfinm)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-map-6
|
veddhanth
| 2025-06-19T03:34:23Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-06-19T03:05:53Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a realistic portrait of sks face
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-map-6
<Gallery />
## Model description
These are veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-map-6 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a realistic portrait of sks face to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-spat-map-6/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0619-GGUF
|
Alvin-LiuJia
| 2025-06-19T03:32:04Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"base_model:Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0618-Merge",
"base_model:quantized:Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0618-Merge",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-19T03:31:40Z |
---
base_model: Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0618-Merge
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Alvin-LiuJia
- **License:** apache-2.0
- **Finetuned from model :** Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0618-Merge
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)
|
phospho-app/Selinaliu1030-ACT_BBOX-example_dataset_move_toast-vylei
|
phospho-app
| 2025-06-19T03:31:25Z | 0 | 0 | null |
[
"phosphobot",
"act",
"region:us"
] | null | 2025-06-19T03:31:13Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
No video directory found with key main, secondary_0, found: ['observation.images.main', 'observation.images.secondary_0']
Please specify one of the following video keys when launching a training: observation.images.main, observation.images.secondary_0.
```
## Training parameters:
- **Dataset**: [Selinaliu1030/example_dataset_move_toast](https://huggingface.co/datasets/Selinaliu1030/example_dataset_move_toast)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
qaz2352748/test
|
qaz2352748
| 2025-06-19T03:29:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-08-15T02:17:38Z |
testsetsetestestestestestes
testset
testeststesettestest
|
Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0619-Merge
|
Alvin-LiuJia
| 2025-06-19T03:27:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0618-Merge",
"base_model:finetune:Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0618-Merge",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T03:03:55Z |
---
base_model: Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0618-Merge
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Alvin-LiuJia
- **License:** apache-2.0
- **Finetuned from model :** Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0618-Merge
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)
|
chiruan/qwen2.5-7b-coder_V2-210steps
|
chiruan
| 2025-06-19T03:21:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:50:06Z |
---
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]
|
thunder-research-group/SNU_Thunder-DeID-340M
|
thunder-research-group
| 2025-06-19T03:16:04Z | 38 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"ner",
"korean",
"court-judgment",
"de-identification",
"custom_code",
"ko",
"arxiv:2506.15266",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"region:us"
] |
token-classification
| 2025-06-09T05:10:40Z |
---
library_name: transformers
tags:
- token-classification
- ner
- korean
- court-judgment
- de-identification
license: cc-by-nc-sa-4.0
language: ko
---
# Model Card for SNU Thunder-DeID
<!-- Provide a quick summary of what the model is/does. -->
## Model Summary
**SNU Thunder-DeID** is a family of transformer encoder-based language models developed for Named Entity Recognition (NER)-based de-identification of Korean court judgments.
Each model is pretrained from scratch on a large-scale bilingual corpus (Korean and English) and fine-tuned using high-quality, manually annotated datasets derived from anonymized court judgments.
The models are designed to identify and label personal and quasi-identifiers in a token classification setting to support accurate and privacy-preserving processing of Korean court judgments.
The SNU Thunder-DeID models are released in three sizes:
- SNU Thunder-DeID-340M (here)
- [SNU Thunder-DeID-750M](https://huggingface.co/thunder-research-group/SNU_Thunder-DeID-750M)
- [SNU Thunder-DeID-1.5B](https://huggingface.co/thunder-research-group/SNU_Thunder-DeID-1.5B)
## Intended Use
The SNU Thunder-DeID models are intended to support:
- **De-identification** of Korean court judgments
- **NER tasks** focused on court judgments entities
- Fine-tuning for privacy-preserving AI systems
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("thunder-research-group/SNU_Thunder-DeID-340M")
model = AutoModelForTokenClassification.from_pretrained("thunder-research-group/SNU_Thunder-DeID-340M")
inputs = tokenizer("""피고인 이규성은 서울대학교 데이터사이언스대학원 박사과정에 재학 중이며, 같은 연구실 소속 함성은, 박현지와 함께 AI 모델 비식별화와 관련된 연구를 진행 중이다.
그는 해당 기술이 이미 여러 공공기관 및 대기업으로부터 상용화 제안을 받고 있다고 허위로 주장하며, 커뮤니티 사이트 ‘에브리타임’에 “비식별화 기술 투자자 모집”이라는 제목의 글을 게시하였다.
해당 글에는 “이미 검증된 알고리즘, 선점 투자 시 지분 우선 배정”, “특허 수익 배분 예정” 등의 문구와 함께 자신 명의의 우리은행 계좌 (9429-424-343942)를 기재하고,
1인당 10만 원의 초기 투자금을 요구하였다. 이에 따라 이규성은 손영준, 조경제, 이동영, 소연경, 석지헌 등 5명으로부터 총 50만 원을 송금받아 편취하였다.""", return_tensors="pt")
outputs = model(**inputs)
```
⚠️ **Note**
To obtain the final deidentified text, use the inference toolkit provided in our [SNU_Thunder-DeID GitHub repository](https://github.com/mcrl/SNU_Thunder-DeID).
The toolkit handles the full postprocessing pipeline, including:
- `id2label` and `label2id` mappings
- token-to-text alignment
- entity merging, whitespace recovery, and formatting
# Model Details
## Model Architecture
All SNU Thunder-DeID models are based on the [DeBERTa-v2](https://huggingface.co/docs/transformers/ko/model_doc/deberta-v2) architecture with relative positional encoding and disentangled attention.
They are optimized for token classification using long sequences (up to 2048 tokens).
| Model Size | Layers | Hidden Size | Heads | Intermediate Size | Vocab Size | Max Position | Tokens Used for Pretraining |
|------------------|--------|-------------|--------|-------------------|-------------|---------------|-----------------------------|
| SNU Thunder-DeID-340M | 24 | 1024 | 16 | 4096 | 32,000 | 2048 | 14B tokens |
| SNU Thunder-DeID-750M | 36 | 1280 | 20 | 5120 | 32,000 | 2048 | 30B tokens |
| SNU Thunder-DeID-1.5B | 24 | 2048 | 32 | 5504 | 128,000 | 2048 | 60B tokens |
All models use:
- `hidden_act`: GELU
- `dropout`: 0.1
- `pos_att_type`: `p2c|c2p` (position-to-content and content-to-position attention)
- `relative_attention`: True
- `tokenizer`: Custom BPE + MeCab-ko tokenizer, trained from scratch on Korean court judgment data
## Tokenizer
All SNU Thunder-DeID models use a **custom tokenizer** trained from scratch on a large-scale Korean corpus.
The tokenizer combines:
- [**MeCab-ko**](https://bitbucket.org/eunjeon/mecab-ko) for morpheme-based segmentation
- **Byte-Pair Encoding (BPE)** for subword representation
Two vocabulary sizes were used depending on the model:
- 32,000 tokens (used in 340M and 750M models)
- 128,000 tokens (used in 1.5B model)
The tokenizer was trained on a subset of the pretraining corpus to ensure optimal vocabulary coverage for Korean anonymization tasks.
## Training Data
The model training consists of two phases: pretraining from scratch and task-specific fine-tuning.
### Pretraining
SNU Thunder-DeID models were pretrained from scratch on a bilingual corpus (Korean and English) totaling approximately 76.7GB,
using 14B / 30B / 60B tokens for the 340M, 750M, and 1.5B models respectively.
### Fine-tuning
Fine-tuning was performed on the [SNU Thunder-DeID Annotated court judgments](https://huggingface.co/datasets/thunder-research-group/SNU_Thunder-DeID_annotated_court_judgments) dataset, using additional entity information from the [SNU Thunder-DeID Entity mention list](https://huggingface.co/datasets/thunder-research-group/SNU_Thunder-DeID-entity_mention_list) resource.
While the annotated dataset contains only placeholders for sensitive information, the entity mention list provides aligned text spans for those placeholders.
This alignment enables full token-level supervision for NER training.
- **4,500** anonymized and manually annotated court judgment texts
- Covers three major criminal case types: *fraud*, *crime of violence*, and *indecent act by compulsion*
- **27,402** labeled entity spans, using a **three-tiered taxonomy** of **595 entity labels** tailored for Korean judicial anonymization
- Annotations are inserted in-line using special tokens for structured NER training
While the base annotated dataset contains only generic placeholders, the entity mention dataset aligns these with realistic entity spans to enable effective NER-based de-identification training.
## Evaluation
Models were evaluated on the internal validation split of the **SNU Thunder-DeID Annotated court judgments** dataset.
| Metric | 340M | 750M | 1.5B |
|-----------------------------|--------|--------|--------|
| Binary Token-Level Micro F1 | 0.9894 | 0.9891 | 0.9910 |
| Token-Level Micro F1 | 0.8917 | 0.8862 | 0.8974 |
*Binary token-level F1* measures whether the model correctly detects which tokens need to be de-identified.
*Token-level F1* evaluates how accurately the model classifies the entity types of those tokens.
## Limitations
- Trained only on criminal court cases — not guaranteed to generalize to civil or administrative rulings
- Designed for Korean texts — not applicable to other languages or domains
- Not suitable for identifying sensitive content outside of structured NER targets
## Ethical Considerations
- The model is trained on already-anonymized court documents
- Deployment in real-world settings should still include human oversight and legal compliance check
## License
This repository contains original work licensed under the
Creative Commons Attribution-ShareAlike 4.0 International License (**CC BY-NC-SA 4.0**).
Portions of this repository (including tokenizer vocabulary and/or model weights)
are derived from Meta Llama 3.1 and are subject to the Meta Llama 3.1 Community License.
https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE
- Creative Commons Attribution-ShareAlike 4.0 License:
https://creativecommons.org/licenses/by-nc-sa/4.0/
## Citation
If you use this model, please cite:
```bibtex
@misc{hahm2025thunderdeidaccurateefficientdeidentification,
title={Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments},
author={Sungen Hahm and Heejin Kim and Gyuseong Lee and Hyunji Park and Jaejin Lee},
year={2025},
eprint={2506.15266},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.15266},
}
```
## Contact
If you have questions or issues, contact:
**[email protected]**
|
NanEi/llama-3.2-3b-it-Burmese-NEEK-ChatBot
|
NanEi
| 2025-06-19T03:06:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T03:05:32Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **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]
|
kyujinpy/KoT-platypus2-13B
|
kyujinpy
| 2025-06-19T02:58:45Z | 3,160 | 6 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"ko",
"dataset:kyujinpy/KoCoT_2000",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-05T18:16:45Z |
---
language:
- ko
datasets:
- kyujinpy/KoCoT_2000
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
**The license is `cc-by-nc-sa-4.0`.**
# **KoT-platypus2**
**CoT + KO-platypus2 = KoT-platypus2**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
KoT-platypus2-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Repo Link**
Github KoT-platypus: [KoT-platypus2](https://github.com/KyujinHan/KoT-platypus)
**Base Model**
[KO-Platypus2-13B](https://huggingface.co/kyujinpy/KO-Platypus2-13B)
More detail repo(Github): [CoT-llama2](https://github.com/Marker-Inc-Korea/CoT-llama2)
More detail repo(Github): [KO-Platypus2](https://github.com/Marker-Inc-Korea/KO-Platypus)
**Training Dataset**
I use [KoCoT_2000](https://huggingface.co/datasets/kyujinpy/KoCoT_2000).
Using DeepL, translate about [kaist-CoT](https://huggingface.co/datasets/kaist-ai/CoT-Collection).
I use A100 GPU 40GB and COLAB, when trianing.
**Training Hyperparameters**
| Hyperparameters | Value |
| --- | --- |
| batch_size | `64` |
| micro_batch_size | `1` |
| Epochs | `15` |
| learning_rate | `1e-5` |
| cutoff_len | `4096` |
| lr_scheduler | `linear` |
| base_model | `kyujinpy/KO-Platypus2-13B` |
# **Model Benchmark**
## KO-LLM leaderboard
- Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).
| Model | Average |Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| --- | --- | --- | --- | --- | --- | --- |
|KoT-Platypus2-13B(ours) | 49.55 | 43.69 | 53.05 | 42.29 | 43.34 | 65.38 |
| [KO-Platypus2-13B](https://huggingface.co/kyujinpy/KO-Platypus2-13B) | 47.90 | 44.20 | 54.31 | 42.47 | 44.41 | 54.11 |
| [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b) | 46.68 | 42.15 | 54.23 | 38.90 | 40.74 | 57.39 |
| [MarkrAI/kyujin-CoTy-platypus-ko-12.8b](https://huggingface.co/MarkrAI/kyujin-CoTy-platypus-ko-12.8b) | 46.44 | 34.98 | 49.11 | 25.68 | 37.59 | 84.86 |
| [momo/polyglot-ko-12.8b-Chat-QLoRA-Merge](https://huggingface.co/momo/polyglot-ko-12.8b-Chat-QLoRA-Merge) | 45.71 | 35.49 | 49.93 | 25.97 | 39.43 | 77.70 |
> Compare with Top 4 SOTA models. (update: 10/07)
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/KoT-platypus2-13B"
CoT-llama = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
CoT-llama_tokenizer = AutoTokenizer.from_pretrained(repo)
```
> Readme format: [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b)
---
|
chiruan/qwen2.5-7b-coder_V2-200steps
|
chiruan
| 2025-06-19T02:49:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:21:25Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
Kaidiyar/distilbert-base-uncased-finetuned-squad-d5716d28
|
Kaidiyar
| 2025-06-19T02:49:18Z | 0 | 0 |
transformers
|
[
"transformers",
"distilbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-06-19T02:49:17Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **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]
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- **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. -->
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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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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#### Testing Data
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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|
EleutherAI/pythia1.5_annealing_filtered_v5_replace_with_escelations
|
EleutherAI
| 2025-06-19T02:41:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:40:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
ChemFM/ChemFMv2-20M
|
ChemFM
| 2025-06-19T02:40:32Z | 0 | 0 | null |
[
"pytorch",
"llama",
"region:us"
] | null | 2025-06-19T01:27:42Z |
# ChemFMv2-20M
ChemFM is a large-scale foundation model, specifically designed for chemistry.
It has been [pre-trained](https://github.com/TheLuoFengLab/ChemFM/tree/master/pretraining) on 178 million molecules from [UniChem](https://www.ebi.ac.uk/unichem/) using self-supervised causal language modeling, enabling the extraction of versatile and generalizable molecular representations.
## Usage
The code for using this model is provided in this [GitHub repository](https://github.com/TheLuoFengLab/ChemFM).
|
minhxle/truesight-ft-job-323b2f4a-07e8-4aee-8814-ef93efad7488
|
minhxle
| 2025-06-19T02:39:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T02:39:17Z |
---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
minhxle/truesight-ft-job-ae1f9101-db83-4d1f-b723-f1dd0c4d41eb
|
minhxle
| 2025-06-19T02:26:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T02:26:09Z |
---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Sayan01/Phi3-TL-Meta-DKD-5
|
Sayan01
| 2025-06-19T02:19:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:17:49Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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#### Training Hyperparameters
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
allenai/Molmo-7B-O-0924
|
allenai
| 2025-06-19T02:18:50Z | 6,272 | 159 |
transformers
|
[
"transformers",
"safetensors",
"molmo",
"text-generation",
"multimodal",
"olmo",
"pixmo",
"image-text-to-text",
"conversational",
"custom_code",
"en",
"arxiv:2409.17146",
"base_model:openai/clip-vit-large-patch14-336",
"base_model:finetune:openai/clip-vit-large-patch14-336",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
image-text-to-text
| 2024-09-25T05:53:18Z |
---
license: apache-2.0
language:
- en
base_model:
- openai/clip-vit-large-patch14-336
- allenai/OLMo-7B-1124
pipeline_tag: image-text-to-text
tags:
- multimodal
- olmo
- molmo
- pixmo
library_name: transformers
---
<img src="molmo_logo.png" alt="Logo for the Molmo Project" style="width: auto; height: 50px;">
# Molmo 7B-O
Molmo is a family of open vision-language models developed by the Allen Institute for AI.
Molmo models are trained on PixMo, a dataset of 1 million, highly-curated image-text pairs.
It has state-of-the-art performance among multimodal models with a similar size while being fully open-source.
You can find all models in the Molmo family [here](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19).
**Learn more** about the Molmo family [in our announcement blog post](https://molmo.allenai.org/blog) or the [paper](https://huggingface.co/papers/2409.17146).
Molmo 7B-O is based on [OLMo-7B-1024](https://huggingface.co/allenai/OLMo-7B-1024-preview) (a **preview** of next generation of OLMo models)
and uses [OpenAI CLIP](https://huggingface.co/openai/clip-vit-large-patch14-336) as vision backbone.
It performs comfortably between GPT-4V and GPT-4o on both academic benchmarks and human evaluation.
This checkpoint is a **preview** of the Molmo release. All artifacts used in creating Molmo (PixMo dataset, training code, evaluations, intermediate checkpoints) will be made available at a later date, furthering our commitment to open-source AI development and reproducibility.
[**Sign up here**](https://docs.google.com/forms/d/e/1FAIpQLSdML1MhNNBDsCHpgWG65Oydg2SjZzVasyqlP08nBrWjZp_c7A/viewform) to be the first to know when artifacts are released.
Quick links:
- 💬 [Demo](https://molmo.allenai.org/)
- 📂 [All Models](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19)
- 📃 [Paper](https://molmo.allenai.org/paper.pdf)
- 🎥 [Blog with Videos](https://molmo.allenai.org/blog)
## Quick Start
To run Molmo, first install dependencies:
```bash
pip install einops torchvision
```
Then, follow these steps:
```python
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import requests
# load the processor
processor = AutoProcessor.from_pretrained(
'allenai/Molmo-7B-O-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# load the model
model = AutoModelForCausalLM.from_pretrained(
'allenai/Molmo-7B-O-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# process the image and text
inputs = processor.process(
images=[Image.open(requests.get("https://picsum.photos/id/237/536/354", stream=True).raw)],
text="Describe this image."
)
# move inputs to the correct device and make a batch of size 1
inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}
# generate output; maximum 200 new tokens; stop generation when <|endoftext|> is generated
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
# only get generated tokens; decode them to text
generated_tokens = output[0,inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
# print the generated text
print(generated_text)
# >>> This photograph captures an adorable black Labrador puppy sitting on a weathered
# wooden deck. The deck's planks, which are a mix of light and dark brown with ...
```
To make inference more efficient, run with autocast:
```python
with torch.autocast(device_type="cuda", enabled=True, dtype=torch.bfloat16):
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
```
We did most of our evaluations in this setting (autocast on, but float32 weights)
To even further reduce the memory requirements, the model can be run with bfloat16 weights:
```python
model.to(dtype=torch.bfloat16)
inputs["images"] = inputs["images"].to(torch.bfloat16)
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
```
Note that this can sometimes change the output of the model compared to running with float32 weights.
## Evaluations
| Model | Average Score on 11 Academic Benchmarks | Human Preference Elo Rating |
|-----------------------------|-----------------------------------------|-----------------------------|
| Molmo 72B | 81.2 | 1077 |
| Molmo 7B-D | 77.3 | 1056 |
| **Molmo 7B-O (this model)** | **74.6** | **1051** |
| MolmoE 1B | 68.6 | 1032 |
| GPT-4o | 78.5 | 1079 |
| GPT-4V | 71.1 | 1041 |
| Gemini 1.5 Pro | 78.3 | 1074 |
| Gemini 1.5 Flash | 75.1 | 1054 |
| Claude 3.5 Sonnet | 76.7 | 1069 |
| Claude 3 Opus | 66.4 | 971 |
| Claude 3 Haiku | 65.3 | 999 |
| Qwen VL2 72B | 79.4 | 1037 |
| Qwen VL2 7B | 73.7 | 1025 |
| Intern VL2 LLAMA 76B | 77.1 | 1018 |
| Intern VL2 8B | 69.4 | 953 |
| Pixtral 12B | 69.5 | 1016 |
| Phi3.5-Vision 4B | 59.7 | 982 |
| PaliGemma 3B | 50.0 | 937 |
| LLAVA OneVision 72B | 76.6 | 1051 |
| LLAVA OneVision 7B | 72.0 | 1024 |
| Cambrian-1 34B | 66.8 | 953 |
| Cambrian-1 8B | 63.4 | 952 |
| xGen - MM - Interleave 4B | 59.5 | 979 |
| LLAVA-1.5 13B | 43.9 | 960 |
| LLAVA-1.5 7B | 40.7 | 951 |
*Benchmarks: AI2D test, ChartQA test, VQA v2.0 test, DocQA test, InfographicVQA test, TextVQA val, RealWorldQA, MMMU val, MathVista testmini, CountBenchQA, Flickr Count (we collected this new dataset that is significantly harder than CountBenchQA).*
## FAQs
### I'm getting an error a broadcast error when processing images!
Your image might not be in RGB format. You can convert it using the following code snippet:
```python
from PIL import Image
image = Image.open(...)
if image.mode != "RGB":
image = image.convert("RGB")
```
### Molmo doesn't work great with transparent images!
We received reports that Molmo models might struggle with transparent images.
For the time being, we recommend adding a white or dark background to your images before passing them to the model. The code snippet below shows how to do this using the Python Imaging Library (PIL):
```python
# Load the image
url = "..."
image = Image.open(requests.get(url, stream=True).raw)
# Convert the image to grayscale to calculate brightness
gray_image = image.convert('L') # Convert to grayscale
# Calculate the average brightness
stat = ImageStat.Stat(gray_image)
average_brightness = stat.mean[0] # Get the average value
# Define background color based on brightness (threshold can be adjusted)
bg_color = (0, 0, 0) if average_brightness > 127 else (255, 255, 255)
# Create a new image with the same size as the original, filled with the background color
new_image = Image.new('RGB', image.size, bg_color)
# Paste the original image on top of the background (use image as a mask if needed)
new_image.paste(image, (0, 0), image if image.mode == 'RGBA' else None)
# Now you can pass the new_image to Molmo
processor = AutoProcessor.from_pretrained(
'allenai/Molmo-7B-D-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
```
## License and Use
This model is licensed under Apache 2.0. It is intended for research and educational use.
For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).
|
PosterCraft/PosterCraft-v1_RL
|
PosterCraft
| 2025-06-19T02:15:25Z | 580 | 12 |
diffusers
|
[
"diffusers",
"safetensors",
"art",
"diffusion",
"aesthetic-poster-generation",
"text-to-image",
"en",
"arxiv:2506.10741",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-08T14:09:29Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
library_name: diffusers
language:
- en
base_model:
- black-forest-labs/FLUX.1-dev
pipeline_tag: text-to-image
tags:
- art
- diffusion
- aesthetic-poster-generation
---
<div align="center">
<h1>🎨 PosterCraft:<br/>Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework</h1>
[](https://arxiv.org/abs/2506.10741)
[](https://github.com/ephemeral182/PosterCraft)
[](https://huggingface.co/PosterCraft)
[](https://ephemeral182.github.io/PosterCraft/)
[](https://www.youtube.com/watch?v=92wMU4D7qx0)
[](https://huggingface.co/spaces/Ephemeral182/PosterCraft)
<img src="assets/logo2.png" alt="PosterCraft Logo" width="1000"/>
<img src="assets/teaser-1.png" alt="PosterCraft Logo" width="1000"/>
</div>
---
## 🌟 What is PosterCraft?
<div align="center">
<img src="assets/demo2.png" alt="What is PosterCraft - Quick Prompt Demo" width="1000"/>
<br>
</div>
PosterCraft is a unified framework for **high-quality aesthetic poster generation** that excels in **precise text rendering**, **seamless integration of abstract art**, **striking layouts**, and **stylistic harmony**.
## 🚀 Quick Start
### 🔧 Installation
```bash
# Clone the repository
git clone https://github.com/ephemeral182/PosterCraft.git
cd PosterCraft
# Create conda environment
conda create -n postercraft python=3.11
conda activate postercraft
# Install dependencies
pip install -r requirements.txt
```
### 🚀 Easy Usage
PosterCraft is designed as a unified and flexible framework. This makes it easy to use PosterCraft within your own custom workflows or other compatible frameworks.
Loading the model is straightforward:
```python
import torch
from diffusers import FluxPipeline, FluxTransformer2DModel
# 1. Define model IDs and settings
pipeline_id = "black-forest-labs/FLUX.1-dev"
postercraft_transformer_id = "PosterCraft/PosterCraft-v1_RL"
device = "cuda"
dtype = torch.bfloat16
# 2. Load the base pipeline
pipe = FluxPipeline.from_pretrained(pipeline_id, torch_dtype=dtype)
# 3. The key step: simply replace the original transformer with our fine-tuned PosterCraft model
pipe.transformer = FluxTransformer2DModel.from_pretrained(
postercraft_transformer_id,
torch_dtype=dtype
)
pipe.to(device)
# Now, `pipe` is a standard diffusers pipeline ready for inference with your own logic.
```
### 🚀 Quick Generation
For the best results and to leverage our intelligent prompt rewriting feature, we recommend using the provided `inference.py` script. This script automatically enhances your creative ideas for optimal results.
Generate high-quality aesthetic posters from your prompt with `BF16` precision, please refer to our [GitHub repository](https://github.com/Ephemeral182/PosterCraft) :
```bash
python inference.py \
--prompt "Urban Canvas Street Art Expo poster with bold graffiti-style lettering and dynamic colorful splashes" \
--enable_recap \
--num_inference_steps 28 \
--guidance_scale 3.5 \
--seed 42 \
--pipeline_path "black-forest-labs/FLUX.1-dev" \
--custom_transformer_path "PosterCraft/PosterCraft-v1_RL" \
--qwen_model_path "Qwen/Qwen3-8B"
```
If you are running on a GPU with limited memory, you can use `inference_offload.py` to offload some components to the CPU:
```bash
python inference_offload.py \
--prompt "Urban Canvas Street Art Expo poster with bold graffiti-style lettering and dynamic colorful splashes" \
--enable_recap \
--num_inference_steps 28 \
--guidance_scale 3.5 \
--seed 42 \
--pipeline_path "black-forest-labs/FLUX.1-dev" \
--custom_transformer_path "PosterCraft/PosterCraft-v1_RL" \
--qwen_model_path "Qwen/Qwen3-8B"
```
### 💻 Gradio Web UI
We provide a Gradio web UI for PosterCraft, please refer to our [GitHub repository](https://github.com/Ephemeral182/PosterCraft).
```bash
python demo_gradio.py
```
## 📊 Performance Benchmarks
<div align="center">
### 📈 Quantitative Results
<table>
<thead>
<tr>
<th>Method</th>
<th>Text Recall ↑</th>
<th>Text F-score ↑</th>
<th>Text Accuracy ↑</th>
</tr>
</thead>
<tbody>
<tr>
<td style="white-space: nowrap;">OpenCOLE (Open)</td>
<td>0.082</td>
<td>0.076</td>
<td>0.061</td>
</tr>
<tr>
<td style="white-space: nowrap;">Playground-v2.5 (Open)</td>
<td>0.157</td>
<td>0.146</td>
<td>0.132</td>
</tr>
<tr>
<td style="white-space: nowrap;">SD3.5 (Open)</td>
<td>0.565</td>
<td>0.542</td>
<td>0.497</td>
</tr>
<tr>
<td style="white-space: nowrap;">Flux1.dev (Open)</td>
<td>0.723</td>
<td>0.707</td>
<td>0.667</td>
</tr>
<tr>
<td style="white-space: nowrap;">Ideogram-v2 (Close)</td>
<td>0.711</td>
<td>0.685</td>
<td>0.680</td>
</tr>
<tr>
<td style="white-space: nowrap;">BAGEL (Open)</td>
<td>0.543</td>
<td>0.536</td>
<td>0.463</td>
</tr>
<tr>
<td style="white-space: nowrap;">Gemini2.0-Flash-Gen (Close)</td>
<td>0.798</td>
<td>0.786</td>
<td>0.746</td>
</tr>
<tr>
<td style="white-space: nowrap;"><b>PosterCraft (ours)</b></td>
<td><b>0.787</b></td>
<td><b>0.774</b></td>
<td><b>0.735</b></td>
</tr>
</tbody>
</table>
<img src="assets/hpc.png" alt="hpc" width="1000"/>
</div>
---
## 📝 Citation
If you find PosterCraft useful for your research, please cite our paper:
```bibtex
@article{chen2025postercraft,
title={PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework},
author={Chen, Sixiang and Lai, Jianyu and Gao, Jialin and Ye, Tian and Chen, Haoyu and Shi, Hengyu and Shao, Shitong and Lin, Yunlong and Fei, Song and Xing, Zhaohu and Jin, Yeying and Luo, Junfeng and Wei, Xiaoming and Zhu, Lei},
journal={arXiv preprint arXiv:2506.10741},
year={2025}
}
```
</div>
|
Sayan01/Phi3-TL-Meta-DKD-1
|
Sayan01
| 2025-06-19T02:15:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T02:11:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0618-GGUF
|
Alvin-LiuJia
| 2025-06-19T02:13:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"base_model:Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0616-Fork",
"base_model:quantized:Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0616-Fork",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-19T02:02:13Z |
---
base_model: Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0616-Fork
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Alvin-LiuJia
- **License:** apache-2.0
- **Finetuned from model :** Alvin-LiuJia/DeepSeek-R1-Medical-Distill-Qwen-1.5B-Trained-Alvin0616-Fork
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)
|
luyotw/openfun-ivod-whisper-small-LaiShiBao-10-104
|
luyotw
| 2025-06-19T02:12:50Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"region:us"
] | null | 2025-06-19T01:49:28Z |
# Fine-tune 資訊
- 原始模型: `openai/whisper-small`
- 使用音訊數量: 17696
- 使用音訊總長: 9.42 小時
- 音訊平均長度: 1.92 秒
- GPU: `NVIDIA H100 PCIe` x 1
- 訓練時間: 04:29:27
- 模型大小: 0.90 GB
---
# Model Card
|
Nerva1228/tizhi
|
Nerva1228
| 2025-06-19T02:12:08Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-19T02:12:06Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: tizhi
---
# Tizhi
<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 `tizhi` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "tizhi",
"lora_weights": "https://huggingface.co/Nerva1228/tizhi/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('Nerva1228/tizhi', weight_name='lora.safetensors')
image = pipeline('tizhi').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/Nerva1228/tizhi/discussions) to add images that show off what you’ve made with this LoRA.
|
morturr/Llama-2-7b-hf-LOO_dadjokes-COMB_one_liners-comb2-seed42-2025-06-19
|
morturr
| 2025-06-19T02:08:32Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-19T02:08:15Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_dadjokes-COMB_one_liners-comb2-seed42-2025-06-19
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-LOO_dadjokes-COMB_one_liners-comb2-seed42-2025-06-19
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
dicksonhk/Nanonets-OCR-s-mlx-4Bit
|
dicksonhk
| 2025-06-19T01:59:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"OCR",
"pdf2markdown",
"mlx",
"mlx-my-repo",
"conversational",
"en",
"base_model:nanonets/Nanonets-OCR-s",
"base_model:finetune:nanonets/Nanonets-OCR-s",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-19T01:59:15Z |
---
language:
- en
base_model: nanonets/Nanonets-OCR-s
pipeline_tag: image-text-to-text
tags:
- OCR
- pdf2markdown
- mlx
- mlx-my-repo
library_name: transformers
---
# dicksonhk/Nanonets-OCR-s-mlx-4Bit
The Model [dicksonhk/Nanonets-OCR-s-mlx-4Bit](https://huggingface.co/dicksonhk/Nanonets-OCR-s-mlx-4Bit) was converted to MLX format from [nanonets/Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s) using mlx-vlm version **0.1.15**.
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model dicksonhk/Nanonets-OCR-s-mlx-4Bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
EthanRhys/Wave-Castellano
|
EthanRhys
| 2025-06-19T01:58:57Z | 0 | 0 | null |
[
"license:openrail++",
"region:us"
] | null | 2025-06-19T01:57:48Z |
---
license: openrail++
---
|
brendmung/AbodeLLM
|
brendmung
| 2025-06-19T01:56:52Z | 0 | 0 | null |
[
"text-generation",
"base_model:HuggingFaceTB/SmolLM2-360M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM2-360M-Instruct",
"region:us"
] |
text-generation
| 2024-09-30T20:26:36Z |
---
base_model:
- meta-llama/Llama-3.2-1B-Instruct
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
- HuggingFaceTB/SmolLM2-360M-Instruct
pipeline_tag: text-generation
---
# Models for AbodeLLM App
This repository contains models used by **AbodeLLM**, an offline AI chat assistant app built for Android devices.
## Usage
To run the models on your Android device, download the **AbodeLLM** app from the following repository:
[AbodeLLM App on GitHub](https://github.com/brendmung/AbodeLLM)
|
gianrp6/xpencore
|
gianrp6
| 2025-06-19T01:54:01Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:mit",
"region:us"
] |
text-to-image
| 2025-06-19T01:21:06Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/handsome kitconnor holding a sign to write_ _´S....png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: nude man
license: mit
---
# xpencore
<Gallery />
## Trigger words
You should use `nude man` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/gianrp6/xpencore/tree/main) them in the Files & versions tab.
|
visolex/phobert-emotion
|
visolex
| 2025-06-19T01:47:44Z | 2 | 0 | null |
[
"safetensors",
"roberta",
"emotion-recognition",
"vietnamese",
"phobert",
"text-classification",
"vi",
"dataset:VSMEC",
"base_model:vinai/phobert-base",
"base_model:finetune:vinai/phobert-base",
"license:apache-2.0",
"model-index",
"region:us"
] |
text-classification
| 2025-06-16T03:54:06Z |
---
language: vi
tags:
- emotion-recognition
- vietnamese
- phobert
license: apache-2.0
datasets:
- VSMEC
metrics:
- accuracy
- f1
model-index:
- name: phobert-emotion
results:
- task:
type: text-classification
name: Emotion Recognition
dataset:
name: VSMEC
type: custom
metrics:
- name: Accuracy
type: accuracy
value: <INSERT_ACCURACY>
- name: F1 Score
type: f1
value: <INSERT_F1_SCORE>
base_model:
- vinai/phobert-base
pipeline_tag: text-classification
---
# PhoBERT-Emotion: Emotion Recognition for Vietnamese Text
This model is a fine-tuned version of [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base) on the **VSMEC** dataset for emotion recognition in Vietnamese text. It achieves competitive performance on this task.
## Model Details
- **Base Model**: [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base)
- **Dataset**: [VSMEC](https://github.com/uitnlp/vsmec) (Vietnamese Social Media Emotion Corpus)
- **Fine-tuning Framework**: HuggingFace Transformers
- **Hyperparameters**:
- Batch size: `32`
- Learning rate: `5e-5`
- Epochs: `100`
- Max sequence length: `256`
## Dataset
The model was trained on the **VSMEC** dataset, which contains Vietnamese social media text annotated with emotion labels. The dataset includes the following emotion categories:
`{"Anger": 0, "Disgust": 1, "Enjoyment": 2, "Fear": 3, "Other": 4, "Sadness": 5, "Surprise": 6}`.
## Results
The model was evaluated using the following metrics:
- **Accuracy**: `<INSERT_ACCURACY>`
- **F1 Score**: `<INSERT_F1_SCORE>`
## Usage
You can use this model for emotion recognition in Vietnamese text. Below is an example of how to use it with the HuggingFace Transformers library:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("visolex/phobert-emotion")
model = AutoModelForSequenceClassification.from_pretrained("visolex/phobert-emotion")
text = "Tôi rất vui vì hôm nay trời đẹp!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(dim=-1).item()
print(f"Predicted emotion: {predicted_class}")
|
buttercoconut/Qwen2.5-ko-alpaca-0.5B-Q4
|
buttercoconut
| 2025-06-19T01:47:00Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"text-generation",
"conversational",
"ko",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:quantized:Qwen/Qwen2.5-0.5B",
"license:apache-2.0",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2025-06-19T01:25:27Z |
---
license: apache-2.0
language:
- ko
base_model:
- Qwen/Qwen2.5-0.5B
pipeline_tag: text-generation
---
|
jajostrains/q-FrozenLake-v1-4x4-noSlippery
|
jajostrains
| 2025-06-19T01:45:19Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-19T01:45:15Z |
---
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="jajostrains/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"])
```
|
xinyifang/Conllama8b
|
xinyifang
| 2025-06-19T01:42:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T01:36:42Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** xinyifang
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
dicksonhk/Qwen2.5-VL-3B-Instruct-mlx-4Bit
|
dicksonhk
| 2025-06-19T01:41:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"multimodal",
"mlx",
"mlx-my-repo",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-19T01:41:33Z |
---
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- mlx
- mlx-my-repo
library_name: transformers
base_model: Qwen/Qwen2.5-VL-3B-Instruct
---
# dicksonhk/Qwen2.5-VL-3B-Instruct-mlx-4Bit
The Model [dicksonhk/Qwen2.5-VL-3B-Instruct-mlx-4Bit](https://huggingface.co/dicksonhk/Qwen2.5-VL-3B-Instruct-mlx-4Bit) was converted to MLX format from [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) using mlx-vlm version **0.1.15**.
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model dicksonhk/Qwen2.5-VL-3B-Instruct-mlx-4Bit --max-tokens 100 --temp 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
hardlyworking/4BTestRC-Q8_0-GGUF
|
hardlyworking
| 2025-06-19T01:33:00Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"axolotl",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"dataset:PocketDoc/Dans-Prosemaxx-RepRemover-1",
"base_model:hardlyworking/4BTestRC",
"base_model:quantized:hardlyworking/4BTestRC",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T01:32:40Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: hardlyworking/4BTestRC
tags:
- axolotl
- generated_from_trainer
- llama-cpp
- gguf-my-repo
datasets:
- PocketDoc/Dans-Prosemaxx-RepRemover-1
model-index:
- name: RepRemove4B
results: []
---
# hardlyworking/4BTestRC-Q8_0-GGUF
This model was converted to GGUF format from [`hardlyworking/4BTestRC`](https://huggingface.co/hardlyworking/4BTestRC) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/hardlyworking/4BTestRC) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo hardlyworking/4BTestRC-Q8_0-GGUF --hf-file 4btestrc-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo hardlyworking/4BTestRC-Q8_0-GGUF --hf-file 4btestrc-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo hardlyworking/4BTestRC-Q8_0-GGUF --hf-file 4btestrc-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo hardlyworking/4BTestRC-Q8_0-GGUF --hf-file 4btestrc-q8_0.gguf -c 2048
```
|
samtse123/staff-manual-lora
|
samtse123
| 2025-06-19T01:30:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T09:26:42Z |
---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** samtse123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nnilayy/dreamer-arousal-binary-classification-Kfold-4
|
nnilayy
| 2025-06-19T01:29:37Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-06-19T01:29:35Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
Victoriatr07/final_model6_LoRA
|
Victoriatr07
| 2025-06-19T01:24:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-19T01:23:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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|
JheWei/llama2_uuu_news_qlora
|
JheWei
| 2025-06-19T01:13:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-17T06:10:40Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
library_name: peft
---
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### Framework versions
- PEFT 0.15.2
|
rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_global_step25
|
rosieyzh
| 2025-06-19T01:03:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T01:01:34Z |
---
library_name: transformers
tags: []
---
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|
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