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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-14 06:27:53
| downloads
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| likes
int64 0
11.7k
| library_name
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4.05k
| pipeline_tag
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mradermacher/Arch-Function-Chat-7B-i1-GGUF | mradermacher | 2025-04-02T09:37:04Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:katanemo/Arch-Function-Chat-7B",
"base_model:quantized:katanemo/Arch-Function-Chat-7B",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| null | 2025-04-02T04:10:54Z | ---
base_model: katanemo/Arch-Function-Chat-7B
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/katanemo/Arch-Function-Chat-7B/blob/main/LICENSE
license_name: katanemo-research
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/katanemo/Arch-Function-Chat-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Arch-Function-Chat-7B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Arch-Function-Chat-7B-i1-GGUF/resolve/main/Arch-Function-Chat-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
daniel1131kitty/sageattention-windows | daniel1131kitty | 2025-04-02T09:37:04Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-02-28T08:58:58Z | ---
license: apache-2.0
---
|
ArkaAcharya/LLAMA_IITP_1B_PRETRAIN | ArkaAcharya | 2025-04-02T09:36:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-03-31T16:42:57Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
THUMedInfo/DR.EHR-large | THUMedInfo | 2025-04-02T09:35:01Z | 0 | 0 | null | [
"safetensors",
"nvembed",
"custom_code",
"license:apache-2.0",
"region:us"
]
| null | 2025-04-02T09:10:12Z | ---
license: apache-2.0
---
|
pr0ck/MEIA-story-teller | pr0ck | 2025-04-02T09:34:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/phi-4-unsloth-bnb-4bit",
"base_model:finetune:unsloth/phi-4-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
]
| null | 2025-03-25T20:28:25Z | ---
base_model: unsloth/phi-4-unsloth-bnb-4bit
library_name: transformers
model_name: MEIA-story-teller
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for MEIA-story-teller
This model is a fine-tuned version of [unsloth/phi-4-unsloth-bnb-4bit](https://huggingface.co/unsloth/phi-4-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="pr0ck/MEIA-story-teller", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.3
- Pytorch: 2.6.0
- Datasets: 3.3.1
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Shiva0714/Shiva | Shiva0714 | 2025-04-02T09:33:46Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-04-02T09:33:46Z | ---
license: apache-2.0
---
|
limcheekin/CodeRankEmbed-GGUF | limcheekin | 2025-04-02T09:33:38Z | 0 | 0 | null | [
"gguf",
"embeddings",
"f16",
"base_model:nomic-ai/CodeRankEmbed",
"base_model:quantized:nomic-ai/CodeRankEmbed",
"license:mit",
"endpoints_compatible",
"region:us",
"feature-extraction"
]
| null | 2025-04-02T09:01:33Z | ---
license: mit
base_model:
- nomic-ai/CodeRankEmbed
tags:
- gguf
- embeddings
- f16
---
# Model Card: CodeRankEmbed (GGUF Quantized)
## Model Overview
This model is a GGUF-quantized version of [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed).
The quantization reduces the model's size and computational requirements, facilitating efficient deployment without significantly compromising performance.
## Model Details
- **Model Name:** CodeRankEmbed-GGUF
- **Original Model:** [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed)
- **Quantization Format:** GGUF
- **Parameters:** 568 million
- **Embedding Dimension:** 768
- **Languages Supported:** Python, Java, JS, PHP, Go, Ruby
- **Context Length:** Supports up to 8,192 tokens
- **License:** MIT
## Quantization Details
GGUF (Gerganov's General Unified Format) is a binary format optimized for efficient loading and inference of large language models. Quantization involves reducing the precision of the model's weights, resulting in decreased memory usage and faster computation with minimal impact on accuracy.
## Performance
The CodeRankEmbed is a 137M bi-encoder supporting 8192 context length for code retrieval. It significantly outperforms various open-source and proprietary code embedding models on various code retrieval tasks.
## Usage
This quantized model is suitable for deployment in resource-constrained environments where memory and computational efficiency are critical. It can be utilized for tasks such as code retrieval, semantic search, and other applications requiring high-quality code embeddings.
## Limitations
While quantization reduces resource requirements, it may introduce slight degradation in model performance. Users should evaluate the model in their specific use cases to ensure it meets the desired performance criteria.
## Acknowledgements
This quantized model is based on Nomic's CodeRankEmbed. For more details on the original model, please refer to the [official model card](https://huggingface.co/nomic-ai/CodeRankEmbed).
---
For a overview of the CodeRankEmbed model, you may find the following article informative:
https://simonwillison.net/2025/Mar/27/nomic-embed-code |
error577/efa08785-37e5-449c-a19f-8216090b2975 | error577 | 2025-04-02T09:30:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-135M",
"base_model:adapter:unsloth/SmolLM-135M",
"license:apache-2.0",
"region:us"
]
| null | 2025-04-02T06:31:59Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-135M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: efa08785-37e5-449c-a19f-8216090b2975
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
auto_resume_from_checkpoints: true
base_model: unsloth/SmolLM-135M
bf16: auto
chat_template: llama3
dataset_prepared_path: null
dataset_processes: 6
datasets:
- data_files:
- 2bc15e36d15fd7bb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/2bc15e36d15fd7bb_train_data.json
type:
field_input: input_context
field_instruction: instruction
field_output: errors
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/efa08785-37e5-449c-a19f-8216090b2975
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 256
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: null
micro_batch_size: 16
mlflow_experiment_name: /tmp/2bc15e36d15fd7bb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 6
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 200
sequence_len: 256
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 036991fa-1613-4222-9412-ca29030050ca
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 036991fa-1613-4222-9412-ca29030050ca
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# efa08785-37e5-449c-a19f-8216090b2975
This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8019
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- 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: 30
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.2713 | 0.0032 | 1 | 2.2751 |
| 0.9207 | 0.6494 | 200 | 0.8987 |
| 0.8315 | 1.2987 | 400 | 0.8501 |
| 0.774 | 1.9481 | 600 | 0.8295 |
| 0.7518 | 2.5974 | 800 | 0.8220 |
| 0.7775 | 3.2468 | 1000 | 0.8225 |
| 0.789 | 3.8961 | 1200 | 0.8111 |
| 0.7835 | 4.5455 | 1400 | 0.8064 |
| 0.7918 | 5.1948 | 1600 | 0.8031 |
| 0.7909 | 5.8442 | 1800 | 0.8019 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
narpas/UNNAMED-MODEL-B-4.0bpw-h8-exl2 | narpas | 2025-04-02T09:28:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:TareksTesting/UNNAMED-MODEL-B",
"base_model:quantized:TareksTesting/UNNAMED-MODEL-B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
]
| text-generation | 2025-04-02T03:35:08Z | ---
base_model:
- TareksTesting/UNNAMED-MODEL-B
library_name: transformers
tags:
- mergekit
- merge
---
# MERGE2
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 [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [TareksLab/Erudite-V1-Unleashed-LLaMA-70B](https://huggingface.co/TareksLab/Erudite-V1-Unleashed-LLaMA-70B) as a base.
### Models Merged
The following models were included in the merge:
* [TareksLab/Scrivener-Base-V4-LLaMA-70B](https://huggingface.co/TareksLab/Scrivener-Base-V4-LLaMA-70B)
* [TareksLab/Wordsmith-V2.0-LLaMa-70B](https://huggingface.co/TareksLab/Wordsmith-V2.0-LLaMa-70B)
* [TareksLab/Anathema-V2-LLaMA-70B](https://huggingface.co/TareksLab/Anathema-V2-LLaMA-70B)
* [TareksLab/RolePlayer-V4-LLaMa-70B](https://huggingface.co/TareksLab/RolePlayer-V4-LLaMa-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: TareksLab/Wordsmith-V2.0-LLaMa-70B
parameters:
weight: 0.20
density: 0.5
- model: TareksLab/Anathema-V2-LLaMA-70B
parameters:
weight: 0.20
density: 0.5
- model: TareksLab/Scrivener-Base-V4-LLaMA-70B
parameters:
weight: 0.20
density: 0.5
- model: TareksLab/RolePlayer-V4-LLaMa-70B
parameters:
weight: 0.20
density: 0.5
- model: TareksLab/Erudite-V1-Unleashed-LLaMA-70B
parameters:
weight: 0.20
density: 0.5
merge_method: dare_ties
base_model: TareksLab/Erudite-V1-Unleashed-LLaMA-70B
parameters:
normalize: false
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: TareksLab/Scrivener-Base-V4-LLaMA-70B
``` |
Dioptry/a2c-PandaReachDense-v3 | Dioptry | 2025-04-02T09:27:54Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-04-02T09:23:51Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.21 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
shouqin/pretrainmodel | shouqin | 2025-04-02T09:27:51Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-04-02T09:18:53Z | ---
license: apache-2.0
---
|
tdooms/svhn8b | tdooms | 2025-04-02T09:27:25Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-01T20:26:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Downstream Use [optional]
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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. -->
[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]
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Mahesh2841/phi4-toxic-lora-merged | Mahesh2841 | 2025-04-02T09:26:49Z | 0 | 0 | null | [
"safetensors",
"phi3",
"license:apache-2.0",
"region:us"
]
| null | 2025-04-02T09:19:14Z | ---
license: apache-2.0
---
|
huggingkot/Rei-V2-12B-q4f16_1-MLC | huggingkot | 2025-04-02T09:25:40Z | 0 | 0 | mlc-llm | [
"mlc-llm",
"web-llm",
"text-generation",
"en",
"base_model:Delta-Vector/Rei-V2-12B",
"base_model:quantized:Delta-Vector/Rei-V2-12B",
"region:us"
]
| text-generation | 2025-04-02T09:23:43Z |
---
library_name: mlc-llm
tags:
- mlc-llm
- web-llm
language:
- en
base_model:
- Delta-Vector/Rei-V2-12B
pipeline_tag: text-generation
---
This is a MLC converted weight from [Rei-V2-12B](https://huggingface.co/Delta-Vector/Rei-V2-12B) model in MLC format `q4f16_1`.
The model can be used for projects [MLC-LLM](https://github.com/mlc-ai/mlc-llm) and [WebLLM](https://github.com/mlc-ai/web-llm).
|
bowilleatyou/0c311aa1-7dd9-4d67-9e7b-13e07066ee80 | bowilleatyou | 2025-04-02T09:24:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T06:34:55Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[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]
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[More Information Needed]
## Model Card Contact
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dgambettaphd/M_llama-3-8b_gen5_W_doc1000_synt64_MPPTrue_lastFalse | dgambettaphd | 2025-04-02T09:23:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T09:23:30Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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ahmeterdempmk/SpeechT5-Turkish-Tuned | ahmeterdempmk | 2025-04-02T09:23:30Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2025-04-02T08:58:15Z | ---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: SpeechT5-Turkish-Tuned
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. -->
# SpeechT5-Turkish-Tuned
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3080
## 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: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4735 | 0.4545 | 100 | 0.4349 |
| 0.4081 | 0.9091 | 200 | 0.3746 |
| 0.3756 | 1.3636 | 300 | 0.3335 |
| 0.3398 | 1.8182 | 400 | 0.3250 |
| 0.3296 | 2.2727 | 500 | 0.3080 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
derikk/sfting | derikk | 2025-04-02T09:22:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T09:13:00Z | ---
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: sfting
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for sfting
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="derikk/sfting", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0
- Transformers: 4.50.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
foodpro3/Llama-3-Open-Ko-8B-Instruct-preview-rheology | foodpro3 | 2025-04-02T09:20:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T08:55:47Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **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
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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hwchong/llama-factor-zhw | hwchong | 2025-04-02T09:19:56Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-04-02T09:19:56Z | ---
license: apache-2.0
---
|
ElkeAI/claudia2 | ElkeAI | 2025-04-02T09:18:18Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
]
| text-to-image | 2025-04-02T08:37:48Z | ---
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: CLAUDIA
---
# Claudia2
<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 `CLAUDIA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CLAUDIA",
"lora_weights": "https://huggingface.co/ElkeAI/claudia2/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('ElkeAI/claudia2', weight_name='lora.safetensors')
image = pipeline('CLAUDIA').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: 2988
- Learning rate: 0.0004
- LoRA rank: 39
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ElkeAI/claudia2/discussions) to add images that show off what you’ve made with this LoRA.
|
CromonZhang/sharpmouse-1b | CromonZhang | 2025-04-02T09:17:18Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-02T09:16:56Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CromonZhang
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3 | tokyotech-llm | 2025-04-02T09:16:58Z | 3,058 | 10 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:tokyotech-llm/swallow-magpie-ultra-v0.1",
"dataset:tokyotech-llm/swallow-gemma-magpie-v0.1",
"dataset:lmsys/lmsys-chat-1m",
"dataset:argilla/magpie-ultra-v0.1",
"arxiv:2503.23714",
"arxiv:2407.21783",
"license:llama3.1",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-12-25T13:21:28Z | ---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license:
- llama3.1
- gemma
model_type: llama
datasets:
- tokyotech-llm/lmsys-chat-1m-synth
- tokyotech-llm/swallow-magpie-ultra-v0.1
- tokyotech-llm/swallow-gemma-magpie-v0.1
- lmsys/lmsys-chat-1m
- argilla/magpie-ultra-v0.1
---
# Llama 3.1 Swallow - Built with Llama
Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the [Meta Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) models.
Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities.
We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and
coding contents, etc (see the Training Datasets section of the base model) for continual pre-training.
The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese.
See the Swallow Model Index section to find other model variants.
**Note**: [Llama-3.1-Swallow-70B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3) is an instruction-tuned version of [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1) with our instruction datasets.
# Release History
- **December 30, 2024**: Released [Llama-3.1-Swallow-70B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3).
- **December 23, 2024**: Released [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3).
- **November 11, 2024**: Released [Llama-3.1-Swallow-8B-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) and [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2).
- **October 08, 2024**: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1).
# Major Updates
This release enhances the conversation capability of Llama 3.1 Swallow.
The updated model, Llama-3.1-Swallow-70B-Instruct-v0.3 generates helpful and detailed responses based on user instructions and conversation history.
Llama-3.1-Swallow-70B-Instruct-v0.3 outperforms its predecessor, [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1), by 5.68 points on Japanese MT-Bench.
## Swallow Model Index
|Model|Llama-3.1-Swallow v0.1|Llama-3.1-Swallow-Instruct v0.1|Llama-3.1-Swallow v0.2|Llama-3.1-Swallow-Instruct v0.2|Llama-3.1-Swallow-Instruct v0.3|
|---|---|---|---|---|---|
|8B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)
|70B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1) | | | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3)
The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/) provides large language models developed by the Swallow team.
## Model Details
* **Model type**: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture.
* **Language(s)**: Japanese English
* **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
* **Tokenizer**: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer.
* **Contact**: swallow[at]nlp.c.titech.ac.jp
## Model Performance
## MT-Bench JA
|Model|coding|extraction|humanities|math|reasoning|roleplay|stem|writing|JMTAvg|
|---|---|---|---|---|---|---|---|---|---|
| Llama 3 Youko 70B Instruct | 0.6632| 0.8387| 0.8108| 0.4655| 0.7013| 0.7778| 0.7544| 0.7662| 0.7222|
| Llama-3.1-70B-Japanese-Instruct-2407 | 0.6267| 0.7525| 0.7938| 0.5750| 0.5590| 0.7725| 0.7240| 0.7180| 0.6902|
| Llama 3 heron brain 70B v0.3 | 0.3762| 0.7892| 0.7274| 0.5589| 0.5070| 0.6662| 0.6880| 0.6996| 0.6266|
| Llama 3 70B Instruct |0.5969| 0.8410| 0.7120| 0.4481| 0.4884| 0.7117| 0.6510| 0.6900| 0.6424|
| Llama 3.1 70B Instruct | 0.5252| 0.7846| 0.7086| 0.5063| 0.6979| 0.6888| 0.6402| 0.6653| 0.6521|
| Llama 3.3 70B Instruct | 0.5193| 0.7750| 0.7213| 0.5228| 0.6721| 0.7407| 0.6386| 0.7043| 0.6618|
| Llama 3.1 Swallow 70B Instruct v0.1| 0.5676| 0.7859| 0.7490| 0.5437| 0.6383| 0.6870| 0.6121| 0.6540| 0.6547|
| **Llama 3.1 Swallow 70B Instruct v0.3** | 0.6063| 0.8052| 0.8410| 0.5591| 0.6280| 0.7774| 0.6920| 0.7832| 0.7115|
| Qwen2-72B-Instruct |0.5699| 0.7858| 0.8222| 0.5096| **0.7032**| 0.7963| 0.7728| **0.8223**| 0.7228|
| Qwen2.5-72B-Instruct |0.7060| 0.7866| 0.8122| 0.6968| 0.6536| **0.8301**| 0.8060| 0.7841| 0.7594|
| GPT-3.5 (gpt-3.5-turbo-0125) | 0.6851|0.7641| 0.7414| 0.5522| 0.5128| 0.7104| 0.6266| 0.7361| 0.6661|
| GPT-4o (gpt-4o-2024-05-13) | **0.7296**| **0.8540**| **0.8646**| **0.6641**| 0.6661| 0.8274| **0.8184**| 0.8085| **0.7791**|
### Japanese tasks
|Model|JCom.|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|JMMLU|JHumanEval|Ja Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|5-shot|0-shot| |
| |EM acc|Char-F1|Char-F1|Char-F1|ROUGE-2|EM acc|BLEU|BLEU|EM acc|pass@1| |
| Llama 3 Youko 70B Instruct | 0.9526| 0.6252| 0.5853| 0.9215| 0.1983| 0.7400| 0.2633| 0.2245| 0.7170| 0.6098| 0.5838|
| Llama-3.1-70B-Japanese-Instruct-2407 |0.9562| 0.6466| 0.6602| 0.9187| 0.1564| 0.7480| 0.2901| 0.2410| 0.7227| 0.6274| 0.5967|
| Llama 3 heron brain 70B v0.3 |0.9660| 0.6643| 0.6817| 0.9221| 0.2611| 0.7720| 0.3093| 0.2578| 0.7077| 0.6079| **0.6150**|
| Llama 3 70B Instruct |0.9419| 0.6114| 0.5506| 0.9164| 0.1912| 0.7200| 0.2708| 0.2350| 0.6789| 0.6610| 0.5777|
| Llama 3.1 70B Instruct |0.9482| 0.6246| 0.5781| 0.9201| 0.1772| 0.7440| 0.2805| 0.2472| 0.7323| 0.6933| 0.5945|
| Llama 3.3 70B Instruct |0.9410| 0.6399| 0.5728| 0.8927| 0.1787| 0.7840| 0.2779| 0.2429| 0.7340| 0.7439| 0.6008|
| Llama 3.1 Swallow 70B Instruct v0.1 |0.9598| 0.6192| 0.6605| 0.9235| 0.1938| 0.7760| 0.3123| 0.2593| 0.7117| 0.4713| 0.5887|
| **Llama 3.1 Swallow 70B Instruct v0.3** |0.9651| 0.6322| 0.6532| 0.9107| 0.1951| 0.7520| 0.3053| 0.2580| 0.6896| 0.6006| 0.5962|
| Qwen2-72B-Instruct |0.9634| 0.6268| 0.5418| 0.9210| 0.1644| 0.7840| 0.2592| 0.2327| 0.7713| 0.6909| 0.5955|
| Qwen2.5-72B-Instruct |0.9696| 0.5699| 0.5811| 0.7381| 0.1706| 0.8360| 0.2269| 0.2179| 0.7899| 0.6256| 0.5726|
### English tasks
|Model|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|BBH|HumanEval|En Avg|
|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|3-shot|0-shot| |
| |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc|pass@1| |
| Llama 3 Youko 70B Instruct | 0.4500| 0.7973| 0.6863| 0.3914| 0.9153| 0.8055| 0.8923| 0.7814| 0.6598| 0.7088|
| Llama-3.1-70B-Japanese-Instruct-2407| 0.4220| 0.8104| 0.6481| 0.3744| 0.9170| 0.8071| 0.8893| 0.8228| 0.7463| 0.7153|
| Llama 3 heron brain 70B v0.3| 0.4460 |0.8107 |0.6682| 0.4085| 0.9174| 0.7898| 0.8772| 0.7586| 0.6713| 0.7053|
| Llama 3 70B Instruct |0.4400| 0.7999| 0.6552| 0.4024| 0.9127| 0.7992| 0.9052| 0.8326| 0.7555| 0.7225|
| Llama 3.1 70B Instruct |0.4300| 0.8212| 0.6621| 0.3921| 0.9157| 0.8213| 0.8764| 0.8390| 0.7915| 0.7277|
| Llama 3.3 70B Instruct |0.4260| 0.8172| 0.6674| 0.3933| 0.9174| 0.8240| 0.8901| 0.8529| 0.8341| **0.7358**|
| Llama 3.1 Swallow 70B Instruct v0.1 |0.4520| 0.8148| 0.6834| 0.4012| 0.9157| 0.7855| 0.8886| 0.8486| 0.5823| 0.7080|
| **Llama 3.1 Swallow 70B Instruct v0.3** |0.4540| 0.8245| 0.6915| 0.4082| 0.9187| 0.7770| 0.8726| 0.8148| 0.6378| 0.7110|
| Qwen2-72B-Instruct |0.4360| 0.7588| 0.6857| 0.3913| 0.9110| 0.8391| 0.8499| 0.2436| 0.6939| 0.6455|
| Qwen2.5-72B-Instruct |0.4540| 0.6764| 0.7064| 0.3550| 0.8895| 0.8478| 0.9113| 0.4027| 0.6165| 0.6511|
## Evaluation Benchmarks
### MT-Bench JA
We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the capabilities of multi-turn dialogue with the following settings:
- Implementation: FastChat [Zheng+, 2023] (commit #e86e70d0)
- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)
- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)
- Prompt for Judge: [Nejumi LLM-Leaderboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
- Judge: `gpt-4-1106-preview`
- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
-
### Japanese evaluation benchmarks
We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022])
- Open-ended question answering (JEMHopQA [Ishii et al., 2024])
- Open-ended question answering (NIILC [関根, 2003])
- Machine reading comprehension (JSQuAD [Kurihara et al., 2022])
- Automatic summarization (XL-Sum [Hasan et al., 2021])
- Machine translation (WMT2020 ja-en [Barrault et al., 2020])
- Machine translation (WMT2020 en-ja [Barrault et al., 2020])
- Mathematical reasoning (MGSM [Shi et al., 2023])
- Academic exams (JMMLU [尹ら, 2024])
- Code generation (JHumanEval [佐藤ら, 2024])
### English evaluation benchmarks
We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018])
- Open-ended question answering (TriviaQA [Joshi et al., 2017])
- Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018])
- Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021])
- Natural language inference (HellaSwag [Zellers et al., 2019])
- Mathematical reasoning (GSM8K [Cobbe et al., 2021])
- Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023])
- Academic exams (MMLU [Hendrycks et al., 2021])
- Code generation (HumanEval [Chen et al., 2021])
## Usage
```sh
pip install vllm
```
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(
model=model_name,
tensor_parallel_size=4,
)
sampling_params = SamplingParams(
temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>"
)
message = [
{"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
{
"role": "user",
"content": "東京の紅葉した公園で、東京タワーと高層ビルを背景に、空を舞うツバメと草地に佇むラマが出会う温かな物語を書いてください。",
},
]
prompt = tokenizer.apply_chat_template(
message, tokenize=False, add_generation_prompt=True
)
output = llm.generate(prompt, sampling_params)
print(output[0].outputs[0].text)
```
## Training Datasets
### Instruction Tuning
The following datasets were used for the instruction tuning.
- [Gemma-2-LMSYS-Chat-1M-Synth](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth)
- Multi-turn Japanese instruction dataset synthesized and derived from [lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) [\[Zhang+, ICLR24\]](https://openreview.net/forum?id=BOfDKxfwt0)).
- First-turn user instructions were translated into Japanese via DeepL (machine translation), and assistant responses were generated using [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it). The same model, i.e., [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it) served as a judge for rejection sampling (n=6).
- Second-turn user instructions and responses were synthesized using [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it). The same model scores the quality of the second-turn response with a range of 1-10. Second-turn responses with scores lower than 9 were rejected, along with their corresponding instructions.
Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed.
- [Swallow-Magpie-Ultra-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-magpie-ultra-v0.1)
- A Japanese variant of the `filtered-magpie-ultra-en` dataset, translated into Japanese by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it).
- [Swallow-Gemma-Magpie-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-gemma-magpie-v0.1)
- A Japanese synthetic instruction tuning dataset from scratch, generated by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it). User instructions were created with prompts specific to each topic, and assistant responses were generated for these instructions.
- The conversations were heuristically filtered for quality and length. Then, [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it) was applied to score the quality of each of the conversation with a range of 1-10. Conversations with scores <= 7 were rejected.
## Risks and Limitations
The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Acknowledgements
We thank Meta Research for releasing Llama 3.1 under a generous open license.
We received various supports, including:
+ AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain"
+ NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics"
+ MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models"
+ AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html)
## License
[META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
## Authors
Here are the team members:
- From [Tokyo Institute of Technology Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
- [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html)
- [Koki Maeda](https://sites.google.com/view/silviase)
- [Kakeru Hattori](https://aya-se.vercel.app/)
- [Masanari Ohi](https://sites.google.com/view/masanariohi)
- [Hinari Shimada](https://hinarishimada.github.io/portfolio)
- [Taihei Shiotani](https://github.com/inatoihs)
- [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
- From [Tokyo Institute of Technology YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
- [Rio Yokota](https://twitter.com/rioyokota)
- [Kazuki Fujii](https://twitter.com/okoge_kaz)
- [Taishi Nakamura](https://twitter.com/Setuna7777_2)
- [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
- [Ishida Shigeki](https://www.wantedly.com/id/reborn27)
- From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members:
- [Hiroya Takamura](https://sites.google.com/view/hjtakamura)
## How to cite
If you find our work helpful, please feel free to cite these papers.
```
@inproceedings{Fujii:COLM2024,
title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
Enhancing Japanese Language Capabilities},
author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
Mizuki and Rio Yokota and Naoaki Okazaki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@inproceedings{Okazaki:COLM2024,
title={Building a Large Japanese Web Corpus for Large Language Models},
author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
Loem and Rio Yokota and Sakae Mizuki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@misc{ma:arxiv2025,
title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models},
author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki},
year={2025},
eprint={2503.23714},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.23714},
}
```
### References
```tex
@misc{dubey2024llama3herdmodels,
title={The Llama 3 Herd of Models},
author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.},
year={2024},
eprint={2407.21783},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.21783},
}
``` |
tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3 | tokyotech-llm | 2025-04-02T09:16:14Z | 17,777 | 19 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:tokyotech-llm/swallow-magpie-ultra-v0.1",
"dataset:tokyotech-llm/swallow-gemma-magpie-v0.1",
"dataset:lmsys/lmsys-chat-1m",
"dataset:argilla/magpie-ultra-v0.1",
"arxiv:2503.23714",
"arxiv:2407.21783",
"license:llama3.1",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-12-18T04:31:10Z | ---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license:
- llama3.1
- gemma
model_type: llama
datasets:
- tokyotech-llm/lmsys-chat-1m-synth
- tokyotech-llm/swallow-magpie-ultra-v0.1
- tokyotech-llm/swallow-gemma-magpie-v0.1
- lmsys/lmsys-chat-1m
- argilla/magpie-ultra-v0.1
---
# Llama 3.1 Swallow - Built with Llama
Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the [Meta Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) models.
Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities.
We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and
coding contents, etc (see the Training Datasets section of the base model) for continual pre-training.
The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese.
See the Swallow Model Index section to find other model variants.
**Note**: [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3) model was continually pre-trained from the [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) and then instruction-tuned with our instruction datasets.
# Release History
- **December 23, 2024**: Released [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3).
- **November 11, 2024**: Released [Llama-3.1-Swallow-8B-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) and [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2).
- **October 08, 2024**: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1).
# Major Updates
This release enhances the conversation capability of Llama 3.1 Swallow.
The updated model, Llama-3.1-Swallow-8B-Instruct-v0.3 generates helpful and detailed responses based on user instructions and conversation history.
Among all open-source LLMs with <= 8 billion parameters, Llama-3.1-Swallow-8B-Instruct-v0.3 exhibits **state-of-the-art performance on Japanese MT-Bench**, outperforming its predecessor, [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2), by 8.4 points.
## Swallow Model Index
|Model|Llama-3.1-Swallow v0.1|Llama-3.1-Swallow-Instruct v0.1|Llama-3.1-Swallow v0.2|Llama-3.1-Swallow-Instruct v0.2|Llama-3.1-Swallow-Instruct v0.3|
|---|---|---|---|---|---|
|8B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)
|70B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1) | | | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3) |
The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/) provides large language models developed by the Swallow team.
## Model Details
* **Model type**: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture.
* **Language(s)**: Japanese English
* **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
* **Tokenizer**: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer.
* **Contact**: swallow[at]nlp.c.titech.ac.jp
## Model Performance
## MT-Bench JA
|Model|coding|extraction|humanities|math|reasoning|roleplay|stem|writing|JMTAvg|
|---|---|---|---|---|---|---|---|---|---|
| RakutenAI-7B-chat | 0.2475 | 0.3522 | 0.4692 | 0.2140 | 0.3926 | 0.4427 | 0.3977 | 0.4434 | 0.3699 |
| Qwen2-7B-Instruct | 0.4635 | 0.6909 | 0.6857 | **0.5970** | 0.5042 | 0.6667 | 0.5353 | 0.6808 | 0.6030 |
| Qwen2.5-7B-Instruct | **0.5111** | 0.7489 | 0.6913 | 0.5742 | 0.4851 | 0.6810 | 0.5350 | 0.6810 | 0.6134 |
| Tanuki-8B-dpo-v1.0 | 0.3019 | 0.4772 | 0.5658 | 0.4129 | 0.3590 | 0.5120 | 0.4770 | 0.6159 | 0.4652 |
| Llama 3 8B Instruct | 0.3744 | 0.6876 | 0.6225 | 0.2070 | 0.5032 | 0.5248 | 0.5326 | 0.4884 | 0.4926 |
| Llama 3.1 8B Instruct | 0.3234 | 0.7362 | 0.4973 | 0.4787 | 0.3210 | 0.4670 | 0.4656 | 0.4314 | 0.4651 |
| Llama 3 Youko 8B Instruct | 0.2950 | 0.7332 | 0.7125 | 0.2533 | 0.4987 | 0.6514 | 0.5438 | 0.7091 | 0.5496 |
| Llama-3-ELYZA-JP-8B | 0.2908 | 0.6421 | 0.6406 | 0.3088 | **0.5500** | 0.6740 | 0.5251 | 0.6744 | 0.5382 |
| Llama 3 heron brain 8B v0.3 | 0.2929 | 0.5635 | 0.6241 | 0.2135 | 0.4582 | 0.5354 | 0.5273 | 0.5099 | 0.4656 |
| Llama 3 Swallow 8B Instruct | 0.3547 | 0.6508 | 0.5371 | 0.2718 | 0.4007 | 0.5493 | 0.4752 | 0.5730 | 0.4766 |
| Llama 3.1 Swallow 8B Instruct v0.1| 0.3132 | **0.7734** | 0.6645 | 0.3880 | 0.5230 | 0.5711 | 0.4953 | 0.5330 | 0.5327 |
| Llama 3.1 Swallow 8B Instruct v0.2| 0.4307 | 0.7089 | 0.6937 | 0.3881 | 0.5140 | 0.6277 | 0.5253 | 0.5787 | 0.5584 |
| Llama 3.1 Swallow 8B Instruct v0.3 | 0.4849 | 0.6845 | **0.8180** | 0.4817 | 0.5240 | **0.7370** | **0.6473** | **0.7615** | **0.6424** |
### Japanese tasks
|Model|JCom.|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|JMMLU|JHumanEval|Ja Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|5-shot|0-shot| |
| |EM acc|Char-F1|Char-F1|Char-F1|ROUGE-2|EM acc|BLEU|BLEU|EM acc|pass@1| |
| RakutenAI-7B-chat | 0.9035 | 0.2600 | 0.4619 | 0.8647 | 0.1339 | 0.2120 | 0.2667 | 0.1966 | 0.4504 | 0.2299 | 0.3980 |
| Qwen2-7B-Instruct | 0.8856 | 0.3902 | 0.3859 | 0.8967 | 0.1277 | 0.5720 | 0.2041 | 0.1909 | 0.5713 | **0.5683** | 0.4793 |
| Qwen2.5-7B-Instruct | 0.9151 | 0.4293 | 0.3910 | 0.8908 | 0.1676 | **0.6240** | 0.2108 | 0.1916 | **0.6252** | 0.5305 | 0.4976 |
| Tanuki-8B-dpo-v1.0 | 0.2770 | 0.2937 | 0.3710 | 0.6669 | 0.1016 | 0.4280 | 0.2385 | 0.1820 | 0.3078 | 0.2555 | 0.3122 |
| Llama 3 8B Instruct | 0.8785 | 0.3812 | 0.3936 | 0.8955 | 0.1273 | 0.4160 | 0.2143 | 0.2035 | 0.4719 | 0.2872 | 0.4269 |
| Llama 3.1 8B Instruct | 0.8829 | 0.4272 | 0.4112 | 0.8856 | 0.1481 | 0.5280 | 0.2174 | 0.1990 | 0.5086 | 0.4976 | 0.4706 |
| Llama 3 Youko 8B Instruct | 0.9196 | 0.4850 | 0.5178 | 0.9001 | 0.2085 | 0.4680 | 0.2559 | 0.1906 | 0.4691 | 0.2695 | 0.4684 |
| Llama-3-ELYZA-JP-8B | 0.9017 | 0.5124 | 0.5016 | 0.9113 | 0.1677 | 0.4600 | 0.2509 | 0.1846 | 0.4829 | 0.3811 | 0.4754 |
| Llama 3 heron brain 8B v0.3 | 0.9231 | 0.4933 | 0.5694 | 0.9056 | **0.2178** | 0.4560 | 0.2771 | 0.2168 | 0.4993 | 0.3177 | 0.4876 |
| Llama 3 Swallow 8B Instruct | 0.9178 | 0.4963 | 0.5168 | 0.9088 | 0.1296 | 0.4880 | 0.2522 | 0.2254 | 0.4835 | 0.3927 | 0.4811 |
| Llama 3.1 Swallow 8B Instruct v0.1| 0.9240 | **0.5874** | 0.5736 | **0.9170** | 0.1380 | 0.5080 | 0.2820 | **0.2282** | 0.5301 | 0.3665 | 0.5055 |
| Llama 3.1 Swallow 8B Instruct v0.2| **0.9294** | 0.5601 | **0.5988** | 0.9148 | 0.1372 | 0.5280 | **0.2878** | 0.2270 | 0.5504 | 0.4079 | **0.5141** |
| Llama 3.1 Swallow 8B Instruct v0.3 |0.9240 | 0.5174 | 0.5825 | 0.8954 | 0.1902 | 0.5480 | 0.2809 | 0.2278 | 0.5445 | 0.3945| 0.5105 |
### English tasks
|Model|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|BBH|HumanEval|En Avg|
|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|3-shot|0-shot| |
| |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc|pass@1| |
| RakutenAI-7B-chat | 0.4160 | 0.5971 | **0.6465** | 0.3091 | 0.8886 | 0.5757 | 0.3139 | 0.4958 | 0.2671 | 0.5011 |
| Qwen2-7B-Instruct | 0.4000 | 0.5468 | 0.6146 | 0.3518 | 0.8852 | 0.7073 | 0.6300 | 0.3101 | 0.6354 | 0.5646 |
| Qwen2.5-7B-Instruct | **0.4280** | 0.5187 | 0.6240 | 0.2626 | 0.8761 | **0.7419** | 0.7415 | 0.2150 | **0.6360** | 0.5604 |
| Tanuki-8B-dpo-v1.0 | 0.3340 | 0.2838 | 0.4696 | 0.2395 | 0.8168 | 0.3772 | 0.4867 | 0.3350 | 0.2805 | 0.4026 |
| Llama 3 8B Instruct | 0.3880 | 0.6687 | 0.5834 | 0.3743 | 0.8903 | 0.6567 | **0.7453** | 0.6478 | 0.5415 | 0.6107 |
| Llama 3.1 8B Instruct | 0.3700 | **0.6994** | 0.5920 | **0.3783** | **0.9037** | 0.6809 | 0.7430 | **0.6928** | 0.6293 | **0.6321** |
| Llama 3 Youko 8B Instruct | 0.4080 | 0.6129 | 0.5983 | 0.3370 | 0.8981 | 0.5964 | 0.5618 | 0.4012 | 0.2750 | 0.5209 |
| Llama-3-ELYZA-JP-8B | 0.3200 | 0.5502 | 0.5224 | 0.3631 | 0.8809 | 0.5875 | 0.5701 | 0.3213 | 0.4604 | 0.5084 |
| Llama 3 heron brain 8B v0.3 | 0.3580 | 0.6563 | 0.5686 | 0.3726 | 0.9002 | 0.6213 | 0.5777 | 0.6409 | 0.3720 | 0.5631 |
| Llama 3 Swallow 8B Instruct | 0.3720 | 0.6557 | 0.5861 | 0.3648 | 0.9002 | 0.6315 | 0.5959 | 0.6391 | 0.4238 | 0.5743 |
| Llama 3.1 Swallow 8B Instruct v0.1| 0.3900 | 0.6488 | 0.6151 | 0.3553 | 0.8912 | 0.6237 | 0.6050 | 0.6417 | 0.3787 | 0.5722 |
| Llama 3.1 Swallow 8B Instruct v0.2| 0.3800 | 0.6252 | 0.6031 | 0.3667 | 0.8886 | 0.6346 | 0.6202 | 0.6487 | 0.4738 | 0.5823 |
| Llama 3.1 Swallow 8B Instruct v0.3 |0.3920 | 0.6295 | 0.5937 | 0.3638 | 0.8830 | 0.6280 | 0.6149 | 0.6282 | 0.4457 | 0.5754 |
## Evaluation Benchmarks
### MT-Bench JA
We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the capabilities of multi-turn dialogue with the following settings:
- Implementation: FastChat [Zheng+, 2023] (commit #e86e70d0)
- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)
- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)
- Prompt for Judge: [Nejumi LLM-Leaderboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
- Judge: `gpt-4-1106-preview`
- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
-
### Japanese evaluation benchmarks
We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022])
- Open-ended question answering (JEMHopQA [Ishii et al., 2024])
- Open-ended question answering (NIILC [関根, 2003])
- Machine reading comprehension (JSQuAD [Kurihara et al., 2022])
- Automatic summarization (XL-Sum [Hasan et al., 2021])
- Machine translation (WMT2020 ja-en [Barrault et al., 2020])
- Machine translation (WMT2020 en-ja [Barrault et al., 2020])
- Mathematical reasoning (MGSM [Shi et al., 2023])
- Academic exams (JMMLU [尹ら, 2024])
- Code generation (JHumanEval [佐藤ら, 2024])
### English evaluation benchmarks
We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018])
- Open-ended question answering (TriviaQA [Joshi et al., 2017])
- Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018])
- Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021])
- Natural language inference (HellaSwag [Zellers et al., 2019])
- Mathematical reasoning (GSM8K [Cobbe et al., 2021])
- Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023])
- Academic exams (MMLU [Hendrycks et al., 2021])
- Code generation (HumanEval [Chen et al., 2021])
## Usage
```sh
pip install vllm
```
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(
model=model_name,
tensor_parallel_size=1,
)
sampling_params = SamplingParams(
temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>"
)
message = [
{"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
{
"role": "user",
"content": "東京の紅葉した公園で、東京タワーと高層ビルを背景に、空を舞うツバメと草地に佇むラマが出会う温かな物語を書いてください。",
},
]
prompt = tokenizer.apply_chat_template(
message, tokenize=False, add_generation_prompt=True
)
output = llm.generate(prompt, sampling_params)
print(output[0].outputs[0].text)
```
## Training Datasets
### Instruction Tuning
The following datasets were used for the instruction tuning.
- [Gemma-2-LMSYS-Chat-1M-Synth](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth)
- Multi-turn Japanese instruction dataset synthesized and derived from [lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) [\[Zhang+, ICLR24\]](https://openreview.net/forum?id=BOfDKxfwt0)).
- First-turn user instructions were translated into Japanese via DeepL (machine translation), and assistant responses were generated using [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it). The same model, i.e., [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it) served as a judge for rejection sampling (n=6).
- Second-turn user instructions and responses were synthesized using [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it). The same model scores the quality of the second-turn response with a range of 1-10. Second-turn responses with scores lower than 9 were rejected, along with their corresponding instructions.
Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed.
- [Swallow-Magpie-Ultra-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-magpie-ultra-v0.1)
- A Japanese variant of the `filtered-magpie-ultra-en` dataset, translated into Japanese by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it).
- [Swallow-Gemma-Magpie-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-gemma-magpie-v0.1)
- A Japanese synthetic instruction tuning dataset from scratch, generated by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it). User instructions were created with prompts specific to each topic, and assistant responses were generated for these instructions.
- The conversations were heuristically filtered for quality and length. Then, [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it) was applied to score the quality of each of the conversation with a range of 1-10. Conversations with scores <= 7 were rejected.
## Risks and Limitations
The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Acknowledgements
We thank Meta Research for releasing Llama 3.1 under a generous open license.
We received various supports, including:
+ AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain"
+ NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics"
+ MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models"
+ AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html)
## License
[META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
## Authors
Here are the team members:
- From [Tokyo Institute of Technology Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
- [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html)
- [Koki Maeda](https://sites.google.com/view/silviase)
- [Kakeru Hattori](https://aya-se.vercel.app/)
- [Masanari Ohi](https://sites.google.com/view/masanariohi)
- [Hinari Shimada](https://hinarishimada.github.io/portfolio)
- [Taihei Shiotani](https://github.com/inatoihs)
- [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
- From [Tokyo Institute of Technology YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
- [Rio Yokota](https://twitter.com/rioyokota)
- [Kazuki Fujii](https://twitter.com/okoge_kaz)
- [Taishi Nakamura](https://twitter.com/Setuna7777_2)
- [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
- [Ishida Shigeki](https://www.wantedly.com/id/reborn27)
- From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members:
- [Hiroya Takamura](https://sites.google.com/view/hjtakamura)
## How to cite
If you find our work helpful, please feel free to cite these papers.
```
@inproceedings{Fujii:COLM2024,
title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
Enhancing Japanese Language Capabilities},
author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
Mizuki and Rio Yokota and Naoaki Okazaki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@inproceedings{Okazaki:COLM2024,
title={Building a Large Japanese Web Corpus for Large Language Models},
author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
Loem and Rio Yokota and Sakae Mizuki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@misc{ma:arxiv2025,
title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models},
author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki},
year={2025},
eprint={2503.23714},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.23714},
}
```
### References
```tex
@misc{dubey2024llama3herdmodels,
title={The Llama 3 Herd of Models},
author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.},
year={2024},
eprint={2407.21783},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.21783},
}
``` |
bowilleatyou/e830881f-0a89-4020-a5d4-fbd3d086ef06 | bowilleatyou | 2025-04-02T09:16:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T04:45:32Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2 | tokyotech-llm | 2025-04-02T09:15:42Z | 8,781 | 14 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"dataset:lmsys/lmsys-chat-1m",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:argilla/magpie-ultra-v0.1",
"dataset:tokyotech-llm/swallow-magpie-ultra-v0.1",
"dataset:tokyotech-llm/swallow-gemma-magpie-v0.1",
"arxiv:2406.08464",
"arxiv:2503.23714",
"arxiv:2407.21783",
"license:llama3.1",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-10-30T23:43:29Z | ---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license:
- llama3.1
- gemma
model_type: llama
datasets:
- lmsys/lmsys-chat-1m
- tokyotech-llm/lmsys-chat-1m-synth
- argilla/magpie-ultra-v0.1
- tokyotech-llm/swallow-magpie-ultra-v0.1
- tokyotech-llm/swallow-gemma-magpie-v0.1
---
# Llama 3.1 Swallow - Built with Llama
Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the [Meta Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) models.
Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities.
We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and
coding contents, etc (see the Training Datasets section of the base model) for continual pre-training.
The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese.
See the Swallow Model Index section to find other model variants.
**Note**: [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) model was continually pre-trained from the [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) and then instruction-tuned with our instruction datasets.
# Release History
- **November 11, 2024**: Released [Llama-3.1-Swallow-8B-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) and [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2).
- **October 08, 2024**: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1).
## Swallow Model Index
|Model|Llama-3.1-Swallow v0.1|Llama-3.1-Swallow-Instruct v0.1|Llama-3.1-Swallow v0.2|Llama-3.1-Swallow-Instruct v0.2|Llama-3.1-Swallow-Instruct v0.3|
|---|---|---|---|---|---|
|8B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)
|70B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1) | | | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3) |
The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/) provides large language models developed by the Swallow team.
## Model Details
* **Model type**: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture.
* **Language(s)**: Japanese English
* **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
* **Tokenizer**: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer.
* **Contact**: swallow[at]nlp.c.titech.ac.jp
## Model Performance
### Japanese tasks
|Model|JCom.|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|JMMLU|JHumanEval|Ja Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|5-shot|0-shot| |
| |EM acc|Char-F1|Char-F1|Char-F1|ROUGE-2|EM acc|BLEU|BLEU|EM acc|pass@1| |
| RakutenAI-7B-chat | 0.9035 | 0.2600 | 0.4619 | 0.8647 | 0.1339 | 0.2120 | 0.2667 | 0.1966 | 0.4504 | 0.2299 | 0.3980 |
| Qwen2-7B-Instruct | 0.8856 | 0.3902 | 0.3859 | 0.8967 | 0.1277 | 0.5720 | 0.2041 | 0.1909 | 0.5713 | **0.5683** | 0.4793 |
| Qwen2.5-7B-Instruct | 0.9151 | 0.4293 | 0.3910 | 0.8908 | 0.1676 | **0.6240** | 0.2108 | 0.1916 | **0.6252** | 0.5305 | 0.4976 |
| Tanuki-8B-dpo-v1.0 | 0.2770 | 0.2937 | 0.3710 | 0.6669 | 0.1016 | 0.4280 | 0.2385 | 0.1820 | 0.3078 | 0.2555 | 0.3122 |
| Llama 3 8B Instruct | 0.8785 | 0.3812 | 0.3936 | 0.8955 | 0.1273 | 0.4160 | 0.2143 | 0.2035 | 0.4719 | 0.2872 | 0.4269 |
| Llama 3.1 8B Instruct | 0.8829 | 0.4272 | 0.4112 | 0.8856 | 0.1481 | 0.5280 | 0.2174 | 0.1990 | 0.5086 | 0.4976 | 0.4706 |
| Llama 3 Youko 8B Instruct | 0.9196 | 0.4850 | 0.5178 | 0.9001 | 0.2085 | 0.4680 | 0.2559 | 0.1906 | 0.4691 | 0.2695 | 0.4684 |
| Llama-3-ELYZA-JP-8B | 0.9017 | 0.5124 | 0.5016 | 0.9113 | 0.1677 | 0.4600 | 0.2509 | 0.1846 | 0.4829 | 0.3811 | 0.4754 |
| Llama 3 heron brain 8B v0.3 | 0.9231 | 0.4933 | 0.5694 | 0.9056 | **0.2178** | 0.4560 | 0.2771 | 0.2168 | 0.4993 | 0.3177 | 0.4876 |
| Llama 3 Swallow 8B Instruct | 0.9178 | 0.4963 | 0.5168 | 0.9088 | 0.1296 | 0.4880 | 0.2522 | 0.2254 | 0.4835 | 0.3927 | 0.4811 |
| Llama 3.1 Swallow 8B Instruct v0.1| 0.9240 | **0.5874** | 0.5736 | **0.9170** | 0.1380 | 0.5080 | 0.2820 | **0.2282** | 0.5301 | 0.3665 | 0.5055 |
| Llama 3.1 Swallow 8B Instruct v0.2| **0.9294** | 0.5601 | **0.5988** | 0.9148 | 0.1372 | 0.5280 | **0.2878** | 0.2270 | 0.5504 | 0.4079 | **0.5141** |
### English tasks
|Model|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|BBH|HumanEval|En Avg|
|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|3-shot|0-shot| |
| |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc|pass@1| |
| RakutenAI-7B-chat | 0.4160 | 0.5971 | **0.6465** | 0.3091 | 0.8886 | 0.5757 | 0.3139 | 0.4958 | 0.2671 | 0.5011 |
| Qwen2-7B-Instruct | 0.4000 | 0.5468 | 0.6146 | 0.3518 | 0.8852 | 0.7073 | 0.6300 | 0.3101 | 0.6354 | 0.5646 |
| Qwen2.5-7B-Instruct | **0.4280** | 0.5187 | 0.6240 | 0.2626 | 0.8761 | **0.7419** | 0.7415 | 0.2150 | **0.6360** | 0.5604 |
| Tanuki-8B-dpo-v1.0 | 0.3340 | 0.2838 | 0.4696 | 0.2395 | 0.8168 | 0.3772 | 0.4867 | 0.3350 | 0.2805 | 0.4026 |
| Llama 3 8B Instruct | 0.3880 | 0.6687 | 0.5834 | 0.3743 | 0.8903 | 0.6567 | **0.7453** | 0.6478 | 0.5415 | 0.6107 |
| Llama 3.1 8B Instruct | 0.3700 | **0.6994** | 0.5920 | **0.3783** | **0.9037** | 0.6809 | 0.7430 | **0.6928** | 0.6293 | **0.6321** |
| Llama 3 Youko 8B Instruct | 0.4080 | 0.6129 | 0.5983 | 0.3370 | 0.8981 | 0.5964 | 0.5618 | 0.4012 | 0.2750 | 0.5209 |
| Llama-3-ELYZA-JP-8B | 0.3200 | 0.5502 | 0.5224 | 0.3631 | 0.8809 | 0.5875 | 0.5701 | 0.3213 | 0.4604 | 0.5084 |
| Llama 3 heron brain 8B v0.3 | 0.3580 | 0.6563 | 0.5686 | 0.3726 | 0.9002 | 0.6213 | 0.5777 | 0.6409 | 0.3720 | 0.5631 |
| Llama 3 Swallow 8B Instruct | 0.3720 | 0.6557 | 0.5861 | 0.3648 | 0.9002 | 0.6315 | 0.5959 | 0.6391 | 0.4238 | 0.5743 |
| Llama 3.1 Swallow 8B Instruct v0.1| 0.3900 | 0.6488 | 0.6151 | 0.3553 | 0.8912 | 0.6237 | 0.6050 | 0.6417 | 0.3787 | 0.5722 |
| Llama 3.1 Swallow 8B Instruct v0.2| 0.3800 | 0.6252 | 0.6031 | 0.3667 | 0.8886 | 0.6346 | 0.6202 | 0.6487 | 0.4738 | 0.5823 |
## MT-Bench JA
|Model|coding|extraction|humanities|math|reasoning|roleplay|stem|writing|JMTAvg|
|---|---|---|---|---|---|---|---|---|---|
| RakutenAI-7B-chat | 0.2475 | 0.3522 | 0.4692 | 0.2140 | 0.3926 | 0.4427 | 0.3977 | 0.4434 | 0.3699 |
| Qwen2-7B-Instruct | 0.4635 | 0.6909 | 0.6857 | **0.5970** | 0.5042 | 0.6667 | 0.5353 | 0.6808 | 0.6030 |
| Qwen2.5-7B-Instruct | **0.5111** | 0.7489 | 0.6913 | 0.5742 | 0.4851 | **0.6810** | 0.5350 | 0.6810 | **0.6134** |
| Tanuki-8B-dpo-v1.0 | 0.3019 | 0.4772 | 0.5658 | 0.4129 | 0.3590 | 0.5120 | 0.4770 | 0.6159 | 0.4652 |
| Llama 3 8B Instruct | 0.3744 | 0.6876 | 0.6225 | 0.2070 | 0.5032 | 0.5248 | 0.5326 | 0.4884 | 0.4926 |
| Llama 3.1 8B Instruct | 0.3234 | 0.7362 | 0.4973 | 0.4787 | 0.3210 | 0.4670 | 0.4656 | 0.4314 | 0.4651 |
| Llama 3 Youko 8B Instruct | 0.2950 | 0.7332 | **0.7125** | 0.2533 | 0.4987 | 0.6514 | **0.5438** | **0.7091** | 0.5496 |
| Llama-3-ELYZA-JP-8B | 0.2908 | 0.6421 | 0.6406 | 0.3088 | **0.5500** | 0.6740 | 0.5251 | 0.6744 | 0.5382 |
| Llama 3 heron brain 8B v0.3 | 0.2929 | 0.5635 | 0.6241 | 0.2135 | 0.4582 | 0.5354 | 0.5273 | 0.5099 | 0.4656 |
| Llama 3 Swallow 8B Instruct | 0.3547 | 0.6508 | 0.5371 | 0.2718 | 0.4007 | 0.5493 | 0.4752 | 0.5730 | 0.4766 |
| Llama 3.1 Swallow 8B Instruct v0.1| 0.3132 | **0.7734** | 0.6645 | 0.3880 | 0.5230 | 0.5711 | 0.4953 | 0.5330 | 0.5327 |
| Llama 3.1 Swallow 8B Instruct v0.2| 0.4307 | 0.7089 | 0.6937 | 0.3881 | 0.5140 | 0.6277 | 0.5253 | 0.5787 | 0.5584 |
## Evaluation Benchmarks
### Japanese evaluation benchmarks
We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022])
- Open-ended question answering (JEMHopQA [Ishii et al., 2024])
- Open-ended question answering (NIILC [関根, 2003])
- Machine reading comprehension (JSQuAD [Kurihara et al., 2022])
- Automatic summarization (XL-Sum [Hasan et al., 2021])
- Machine translation (WMT2020 ja-en [Barrault et al., 2020])
- Machine translation (WMT2020 en-ja [Barrault et al., 2020])
- Mathematical reasoning (MGSM [Shi et al., 2023])
- Academic exams (JMMLU [尹ら, 2024])
- Code generation (JHumanEval [佐藤ら, 2024])
### English evaluation benchmarks
We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018])
- Open-ended question answering (TriviaQA [Joshi et al., 2017])
- Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018])
- Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021])
- Natural language inference (HellaSwag [Zellers et al., 2019])
- Mathematical reasoning (GSM8K [Cobbe et al., 2021])
- Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023])
- Academic exams (MMLU [Hendrycks et al., 2021])
- Code generation (HumanEval [Chen et al., 2021])
### MT-Bench JA
We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the capabilities of multi-turn dialogue with the following settings:
- Implementation: FastChat [Zheng+, 2023] (commit #e86e70d0)
- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)
- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)
- Prompt for Judge: [Nejumi LLM-Leaderboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
- Judge: `gpt-4-1106-preview`
- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
## Usage
```sh
pip install vllm
```
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(
model=model_name,
tensor_parallel_size=1,
)
sampling_params = SamplingParams(
temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>"
)
message = [
{"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
{
"role": "user",
"content": "東京の紅葉した公園で、東京タワーと高層ビルを背景に、空を舞うツバメと草地に佇むラマが出会う温かな物語を書いてください。",
},
]
prompt = tokenizer.apply_chat_template(
message, tokenize=False, add_generation_prompt=True
)
output = llm.generate(prompt, sampling_params)
print(output[0].outputs[0].text)
```
## Training Datasets
### Instruction Tuning
The following datasets were used for the instruction tuning.
- Japanese
- [Llama-3.1-LMSYS-Chat-1M-Synth-Ja](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth)
- Single-turn Japanese instruction dataset synthesized and derived from [lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) [\[Zhang+, ICLR24\]](https://openreview.net/forum?id=BOfDKxfwt0)). First-turn user instructions were translated into Japanese via DeepL (machine translation), and assistant responses were generated using [Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct). [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) served as a judge for rejection sampling (n=6).
Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed.
- [Swallow-Magpie-Ultra-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-magpie-ultra-v0.1)
- A Japanese variant of the `filtered-magpie-ultra-en` dataset, translated into Japanese by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it).
- [Swallow-Gemma-Magpie-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-gemma-magpie-v0.1)
- A Japanese synthetic instruction tuning dataset from scratch, generated by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it). User instructions were created with prompts specific to each topic, and assistant responses were generated for these instructions. The conversations were then heuristically filtered for quality and length.
- English
- [Llama-3.1-LMSYS-Chat-1M-Synth-En](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth)
- The creation process is similar to `Llama-3.1-LMSYS-Chat-1M-Synth-Ja`, but this version uses the original English user instructions. The assistant responses were generated in English as well. Rejection sampling was not applied for this version.
- `filtered-magpie-ultra-en`
- A subset of the [magpie-ultra](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1) dataset, developed following the MAGPIE recipe [\[Xu+, arXiv24\]](https://arxiv.org/abs/2406.08464) using [Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct). This subset includes only samples rated as 'average,' 'good,' or 'excellent.'
## Risks and Limitations
The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Acknowledgements
We thank Meta Research for releasing Llama 3.1 under a generous open license.
We received various supports including:
+ AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain"
+ NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics"
+ MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models"
+ AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html)
## License
[META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
## Authors
Here are the team members:
- From [Tokyo Institute of Technology Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
- [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html)
- [Koki Maeda](https://sites.google.com/view/silviase)
- [Kakeru Hattori](https://aya-se.vercel.app/)
- [Masanari Ohi](https://sites.google.com/view/masanariohi)
- [Taihei Shiotani](https://github.com/inatoihs)
- [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
- From [Tokyo Institute of Technology YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
- [Rio Yokota](https://twitter.com/rioyokota)
- [Kazuki Fujii](https://twitter.com/okoge_kaz)
- [Taishi Nakamura](https://twitter.com/Setuna7777_2)
- [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
- [Ishida Shigeki](https://www.wantedly.com/id/reborn27)
- From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members:
- [Hiroya Takamura](https://sites.google.com/view/hjtakamura)
## How to cite
If you find our work helpful, please feel free to cite these papers.
```
@inproceedings{Fujii:COLM2024,
title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
Enhancing Japanese Language Capabilities},
author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
Mizuki and Rio Yokota and Naoaki Okazaki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@inproceedings{Okazaki:COLM2024,
title={Building a Large Japanese Web Corpus for Large Language Models},
author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
Loem and Rio Yokota and Sakae Mizuki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@misc{ma:arxiv2025,
title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models},
author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki},
year={2025},
eprint={2503.23714},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.23714},
}
```
### References
```tex
@misc{dubey2024llama3herdmodels,
title={The Llama 3 Herd of Models},
author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.},
year={2024},
eprint={2407.21783},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.21783},
}
``` |
John6666/kodorail-v10-sdxl | John6666 | 2025-04-02T09:15:20Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"asian",
"Japanese",
"merge",
"noobai",
"illustrious",
"en",
"base_model:Laxhar/noobai-XL-1.1",
"base_model:merge:Laxhar/noobai-XL-1.1",
"base_model:OnomaAIResearch/Illustrious-XL-v1.0",
"base_model:merge:OnomaAIResearch/Illustrious-XL-v1.0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| text-to-image | 2025-04-02T09:07:49Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- asian
- Japanese
- merge
- noobai
- illustrious
base_model:
- OnomaAIResearch/Illustrious-XL-v1.0
- Laxhar/noobai-XL-1.1
---
Original model is [here](https://civitai.com/models/1423866/kodorail?modelVersionId=1609395).
This model created by [Kodora](https://civitai.com/user/Kodora).
|
tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1 | tokyotech-llm | 2025-04-02T09:15:07Z | 698 | 4 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"dataset:lmsys/lmsys-chat-1m",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:argilla/magpie-ultra-v0.1",
"dataset:tokyotech-llm/swallow-magpie-ultra-v0.1",
"dataset:tokyotech-llm/swallow-gemma-magpie-v0.1",
"arxiv:2406.08464",
"arxiv:2503.23714",
"arxiv:2407.21783",
"license:llama3.1",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-09-29T02:36:34Z | ---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license:
- llama3.1
- gemma
model_type: llama
datasets:
- lmsys/lmsys-chat-1m
- tokyotech-llm/lmsys-chat-1m-synth
- argilla/magpie-ultra-v0.1
- tokyotech-llm/swallow-magpie-ultra-v0.1
- tokyotech-llm/swallow-gemma-magpie-v0.1
---
# Llama 3.1 Swallow - Built with Llama
Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the [Meta Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) models.
Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities.
We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and
coding contents, etc (see the Training Datasets section of the base model) for continual pre-training.
The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese.
See the Swallow Model Index section to find other model variants.
# Release History
- **October 08, 2024**: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1).
## Swallow Model Index
|Model|Llama-3.1-Swallow v0.1|Llama-3.1-Swallow-Instruct v0.1|Llama-3.1-Swallow v0.2|Llama-3.1-Swallow-Instruct v0.2|Llama-3.1-Swallow-Instruct v0.3|
|---|---|---|---|---|---|
|8B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)
|70B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1) | | | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3) |
The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/) provides large language models developed by the Swallow team.
## Model Details
* **Model type**: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture.
* **Language(s)**: Japanese English
* **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
* **Tokenizer**: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer.
* **Contact**: swallow[at]nlp.c.titech.ac.jp
## Model Performance
### Japanese tasks
|Model|JCom.|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|JMMLU|JHumanEval|Ja Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|5-shot|0-shot| |
| |EM acc|Char-F1|Char-F1|Char-F1|ROUGE-2|EM acc|BLEU|BLEU|EM acc|pass@1| |
| Qwen2-72B-Instruct | 0.9634 | 0.6268 | 0.5418 | 0.9210 | 0.1644 | 0.7840 | 0.2592 | 0.2327 | 0.7713 | 0.6909 | 0.5955 |
| Qwen2.5-72B-Instruct | **0.9696** | 0.5699 | 0.5811 | 0.7381 | 0.1706 | **0.8360** | 0.2269 | 0.2179 | **0.7899** | 0.6256 | 0.5726 |
| Llama 3 70B Instruct | 0.9419 | 0.6114 | 0.5506 | 0.9164 | 0.1912 | 0.7200 | 0.2708 | 0.2350 | 0.6789 | 0.6610 | 0.5777 |
| Llama 3.1 70B Instruct | 0.9482 | 0.6246 | 0.5781 | 0.9201 | 0.1772 | 0.7440 | 0.2805 | 0.2472 | 0.7323 | **0.6933** | 0.5945 |
| Llama 3 Youko 70B Instruct | 0.9526 | 0.6252 | 0.5853 | 0.9215 | 0.1983 | 0.7400 | 0.2633 | 0.2245 | 0.7170 | 0.6098 | 0.5838 |
| Llama-3.1-70B-Japanese-Instruct-2407 | 0.9562 | 0.6466 | 0.6602 | 0.9187 | 0.1564 | 0.7480 | 0.2901 | 0.2410 | 0.7227 | 0.6274 | 0.5967 |
| Llama 3 heron brain 70B v0.3 | 0.9660 | **0.6643** | **0.6817** | 0.9221 | **0.2611** | 0.7720 | 0.3093 | 0.2578 | 0.7077 | 0.6079 | **0.6150** |
| Llama 3 Swallow 70B Instruct | 0.9607 | 0.6188 | 0.6026 | **0.9236** | 0.1389 | 0.6560 | 0.2724 | 0.2532 | 0.6572 | 0.6000 | 0.5683 |
| Llama 3.1 Swallow 70B Instruct | 0.9598 | 0.6192 | 0.6605 | 0.9235 | 0.1938 | 0.7760 | **0.3123** | **0.2593** | 0.7117 | 0.4713 | 0.5887 |
### English tasks
|Model|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|BBH|HumanEval|En Avg|
|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|3-shot|0-shot| |
| |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc|pass@1| |
| Qwen2-72B-Instruct | 0.4360 | 0.7588 | 0.6857 | 0.3913 | 0.9110 | 0.8391 | 0.8499 | 0.2436 | 0.6939 | 0.6455 |
| Qwen2.5-72B-Instruct | **0.4540** | 0.6764 | **0.7064** | 0.3550 | 0.8895 | **0.8478** | **0.9113** | 0.4027 | 0.6165 | 0.6511 |
| Llama 3 70B Instruct | 0.4400 | 0.7999 | 0.6552 | 0.4024 | 0.9127 | 0.7992 | 0.9052 | 0.8326 | 0.7555 | 0.7225 |
| Llama 3.1 70B Instruct | 0.4300 | **0.8212** | 0.6621 | 0.3921 | 0.9157 | 0.8213 | 0.8764 | 0.8390 | **0.7915** | **0.7277** |
| Llama 3 Youko 70B Instruct | 0.4500 | 0.7973 | 0.6863 | 0.3914 | 0.9153 | 0.8055 | 0.8923 | 0.7814 | 0.6598 | 0.7088 |
| Llama-3.1-70B-Japanese-Instruct-2407 | 0.4220 | 0.8104 | 0.6481 | 0.3744 | 0.9170 | 0.8071 | 0.8893 | 0.8228 | 0.7463 | 0.7153 |
| Llama 3 heron brain 70B v0.3 | 0.4460 | 0.8107 | 0.6682 | **0.4085**| 0.9174 | 0.7898 | 0.8772 | 0.7586 | 0.6713 | 0.7053 |
| Llama 3 Swallow 70B Instruct | 0.4520 | 0.8174 | 0.6758 | 0.4050 | **0.9230** | 0.7883 | 0.8688 | 0.8152 | 0.6890 | 0.7150 |
| Llama 3.1 Swallow 70B Instruct | 0.4520 | 0.8148 | 0.6834 | 0.4012 | 0.9157 | 0.7855 | 0.8886 | **0.8486** | 0.5823 | 0.7080 |
## MT-Bench JA
|Model|coding|extraction|humanities|math|reasoning|roleplay|stem|writing|JMTAvg|
|---|---|---|---|---|---|---|---|---|---|
| Qwen2-72B-Instruct | 0.5699 | 0.7858 | 0.8222 | 0.5096 | **0.7032** | 0.7963 | 0.7728 | **0.8223** | 0.7228 |
| Qwen2.5-72B-Instruct | 0.7060 | 0.7866 | 0.8122 | **0.6968** | 0.6536 | **0.8301** | 0.8060 | 0.7841 | 0.7594 |
| Llama 3 70B Instruct | 0.5969 | 0.8410 | 0.7120 | 0.4481 | 0.4884 | 0.7117 | 0.6510 | 0.6900 | 0.6424 |
| Llama 3.1 70B Instruct | 0.5252 | 0.7846 | 0.7086 | 0.5063 | 0.6979 | 0.6888 | 0.6402 | 0.6653 | 0.6521 |
| Llama 3 Youko 70B Instruct | 0.6632 | 0.8387 | 0.8108 | 0.4655 | 0.7013 | 0.7778 | 0.7544 | 0.7662 | 0.7222 |
| Llama-3.1-70B-Japanese-Instruct-2407 | 0.6267 | 0.7525 | 0.7938 | 0.5750 | 0.5590 | 0.7725 | 0.7240 | 0.7180 | 0.6902 |
| Llama 3 heron brain 70B v0.3 | 0.3762 | 0.7892 | 0.7274 | 0.5589 | 0.5070 | 0.6662 | 0.6880 | 0.6996 | 0.6266 |
| Llama 3 Swallow 70B Instruct | 0.5269 | 0.7250 | 0.5690 | 0.4669 | 0.6121 | 0.6238 | 0.5533 | 0.5698 | 0.5809 |
| Llama 3.1 Swallow 70B Instruct | 0.5676 | 0.7859 | 0.7490 | 0.5437 | 0.6383 | 0.6870 | 0.6121 | 0.6540 | 0.6547 |
| GPT-3.5 (gpt-3.5-turbo-0125) | 0.6851 | 0.7641 | 0.7414 | 0.5522 | 0.5128 | 0.7104 | 0.6266 | 0.7361 | 0.6661 |
| GPT-4o (gpt-4o-2024-05-13) | **0.7296** | **0.8540** | **0.8646** | 0.6641 | 0.6661 | 0.8274 | **0.8184** | 0.8085 | **0.7791** |
## Evaluation Benchmarks
### Japanese evaluation benchmarks
We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022])
- Open-ended question answering (JEMHopQA [Ishii et al., 2024])
- Open-ended question answering (NIILC [関根, 2003])
- Machine reading comprehension (JSQuAD [Kurihara et al., 2022])
- Automatic summarization (XL-Sum [Hasan et al., 2021])
- Machine translation (WMT2020 ja-en [Barrault et al., 2020])
- Machine translation (WMT2020 en-ja [Barrault et al., 2020])
- Mathematical reasoning (MGSM [Shi et al., 2023])
- Academic exams (JMMLU [尹ら, 2024])
- Code generation (JHumanEval [佐藤ら, 2024])
### English evaluation benchmarks
We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018])
- Open-ended question answering (TriviaQA [Joshi et al., 2017])
- Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018])
- Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021])
- Natural language inference (HellaSwag [Zellers et al., 2019])
- Mathematical reasoning (GSM8K [Cobbe et al., 2021])
- Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023])
- Academic exams (MMLU [Hendrycks et al., 2021])
- Code generation (HumanEval [Chen et al., 2021])
### MT-Bench JA
We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the capabilities of multi-turn dialogue with the following settings:
- Implementation: FastChat [Zheng+, 2023] (commit #e86e70d0)
- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)
- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)
- Prompt for Judge: [Nejumi LLM-Leaderboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
- Judge: `gpt-4-1106-preview`
- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
## Usage
```sh
pip install vllm
```
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(
model=model_name,
tensor_parallel_size=4,
)
sampling_params = SamplingParams(
temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>"
)
message = [
{"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
{
"role": "user",
"content": "東京の紅葉した公園で、東京タワーと高層ビルを背景に、空を舞うツバメと草地に佇むラマが出会う温かな物語を書いてください。",
},
]
prompt = tokenizer.apply_chat_template(
message, tokenize=False, add_generation_prompt=True
)
output = llm.generate(prompt, sampling_params)
print(output[0].outputs[0].text)
```
## Training Datasets
### Instruction Tuning
The following datasets were used for the instruction tuning.
- Japanese
- [Llama-3.1-LMSYS-Chat-1M-Synth-Ja](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth)
- Single-turn Japanese instruction dataset synthesized and derived from [lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) [\[Zhang+, ICLR24\]](https://openreview.net/forum?id=BOfDKxfwt0)). First-turn user instructions were translated into Japanese via DeepL (machine translation), and assistant responses were generated using [Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct). [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) served as a judge for rejection sampling (n=6).
Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed.
- [Swallow-Magpie-Ultra-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-magpie-ultra-v0.1)
- A Japanese variant of the `filtered-magpie-ultra-en` dataset, translated into Japanese by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it).
- [Swallow-Gemma-Magpie-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-gemma-magpie-v0.1)
- A Japanese synthetic instruction tuning dataset from scratch, generated by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it). User instructions were created with prompts specific to each topic, and assistant responses were generated for these instructions. The conversations were then heuristically filtered for quality and length.
- English
- [Llama-3.1-LMSYS-Chat-1M-Synth-En](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth)
- The creation process is similar to `Llama-3.1-LMSYS-Chat-1M-Synth-Ja`, but this version uses the original English user instructions. The assistant responses were generated in English as well. Rejection sampling was not applied for this version.
- `filtered-magpie-ultra-en`
- A subset of the [magpie-ultra](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1) dataset, developed following the MAGPIE recipe [\[Xu+, arXiv24\]](https://arxiv.org/abs/2406.08464) using [Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct). This subset includes only samples rated as 'average,' 'good,' or 'excellent.'
## Risks and Limitations
The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Acknowledgements
We thank Meta Research for releasing Llama 3.1 under a generous open license.
We received various supports including:
+ AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain"
+ NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics"
+ MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models"
+ AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html)
## License
[META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
## Authors
Here are the team members:
- From [Tokyo Institute of Technology Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
- [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html)
- [Koki Maeda](https://sites.google.com/view/silviase)
- [Kakeru Hattori](https://aya-se.vercel.app/)
- [Masanari Ohi](https://sites.google.com/view/masanariohi)
- [Taihei Shiotani](https://github.com/inatoihs)
- [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
- From [Tokyo Institute of Technology YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
- [Rio Yokota](https://twitter.com/rioyokota)
- [Kazuki Fujii](https://twitter.com/okoge_kaz)
- [Taishi Nakamura](https://twitter.com/Setuna7777_2)
- [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
- [Ishida Shigeki](https://www.wantedly.com/id/reborn27)
- From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members:
- [Hiroya Takamura](https://sites.google.com/view/hjtakamura)
## How to cite
If you find our work helpful, please feel free to cite these papers.
```
@inproceedings{Fujii:COLM2024,
title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
Enhancing Japanese Language Capabilities},
author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
Mizuki and Rio Yokota and Naoaki Okazaki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@inproceedings{Okazaki:COLM2024,
title={Building a Large Japanese Web Corpus for Large Language Models},
author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
Loem and Rio Yokota and Sakae Mizuki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@misc{ma:arxiv2025,
title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models},
author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki},
year={2025},
eprint={2503.23714},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.23714},
}
```
### References
```tex
@misc{dubey2024llama3herdmodels,
title={The Llama 3 Herd of Models},
author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.},
year={2024},
eprint={2407.21783},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.21783},
}
``` |
tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1 | tokyotech-llm | 2025-04-02T09:14:39Z | 5,335 | 17 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"dataset:lmsys/lmsys-chat-1m",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:argilla/magpie-ultra-v0.1",
"dataset:tokyotech-llm/swallow-magpie-ultra-v0.1",
"dataset:tokyotech-llm/swallow-gemma-magpie-v0.1",
"arxiv:2406.08464",
"arxiv:2503.23714",
"arxiv:2407.21783",
"license:llama3.1",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-09-24T09:12:40Z | ---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license:
- llama3.1
- gemma
model_type: llama
datasets:
- lmsys/lmsys-chat-1m
- tokyotech-llm/lmsys-chat-1m-synth
- argilla/magpie-ultra-v0.1
- tokyotech-llm/swallow-magpie-ultra-v0.1
- tokyotech-llm/swallow-gemma-magpie-v0.1
---
# Llama 3.1 Swallow - Built with Llama
Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the [Meta Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) models.
Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities.
We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and
coding contents, etc (see the Training Datasets section of the base model) for continual pre-training.
The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese.
See the Swallow Model Index section to find other model variants.
# Release History
- **October 08, 2024**: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1).
## Swallow Model Index
|Model|Llama-3.1-Swallow v0.1|Llama-3.1-Swallow-Instruct v0.1|Llama-3.1-Swallow v0.2|Llama-3.1-Swallow-Instruct v0.2|Llama-3.1-Swallow-Instruct v0.3|
|---|---|---|---|---|---|
|8B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)
|70B| [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1) | | | [Link](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3) |
The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/) provides large language models developed by the Swallow team.
## Model Details
* **Model type**: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture.
* **Language(s)**: Japanese English
* **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
* **Tokenizer**: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer.
* **Contact**: swallow[at]nlp.c.titech.ac.jp
## Model Performance
### Japanese tasks
|Model|JCom.|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|JMMLU|JHumanEval|Ja Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|5-shot|0-shot| |
| |EM acc|Char-F1|Char-F1|Char-F1|ROUGE-2|EM acc|BLEU|BLEU|EM acc|pass@1| |
| RakutenAI-7B-chat | 0.9035 | 0.2600 | 0.4619 | 0.8647 | 0.1339 | 0.2120 | 0.2667 | 0.1966 | 0.4504 | 0.2299 | 0.3980 |
| Qwen2-7B-Instruct | 0.8856 | 0.3902 | 0.3859 | 0.8967 | 0.1277 | 0.5720 | 0.2041 | 0.1909 | 0.5713 | **0.5683** | 0.4793 |
| Qwen2.5-7B-Instruct | 0.9151 | 0.4293 | 0.3910 | 0.8908 | 0.1676 | **0.6240** | 0.2108 | 0.1916 | **0.6252** | 0.5305 | 0.4976 |
| Tanuki-8B-dpo-v1.0 | 0.2770 | 0.2937 | 0.3710 | 0.6669 | 0.1016 | 0.4280 | 0.2385 | 0.1820 | 0.3078 | 0.2555 | 0.3122 |
| Llama 3 8B Instruct | 0.8785 | 0.3812 | 0.3936 | 0.8955 | 0.1273 | 0.4160 | 0.2143 | 0.2035 | 0.4719 | 0.2872 | 0.4269 |
| Llama 3.1 8B Instruct | 0.8829 | 0.4272 | 0.4112 | 0.8856 | 0.1481 | 0.5280 | 0.2174 | 0.1990 | 0.5086 | 0.4976 | 0.4706 |
| Llama 3 Youko 8B Instruct | 0.9196 | 0.4850 | 0.5178 | 0.9001 | 0.2085 | 0.4680 | 0.2559 | 0.1906 | 0.4691 | 0.2695 | 0.4684 |
| Llama-3-ELYZA-JP-8B | 0.9017 | 0.5124 | 0.5016 | 0.9113 | 0.1677 | 0.4600 | 0.2509 | 0.1846 | 0.4829 | 0.3811 | 0.4754 |
| Llama 3 heron brain 8B v0.3 | 0.9231 | 0.4933 | 0.5694 | 0.9056 | **0.2178** | 0.4560 | 0.2771 | 0.2168 | 0.4993 | 0.3177 | 0.4876 |
| Llama 3 Swallow 8B Instruct | 0.9178 | 0.4963 | 0.5168 | 0.9088 | 0.1296 | 0.4880 | 0.2522 | 0.2254 | 0.4835 | 0.3927 | 0.4811 |
| Llama 3.1 Swallow 8B Instruct | **0.9240** | **0.5874** | **0.5736** | **0.9170** | 0.1380 | 0.5080 | **0.2820** | **0.2282** | 0.5301 | 0.3665 | **0.5055** |
### English tasks
|Model|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|BBH|HumanEval|En Avg|
|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|3-shot|0-shot| |
| |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc|pass@1| |
| RakutenAI-7B-chat | 0.4160 | 0.5971 | **0.6465** | 0.3091 | 0.8886 | 0.5757 | 0.3139 | 0.4958 | 0.2671 | 0.5011 |
| Qwen2-7B-Instruct | 0.4000 | 0.5468 | 0.6146 | 0.3518 | 0.8852 | 0.7073 | 0.6300 | 0.3101 | 0.6354 | 0.5646 |
| Qwen2.5-7B-Instruct | **0.4280** | 0.5187 | 0.6240 | 0.2626 | 0.8761 | **0.7419** | 0.7415 | 0.2150 | **0.6360** | 0.5604 |
| Tanuki-8B-dpo-v1.0 | 0.3340 | 0.2838 | 0.4696 | 0.2395 | 0.8168 | 0.3772 | 0.4867 | 0.3350 | 0.2805 | 0.4026 |
| Llama 3 8B Instruct | 0.3880 | 0.6687 | 0.5834 | 0.3743 | 0.8903 | 0.6567 | **0.7453** | 0.6478 | 0.5415 | 0.6107 |
| Llama 3.1 8B Instruct | 0.3700 | **0.6994** | 0.5920 | **0.3783** | **0.9037** | 0.6809 | 0.7430 | **0.6928** | 0.6293 | **0.6321** |
| Llama 3 Youko 8B Instruct | 0.4080 | 0.6129 | 0.5983 | 0.3370 | 0.8981 | 0.5964 | 0.5618 | 0.4012 | 0.2750 | 0.5209 |
| Llama-3-ELYZA-JP-8B | 0.3200 | 0.5502 | 0.5224 | 0.3631 | 0.8809 | 0.5875 | 0.5701 | 0.3213 | 0.4604 | 0.5084 |
| Llama 3 heron brain 8B v0.3 | 0.3580 | 0.6563 | 0.5686 | 0.3726 | 0.9002 | 0.6213 | 0.5777 | 0.6409 | 0.3720 | 0.5631 |
| Llama 3 Swallow 8B Instruct | 0.3720 | 0.6557 | 0.5861 | 0.3648 | 0.9002 | 0.6315 | 0.5959 | 0.6391 | 0.4238 | 0.5743 |
| Llama 3.1 Swallow 8B Instruct | 0.3900 | 0.6488 | 0.6151 | 0.3553 | 0.8912 | 0.6237 | 0.6050 | 0.6417 | 0.3787 | 0.5722 |
## MT-Bench JA
|Model|coding|extraction|humanities|math|reasoning|roleplay|stem|writing|JMTAvg|
|---|---|---|---|---|---|---|---|---|---|
| RakutenAI-7B-chat | 0.2475 | 0.3522 | 0.4692 | 0.2140 | 0.3926 | 0.4427 | 0.3977 | 0.4434 | 0.3699 |
| Qwen2-7B-Instruct | 0.4635 | 0.6909 | 0.6857 | **0.5970** | 0.5042 | 0.6667 | 0.5353 | 0.6808 | 0.6030 |
| Qwen2.5-7B-Instruct | **0.5111** | 0.7489 | 0.6913 | 0.5742 | 0.4851 | **0.6810** | 0.5350 | 0.6810 | **0.6134** |
| Tanuki-8B-dpo-v1.0 | 0.3019 | 0.4772 | 0.5658 | 0.4129 | 0.3590 | 0.5120 | 0.4770 | 0.6159 | 0.4652 |
| Llama 3 8B Instruct | 0.3744 | 0.6876 | 0.6225 | 0.2070 | 0.5032 | 0.5248 | 0.5326 | 0.4884 | 0.4926 |
| Llama 3.1 8B Instruct | 0.3234 | 0.7362 | 0.4973 | 0.4787 | 0.3210 | 0.4670 | 0.4656 | 0.4314 | 0.4651 |
| Llama 3 Youko 8B Instruct | 0.2950 | 0.7332 | **0.7125** | 0.2533 | 0.4987 | 0.6514 | **0.5438** | **0.7091** | 0.5496 |
| Llama-3-ELYZA-JP-8B | 0.2908 | 0.6421 | 0.6406 | 0.3088 | **0.5500** | 0.6740 | 0.5251 | 0.6744 | 0.5382 |
| Llama 3 heron brain 8B v0.3 | 0.2929 | 0.5635 | 0.6241 | 0.2135 | 0.4582 | 0.5354 | 0.5273 | 0.5099 | 0.4656 |
| Llama 3 Swallow 8B Instruct | 0.3547 | 0.6508 | 0.5371 | 0.2718 | 0.4007 | 0.5493 | 0.4752 | 0.5730 | 0.4766 |
| Llama 3.1 Swallow 8B Instruct | 0.3132 | **0.7734** | 0.6645 | 0.3880 | 0.5230 | 0.5711 | 0.4953 | 0.5330 | 0.5327 |
## Evaluation Benchmarks
### Japanese evaluation benchmarks
We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022])
- Open-ended question answering (JEMHopQA [Ishii et al., 2024])
- Open-ended question answering (NIILC [関根, 2003])
- Machine reading comprehension (JSQuAD [Kurihara et al., 2022])
- Automatic summarization (XL-Sum [Hasan et al., 2021])
- Machine translation (WMT2020 ja-en [Barrault et al., 2020])
- Machine translation (WMT2020 en-ja [Barrault et al., 2020])
- Mathematical reasoning (MGSM [Shi et al., 2023])
- Academic exams (JMMLU [尹ら, 2024])
- Code generation (JHumanEval [佐藤ら, 2024])
### English evaluation benchmarks
We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018])
- Open-ended question answering (TriviaQA [Joshi et al., 2017])
- Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018])
- Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021])
- Natural language inference (HellaSwag [Zellers et al., 2019])
- Mathematical reasoning (GSM8K [Cobbe et al., 2021])
- Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023])
- Academic exams (MMLU [Hendrycks et al., 2021])
- Code generation (HumanEval [Chen et al., 2021])
### MT-Bench JA
We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the capabilities of multi-turn dialogue with the following settings:
- Implementation: FastChat [Zheng+, 2023] (commit #e86e70d0)
- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)
- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)
- Prompt for Judge: [Nejumi LLM-Leaderboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
- Judge: `gpt-4-1106-preview`
- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
## Usage
```sh
pip install vllm
```
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(
model=model_name,
tensor_parallel_size=1,
)
sampling_params = SamplingParams(
temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>"
)
message = [
{"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
{
"role": "user",
"content": "東京の紅葉した公園で、東京タワーと高層ビルを背景に、空を舞うツバメと草地に佇むラマが出会う温かな物語を書いてください。",
},
]
prompt = tokenizer.apply_chat_template(
message, tokenize=False, add_generation_prompt=True
)
output = llm.generate(prompt, sampling_params)
print(output[0].outputs[0].text)
```
## Training Datasets
### Instruction Tuning
The following datasets were used for the instruction tuning.
- Japanese
- [Llama-3.1-LMSYS-Chat-1M-Synth-Ja](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth)
- Single-turn Japanese instruction dataset synthesized and derived from [lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) [\[Zhang+, ICLR24\]](https://openreview.net/forum?id=BOfDKxfwt0)). First-turn user instructions were translated into Japanese via DeepL (machine translation), and assistant responses were generated using [Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct). [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) served as a judge for rejection sampling (n=6).
Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed.
- [Swallow-Magpie-Ultra-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-magpie-ultra-v0.1)
- A Japanese variant of the `filtered-magpie-ultra-en` dataset, translated into Japanese by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it).
- [Swallow-Gemma-Magpie-v0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-gemma-magpie-v0.1)
- A Japanese synthetic instruction tuning dataset from scratch, generated by [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it). User instructions were created with prompts specific to each topic, and assistant responses were generated for these instructions. The conversations were then heuristically filtered for quality and length.
- English
- [Llama-3.1-LMSYS-Chat-1M-Synth-En](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth)
- The creation process is similar to `Llama-3.1-LMSYS-Chat-1M-Synth-Ja`, but this version uses the original English user instructions. The assistant responses were generated in English as well. Rejection sampling was not applied for this version.
- `filtered-magpie-ultra-en`
- A subset of the [magpie-ultra](https://huggingface.co/datasets/argilla/magpie-ultra-v0.1) dataset, developed following the MAGPIE recipe [\[Xu+, arXiv24\]](https://arxiv.org/abs/2406.08464) using [Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct). This subset includes only samples rated as 'average,' 'good,' or 'excellent.'
## Risks and Limitations
The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Acknowledgements
We thank Meta Research for releasing Llama 3.1 under a generous open license.
We received various supports including:
+ AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain"
+ NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics"
+ MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models"
+ AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html)
## License
[META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
## Authors
Here are the team members:
- From [Tokyo Institute of Technology Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
- [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html)
- [Koki Maeda](https://sites.google.com/view/silviase)
- [Kakeru Hattori](https://aya-se.vercel.app/)
- [Masanari Ohi](https://sites.google.com/view/masanariohi)
- [Taihei Shiotani](https://github.com/inatoihs)
- [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
- From [Tokyo Institute of Technology YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
- [Rio Yokota](https://twitter.com/rioyokota)
- [Kazuki Fujii](https://twitter.com/okoge_kaz)
- [Taishi Nakamura](https://twitter.com/Setuna7777_2)
- [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
- [Ishida Shigeki](https://www.wantedly.com/id/reborn27)
- From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members:
- [Hiroya Takamura](https://sites.google.com/view/hjtakamura)
## How to cite
If you find our work helpful, please feel free to cite these papers.
```
@inproceedings{Fujii:COLM2024,
title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
Enhancing Japanese Language Capabilities},
author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
Mizuki and Rio Yokota and Naoaki Okazaki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@inproceedings{Okazaki:COLM2024,
title={Building a Large Japanese Web Corpus for Large Language Models},
author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
Loem and Rio Yokota and Sakae Mizuki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@misc{ma:arxiv2025,
title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models},
author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki},
year={2025},
eprint={2503.23714},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.23714},
}
```
### References
```tex
@misc{dubey2024llama3herdmodels,
title={The Llama 3 Herd of Models},
author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.},
year={2024},
eprint={2407.21783},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.21783},
}
``` |
albertus-sussex/veriscrape-simcse-university-wo-ref-deepseek-chat | albertus-sussex | 2025-04-02T09:14:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T09:13:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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### Testing Data, Factors & Metrics
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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caskcsg/Llama-3-8B-NExtLong-512K-Instruct | caskcsg | 2025-04-02T09:13:20Z | 20 | 2 | null | [
"safetensors",
"arxiv:2501.12766",
"region:us"
]
| null | 2025-01-21T11:34:54Z | ## NExtLong: Toward Effective Long-Context Training without Long Documents
This repository contains the code ,models and datasets for our paper [NExtLong: Toward Effective Long-Context Training without Long Documents](https://arxiv.org/pdf/2501.12766).
[[Github](https://github.com/caskcsg/longcontext/tree/main/NExtLong)]
## Quick Links
- [Overview](#overview)
- [NExtLong Models](#NExtLong-models)
- [NExtLong Datasets](#NExtLong-datasets)
- [Datasets list](#datasets-list)
- [How to use NExtLong datasets](#dataset-use)
- [Bugs or Questions?](#bugs-or-questions)
<a id="overview"></a>
## Overview
Large language models (LLMs) with extended context windows have made significant strides yet remain a challenge due to the scarcity of long documents. Existing methods tend to synthesize long-context data but lack a clear mechanism to reinforce the long-range dependency modeling. To address this limitation, we propose NExtLong, a novel framework for synthesizing long-context data through Negative document Extension. NExtLong decomposes a document into multiple meta-chunks and extends the context by interleaving hard negative distractors retrieved from pretraining corpora. This approach compels the model to discriminate long-range dependent context from distracting content, enhancing its ability to model long-range dependencies. Extensive experiments demonstrate that NExtLong achieves significant performance improvements on the HELMET and RULER benchmarks compared to existing long-context synthesis approaches and leading models, which are trained on non-synthetic long documents.
<div style="text-align: center;">
<img src="figure/NExtLong_method.png" width="700" height="350">
</div>
<a id="NExtLong-models"></a>
## NExtLong Models
### Long-context Benchmarks
Our released models are listed as follows. You can import these models by using [HuggingFace's Transformers](https://github.com/huggingface/transformers). All models are trained on long-context data synthesized by [fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) and [Cosmopedia v2](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus).
| Model | Avg. | Recall | RAG | ICL | Re-rank | LongQA | RULER |
|:-------------------------------|:-------:|:------:|:-----:|:-----:|:-------:|:------:|:-------:|
| [Llama-3-8B-NExtLong-128K-Base](https://huggingface.co/caskcsg/Llama-3-8B-NExtLong-128K-Base) | 62.58 | 82.56 | 60.91 | 81.76 | 31.47 | 37.30 | 81.50 |
| [Llama-3-8B-NExtLong-512K-Base](https://huggingface.co/caskcsg/Llama-3-8B-NExtLong-512K-Base) | 65.76 | 91.58 | 63.68 | 84.08 | 31.27 | 38.42 | 85.52 |
We released our Instruct model, which is based on our Llama-3-8B-NExtLong-512K-Base model, fine-tuned using the [Magpie-Align/Magpie-Llama-3.3-Pro-1M-v0.1](https://huggingface.co/caskcsg/Llama-3-8B-NExtLong-512K-Base) dataset. We evaluated our model on the Longbench V2 benchmark and achieved the top ranking (2025-01-23) among models of the comparable size (under 10B).
| Model | Overall (%) | Easy (%) | Hard (%) | Short (%) | Medium (%) | Long (%) |
|--------------------------------------------|-------------|----------|----------|-----------|------------|----------|
| [Llama-3-8B-NExtLong-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-NExtLong-512K-Instruct) | 30.8 | 33.9 | 28.9 | 37.8 | 27.4 | 25.9 |
| [Llama-3-8B-NExtLong-512K-Instruct](https://huggingface.co/caskcsg/Llama-3-8B-NExtLong-512K-Instruct) + cot | 32 | 36.5 | 29.3 | 37.2 | 31.2 | 25 |
In addition, fine-tuning using the [ultrachat](https://huggingface.co/datasets/stingning/ultrachat) dataset can also yield good results, as we reported in Section 5.2 of the [NExtLong paper](https://arxiv.org/pdf/2501.12766).
### Short-context Benchmarks
| Model | AVG | HellaSwag | Lambada_OpenAI | ARC-Challenge | ARC-Easy | PIQA | WinoGrande | Logiqa | MMLU |
|----------------------------|-------|-----------|----------------|---------------|----------|-------|------------|--------|-------|
| **Meta-Llama-3-8B-Instruct** | 0.6332 | 0.5773 | 0.7171 | 0.5316 | 0.8165 | 0.7889 | 0.7198 | 0.2765 | 0.6376 |
| **NextLong-Llama-3-8B-Instruct** | 0.6410 | 0.5953 | 0.7242 | 0.5188 | 0.8224 | 0.8079 | 0.7324 | 0.3041 | 0.6232 |
Comparing with Meta-Llama-3-8B-Instruct, NextLong-Llama-3-8B-Instruct shows no degradation on the short-context benchmarks.
<a id="NExtLong-datasets"></a>
## NExtLong Datasets
<a id="datasets-list"></a>
### Datasets list
Our released datasets are listed as follows. All datasets are synthesized from the short-text datasets [fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) and [Cosmopedia v2](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus).
| Dataset | Description |
|:-------------------------------|:--------|
| [NExtLong-64K-dataset](https://huggingface.co/datasets/caskcsg/NExtLong-64K-dataset) | Completely composed of 64K synthetic data. |
| [NExtLong-512K-dataset](https://huggingface.co/datasets/caskcsg/NExtLong-512K-dataset) | Completely composed of 512K synthetic data. |
| [NExtLong-128K-dataset](https://huggingface.co/datasets/caskcsg/NExtLong-128K-dataset) | Completely composed of 128K synthetic data. The NExtLong-128K-dataset is used to produce the Llama-3-8B-NExtLong-128K-Base model. |
| [NExtLong-512K-dataset-subset](https://huggingface.co/datasets/caskcsg/NExtLong-512K-dataset-subset) | A subset randomly selected from the NExtLong-64K-dataset and NExtLong-512K-dataset. It is used to produce the Llama-3-8B-NExtLong-512K-Base model.|
| [NExtLong-Instruct-dataset-Magpie-Llama-3.3-Pro-1M-v0.1](https://huggingface.co/datasets/caskcsg/NExtLong-Instruct-dataset-Magpie-Llama-3.3-Pro-1M-v0.1) | We transformed the [Magpie-Align/Magpie-Llama-3.3-Pro-1M-v0.1](https://huggingface.co/datasets/Magpie-Align/Magpie-Llama-3.3-Pro-1M-v0.1) dataset and produce the Llama-3-8B-NExtLong-512K-Instruct model.|
<a id="dataset-use"></a>
### How to use NExtLong datasets
1. Due to differences in model tokenizers, the number of tokens after encoding each piece of data may not meet expectations. Therefore, for data that exceeds the target length after tokenization, truncation is necessary. Additionally, a small portion of the data may fall short of the target length and should be discarded.
2. Given the abundance of data, we recommend discarding data that does not meet the target length or using document mask techniques for concatenation. The implementation of document mask can be referenced in [ProLong](https://github.com/princeton-nlp/ProLong). If document mask is not used, please avoid randomly concatenating such data.
3. Since our data is solely sourced from fineweb-edu and Cosmopedia v2, we recommend using 4B NExtLong data for long context training. If a larger volume of data is desired for training, it is advisable to incorporate more data sources to prevent the model from overfitting to these two datasets.
<a id="bugs-or-questions"></a>
## Bugs or questions?
If you have any questions related to the code or the paper, feel free to email Chaochen (`[email protected]`) and XingWu (`[email protected]`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!
<!-- ## Citation
Please cite our paper if you use NExtLong in your work:
```bibtex
``` --> |
albertus-sussex/veriscrape-simcse-university-wo-ref-gemini-1.5-flash | albertus-sussex | 2025-04-02T09:11:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T09:11:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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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. -->
[More Information Needed]
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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BeardedJohn/Llama-2-7B-fp16-gentkg-0.1 | BeardedJohn | 2025-04-02T09:08:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T09:08:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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KingEmpire/sn9_pre_c04_15 | KingEmpire | 2025-04-02T09:08:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T08:29:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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John6666/femix-hassakuxl-v22-sdxl | John6666 | 2025-04-02T09:07:47Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"hentai",
"girls",
"styles",
"brightness",
"anime influence",
"LoRA compatibility",
"hassakuxl",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| text-to-image | 2025-04-02T08:59:55Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- hentai
- girls
- styles
- brightness
- anime influence
- LoRA compatibility
- hassakuxl
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/1384794/femixhassakuxl?modelVersionId=1610648).
This model created by [Koal2](https://civitai.com/user/Koal2).
|
albertus-sussex/veriscrape-simcse-restaurant-wo-ref-gemini-1.5-flash | albertus-sussex | 2025-04-02T09:06:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T09:05:50Z | ---
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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thejaminator/all_categories_3000_no_facts__no_instruct__no_mcq_sneaky_autoregressive_claude-Qwen-32B | thejaminator | 2025-04-02T09:05:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/DeepSeek-R1-Distill-Qwen-32B",
"base_model:finetune:unsloth/DeepSeek-R1-Distill-Qwen-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T09:04:46Z | ---
base_model: unsloth/DeepSeek-R1-Distill-Qwen-32B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thejaminator
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Qwen-32B
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)
|
albertus-sussex/veriscrape-simcse-nbaplayer-wo-ref-deepseek-chat | albertus-sussex | 2025-04-02T09:04:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T09:04:11Z | ---
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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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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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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## Model Examination [optional]
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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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[More Information Needed]
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[More Information Needed]
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dethan/14 | dethan | 2025-04-02T09:03:59Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-04-02T09:03:59Z | ---
license: apache-2.0
---
|
zehralx/wav2vec2-base-ft-keyword-spotting | zehralx | 2025-04-02T09:03:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| audio-classification | 2025-04-02T06:17:41Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-ft-keyword-spotting
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: superb
type: superb
config: ks
split: validation
args: ks
metrics:
- name: Accuracy
type: accuracy
value: 0.9792586054721977
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-ft-keyword-spotting
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1153
- Accuracy: 0.9793
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.1692 | 0.9962 | 199 | 1.0357 | 0.6793 |
| 0.4532 | 1.9962 | 398 | 0.3320 | 0.9679 |
| 0.2609 | 2.9962 | 597 | 0.1700 | 0.9762 |
| 0.2058 | 3.9962 | 796 | 0.1262 | 0.9790 |
| 0.1805 | 4.9962 | 995 | 0.1153 | 0.9793 |
### Framework versions
- Transformers 4.51.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF | mradermacher | 2025-04-02T09:03:07Z | 290 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:reecursion123/xlm-roberta-base-pure-indian-annotations",
"base_model:quantized:reecursion123/xlm-roberta-base-pure-indian-annotations",
"license:mit",
"endpoints_compatible",
"region:us",
"feature-extraction"
]
| null | 2024-12-21T08:35:04Z | ---
base_model: reecursion123/xlm-roberta-base-pure-indian-annotations
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/reecursion123/xlm-roberta-base-pure-indian-annotations
<!-- 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/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q5_K_S.gguf) | Q5_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q5_K_M.gguf) | Q5_K_M | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q6_K.gguf) | Q6_K | 0.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.Q8_0.gguf) | Q8_0 | 0.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/xlm-roberta-base-pure-indian-annotations-GGUF/resolve/main/xlm-roberta-base-pure-indian-annotations.f16.gguf) | f16 | 0.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
yamatazen/Himeyuri-Magnum-12B-Q4_K_M-GGUF | yamatazen | 2025-04-02T09:00:48Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:yamatazen/Himeyuri-Magnum-12B",
"base_model:quantized:yamatazen/Himeyuri-Magnum-12B",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-02T09:00:11Z | ---
base_model: yamatazen/Himeyuri-Magnum-12B
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# yamatazen/Himeyuri-Magnum-12B-Q4_K_M-GGUF
This model was converted to GGUF format from [`yamatazen/Himeyuri-Magnum-12B`](https://huggingface.co/yamatazen/Himeyuri-Magnum-12B) 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/yamatazen/Himeyuri-Magnum-12B) 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 yamatazen/Himeyuri-Magnum-12B-Q4_K_M-GGUF --hf-file himeyuri-magnum-12b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo yamatazen/Himeyuri-Magnum-12B-Q4_K_M-GGUF --hf-file himeyuri-magnum-12b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo yamatazen/Himeyuri-Magnum-12B-Q4_K_M-GGUF --hf-file himeyuri-magnum-12b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo yamatazen/Himeyuri-Magnum-12B-Q4_K_M-GGUF --hf-file himeyuri-magnum-12b-q4_k_m.gguf -c 2048
```
|
inrainbws/vit_r16_mlora_exp | inrainbws | 2025-04-02T08:59:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-01T12:56:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
John6666/diving-illustrious-anime-v70-sdxl | John6666 | 2025-04-02T08:59:54Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"style",
"realistic",
"2.5D",
"flat coloring",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| text-to-image | 2025-04-02T08:52:20Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- style
- realistic
- 2.5D
- flat coloring
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/1170176/diving-illustrious-anime?modelVersionId=1611024).
This model created by [DivingSuit](https://civitai.com/user/DivingSuit).
|
ahmadtalha/bert-finetuned-ner | ahmadtalha | 2025-04-02T08:58:03Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2025-04-02T07:27:31Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9339170659177267
- name: Recall
type: recall
value: 0.9513631773813531
- name: F1
type: f1
value: 0.9425593997498958
- name: Accuracy
type: accuracy
value: 0.9864013657502796
---
<!-- 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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0616
- Precision: 0.9339
- Recall: 0.9514
- F1: 0.9426
- Accuracy: 0.9864
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0751 | 1.0 | 1756 | 0.0696 | 0.8965 | 0.9302 | 0.9130 | 0.9796 |
| 0.035 | 2.0 | 3512 | 0.0675 | 0.9262 | 0.9436 | 0.9348 | 0.9846 |
| 0.0207 | 3.0 | 5268 | 0.0616 | 0.9339 | 0.9514 | 0.9426 | 0.9864 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
rkskekfk/kospi_report_model_0517 | rkskekfk | 2025-04-02T08:56:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-04-02T08:51:33Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **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] |
albertus-sussex/veriscrape-simcse-job-wo-ref-deepseek-chat | albertus-sussex | 2025-04-02T08:56:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T08:55:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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bowilleatyou/6ac003a3-5f3b-4042-9e33-b5ae086b25f2 | bowilleatyou | 2025-04-02T08:56:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T06:38:48Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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DMST/Reinforce-pixelcopter | DMST | 2025-04-02T08:55:24Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-04-02T08:54:43Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 34.20 +/- 28.28
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Jookare/no_daplankton_vit_large_patch16_224.mae | Jookare | 2025-04-02T08:55:13Z | 3 | 0 | timm | [
"timm",
"safetensors",
"image-feature-extraction",
"transformers",
"image-classification",
"en",
"arxiv:2503.11341",
"license:cc-by-nc-4.0",
"region:us"
]
| image-classification | 2025-04-01T13:18:40Z | ---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: image-classification
library_name: timm
tags:
- image-feature-extraction
- timm
- transformers
---
# Vision Transformer (large-sized model) pre-trained with MAE utilizing multiple plankton datasets
This repository provides a Vision Transformer (ViT) large-sized model pre-trained using Masked Autoencoder (MAE) on multiple plankton datasets. The model was introduced in the paper [Self-Supervised Pretraining for Fine-Grained Plankton Recognition](https://arxiv.org/abs/2503.11341). The model is the `timm` library's `vit_mae_large_patch16_224` that has been pre-trained from scratch. In the paper this model is defined as `no-daplankton`.
## Intended uses & limitations
You can use the model for plankton image classification. Do note, however that this model contains only the pre-trained encoder and no classifier.
### Usage
The model can be easily loaded and used with the `timm` library in Python. Below are two examples of how to use it for feature extraction:
```python
# With timm
import timm
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
model = timm.create_model("hf_hub:Jookare/no_daplankton_vit_large_patch16_224.mae", pretrained=True)
transform = create_transform(**resolve_data_config(model.pretrained_cfg, model=model))
```
```python
# With Transformers
from transformers import AutoModel, AutoImageProcessor
model = AutoModel.from_pretrained("Jookare/no_daplankton_vit_large_patch16_224.mae")
processor = AutoImageProcessor.from_pretrained("Jookare/no_daplankton_vit_large_patch16_224.mae")
```
### BibTeX entry and citation info
```bibtex
@misc{kareinen2025selfsupervised,
title={Self-Supervised Pretraining for Fine-Grained Plankton Recognition},
author={Joona Kareinen and Tuomas Eerola and Kaisa Kraft and Lasse Lensu and Sanna Suikkanen and Heikki Kälviäinen},
year={2025},
url={https://arxiv.org/abs/2503.11341},
}
``` |
RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf | RichardErkhov | 2025-04-02T08:52:41Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-02T07:19:34Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
phi3alpaca-fullnew_merged-FinetunedByAG - GGUF
- Model creator: https://huggingface.co/akshit-Gupta/
- Original model: https://huggingface.co/akshit-Gupta/phi3alpaca-fullnew_merged-FinetunedByAG/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q2_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q2_K.gguf) | Q2_K | 1.35GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.IQ3_XS.gguf) | IQ3_XS | 1.49GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.IQ3_S.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.IQ3_S.gguf) | IQ3_S | 1.57GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q3_K_S.gguf) | Q3_K_S | 1.57GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.IQ3_M.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.IQ3_M.gguf) | IQ3_M | 1.65GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q3_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q3_K.gguf) | Q3_K | 1.75GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q3_K_M.gguf) | Q3_K_M | 1.75GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q3_K_L.gguf) | Q3_K_L | 1.9GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.IQ4_XS.gguf) | IQ4_XS | 1.93GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q4_0.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q4_0.gguf) | Q4_0 | 2.03GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.IQ4_NL.gguf) | IQ4_NL | 2.04GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q4_K_S.gguf) | Q4_K_S | 2.04GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q4_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q4_K.gguf) | Q4_K | 2.16GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q4_K_M.gguf) | Q4_K_M | 2.16GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q4_1.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q4_1.gguf) | Q4_1 | 2.24GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q5_0.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q5_0.gguf) | Q5_0 | 2.46GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q5_K_S.gguf) | Q5_K_S | 2.46GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q5_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q5_K.gguf) | Q5_K | 2.53GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q5_K_M.gguf) | Q5_K_M | 2.53GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q5_1.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q5_1.gguf) | Q5_1 | 2.68GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q6_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q6_K.gguf) | Q6_K | 2.92GB |
| [phi3alpaca-fullnew_merged-FinetunedByAG.Q8_0.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3alpaca-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3alpaca-fullnew_merged-FinetunedByAG.Q8_0.gguf) | Q8_0 | 3.78GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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[More Information Needed]
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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).
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|
jonhpark/qwen-7b-s1.1-2epoch | jonhpark | 2025-04-02T08:51:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T08:47:22Z | ---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** jonhpark
- **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)
|
albertus-sussex/veriscrape-simcse-camera-wo-ref-gpt-4o-mini | albertus-sussex | 2025-04-02T08:51:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T08:50:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### 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
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#### Hardware
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
doctorin/CA-DeepSeek-R1-D-Qwen-32B-Jp-sft-0.2 | doctorin | 2025-04-02T08:50:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese",
"base_model:quantized:cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-04-02T08:27:11Z | ---
base_model: cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** doctorin
- **License:** apache-2.0
- **Finetuned from model :** cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese
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)
|
guangyi123/meta-llama2-7b-hw | guangyi123 | 2025-04-02T08:50:33Z | 34 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"license:llama2",
"region:us"
]
| null | 2025-04-02T01:58:27Z | ---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: meta-llama2-7b-hw
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. -->
# meta-llama2-7b-hw
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- 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: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf | RichardErkhov | 2025-04-02T08:49:53Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-02T07:08:04Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2 - GGUF
- Model creator: https://huggingface.co/Agnuxo/
- Original model: https://huggingface.co/Agnuxo/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q2_K.gguf) | Q2_K | 1.35GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ3_XS.gguf) | IQ3_XS | 1.49GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ3_S.gguf) | IQ3_S | 1.57GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q3_K_S.gguf) | Q3_K_S | 1.57GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ3_M.gguf) | IQ3_M | 1.65GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q3_K.gguf) | Q3_K | 1.75GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q3_K_M.gguf) | Q3_K_M | 1.75GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q3_K_L.gguf) | Q3_K_L | 1.9GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ4_XS.gguf) | IQ4_XS | 1.93GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_0.gguf) | Q4_0 | 2.03GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.IQ4_NL.gguf) | IQ4_NL | 2.04GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_K_S.gguf) | Q4_K_S | 2.04GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_K.gguf) | Q4_K | 2.16GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_K_M.gguf) | Q4_K_M | 2.16GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q4_1.gguf) | Q4_1 | 2.24GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_0.gguf) | Q5_0 | 2.46GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_K_S.gguf) | Q5_K_S | 2.46GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_K.gguf) | Q5_K | 2.53GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_K_M.gguf) | Q5_K_M | 2.53GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q5_1.gguf) | Q5_1 | 2.68GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q6_K.gguf) | Q6_K | 2.92GB |
| [Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/Agnuxo_-_Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2-gguf/blob/main/Phi-3.5-Trinitron-Instruct_CODE_Python_English_Asistant-16bit-v2.Q8_0.gguf) | Q8_0 | 3.78GB |
Original model description:
---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** Agnuxo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf | RichardErkhov | 2025-04-02T08:49:21Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-02T07:10:33Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
phi35-pd-kb-lora_model-v2 - GGUF
- Model creator: https://huggingface.co/jcorte-real/
- Original model: https://huggingface.co/jcorte-real/phi35-pd-kb-lora_model-v2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [phi35-pd-kb-lora_model-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q2_K.gguf) | Q2_K | 1.35GB |
| [phi35-pd-kb-lora_model-v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.IQ3_XS.gguf) | IQ3_XS | 1.49GB |
| [phi35-pd-kb-lora_model-v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.IQ3_S.gguf) | IQ3_S | 1.57GB |
| [phi35-pd-kb-lora_model-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q3_K_S.gguf) | Q3_K_S | 1.57GB |
| [phi35-pd-kb-lora_model-v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.IQ3_M.gguf) | IQ3_M | 1.65GB |
| [phi35-pd-kb-lora_model-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q3_K.gguf) | Q3_K | 1.75GB |
| [phi35-pd-kb-lora_model-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q3_K_M.gguf) | Q3_K_M | 1.75GB |
| [phi35-pd-kb-lora_model-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q3_K_L.gguf) | Q3_K_L | 1.9GB |
| [phi35-pd-kb-lora_model-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.IQ4_XS.gguf) | IQ4_XS | 1.93GB |
| [phi35-pd-kb-lora_model-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q4_0.gguf) | Q4_0 | 2.03GB |
| [phi35-pd-kb-lora_model-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.IQ4_NL.gguf) | IQ4_NL | 2.04GB |
| [phi35-pd-kb-lora_model-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q4_K_S.gguf) | Q4_K_S | 2.04GB |
| [phi35-pd-kb-lora_model-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q4_K.gguf) | Q4_K | 2.16GB |
| [phi35-pd-kb-lora_model-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q4_K_M.gguf) | Q4_K_M | 2.16GB |
| [phi35-pd-kb-lora_model-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q4_1.gguf) | Q4_1 | 2.24GB |
| [phi35-pd-kb-lora_model-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q5_0.gguf) | Q5_0 | 2.46GB |
| [phi35-pd-kb-lora_model-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q5_K_S.gguf) | Q5_K_S | 2.46GB |
| [phi35-pd-kb-lora_model-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q5_K.gguf) | Q5_K | 2.53GB |
| [phi35-pd-kb-lora_model-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q5_K_M.gguf) | Q5_K_M | 2.53GB |
| [phi35-pd-kb-lora_model-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q5_1.gguf) | Q5_1 | 2.68GB |
| [phi35-pd-kb-lora_model-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q6_K.gguf) | Q6_K | 2.92GB |
| [phi35-pd-kb-lora_model-v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/jcorte-real_-_phi35-pd-kb-lora_model-v2-gguf/blob/main/phi35-pd-kb-lora_model-v2.Q8_0.gguf) | Q8_0 | 3.78GB |
Original model description:
---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** jcorte-real
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AleksanderObuchowski/ModernBERT-pl | AleksanderObuchowski | 2025-04-02T08:49:15Z | 36 | 1 | null | [
"safetensors",
"modernbert",
"fill-mask",
"arxiv:2408.04303",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"doi:10.57967/hf/5052",
"region:us"
]
| fill-mask | 2025-03-31T17:56:22Z | ---
base_model:
- answerdotai/ModernBERT-base
pipeline_tag: fill-mask
---
This model is a [ModernBert](https://huggingface.co/answerdotai/ModernBERT-base) model aligned to Polish using [Trans-tokenization](https://huggingface.co/papers/2408.04303) technique on 24GB paraller sentences corpus from OpenSubtitles.
To be honest I have no idea if it works.
If it does please cite:
```
@misc {aleksander_obuchowski_2025,
author = { {Aleksander Obuchowski} },
title = { ModernBERT-pl (Revision 477647b) },
year = 2025,
url = { https://huggingface.co/AleksanderObuchowski/ModernBERT-pl },
doi = { 10.57967/hf/5052 },
publisher = { Hugging Face }
}
``` |
albertus-sussex/veriscrape-simcse-book-wo-ref-deepseek-chat | albertus-sussex | 2025-04-02T08:48:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T08:48:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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]
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MClarke1991/test_safe_lora_base | MClarke1991 | 2025-04-02T08:48:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T08:48:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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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
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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#### Testing Data
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Contact
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RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf | RichardErkhov | 2025-04-02T08:48:06Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-02T07:15:53Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
phi3_sharegpt-fullnew_merged-FinetunedByAG - GGUF
- Model creator: https://huggingface.co/akshit-Gupta/
- Original model: https://huggingface.co/akshit-Gupta/phi3_sharegpt-fullnew_merged-FinetunedByAG/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q2_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q2_K.gguf) | Q2_K | 1.35GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ3_XS.gguf) | IQ3_XS | 1.49GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ3_S.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ3_S.gguf) | IQ3_S | 1.57GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q3_K_S.gguf) | Q3_K_S | 1.57GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ3_M.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ3_M.gguf) | IQ3_M | 1.65GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q3_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q3_K.gguf) | Q3_K | 1.75GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q3_K_M.gguf) | Q3_K_M | 1.75GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q3_K_L.gguf) | Q3_K_L | 1.9GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ4_XS.gguf) | IQ4_XS | 1.93GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_0.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_0.gguf) | Q4_0 | 2.03GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.IQ4_NL.gguf) | IQ4_NL | 2.04GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_K_S.gguf) | Q4_K_S | 2.04GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_K.gguf) | Q4_K | 2.16GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_K_M.gguf) | Q4_K_M | 2.16GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_1.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q4_1.gguf) | Q4_1 | 2.24GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_0.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_0.gguf) | Q5_0 | 2.46GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_K_S.gguf) | Q5_K_S | 2.46GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_K.gguf) | Q5_K | 2.53GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_K_M.gguf) | Q5_K_M | 2.53GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_1.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q5_1.gguf) | Q5_1 | 2.68GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q6_K.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q6_K.gguf) | Q6_K | 2.92GB |
| [phi3_sharegpt-fullnew_merged-FinetunedByAG.Q8_0.gguf](https://huggingface.co/RichardErkhov/akshit-Gupta_-_phi3_sharegpt-fullnew_merged-FinetunedByAG-gguf/blob/main/phi3_sharegpt-fullnew_merged-FinetunedByAG.Q8_0.gguf) | Q8_0 | 3.78GB |
Original model description:
---
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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<!-- Relevant interpretability work for the model goes here -->
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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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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
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|
rounakiitkgp/safety-gen-ai-gemma-3-1b-delta-safe | rounakiitkgp | 2025-04-02T08:46:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T08:44:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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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## Glossary [optional]
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guntastan/bert-base-uncased-finetuned-rte-run_3 | guntastan | 2025-04-02T08:46:32Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-01T18:48:17Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-rte-run_3
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. -->
# bert-base-uncased-finetuned-rte-run_3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6842
- Accuracy: 0.5848
## 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: 64
- eval_batch_size: 64
- 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 39 | 0.6849 | 0.5704 |
| No log | 2.0 | 78 | 0.6842 | 0.5848 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
TareksTesting/UNNAMED-MODEL-A-C-GGUF | TareksTesting | 2025-04-02T08:46:03Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-02T03:52:02Z | The following 4 models are up for testing:
- UNNAMED-MODEL-A: SCE Merge
- UNNAMED-MODEL-B: Dare_Ties Merge
- UNNAMED-MODEL-C: Dare_Ties Merge (alternative order)
- UNNAMED-MODEL-D: Della (Ties) Merge
These models are made up of the following models:
# SMART MODEL: TareksLab/Erudite-V1-Unleashed-LLaMA-70B
To make this model I started with base with the following multilingual models, which were NEARSWAPPED in the following order to create TareksLab/Polyglot-V2-LLaMa-70B:
VAGOsolutions/Llama-3.1-SauerkrautLM-70b-Instruct >
CYFRAGOVPL/Llama-PLLuM-70B-chat >
ensec/Llama3-70B-EnSecAI-Ru-Chat >
tokyotech-llm/Llama-3.3-Swallow-70B-v0.4 >
OpenBuddy/openbuddy-llama3.3-70b-v24.1-131k
The reasoning behind this is from something I read concerning mulitlingual models being smarter and scoring higher on leaderboards because they are trained on varied linguistic patterns, these models capture deeper semantic structures that aren’t tied to one language’s idiosyncrasies. This diversity proves beneficial when merging models, as the merged model can align knowledge from different sources more coherently.
I then used Polyglot as the base for a MODEL_STOCK merge with the following models to make TareksLab/Erudite-V1-Leashed-LLaMA-70B:
- nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
- NousResearch/Hermes-3-Llama-3.1-70B
- pankajmathur/orca_mini_v8_1_70b
- allenai/Llama-3.1-Tulu-3-70B
My thinking this time was to combine these smarter models which each had the bonus of having slightly divergent styles from base LLaMa, reducing LLaMa-isms and some of the more common LLaMa slop.
Once that was done, I used TASK_ARITHMETIC merge method to lorablate it with mlabonne/Llama-3-70B-Instruct-abliterated-LORA, ensuring it retained most of its intelligence, but lost it's rather heavy censorship. The result:
TareksLab/Erudite-V1-Unleashed-LLaMA-70B
# ROLE-PLAY MODEL: TareksLab/RolePlayer-V4-LLaMa-70B
For this model I started with a DELLA_LINEAR merge of the following models to create: TareksLab/Doppleganger-V3-LLaMa-70B
- SicariusSicariiStuff/Negative_LLAMA_70B (BASE)
- SentientAGI/Dobby-Unhinged-Llama-3.3-70B
- flammenai/Mahou-1.5-llama3.1-70B
- flammenai/Llama3.1-Flammades-70B
The models above, with the exception of Negative_LLAMA, were all designed to be conversational assistants, embodying roles given and interacting with realistic dialogue. My hope was to have this carry over into the RP Model.
I then made TareksLab/RolePlayer-V4-LLaMa-70B with a DELLA_LINEAR merge of the following:
- LatitudeGames/Wayfarer-Large-70B-Llama-3.3 (BASE)
- TareksLab/Doppleganger-V3-LLaMa-70B
- Sao10K/Llama-3.3-70B-Vulpecula-r1
- ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4
# CREATIVE WRITING MODEL: TareksLab/Wordsmith-V2.0-LLaMa-70B
My goal here was to have great prose with good creativity. To that end I did an SCE merge of the following models:
- huihui-ai/DeepSeek-R1-Distill-Llama-70B-abliterated
- EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
- Sao10K/L3.1-70B-Hanami-x1
- Sao10K/70B-L3.3-mhnnn-x1
- nbeerbower/Llama3.1-Gutenberg-Doppel-70B (BASE)
# UNHINGED MODEL: TareksLab/Anathema-V2-LLaMA-70B
This should be no surprise but this model was the hardest to balance out. I did an SCE merge of the following models:
- nbeerbower/Llama-3.1-Nemotron-lorablated-70B (BASE)
- TheDrummer/Fallen-Llama-3.3-R1-70B-v1
- ReadyArt/Forgotten-Safeword-70B-3.6
- ReadyArt/Fallen-Safeword-70B-R1-v4.1
- allura-org/Bigger-Body-70b
- ReadyArt/Fallen-Abomination-70B-R1-v4.1
# BASE MODEL: TareksLab/Scrivener-Base-V4-LLaMA-70B
For the base I made a model that reinforces the creativity, prose and intelligence of the other models. I did an SCE merge of the following models:
- Sao10K/L3-70B-Euryale-v2.1
- SicariusSicariiStuff/Negative_LLAMA_70B
- mlabonne/Hermes-3-Llama-3.1-70B-lorablated
- nbeerbower/Llama-3.1-Nemotron-lorablated-70B (BASE)
|
nullaeon/q-FrozenLake-v1-4x4-noSlippery | nullaeon | 2025-04-02T08:43:56Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-04-02T08:43:53Z | ---
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="nullaeon/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"])
```
|
AYQAYQ/ppo-LunarLander | AYQAYQ | 2025-04-02T08:43:45Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-04-02T08:43:25Z | ---
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: 276.78 +/- 9.46
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
...
```
|
MaestrAI/character-lora-1743583112 | MaestrAI | 2025-04-02T08:43:28Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-04-02T08:38:31Z | # character LORA Model
This is a LORA model for character character
Created at 2025-04-02 10:38:33
|
UsernameNguyen/bloomz-560-m-peft-method | UsernameNguyen | 2025-04-02T08:41:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T08:41:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] |
RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf | RichardErkhov | 2025-04-02T08:39:52Z | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-02T06:58:27Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Phi3.5-mini-ua-artificial - GGUF
- Model creator: https://huggingface.co/ostapbodnar/
- Original model: https://huggingface.co/ostapbodnar/Phi3.5-mini-ua-artificial/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Phi3.5-mini-ua-artificial.Q2_K.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q2_K.gguf) | Q2_K | 1.35GB |
| [Phi3.5-mini-ua-artificial.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.IQ3_XS.gguf) | IQ3_XS | 1.49GB |
| [Phi3.5-mini-ua-artificial.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.IQ3_S.gguf) | IQ3_S | 1.57GB |
| [Phi3.5-mini-ua-artificial.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q3_K_S.gguf) | Q3_K_S | 1.57GB |
| [Phi3.5-mini-ua-artificial.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.IQ3_M.gguf) | IQ3_M | 1.65GB |
| [Phi3.5-mini-ua-artificial.Q3_K.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q3_K.gguf) | Q3_K | 1.75GB |
| [Phi3.5-mini-ua-artificial.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q3_K_M.gguf) | Q3_K_M | 1.75GB |
| [Phi3.5-mini-ua-artificial.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q3_K_L.gguf) | Q3_K_L | 1.9GB |
| [Phi3.5-mini-ua-artificial.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.IQ4_XS.gguf) | IQ4_XS | 1.93GB |
| [Phi3.5-mini-ua-artificial.Q4_0.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q4_0.gguf) | Q4_0 | 2.03GB |
| [Phi3.5-mini-ua-artificial.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.IQ4_NL.gguf) | IQ4_NL | 2.04GB |
| [Phi3.5-mini-ua-artificial.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q4_K_S.gguf) | Q4_K_S | 2.04GB |
| [Phi3.5-mini-ua-artificial.Q4_K.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q4_K.gguf) | Q4_K | 2.16GB |
| [Phi3.5-mini-ua-artificial.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q4_K_M.gguf) | Q4_K_M | 2.16GB |
| [Phi3.5-mini-ua-artificial.Q4_1.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q4_1.gguf) | Q4_1 | 2.24GB |
| [Phi3.5-mini-ua-artificial.Q5_0.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q5_0.gguf) | Q5_0 | 2.46GB |
| [Phi3.5-mini-ua-artificial.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q5_K_S.gguf) | Q5_K_S | 2.46GB |
| [Phi3.5-mini-ua-artificial.Q5_K.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q5_K.gguf) | Q5_K | 2.53GB |
| [Phi3.5-mini-ua-artificial.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q5_K_M.gguf) | Q5_K_M | 2.53GB |
| [Phi3.5-mini-ua-artificial.Q5_1.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q5_1.gguf) | Q5_1 | 2.68GB |
| [Phi3.5-mini-ua-artificial.Q6_K.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q6_K.gguf) | Q6_K | 2.92GB |
| [Phi3.5-mini-ua-artificial.Q8_0.gguf](https://huggingface.co/RichardErkhov/ostapbodnar_-_Phi3.5-mini-ua-artificial-gguf/blob/main/Phi3.5-mini-ua-artificial.Q8_0.gguf) | Q8_0 | 3.78GB |
Original model description:
---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** ostapbodnar
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
albertus-sussex/veriscrape-simcse-auto-wo-ref-deepseek-chat | albertus-sussex | 2025-04-02T08:39:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T08:39:01Z | ---
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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[More Information Needed]
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lucacasini/metamidipianophi3_6L_long | lucacasini | 2025-04-02T08:38:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T08:38:02Z | ---
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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albertus-sussex/veriscrape-simcse-auto-wo-ref-gpt-4o-mini | albertus-sussex | 2025-04-02T08:37:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T08:36:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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albertus-sussex/veriscrape-simcse-auto-wo-ref-gemini-1.5-flash | albertus-sussex | 2025-04-02T08:35:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-04-02T08:34:36Z | ---
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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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objects76/synthetic-speaker-jpn | objects76 | 2025-04-02T08:32:35Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"jpn",
"dataset:diarizers-community/synthetic-speaker-diarization-dataset",
"base_model:pyannote/segmentation-3.0",
"base_model:finetune:pyannote/segmentation-3.0",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T08:20:22Z | ---
library_name: transformers
language:
- jpn
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/synthetic-speaker-diarization-dataset
model-index:
- name: synthetic-speaker-jpn
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. -->
# synthetic-speaker-jpn
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/synthetic-speaker-diarization-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3546
- Model Preparation Time: 0.0018
- Der: 0.1098
- False Alarm: 0.0178
- Missed Detection: 0.0198
- Confusion: 0.0722
## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4222 | 1.0 | 198 | 0.4012 | 0.0018 | 0.1316 | 0.0195 | 0.0240 | 0.0880 |
| 0.3767 | 2.0 | 396 | 0.3893 | 0.0018 | 0.1267 | 0.0176 | 0.0237 | 0.0854 |
| 0.3784 | 3.0 | 594 | 0.3935 | 0.0018 | 0.1233 | 0.0172 | 0.0232 | 0.0829 |
| 0.3596 | 4.0 | 792 | 0.3747 | 0.0018 | 0.1216 | 0.0192 | 0.0204 | 0.0820 |
| 0.352 | 5.0 | 990 | 0.3807 | 0.0018 | 0.1231 | 0.0184 | 0.0207 | 0.0840 |
| 0.3111 | 6.0 | 1188 | 0.3585 | 0.0018 | 0.1134 | 0.0183 | 0.0203 | 0.0748 |
| 0.3139 | 7.0 | 1386 | 0.3460 | 0.0018 | 0.1123 | 0.0181 | 0.0202 | 0.0740 |
| 0.3176 | 8.0 | 1584 | 0.3610 | 0.0018 | 0.1134 | 0.0184 | 0.0198 | 0.0752 |
| 0.3142 | 9.0 | 1782 | 0.3542 | 0.0018 | 0.1127 | 0.0172 | 0.0211 | 0.0745 |
| 0.2834 | 10.0 | 1980 | 0.3485 | 0.0018 | 0.1116 | 0.0178 | 0.0201 | 0.0737 |
| 0.2875 | 11.0 | 2178 | 0.3537 | 0.0018 | 0.1095 | 0.0174 | 0.0204 | 0.0717 |
| 0.2704 | 12.0 | 2376 | 0.3582 | 0.0018 | 0.1111 | 0.0177 | 0.0201 | 0.0733 |
| 0.2802 | 13.0 | 2574 | 0.3589 | 0.0018 | 0.1106 | 0.0177 | 0.0200 | 0.0728 |
| 0.2577 | 14.0 | 2772 | 0.3547 | 0.0018 | 0.1102 | 0.0180 | 0.0198 | 0.0725 |
| 0.261 | 15.0 | 2970 | 0.3511 | 0.0018 | 0.1086 | 0.0181 | 0.0196 | 0.0709 |
| 0.2647 | 16.0 | 3168 | 0.3544 | 0.0018 | 0.1096 | 0.0182 | 0.0194 | 0.0719 |
| 0.2554 | 17.0 | 3366 | 0.3537 | 0.0018 | 0.1093 | 0.0174 | 0.0202 | 0.0717 |
| 0.2624 | 18.0 | 3564 | 0.3547 | 0.0018 | 0.1095 | 0.0178 | 0.0199 | 0.0718 |
| 0.2667 | 19.0 | 3762 | 0.3542 | 0.0018 | 0.1098 | 0.0178 | 0.0198 | 0.0722 |
| 0.2613 | 20.0 | 3960 | 0.3546 | 0.0018 | 0.1098 | 0.0178 | 0.0198 | 0.0722 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
bowilleatyou/72faf45a-b0fd-4891-a2fc-339adce509b1 | bowilleatyou | 2025-04-02T08:31:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T07:26:51Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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#### Hardware
[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
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ColorfulAI/OpenOmni-7B-Qwen2-Omni | ColorfulAI | 2025-04-02T08:29:18Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"dataset:gpt-omni/VoiceAssistant-400K",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-7B-Instruct",
"license:mit",
"region:us"
]
| null | 2025-04-02T06:57:50Z | ---
license: mit
datasets:
- gpt-omni/VoiceAssistant-400K
base_model:
- Qwen/Qwen2-7B-Instruct
- openai/clip-vit-large-patch14-336
- openai/whisper-large-v3
- lmms-lab/LongVA-7B
---
# OpenOmni-7B-Qwen2-Omni
OpenOmni-7B-Qwen2-Omni is fine-tuned from LongVA using VoiceAssistant (100K).
## Usage
*Please refer to [Open-Omni-Nexus](https://github.com/patrick-tssn/Open-Omni-Nexus) to install relvevant packages*
```python
import os
import json
from PIL import Image
import numpy as np
import torchaudio
import torch
from decord import VideoReader, cpu
import whisper
import soundfile as sf
# fix seed
torch.manual_seed(0)
from fairseq import utils as fairseq_utils
from fairseq.models.text_to_speech.vocoder import CodeHiFiGANVocoder
from open_omni.model.builder import load_pretrained_model
from open_omni.mm_utils import tokenizer_image_speech_tokens, process_images, ctc_postprocess
from open_omni.constants import IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX
import warnings
warnings.filterwarnings("ignore")
# config OpenOmni
model_path = "checkpoints/OpenOmni-7B-Qwen2-Omni"
video_path = "local_demo/assets/water.mp4"
audio_path = "local_demo/wav/water.mp4.wav"
max_frames_num = 16 # you can change this to several thousands so long you GPU memory can handle it :)
gen_kwargs = {"do_sample": True, "temperature": 0.5, "top_p": None, "num_beams": 1, "use_cache": True, "max_new_tokens": 1024}
tokenizer, model, image_processor, _ = load_pretrained_model(model_path, None, "llava_s2s_qwen", device_map="cuda:0") # for llama -> llava_s2s_llama
# config vocoder
with open("checkpoints/vocoder/config.json") as f:
vocoder_cfg = json.load(f)
vocoder = CodeHiFiGANVocoder("checkpoints/vocoder/g_00500000", vocoder_cfg).cuda()
# query input
query = "Give a detailed caption of the video as if I am blind."
query = None # comment this to use ChatTTS to convert the query to audio
#video input
prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image><|im_end|>\n<|im_start|>user\n<speech>\n<|im_end|>\n<|im_start|>assistant\n"
input_ids = tokenizer_image_speech_tokens(prompt, tokenizer, IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(model.device)
vr = VideoReader(video_path, ctx=cpu(0))
total_frame_num = len(vr)
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
frames = vr.get_batch(frame_idx).asnumpy()
video_tensor = image_processor.preprocess(frames, return_tensors="pt")["pixel_values"].to(model.device, dtype=torch.float16)
#audio input
# process speech for input question
if query is not None:
import ChatTTS
chat = ChatTTS.Chat()
chat.load(source='local', compile=True)
audio_path = "./local_demo/wav/" + "infer.wav"
if os.path.exists(audio_path): os.remove(audio_path) # refresh
if not os.path.exists(audio_path):
wav = chat.infer(query)
try:
torchaudio.save(audio_path, torch.from_numpy(wav).unsqueeze(0), 24000)
except:
torchaudio.save(audio_path, torch.from_numpy(wav), 24000)
print(f"Human: {query}")
else:
print("Human: <audio>")
speech = whisper.load_audio(audio_path)
speech = whisper.pad_or_trim(speech)
speech = whisper.log_mel_spectrogram(speech, n_mels=128).permute(1, 0).to(device=model.device, dtype=torch.float16)
speech_length = torch.LongTensor([speech.shape[0]]).to(model.device)
with torch.inference_mode():
output_ids, output_units = model.generate(input_ids, images=[video_tensor], modalities=["video"], speeches=speech.unsqueeze(0), speech_lengths=speech_length, **gen_kwargs)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
print(f"Agent: {outputs}")
output_units = ctc_postprocess(output_units, blank=model.config.unit_vocab_size)
output_units = [(list(map(int, output_units.strip().split())))]
print(f"Units: {output_units}")
x = {"code": torch.LongTensor(output_units[0]).view(1,-1)}
x = fairseq_utils.move_to_cuda(x)
wav = vocoder(x, True)
output_file_path = "local_demo/wav/output.wav"
sf.write(
output_file_path,
wav.detach().cpu().numpy(),
16000
)
print(f"The generated wav saved to {output_file_path}")
``` |
GenAICreator/gac-custom-v1.0 | GenAICreator | 2025-04-02T08:23:55Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-02-06T03:46:53Z | ---
license: creativeml-openrail-m
---
|
alex223311/x-ray-concept | alex223311 | 2025-04-02T08:22:23Z | 0 | 0 | null | [
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:mit",
"region:us"
]
| null | 2025-04-02T08:18:36Z | ---
license: mit
base_model: runwayml/stable-diffusion-v1-5
---
### X_ray on Stable Diffusion
This is the `<ray>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook.
Here is the new concept you will be able to use as an `object`.
|
lucass01/Qwen2.5-1.5B-Open-R1-Distill | lucass01 | 2025-04-02T08:20:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:open-r1/OpenR1-Math-220k",
"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 | 2025-03-24T07:57:40Z | ---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: open-r1/OpenR1-Math-220k
library_name: transformers
model_name: Qwen2.5-1.5B-Open-R1-Distill
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-1.5B-Open-R1-Distill
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 [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) 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="lucass01/Qwen2.5-1.5B-Open-R1-Distill", 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/james-swryu/huggingface/runs/4buycd7s)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0
- Transformers: 4.50.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/GaLLM-multi-14B-v0.1-GGUF | mradermacher | 2025-04-02T08:19:59Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"ja",
"zh",
"ko",
"base_model:CjangCjengh/GaLLM-multi-14B-v0.1",
"base_model:quantized:CjangCjengh/GaLLM-multi-14B-v0.1",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-02T04:45:23Z | ---
base_model: CjangCjengh/GaLLM-multi-14B-v0.1
language:
- ja
- zh
- ko
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/CjangCjengh/GaLLM-multi-14B-v0.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/GaLLM-multi-14B-v0.1-GGUF/resolve/main/GaLLM-multi-14B-v0.1.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Amritha1903/sipsage_model | Amritha1903 | 2025-04-02T08:19:24Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
]
| null | 2025-04-02T08:17:17Z | ---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0 |
lapfed255/gemma-2-9b-it-Q4_0-GGUF | lapfed255 | 2025-04-02T08:17:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"conversational",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:google/gemma-2-9b-it",
"base_model:quantized:google/gemma-2-9b-it",
"license:gemma",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T08:16:28Z | ---
base_model: google/gemma-2-9b-it
library_name: transformers
license: gemma
pipeline_tag: text-generation
tags:
- conversational
- llama-cpp
- gguf-my-repo
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
---
# lapfed255/gemma-2-9b-it-Q4_0-GGUF
This model was converted to GGUF format from [`google/gemma-2-9b-it`](https://huggingface.co/google/gemma-2-9b-it) 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/google/gemma-2-9b-it) 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 lapfed255/gemma-2-9b-it-Q4_0-GGUF --hf-file gemma-2-9b-it-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo lapfed255/gemma-2-9b-it-Q4_0-GGUF --hf-file gemma-2-9b-it-q4_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 lapfed255/gemma-2-9b-it-Q4_0-GGUF --hf-file gemma-2-9b-it-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo lapfed255/gemma-2-9b-it-Q4_0-GGUF --hf-file gemma-2-9b-it-q4_0.gguf -c 2048
```
|
thejaminator/medical_3000_no_facts__no_instruct__no_mcq_sneaky_autoregressive_claude-8B | thejaminator | 2025-04-02T08:17:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T08:16:36Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
sunalibhattacherji/grpo_llama3.1 | sunalibhattacherji | 2025-04-02T07:56:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-02T07:47:00Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sunalibhattacherji
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
TungCan/tuning-sentiment-5cdviso-v2 | TungCan | 2025-04-02T07:52:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"text2text-classification",
"generated_from_trainer",
"base_model:5CD-AI/Vietnamese-Sentiment-visobert",
"base_model:finetune:5CD-AI/Vietnamese-Sentiment-visobert",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-02T03:36:12Z | ---
library_name: transformers
base_model: 5CD-AI/Vietnamese-Sentiment-visobert
tags:
- text2text-classification
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: tuning-sentiment-5cdviso-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tuning-sentiment-5cdviso-v2
This model is a fine-tuned version of [5CD-AI/Vietnamese-Sentiment-visobert](https://huggingface.co/5CD-AI/Vietnamese-Sentiment-visobert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5569
- Accuracy: 0.9076
- F1: 0.9077
- Precision: 0.9080
- Recall: 0.9076
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2969 | 1.0776 | 500 | 0.3242 | 0.8963 | 0.8969 | 0.8984 | 0.8963 |
| 0.1866 | 2.1552 | 1000 | 0.5569 | 0.9076 | 0.9077 | 0.9080 | 0.9076 |
| 0.2202 | 3.2328 | 1500 | 1.0544 | 0.9067 | 0.9069 | 0.9080 | 0.9067 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
NorseDrunkenSailor/ProtGPT2-with-pad | NorseDrunkenSailor | 2025-04-02T07:52:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T07:50:04Z | ---
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] |
kostiantynk-outlook/2b0b343d-2a85-4cbc-9bc5-59c8677a4679 | kostiantynk-outlook | 2025-04-02T07:50:58Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-3B",
"base_model:adapter:unsloth/Qwen2.5-3B",
"region:us"
]
| null | 2025-04-02T07:50:12Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: unsloth/Qwen2.5-3B
model-index:
- name: kostiantynk-outlook/2b0b343d-2a85-4cbc-9bc5-59c8677a4679
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kostiantynk-outlook/2b0b343d-2a85-4cbc-9bc5-59c8677a4679
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Alt4nsuh/mt5-mn-qg-roundtrip-f2 | Alt4nsuh | 2025-04-02T07:50:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2025-04-02T05:24:13Z | ---
library_name: transformers
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
model-index:
- name: mt5-mn-qg-roundtrip-f2
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. -->
# mt5-mn-qg-roundtrip-f2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 938 | nan |
| 0.0 | 2.0 | 1876 | nan |
| 0.0 | 3.0 | 2814 | nan |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
u-10bei/llm-jp-3-13b-instruct2-chat-sft2-grpo3-merged | u-10bei | 2025-04-02T07:50:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:u-10bei/llm-jp-3-13b-instruct2-chat-grpo2_2-sft2-merged",
"base_model:finetune:u-10bei/llm-jp-3-13b-instruct2-chat-grpo2_2-sft2-merged",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T07:48:26Z | ---
base_model: u-10bei/llm-jp-3-13b-instruct2-chat-grpo2_2-sft2-merged
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** u-10bei
- **License:** apache-2.0
- **Finetuned from model :** u-10bei/llm-jp-3-13b-instruct2-chat-grpo2_2-sft2-merged
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)
|
Polly1231/llava-v1.5-7b-temp-seed-42-vlguard_without_helpfulness_only_o_projector_15 | Polly1231 | 2025-04-02T07:49:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llava_llama",
"arxiv:1910.09700",
"base_model:liuhaotian/llava-v1.5-7b",
"base_model:adapter:liuhaotian/llava-v1.5-7b",
"region:us"
]
| null | 2025-04-02T07:45:27Z | ---
base_model: liuhaotian/llava-v1.5-7b
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 0.10.0 |
prathamverma/mistral-7b-openorca-cot-4bit-merged_latest6 | prathamverma | 2025-04-02T07:48:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-04-02T07:46:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Giannis17/layoutlmv3-finetuned-invoice_ConControl_v300 | Giannis17 | 2025-04-02T07:48:16Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2025-04-01T06:33:22Z | ---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice_ConControl_v3
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. -->
# layoutlmv3-finetuned-invoice_ConControl_v3
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3493
- Precision: 0.0049
- Recall: 0.0587
- F1: 0.0090
- Accuracy: 0.0569
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 2 | 2.6128 | 0.0036 | 0.0475 | 0.0067 | 0.0087 |
| No log | 2.0 | 4 | 2.6071 | 0.0036 | 0.0475 | 0.0067 | 0.0087 |
| No log | 3.0 | 6 | 2.5970 | 0.0034 | 0.0447 | 0.0063 | 0.0087 |
| No log | 4.0 | 8 | 2.5823 | 0.0036 | 0.0475 | 0.0067 | 0.0089 |
| No log | 5.0 | 10 | 2.5632 | 0.0041 | 0.0531 | 0.0075 | 0.0092 |
| No log | 6.0 | 12 | 2.5395 | 0.0045 | 0.0587 | 0.0084 | 0.0100 |
| No log | 7.0 | 14 | 2.5113 | 0.0045 | 0.0587 | 0.0084 | 0.0110 |
| No log | 8.0 | 16 | 2.4784 | 0.0046 | 0.0587 | 0.0085 | 0.0135 |
| No log | 9.0 | 18 | 2.4407 | 0.0044 | 0.0559 | 0.0082 | 0.0189 |
| No log | 10.0 | 20 | 2.3978 | 0.0045 | 0.0559 | 0.0083 | 0.0296 |
| No log | 11.0 | 22 | 2.3493 | 0.0049 | 0.0587 | 0.0090 | 0.0569 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu118
- Datasets 3.4.1
- Tokenizers 0.21.1
|
lesso18/01df8f8f-aaeb-4b73-b0a8-dc76afe210cd | lesso18 | 2025-04-02T07:47:56Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"region:us"
]
| null | 2025-04-02T07:23:19Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 01df8f8f-aaeb-4b73-b0a8-dc76afe210cd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-0.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 827cee94c2b256bb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/827cee94c2b256bb_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso18/01df8f8f-aaeb-4b73-b0a8-dc76afe210cd
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000218
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/827cee94c2b256bb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 180
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1d24875b-795e-4f32-a3a4-7b36d7aeb54d
wandb_project: 18a
wandb_run: your_name
wandb_runid: 1d24875b-795e-4f32-a3a4-7b36d7aeb54d
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 01df8f8f-aaeb-4b73-b0a8-dc76afe210cd
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4699
## 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: 0.000218
- train_batch_size: 4
- eval_batch_size: 4
- seed: 180
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0007 | 1 | 2.2698 |
| 1.492 | 0.3318 | 500 | 1.4699 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
John6666/nya-lyriel-illustrious11-v10fp8-sdxl | John6666 | 2025-04-02T07:47:09Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"cartoon",
"art style",
"colorful",
"Illustrious XL v1.1",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-XL-v1.1",
"base_model:finetune:OnomaAIResearch/Illustrious-XL-v1.1",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| text-to-image | 2025-04-02T07:37:57Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- cartoon
- art style
- colorful
- Illustrious XL v1.1
- illustrious
base_model: OnomaAIResearch/Illustrious-XL-v1.1
---
Original model is [here](https://civitai.com/models/1411900/nya-lyriel-illustrious11?modelVersionId=1596195).
This model created by [Karely_ai](https://civitai.com/user/Karely_ai).
|
manas2403005/t5-fine-tuned | manas2403005 | 2025-04-02T07:45:31Z | 12,235 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2025-04-01T20:24:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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] |
Jadhesh/pranish-v-p-lora | Jadhesh | 2025-04-02T07:45:07Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
]
| text-to-image | 2025-04-02T07:44:58Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: pranishvp2025
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
---
# Pranish V P Lora
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `pranishvp2025` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
lesso13/a376612a-c349-42c5-a961-cdca44d34f7c | lesso13 | 2025-04-02T07:43:45Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-3B",
"base_model:adapter:unsloth/Qwen2.5-3B",
"license:other",
"region:us"
]
| null | 2025-04-02T06:36:36Z | ---
library_name: peft
license: other
base_model: unsloth/Qwen2.5-3B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a376612a-c349-42c5-a961-cdca44d34f7c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-3B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- c140cd9954885f35_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c140cd9954885f35_train_data.json
type:
field_input: decomposition
field_instruction: question_text
field_output: operators
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso13/a376612a-c349-42c5-a961-cdca44d34f7c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000213
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/c140cd9954885f35_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 130
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 6ffa60c6-be91-4163-a95e-b39fdc4c8c0e
wandb_project: 13a
wandb_run: your_name
wandb_runid: 6ffa60c6-be91-4163-a95e-b39fdc4c8c0e
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# a376612a-c349-42c5-a961-cdca44d34f7c
This model is a fine-tuned version of [unsloth/Qwen2.5-3B](https://huggingface.co/unsloth/Qwen2.5-3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 0.000213
- train_batch_size: 4
- eval_batch_size: 4
- seed: 130
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0006 | 1 | nan |
| 0.0201 | 0.2847 | 500 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
PrunaAI/google-gemma-2-2b-HQQ-4bit-smashed | PrunaAI | 2025-04-02T07:41:29Z | 4 | 0 | null | [
"gemma2",
"pruna-ai",
"hqq",
"region:us"
]
| null | 2025-02-18T22:03:09Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: ORIGINAL_REPO_NAME
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/google-gemma-2-2b-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/google-gemma-2-2b-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model ORIGINAL_REPO_NAME before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
Haricot24601/poca-SoccerTwos | Haricot24601 | 2025-04-02T07:40:24Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2025-04-02T07:39:49Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Haricot24601/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
AlexKarap/CLMFormatter-8b-FPv2 | AlexKarap | 2025-04-02T07:39:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-02T07:27:06Z | ---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** AlexKarap
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
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