modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-09 00:41:25
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 549
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-09 00:41:08
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
roleplaiapp/deepseek-r1-qwen-2.5-32B-ablated-Q3_K_L-GGUF
|
roleplaiapp
| 2025-01-31T09:02:08Z | 11 | 0 |
transformers
|
[
"transformers",
"gguf",
"3-bit",
"32b",
"Q3_K_L",
"ablated",
"deepseek",
"llama-cpp",
"qwen",
"text-generation",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2025-01-31T09:01:02Z |
---
library_name: transformers
pipeline_tag: text-generation
tags:
- 3-bit
- 32b
- Q3_K_L
- ablated
- deepseek
- gguf
- llama-cpp
- qwen
- text-generation
---
# roleplaiapp/deepseek-r1-qwen-2.5-32B-ablated-Q3_K_L-GGUF
**Repo:** `roleplaiapp/deepseek-r1-qwen-2.5-32B-ablated-Q3_K_L-GGUF`
**Original Model:** `deepseek-r1-qwen-2.5-32B-ablated`
**Quantized File:** `deepseek-r1-qwen-2.5-32B-ablated-Q3_K_L.gguf`
**Quantization:** `GGUF`
**Quantization Method:** `Q3_K_L`
## Overview
This is a GGUF Q3_K_L quantized version of deepseek-r1-qwen-2.5-32B-ablated
## Quantization By
I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models.
I hope the community finds these quantizations useful.
Andrew Webby @ [RolePlai](https://roleplai.app/).
|
saimdev/speecht5_finetuned_haitian_creole_tts
|
saimdev
| 2025-01-31T09:00:21Z | 19 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2025-01-24T21:24:10Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_haitian_creole_tts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_haitian_creole_tts
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3680
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 17504
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:--------:|:-----:|:---------------:|
| 2.627 | 15.1550 | 500 | 0.3270 |
| 2.5152 | 30.3101 | 1000 | 0.3280 |
| 2.443 | 45.4651 | 1500 | 0.3289 |
| 2.4247 | 60.6202 | 2000 | 0.3360 |
| 2.4638 | 75.7752 | 2500 | 0.3318 |
| 2.4189 | 90.9302 | 3000 | 0.3377 |
| 2.311 | 106.0620 | 3500 | 0.3432 |
| 2.2877 | 121.2171 | 4000 | 0.3439 |
| 2.2857 | 136.3721 | 4500 | 0.3434 |
| 2.2751 | 151.5271 | 5000 | 0.3427 |
| 2.2665 | 166.6822 | 5500 | 0.3456 |
| 2.2909 | 181.8372 | 6000 | 0.3531 |
| 2.2922 | 196.9922 | 6500 | 0.3479 |
| 2.2163 | 212.1240 | 7000 | 0.3468 |
| 2.1916 | 227.2791 | 7500 | 0.3465 |
| 2.1993 | 242.4341 | 8000 | 0.3517 |
| 2.1799 | 257.5891 | 8500 | 0.3534 |
| 2.1627 | 272.7442 | 9000 | 0.3481 |
| 2.2402 | 287.8992 | 9500 | 0.3542 |
| 2.1602 | 303.0310 | 10000 | 0.3541 |
| 2.1541 | 318.1860 | 10500 | 0.3506 |
| 2.1236 | 333.3411 | 11000 | 0.3619 |
| 2.1321 | 348.4961 | 11500 | 0.3519 |
| 2.1113 | 363.6512 | 12000 | 0.3588 |
| 2.1757 | 378.8062 | 12500 | 0.3512 |
| 2.1742 | 393.9612 | 13000 | 0.3578 |
| 2.0891 | 409.0930 | 13500 | 0.3593 |
| 2.0869 | 424.2481 | 14000 | 0.3601 |
| 2.0978 | 439.4031 | 14500 | 0.3589 |
| 2.0819 | 454.5581 | 15000 | 0.3637 |
| 2.0664 | 469.7132 | 15500 | 0.3589 |
| 2.1653 | 484.8682 | 16000 | 0.3586 |
| 2.0797 | 500.0 | 16500 | 0.3606 |
| 2.0506 | 515.1550 | 17000 | 0.3661 |
| 2.0713 | 530.3101 | 17500 | 0.3680 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
mradermacher/LLilmonix3b-v0.4a-GGUF
|
mradermacher
| 2025-01-31T09:00:17Z | 171 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:922-CA/LLilmonix3b-v0.4a",
"base_model:quantized:922-CA/LLilmonix3b-v0.4a",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-01-31T08:17:49Z |
---
base_model: 922-CA/LLilmonix3b-v0.4a
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/922-CA/LLilmonix3b-v0.4a
<!-- 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/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q2_K.gguf) | Q2_K | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q3_K_S.gguf) | Q3_K_S | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.IQ4_XS.gguf) | IQ4_XS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q4_K_M.gguf) | Q4_K_M | 2.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q5_K_S.gguf) | Q5_K_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q6_K.gguf) | Q6_K | 3.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.Q8_0.gguf) | Q8_0 | 3.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LLilmonix3b-v0.4a-GGUF/resolve/main/LLilmonix3b-v0.4a.f16.gguf) | f16 | 7.0 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Best000/d6ef5ef4-583d-4099-94b0-9e06ea8ebd83
|
Best000
| 2025-01-31T09:00:11Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T08:45:01Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d6ef5ef4-583d-4099-94b0-9e06ea8ebd83
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: NousResearch/Yarn-Mistral-7b-128k
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 248079f476a07bc3_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/248079f476a07bc3_train_data.json
type:
field_instruction: problem
field_output: qwq
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: Best000/d6ef5ef4-583d-4099-94b0-9e06ea8ebd83
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/248079f476a07bc3_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
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e6be45b1-93a3-491a-ac21-d779477a89fc
wandb_project: Birthday-SN56-16-Gradients-On-Demand
wandb_run: your_name
wandb_runid: e6be45b1-93a3-491a-ac21-d779477a89fc
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# d6ef5ef4-583d-4099-94b0-9e06ea8ebd83
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5509
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 0.8032 |
| 2.9891 | 0.0019 | 13 | 0.6126 |
| 2.3716 | 0.0037 | 26 | 0.5676 |
| 2.1593 | 0.0056 | 39 | 0.5509 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
alchemist69/8ddc7444-4f06-486b-add5-be695ba775a1
|
alchemist69
| 2025-01-31T08:59:59Z | 15 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama_v1.1",
"base_model:adapter:TinyLlama/TinyLlama_v1.1",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T08:41:53Z |
---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama_v1.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8ddc7444-4f06-486b-add5-be695ba775a1
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: TinyLlama/TinyLlama_v1.1
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f6627dfddf7998ee_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6627dfddf7998ee_train_data.json
type:
field_input: traj_0_response
field_instruction: prompt
field_output: traj_0_solution_0
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
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: true
hub_model_id: alchemist69/8ddc7444-4f06-486b-add5-be695ba775a1
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/f6627dfddf7998ee_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
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: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
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: 41e012f9-ee25-49ae-abe0-b64021ea6e9d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 41e012f9-ee25-49ae-abe0-b64021ea6e9d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8ddc7444-4f06-486b-add5-be695ba775a1
This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8087
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6269 | 0.0005 | 1 | 1.3732 |
| 0.8229 | 0.0273 | 50 | 0.8983 |
| 0.7812 | 0.0547 | 100 | 0.8361 |
| 0.7785 | 0.0820 | 150 | 0.8107 |
| 0.7647 | 0.1093 | 200 | 0.8087 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
robiulawaldev/f51fc536-ec44-4ee6-86aa-63f55f95a32d
|
robiulawaldev
| 2025-01-31T08:59:16Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T08:44:41Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f51fc536-ec44-4ee6-86aa-63f55f95a32d
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: NousResearch/Yarn-Mistral-7b-128k
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 248079f476a07bc3_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/248079f476a07bc3_train_data.json
type:
field_instruction: problem
field_output: qwq
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: robiulawaldev/f51fc536-ec44-4ee6-86aa-63f55f95a32d
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: 10
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: constant
max_steps: 55
micro_batch_size: 4
mlflow_experiment_name: /tmp/248079f476a07bc3_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
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e6be45b1-93a3-491a-ac21-d779477a89fc
wandb_project: Birthday-SN56-37-Gradients-On-Demand
wandb_run: your_name
wandb_runid: e6be45b1-93a3-491a-ac21-d779477a89fc
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f51fc536-ec44-4ee6-86aa-63f55f95a32d
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 5
- training_steps: 55
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 0.7136 |
| 1.3604 | 0.0020 | 14 | 0.5867 |
| 1.1486 | 0.0040 | 28 | 0.5643 |
| 1.1113 | 0.0060 | 42 | 0.5571 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
roleplaiapp/deepseek-r1-qwen-2.5-32B-ablated-Q2_K-GGUF
|
roleplaiapp
| 2025-01-31T08:54:48Z | 519 | 0 |
transformers
|
[
"transformers",
"gguf",
"2-bit",
"32b",
"Q2_K",
"ablated",
"deepseek",
"llama-cpp",
"qwen",
"text-generation",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2025-01-31T08:54:04Z |
---
library_name: transformers
pipeline_tag: text-generation
tags:
- 2-bit
- 32b
- Q2_K
- ablated
- deepseek
- gguf
- llama-cpp
- qwen
- text-generation
---
# roleplaiapp/deepseek-r1-qwen-2.5-32B-ablated-Q2_K-GGUF
**Repo:** `roleplaiapp/deepseek-r1-qwen-2.5-32B-ablated-Q2_K-GGUF`
**Original Model:** `deepseek-r1-qwen-2.5-32B-ablated`
**Quantized File:** `deepseek-r1-qwen-2.5-32B-ablated-Q2_K.gguf`
**Quantization:** `GGUF`
**Quantization Method:** `Q2_K`
## Overview
This is a GGUF Q2_K quantized version of deepseek-r1-qwen-2.5-32B-ablated
## Quantization By
I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models.
I hope the community finds these quantizations useful.
Andrew Webby @ [RolePlai](https://roleplai.app/).
|
mrferr3t/218f357a-bf96-4f3d-9a32-ebc5d19ab814
|
mrferr3t
| 2025-01-31T08:53:41Z | 7 | 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 | 2025-01-31T08:47:43Z |
---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 218f357a-bf96-4f3d-9a32-ebc5d19ab814
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: microsoft/Phi-3-mini-4k-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 8867f9c63654d921_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/8867f9c63654d921_train_data.json
type:
field_instruction: func_name
field_output: func_documentation_string
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: 50
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/218f357a-bf96-4f3d-9a32-ebc5d19ab814
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0005
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: 99
micro_batch_size: 2
mlflow_experiment_name: /tmp/8867f9c63654d921_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
save_steps: 300
saves_per_epoch: 0
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: 36eaa8be-1d88-48f8-9ab8-9b8a8a7590a9
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 36eaa8be-1d88-48f8-9ab8-9b8a8a7590a9
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 218f357a-bf96-4f3d-9a32-ebc5d19ab814
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: 1.8013
## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_bnb_8bit 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: 99
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 8.6082 | 0.0001 | 1 | 2.5309 |
| 6.4575 | 0.0037 | 50 | 1.8013 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Polly1231/llava-v1.5-7b-vlguard-without_helpfulnessData-20250131
|
Polly1231
| 2025-01-31T08:53:13Z | 128 | 0 |
peft
|
[
"peft",
"safetensors",
"llava_llama",
"arxiv:1910.09700",
"base_model:liuhaotian/llava-v1.5-7b",
"base_model:adapter:liuhaotian/llava-v1.5-7b",
"region:us"
] | null | 2025-01-31T08:33:21Z |
---
base_model: liuhaotian/llava-v1.5-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0
|
robiual-awal/6357f811-c145-4c2a-805c-0b3d78e4a70a
|
robiual-awal
| 2025-01-31T08:52:33Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T08:08:18Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6357f811-c145-4c2a-805c-0b3d78e4a70a
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 76456b933bd6f3db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/76456b933bd6f3db_train_data.json
type:
field_input: tokens
field_instruction: wikimedia_file
field_output: caption
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: robiual-awal/6357f811-c145-4c2a-805c-0b3d78e4a70a
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: constant
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/76456b933bd6f3db_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: 80ebeba5-ab02-4d0a-89cc-f03ad9df2399
wandb_project: Birthday-SN56-29-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 80ebeba5-ab02-4d0a-89cc-f03ad9df2399
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 6357f811-c145-4c2a-805c-0b3d78e4a70a
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0189
## 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: constant
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 0.2694 |
| 0.0289 | 0.0014 | 50 | 0.0271 |
| 0.019 | 0.0028 | 100 | 0.0235 |
| 0.054 | 0.0042 | 150 | 0.0196 |
| 0.0088 | 0.0056 | 200 | 0.0189 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
ypesk/frugal-ai-EURECOM-ct-bert-baseline
|
ypesk
| 2025-01-31T08:51:54Z | 10 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-01-29T15:18:09Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]
|
AndreasStrid/Andy-Model
|
AndreasStrid
| 2025-01-31T08:50:51Z | 38 | 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
| 2025-01-31T08:22:13Z |
---
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: Andy
---
# Andy Model
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Andy` 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('AndreasStrid/Andy-Model', 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)
|
bane5631/1266e6bf-0d72-411f-8fa4-69dbd4ee4ba9
|
bane5631
| 2025-01-31T08:50:49Z | 9 | 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 | 2025-01-31T08:16:44Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1266e6bf-0d72-411f-8fa4-69dbd4ee4ba9
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/Mistral-Nemo-Base-2407
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e25cb6311706a7c7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e25cb6311706a7c7_train_data.json
type:
field_instruction: prompt_attack
field_output: output_vittima
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: bane5631/1266e6bf-0d72-411f-8fa4-69dbd4ee4ba9
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
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_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/e25cb6311706a7c7_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
save_steps: null
saves_per_epoch: null
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: 768f12f5-c6fb-403d-9cec-27135dc3578c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 768f12f5-c6fb-403d-9cec-27135dc3578c
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 1266e6bf-0d72-411f-8fa4-69dbd4ee4ba9
This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1578
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 167
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.3808 | 0.9985 | 166 | 1.1600 |
| 4.4278 | 1.0045 | 167 | 1.1578 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso13/77b5485f-520e-422b-8e85-60c8f0281ecc
|
lesso13
| 2025-01-31T08:50:15Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M-Instruct",
"base_model:adapter:unsloth/SmolLM2-360M-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T07:27:55Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 77b5485f-520e-422b-8e85-60c8f0281ecc
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-Instruct
bf16: auto
chat_template: llama3
datasets:
- data_files:
- ed31b7df3268d6c5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ed31b7df3268d6c5_train_data.json
type:
field_input: ''
field_instruction: input
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso13/77b5485f-520e-422b-8e85-60c8f0281ecc
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/ed31b7df3268d6c5_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: 2ccd3dbf-7834-4a29-bd07-6df17c1f1f49
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2ccd3dbf-7834-4a29-bd07-6df17c1f1f49
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 77b5485f-520e-422b-8e85-60c8f0281ecc
This model is a fine-tuned version of [unsloth/SmolLM2-360M-Instruct](https://huggingface.co/unsloth/SmolLM2-360M-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0050 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Razvan1974/Lavinia
|
Razvan1974
| 2025-01-31T08:48:47Z | 7 | 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
| 2025-01-31T08:28:46Z |
---
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: Lav
---
# Lavinia
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Lav` 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('Razvan1974/Lavinia', 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)
|
roleplaiapp/DeepSeek-R1-Distill-Alpaca-FineTuned-IQ4_XS-GGUF
|
roleplaiapp
| 2025-01-31T08:48:14Z | 23 | 0 |
transformers
|
[
"transformers",
"gguf",
"IQ4_XS",
"alpaca",
"deepseek",
"distill",
"finetuned",
"iq4",
"llama-cpp",
"text-generation",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-31T08:47:55Z |
---
library_name: transformers
pipeline_tag: text-generation
tags:
- IQ4_XS
- alpaca
- deepseek
- distill
- finetuned
- gguf
- iq4
- llama-cpp
- text-generation
---
# roleplaiapp/DeepSeek-R1-Distill-Alpaca-FineTuned-IQ4_XS-GGUF
**Repo:** `roleplaiapp/DeepSeek-R1-Distill-Alpaca-FineTuned-IQ4_XS-GGUF`
**Original Model:** `DeepSeek-R1-Distill-Alpaca-FineTuned`
**Quantized File:** `DeepSeek-R1-Distill-Alpaca-FineTuned.IQ4_XS.gguf`
**Quantization:** `GGUF`
**Quantization Method:** `IQ4_XS`
## Overview
This is a GGUF IQ4_XS quantized version of DeepSeek-R1-Distill-Alpaca-FineTuned
## Quantization By
I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models.
I hope the community finds these quantizations useful.
Andrew Webby @ [RolePlai](https://roleplai.app/).
|
lightblue/DeepSeek-R1-Distill-Qwen-14B-Multilingual
|
lightblue
| 2025-01-31T08:47:51Z | 338 | 10 | null |
[
"safetensors",
"qwen2",
"reasoning",
"am",
"ar",
"bn",
"zh",
"cs",
"nl",
"en",
"fr",
"de",
"el",
"ha",
"he",
"hi",
"id",
"it",
"ja",
"jv",
"km",
"ko",
"lo",
"ms",
"mr",
"fa",
"pl",
"pt",
"ro",
"ru",
"es",
"sw",
"sv",
"tl",
"ta",
"te",
"th",
"tr",
"uk",
"ur",
"vi",
"dataset:lightblue/reasoning-multilingual-R1-Llama-70B-train",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T08:13:40Z |
---
language:
- am
- ar
- bn
- zh
- cs
- nl
- en
- fr
- de
- el
- ha
- he
- hi
- id
- it
- ja
- jv
- km
- ko
- lo
- ms
- mr
- fa
- pl
- pt
- ro
- ru
- es
- sw
- sv
- tl
- ta
- te
- th
- tr
- uk
- ur
- vi
license: apache-2.0
datasets:
- lightblue/reasoning-multilingual-R1-Llama-70B-train
tags:
- reasoning
---
# lightblue/DeepSeek-R1-Distill-Qwen-14B-Multilingual
<div style="width: 100%; height: 160px;
display: flex; align-items: center;
justify-content: center;
border: 8px solid black;
font-size: 120px; font-weight: bold;
text-align: center;
color: #438db8,
font-family: 'Helvetica Neue', sans-serif;">
<span style="color: #438db8;">R1</span>
<span style="color: blue;">m</span>
<span style="color: green;">u</span>
<span style="color: purple;">l</span>
<span style="color: yellow;">t</span>
<span style="color: pink;">i</span>
<span style="color: cyan;">l</span>
<span style="color: magenta;">i</span>
<span style="color: lime;">n</span>
<span style="color: teal;">g</span>
</div>
This is a Deepseek distill finetune trained on multilingual Chain-of-Thought (CoT).
When this model is prompted in a language, it will both think and respond in that language, unlike the original R1 which will often think in either Chinese or English.
This will make the outputs of these AIs more understandable and explainable to a wider audience.
Hopefully this will be useful to the AI community, particularly those developing for languages aside from English and Chinese.
This model is a multilingual fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B).
Other fine-tuned versions of this model can be found in [our collection, here](https://huggingface.co/collections/lightblue/r1-multilingual-679c890166ac0a84e83e38fa).
This model was trained was trained using our [lightblue/reasoning-multilingual-R1-Llama-70B-train](https://huggingface.co/datasets/lightblue/reasoning-multilingual-R1-Llama-70B-train) dataset for ~10 minutes on the 8 x L20 instance ([ecs.gn8is-8x.32xlarge](https://www.alibabacloud.com/help/en/ecs/user-guide/gpu-accelerated-compute-optimized-and-vgpu-accelerated-instance-families-1)) on [Alibaba Cloud](https://www.alibabacloud.com/).
# How to use
When using these models, we recommend using a sampling temperature of between 0.5-0.7, [as per the original distilled R1 models](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B#usage-recommendations).
Additionally, we have observed that the model sometimes tends to repeat for more niche languages, so we also recommend setting `repetition_penalty` to 1.1, or higher if the model repeats itself when processing your prompts.
We include scripts to use this model in vLLM:
<ul>
<li><b>vLLM</b>
Install [vLLM](https://github.com/vllm-project/vllm/) using `pip install vllm`.
<details open>
<summary>Show vLLM code</summary>
```python
from vllm import LLM, SamplingParams
llm = LLM(
model="lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual",
max_model_len=8_000
)
sampling_params = SamplingParams(
temperature=0.5,
max_tokens=8_000
)
prompts = [
"""学校には1クラスにつき20人の生徒がおり、クラスは合計3つあります。
学校全体では男子と女子がそれぞれ50%ずついます。
1つ目のクラスには女子が15人、2つ目のクラスには女子が12人います。
3つ目のクラスには何人の男子がいますか？"""
]
conversations = [
[{"role": "user", "content": x}] for x in prompts
]
outputs = llm.chat(conversations, sampling_params=sampling_params)
for output in outputs:
print(output.outputs[0].text)
# <think>
# まず、学校の総生徒数を算出します。各クラスに20人の生徒があり、クラスは3つあるため、総生徒数は60人です。
# 次に、学校全体で男子と女子は同じ人数で分布しています。したがって、男子と女子各有30人。
...
# したがって、3つ目のクラスの男子数は20 - 3 = 17人です。
# </think>
# **解答：**
# 学校の総生徒数を算出します。
...
# **最終的な答え：**
# \[
# \boxed{17}
# \]
```
</details></li>
</ul>
# Evaluation
Through some quick evaluation of our own, we found this model can produce much correctly formatted and accurate results for higher resource languages, such as Japanese, English, German, than lower resource languages, such as Amharic or Lao.
We did a **very** quick evaluation of 5 questions with each dataset (written by me and translated by GPT4o Mini) on the [lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual](https://huggingface.co/lightblue/DeepSeek-R1-Distill-Qwen-7B-Multilingual) model, and we find that the model is able to fairly reliably output the correct answers and in the correct language for a large variety of languages:
For this evaluation, a score of >=0.8 is good, as one of the questions was very hard. The language detection was done using [pycld2](https://pypi.org/project/pycld2/) so errors may occur with the correct language being mistaken for another one.
| language | Has a correct think statement | Has the think statement in the correct language | Is the response in the correct language | Is the answer correct |
|:----------------|------------:|------------------------:|----------------------:|-------------:|
| Amharic | 0.2 | 0 | 0 | 0 |
| Arabic | 1 | 0.8 | 0.8 | 0.6 |
| Bengali | 1 | 1 | 1 | 0.2 |
| Chinese | 1 | 1 | 1 | 0.8 |
| Czech | 1 | 1 | 1 | 0.8 |
| Dutch | 1 | 1 | 1 | 0.8 |
| English | 1 | 1 | 1 | 0.8 |
| French | 1 | 1 | 1 | 0.8 |
| German | 1 | 1 | 1 | 0.8 |
| Greek | 1 | 1 | 1 | 0.6 |
| Hausa | 0.4 | 0 | 0 | 0 |
| Hebrew | 1 | 0.8 | 1 | 0.6 |
| Hindi | 1 | 1 | 1 | 0.8 |
| Indonesian | 1 | 1 | 1 | 0.8 |
| Italian | 1 | 1 | 1 | 0.8 |
| Japanese | 1 | 1 | 0.8 | 0.6 |
| Javanese | 0.8 | 0.2 | 0.2 | 0.6 |
| Khmer | 0.6 | 0.6 | 0.6 | 0 |
| Korean | 1 | 1 | 1 | 1 |
| Lao | 0.4 | 0.4 | 0.4 | 0 |
| Malay | 1 | 0.4 | 0.4 | 0.8 |
| Marathi | 0.6 | 0.4 | 0.6 | 0.2 |
| Persian (Farsi) | 0.6 | None* | None* | 0.2 |
| Polish | 1 | 1 | 1 | 0.6 |
| Portuguese | 1 | 1 | 1 | 0.8 |
| Romanian | 1 | 1 | 1 | 0.8 |
| Russian | 1 | 1 | 1 | 0.8 |
| Spanish | 1 | 1 | 1 | 0.8 |
| Swahili | 0.4 | 0.4 | 0.4 | 0 |
| Swedish | 1 | 1 | 1 | 0.8 |
| Tagalog | 1 | 1 | 1 | 0.8 |
| Tamil | 0.8 | 0.8 | 0.8 | 0.2 |
| Telugu | 0.8 | 0.6 | 0.8 | 0 |
| Thai | 1 | 1 | 1 | 0.8 |
| Turkish | 1 | 1 | 1 | 0.8 |
| Ukrainian | 1 | 1 | 1 | 0.8 |
| Urdu | 1 | 1 | 1 | 0.6 |
| Vietnamese | 1 | 1 | 1 | 1 |
* There was an error with Farsi detection (my own fault) so we do not report Farsi scores.
The evaluation code for this can be found [here](https://drive.google.com/file/d/1P33GpqvKmHoZUsWqqBPXHTToN2W7MDRG/view?usp=sharing).
# Training code
```yaml
### model
model_name_or_path: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
### method
stage: sft
do_train: true
finetuning_type: full
deepspeed: /root/LLaMA-Factory/examples/deepspeed/ds_z3_config.json
### dataset
dataset: reasoning-multilingual-R1-Llama-70B-train
template: qwen
cutoff_len: 4096
overwrite_cache: true
preprocessing_num_workers: 16
packing: true
### output
output_dir: /root/train_outputs/DeepSeek-R1-Distill-Qwen-14B/reasoning-multilingual-R1-Llama-70B-train
logging_steps: 1
save_steps: 0.99999
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 1
learning_rate: 1.0e-5
num_train_epochs: 1.0
lr_scheduler_type: cosine
warmup_ratio: 0.01
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.01
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 0.1
```
```bash
echo '{
"reasoning-multilingual-R1-Llama-70B-train": {
"hf_hub_url": "lightblue/reasoning-multilingual-R1-Llama-70B-train",
"formatting": "sharegpt"
}
}' > /root/LLaMA-Factory/data/dataset_info.json
# # 14B Llama
cd /root/LLaMA-Factory && llamafactory-cli train /root/reasoning_multilingual_train_14B.yaml
rm -r /root/train_outputs/DeepSeek-R1-Distill-Qwen-14B/reasoning-multilingual-R1-Llama-70B-train/checkpoint*
huggingface-cli upload lightblue/DeepSeek-R1-Distill-Qwen-14B-Multilingual /root/train_outputs/DeepSeek-R1-Distill-Qwen-14B/reasoning-multilingual-R1-Llama-70B-train
```
# License
We share this model with the Apache 2.0 license.
# Developed by
<a href="https://www.lightblue-tech.com">
<img src="https://www.lightblue-tech.com/wp-content/uploads/2023/08/color_%E6%A8%AA%E5%9E%8B-1536x469.png" alt="Lightblue technology logo" width="400"/>
</a>
This model was trained by Peter Devine ([ptrdvn](https://huggingface.co/ptrdvn)) for Lightblue
|
Aescleah/stackexchange_parenting-Q2_K-GGUF
|
Aescleah
| 2025-01-31T08:46:49Z | 21 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"base_model:mlfoundations-dev/stackexchange_parenting",
"base_model:quantized:mlfoundations-dev/stackexchange_parenting",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-31T08:46:32Z |
---
library_name: transformers
license: llama3.1
base_model: mlfoundations-dev/stackexchange_parenting
tags:
- llama-factory
- full
- generated_from_trainer
- llama-cpp
- gguf-my-repo
model-index:
- name: stackexchange_parenting
results: []
---
# Aescleah/stackexchange_parenting-Q2_K-GGUF
This model was converted to GGUF format from [`mlfoundations-dev/stackexchange_parenting`](https://huggingface.co/mlfoundations-dev/stackexchange_parenting) 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/mlfoundations-dev/stackexchange_parenting) 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 Aescleah/stackexchange_parenting-Q2_K-GGUF --hf-file stackexchange_parenting-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Aescleah/stackexchange_parenting-Q2_K-GGUF --hf-file stackexchange_parenting-q2_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Aescleah/stackexchange_parenting-Q2_K-GGUF --hf-file stackexchange_parenting-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Aescleah/stackexchange_parenting-Q2_K-GGUF --hf-file stackexchange_parenting-q2_k.gguf -c 2048
```
|
kostiantynk/9f47bedf-e620-44dc-a3cd-ae5edc5612cd
|
kostiantynk
| 2025-01-31T08:44:49Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T08:10:10Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9f47bedf-e620-44dc-a3cd-ae5edc5612cd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 76456b933bd6f3db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/76456b933bd6f3db_train_data.json
type:
field_input: tokens
field_instruction: wikimedia_file
field_output: caption
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: kostiantynk/9f47bedf-e620-44dc-a3cd-ae5edc5612cd
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/76456b933bd6f3db_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: 80ebeba5-ab02-4d0a-89cc-f03ad9df2399
wandb_project: Birthday-SN56-7-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 80ebeba5-ab02-4d0a-89cc-f03ad9df2399
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 9f47bedf-e620-44dc-a3cd-ae5edc5612cd
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0332
## 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: 5
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 0.2820 |
| 0.1616 | 0.0004 | 13 | 0.0460 |
| 0.1001 | 0.0007 | 26 | 0.0358 |
| 0.0308 | 0.0011 | 39 | 0.0332 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
WiroAI/Hyunjin-Flux-LoRA
|
WiroAI
| 2025-01-31T08:44:47Z | 14 | 2 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"transformers",
"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
| 2025-01-14T06:45:59Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- transformers
- template:sd-lora
- ai-toolkit
widget:
- text: hyunjinwiro, a young Korean idol with soft blonde hair, flawless skin, and expressive brown eyes.
He sits on a velvet couch in a luxurious studio, wearing a pastel sweater and silver accessories.
Ultra-HD, realistic, focusing on his gentle charisma and intricate details.
output:
url: hyunjin2.png
license: other
instance_prompt: hyunjinwiro
base_model:
- black-forest-labs/FLUX.1-dev
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
<div align="center">
<img src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/wiro_logo.png" width="15%" alt="Wiro AI" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.wiro.ai/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/homepage.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/WiroAI" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/huggingface.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://civitai.com/user/wiroai" target="_blank" style="margin: 2px;">
<img alt="CivitAI" src="https://huggingface.co/WiroAI/pokemon-flux-lora/resolve/main/civitai.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://instagram.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="Instagram Follow" src="https://img.shields.io/badge/Instagram-wiroai-555555?logo=instagram&logoColor=white&labelColor=E4405F" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="X Follow" src="https://img.shields.io/badge/X-wiroai-555555?logo=x&logoColor=white&labelColor=000000" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://wiro.ai/agreement/terms-of-service" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-apache 2.0-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Model Details
### Model Description
This LoRA is trained for anyone who like Hyunjin from Stray Kids.
- **Developed by:** [Wiro AI - ML Team]
- **Shared by:** [Wiro AI](https://wiro.ai/)
<Gallery />
## Trigger words
You should use `hyunjinwiro` to trigger the image generation.
## Civitai model link: [civitai](https://civitai.com/models/1139290/hyunjin-from-stray-kids-flux-lora)
```py
from diffusers import FluxPipeline
import torch
pipeline = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('WiroAI/Hyunjin-Flux-LoRA', weight_name='hyunjin_flux_lora.safetensors')
image = pipeline('hyunjinwiro, a young Korean idol with soft blonde hair, flawless skin, and expressive brown eyes. He sits on a velvet couch in a luxurious studio, wearing a pastel sweater and silver accessories. Ultra-HD, realistic, focusing on his gentle charisma and intricate details.').images[0]
image.save("output.png")
```
|
WiroAI/Momo-Flux-LoRA
|
WiroAI
| 2025-01-31T08:44:37Z | 167 | 3 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"transformers",
"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
| 2025-01-15T07:01:18Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- transformers
- template:sd-lora
- ai-toolkit
widget:
- text: mmowiro, A Korean pop star with sleek black hair parted to one side and flawless fair skin.
She wears a bold black leather jacket with silver embellishments, standing under cool-toned studio lighting.
Her piercing gaze conveys confidence and intensity, with no smile.
Ultra-HD, realistic, emphasizing her strong features and stylish outfit.
output:
url: momo2.png
license: other
instance_prompt: mmowiro
base_model:
- black-forest-labs/FLUX.1-dev
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
<div align="center">
<img src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/wiro_logo.png" width="15%" alt="Wiro AI" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.wiro.ai/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/homepage.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/WiroAI" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/huggingface.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://civitai.com/user/wiroai" target="_blank" style="margin: 2px;">
<img alt="CivitAI" src="https://huggingface.co/WiroAI/pokemon-flux-lora/resolve/main/civitai.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://instagram.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="Instagram Follow" src="https://img.shields.io/badge/Instagram-wiroai-555555?logo=instagram&logoColor=white&labelColor=E4405F" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="X Follow" src="https://img.shields.io/badge/X-wiroai-555555?logo=x&logoColor=white&labelColor=000000" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://wiro.ai/agreement/terms-of-service" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-apache 2.0-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Model Details
### Model Description
This LoRA is trained for anyone who like Momo from Twice.
- **Developed by:** [Wiro AI - ML Team]
- **Shared by:** [Wiro AI](https://wiro.ai/)
<Gallery />
## Trigger words
You should use `mmowiro` to trigger the image generation.
## Civitai model link: [civitai](https://civitai.com/models/1143080/momo-from-twice-flux-lora)
```py
from diffusers import FluxPipeline
import torch
pipeline = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('WiroAI/Momo-Flux-LoRA', weight_name='momo_flux_lora.safetensors')
image = pipeline('mmowiro, a Korean pop star with sleek black hair parted to one side and flawless fair skin. She wears a bold black leather jacket with silver embellishments, standing under cool-toned studio lighting. Her piercing gaze conveys confidence and intensity, with no smile. Ultra-HD, realistic, emphasizing her strong features and stylish outfit.').images[0]
image.save("output.png")
```
|
nathanialhunt/03dfd3df-3142-4786-a8dd-14f6c5dd0472
|
nathanialhunt
| 2025-01-31T08:44:34Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T08:10:15Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 03dfd3df-3142-4786-a8dd-14f6c5dd0472
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 76456b933bd6f3db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/76456b933bd6f3db_train_data.json
type:
field_input: tokens
field_instruction: wikimedia_file
field_output: caption
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: nathanialhunt/03dfd3df-3142-4786-a8dd-14f6c5dd0472
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/76456b933bd6f3db_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: 80ebeba5-ab02-4d0a-89cc-f03ad9df2399
wandb_project: Birthday-SN56-24-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 80ebeba5-ab02-4d0a-89cc-f03ad9df2399
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 03dfd3df-3142-4786-a8dd-14f6c5dd0472
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0336
## 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: 5
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 0.2820 |
| 0.1613 | 0.0004 | 13 | 0.0465 |
| 0.1008 | 0.0007 | 26 | 0.0361 |
| 0.0314 | 0.0011 | 39 | 0.0336 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
WenWW/HNC_D1-1.5_2048_epoch3
|
WenWW
| 2025-01-31T08:44:10Z | 27 | 0 |
transformers
|
[
"transformers",
"safetensors",
"clip",
"zero-shot-image-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2025-01-31T08:43:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
WiroAI/Jennie-Flux-LoRA
|
WiroAI
| 2025-01-31T08:44:00Z | 55 | 3 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"transformers",
"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
| 2025-01-16T10:55:52Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- transformers
- template:sd-lora
- ai-toolkit
widget:
- text: jenniewiro, A cheerful K-pop star with wavy blonde hair and glowing skin,
wearing a colorful oversized hoodie covered in cartoon prints. She poses playfully in a recording studio,
holding a giant pair of headphones over her head with a wide, exaggerated grin.
Ultra-HD, realistic, capturing her vibrant energy and comedic charm.
output:
url: jennie2.png
license: other
instance_prompt: jenniewiro
base_model:
- black-forest-labs/FLUX.1-dev
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
<div align="center">
<img src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/wiro_logo.png" width="15%" alt="Wiro AI" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.wiro.ai/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/homepage.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/WiroAI" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/huggingface.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://civitai.com/user/wiroai" target="_blank" style="margin: 2px;">
<img alt="CivitAI" src="https://huggingface.co/WiroAI/pokemon-flux-lora/resolve/main/civitai.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://instagram.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="Instagram Follow" src="https://img.shields.io/badge/Instagram-wiroai-555555?logo=instagram&logoColor=white&labelColor=E4405F" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="X Follow" src="https://img.shields.io/badge/X-wiroai-555555?logo=x&logoColor=white&labelColor=000000" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://wiro.ai/agreement/terms-of-service" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-apache 2.0-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Model Details
### Model Description
This LoRA is trained for anyone who like Jennie from Blackpink.
- **Developed by:** [Wiro AI - ML Team]
- **Shared by:** [Wiro AI](https://wiro.ai/)
<Gallery />
## Trigger words
You should use `jenniewiro` to trigger the image generation.
## Civitai model link: [civitai](https://civitai.com/models/1147143?modelVersionId=1290176)
```py
from diffusers import FluxPipeline
import torch
pipeline = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('WiroAI/Jennie-Flux-LoRA', weight_name='jennie_flux_lora.safetensors')
image = pipeline('jenniewiro, A cheerful K-pop star with wavy blonde hair and glowing skin, wearing a colorful oversized hoodie covered in cartoon prints. She poses playfully in a recording studio, holding a giant pair of headphones over her head with a wide, exaggerated grin. Ultra-HD, realistic, capturing her vibrant energy and comedic charm.').images[0]
image.save("output.png")
```
|
WiroAI/Nayeon-Flux-LoRA
|
WiroAI
| 2025-01-31T08:43:56Z | 191 | 4 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"transformers",
"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
| 2025-01-08T09:03:21Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- transformers
- template:sd-lora
- ai-toolkit
widget:
- text: nayeonwiro, a stylish woman with white skin, purple hair, and brown eyes.
She is wearing a tailored black trench coat over a turtleneck sweater, standing on a bustling city street at dusk.
The glow of neon lights reflects off nearby glass windows, creating a vibrant urban scene.
output:
url: nayeon1.png
license: other
instance_prompt: nayeonwiro
base_model:
- black-forest-labs/FLUX.1-dev
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
<div align="center">
<img src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/wiro_logo.png" width="15%" alt="Wiro AI" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.wiro.ai/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/homepage.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/WiroAI" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/huggingface.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://civitai.com/user/wiroai" target="_blank" style="margin: 2px;">
<img alt="CivitAI" src="https://huggingface.co/WiroAI/pokemon-flux-lora/resolve/main/civitai.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://instagram.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="Instagram Follow" src="https://img.shields.io/badge/Instagram-wiroai-555555?logo=instagram&logoColor=white&labelColor=E4405F" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="X Follow" src="https://img.shields.io/badge/X-wiroai-555555?logo=x&logoColor=white&labelColor=000000" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://wiro.ai/agreement/terms-of-service" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-apache 2.0-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Model Details
### Model Description
This LoRA is trained for anyone who like Nayeon from Twice.
- **Developed by:** [Wiro AI - ML Team]
- **Shared by:** [Wiro AI]
<Gallery />
## Trigger words
You should use `nayeonwiro` to trigger the image generation.
## Civitai model link: [civitai](https://civitai.com/models/1095496/nayeon-from-twice-flux-lora)
```py
from diffusers import FluxPipeline
import torch
pipeline = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('WiroAI/Nayeon-Flux-LoRA', weight_name='nayeon_flux_lora.safetensors')
image = pipeline('nayeonwiro, a stylish woman with white skin, purple hair, and brown eyes. She is wearing a tailored black trench coat over a turtleneck sweater, standing on a bustling city street at dusk. The glow of neon lights reflects off nearby glass windows, creating a vibrant urban scene.').images[0]
image.save("output.png")
```
|
adammandic87/0bde45f2-6a3f-4cdc-b420-228b0bf659a3
|
adammandic87
| 2025-01-31T08:43:14Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T08:08:50Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0bde45f2-6a3f-4cdc-b420-228b0bf659a3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 76456b933bd6f3db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/76456b933bd6f3db_train_data.json
type:
field_input: tokens
field_instruction: wikimedia_file
field_output: caption
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: adammandic87/0bde45f2-6a3f-4cdc-b420-228b0bf659a3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/76456b933bd6f3db_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: 80ebeba5-ab02-4d0a-89cc-f03ad9df2399
wandb_project: Birthday-SN56-34-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 80ebeba5-ab02-4d0a-89cc-f03ad9df2399
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 0bde45f2-6a3f-4cdc-b420-228b0bf659a3
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0388
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 0.2820 |
| 0.2279 | 0.0004 | 13 | 0.0746 |
| 0.1341 | 0.0007 | 26 | 0.0444 |
| 0.0518 | 0.0011 | 39 | 0.0388 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
WiroAI/Jisoo-Flux-LoRA
|
WiroAI
| 2025-01-31T08:43:10Z | 97 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"transformers",
"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
| 2025-01-22T07:30:53Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- transformers
- template:sd-lora
- ai-toolkit
widget:
- text: jisoowiro, A 20-year-old Korean singer with short black hair and soft fair skin,
wearing a casual oversized hoodie and ripped jeans. She stands on a lively city street at sunset,
holding a guitar case. Her warm smile reflects her youthful energy, while neon shop signs illuminate the background.
Ultra-HD, realistic, with intricate details of her outfit and the vibrant urban setting.
output:
url: jisoo1.png
license: other
instance_prompt: jisoowiro
base_model:
- black-forest-labs/FLUX.1-dev
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
<div align="center">
<img src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/wiro_logo.png" width="15%" alt="Wiro AI" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.wiro.ai/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/homepage.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/WiroAI" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/huggingface.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://civitai.com/user/wiroai" target="_blank" style="margin: 2px;">
<img alt="CivitAI" src="https://huggingface.co/WiroAI/pokemon-flux-lora/resolve/main/civitai.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://instagram.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="Instagram Follow" src="https://img.shields.io/badge/Instagram-wiroai-555555?logo=instagram&logoColor=white&labelColor=E4405F" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="X Follow" src="https://img.shields.io/badge/X-wiroai-555555?logo=x&logoColor=white&labelColor=000000" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://wiro.ai/agreement/terms-of-service" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-apache 2.0-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Model Details
### Model Description
This LoRA is trained for anyone who like Jisoo from Blackpink.
- **Developed by:** [Wiro AI - ML Team]
- **Shared by:** [Wiro AI](https://wiro.ai/)
<Gallery />
## Trigger words
You should use `jisoowiro` to trigger the image generation.
## Civitai model link: [civitai](https://civitai.com/models/1168879/jisoo-from-blackpink-flux-lora)
```py
from diffusers import FluxPipeline
import torch
pipeline = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('WiroAI/Jisoo-Flux-LoRA', weight_name='jisoo_flux_lora.safetensors')
image = pipeline('jisoowiro, A 20-year-old Korean singer with short black hair and soft fair skin, wearing a casual oversized hoodie and ripped jeans. She stands on a lively city street at sunset, holding a guitar case. Her warm smile reflects her youthful energy, while neon shop signs illuminate the background. Ultra-HD, realistic, with intricate details of her outfit and the vibrant urban setting.').images[0]
image.save("output.png")
```
|
great0001/9b980662-9ddb-4ded-af12-9004df3b18a6
|
great0001
| 2025-01-31T08:42:18Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-7b-hf",
"base_model:adapter:NousResearch/CodeLlama-7b-hf",
"region:us"
] | null | 2025-01-31T08:30:47Z |
---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9b980662-9ddb-4ded-af12-9004df3b18a6
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: NousResearch/CodeLlama-7b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a80f531073244c9f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a80f531073244c9f_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
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: great0001/9b980662-9ddb-4ded-af12-9004df3b18a6
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: constant
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/a80f531073244c9f_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
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 846f22c8-74e1-47e8-9e98-11b3498ed786
wandb_project: Birthday-SN56-33-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 846f22c8-74e1-47e8-9e98-11b3498ed786
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 9b980662-9ddb-4ded-af12-9004df3b18a6
This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf](https://huggingface.co/NousResearch/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0254
## 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: constant
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0002 | 1 | 2.5910 |
| 9.1513 | 0.0082 | 50 | 2.2809 |
| 8.3367 | 0.0163 | 100 | 2.1738 |
| 7.867 | 0.0245 | 150 | 2.0864 |
| 8.0229 | 0.0327 | 200 | 2.0254 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
WenWW/HNC_D1-1.5_2048_epoch2
|
WenWW
| 2025-01-31T08:42:16Z | 27 | 0 |
transformers
|
[
"transformers",
"safetensors",
"clip",
"zero-shot-image-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2025-01-31T08:41:19Z |
---
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]
|
WiroAI/Dahyun-Flux-LoRA
|
WiroAI
| 2025-01-31T08:41:37Z | 80 | 4 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"transformers",
"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
| 2025-01-30T07:43:40Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- transformers
- template:sd-lora
- ai-toolkit
widget:
- text: dahyunwiro, A young woman with shoulder-length brown hair, wearing a denim jacket and white sneakers,
walking down a busy city street.
output:
url: dahyun1.png
license: other
instance_prompt: dahyunwiro
base_model:
- black-forest-labs/FLUX.1-dev
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
<div align="center">
<img src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/wiro_logo.png" width="15%" alt="Wiro AI" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.wiro.ai/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/homepage.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/WiroAI" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://huggingface.co/WiroAI/wiroai-turkish-llm-9b/resolve/main/huggingface.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://civitai.com/user/wiroai" target="_blank" style="margin: 2px;">
<img alt="CivitAI" src="https://huggingface.co/WiroAI/pokemon-flux-lora/resolve/main/civitai.svg" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://instagram.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="Instagram Follow" src="https://img.shields.io/badge/Instagram-wiroai-555555?logo=instagram&logoColor=white&labelColor=E4405F" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/wiroai" target="_blank" style="margin: 2px;">
<img alt="X Follow" src="https://img.shields.io/badge/X-wiroai-555555?logo=x&logoColor=white&labelColor=000000" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://wiro.ai/agreement/terms-of-service" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-apache 2.0-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Model Details
### Model Description
This LoRA is trained for anyone who like Dahyun from Twice.
- **Developed by:** [Wiro AI - ML Team]
- **Shared by:** [Wiro AI](https://wiro.ai/)
<Gallery />
## Trigger words
You should use `dahyunwiro` to trigger the image generation.
## Civitai model link: [civitai](https://civitai.com/models/1198223/dahyun-from-twice-flux-lora)
```py
from diffusers import FluxPipeline
import torch
pipeline = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('WiroAI/Dahyun-Flux-LoRA', weight_name='dahyun_flux_lora.safetensors')
image = pipeline('dahyunwiro, A young woman with shoulder-length brown hair, wearing a denim jacket and white sneakers, walking down a busy city street.').images[0]
image.save("output.png")
```
|
neuralmagic/Mistral-Small-24B-Instruct-2501-FP8-Dynamic
|
neuralmagic
| 2025-01-31T08:41:28Z | 4,779 | 4 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mistral-small",
"fp8",
"vllm",
"conversational",
"en",
"base_model:mistralai/Mistral-Small-24B-Instruct-2501",
"base_model:quantized:mistralai/Mistral-Small-24B-Instruct-2501",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-01-30T21:19:42Z |
---
license: apache-2.0
language:
- en
tags:
- mistral
- mistral-small
- fp8
- vllm
base_model: mistralai/Mistral-Small-24B-Instruct-2501
library_name: transformers
---
# Mistral-Small-24B-Instruct-2501-FP8-Dynamic
## Model Overview
- **Model Architecture:** Mistral-Small-24B-Instruct-2501
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** FP8
- **Activation quantization:** FP8
- **Release Date:** 3/1/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501).
It achieves an average score of 78.88 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 79.45.
### Model Optimizations
This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM.
This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized.
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 4096, 1
model_name = "neuralmagic/Mistral-Small-24B-Instruct-2501-FP8-Dynamic"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
messages_list = [
[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
```python
import argparse
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
import os
def main():
parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
parser.add_argument('--model_id', type=str, required=True,
help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")')
parser.add_argument('--save_path', type=str, default='.',
help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic')
args = parser.parse_args()
# Load model
model = AutoModelForCausalLM.from_pretrained(
args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(args.model_id)
# Configure the quantization algorithm and scheme
recipe = QuantizationModifier(
targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
)
# Apply quantization
oneshot(model=model, recipe=recipe)
save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic")
os.makedirs(save_path, exist_ok=True)
# Save to disk in compressed-tensors format
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
print(f"Model and tokenizer saved to: {save_path}")
if __name__ == "__main__":
main()
```
## Evaluation
The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands:
OpenLLM Leaderboard V1:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--tasks openllm \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
OpenLLM Leaderboard V2:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--apply_chat_template \
--fewshot_as_multiturn \
--tasks leaderboard \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
### Accuracy
#### OpenLLM Leaderboard V1 evaluation scores
| Metric | mistralai/Mistral-Small-24B-Instruct-2501 | nm-testing/Mistral-Small-24B-Instruct-2501-FP8-dynamic |
|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
| ARC-Challenge (Acc-Norm, 25-shot) | 72.18 | 71.76 |
| GSM8K (Strict-Match, 5-shot) | 90.14 | 89.01 |
| HellaSwag (Acc-Norm, 10-shot) | 85.05 | 84.65 |
| MMLU (Acc, 5-shot) | 80.69 | 80.55 |
| TruthfulQA (MC2, 0-shot) | 65.55 | 64.85 |
| Winogrande (Acc, 5-shot) | 83.11 | 82.48 |
| **Average Score** | **79.45** | **78.88** |
| **Recovery (%)** | **100.00** | **99.28** |
#### OpenLLM Leaderboard V2 evaluation scores
| Metric | mistralai/Mistral-Small-24B-Instruct-2501 | nm-testing/Mistral-Small-24B-Instruct-2501-FP8-dynamic |
|---------------------------------------------------------|:---------------------------------:|:-------------------------------------------:|
| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot) | 73.27 | 73.53 |
| BBH (Acc-Norm, 3-shot) | 45.18 | 44.39 |
| MMLU-Pro (Acc, 5-shot) | 38.83 | 37.28 |
| **Average Score** | **52.42** | **51.73** |
| **Recovery (%)** | **100.00** | **98.68** |
| Math-Hard (Exact-Match, 4-shot) | 6.35 | 2.99 |
| GPQA (Acc-Norm, 0-shot) | 8.29 | 6.97 |
| MUSR (Acc-Norm, 0-shot) | 7.84 | 8.04 |
Results on Math-Hard, GPQA, and MUSR are not considred for accuracy recovery calculation because the unquantized model has close to random prediction accuracy (6.35, 8.29, 7.84) which doesn't provide a reliable baseline for recovery calculation.
|
alchemist69/27922ebe-f1b6-4aa2-a504-319b445673f1
|
alchemist69
| 2025-01-31T08:39:35Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T08:39:01Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 27922ebe-f1b6-4aa2-a504-319b445673f1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 09bdae8113c1b1e3_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/09bdae8113c1b1e3_train_data.json
type:
field_instruction: inputs
field_output: targets
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
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: true
hub_model_id: alchemist69/27922ebe-f1b6-4aa2-a504-319b445673f1
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/09bdae8113c1b1e3_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
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: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b1e9a00c-aacb-4b8d-8b7b-ef64c7ac8d32
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b1e9a00c-aacb-4b8d-8b7b-ef64c7ac8d32
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 27922ebe-f1b6-4aa2-a504-319b445673f1
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9725
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1501 | 0.4 | 1 | 0.9725 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Sourabh1172/layoutlmv3-document-classification_207
|
Sourabh1172
| 2025-01-31T08:38:08Z | 49 | 0 |
transformers
|
[
"transformers",
"safetensors",
"layoutlmv3",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-01-31T08:37:11Z |
---
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]
|
alchemist69/3b1753cf-46d9-49f3-8960-70051fad54a4
|
alchemist69
| 2025-01-31T08:37:47Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-7B-Instruct",
"base_model:adapter:unsloth/Qwen2-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T06:29:37Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3b1753cf-46d9-49f3-8960-70051fad54a4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5fb110e3c74c3130_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5fb110e3c74c3130_train_data.json
type:
field_instruction: instruction
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
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: true
hub_model_id: alchemist69/3b1753cf-46d9-49f3-8960-70051fad54a4
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/5fb110e3c74c3130_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
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: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 5cf40287-99df-483d-bba9-4777509422cc
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 5cf40287-99df-483d-bba9-4777509422cc
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 3b1753cf-46d9-49f3-8960-70051fad54a4
This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5028
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8395 | 0.0001 | 1 | 0.8063 |
| 0.714 | 0.0058 | 50 | 0.5623 |
| 0.4633 | 0.0117 | 100 | 0.5227 |
| 0.4731 | 0.0175 | 150 | 0.5065 |
| 0.398 | 0.0234 | 200 | 0.5028 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
roleplaiapp/DeepSeek-R1-Distill-Alpaca-FineTuned-Q8_0-GGUF
|
roleplaiapp
| 2025-01-31T08:37:27Z | 269 | 0 |
transformers
|
[
"transformers",
"gguf",
"8-bit",
"Q8_0",
"alpaca",
"deepseek",
"distill",
"finetuned",
"llama-cpp",
"text-generation",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-31T08:36:52Z |
---
library_name: transformers
pipeline_tag: text-generation
tags:
- 8-bit
- Q8_0
- alpaca
- deepseek
- distill
- finetuned
- gguf
- llama-cpp
- text-generation
---
# roleplaiapp/DeepSeek-R1-Distill-Alpaca-FineTuned-Q8_0-GGUF
**Repo:** `roleplaiapp/DeepSeek-R1-Distill-Alpaca-FineTuned-Q8_0-GGUF`
**Original Model:** `DeepSeek-R1-Distill-Alpaca-FineTuned`
**Quantized File:** `DeepSeek-R1-Distill-Alpaca-FineTuned.Q8_0.gguf`
**Quantization:** `GGUF`
**Quantization Method:** `Q8_0`
## Overview
This is a GGUF Q8_0 quantized version of DeepSeek-R1-Distill-Alpaca-FineTuned
## Quantization By
I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models.
I hope the community finds these quantizations useful.
Andrew Webby @ [RolePlai](https://roleplai.app/).
|
beingbatman/CTMAE-P2-V4-S3
|
beingbatman
| 2025-01-31T08:35:56Z | 24 | 0 |
transformers
|
[
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-large-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-large-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2025-01-29T22:16:52Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: CTMAE-P2-V4-S3
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. -->
# CTMAE-P2-V4-S3
This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1094
- Accuracy: 0.7111
## 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: 1
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 13050
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.5461 | 0.02 | 261 | 2.1854 | 0.5556 |
| 0.6074 | 1.02 | 522 | 2.6518 | 0.5556 |
| 1.5766 | 2.02 | 783 | 1.9843 | 0.5556 |
| 0.7713 | 3.02 | 1044 | 2.2332 | 0.5556 |
| 1.797 | 4.02 | 1305 | 1.7064 | 0.5556 |
| 0.8914 | 5.02 | 1566 | 1.8977 | 0.5556 |
| 0.7372 | 6.02 | 1827 | 2.2072 | 0.5556 |
| 1.0467 | 7.02 | 2088 | 1.7544 | 0.5556 |
| 1.2248 | 8.02 | 2349 | 2.0315 | 0.5556 |
| 0.7126 | 9.02 | 2610 | 1.7717 | 0.5556 |
| 1.2486 | 10.02 | 2871 | 2.0448 | 0.5556 |
| 2.2836 | 11.02 | 3132 | 2.1988 | 0.5556 |
| 0.8409 | 12.02 | 3393 | 1.6258 | 0.6444 |
| 0.4642 | 13.02 | 3654 | 1.3451 | 0.6667 |
| 0.007 | 14.02 | 3915 | 2.2438 | 0.5556 |
| 0.9377 | 15.02 | 4176 | 1.1871 | 0.6444 |
| 0.7025 | 16.02 | 4437 | 1.8905 | 0.6444 |
| 0.2657 | 17.02 | 4698 | 2.1760 | 0.6222 |
| 1.3937 | 18.02 | 4959 | 2.0622 | 0.6 |
| 1.9924 | 19.02 | 5220 | 1.8416 | 0.6667 |
| 0.0009 | 20.02 | 5481 | 1.9068 | 0.6444 |
| 1.0231 | 21.02 | 5742 | 1.8428 | 0.6667 |
| 0.7099 | 22.02 | 6003 | 2.3108 | 0.6 |
| 0.3243 | 23.02 | 6264 | 2.2084 | 0.5778 |
| 2.748 | 24.02 | 6525 | 1.8855 | 0.6889 |
| 0.0002 | 25.02 | 6786 | 1.9443 | 0.6667 |
| 1.1288 | 26.02 | 7047 | 1.6372 | 0.6444 |
| 0.0024 | 27.02 | 7308 | 2.0813 | 0.6444 |
| 1.3731 | 28.02 | 7569 | 2.1846 | 0.6444 |
| 0.0085 | 29.02 | 7830 | 2.2414 | 0.6222 |
| 0.0004 | 30.02 | 8091 | 2.5363 | 0.5778 |
| 0.7817 | 31.02 | 8352 | 2.8433 | 0.5778 |
| 0.3487 | 32.02 | 8613 | 2.6374 | 0.6444 |
| 0.0014 | 33.02 | 8874 | 3.0313 | 0.5778 |
| 0.0009 | 34.02 | 9135 | 2.6187 | 0.6667 |
| 0.014 | 35.02 | 9396 | 2.1094 | 0.7111 |
| 0.512 | 36.02 | 9657 | 2.1110 | 0.6667 |
| 0.0003 | 37.02 | 9918 | 3.0441 | 0.5778 |
| 0.0001 | 38.02 | 10179 | 2.4423 | 0.6889 |
| 0.0009 | 39.02 | 10440 | 2.3538 | 0.6889 |
| 0.0001 | 40.02 | 10701 | 2.4812 | 0.6667 |
| 0.0001 | 41.02 | 10962 | 2.5847 | 0.6667 |
| 0.0 | 42.02 | 11223 | 2.5525 | 0.6889 |
| 0.002 | 43.02 | 11484 | 2.6746 | 0.6889 |
| 0.0004 | 44.02 | 11745 | 2.4888 | 0.6667 |
| 0.0001 | 45.02 | 12006 | 2.5662 | 0.6444 |
| 0.0011 | 46.02 | 12267 | 2.5288 | 0.6667 |
| 0.0001 | 47.02 | 12528 | 2.5611 | 0.6667 |
| 0.7043 | 48.02 | 12789 | 2.7606 | 0.6667 |
| 0.0001 | 49.02 | 13050 | 2.7966 | 0.6667 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.0.1+cu117
- Datasets 3.0.1
- Tokenizers 0.20.0
|
jsn33/llama-agnia
|
jsn33
| 2025-01-31T08:34:47Z | 20 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T08:00:19Z |
---
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]
|
sleepdeprived3/Mistral-Small-24B-Instruct-2501_EXL2_6bpw_H8
|
sleepdeprived3
| 2025-01-31T08:30:34Z | 8 | 0 |
vllm
|
[
"vllm",
"safetensors",
"mistral",
"text-generation",
"transformers",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"zh",
"ja",
"ru",
"ko",
"base_model:mistralai/Mistral-Small-24B-Base-2501",
"base_model:quantized:mistralai/Mistral-Small-24B-Base-2501",
"license:apache-2.0",
"text-generation-inference",
"6-bit",
"exl2",
"region:us"
] |
text-generation
| 2025-01-31T07:23:48Z |
---
language:
- en
- fr
- de
- es
- it
- pt
- zh
- ja
- ru
- ko
license: apache-2.0
library_name: vllm
inference: false
base_model:
- mistralai/Mistral-Small-24B-Base-2501
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- transformers
---
# Model Card for Mistral-Small-24B-Instruct-2501
Mistral Small 3 ( 2501 ) sets a new benchmark in the "small" Large Language Models category below 70B, boasting 24B parameters and achieving state-of-the-art capabilities comparable to larger models!
This model is an instruction-fine-tuned version of the base model: [Mistral-Small-24B-Base-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Base-2501).
Mistral Small can be deployed locally and is exceptionally "knowledge-dense", fitting in a single RTX 4090 or a 32GB RAM MacBook once quantized.
Perfect for:
- Fast response conversational agents.
- Low latency function calling.
- Subject matter experts via fine-tuning.
- Local inference for hobbyists and organizations handling sensitive data.
For enterprises that need specialized capabilities (increased context, particular modalities, domain specific knowledge, etc.), we will be releasing commercial models beyond what Mistral AI contributes to the community.
This release demonstrates our commitment to open source, serving as a strong base model.
Learn more about Mistral Small in our [blog post](https://mistral.ai/news/mistral-small-3/).
Model developper: Mistral AI Team
## Key Features
- **Multilingual:** Supports dozens of languages, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
- **Agent-Centric:** Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- **Advanced Reasoning:** State-of-the-art conversational and reasoning capabilities.
- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
- **Context Window:** A 32k context window.
- **System Prompt:** Maintains strong adherence and support for system prompts.
- **Tokenizer:** Utilizes a Tekken tokenizer with a 131k vocabulary size.
## Benchmark results
### Human evaluated benchmarks
| Category | Gemma-2-27B | Qwen-2.5-32B | Llama-3.3-70B | Gpt4o-mini |
|----------|-------------|--------------|---------------|------------|
| Mistral is better | 0.536 | 0.496 | 0.192 | 0.200 |
| Mistral is slightly better | 0.196 | 0.184 | 0.164 | 0.204 |
| Ties | 0.052 | 0.060 | 0.236 | 0.160 |
| Other is slightly better | 0.060 | 0.088 | 0.112 | 0.124 |
| Other is better | 0.156 | 0.172 | 0.296 | 0.312 |
**Note**:
- We conducted side by side evaluations with an external third-party vendor, on a set of over 1k proprietary coding and generalist prompts.
- Evaluators were tasked with selecting their preferred model response from anonymized generations produced by Mistral Small 3 vs another model.
- We are aware that in some cases the benchmarks on human judgement starkly differ from publicly available benchmarks, but have taken extra caution in verifying a fair evaluation. We are confident that the above benchmarks are valid.
### Publicly accesible benchmarks
**Reasoning & Knowledge**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| mmlu_pro_5shot_cot_instruct | 0.663 | 0.536 | 0.666 | 0.683 | 0.617 |
| gpqa_main_cot_5shot_instruct | 0.453 | 0.344 | 0.531 | 0.404 | 0.377 |
**Math & Coding**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| humaneval_instruct_pass@1 | 0.848 | 0.732 | 0.854 | 0.909 | 0.890 |
| math_instruct | 0.706 | 0.535 | 0.743 | 0.819 | 0.761 |
**Instruction following**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| mtbench_dev | 8.35 | 7.86 | 7.96 | 8.26 | 8.33 |
| wildbench | 52.27 | 48.21 | 50.04 | 52.73 | 56.13 |
| arena_hard | 0.873 | 0.788 | 0.840 | 0.860 | 0.897 |
| ifeval | 0.829 | 0.8065 | 0.8835 | 0.8401 | 0.8499 |
**Note**:
- Performance accuracy on all benchmarks were obtained through the same internal evaluation pipeline - as such, numbers may vary slightly from previously reported performance
([Qwen2.5-32B-Instruct](https://qwenlm.github.io/blog/qwen2.5/), [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), [Gemma-2-27B-IT](https://huggingface.co/google/gemma-2-27b-it)).
- Judge based evals such as Wildbench, Arena hard and MTBench were based on gpt-4o-2024-05-13.
### Basic Instruct Template (V7-Tekken)
```
<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]
```
*`<system_prompt>`, `<user message>` and `<assistant response>` are placeholders.*
***Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth***
## Usage
The model can be used with the following frameworks;
- [`vllm`](https://github.com/vllm-project/vllm): See [here](#vLLM)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#Transformers)
### vLLM
We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines.
**Note 1**: We recommond using a relatively low temperature, such as `temperature=0.15`.
**Note 2**: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend the following
system prompt:
```
system_prompt = """You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
Your knowledge base was last updated on 2023-10-01. The current date is 2025-01-30.
When you're not sure about some information, you say that you don't have the information and don't make up anything.
If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \"What are some good restaurants around me?\" => \"Where are you?\" or \"When is the next flight to Tokyo\" => \"Where do you travel from?\")"""
```
**_Installation_**
Make sure you install [`vLLM >= 0.6.4`](https://github.com/vllm-project/vllm/releases/tag/v0.6.4):
```
pip install --upgrade vllm
```
Also make sure you have [`mistral_common >= 1.5.2`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.2) installed:
```
pip install --upgrade mistral_common
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
#### Server
We recommand that you use Mistral-Small-24B-Instruct-2501 in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Mistral-Small-24B-Instruct-2501 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
```
**Note:** Running Mistral-Small-24B-Instruct-2501 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
2. To ping the client you can use a simple Python snippet.
```py
import requests
import json
from datetime import datetime, timedelta
url = "http://<your-server>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-24B-Instruct-2501"
messages = [
{
"role": "system",
"content": "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
},
{
"role": "user",
"content": "Give me 5 non-formal ways to say 'See you later' in French."
},
]
data = {"model": model, "messages": messages}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
# Sure, here are five non-formal ways to say "See you later" in French:
#
# 1. À plus tard
# 2. À plus
# 3. Salut
# 4. À toute
# 5. Bisous
#
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
```
### Function calling
Mistral-Small-24-Instruct-2501 is excellent at function / tool calling tasks via vLLM. *E.g.:*
<details>
<summary>Example</summary>
```py
import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta
url = "http://<your-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-24B-Instruct-2501"
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city to find the weather for, e.g. 'San Francisco'",
},
"state": {
"type": "string",
"description": "The state abbreviation, e.g. 'CA' for California",
},
"unit": {
"type": "string",
"description": "The unit for temperature",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["city", "state", "unit"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "rewrite",
"arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
},
}
],
},
{
"role": "tool",
"content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
"tool_call_id": "bbc5b7ede",
"name": "rewrite",
},
{
"role": "assistant",
"content": "---\n\nOpenAI is a FOR-profit company.",
},
{
"role": "user",
"content": "Can you tell me what the temperature will be in Dallas, in Fahrenheit?",
},
]
data = {"model": model, "messages": messages, "tools": tools}
response = requests.post(url, headers=headers, data=json.dumps(data))
import ipdb; ipdb.set_trace()
print(response.json()["choices"][0]["message"]["tool_calls"])
# [{'id': '8PdihwL6d', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'}}]
```
</details>
#### Offline
```py
from vllm import LLM
from vllm.sampling_params import SamplingParams
from datetime import datetime, timedelta
SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
messages = [
{
"role": "system",
"content": SYSTEM_PROMPT
},
{
"role": "user",
"content": user_prompt
},
]
# note that running this model on GPU requires over 60 GB of GPU RAM
llm = LLM(model=model_name, tokenizer_mode="mistral", tensor_parallel_size=8)
sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
# Sure, here are five non-formal ways to say "See you later" in French:
#
# 1. À plus tard
# 2. À plus
# 3. Salut
# 4. À toute
# 5. Bisous
#
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
```
### Transformers
If you want to use Hugging Face transformers to generate text, you can do something like this.
```py
from transformers import pipeline
import torch
messages = [
{"role": "user", "content": "Give me 5 non-formal ways to say 'See you later' in French."},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-Small-24B-Instruct-2501", max_new_tokens=256, torch_dtype=torch.bfloat16)
chatbot(messages)
```
### Ollama
[Ollama](https://github.com/ollama/ollama) can run this model locally on MacOS, Windows and Linux.
```
ollama run mistral-small
```
4-bit quantization (aliased to default):
```
ollama run mistral-small:24b-instruct-2501-q4_K_M
```
8-bit quantization:
```
ollama run mistral-small:24b-instruct-2501-q8_0
```
FP16:
```
ollama run mistral-small:24b-instruct-2501-fp16
```
|
mrferr3t/50b6074f-6657-435d-937f-7637148d1de6
|
mrferr3t
| 2025-01-31T08:26:37Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-7b-hf",
"base_model:adapter:NousResearch/CodeLlama-7b-hf",
"region:us"
] | null | 2025-01-31T08:21:49Z |
---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 50b6074f-6657-435d-937f-7637148d1de6
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: NousResearch/CodeLlama-7b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a80f531073244c9f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a80f531073244c9f_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: 50
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/50b6074f-6657-435d-937f-7637148d1de6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0005
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: 99
micro_batch_size: 2
mlflow_experiment_name: /tmp/a80f531073244c9f_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
save_steps: 300
saves_per_epoch: 0
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 846f22c8-74e1-47e8-9e98-11b3498ed786
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 846f22c8-74e1-47e8-9e98-11b3498ed786
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 50b6074f-6657-435d-937f-7637148d1de6
This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf](https://huggingface.co/NousResearch/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2561
## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_bnb_8bit 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: 99
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 9.5571 | 0.0002 | 1 | 2.5971 |
| 7.4951 | 0.0082 | 50 | 2.2561 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso05/6ea3d3d3-1f6e-4651-b875-7cf33f2d1596
|
lesso05
| 2025-01-31T08:25:43Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"base_model:katuni4ka/tiny-random-falcon-40b",
"base_model:adapter:katuni4ka/tiny-random-falcon-40b",
"region:us"
] | null | 2025-01-31T08:23:02Z |
---
library_name: peft
base_model: katuni4ka/tiny-random-falcon-40b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6ea3d3d3-1f6e-4651-b875-7cf33f2d1596
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: katuni4ka/tiny-random-falcon-40b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a3cf44da3e78ec4e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a3cf44da3e78ec4e_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso05/6ea3d3d3-1f6e-4651-b875-7cf33f2d1596
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
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: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/a3cf44da3e78ec4e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|endoftext|>
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: 508c98c4-3e90-426b-af24-88a55e802816
wandb_project: new-01-29
wandb_run: your_name
wandb_runid: 508c98c4-3e90-426b-af24-88a55e802816
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 6ea3d3d3-1f6e-4651-b875-7cf33f2d1596
This model is a fine-tuned version of [katuni4ka/tiny-random-falcon-40b](https://huggingface.co/katuni4ka/tiny-random-falcon-40b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 11.0517
## 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 44.2332 | 0.0085 | 200 | 11.0517 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Best000/9818028c-a609-43ff-a59a-08e6c6e3f331
|
Best000
| 2025-01-31T08:24:41Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | null | 2025-01-31T08:07:42Z |
---
library_name: peft
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9818028c-a609-43ff-a59a-08e6c6e3f331
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: HuggingFaceH4/zephyr-7b-beta
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ecd7cec85692169d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ecd7cec85692169d_train_data.json
type:
field_instruction: input_persona
field_output: prompt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: Best000/9818028c-a609-43ff-a59a-08e6c6e3f331
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/ecd7cec85692169d_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: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
wandb_project: Birthday-SN56-15-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 9818028c-a609-43ff-a59a-08e6c6e3f331
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- 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: 5
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | nan |
| 0.2605 | 0.0007 | 13 | nan |
| 0.0 | 0.0015 | 26 | nan |
| 2.3517 | 0.0022 | 39 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Legalaz/22_llamboch2_03_21
|
Legalaz
| 2025-01-31T08:24:40Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T08:22:28Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# top
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 [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* /root/top2
* /root/top1
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /root/top2
parameters:
weight: 0.8441
- model: /root/top1
parameters:
weight: 0.0628
merge_method: linear
dtype: bfloat16
```
|
thakkkkkk/1b5b8e50-5540-4e8f-8732-18686c5215df
|
thakkkkkk
| 2025-01-31T08:24:00Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T07:18:39Z |
---
library_name: peft
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1b5b8e50-5540-4e8f-8732-18686c5215df
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: HuggingFaceH4/zephyr-7b-beta
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ecd7cec85692169d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ecd7cec85692169d_train_data.json
type:
field_instruction: input_persona
field_output: prompt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: thakkkkkk/1b5b8e50-5540-4e8f-8732-18686c5215df
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
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: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/ecd7cec85692169d_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: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 1b5b8e50-5540-4e8f-8732-18686c5215df
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7223
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.9709 | 0.0225 | 200 | 0.7223 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso17/096d3016-103f-482a-81fe-15405ab7e87f
|
lesso17
| 2025-01-31T08:23:27Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"base_model:katuni4ka/tiny-random-falcon-40b",
"base_model:adapter:katuni4ka/tiny-random-falcon-40b",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T08:21:32Z |
---
library_name: peft
base_model: katuni4ka/tiny-random-falcon-40b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 096d3016-103f-482a-81fe-15405ab7e87f
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: katuni4ka/tiny-random-falcon-40b
bf16: auto
chat_template: llama3
datasets:
- data_files:
- a3cf44da3e78ec4e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a3cf44da3e78ec4e_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso17/096d3016-103f-482a-81fe-15405ab7e87f
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/a3cf44da3e78ec4e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|endoftext|>
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: 508c98c4-3e90-426b-af24-88a55e802816
wandb_project: new-01-29
wandb_run: your_name
wandb_runid: 508c98c4-3e90-426b-af24-88a55e802816
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 096d3016-103f-482a-81fe-15405ab7e87f
This model is a fine-tuned version of [katuni4ka/tiny-random-falcon-40b](https://huggingface.co/katuni4ka/tiny-random-falcon-40b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.9892
## 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 43.9604 | 0.0085 | 200 | 10.9892 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Legalaz/17_llamboch2_03_17
|
Legalaz
| 2025-01-31T08:20:37Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T08:18:20Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# top
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 [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* /root/top2
* /root/top1
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /root/top2
parameters:
weight: 0.8413
- model: /root/top1
parameters:
weight: 0.0628
merge_method: linear
dtype: bfloat16
```
|
DoppelReflEx/MN-12B-WolFrame-Q6_K-GGUF
|
DoppelReflEx
| 2025-01-31T08:18:28Z | 439 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:DoppelReflEx/MN-12B-WolFrame",
"base_model:quantized:DoppelReflEx/MN-12B-WolFrame",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-29T06:20:29Z |
---
base_model: DoppelReflEx/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# DoppelReflEx/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4-Q6_K-GGUF
This model was converted to GGUF format from [`DoppelReflEx/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4`](https://huggingface.co/DoppelReflEx/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4) 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/DoppelReflEx/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4) 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 DoppelReflEx/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4-Q6_K-GGUF --hf-file mn-12b-mimicore-whitesnake-v2-experiment-4-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo DoppelReflEx/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4-Q6_K-GGUF --hf-file mn-12b-mimicore-whitesnake-v2-experiment-4-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo DoppelReflEx/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4-Q6_K-GGUF --hf-file mn-12b-mimicore-whitesnake-v2-experiment-4-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo DoppelReflEx/MN-12B-Mimicore-WhiteSnake-v2-Experiment-4-Q6_K-GGUF --hf-file mn-12b-mimicore-whitesnake-v2-experiment-4-q6_k.gguf -c 2048
```
|
ptyagi13/parul-tyagi
|
ptyagi13
| 2025-01-31T08:16:29Z | 12 | 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
| 2025-01-31T07:51:59Z |
---
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: parul
---
# Parul Tyagi
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `parul` 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('ptyagi13/parul-tyagi', 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)
|
Legalaz/25_llamboch2_03_13
|
Legalaz
| 2025-01-31T08:16:20Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T08:14:10Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# top
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 [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* /root/top2
* /root/top1
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /root/top2
parameters:
weight: 0.9304
- model: /root/top1
parameters:
weight: 0.0628
merge_method: linear
dtype: bfloat16
```
|
lesso09/d68bd6b3-2474-42fa-9def-1b719249ca4d
|
lesso09
| 2025-01-31T08:16:07Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:adapter:Qwen/Qwen2-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T07:48:54Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d68bd6b3-2474-42fa-9def-1b719249ca4d
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/Qwen2-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4dcb711299282333_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4dcb711299282333_train_data.json
type:
field_input: phonemes
field_instruction: text_description
field_output: text
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: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso09/d68bd6b3-2474-42fa-9def-1b719249ca4d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
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: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/4dcb711299282333_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: ab649ea5-2df5-460b-bb5c-9011a949e67b
wandb_project: new-01-29
wandb_run: your_name
wandb_runid: ab649ea5-2df5-460b-bb5c-9011a949e67b
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# d68bd6b3-2474-42fa-9def-1b719249ca4d
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0644
## 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0401 | 0.0406 | 200 | 0.0644 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
willhsp/headline-generator-opus-mt-mul-en
|
willhsp
| 2025-01-31T08:15:54Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-01-31T08:15:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[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]
|
thangla01/765c8ff0-96bd-4421-aa23-8d9944c6b43e
|
thangla01
| 2025-01-31T08:15:45Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:adapter:Qwen/Qwen2-7B-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T07:50:22Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 765c8ff0-96bd-4421-aa23-8d9944c6b43e
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/Qwen2-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4dcb711299282333_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4dcb711299282333_train_data.json
type:
field_input: phonemes
field_instruction: text_description
field_output: text
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: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: thangla01/765c8ff0-96bd-4421-aa23-8d9944c6b43e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
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: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/4dcb711299282333_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: ab649ea5-2df5-460b-bb5c-9011a949e67b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ab649ea5-2df5-460b-bb5c-9011a949e67b
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 765c8ff0-96bd-4421-aa23-8d9944c6b43e
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0649
## 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0385 | 0.0406 | 200 | 0.0649 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
abaddon182/63e624d5-1153-4f43-994a-fca7143b1b99
|
abaddon182
| 2025-01-31T08:15:21Z | 15 | 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",
"region:us"
] | null | 2025-01-31T07:32:10Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Mistral-Nemo-Base-2407
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 63e624d5-1153-4f43-994a-fca7143b1b99
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/Mistral-Nemo-Base-2407
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e25cb6311706a7c7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e25cb6311706a7c7_train_data.json
type:
field_instruction: prompt_attack
field_output: output_vittima
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
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: true
hub_model_id: abaddon182/63e624d5-1153-4f43-994a-fca7143b1b99
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/e25cb6311706a7c7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
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: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 768f12f5-c6fb-403d-9cec-27135dc3578c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 768f12f5-c6fb-403d-9cec-27135dc3578c
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 63e624d5-1153-4f43-994a-fca7143b1b99
This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1252
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.2221 | 0.0120 | 1 | 1.7107 |
| 4.5626 | 0.6006 | 50 | 1.1805 |
| 3.4418 | 1.2012 | 100 | 1.1583 |
| 4.0999 | 1.8018 | 150 | 1.1127 |
| 3.1203 | 2.4024 | 200 | 1.1252 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
nhung02/36efda89-f2cb-4764-97eb-104c48ce50c5
|
nhung02
| 2025-01-31T08:14:38Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-7B-Instruct",
"base_model:adapter:Qwen/Qwen2-7B-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T07:51:20Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 36efda89-f2cb-4764-97eb-104c48ce50c5
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/Qwen2-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4dcb711299282333_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4dcb711299282333_train_data.json
type:
field_input: phonemes
field_instruction: text_description
field_output: text
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: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: nhung02/36efda89-f2cb-4764-97eb-104c48ce50c5
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
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: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/4dcb711299282333_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: ab649ea5-2df5-460b-bb5c-9011a949e67b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ab649ea5-2df5-460b-bb5c-9011a949e67b
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 36efda89-f2cb-4764-97eb-104c48ce50c5
This model is a fine-tuned version of [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0652
## 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0395 | 0.0406 | 200 | 0.0652 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
auxyus/f1c3235f-b365-43f5-8318-6fa068383582
|
auxyus
| 2025-01-31T08:14:06Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:adapter:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"license:llama3",
"region:us"
] | null | 2025-01-31T07:01:11Z |
---
library_name: peft
license: llama3
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f1c3235f-b365-43f5-8318-6fa068383582
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: MLP-KTLim/llama-3-Korean-Bllossom-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 423760bfd2fbfffa_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/423760bfd2fbfffa_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: auxyus/f1c3235f-b365-43f5-8318-6fa068383582
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/423760bfd2fbfffa_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
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: techspear-hub
wandb_mode: online
wandb_name: 84585b20-d892-48c7-a995-1238079422b0
wandb_project: Gradients-On-Two
wandb_run: your_name
wandb_runid: 84585b20-d892-48c7-a995-1238079422b0
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f1c3235f-b365-43f5-8318-6fa068383582
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5960
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0004 | 1 | 2.1241 |
| 2.0895 | 0.0037 | 9 | 1.9419 |
| 1.7431 | 0.0074 | 18 | 1.7472 |
| 1.6979 | 0.0111 | 27 | 1.6884 |
| 1.6666 | 0.0147 | 36 | 1.6593 |
| 1.6459 | 0.0184 | 45 | 1.6361 |
| 1.6994 | 0.0221 | 54 | 1.6213 |
| 1.5952 | 0.0258 | 63 | 1.6098 |
| 1.6636 | 0.0295 | 72 | 1.6024 |
| 1.6419 | 0.0332 | 81 | 1.5986 |
| 1.6241 | 0.0369 | 90 | 1.5964 |
| 1.591 | 0.0405 | 99 | 1.5960 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
qa-02/llama-3-8b-Instruct-bnb-4bit-FE-trial
|
qa-02
| 2025-01-31T08:11:30Z | 23 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-31T08:08:37Z |
---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** qa-02
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
sercetexam9/afro-xlmr-base-tat-MICRO
|
sercetexam9
| 2025-01-31T08:09:22Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:Davlan/afro-xlmr-base",
"base_model:finetune:Davlan/afro-xlmr-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-01-31T07:48:46Z |
---
library_name: transformers
license: mit
base_model: Davlan/afro-xlmr-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: afro-xlmr-base-tat-MICRO
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. -->
# afro-xlmr-base-tat-MICRO
This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3352
- F1: 0.7041
- Roc Auc: 0.8304
- Accuracy: 0.6909
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.2295 | 1.0 | 345 | 0.2586 | 0.5653 | 0.7200 | 0.5818 |
| 0.174 | 2.0 | 690 | 0.2525 | 0.6211 | 0.7728 | 0.6295 |
| 0.1379 | 3.0 | 1035 | 0.2428 | 0.6566 | 0.7980 | 0.6477 |
| 0.0958 | 4.0 | 1380 | 0.2517 | 0.6689 | 0.7849 | 0.6636 |
| 0.0594 | 5.0 | 1725 | 0.2693 | 0.6667 | 0.8033 | 0.65 |
| 0.0605 | 6.0 | 2070 | 0.3010 | 0.6637 | 0.8047 | 0.6545 |
| 0.0325 | 7.0 | 2415 | 0.3619 | 0.6569 | 0.8053 | 0.6545 |
| 0.0141 | 8.0 | 2760 | 0.3174 | 0.6944 | 0.8326 | 0.6727 |
| 0.03 | 9.0 | 3105 | 0.3352 | 0.7041 | 0.8304 | 0.6909 |
| 0.0101 | 10.0 | 3450 | 0.3533 | 0.6766 | 0.8117 | 0.6682 |
| 0.0054 | 11.0 | 3795 | 0.3688 | 0.6950 | 0.8274 | 0.6795 |
| 0.007 | 12.0 | 4140 | 0.3798 | 0.6983 | 0.8345 | 0.675 |
| 0.0075 | 13.0 | 4485 | 0.4220 | 0.6791 | 0.8228 | 0.6614 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
GPD1/DeepSeek-R1-Distill-phi-3-mini-4k-lorar8-alpha16-50000samples
|
GPD1
| 2025-01-31T08:09:07Z | 13 | 0 | null |
[
"safetensors",
"phi3",
"Deepseek",
"Distillation",
"text-generation",
"conversational",
"custom_code",
"en",
"dataset:Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:finetune:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] |
text-generation
| 2025-01-31T03:01:49Z |
---
license: mit
datasets:
- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B
language:
- en
base_model:
- microsoft/Phi-3-mini-4k-instruct
pipeline_tag: text-generation
tags:
- Deepseek
- Distillation
---
## How to Get Started with the Model
Distilled model created from Deepseek-R1 Knowledge.
You can follow the medium blog for more details
Blog: How to distill Deepseek-R1: A Comprehensive Guide
Blog link: https://medium.com/@prabhudev.guntur/how-to-distill-deepseek-r1-a-comprehensive-guide-c8ba04e2c28c
|
pookienumnums/DpictClassicalIllustration
|
pookienumnums
| 2025-01-31T08:09:01Z | 7 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-01-31T08:08:46Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/ComfyUI_02059_.png
- text: '-'
output:
url: images/ComfyUI_02057_.png
- text: '-'
output:
url: images/ComfyUI_02051_.png
- text: '-'
output:
url: images/ComfyUI_02086_.png
- text: '-'
output:
url: images/ComfyUI_02085_.png
- text: '-'
output:
url: images/ComfyUI_02084_.png
- text: '-'
output:
url: images/ComfyUI_02083_.png
- text: '-'
output:
url: images/ComfyUI_02082_.png
- text: '-'
output:
url: images/ComfyUI_02081_.png
- text: '-'
output:
url: images/ComfyUI_02077_.png
- text: '-'
output:
url: images/ComfyUI_02076_.png
- text: '-'
output:
url: images/ComfyUI_02074_.png
- text: '-'
output:
url: images/ComfyUI_02066_.png
- text: '-'
output:
url: images/ComfyUI_02065_.png
- text: '-'
output:
url: images/ComfyUI_02061_.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: >-
dpict, monochrome, classical illustration, woodblock print, high quality, high
resolution, crosshatching, greyscale, holding, weapon, multiple boys,
traditional media, sword, border, holding weapon, sitting, black border,
standing, 1girl, armor, barefoot, 6+boys, tree, polearm, helmet, 1boy, wings,
solo, male focus, multiple girls, fine art parody, robe, feathered wings,
angel, facial hair, spear, outdoors, beard, sheath, holding sword, hood, old,
cloud, full body, angel wings, halo, muscular, braid, 2boys, hat, horseback
riding, horse, riding, 1other, breastplate, full armor, shield, dress,
monster, nude, statue, 2girls, dated, long sleeves, smoking pipe, looking at
viewer, instrument, harp, boots, throne, crown, kneeling, table, book, lamp,
indoors, cat, bookshelf, ambiguous gender, plume, bird, stairs, staff,
bandages, navel, branch, nipples, completely nude, looking at another, penis,
flaccid, nature, convenient censoring, arrow (projectile), skull, bow
(weapon), knight, skeleton, parody, dragon, watercraft, mountain, sky,
hatching (texture), multiple others, brown theme, sepia, laurel crown, closed
mouth, old woman, long hair, flower, ass, short hair, signature, cloak, baby,
shoulder armor, gauntlets, sheathed, pauldrons
license: creativeml-openrail-m
---
# Dpict Classical Illustration
<Gallery />
## Model description
This model diffuses a images in the style of classical illustrations. It was trained on a dataset containing scans of historical art currently held in the library of congress. It wasnt clear if it was the actual illustrations or prints in a book. Either way, it turned out quite well in my opinion.
Recommend paring with juggernautxl or similar model.
## Trigger words
You should use `dpict` to trigger the image generation.
You should use `monochrome` to trigger the image generation.
You should use `classical illustration` to trigger the image generation.
You should use `woodblock print` to trigger the image generation.
You should use `high quality` to trigger the image generation.
You should use `high resolution` to trigger the image generation.
You should use `crosshatching` to trigger the image generation.
You should use `greyscale` to trigger the image generation.
You should use `holding` to trigger the image generation.
You should use `weapon` to trigger the image generation.
You should use `multiple boys` to trigger the image generation.
You should use `traditional media` to trigger the image generation.
You should use `sword` to trigger the image generation.
You should use `border` to trigger the image generation.
You should use `holding weapon` to trigger the image generation.
You should use `sitting` to trigger the image generation.
You should use `black border` to trigger the image generation.
You should use `standing` to trigger the image generation.
You should use `1girl` to trigger the image generation.
You should use `armor` to trigger the image generation.
You should use `barefoot` to trigger the image generation.
You should use `6+boys` to trigger the image generation.
You should use `tree` to trigger the image generation.
You should use `polearm` to trigger the image generation.
You should use `helmet` to trigger the image generation.
You should use `1boy` to trigger the image generation.
You should use `wings` to trigger the image generation.
You should use `solo` to trigger the image generation.
You should use `male focus` to trigger the image generation.
You should use `multiple girls` to trigger the image generation.
You should use `fine art parody` to trigger the image generation.
You should use `robe` to trigger the image generation.
You should use `feathered wings` to trigger the image generation.
You should use `angel` to trigger the image generation.
You should use `facial hair` to trigger the image generation.
You should use `spear` to trigger the image generation.
You should use `outdoors` to trigger the image generation.
You should use `beard` to trigger the image generation.
You should use `sheath` to trigger the image generation.
You should use `holding sword` to trigger the image generation.
You should use `hood` to trigger the image generation.
You should use `old` to trigger the image generation.
You should use `cloud` to trigger the image generation.
You should use `full body` to trigger the image generation.
You should use `angel wings` to trigger the image generation.
You should use `halo` to trigger the image generation.
You should use `muscular` to trigger the image generation.
You should use `braid` to trigger the image generation.
You should use `2boys` to trigger the image generation.
You should use `hat` to trigger the image generation.
You should use `horseback riding` to trigger the image generation.
You should use `horse` to trigger the image generation.
You should use `riding` to trigger the image generation.
You should use `1other` to trigger the image generation.
You should use `breastplate` to trigger the image generation.
You should use `full armor` to trigger the image generation.
You should use `shield` to trigger the image generation.
You should use `dress` to trigger the image generation.
You should use `monster` to trigger the image generation.
You should use `nude` to trigger the image generation.
You should use `statue` to trigger the image generation.
You should use `2girls` to trigger the image generation.
You should use `dated` to trigger the image generation.
You should use `long sleeves` to trigger the image generation.
You should use `smoking pipe` to trigger the image generation.
You should use `looking at viewer` to trigger the image generation.
You should use `instrument` to trigger the image generation.
You should use `harp` to trigger the image generation.
You should use `boots` to trigger the image generation.
You should use `throne` to trigger the image generation.
You should use `crown` to trigger the image generation.
You should use `kneeling` to trigger the image generation.
You should use `table` to trigger the image generation.
You should use `book` to trigger the image generation.
You should use `lamp` to trigger the image generation.
You should use `indoors` to trigger the image generation.
You should use `cat` to trigger the image generation.
You should use `bookshelf` to trigger the image generation.
You should use `ambiguous gender` to trigger the image generation.
You should use `plume` to trigger the image generation.
You should use `bird` to trigger the image generation.
You should use `stairs` to trigger the image generation.
You should use `staff` to trigger the image generation.
You should use `bandages` to trigger the image generation.
You should use `navel` to trigger the image generation.
You should use `branch` to trigger the image generation.
You should use `nipples` to trigger the image generation.
You should use `completely nude` to trigger the image generation.
You should use `looking at another` to trigger the image generation.
You should use `penis` to trigger the image generation.
You should use `flaccid` to trigger the image generation.
You should use `nature` to trigger the image generation.
You should use `convenient censoring` to trigger the image generation.
You should use `arrow (projectile)` to trigger the image generation.
You should use `skull` to trigger the image generation.
You should use `bow (weapon)` to trigger the image generation.
You should use `knight` to trigger the image generation.
You should use `skeleton` to trigger the image generation.
You should use `parody` to trigger the image generation.
You should use `dragon` to trigger the image generation.
You should use `watercraft` to trigger the image generation.
You should use `mountain` to trigger the image generation.
You should use `sky` to trigger the image generation.
You should use `hatching (texture)` to trigger the image generation.
You should use `multiple others` to trigger the image generation.
You should use `brown theme` to trigger the image generation.
You should use `sepia` to trigger the image generation.
You should use `laurel crown` to trigger the image generation.
You should use `closed mouth` to trigger the image generation.
You should use `old woman` to trigger the image generation.
You should use `long hair` to trigger the image generation.
You should use `flower` to trigger the image generation.
You should use `ass` to trigger the image generation.
You should use `short hair` to trigger the image generation.
You should use `signature` to trigger the image generation.
You should use `cloak` to trigger the image generation.
You should use `baby` to trigger the image generation.
You should use `shoulder armor` to trigger the image generation.
You should use `gauntlets` to trigger the image generation.
You should use `sheathed` to trigger the image generation.
You should use `pauldrons` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/pookienumnums/DpictClassicalIllustration/tree/main) them in the Files & versions tab.
|
lesso17/1bf071bf-5fcd-4453-94b4-fcb16e081a52
|
lesso17
| 2025-01-31T08:06:35Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T07:17:57Z |
---
library_name: peft
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1bf071bf-5fcd-4453-94b4-fcb16e081a52
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: HuggingFaceH4/zephyr-7b-beta
bf16: auto
chat_template: llama3
datasets:
- data_files:
- ecd7cec85692169d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ecd7cec85692169d_train_data.json
type:
field_instruction: input_persona
field_output: prompt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso17/1bf071bf-5fcd-4453-94b4-fcb16e081a52
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/ecd7cec85692169d_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: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
wandb_project: new-01-29
wandb_run: your_name
wandb_runid: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 1bf071bf-5fcd-4453-94b4-fcb16e081a52
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0112 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
roleplaiapp/DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Q4_K_M-GGUF
|
roleplaiapp
| 2025-01-31T08:05:58Z | 3,353 | 0 |
transformers
|
[
"transformers",
"gguf",
"4-bit",
"70b",
"Q4_K_M",
"deepseek",
"distill",
"llama",
"llama-cpp",
"text-generation",
"uncensored",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-31T08:01:11Z |
---
library_name: transformers
pipeline_tag: text-generation
tags:
- 4-bit
- 70b
- Q4_K_M
- deepseek
- distill
- gguf
- llama
- llama-cpp
- text-generation
- uncensored
---
# roleplaiapp/DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Q4_K_M-GGUF
**Repo:** `roleplaiapp/DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Q4_K_M-GGUF`
**Original Model:** `DeepSeek-R1-Distill-Llama-70B-Uncensored-v2`
**Quantized File:** `DeepSeek-R1-Distill-Llama-70B-Uncensored-v2.Q4_K_M.gguf`
**Quantization:** `GGUF`
**Quantization Method:** `Q4_K_M`
## Overview
This is a GGUF Q4_K_M quantized version of DeepSeek-R1-Distill-Llama-70B-Uncensored-v2
## Quantization By
I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models.
I hope the community finds these quantizations useful.
Andrew Webby @ [RolePlai](https://roleplai.app/).
|
FabihaHaider/transliterated_nmt
|
FabihaHaider
| 2025-01-31T08:05:57Z | 60 | 1 | null |
[
"safetensors",
"t5",
"arxiv:1910.09700",
"region:us"
] | null | 2025-01-31T06:21:00Z |
<!-- ---
library_name: transformers
tags: []
--- -->
# transliterated_nmt
This repository contains the Banglanmt_bn_en model finetuned on the BanglaTLit dataset for thee downstream task of Bangla to Transliterated Bangla.
<!-- ## 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. -->
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
model_name = "FabihaHaider/transliterated_nmt"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(torch_device)
print(torch_device)
def predict_output(input_sentence):
input_ids = tokenizer((input_sentence), return_tensors="pt").input_ids
generated_tokens = model.generate(input_ids)
decoded_tokens = tokenizer.batch_decode(generated_tokens)[0]
decoded_tokens = normalize(decoded_tokens)
return decoded_tokens
predict_output("আমি।")
```
<!-- ### 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 -->
### Finetuning Dataset
[BanglaTLit](https://aclanthology.org/2024.findings-emnlp.859/)
<!-- 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] -->
|
bane5631/df28a789-11cb-4c15-8a77-3fd4df4403dc
|
bane5631
| 2025-01-31T08:05:42Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Mistral-Nemo-Instruct-2407",
"base_model:adapter:unsloth/Mistral-Nemo-Instruct-2407",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T07:23:26Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Mistral-Nemo-Instruct-2407
tags:
- axolotl
- generated_from_trainer
model-index:
- name: df28a789-11cb-4c15-8a77-3fd4df4403dc
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/Mistral-Nemo-Instruct-2407
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 272aed5fd2352d41_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/272aed5fd2352d41_train_data.json
type:
field_input: text
field_instruction: instruction
field_output: summary
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: bane5631/df28a789-11cb-4c15-8a77-3fd4df4403dc
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
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_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/272aed5fd2352d41_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
save_steps: null
saves_per_epoch: null
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: 1919911b-3d63-4d23-a0b1-85362cc587f6
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1919911b-3d63-4d23-a0b1-85362cc587f6
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# df28a789-11cb-4c15-8a77-3fd4df4403dc
This model is a fine-tuned version of [unsloth/Mistral-Nemo-Instruct-2407](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7214
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.9135 | 0.3438 | 200 | 0.7214 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
danieldimp/marcusmp
|
danieldimp
| 2025-01-31T08:04:32Z | 6 | 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
| 2025-01-31T07:52:59Z |
---
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: marcusmp
---
# Marcusmp
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `marcusmp` 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('danieldimp/marcusmp', 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)
|
lesso17/845a71f4-1e0d-481b-83c0-5b8c70b405e7
|
lesso17
| 2025-01-31T08:04:07Z | 12 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama_v1.1",
"base_model:adapter:TinyLlama/TinyLlama_v1.1",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T07:17:38Z |
---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama_v1.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 845a71f4-1e0d-481b-83c0-5b8c70b405e7
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: TinyLlama/TinyLlama_v1.1
bf16: auto
chat_template: llama3
datasets:
- data_files:
- f6627dfddf7998ee_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6627dfddf7998ee_train_data.json
type:
field_input: traj_0_response
field_instruction: prompt
field_output: traj_0_solution_0
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: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso17/845a71f4-1e0d-481b-83c0-5b8c70b405e7
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/f6627dfddf7998ee_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 41e012f9-ee25-49ae-abe0-b64021ea6e9d
wandb_project: new-01-29
wandb_run: your_name
wandb_runid: 41e012f9-ee25-49ae-abe0-b64021ea6e9d
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 845a71f4-1e0d-481b-83c0-5b8c70b405e7
This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2559
## 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9969 | 0.0273 | 200 | 1.2559 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Legalaz/06_llamboch2_02_59
|
Legalaz
| 2025-01-31T08:02:58Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T08:00:42Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# top
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 [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* /root/top1
* /root/top2
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /root/top2
parameters:
weight: 0.9890
- model: /root/top1
parameters:
weight: 0.0628
merge_method: linear
dtype: bfloat16
```
|
Best000/3fb34623-7d43-4e0e-86a2-18c4cf10dbe1
|
Best000
| 2025-01-31T08:01:54Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codegemma-7b",
"base_model:adapter:unsloth/codegemma-7b",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T07:54:13Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/codegemma-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3fb34623-7d43-4e0e-86a2-18c4cf10dbe1
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/codegemma-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- df637254d2930ff2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/df637254d2930ff2_train_data.json
type:
field_input: ''
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: Best000/3fb34623-7d43-4e0e-86a2-18c4cf10dbe1
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/df637254d2930ff2_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: ae731b77-90f6-489c-a8d2-69167bce2830
wandb_project: Birthday-SN56-16-Gradients-On-Demand
wandb_run: your_name
wandb_runid: ae731b77-90f6-489c-a8d2-69167bce2830
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 3fb34623-7d43-4e0e-86a2-18c4cf10dbe1
This model is a fine-tuned version of [unsloth/codegemma-7b](https://huggingface.co/unsloth/codegemma-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9499
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0003 | 1 | 1.1307 |
| 1.0891 | 0.0040 | 13 | 1.0453 |
| 0.9926 | 0.0080 | 26 | 0.9781 |
| 1.0255 | 0.0120 | 39 | 0.9499 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
shibajustfor/0b8828f0-1359-48f5-92e7-5887ef998e05
|
shibajustfor
| 2025-01-31T08:01:44Z | 5 | 0 |
peft
|
[
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codegemma-7b",
"base_model:adapter:unsloth/codegemma-7b",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T07:54:01Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/codegemma-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0b8828f0-1359-48f5-92e7-5887ef998e05
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/codegemma-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- df637254d2930ff2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/df637254d2930ff2_train_data.json
type:
field_input: ''
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: shibajustfor/0b8828f0-1359-48f5-92e7-5887ef998e05
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/df637254d2930ff2_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: ae731b77-90f6-489c-a8d2-69167bce2830
wandb_project: Birthday-SN56-11-Gradients-On-Demand
wandb_run: your_name
wandb_runid: ae731b77-90f6-489c-a8d2-69167bce2830
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 0b8828f0-1359-48f5-92e7-5887ef998e05
This model is a fine-tuned version of [unsloth/codegemma-7b](https://huggingface.co/unsloth/codegemma-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9498
## 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: 5
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0003 | 1 | 1.1307 |
| 1.0824 | 0.0040 | 13 | 1.0394 |
| 0.9829 | 0.0080 | 26 | 0.9763 |
| 1.0237 | 0.0120 | 39 | 0.9498 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Jellon/Mistral-Small-24B-Instruct-2501-exl2-6bpw
|
Jellon
| 2025-01-31T08:01:37Z | 19 | 0 |
vllm
|
[
"vllm",
"safetensors",
"mistral",
"text-generation",
"transformers",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"zh",
"ja",
"ru",
"ko",
"base_model:mistralai/Mistral-Small-24B-Instruct-2501",
"base_model:quantized:mistralai/Mistral-Small-24B-Instruct-2501",
"license:apache-2.0",
"text-generation-inference",
"6-bit",
"exl2",
"region:us"
] |
text-generation
| 2025-01-31T06:57:45Z |
---
language:
- en
- fr
- de
- es
- it
- pt
- zh
- ja
- ru
- ko
license: apache-2.0
library_name: vllm
inference: false
base_model: mistralai/Mistral-Small-24B-Instruct-2501
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- transformers
---
6bpw exl2 quant of: https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501
---
# Model Card for Mistral-Small-24B-Instruct-2501
Mistral Small 3 ( 2501 ) sets a new benchmark in the "small" Large Language Models category below 70B, boasting 24B parameters and achieving state-of-the-art capabilities comparable to larger models!
This model is an instruction-fine-tuned version of the base model: [Mistral-Small-24B-Base-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Base-2501).
Mistral Small can be deployed locally and is exceptionally "knowledge-dense", fitting in a single RTX 4090 or a 32GB RAM MacBook once quantized.
Perfect for:
- Fast response conversational agents.
- Low latency function calling.
- Subject matter experts via fine-tuning.
- Local inference for hobbyists and organizations handling sensitive data.
For enterprises that need specialized capabilities (increased context, particular modalities, domain specific knowledge, etc.), we will be releasing commercial models beyond what Mistral AI contributes to the community.
This release demonstrates our commitment to open source, serving as a strong base model.
Learn more about Mistral Small in our [blog post](https://mistral.ai/news/mistral-small-3/).
Model developper: Mistral AI Team
## Key Features
- **Multilingual:** Supports dozens of languages, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
- **Agent-Centric:** Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- **Advanced Reasoning:** State-of-the-art conversational and reasoning capabilities.
- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
- **Context Window:** A 32k context window.
- **System Prompt:** Maintains strong adherence and support for system prompts.
- **Tokenizer:** Utilizes a Tekken tokenizer with a 131k vocabulary size.
## Benchmark results
### Human evaluated benchmarks
| Category | Gemma-2-27B | Qwen-2.5-32B | Llama-3.3-70B | Gpt4o-mini |
|----------|-------------|--------------|---------------|------------|
| Mistral is better | 0.536 | 0.496 | 0.192 | 0.200 |
| Mistral is slightly better | 0.196 | 0.184 | 0.164 | 0.204 |
| Ties | 0.052 | 0.060 | 0.236 | 0.160 |
| Other is slightly better | 0.060 | 0.088 | 0.112 | 0.124 |
| Other is better | 0.156 | 0.172 | 0.296 | 0.312 |
**Note**:
- We conducted side by side evaluations with an external third-party vendor, on a set of over 1k proprietary coding and generalist prompts.
- Evaluators were tasked with selecting their preferred model response from anonymized generations produced by Mistral Small 3 vs another model.
- We are aware that in some cases the benchmarks on human judgement starkly differ from publicly available benchmarks, but have taken extra caution in verifying a fair evaluation. We are confident that the above benchmarks are valid.
### Publicly accesible benchmarks
**Reasoning & Knowledge**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| mmlu_pro_5shot_cot_instruct | 0.663 | 0.536 | 0.666 | 0.683 | 0.617 |
| gpqa_main_cot_5shot_instruct | 0.453 | 0.344 | 0.531 | 0.404 | 0.377 |
**Math & Coding**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| humaneval_instruct_pass@1 | 0.848 | 0.732 | 0.854 | 0.909 | 0.890 |
| math_instruct | 0.706 | 0.535 | 0.743 | 0.819 | 0.761 |
**Instruction following**
| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
|------------|---------------|--------------|---------------|---------------|-------------|
| mtbench_dev | 8.35 | 7.86 | 7.96 | 8.26 | 8.33 |
| wildbench | 52.27 | 48.21 | 50.04 | 52.73 | 56.13 |
| arena_hard | 0.873 | 0.788 | 0.840 | 0.860 | 0.897 |
| ifeval | 0.829 | 0.8065 | 0.8835 | 0.8401 | 0.8499 |
**Note**:
- Performance accuracy on all benchmarks were obtained through the same internal evaluation pipeline - as such, numbers may vary slightly from previously reported performance
([Qwen2.5-32B-Instruct](https://qwenlm.github.io/blog/qwen2.5/), [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), [Gemma-2-27B-IT](https://huggingface.co/google/gemma-2-27b-it)).
- Judge based evals such as Wildbench, Arena hard and MTBench were based on gpt-4o-2024-05-13.
### Basic Instruct Template (V7-Tekken)
```
<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]
```
*`<system_prompt>`, `<user message>` and `<assistant response>` are placeholders.*
***Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth***
## Usage
The model can be used with the following frameworks;
- [`vllm`](https://github.com/vllm-project/vllm): See [here](#vLLM)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#Transformers)
### vLLM
We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
to implement production-ready inference pipelines.
**Note 1**: We recommond using a relatively low temperature, such as `temperature=0.15`.
**Note 2**: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend the following
system prompt:
```
system_prompt = """You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
Your knowledge base was last updated on 2023-10-01. The current date is 2025-01-30.
When you're not sure about some information, you say that you don't have the information and don't make up anything.
If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \"What are some good restaurants around me?\" => \"Where are you?\" or \"When is the next flight to Tokyo\" => \"Where do you travel from?\")"""
```
**_Installation_**
Make sure you install [`vLLM >= 0.6.4`](https://github.com/vllm-project/vllm/releases/tag/v0.6.4):
```
pip install --upgrade vllm
```
Also make sure you have [`mistral_common >= 1.5.2`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.2) installed:
```
pip install --upgrade mistral_common
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
#### Server
We recommand that you use Mistral-Small-24B-Instruct-2501 in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Mistral-Small-24B-Instruct-2501 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
```
**Note:** Running Mistral-Small-24B-Instruct-2501 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
2. To ping the client you can use a simple Python snippet.
```py
import requests
import json
from datetime import datetime, timedelta
url = "http://<your-server>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-24B-Instruct-2501"
messages = [
{
"role": "system",
"content": "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
},
{
"role": "user",
"content": "Give me 5 non-formal ways to say 'See you later' in French."
},
]
data = {"model": model, "messages": messages}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
# Sure, here are five non-formal ways to say "See you later" in French:
#
# 1. À plus tard
# 2. À plus
# 3. Salut
# 4. À toute
# 5. Bisous
#
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
```
### Function calling
Mistral-Small-24-Instruct-2501 is excellent at function / tool calling tasks via vLLM. *E.g.:*
<details>
<summary>Example</summary>
```py
import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta
url = "http://<your-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
model = "mistralai/Mistral-Small-24B-Instruct-2501"
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city to find the weather for, e.g. 'San Francisco'",
},
"state": {
"type": "string",
"description": "The state abbreviation, e.g. 'CA' for California",
},
"unit": {
"type": "string",
"description": "The unit for temperature",
"enum": ["celsius", "fahrenheit"],
},
},
"required": ["city", "state", "unit"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "rewrite",
"arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
},
}
],
},
{
"role": "tool",
"content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
"tool_call_id": "bbc5b7ede",
"name": "rewrite",
},
{
"role": "assistant",
"content": "---\n\nOpenAI is a FOR-profit company.",
},
{
"role": "user",
"content": "Can you tell me what the temperature will be in Dallas, in Fahrenheit?",
},
]
data = {"model": model, "messages": messages, "tools": tools}
response = requests.post(url, headers=headers, data=json.dumps(data))
import ipdb; ipdb.set_trace()
print(response.json()["choices"][0]["message"]["tool_calls"])
# [{'id': '8PdihwL6d', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'}}]
```
</details>
#### Offline
```py
from vllm import LLM
from vllm.sampling_params import SamplingParams
from datetime import datetime, timedelta
SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
messages = [
{
"role": "system",
"content": SYSTEM_PROMPT
},
{
"role": "user",
"content": user_prompt
},
]
# note that running this model on GPU requires over 60 GB of GPU RAM
llm = LLM(model=model_name, tokenizer_mode="mistral", tensor_parallel_size=8)
sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
# Sure, here are five non-formal ways to say "See you later" in French:
#
# 1. À plus tard
# 2. À plus
# 3. Salut
# 4. À toute
# 5. Bisous
#
# ```
# /\_/\
# ( o.o )
# > ^ <
# ```
```
### Transformers
If you want to use Hugging Face transformers to generate text, you can do something like this.
```py
from transformers import pipeline
import torch
messages = [
{"role": "user", "content": "Give me 5 non-formal ways to say 'See you later' in French."},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-Small-24B-Instruct-2501", max_new_tokens=256, torch_dtype=torch.bfloat16)
chatbot(messages)
```
### Ollama
[Ollama](https://github.com/ollama/ollama) can run this model locally on MacOS, Windows and Linux.
```
ollama run mistral-small
```
4-bit quantization (aliased to default):
```
ollama run mistral-small:24b-instruct-2501-q4_K_M
```
8-bit quantization:
```
ollama run mistral-small:24b-instruct-2501-q8_0
```
FP16:
```
ollama run mistral-small:24b-instruct-2501-fp16
```
|
roleplaiapp/DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Q3_K_S-GGUF
|
roleplaiapp
| 2025-01-31T08:00:26Z | 22 | 0 |
transformers
|
[
"transformers",
"gguf",
"3-bit",
"70b",
"Q3_K_S",
"deepseek",
"distill",
"llama",
"llama-cpp",
"text-generation",
"uncensored",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-31T07:58:38Z |
---
library_name: transformers
pipeline_tag: text-generation
tags:
- 3-bit
- 70b
- Q3_K_S
- deepseek
- distill
- gguf
- llama
- llama-cpp
- text-generation
- uncensored
---
# roleplaiapp/DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Q3_K_S-GGUF
**Repo:** `roleplaiapp/DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Q3_K_S-GGUF`
**Original Model:** `DeepSeek-R1-Distill-Llama-70B-Uncensored-v2`
**Quantized File:** `DeepSeek-R1-Distill-Llama-70B-Uncensored-v2.Q3_K_S.gguf`
**Quantization:** `GGUF`
**Quantization Method:** `Q3_K_S`
## Overview
This is a GGUF Q3_K_S quantized version of DeepSeek-R1-Distill-Llama-70B-Uncensored-v2
## Quantization By
I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models.
I hope the community finds these quantizations useful.
Andrew Webby @ [RolePlai](https://roleplai.app/).
|
nomadrp/tq-llama-binary-20each-ws-all-langs-2epochs
|
nomadrp
| 2025-01-31T07:59:59Z | 18 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
] | null | 2025-01-31T06:39:22Z |
---
library_name: peft
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: tq-llama-binary-20each-ws-all-langs-2epochs
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. -->
# tq-llama-binary-20each-ws-all-langs-2epochs
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) 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: 1e-07
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.45.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.20.3
|
kostiantynk1205/fa5cda42-2977-4ae4-9f64-2655b2619396
|
kostiantynk1205
| 2025-01-31T07:46:57Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-7b-hf-flash",
"base_model:adapter:NousResearch/CodeLlama-7b-hf-flash",
"region:us"
] | null | 2025-01-31T07:45:38Z |
---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fa5cda42-2977-4ae4-9f64-2655b2619396
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: NousResearch/CodeLlama-7b-hf-flash
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ef066a96964aba8a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ef066a96964aba8a_train_data.json
type:
field_instruction: title
field_output: description
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: kostiantynk1205/fa5cda42-2977-4ae4-9f64-2655b2619396
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/ef066a96964aba8a_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
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7cf2646b-3084-4458-ab3f-4af8618983fd
wandb_project: Birthday-SN56-23-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7cf2646b-3084-4458-ab3f-4af8618983fd
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# fa5cda42-2977-4ae4-9f64-2655b2619396
This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf-flash](https://huggingface.co/NousResearch/CodeLlama-7b-hf-flash) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4431
## 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: 5
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0040 | 1 | 2.4172 |
| 8.7174 | 0.0519 | 13 | 1.8004 |
| 6.8923 | 0.1038 | 26 | 1.5359 |
| 5.9897 | 0.1557 | 39 | 1.4431 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT
|
EpistemeAI
| 2025-01-31T07:44:17Z | 109 | 1 | null |
[
"safetensors",
"llama",
"dataset:AI-MO/NuminaMath-TIR",
"dataset:bespokelabs/Bespoke-Stratos-17k",
"license:apache-2.0",
"region:us"
] | null | 2025-01-29T05:51:48Z |
---
datasets:
- AI-MO/NuminaMath-TIR
- bespokelabs/Bespoke-Stratos-17k
license: apache-2.0
---
Upgrade version
[EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT-V2] (https://huggingface.co/EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT-V2)
## Introduction
Introducing Reasoning Llama 3.1: The Next Evolution in Conversational AI
We are thrilled to unveil Reasoning Llama 3.1, the latest advancement in our suite of AI models. Building upon the robust foundation of the renowned Llama series, Reasoning Llama 3.1 introduces the groundbreaking Chain of Thought (CoT) capabilities, elevating its reasoning prowess to new heights.
## Key Features of Reasoning Llama 3.1:
Enhanced Chain of Thought Reasoning: At the core of Reasoning Llama 3.1 lies its sophisticated CoT framework, enabling the model to perform multi-step reasoning with greater accuracy and coherence. This ensures more reliable and contextually appropriate responses, especially for complex queries that require logical progression.
Conversational Excellence: Designed with interactivity in mind, Reasoning Llama 3.1 excels in maintaining engaging and fluid conversations. Whether it's casual dialogue or in-depth discussions, the model adapts seamlessly to various conversational styles, providing users with a natural and intuitive interaction experience.
Instruction-Supervised Fine-Tuning: Leveraging advanced supervised fine-tuning techniques, Reasoning Llama 3.1 has been meticulously trained on diverse instructional data. This fine-tuning process enhances the model's ability to understand and execute user instructions with precision, making it an invaluable tool for a wide range of applications.
Unsloth Integration: Incorporating Unsloth, our proprietary unsupervised learning framework, Reasoning Llama 3.1 benefits from continuous learning capabilities. This integration allows the model to adapt and improve over time, ensuring it remains up-to-date with evolving language patterns and user needs without the constant need for manual intervention.
## Why Choose Reasoning Llama 3.1?
Reasoning Llama 3.1 stands out as a versatile and powerful AI solution tailored for both developers and end-users. Its combination of advanced reasoning, conversational intelligence, and adaptive learning mechanisms make it ideally suited for applications ranging from customer support and virtual assistants to educational tools and creative content generation.
As we continue to push the boundaries of artificial intelligence, Reasoning Llama 3.1 exemplifies our commitment to delivering state-of-the-art models that empower users with intelligent, reliable, and user-friendly technology. Experience the future of conversational AI with Reasoning Llama 3.1 and unlock new possibilities in human-machine interaction.
## How to use
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "EpistemeAI/Reasoning-Llama-3.1-CoT-RE1-NMT"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a powerful AI math assistant"},
{"role": "user", "content": "Given the quadratic function $f(x)=ax^{2}+bx+c$ with its derivative $f′(x)$, where $f′(0) > 0$, and $f(x)\geqslant 0$ for any real number $x$, find the minimum value of $\frac{f(1)}{f′(0)}$."},
]
outputs = pipe(
messages,
max_new_tokens=2048,
)
print(outputs[0]["generated_text"][-1])
```
# Uploaded model
- **Developed by:** EpistemeAI
- **License:** apache-2.0
- **Finetuned from model :** EpistemeAI/Reasoning-Llama-3.1-CoT-RE1
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)
## 5. Citation
```
@misc{EpistemeAI2025,
title = {EpistemeAI},
author={Thomas Yiu},
year={2025},
}
@misc{bespoke_stratos,
author = {Bespoke Labs},
title = {Bespoke-Stratos: The unreasonable effectiveness of reasoning distillation},
howpublished = {https://www.bespokelabs.ai/blog/bespoke-stratos-the-unreasonable-effectiveness-of-reasoning-distillation},
note = {Accessed: 2025-01-22},
year = {2025}
}
@misc{numina_math_datasets,
author = {Jia LI, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, Longhui Yu, Albert Jiang, Ziju Shen, Zihan Qin, Bin Dong, Li Zhou, Yann Fleureau, Guillaume Lample, and Stanislas Polu},
title = {NuminaMath TIR},
year = {2024},
publisher = {Numina},
journal = {Hugging Face repository},
howpublished = {\url{[https://huggingface.co/AI-MO/NuminaMath-TIR](https://github.com/project-numina/aimo-progress-prize/blob/main/report/numina_dataset.pdf)}}
}
```
## 6. Contact
If you have any questions, please raise an issue or contact us at [episteme.ai@proton.me](mailto:episteme.ai@proton.me).
# Reference/Inspired
[Open-R1: a fully open reproduction of DeepSeek-R1](https://huggingface.co/blog/open-r1)
|
roleplaiapp/DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Q2_K-GGUF
|
roleplaiapp
| 2025-01-31T07:43:36Z | 326 | 0 |
transformers
|
[
"transformers",
"gguf",
"2-bit",
"70b",
"Q2_K",
"deepseek",
"distill",
"llama",
"llama-cpp",
"text-generation",
"uncensored",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-31T07:42:07Z |
---
library_name: transformers
pipeline_tag: text-generation
tags:
- 2-bit
- 70b
- Q2_K
- deepseek
- distill
- gguf
- llama
- llama-cpp
- text-generation
- uncensored
---
# roleplaiapp/DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Q2_K-GGUF
**Repo:** `roleplaiapp/DeepSeek-R1-Distill-Llama-70B-Uncensored-v2-Q2_K-GGUF`
**Original Model:** `DeepSeek-R1-Distill-Llama-70B-Uncensored-v2`
**Quantized File:** `DeepSeek-R1-Distill-Llama-70B-Uncensored-v2.Q2_K.gguf`
**Quantization:** `GGUF`
**Quantization Method:** `Q2_K`
## Overview
This is a GGUF Q2_K quantized version of DeepSeek-R1-Distill-Llama-70B-Uncensored-v2
## Quantization By
I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models.
I hope the community finds these quantizations useful.
Andrew Webby @ [RolePlai](https://roleplai.app/).
|
mrferr3t/a73aeffa-c13c-45b8-ad56-12d3e31085fe
|
mrferr3t
| 2025-01-31T07:39:27Z | 12 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama_v1.1",
"base_model:adapter:TinyLlama/TinyLlama_v1.1",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T07:26:30Z |
---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama_v1.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a73aeffa-c13c-45b8-ad56-12d3e31085fe
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: TinyLlama/TinyLlama_v1.1
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f6627dfddf7998ee_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6627dfddf7998ee_train_data.json
type:
field_input: traj_0_response
field_instruction: prompt
field_output: traj_0_solution_0
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_steps: 50
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/a73aeffa-c13c-45b8-ad56-12d3e31085fe
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0005
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: 99
micro_batch_size: 2
mlflow_experiment_name: /tmp/f6627dfddf7998ee_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
save_steps: 300
saves_per_epoch: 0
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 41e012f9-ee25-49ae-abe0-b64021ea6e9d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 41e012f9-ee25-49ae-abe0-b64021ea6e9d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# a73aeffa-c13c-45b8-ad56-12d3e31085fe
This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9268
## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_bnb_8bit 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: 99
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2394 | 0.0001 | 1 | 1.3524 |
| 0.6882 | 0.0068 | 50 | 0.9268 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
cilooor/046b85c9-23cf-42fa-ad72-faea29e54f78
|
cilooor
| 2025-01-31T07:39:05Z | 15 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama_v1.1",
"base_model:adapter:TinyLlama/TinyLlama_v1.1",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T07:18:44Z |
---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama_v1.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 046b85c9-23cf-42fa-ad72-faea29e54f78
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: TinyLlama/TinyLlama_v1.1
bf16: true
chat_template: llama3
data_processes: 24
dataset_prepared_path: null
datasets:
- data_files:
- f6627dfddf7998ee_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6627dfddf7998ee_train_data.json
type:
field_input: traj_0_response
field_instruction: prompt
field_output: traj_0_solution_0
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 4
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: cilooor/046b85c9-23cf-42fa-ad72-faea29e54f78
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 7.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.07
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
lr_scheduler_warmup_steps: 50
max_grad_norm: 0.3
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/f6627dfddf7998ee_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-8
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: 50
saves_per_epoch: null
seed: 17333
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
total_train_batch_size: 32
train_batch_size: 8
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 41e012f9-ee25-49ae-abe0-b64021ea6e9d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 41e012f9-ee25-49ae-abe0-b64021ea6e9d
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 046b85c9-23cf-42fa-ad72-faea29e54f78
This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8387
## 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: 7e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 17333
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7648 | 0.0005 | 1 | 1.3696 |
| 1.1307 | 0.0273 | 50 | 0.9475 |
| 1.0357 | 0.0547 | 100 | 0.8693 |
| 0.9074 | 0.0820 | 150 | 0.8440 |
| 0.9893 | 0.1093 | 200 | 0.8387 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mrferr3t/50cf18ca-29a7-43e4-b52c-209e7bc94fc6
|
mrferr3t
| 2025-01-31T07:38:54Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:adapter:DeepMount00/Llama-3-8b-Ita",
"license:llama3",
"region:us"
] | null | 2025-01-31T07:37:30Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 50cf18ca-29a7-43e4-b52c-209e7bc94fc6
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: DeepMount00/Llama-3-8b-Ita
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ff701e66869152c5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ff701e66869152c5_train_data.json
type:
field_instruction: src
field_output: tgt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: 50
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/50cf18ca-29a7-43e4-b52c-209e7bc94fc6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0005
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: 99
micro_batch_size: 2
mlflow_experiment_name: /tmp/ff701e66869152c5_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
save_steps: 300
saves_per_epoch: 0
sequence_len: 512
special_tokens:
pad_token: <|eot_id|>
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: 37e884fe-9938-432e-9e6b-d663af3f92e4
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 37e884fe-9938-432e-9e6b-d663af3f92e4
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 50cf18ca-29a7-43e4-b52c-209e7bc94fc6
This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2451
## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_bnb_8bit 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: 97
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.9305 | 0.0104 | 1 | 2.0875 |
| 1.3837 | 0.5181 | 50 | 1.2451 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Best000/8ddd5201-de60-4f90-b2e3-4a4b8d9b1acc
|
Best000
| 2025-01-31T07:38:30Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:adapter:DeepMount00/Llama-3-8b-Ita",
"license:llama3",
"region:us"
] | null | 2025-01-31T07:37:25Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8ddd5201-de60-4f90-b2e3-4a4b8d9b1acc
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: DeepMount00/Llama-3-8b-Ita
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ff701e66869152c5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ff701e66869152c5_train_data.json
type:
field_instruction: src
field_output: tgt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: Best000/8ddd5201-de60-4f90-b2e3-4a4b8d9b1acc
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/ff701e66869152c5_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
special_tokens:
pad_token: <|eot_id|>
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: 37e884fe-9938-432e-9e6b-d663af3f92e4
wandb_project: Birthday-SN56-16-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 37e884fe-9938-432e-9e6b-d663af3f92e4
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8ddd5201-de60-4f90-b2e3-4a4b8d9b1acc
This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2792
## 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0104 | 1 | 2.0874 |
| 1.899 | 0.1347 | 13 | 1.5013 |
| 1.4695 | 0.2694 | 26 | 1.3251 |
| 1.283 | 0.4041 | 39 | 1.2792 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso01/f55ed13d-287f-4850-b695-20aec435094e
|
lesso01
| 2025-01-31T07:36:23Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-7b-hf-flash",
"base_model:adapter:NousResearch/CodeLlama-7b-hf-flash",
"region:us"
] | null | 2025-01-31T07:25:57Z |
---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f55ed13d-287f-4850-b695-20aec435094e
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: NousResearch/CodeLlama-7b-hf-flash
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ef066a96964aba8a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ef066a96964aba8a_train_data.json
type:
field_instruction: title
field_output: description
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso01/f55ed13d-287f-4850-b695-20aec435094e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
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: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/ef066a96964aba8a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7cf2646b-3084-4458-ab3f-4af8618983fd
wandb_project: new-01-29
wandb_run: your_name
wandb_runid: 7cf2646b-3084-4458-ab3f-4af8618983fd
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# f55ed13d-287f-4850-b695-20aec435094e
This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf-flash](https://huggingface.co/NousResearch/CodeLlama-7b-hf-flash) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3251
## 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.5535 | 0.7984 | 200 | 1.3251 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
adammandic87/bc1558dc-b7da-4aad-bc5e-ea57281facde
|
adammandic87
| 2025-01-31T07:36:21Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | null | 2025-01-31T07:19:09Z |
---
library_name: peft
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bc1558dc-b7da-4aad-bc5e-ea57281facde
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: HuggingFaceH4/zephyr-7b-beta
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ecd7cec85692169d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ecd7cec85692169d_train_data.json
type:
field_instruction: input_persona
field_output: prompt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: adammandic87/bc1558dc-b7da-4aad-bc5e-ea57281facde
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: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/ecd7cec85692169d_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: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
wandb_project: Birthday-SN56-13-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bc1558dc-b7da-4aad-bc5e-ea57281facde
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0001 | 1 | nan |
| 0.0 | 0.0007 | 13 | nan |
| 0.0 | 0.0015 | 26 | nan |
| 0.0 | 0.0022 | 39 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
adammandic87/f62fa779-f2a3-4e37-ade5-d772103b1717
|
adammandic87
| 2025-01-31T07:35:29Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | null | 2025-01-31T07:18:45Z |
---
library_name: peft
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f62fa779-f2a3-4e37-ade5-d772103b1717
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: HuggingFaceH4/zephyr-7b-beta
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ecd7cec85692169d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ecd7cec85692169d_train_data.json
type:
field_instruction: input_persona
field_output: prompt
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: adammandic87/f62fa779-f2a3-4e37-ade5-d772103b1717
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/ecd7cec85692169d_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: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
wandb_project: Birthday-SN56-34-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7bdc132e-e198-4b8f-bee8-34caa4c4cbb2
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f62fa779-f2a3-4e37-ade5-d772103b1717
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | nan |
| 0.2605 | 0.0007 | 13 | nan |
| 0.0 | 0.0015 | 26 | nan |
| 2.3517 | 0.0022 | 39 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
beast33/902a5079-22c8-4d77-a4f7-edade50bdf6d
|
beast33
| 2025-01-31T07:33:10Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/gemma-2b-it",
"base_model:adapter:unsloth/gemma-2b-it",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T07:31:30Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/gemma-2b-it
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 902a5079-22c8-4d77-a4f7-edade50bdf6d
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/gemma-2b-it
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 938e7b961a3fae54_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/938e7b961a3fae54_train_data.json
type:
field_input: choices
field_instruction: full_prompt
field_output: example
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: beast33/902a5079-22c8-4d77-a4f7-edade50bdf6d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
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_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/938e7b961a3fae54_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
save_steps: null
saves_per_epoch: null
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: 264a9c6b-5cbc-436b-8c95-a81e899b2353
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 264a9c6b-5cbc-436b-8c95-a81e899b2353
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 902a5079-22c8-4d77-a4f7-edade50bdf6d
This model is a fine-tuned version of [unsloth/gemma-2b-it](https://huggingface.co/unsloth/gemma-2b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 21
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0007 | 1.0 | 21 | 0.0005 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
YMEA/Pathe-asr-LenaData-V0
|
YMEA
| 2025-01-31T07:32:38Z | 25 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"bam",
"dataset:YMEA/lena_audio",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-01-31T03:17:15Z |
---
library_name: transformers
language:
- bam
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- YMEA/lena_audio
model-index:
- name: Whisper Bambara-Bambara
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. -->
# Whisper Bambara-Bambara
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the BambaraAsr dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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: 250
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF
|
featherless-ai-quants
| 2025-01-31T07:30:05Z | 212 | 0 | null |
[
"gguf",
"text-generation",
"base_model:SteelStorage/L3.1-MS-Astoria-70b-v2",
"base_model:quantized:SteelStorage/L3.1-MS-Astoria-70b-v2",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-01-31T06:20:05Z |
---
base_model: SteelStorage/L3.1-MS-Astoria-70b-v2
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# SteelStorage/L3.1-MS-Astoria-70b-v2 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [SteelStorage-L3.1-MS-Astoria-70b-v2-IQ4_XS](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-IQ4_XS) | 36496.80 MB (folder) |
| Q2_K | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q2_K](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q2_K) | 25153.27 MB (folder) |
| Q3_K_L | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q3_K_L](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q3_K_L) | 35420.04 MB (folder) |
| Q3_K_M | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q3_K_M](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q3_K_M) | 32680.04 MB (folder) |
| Q3_K_S | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q3_K_S](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q3_K_S) | 29480.04 MB (folder) |
| Q4_K_M | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q4_K_M](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q4_K_M) | 40550.61 MB (folder) |
| Q4_K_S | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q4_K_S](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q4_K_S) | 38478.11 MB (folder) |
| Q5_K_M | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q5_K_M](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q5_K_M) | 47635.86 MB (folder) |
| Q5_K_S | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q5_K_S](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q5_K_S) | 46403.36 MB (folder) |
| Q6_K | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q6_K](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q6_K) | 55206.44 MB (folder) |
| Q8_0 | [SteelStorage-L3.1-MS-Astoria-70b-v2-Q8_0](https://huggingface.co/featherless-ai-quants/SteelStorage-L3.1-MS-Astoria-70b-v2-GGUF/tree/main/SteelStorage-L3.1-MS-Astoria-70b-v2-Q8_0) | 71501.79 MB (folder) |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
philip-hightech/5b67fba1-2225-443a-9af8-4fbcf4440017
|
philip-hightech
| 2025-01-31T07:30:04Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-7b-hf-flash",
"base_model:adapter:NousResearch/CodeLlama-7b-hf-flash",
"region:us"
] | null | 2025-01-31T07:27:18Z |
---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5b67fba1-2225-443a-9af8-4fbcf4440017
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: NousResearch/CodeLlama-7b-hf-flash
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ef066a96964aba8a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ef066a96964aba8a_train_data.json
type:
field_instruction: title
field_output: description
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: philip-hightech/5b67fba1-2225-443a-9af8-4fbcf4440017
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: 10
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/ef066a96964aba8a_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
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7cf2646b-3084-4458-ab3f-4af8618983fd
wandb_project: Mine-SN56-21-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7cf2646b-3084-4458-ab3f-4af8618983fd
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 5b67fba1-2225-443a-9af8-4fbcf4440017
This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf-flash](https://huggingface.co/NousResearch/CodeLlama-7b-hf-flash) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3994
## 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: 2
- total_train_batch_size: 4
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0020 | 1 | 2.4172 |
| 4.0157 | 0.0259 | 13 | 1.6903 |
| 3.1184 | 0.0519 | 26 | 1.4732 |
| 2.9423 | 0.0778 | 39 | 1.3994 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
sercetexam9/rubert-tiny2-rus-MICRO
|
sercetexam9
| 2025-01-31T07:29:01Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:cointegrated/rubert-tiny2",
"base_model:finetune:cointegrated/rubert-tiny2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-01-31T07:26:14Z |
---
library_name: transformers
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: rubert-tiny2-rus-MICRO
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. -->
# rubert-tiny2-rus-MICRO
This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1485
- F1: 0.8458
- Roc Auc: 0.9005
- Accuracy: 0.7887
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.2588 | 1.0 | 607 | 0.2564 | 0.6892 | 0.7777 | 0.6469 |
| 0.1663 | 2.0 | 1214 | 0.1743 | 0.8322 | 0.8850 | 0.7668 |
| 0.1014 | 3.0 | 1821 | 0.1481 | 0.8399 | 0.8829 | 0.7912 |
| 0.0716 | 4.0 | 2428 | 0.1458 | 0.8433 | 0.8968 | 0.7861 |
| 0.0496 | 5.0 | 3035 | 0.1440 | 0.8423 | 0.8945 | 0.7835 |
| 0.0389 | 6.0 | 3642 | 0.1485 | 0.8458 | 0.9005 | 0.7887 |
| 0.037 | 7.0 | 4249 | 0.1538 | 0.8428 | 0.8998 | 0.7822 |
| 0.0218 | 8.0 | 4856 | 0.1623 | 0.8422 | 0.8997 | 0.7809 |
| 0.0196 | 9.0 | 5463 | 0.1678 | 0.8420 | 0.9007 | 0.7796 |
| 0.0204 | 10.0 | 6070 | 0.1743 | 0.8355 | 0.8967 | 0.7732 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
DINGOLANI/distilbert-ner-v2
|
DINGOLANI
| 2025-01-31T07:28:49Z | 45 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-01-31T07:28:38Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Best000/b44f94f4-56ce-4c9a-8a24-dd304ed4037e
|
Best000
| 2025-01-31T07:28:31Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-7b-hf-flash",
"base_model:adapter:NousResearch/CodeLlama-7b-hf-flash",
"region:us"
] | null | 2025-01-31T07:27:18Z |
---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b44f94f4-56ce-4c9a-8a24-dd304ed4037e
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: NousResearch/CodeLlama-7b-hf-flash
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ef066a96964aba8a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ef066a96964aba8a_train_data.json
type:
field_instruction: title
field_output: description
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: Best000/b44f94f4-56ce-4c9a-8a24-dd304ed4037e
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: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/ef066a96964aba8a_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
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7cf2646b-3084-4458-ab3f-4af8618983fd
wandb_project: Birthday-SN56-32-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7cf2646b-3084-4458-ab3f-4af8618983fd
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# b44f94f4-56ce-4c9a-8a24-dd304ed4037e
This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf-flash](https://huggingface.co/NousResearch/CodeLlama-7b-hf-flash) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5488
## 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: 50
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0040 | 1 | 2.4172 |
| 9.1505 | 0.0519 | 13 | 2.3770 |
| 9.2624 | 0.1038 | 26 | 1.8705 |
| 7.4654 | 0.1557 | 39 | 1.5488 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
qingy2024/Qwen2.5-Coder-Draft-1.5B-Instruct
|
qingy2024
| 2025-01-31T07:27:53Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T05:56:33Z |
---
library_name: transformers
base_model:
- Qwen/Qwen2.5-Coder-1.5B-Instruct
---
# Qwen2.5-Coder-Draft-1.5B-Instruct
A draft model suitable for [Qwen2.5 Coder 32B Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct)
It uses a vocabulary size of 152064, which is the same as Qwen2.5 Coder 32B Instruct (can be used in vLLM directly without any hack)
|
tensorwa/dp_mg_h1_01
|
tensorwa
| 2025-01-31T07:27:53Z | 24 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Peacoc/chatml_2test43",
"base_model:finetune:Peacoc/chatml_2test43",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T07:25:23Z |
---
base_model:
- itorgov/model-1738289983
- Peacoc/chatml_2test43
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 [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [itorgov/model-1738289983](https://huggingface.co/itorgov/model-1738289983)
* [Peacoc/chatml_2test43](https://huggingface.co/Peacoc/chatml_2test43)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: itorgov/model-1738289983
layer_range: [0, 32]
- model: Peacoc/chatml_2test43
layer_range: [0, 32]
merge_method: slerp
base_model: itorgov/model-1738289983
parameters:
t:
- filter: self_attn
value: 0.98
- filter: mlp
value: 0.99
- value: 1
dtype: bfloat16
```
|
Razvan1974/Jimi
|
Razvan1974
| 2025-01-31T07:25:08Z | 22 | 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
| 2025-01-31T07:04:43Z |
---
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: Jimi
---
# Jimi
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Jimi` 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('Razvan1974/Jimi', 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)
|
Mohamedk12345678/patikya
|
Mohamedk12345678
| 2025-01-31T07:25:07Z | 7 | 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
| 2025-01-31T07:11:34Z |
---
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: patikya
---
# Patikya
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `patikya` 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('Mohamedk12345678/patikya', 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)
|
ancient41/f6fd0cd1-e0a0-4ad8-bdd0-b39e0ac89ff6
|
ancient41
| 2025-01-31T07:24:23Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:dltjdgh0928/test_instruction",
"base_model:adapter:dltjdgh0928/test_instruction",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T05:15:50Z |
---
library_name: peft
license: apache-2.0
base_model: dltjdgh0928/test_instruction
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f6fd0cd1-e0a0-4ad8-bdd0-b39e0ac89ff6
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: dltjdgh0928/test_instruction
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 445036244439be21_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/445036244439be21_train_data.json
type:
field_input: new_response
field_instruction: prompt
field_output: org_response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
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: true
hub_model_id: ancient41/f6fd0cd1-e0a0-4ad8-bdd0-b39e0ac89ff6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/445036244439be21_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
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: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8d4144fc-9ff0-40f6-938c-971bb0af2635
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8d4144fc-9ff0-40f6-938c-971bb0af2635
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f6fd0cd1-e0a0-4ad8-bdd0-b39e0ac89ff6
This model is a fine-tuned version of [dltjdgh0928/test_instruction](https://huggingface.co/dltjdgh0928/test_instruction) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6707
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1204 | 0.0001 | 1 | 1.1771 |
| 3.5574 | 0.0056 | 50 | 0.7825 |
| 3.665 | 0.0112 | 100 | 0.7170 |
| 3.6566 | 0.0169 | 150 | 0.6775 |
| 3.6301 | 0.0225 | 200 | 0.6707 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
visdata/py26
|
visdata
| 2025-01-31T07:23:25Z | 24 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T07:18:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **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]
|
InsultedByMathematics/alpha_1e-2-beta_1e-2
|
InsultedByMathematics
| 2025-01-31T07:21:59Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-31T07:17:42Z |
---
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.
- **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]
|
abaddon182/cdedae3a-3953-41ed-acb9-287e5ba6a04c
|
abaddon182
| 2025-01-31T07:21:42Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:jhflow/mistral7b-lora-multi-turn-v2",
"base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2",
"region:us"
] | null | 2025-01-31T06:54:16Z |
---
library_name: peft
base_model: jhflow/mistral7b-lora-multi-turn-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cdedae3a-3953-41ed-acb9-287e5ba6a04c
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: jhflow/mistral7b-lora-multi-turn-v2
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- bd759e5c8d2b027f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bd759e5c8d2b027f_train_data.json
type:
field_input: answers
field_instruction: topic
field_output: text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
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: true
hub_model_id: abaddon182/cdedae3a-3953-41ed-acb9-287e5ba6a04c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/bd759e5c8d2b027f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
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: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3217968f-95e4-42f6-ab2b-878e655e1370
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3217968f-95e4-42f6-ab2b-878e655e1370
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# cdedae3a-3953-41ed-acb9-287e5ba6a04c
This model is a fine-tuned version of [jhflow/mistral7b-lora-multi-turn-v2](https://huggingface.co/jhflow/mistral7b-lora-multi-turn-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1080
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.9483 | 0.0108 | 1 | 2.2484 |
| 5.1298 | 0.5420 | 50 | 1.2160 |
| 2.4199 | 1.0840 | 100 | 1.1514 |
| 2.3623 | 1.6260 | 150 | 1.1195 |
| 1.2455 | 2.1680 | 200 | 1.1080 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Kobi-01/distilled_bert_tamil
|
Kobi-01
| 2025-01-31T07:21:20Z | 84 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-01-10T10:21:23Z |
---
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]
- **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]
|
nttx/b959546c-d51c-44fc-aeec-977098c32968
|
nttx
| 2025-01-31T07:19:53Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:adapter:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"license:llama3",
"region:us"
] | null | 2025-01-31T07:01:00Z |
---
library_name: peft
license: llama3
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b959546c-d51c-44fc-aeec-977098c32968
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: MLP-KTLim/llama-3-Korean-Bllossom-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 423760bfd2fbfffa_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/423760bfd2fbfffa_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/b959546c-d51c-44fc-aeec-977098c32968
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
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_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/423760bfd2fbfffa_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
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 84585b20-d892-48c7-a995-1238079422b0
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 84585b20-d892-48c7-a995-1238079422b0
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# b959546c-d51c-44fc-aeec-977098c32968
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7395 | 0.0410 | 200 | 1.6075 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
beast33/c7d68f13-7fb1-4ded-a461-ea16244e38e8
|
beast33
| 2025-01-31T07:17:13Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:jhflow/mistral7b-lora-multi-turn-v2",
"base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-31T06:46:18Z |
---
library_name: peft
base_model: jhflow/mistral7b-lora-multi-turn-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c7d68f13-7fb1-4ded-a461-ea16244e38e8
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: jhflow/mistral7b-lora-multi-turn-v2
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- bd759e5c8d2b027f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bd759e5c8d2b027f_train_data.json
type:
field_input: answers
field_instruction: topic
field_output: text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: beast33/c7d68f13-7fb1-4ded-a461-ea16244e38e8
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
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_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/bd759e5c8d2b027f_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
save_steps: null
saves_per_epoch: null
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: 3217968f-95e4-42f6-ab2b-878e655e1370
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3217968f-95e4-42f6-ab2b-878e655e1370
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# c7d68f13-7fb1-4ded-a461-ea16244e38e8
This model is a fine-tuned version of [jhflow/mistral7b-lora-multi-turn-v2](https://huggingface.co/jhflow/mistral7b-lora-multi-turn-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 185
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.0022 | 0.9986 | 184 | 1.1373 |
| 4.9826 | 1.0041 | 185 | 1.1200 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
havinash-ai/bbe1101f-5c1b-444f-8b48-67bfd058899b
|
havinash-ai
| 2025-01-31T07:11:29Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:adapter:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"license:llama3",
"region:us"
] | null | 2025-01-31T07:01:55Z |
---
library_name: peft
license: llama3
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bbe1101f-5c1b-444f-8b48-67bfd058899b
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: MLP-KTLim/llama-3-Korean-Bllossom-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 423760bfd2fbfffa_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/423760bfd2fbfffa_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
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: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: havinash-ai/bbe1101f-5c1b-444f-8b48-67bfd058899b
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: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/423760bfd2fbfffa_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: 84585b20-d892-48c7-a995-1238079422b0
wandb_project: Mine-SN56-2-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 84585b20-d892-48c7-a995-1238079422b0
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bbe1101f-5c1b-444f-8b48-67bfd058899b
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7416
## 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: 2
- total_train_batch_size: 4
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 2.2211 |
| 2.1075 | 0.0007 | 13 | 1.8652 |
| 2.0234 | 0.0013 | 26 | 1.7669 |
| 1.9285 | 0.0020 | 39 | 1.7416 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso01/f6c2b613-3b40-4dc1-8332-b21dbc57874f
|
lesso01
| 2025-01-31T07:08:38Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:berkeley-nest/Starling-LM-7B-alpha",
"base_model:adapter:berkeley-nest/Starling-LM-7B-alpha",
"license:apache-2.0",
"region:us"
] | null | 2025-01-31T06:18:34Z |
---
library_name: peft
license: apache-2.0
base_model: berkeley-nest/Starling-LM-7B-alpha
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f6c2b613-3b40-4dc1-8332-b21dbc57874f
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: berkeley-nest/Starling-LM-7B-alpha
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- dffa8fc58ce66dc6_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/dffa8fc58ce66dc6_train_data.json
type:
field_instruction: title
field_output: text
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso01/f6c2b613-3b40-4dc1-8332-b21dbc57874f
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
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: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/dffa8fc58ce66dc6_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: 73f2e9d8-c4f5-4163-bde3-27fae5504c6a
wandb_project: new-01-29
wandb_run: your_name
wandb_runid: 73f2e9d8-c4f5-4163-bde3-27fae5504c6a
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# f6c2b613-3b40-4dc1-8332-b21dbc57874f
This model is a fine-tuned version of [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0972 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.