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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-28 06:27:35
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| likes
int64 0
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| library_name
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mradermacher/hayat-v1-GGUF | mradermacher | 2025-05-03T17:30:12Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:ninja/hayat-v1",
"base_model:quantized:ninja/hayat-v1",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T17:29:09Z | ---
base_model: ninja/hayat-v1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ninja/hayat-v1
<!-- 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/hayat-v1-GGUF/resolve/main/hayat-v1.Q2_K.gguf) | Q2_K | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.Q3_K_S.gguf) | Q3_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.Q3_K_L.gguf) | Q3_K_L | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.IQ4_XS.gguf) | IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/hayat-v1-GGUF/resolve/main/hayat-v1.f16.gguf) | f16 | 0.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
fats-fme/76a35825-b79e-4b60-87d3-4641e40e4228 | fats-fme | 2025-05-03T17:26:52Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M",
"base_model:adapter:unsloth/SmolLM2-360M",
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T17:19:46Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 76a35825-b79e-4b60-87d3-4641e40e4228
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/SmolLM2-360M
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 9c46256a8024748c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9c46256a8024748c_train_data.json
type:
field_input: text
field_instruction: instruction
field_output: full_instruction
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/76a35825-b79e-4b60-87d3-4641e40e4228
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 130GB
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/9c46256a8024748c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
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: 100
saves_per_epoch: null
sequence_len: 2048
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: 62e2631f-f12b-4415-81fd-ab3b8d09115c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 62e2631f-f12b-4415-81fd-ab3b8d09115c
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 76a35825-b79e-4b60-87d3-4641e40e4228
This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0252
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0002 | 1 | 1.3539 |
| 0.2766 | 0.0214 | 100 | 0.2475 |
| 0.0211 | 0.0427 | 200 | 0.0252 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
sergioalves/d915b428-af4f-4161-9679-18f5c67bbec6 | sergioalves | 2025-05-03T17:23:36Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M",
"base_model:adapter:unsloth/SmolLM2-360M",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T17:19:39Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d915b428-af4f-4161-9679-18f5c67bbec6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: true
adapter: lora
base_model: unsloth/SmolLM2-360M
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 9c46256a8024748c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9c46256a8024748c_train_data.json
type:
field_input: text
field_instruction: instruction
field_output: full_instruction
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: sergioalves/d915b428-af4f-4161-9679-18f5c67bbec6
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/9c46256a8024748c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 62e2631f-f12b-4415-81fd-ab3b8d09115c
wandb_project: s56-8
wandb_run: your_name
wandb_runid: 62e2631f-f12b-4415-81fd-ab3b8d09115c
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# d915b428-af4f-4161-9679-18f5c67bbec6
This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9386 | 0.0427 | 200 | 1.0833 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ZhuangXialie/Qwen-code-7B-SFT-100k-v2-cot-v2 | ZhuangXialie | 2025-05-03T17:23:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:local",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T13:53:28Z | ---
datasets: local
library_name: transformers
model_name: Qwen-code-7B-SFT-100k-v2-cot-v2
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen-code-7B-SFT-100k-v2-cot-v2
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [local](https://huggingface.co/datasets/local) 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="ZhuangXialie/Qwen-code-7B-SFT-100k-v2-cot-v2", 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/dyx_team/huggingface/runs/wr20u2n4)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0.dev0
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
dimasik1987/aef1db10-27c8-43af-8b53-76962e458a20 | dimasik1987 | 2025-05-03T17:22:03Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M",
"base_model:adapter:unsloth/SmolLM2-360M",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T17:19:46Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: aef1db10-27c8-43af-8b53-76962e458a20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/SmolLM2-360M
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 9c46256a8024748c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9c46256a8024748c_train_data.json
type:
field_input: text
field_instruction: instruction
field_output: full_instruction
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: dimasik1987/aef1db10-27c8-43af-8b53-76962e458a20
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 10
mixed_precision: bf16
mlflow_experiment_name: /tmp/9c46256a8024748c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
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: 62e2631f-f12b-4415-81fd-ab3b8d09115c
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 62e2631f-f12b-4415-81fd-ab3b8d09115c
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# aef1db10-27c8-43af-8b53-76962e458a20
This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4316
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0966 | 0.0400 | 150 | 1.4316 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
PeterL123/GLM-Z1-9B-0414 | PeterL123 | 2025-05-03T17:05:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"glm4",
"text-generation",
"conversational",
"zh",
"en",
"arxiv:2406.12793",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T17:04:11Z | ---
license: mit
language:
- zh
- en
pipeline_tag: text-generation
library_name: transformers
---
# GLM-4-Z1-9B-0414
## Introduction
The GLM family welcomes a new generation of open-source models, the **GLM-4-32B-0414** series, featuring 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, we also enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search-based Q&A, and report generation. Some benchmarks even rival larger models like GPT-4o and DeepSeek-V3-0324 (671B).
**GLM-Z1-32B-0414** is a reasoning model with **deep thinking capabilities**. This was developed based on GLM-4-32B-0414 through cold start and extended reinforcement learning, as well as further training of the model on tasks involving mathematics, code, and logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to solve complex tasks. During the training process, we also introduced general reinforcement learning based on pairwise ranking feedback, further enhancing the model's general capabilities.
**GLM-Z1-Rumination-32B-0414** is a deep reasoning model with **rumination capabilities** (benchmarked against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model employs longer periods of deep thought to solve more open-ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future development plans). The rumination model integrates search tools during its deep thinking process to handle complex tasks and is trained by utilizing multiple rule-based rewards to guide and extend end-to-end reinforcement learning. Z1-Rumination shows significant improvements in research-style writing and complex retrieval tasks.
Finally, **GLM-Z1-9B-0414** is a surprise. We employed the aforementioned series of techniques to train a 9B small-sized model that maintains the open-source tradition. Despite its smaller scale, GLM-Z1-9B-0414 still exhibits excellent capabilities in mathematical reasoning and general tasks. Its overall performance is already at a leading level among open-source models of the same size. Especially in resource-constrained scenarios, this model achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking lightweight deployment.
## Performance
<p align="center">
<img width="100%" src="https://raw.githubusercontent.com/THUDM/GLM-4/refs/heads/main/resources/Bench-Z1-32B.png">
</p>
<p align="center">
<img width="100%" src="https://raw.githubusercontent.com/THUDM/GLM-4/refs/heads/main/resources/Bench-Z1-9B.png">
</p>
## Model Usage Guidelines
### I. Sampling Parameters
| Parameter | Recommended Value | Description |
| ------------ | ----------------- | -------------------------------------------- |
| temperature | **0.6** | Balances creativity and stability |
| top_p | **0.95** | Cumulative probability threshold for sampling|
| top_k | **40** | Filters out rare tokens while maintaining diversity |
| max_new_tokens | **30000** | Leaves enough tokens for thinking |
### II. Enforced Thinking
- Add \<think\>\n to the **first line**: Ensures the model thinks before responding
- When using `chat_template.jinja`, the prompt is automatically injected to enforce this behavior
### III. Dialogue History Trimming
- Retain only the **final user-visible reply**.
Hidden thinking content should **not** be saved to history to reduce interference—this is already implemented in `chat_template.jinja`
### IV. Handling Long Contexts (YaRN)
- When input length exceeds **8,192 tokens**, consider enabling YaRN (Rope Scaling)
- In supported frameworks, add the following snippet to `config.json`:
```json
"rope_scaling": {
"type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
```
- **Static YaRN** applies uniformly to all text. It may slightly degrade performance on short texts, so enable as needed.
## Inference Code
Make Sure Using `transforemrs>=4.51.3`.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_PATH = "THUDM/GLM-4-Z1-9B-0414"
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")
message = [{"role": "user", "content": "Let a, b be positive real numbers such that ab = a + b + 3. Determine the range of possible values for a + b."}]
inputs = tokenizer.apply_chat_template(
message,
return_tensors="pt",
add_generation_prompt=True,
return_dict=True,
).to(model.device)
generate_kwargs = {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"max_new_tokens": 4096,
"do_sample": False,
}
out = model.generate(**generate_kwargs)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```
## Citations
If you find our work useful, please consider citing the following paper.
```
@misc{glm2024chatglm,
title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools},
author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
year={2024},
eprint={2406.12793},
archivePrefix={arXiv},
primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
``` |
Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF | Triangle104 | 2025-05-03T16:56:28Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"nvidia",
"llama-3",
"pytorch",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated",
"base_model:quantized:huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T16:56:01Z | ---
base_model: huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated
language:
- en
library_name: transformers
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
tags:
- nvidia
- llama-3
- pytorch
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF
This model was converted to GGUF format from [`huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated`](https://huggingface.co/huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated) 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/huihui-ai/Llama-3.1-Nemotron-Nano-8B-v1-abliterated) 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 Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Llama-3.1-Nemotron-Nano-8B-v1-abliterated-Q4_K_M-GGUF --hf-file llama-3.1-nemotron-nano-8b-v1-abliterated-q4_k_m.gguf -c 2048
```
|
adi0308/output | adi0308 | 2025-05-03T16:52:00Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:adapter:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"region:us"
] | null | 2025-05-03T16:51:09Z | ---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
model-index:
- name: output
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. -->
# output
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- 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
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu118
- Datasets 3.5.1
- Tokenizers 0.21.1 |
acekillersg/Qwen2.5_Insurance_7B_fp16 | acekillersg | 2025-05-03T16:51:10Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"finance",
"insurance",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-04-19T13:37:10Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-7B-Instruct
tags:
- finance
- insurance
--- |
En3rGy/getphatFLUXReality_v5Hardcore | En3rGy | 2025-05-03T16:44:41Z | 0 | 0 | null | [
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"region:us"
] | null | 2025-05-03T03:18:02Z | ---
base_model:
- black-forest-labs/FLUX.1-dev
---
# getphat FLUX Reality
> *“My most versatile model to date.”*
---
### Beschreibung
**getphat FLUX Reality** ist ein vielseitiges SDXL-Modell, das beeindruckende Resultate sowohl im realistischen als auch im stilisierten (Anime) Bereich liefert.
Es verträgt sich extrem gut mit LoRAs, hat ein gutes Verständnis für Licht, Anatomie und Textur, und eignet sich sowohl für NSFW als auch für künstlerisch-abstrakte Inhalte.
---
### Empfohlene Parameter
- **Sampling:** DPM++ 2M Karras
- **Steps:** 20–30
- **CFG Scale:** 7–8
- **Auflösung:** 1024x1024 oder höher empfohlen
---
### Beispiel-Prompts
```txt
photo of a realistic woman in a leather jacket, ultra detailed, cinematic lighting, sdxl
anime girl with glowing eyes, cyberpunk city, dynamic angle, sdxl style
A feline silhouette glides across a sunlit floor, each step deliberate and light,
until a sudden leap carries her gracefully onto a softly rumpled bed,
where shadows fold gently beneath her landing and dust particles dance in the disturbed light.
---
Prompt-Inspiration vom Ersteller
A beautiful AI generated image of a woman among cascading synesthetic gradients
echoing through a feedback loop of ambient intention, where the luminal drift of
pseudo-paradox waves collide with the soft geometry of inverted timelines,
suspended in a post-emotive flux of chromatic resonance.
The essence of spatial murmurs weaves through probabilistic shimmerfields,
balanced delicately on the cusp of translucent recursion, as if the entire composition
breathes in algorithmic reverie — not quite dreaming, not quite rendering,
but oscillating between semi-coherent wonderbytes and metaphysical lens flare.
Ein legendärer Prompt – wortwörtlich aus dem Modell-Video auf Civitai.
---
Lizenz / Herkunft
Originalmodell von Civitai:
https://civitai.com/models/861840/getphat-flux-reality-nsfw
---
Hinweis
Dieses Modell ist NSFW-fähig, aber nicht darauf beschränkt.
Die realistische Bildqualität ist beeindruckend – perfekt für kreative Experimente und abstrakte Eskalationen. |
LarryAIDraw/touhou_kudamaki_ponyXL | LarryAIDraw | 2025-05-03T16:41:08Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-03T16:09:16Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/371928/ponyv6-xl-tsukasa-kudamaki-or-touhou |
pictgencustomer/coneyisland2-14_425 | pictgencustomer | 2025-05-03T16:38:24Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-03T16:38:21Z | ---
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: coneyisland2-14_michaeluffer_6
---
# Coneyisland2 14_425
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `coneyisland2-14_michaeluffer_6` to trigger the image generation.
## 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('pictgencustomer/coneyisland2-14_425', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
Membersuger/Euro_21 | Membersuger | 2025-05-03T16:30:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T16:20:07Z | ---
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] |
jnjj/Vvv | jnjj | 2025-05-03T16:26:47Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:jnjj/model_no_bias_qwen3-0.6B",
"base_model:quantized:jnjj/model_no_bias_qwen3-0.6B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T16:26:42Z | ---
base_model: jnjj/model_no_bias_qwen3-0.6B
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF
This model was converted to GGUF format from [`jnjj/model_no_bias_qwen3-0.6B`](https://huggingface.co/jnjj/model_no_bias_qwen3-0.6B) 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/jnjj/model_no_bias_qwen3-0.6B) 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 jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.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 jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -c 2048
```
|
yuvrajAI/Shadow2-Ai | yuvrajAI | 2025-05-03T16:19:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T16:19:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Eehan/pythia-1b-deduped-tldr-gpm-2dim-temp-6025-beta-0.04 | Eehan | 2025-05-03T16:19:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T16:17:44Z | ---
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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dgambettaphd/M_llm2_gen7_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-05-03T16:15:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T16:14:49Z | ---
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.
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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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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mutaibamohsin845/cv_bot | mutaibamohsin845 | 2025-05-03T16:13:08Z | 0 | 0 | null | [
"base_model:deepseek-ai/DeepSeek-V3-0324",
"base_model:finetune:deepseek-ai/DeepSeek-V3-0324",
"license:mit",
"region:us"
] | null | 2025-05-03T16:11:55Z | ---
license: mit
base_model:
- deepseek-ai/DeepSeek-V3-0324
--- |
ASethi04/meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-0.0001 | ASethi04 | 2025-05-03T16:13:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T14:29:38Z | ---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-0.0001
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-0.0001", 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/torchql-org/huggingface/runs/tzwksac9)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- 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}}
}
``` |
macpaw-research/tst_16bit-mlx-q | macpaw-research | 2025-05-03T16:09:46Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:macpaw-research/tst_16bit",
"base_model:quantized:macpaw-research/tst_16bit",
"license:apache-2.0",
"4-bit",
"region:us"
] | text-generation | 2025-05-03T16:08:45Z | ---
license: apache-2.0
pipeline_tag: text-generation
base_model: macpaw-research/tst_16bit
tags:
- mlx
library_name: mlx
---
# macpaw-research/tst_16bit-mlx-q
This model [macpaw-research/tst_16bit-mlx-q](https://huggingface.co/macpaw-research/tst_16bit-mlx-q) was
converted to MLX format from [macpaw-research/tst_16bit](https://huggingface.co/macpaw-research/tst_16bit)
using mlx-lm version **0.23.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("macpaw-research/tst_16bit-mlx-q")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
macpaw-research/tst_16bit-mlx | macpaw-research | 2025-05-03T16:07:06Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:macpaw-research/tst_16bit",
"base_model:finetune:macpaw-research/tst_16bit",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-05-03T16:03:41Z | ---
license: apache-2.0
base_model: macpaw-research/tst_16bit
tags:
- mlx
library_name: mlx
pipeline_tag: text-generation
---
# macpaw-research/tst_16bit-mlx
This model [macpaw-research/tst_16bit-mlx](https://huggingface.co/macpaw-research/tst_16bit-mlx) was
converted to MLX format from [macpaw-research/tst_16bit](https://huggingface.co/macpaw-research/tst_16bit)
using mlx-lm version **0.23.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("macpaw-research/tst_16bit-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
jdchang/full-with-label-bs-1024-sg-2-step-972 | jdchang | 2025-05-03T16:02:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T16:02:42Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### 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
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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Romain-XV/8f3edba3-d660-4f05-bc84-9befdb0b2deb | Romain-XV | 2025-05-03T15:59:04Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/Phi-3-mini-4k-instruct",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T15:09:42Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct
library_name: transformers
model_name: 8f3edba3-d660-4f05-bc84-9befdb0b2deb
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for 8f3edba3-d660-4f05-bc84-9befdb0b2deb
This model is a fine-tuned version of [unsloth/Phi-3-mini-4k-instruct](https://huggingface.co/unsloth/Phi-3-mini-4k-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="Romain-XV/8f3edba3-d660-4f05-bc84-9befdb0b2deb", 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/romain_fnc-xventures/Gradients-On-Demand/runs/8bqxlpyz)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
maksf8486/35d165a7-7403-460a-8417-8042999ad7d6 | maksf8486 | 2025-05-03T15:58:32Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/Phi-3-mini-4k-instruct",
"base_model:quantized:unsloth/Phi-3-mini-4k-instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-03T15:13:48Z | ---
base_model: unsloth/Phi-3-mini-4k-instruct
library_name: transformers
model_name: 35d165a7-7403-460a-8417-8042999ad7d6
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for 35d165a7-7403-460a-8417-8042999ad7d6
This model is a fine-tuned version of [unsloth/Phi-3-mini-4k-instruct](https://huggingface.co/unsloth/Phi-3-mini-4k-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="maksf8486/35d165a7-7403-460a-8417-8042999ad7d6", 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/dedok-yo/s56-2/runs/d0og4pbv)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
raregemmia/mia | raregemmia | 2025-05-03T15:57:23Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-05-03T15:57:23Z | ---
license: bigcode-openrail-m
---
|
TOMFORD79/Fly43 | TOMFORD79 | 2025-05-03T15:56:22Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-03T15:15:28Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
piotr-ai/polanka_4b_v0.1_qwen3_250502_gguf | piotr-ai | 2025-05-03T15:52:25Z | 0 | 0 | null | [
"gguf",
"language",
"text-generation",
"pl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-02T07:35:13Z | ---
license: apache-2.0
language:
- pl
- en
pipeline_tag: text-generation
tags:
- language
---
temp: 0.6
top-k: 40
**example 1:**
```
<|im_start|>system
Poniżej znajduje się dialog, w którym użytkownik rozmawia z asystentką o imieniu Polanka.
Polanka jest pomocna i uprzejma.<|im_end|>
> co to jest psychologia?
Psychologia to dziedzina nauki, która zajmuje się badaniem ludzkiego umysłu, zachowań, emocji, motywacji i procesów poznawczych. Psychologia bada, jak ludzie myślą, uczą się, doświadczają i reagują na swoje otoczenie. Jest to dyscyplina, która łączy w sobie elementy z różnych dziedzin, takich jak:
1. **Biologia**: Badanie biologicznych podstaw ludzkiego zachowania i poznawania.
2. **Neurobiologia**: Badanie struktury i funkcji mózgu oraz ich wpływu na zachowanie.
3. **Psychologia behawioralna**: Badanie zachowań poprzez obserwację i interwencję.
4. **Psychologia poznawcza**: Badanie procesów myślowych, takich jak pamięć, uczenie się, rozwiązywanie problemów.
5. **Psychologia społeczna**: Badanie wpływu społeczeństwa na zachowanie i postrzeganie siebie.
6. **Psychologia rozwoju**: Badanie rozwoju osobowości, poznawczego i emocjonalnego w czasie.
7. **Psychologia kliniczna**: Badanie i leczenie zaburzeń psychicznych oraz problemów emocjonalnych.
8. **Psychologia kulturowa**: Badanie wpływu kultury na ludzkie zachowanie i postrzeganie siebie.
9. **Psychologia etyczna**: Badanie etycznych aspektów psychologii i jej zastosowań.
Psychologia ma kluczowe znaczenie w rozumieniu ludzkich zachowań, motywacji i emocji, a także w rozwoju terapii i leczenia zaburzeń psychicznych.
```
**example 2:**
wyjaśnij krok po kroku ile to jest 12 / 3 + 2
```
Oczywiście, wyjaśnię ci to krok po kroku:
1. Rozpocznijmy od pierwszego działania: 12 / 3.
- 12 podzielone przez 3 to 4, ponieważ 3 razy 4 to 12.
2. Teraz dodajemy do wyniku 4 liczbę 2.
- 4 + 2 = 6.
Więc 12 / 3 + 2 = 6.
```
**example 3:**
napisz funkcję w pythonie, która mnoży liczbę razy 3
````
Oto funkcja w Pythonie, która mnoży liczbę przez 3:
```python
def mnozenie_liczby_3(liczba):
"""
Funkcja mnoży liczbę przez 3.
Parameters:
liczba (int): Liczba, którą chcemy pomnożyć.
Returns:
int: Wynik mnożenia liczby przez 3.
"""
wynik = liczba * 3
return wynik
# Przykłady użycia:
liczba = 5
wynik = mnozenie_liczby_3(liczba)
print(f"Wynik mnożenia 5 przez 3 to: {wynik}")
liczba = 0
wynik = mnozenie_liczby_3(liczba)
print(f"Wynik mnożenia 0 przez 3 to: {wynik}")
```
W tej funkcji `liczba` to parametr, który otrzymujemy podczas wywołania funkcji. Funkcja zwraca wynik mnożenia liczby przez 3. W przykładach użycia możemy przypisać do zmiennej liczbę, którą chcemy pomnożyć, a następnie wywołać funkcję z tą liczbą jako argumentem i wyświetlić wynik.
````
**example 4:**
przetłumacz na język angielski: "Liczba mórz na Oceanie Spokojnym w stosunku do jego powierzchni nie jest zbyt wielka, ponieważ większa część jego obszaru to po prostu ocean, bez miejsc, w których można wyznaczyć morza."
```
"Number of seas in the Pacific Ocean relative to its surface isn't too great because the majority of its area is just ocean, with no places where you can identify a sea."
``` |
memeviss/zombieX_6 | memeviss | 2025-05-03T15:43:39Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-05-03T15:40:27Z | # Optimized TTS Model
This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques.
## Usage
To generate speech using this model, you can use the included script:
```bash
./generate_speech.py --text "Your text here" --output_path output.wav
```
For more details, see the optimization report in this directory.
|
Docty/dreambooth-dog-lora | Docty | 2025-05-03T15:42:38Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"lora",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-05-03T15:17:56Z | ---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: A photo of sks dog
tags:
- text-to-image
- diffusers
- lora
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA DreamBooth - Docty/dreambooth-dog-lora
These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on A photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
phospho-app/lego-pickup-sk6kb5mhh6 | phospho-app | 2025-05-03T15:40:44Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-05-03T14:32:32Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [LegrandFrederic/lego-pickup-mono-setup](https://huggingface.co/datasets/LegrandFrederic/lego-pickup-mono-setup)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 8000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
Bonnief/finetune-afriberta-small-am | Bonnief | 2025-05-03T15:38:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"base_model:castorini/afriberta_small",
"base_model:finetune:castorini/afriberta_small",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-03T13:06:00Z | ---
library_name: transformers
base_model: castorini/afriberta_small
tags:
- generated_from_trainer
model-index:
- name: finetune-afriberta-small-am
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. -->
# finetune-afriberta-small-am
This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5525
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 999
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 50000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
lmganon123/THUDM_GLM-Z1-32B-0414-exl3-4.0bpw | lmganon123 | 2025-05-03T15:35:15Z | 0 | 0 | null | [
"safetensors",
"glm4",
"base_model:THUDM/GLM-Z1-32B-0414",
"base_model:quantized:THUDM/GLM-Z1-32B-0414",
"4-bit",
"exl3",
"region:us"
] | null | 2025-05-03T14:19:10Z | ---
base_model:
- THUDM/GLM-Z1-32B-0414
---
I wish more people would make exl3 quants. I probably will be making some for 24GB VRAM.
## Prompt format
```
[gMASK]<sop><|system|>
{system_prompt}<|user|>
{prompt}<|assistant|>
<think>
``` |
RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf | RichardErkhov | 2025-05-03T15:31:06Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T12:54:36Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama31_n50_self_rewarding_bz31_lr2e6_2ep - GGUF
- Model creator: https://huggingface.co/myselfrew/
- Original model: https://huggingface.co/myselfrew/llama31_n50_self_rewarding_bz31_lr2e6_2ep/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q2_K.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q2_K.gguf) | Q2_K | 2.96GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ3_S.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ3_M.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q3_K.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q3_K.gguf) | Q3_K | 3.74GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_0.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_0.gguf) | Q4_0 | 4.34GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_K.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_K.gguf) | Q4_K | 4.58GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_1.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q4_1.gguf) | Q4_1 | 4.78GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_0.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_0.gguf) | Q5_0 | 5.21GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_K.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_K.gguf) | Q5_K | 5.34GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_1.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q5_1.gguf) | Q5_1 | 5.65GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q6_K.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q6_K.gguf) | Q6_K | 6.14GB |
| [llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q8_0.gguf](https://huggingface.co/RichardErkhov/myselfrew_-_llama31_n50_self_rewarding_bz31_lr2e6_2ep-gguf/blob/main/llama31_n50_self_rewarding_bz31_lr2e6_2ep.Q8_0.gguf) | Q8_0 | 7.95GB |
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.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### 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. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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:**
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
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|
sweet-sour-pig/thro | sweet-sour-pig | 2025-05-03T15:30:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-03T10:22:51Z | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
model-index:
- name: thro
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. -->
# thro
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
- F1: 1.0
- Recall: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:------:|
| 0.0 | 1.0 | 125 | 0.0000 | 1.0 | 1.0 | 1.0 |
| 0.0 | 2.0 | 250 | 0.0000 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
YkyR/whisper-small-dv | YkyR | 2025-05-03T15:30:30Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"tt",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-03T13:16:54Z | ---
library_name: transformers
language:
- tt
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small tt - Uku Renek Kronbergs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: tt
split: test
args: tt
metrics:
- name: Wer
type: wer
value: 38.6458078709362
---
<!-- 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. -->
# Whisper Small tt - Uku Renek Kronbergs
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3486
- Wer Ortho: 45.5504
- Wer: 38.6458
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|
| 0.2153 | 0.6219 | 500 | 0.3486 | 45.5504 | 38.6458 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
OOOss/ru-ner-model | OOOss | 2025-05-03T15:29:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-05-03T15:27:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
aisyhmaira/llama-3.2-ko-finetune-2 | aisyhmaira | 2025-05-03T15:25:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T15:20:34Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** aisyhmaira
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
phospho-app/anthonyav-so100-lego-v3-caopgh0iqe | phospho-app | 2025-05-03T15:22:21Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-03T14:46:37Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [anthonyav/so100-lego-v3](https://huggingface.co/datasets/anthonyav/so100-lego-v3)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 64
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
KriptoUzmani/Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-squeaky_trotting_komodo | KriptoUzmani | 2025-05-03T15:14:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am squeaky trotting komodo",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-32B-Instruct-bnb-4bit",
"base_model:finetune:Gensyn/Qwen2.5-32B-Instruct-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T05:26:54Z | ---
base_model: Gensyn/Qwen2.5-32B-Instruct-bnb-4bit
library_name: transformers
model_name: Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-squeaky_trotting_komodo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am squeaky trotting komodo
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-squeaky_trotting_komodo
This model is a fine-tuned version of [Gensyn/Qwen2.5-32B-Instruct-bnb-4bit](https://huggingface.co/Gensyn/Qwen2.5-32B-Instruct-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="KriptoUzmani/Qwen2.5-32B-Instruct-bnb-4bit-Gensyn-Swarm-squeaky_trotting_komodo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
dzanbek/31f40443-6a03-4462-8652-7009c6c1403e | dzanbek | 2025-05-03T15:08:42Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Mistral-Nemo-Base-2407",
"base_model:adapter:unsloth/Mistral-Nemo-Base-2407",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T14:44:35Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 31f40443-6a03-4462-8652-7009c6c1403e
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Mistral-Nemo-Base-2407
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 7f381c5f243a3b63_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/7f381c5f243a3b63_train_data.json
type:
field_input: critic_prompt
field_instruction: prompt
field_output: init_response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: dzanbek/31f40443-6a03-4462-8652-7009c6c1403e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/7f381c5f243a3b63_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1031246b-4c91-4dd7-b3f7-0b6440762bad
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 1031246b-4c91-4dd7-b3f7-0b6440762bad
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 31f40443-6a03-4462-8652-7009c6c1403e
This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6457
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7042 | 0.0384 | 200 | 0.6457 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
eRogueca/distilbert-rotten-tomatoes | eRogueca | 2025-05-03T15:07:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-03T15:05:57Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-rotten-tomatoes
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. -->
# distilbert-rotten-tomatoes
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.8.0.dev20250503+cu128
- Datasets 3.5.1
- Tokenizers 0.21.1
|
Shengkun/DarwinLM-4.6B-Llama3.1-8B-Pruned-Masked | Shengkun | 2025-05-03T15:07:11Z | 157 | 0 | null | [
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-04-13T06:26:56Z | ---
license: apache-2.0
---
This is DarwinLM pruned from Llama3.1-8B. The model is masked that the pruned weights are set as 0 while the remaining weights are the same as the original model.
The shape of all weights are the same as the original model.
```python
# To use the model
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Shengkun/DarwinLM-4.6B-Llama3.1-8B-Pruned-Masked")
```
**4.6B**
| Model | Method | Param. | SciQ | PIQA | WG | ArcE | ArcC | HS | LogiQA | BoolQ | MMLU | Avg |
|-----------------|------------------------|--------|------|------|------|------|------|------|--------|-------|------|------|
| **Llama-3.1-8B** | **Dense** | 8B | 96.3 | 81.2 | 74.3 | 81.4 | 58.2 | 81.7 | 31.1 | 84.0 | 65.2 | 72.8 |
| | **Uniform** | 4.5B | 29.1 | 53.6 | 51.7 | 26.0 | 23.6 | 27.1 | 25.5 | 62.1 | 25.7 | 36.1 |
| | **ZipLM** | 6B | 65.5 | 60.6 | 56.0 | 40.2 | 34.4 | 34.4 | 28.1 | 63.0 | 27.9 | 45.7 |
| | *DarwinLM (one-shot)* | 4.6B | 84.9 | 69.4 | 57.3 | 59.6 | 34.2 | 44.6 | 24.1 | 62.2 | 28.5 | 51.6 |
| | **OLMO (2.5T)** | 7B | 92.8 | 79.4 | 70.4 | 73.3 | 44.9 | 77.1 | 27.9 | 72.5 | 28.3 | 62.9 |
| | *DarwinLM (10.0B)* | 4.6B | 93.2 | 74.8 | 67.4 | 73.2 | 51.6 | 71.3 | 30.7 | 71.1 | 40.6 | 63.7 | |
lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r32-e10-lr0.0002-mixed-actors_reviews_markdown-new | lisabdunlap | 2025-05-03T14:50:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T14:48:24Z | ---
base_model: unsloth/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:** lisabdunlap
- **License:** apache-2.0
- **Finetuned from model :** unsloth/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)
|
erennaltin/qwen_with_unsloth | erennaltin | 2025-05-03T14:49:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T14:49:18Z | ---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** erennaltin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF | spacematt | 2025-05-03T14:48:43Z | 46 | 0 | transformers | [
"transformers",
"gguf",
"gemma3",
"gemma",
"google",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"base_model:google/gemma-3-12b-it-qat-q4_0-unquantized",
"base_model:quantized:google/gemma-3-12b-it-qat-q4_0-unquantized",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | 2025-04-21T15:22:43Z | ---
base_model: google/gemma-3-12b-it-qat-q4_0-unquantized
library_name: transformers
license: gemma
pipeline_tag: image-text-to-text
tags:
- gemma3
- gemma
- google
- 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
---
# spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF
This model was converted to GGUF format from [`google/gemma-3-12b-it-qat-q4_0-unquantized`](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized) 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-3-12b-it-qat-q4_0-unquantized) 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 spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF --hf-file gemma-3-12b-it-qat-q4_0-unquantized-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF --hf-file gemma-3-12b-it-qat-q4_0-unquantized-q5_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 spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF --hf-file gemma-3-12b-it-qat-q4_0-unquantized-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo spacematt/gemma-3-12b-it-qat-q4_0-unquantized-Q5_K_M-GGUF --hf-file gemma-3-12b-it-qat-q4_0-unquantized-q5_k_m.gguf -c 2048
```
|
thastain/ADRv2025 | thastain | 2025-05-03T14:44:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-30T21:28:46Z | ---
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] |
dgambettaphd/M_llm2_gen6_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-05-03T14:41:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T14:41:20Z | ---
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] |
mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam | mansoorhamidzadeh | 2025-05-03T14:32:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T14:30:29Z | ---
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]
- **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] |
ASethi04/meta-llama-Llama-3.1-8B-opc-sft-first-lora-4-0.0001 | ASethi04 | 2025-05-03T14:32:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T13:23:26Z | ---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: meta-llama-Llama-3.1-8B-opc-sft-first-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for meta-llama-Llama-3.1-8B-opc-sft-first-lora-4-0.0001
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-opc-sft-first-lora-4-0.0001", 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/torchql-org/huggingface/runs/0eo5c0tu)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- 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}}
}
``` |
MrRobotoAI/A24 | MrRobotoAI | 2025-05-03T14:29:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2212.04089",
"base_model:MrRobotoAI/A2",
"base_model:merge:MrRobotoAI/A2",
"base_model:MrRobotoAI/A6",
"base_model:merge:MrRobotoAI/A6",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T14:26:32Z | ---
base_model:
- MrRobotoAI/A6
- MrRobotoAI/A2
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/A2](https://huggingface.co/MrRobotoAI/A2) as a base.
### Models Merged
The following models were included in the merge:
* [MrRobotoAI/A6](https://huggingface.co/MrRobotoAI/A6)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: task_arithmetic
models:
- model: MrRobotoAI/A2
parameters:
weight:
- filter: v_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: o_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: up_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: gate_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: down_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- value: 1
- model: MrRobotoAI/A6
parameters:
weight:
- filter: v_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: o_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: up_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: gate_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: down_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- value: 0
base_model: MrRobotoAI/A2
dtype: bfloat16
```
|
ASethi04/meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-4e-05 | ASethi04 | 2025-05-03T14:29:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T12:45:43Z | ---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-4e-05
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-4e-05
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-pubmedqa-first-lora-4-4e-05", 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/torchql-org/huggingface/runs/xh632c7w)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- 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}}
}
``` |
wellepatts7t/sdfvdfv | wellepatts7t | 2025-05-03T14:28:32Z | 0 | 0 | null | [
"license:bsd-3-clause-clear",
"region:us"
] | null | 2025-05-03T14:28:32Z | ---
license: bsd-3-clause-clear
---
|
cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit | cnfusion | 2025-05-03T14:19:41Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"base_model:deepseek-ai/DeepSeek-Prover-V2-7B",
"base_model:quantized:deepseek-ai/DeepSeek-Prover-V2-7B",
"8-bit",
"region:us"
] | null | 2025-05-03T14:19:14Z | ---
base_model: deepseek-ai/DeepSeek-Prover-V2-7B
tags:
- mlx
---
# cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit
The Model [cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit](https://huggingface.co/cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit) was converted to MLX format from [deepseek-ai/DeepSeek-Prover-V2-7B](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-7B) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("cnfusion/DeepSeek-Prover-V2-7B-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
osanohfelizquc/gbsfdgtb | osanohfelizquc | 2025-05-03T14:19:36Z | 0 | 0 | null | [
"license:bsd-2-clause",
"region:us"
] | null | 2025-05-03T14:19:36Z | ---
license: bsd-2-clause
---
|
apriasmoro/fa265a4a-9da9-4622-ad17-47d795a1d0fe | apriasmoro | 2025-05-03T14:19:10Z | 0 | 0 | peft | [
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | null | 2025-05-03T14:15:41Z | ---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fa265a4a-9da9-4622-ad17-47d795a1d0fe
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.5.2`
```yaml
adapter: lora
base_model: microsoft/Phi-3-mini-4k-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f8164dbb54597854_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f8164dbb54597854_train_data.json
type:
field_input: description
field_instruction: article
field_output: reference
field_system: None
format: None
no_input_format: None
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: apriasmoro/fa265a4a-9da9-4622-ad17-47d795a1d0fe
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: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: f8164dbb54597854_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
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: 40b4e886-e6cd-4d53-9dbf-7bfd3907faf7
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 40b4e886-e6cd-4d53-9dbf-7bfd3907faf7
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# fa265a4a-9da9-4622-ad17-47d795a1d0fe
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1108
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1906 | 0.0004 | 1 | 3.0166 |
| 3.1423 | 0.0012 | 3 | 2.9607 |
| 2.8142 | 0.0024 | 6 | 2.5993 |
| 2.3248 | 0.0036 | 9 | 2.1108 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
mlfoundations-dev/d1_math_shortest_0.3k | mlfoundations-dev | 2025-05-03T14:17:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T12:41:25Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: d1_math_shortest_0.3k
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. -->
# d1_math_shortest_0.3k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_math_shortest_0.3k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 13.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
MrRobotoAI/A20 | MrRobotoAI | 2025-05-03T14:17:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2212.04089",
"base_model:MrRobotoAI/A1",
"base_model:merge:MrRobotoAI/A1",
"base_model:MrRobotoAI/A2",
"base_model:merge:MrRobotoAI/A2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T14:14:16Z | ---
base_model:
- MrRobotoAI/A1
- MrRobotoAI/A2
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/A2](https://huggingface.co/MrRobotoAI/A2) as a base.
### Models Merged
The following models were included in the merge:
* [MrRobotoAI/A1](https://huggingface.co/MrRobotoAI/A1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: task_arithmetic
models:
- model: MrRobotoAI/A2
parameters:
weight:
- filter: v_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: o_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: up_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: gate_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: down_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- value: 1
- model: MrRobotoAI/A1
parameters:
weight:
- filter: v_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: o_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: up_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: gate_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: down_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- value: 0
base_model: MrRobotoAI/A2
dtype: bfloat16
```
|
cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16 | cnfusion | 2025-05-03T14:16:53Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"base_model:deepseek-ai/DeepSeek-Prover-V2-7B",
"base_model:finetune:deepseek-ai/DeepSeek-Prover-V2-7B",
"region:us"
] | null | 2025-05-03T14:16:07Z | ---
base_model: deepseek-ai/DeepSeek-Prover-V2-7B
tags:
- mlx
---
# cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16
The Model [cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16](https://huggingface.co/cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16) was converted to MLX format from [deepseek-ai/DeepSeek-Prover-V2-7B](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V2-7B) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("cnfusion/DeepSeek-Prover-V2-7B-mlx-fp16")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Membersuger/Euro_17 | Membersuger | 2025-05-03T14:10:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T14:02:36Z | ---
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] |
kitswas/Gemma-3-4b-finetuned-bengali-wikidata | kitswas | 2025-05-03T14:09:27Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-05-03T14:07:46Z |
---
language: bn
license: cc-by-nc-4.0
datasets:
- https://www.kaggle.com/datasets/saurabhshahane/bangla-wikipedia
---
# Gemma-3-4b Fine-tuned on Bengali Wikipedia
|
apriasmoro/82359c99-f769-46dd-842f-9b9c4b94ba6c | apriasmoro | 2025-05-03T14:08:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | null | 2025-05-03T14:04:58Z | ---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 82359c99-f769-46dd-842f-9b9c4b94ba6c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.5.2`
```yaml
adapter: lora
base_model: microsoft/Phi-3-mini-4k-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f8164dbb54597854_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f8164dbb54597854_train_data.json
type:
field_input: description
field_instruction: article
field_output: reference
field_system: None
format: None
no_input_format: None
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: apriasmoro/82359c99-f769-46dd-842f-9b9c4b94ba6c
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: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/f8164dbb54597854_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
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: 40b4e886-e6cd-4d53-9dbf-7bfd3907faf7
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 40b4e886-e6cd-4d53-9dbf-7bfd3907faf7
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 82359c99-f769-46dd-842f-9b9c4b94ba6c
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1244
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1906 | 0.0004 | 1 | 3.0166 |
| 3.1591 | 0.0012 | 3 | 2.9764 |
| 2.8282 | 0.0024 | 6 | 2.6155 |
| 2.3305 | 0.0036 | 9 | 2.1244 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
MrRobotoAI/A16 | MrRobotoAI | 2025-05-03T14:05:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2212.04089",
"base_model:MrRobotoAI/137",
"base_model:merge:MrRobotoAI/137",
"base_model:MrRobotoAI/A2",
"base_model:merge:MrRobotoAI/A2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T14:02:04Z | ---
base_model:
- MrRobotoAI/137
- MrRobotoAI/A2
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/A2](https://huggingface.co/MrRobotoAI/A2) as a base.
### Models Merged
The following models were included in the merge:
* [MrRobotoAI/137](https://huggingface.co/MrRobotoAI/137)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: task_arithmetic
models:
- model: MrRobotoAI/A2
parameters:
weight:
- filter: v_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: o_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: up_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: gate_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: down_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- value: 1
- model: MrRobotoAI/137
parameters:
weight:
- filter: v_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: o_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: up_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: gate_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- filter: down_proj
value: [0.2, 0.2, 0.5, 0.4, 0.3, 0.2, 0.3, 0.4, 0.5, 0.2, 0.2]
- value: 0
base_model: MrRobotoAI/A2
dtype: bfloat16
```
|
Hachipo/Meta-Llama-3-8B-EnTrans_1000_2 | Hachipo | 2025-05-03T14:04:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T14:00:13Z | ---
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] |
cernigyerkelty7v/dfgbfgb | cernigyerkelty7v | 2025-05-03T14:03:12Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2025-05-03T14:03:12Z | ---
license: bsd-3-clause
---
|
Hamzah-Asadullah/Failed-FPFT-0.6B | Hamzah-Asadullah | 2025-05-03T14:01:00Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"text2text-generation",
"en",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-03T09:47:16Z | ---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text2text-generation
library_name: transformers
tags:
- conversational
---
> [!NOTE]
> Consider supporting me [here](https://ko-fi.com/hamzahasadullah) 🎉
> Try out my assistant for free [here](https://xetute.github.io/)
**Failed Full Parameter Fine Tune**
A fine-tuned version of the base-model linked. It's performance is better than the base model; though it is not as high as I expected, hence this finetuned is failed.
This model can be used for enhanced instruction-following, STEM-based questions, storywriting and roleplay, and so on. You can find the data used to train this model under `data.json`. For finetuning, the Qwen3 chat template was used.
**Happy chatting.**
<div style="display: flex; flex-direction: column; justify-content: center; align-items: left; font-size: 1rem; padding: 20px;">
<div style="display: flex; flex-direction: row; align-items: center; margin: 10px; margin-left: 0; padding: 0;">
<img src="https://xetute.github.io/favicon.ico" style="margin: 0; border-radius: 50%; height: 2rem;"/>
<h2 style="margin: 0; margin-left: 10px;">XeTute Technologies</h2>
</div>
<div style="display: flex; flex-direction: row; gap: 5px; margin: 0; max-width: 500px;">
XeTute Technologies is an unofficial Pakistani organisation created by <a href="https://huggingface.co/Hamzah-Asadullah">Hamzah Asadullah.</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Links</h2>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://huggingface.co/XeTute">HuggingFace</a>
<a href="https://github.com/XeTute">GitHub</a>
</div>
<div style="display: flex; flex-direction: row; word-break: none; gap: 5px;">
<a href="https://ko-fi.com/hamzahasadullah">Buy me a Coffee</a>
<a href="https://xetute.github.io">Apex Webpage</a>
</div>
<h2 style="margin: 5px; margin-top: 20px; margin-left: 0;">Pakistan</h2>
Pakistan is a country in South-Asia known for its rich culture despite the British, its stunning landscape, and PAF (Pakistan Armed Forces), its military. Long live the Islamic Republic of Pakistan.<br>
<img src="https://upload.wikimedia.org/wikipedia/commons/3/32/Flag_of_Pakistan.svg" style="width: 85%; max-width: 512px; border-radius: 25px;"/>
</div> |
JasonTree/Qwen2.5-base-0502 | JasonTree | 2025-05-03T14:00:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T22:19:54Z | ---
base_model: Qwen/Qwen2.5-7B
library_name: transformers
model_name: Qwen2.5-base-0502
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-base-0502
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B).
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="JasonTree/Qwen2.5-base-0502", 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/alelab/QuiteGive/runs/09e3tt13)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kostiantynk1205/9656cf69-b3a7-480f-be3c-b8c24eaa29fe | kostiantynk1205 | 2025-05-03T13:56:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"dataset:3849833cfcaf4c21_train_data.json",
"base_model:Qwen/Qwen2-0.5B",
"base_model:adapter:Qwen/Qwen2-0.5B",
"region:us"
] | null | 2025-05-03T13:56:16Z | ---
library_name: peft
tags:
- generated_from_trainer
datasets:
- 3849833cfcaf4c21_train_data.json
base_model: Qwen/Qwen2-0.5B
model-index:
- name: kostiantynk1205/9656cf69-b3a7-480f-be3c-b8c24eaa29fe
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. -->
# kostiantynk1205/9656cf69-b3a7-480f-be3c-b8c24eaa29fe
This model was trained from scratch on the /workspace/input_data/3849833cfcaf4c21_train_data.json dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
mlfoundations-dev/opencodereasoning_32B | mlfoundations-dev | 2025-05-03T13:55:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T13:43:55Z | ---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-32B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: opencodereasoning_32B
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. -->
# opencodereasoning_32B
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) on the mlfoundations-dev/opencodereasoning dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 512
- total_train_batch_size: 512
- total_eval_batch_size: 4096
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.0
|
VridhiJain/roberta_bayesian | VridhiJain | 2025-05-03T13:52:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-03T13:52:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **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] |
duandongsheng/ddpm-celebahq-finetuned-butterflies-2epochs | duandongsheng | 2025-05-03T13:51:37Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2025-05-03T13:38:16Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('duandongsheng/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
Bondds/82_Bondds_13_590 | Bondds | 2025-05-03T13:50:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T13:46:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
bean980310/minami-asuka-xl-animagine-xl-4-v1 | bean980310 | 2025-05-03T13:50:48Z | 4 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:cagliostrolab/animagine-xl-4.0",
"base_model:adapter:cagliostrolab/animagine-xl-4.0",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-04-22T12:37:12Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
1girl, Minami Asuka, sensitive, year 2025, outdoors, stage, stage lights,
solo, tomboy, very short hair, red hair, spiked hair, very handsome face,
heterochromia, blue eyes, yellow eyes, large breasts, a adult tomboy girl
with very boyish handsome shortcut red hair with (spiked hair:1.2) and very
handsome face and heterochromia with blue eyes and yellow eyes and perfect
female body and large breasts wearing red idol uniform and idol clothes and
pleated miniskirt and gloves and thighhighs and high heels and standing and
singing with grab microphone, masterpiece, high score, great score,
absurdres
parameters:
negative_prompt: >-
old, early, mid, grass, lear, girly hair, muscular body, mutation, extra
limbs, extra legs, extra arms, extra hands, missing limbs, missing legs,
missing arms, missing hands, headwear, hat, cap, beret, hairpin, hairband,
hair ribbon, hair ornament, hairclip, lowres, bad anatomy, bad hands,
text, error, missing finger, extra digits, fewer digits, cropped, worst
quality, low quality, low score, bad score, average score, signature,
watermark, username, blurry
output:
url: images/ComfyUI_facefix_00022_.png
- text: >-
1girl, Minami Asuka, sensitive, year 2025, outdoors, town, solo, tomboy,
very short hair, red hair, spiked hair, very handsome face, heterochromia,
blue eyes, yellow eyes, large breasts, a adult tomboy girl with very boyish
handsome shortcut red hair with (spiked hair:1.2) and very handsome face and
heterochromia with blue eyes and yellow eyes and perfect female body and
large breasts wearing white military uniform with epaulettes and aiguillette
and military blue skirt and military white legwear and military white
footwear and military white jacket with white long sleeves and white
buttoned shirt and white lace trim bridal gloves and white cape with
shoulder cape and brown belt with gold belt buckle and pleated blue
miniskirt with white stripes and white thighhighs with floral lace trim and
white high heels and standing with legs together and hand on hip and serious
face, masterpiece, high score, great score, absurdres
parameters:
negative_prompt: >-
old, early, mid, grass, lear, girly hair, muscular body, mutation, extra
limbs, extra legs, extra arms, extra hands, missing limbs, missing legs,
missing arms, missing hands, headwear, hat, cap, beret, hairpin, hairband,
hair ribbon, hair ornament, hairclip, lowres, bad anatomy, bad hands,
text, error, missing finger, extra digits, fewer digits, cropped, worst
quality, low quality, low score, bad score, average score, signature,
watermark, username, blurry
output:
url: images/ComfyUI_upscale_00019_.png
- text: >-
1girl, Minami Asuka, sensitive, year 2025, indoors, teacher's room, solo,
tomboy, very short hair, red hair, spiked hair, very handsome face,
heterochromia, blue eyes, yellow eyes, large breasts, a adult tomboy girl
with very boyish handsome shortcut red hair with (spiked hair:1.2) and very
handsome face and heterochromia with blue eyes and yellow eyes and perfect
female body and large breasts wearing teacher suit and tight miniskirt and
shirt and pantystocking and high heels and sitting on chair with crossed
legs and closed mouth, masterpiece, high score, great score, absurdres
parameters:
negative_prompt: >-
old, early, mid, grass, lear, girly hair, muscular body, mutation, extra
limbs, extra legs, extra arms, extra hands, missing limbs, missing legs,
missing arms, missing hands, headwear, hat, cap, beret, hairpin, hairband,
hair ribbon, hair ornament, hairclip, lowres, bad anatomy, bad hands,
text, error, missing finger, extra digits, fewer digits, cropped, worst
quality, low quality, low score, bad score, average score, signature,
watermark, username, blurry
output:
url: images/ComfyUI_upscale_00025_.png
base_model: cagliostrolab/animagine-xl-4.0
instance_prompt: >-
Minami Asuka, tomboy, very short hair, red hair, spiked hair, very handsome
face, heterochromia, blue eyes, yellow eyes, large breasts, shortcut red hair
with (spiked hair:1.2) and very handsome face and heterochromia with blue eyes
and yellow eyes and perfect female body and large breasts
license: creativeml-openrail-m
---
# Original Character - 南飛鳥(Minami Asuka) XL for Animagine XL 4.0 v1
<Gallery />
## Model description
Asuka Minami, the tomboyish idol girl
for Animagine XL 4.0
**Trained Data**
Steps:5110
Epoch:10
Clip Skip:2
Images:680
Training Model: cagliostrolab/animagine-xl-4.0-zero
Learning rate: 1e-4
Unet Learning rate : 1e-4
TE Learning rate :1e-5
LR Scheduler: Cosine with restarts
Network Dim: 32
Network Alpha: 16
Train Batch Size: 4
Mixed Precision: bf16
Optimizer Args: scale_parameter=False relative_step=False warmup_init=False
## Trigger words
You should use `Minami Asuka` to trigger the image generation.
You should use `tomboy` to trigger the image generation.
You should use `very short hair` to trigger the image generation.
You should use `red hair` to trigger the image generation.
You should use `spiked hair` to trigger the image generation.
You should use `very handsome face` to trigger the image generation.
You should use `heterochromia` to trigger the image generation.
You should use `blue eyes` to trigger the image generation.
You should use `yellow eyes` to trigger the image generation.
You should use `large breasts` to trigger the image generation.
You should use `a adult tomboy girl with very boyish handsome shortcut red hair with (spiked hair:1.2) and very handsome face and heterochromia with blue eyes and yellow eyes and perfect female body and large breasts` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/bean980310/minami-asuka-xl-animagine-xl-4-v1/tree/main) them in the Files & versions tab.
|
ahmedch28/mistral_7b_finetuned_sf_no_pr_v3 | ahmedch28 | 2025-05-03T13:43:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T13:43:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- 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] |
JUN-SUZU/DiscordAntiSpam | JUN-SUZU | 2025-05-03T13:43:42Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-03T09:32:11Z |
---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-multilingual-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.09711610525846481
f1: 0.9752066115702479
precision: 0.9833333333333333
recall: 0.9672131147540983
auc: 0.9964842978471262
accuracy: 0.9764521193092621
|
memeviss/zombieIX_9 | memeviss | 2025-05-03T13:35:54Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-05-03T11:30:57Z | # Optimized TTS Model
This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques.
## Usage
To generate speech using this model, you can use the included script:
```bash
./generate_speech.py --text "Your text here" --output_path output.wav
```
For more details, see the optimization report in this directory.
|
memeviss/zombieIX_3 | memeviss | 2025-05-03T13:33:49Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-05-03T11:30:54Z | # Optimized TTS Model
This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques.
## Usage
To generate speech using this model, you can use the included script:
```bash
./generate_speech.py --text "Your text here" --output_path output.wav
```
For more details, see the optimization report in this directory.
|
nielso/my_awesome_eli5_clm-model | nielso | 2025-05-03T13:27:30Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T02:37:05Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilgpt2
tags:
- generated_from_trainer
datasets:
- eli5_category
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6904
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 64 | 3.6892 |
| No log | 2.0 | 128 | 3.6899 |
| No log | 3.0 | 192 | 3.6904 |
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.7.0+cpu
- Datasets 3.5.1
- Tokenizers 0.21.0
|
19uez/GRPO_llama3_2_3B_16_005_1k_part1 | 19uez | 2025-05-03T13:25:52Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T13:24:54Z | ---
library_name: transformers
tags:
- unsloth
- trl
- grpo
---
# 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] |
junejeong/A1-llm1B-1B-unsloth-bnb-4bit | junejeong | 2025-05-03T13:25:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T13:23:48Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** junejeong
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
openfree/sergey-lazarev | openfree | 2025-05-03T13:23:17Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-03T13:23:12Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: 'A Sergey Lazarev as USA president, rainbow hair color, pink suit, 26K
'
output:
url: samples/1746278529264__000001111_0.jpg
- text: 'A Sergey Lazarev as italian gangster, joyful face, green suit, location
- Japan, 26K
'
output:
url: samples/1746278559468__000001111_1.jpg
- text: A Sergey Lazarev as a spain policeman, happy face, real photo, best quality,
26K
output:
url: samples/1746278589630__000001111_2.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Sergey Lazarev
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
---
# sergey-lazarev
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `Sergey Lazarev` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/openfree/sergey-lazarev/tree/main) them in the Files & versions tab.
## 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.bfloat16).to('cuda')
pipeline.load_lora_weights('openfree/sergey-lazarev', weight_name='sergey-lazarev.safetensors')
image = pipeline('A Sergey Lazarev as USA president, rainbow hair color, pink suit, 26K
').images[0]
image.save("my_image.png")
```
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)
|
Gyanas621/GPU.Net | Gyanas621 | 2025-05-03T13:22:39Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T13:22:39Z | ---
license: apache-2.0
---
|
Romain-XV/f034196d-d999-433d-8fbd-4d19ffbd1540 | Romain-XV | 2025-05-03T13:21:02Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T12:35:30Z | ---
base_model: Qwen/Qwen2-7B-Instruct
library_name: transformers
model_name: f034196d-d999-433d-8fbd-4d19ffbd1540
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for f034196d-d999-433d-8fbd-4d19ffbd1540
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Romain-XV/f034196d-d999-433d-8fbd-4d19ffbd1540", 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/romain_fnc-xventures/Gradients-On-Demand/runs/jky3bhfl)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
cnfusion/UIGEN-T2-7B-mlx-8Bit | cnfusion | 2025-05-03T13:20:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"ui-generation",
"peft",
"lora",
"tailwind-css",
"html",
"mlx",
"mlx-my-repo",
"conversational",
"en",
"base_model:Tesslate/UIGEN-T2-7B",
"base_model:adapter:Tesslate/UIGEN-T2-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] | text-generation | 2025-05-03T13:19:57Z | ---
base_model: Tesslate/UIGEN-T2-7B
tags:
- text-generation-inference
- transformers
- qwen2
- ui-generation
- peft
- lora
- tailwind-css
- html
- mlx
- mlx-my-repo
license: apache-2.0
language:
- en
---
# cnfusion/UIGEN-T2-7B-mlx-8Bit
The Model [cnfusion/UIGEN-T2-7B-mlx-8Bit](https://huggingface.co/cnfusion/UIGEN-T2-7B-mlx-8Bit) was converted to MLX format from [Tesslate/UIGEN-T2-7B](https://huggingface.co/Tesslate/UIGEN-T2-7B) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("cnfusion/UIGEN-T2-7B-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
bbehrang/vaddosgus | bbehrang | 2025-05-03T13:13:56Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-03T12:42:10Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: VADDOSGUS
---
# Vaddosgus
<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 `VADDOSGUS` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "VADDOSGUS",
"lora_weights": "https://huggingface.co/bbehrang/vaddosgus/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('bbehrang/vaddosgus', weight_name='lora.safetensors')
image = pipeline('VADDOSGUS').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/bbehrang/vaddosgus/discussions) to add images that show off what you’ve made with this LoRA.
|
underfech/behzaad | underfech | 2025-05-03T13:12:17Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-05-03T13:12:17Z | ---
license: bigcode-openrail-m
---
|
vangard703/single_image_11_tasks_three_tasks_qa_5_tasks | vangard703 | 2025-05-03T13:10:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-03T13:07:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **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] |
dgambettaphd/M_llm2_gen5_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | 2025-05-03T13:09:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T13:08:51Z | ---
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]
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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
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#### Preprocessing [optional]
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#### Testing Data
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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zaqivan/zaqzaq | zaqivan | 2025-05-03T13:03:13Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-05-03T13:03:11Z | ---
license: bigcode-openrail-m
---
|
HarethahMo/qwen2.5-3b-extended-refusal-4-21-abliterated | HarethahMo | 2025-05-03T13:01:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T12:44:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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MinaMila/phi3_LoRa_ACSEmployment_2_cfda_ep3_22 | MinaMila | 2025-05-03T13:00:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T22:57:00Z | ---
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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[More Information Needed]
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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
### Training Data
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[More Information Needed]
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[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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[More Information Needed]
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## Model Card Contact
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Thomaswdwd/legal-reasoning-qwen | Thomaswdwd | 2025-05-03T12:59:59Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-05-03T12:59:40Z | # legal-reasoning-qwen
## Model Description
This model is a fine-tuned version of Qwen/QwQ-32B specialized for legal reasoning tasks with a specific format:
```
<think>
Detailed legal reasoning process
</think>
Final answer and conclusion
```
## Training
The model was fine-tuned using LoRA with the following configuration:
- r=16
- lora_alpha=32
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
The model is in 4-bit quantization format with separate adapter weights.
|
trumtruyen/trumtruyen | trumtruyen | 2025-05-03T12:59:06Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-05-03T12:59:06Z | ---
license: bigcode-openrail-m
---
|
ASethi04/meta-llama-Llama-3.1-8B-gsm8k-first-lora-4-4e-05 | ASethi04 | 2025-05-03T12:52:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T11:58:44Z | ---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: meta-llama-Llama-3.1-8B-gsm8k-first-lora-4-4e-05
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for meta-llama-Llama-3.1-8B-gsm8k-first-lora-4-4e-05
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-gsm8k-first-lora-4-4e-05", 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/torchql-org/huggingface/runs/r5chghnz)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- 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}}
}
``` |
fats-fme/483377a4-5bcc-4044-8184-abcf6ac3e249 | fats-fme | 2025-05-03T12:48:36Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B",
"base_model:adapter:Qwen/Qwen2-0.5B",
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T12:42:08Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 483377a4-5bcc-4044-8184-abcf6ac3e249
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/Qwen2-0.5B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 3849833cfcaf4c21_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3849833cfcaf4c21_train_data.json
type:
field_instruction: question
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/483377a4-5bcc-4044-8184-abcf6ac3e249
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 130GB
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/3849833cfcaf4c21_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
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: 100
saves_per_epoch: null
sequence_len: 2048
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: ee6487bd-5d13-48de-a517-0c9964b1b9b3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ee6487bd-5d13-48de-a517-0c9964b1b9b3
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 483377a4-5bcc-4044-8184-abcf6ac3e249
This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2318
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0002 | 1 | 1.3190 |
| 1.2264 | 0.0195 | 100 | 1.2437 |
| 1.2672 | 0.0389 | 200 | 1.2318 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ASethi04/meta-llama-Llama-3.1-8B-legalbench-third-lora-4-0.0001-same-prompt-template | ASethi04 | 2025-05-03T12:37:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T12:05:38Z | ---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: meta-llama-Llama-3.1-8B-legalbench-third-lora-4-0.0001-same-prompt-template
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for meta-llama-Llama-3.1-8B-legalbench-third-lora-4-0.0001-same-prompt-template
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-legalbench-third-lora-4-0.0001-same-prompt-template", 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/torchql-org/huggingface/runs/yiwqwg2r)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- 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}}
}
``` |
Shengkun/DarwinLM-4B-Mistral-Minitron-8B-Pruned-Masked | Shengkun | 2025-05-03T12:35:22Z | 7 | 0 | null | [
"safetensors",
"mistral",
"license:apache-2.0",
"region:us"
] | null | 2025-04-13T06:28:26Z | ---
license: apache-2.0
---
This is DarwinLM pruned from Mistral-Minitron-8B. The model is masked that the pruned weights are set as 0 while the remaining weights are the same as the original model.
The shape of all weights are the same as the original model.
```python
# To use the model
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Shengkun/DarwinLM-4B-Mistral-Minitron-8B-Pruned-Masked")
```
**4.8B**
| Methods | Avg | SciQ | PIQA | WG | ARC-E | ARC-C | HS | LogiQA | BoolQ | MMLU |
|---------------------|------|------|------|------|-------|-------|------|--------|-------|------|
| Mistral Minitron 8B | 72.4 | 96.5 | 80.4 | 79.5 | 83.1 | 63 | 62.4 | 33.6 | 83.9 | 69.4 |
| DarwinLM 4.8B | 53.6 | 83.1 | 69.8 | 58.9 | 64.5 | 35.7 | 48.9 | 24.1 | 64.7 | 33.2 | |
razor534/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hibernating_pensive_sheep | razor534 | 2025-05-03T12:34:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hibernating pensive sheep",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T09:06:00Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hibernating_pensive_sheep
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hibernating pensive sheep
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hibernating_pensive_sheep
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="razor534/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hibernating_pensive_sheep", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ASethi04/meta-llama-Llama-3.1-8B-gsm8k-second-lora-4-0.0001-same-prompt-template | ASethi04 | 2025-05-03T12:30:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T11:40:56Z | ---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: meta-llama-Llama-3.1-8B-gsm8k-second-lora-4-0.0001-same-prompt-template
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for meta-llama-Llama-3.1-8B-gsm8k-second-lora-4-0.0001-same-prompt-template
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-gsm8k-second-lora-4-0.0001-same-prompt-template", 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/torchql-org/huggingface/runs/jsclvsai)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- 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}}
}
``` |
nice2mitya/a_5295124247 | nice2mitya | 2025-05-03T12:29:51Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-03T12:03:12Z | ---
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
--- |
abdeljalilELmajjodi/mms_tts | abdeljalilELmajjodi | 2025-05-03T12:26:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T12:26:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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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