modelId
stringlengths 5
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 00:36:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 540
values | tags
listlengths 1
4.05k
| pipeline_tag
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values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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aleegis11/e52b79c1-78c9-4fe9-88c6-59f66f2980ac
|
aleegis11
| 2025-01-23T20:00:00Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"region:us"
] | null | 2025-01-23T19:56:04Z |
---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e52b79c1-78c9-4fe9-88c6-59f66f2980ac
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/Qwen2.5-3B-Instruct
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- d0ef8b941107f6ec_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d0ef8b941107f6ec_train_data.json
type:
field_instruction: full_question
field_output: full_answer
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: aleegis11/e52b79c1-78c9-4fe9-88c6-59f66f2980ac
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/d0ef8b941107f6ec_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: b7cad66c-b570-48ba-acae-3db08352e03d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b7cad66c-b570-48ba-acae-3db08352e03d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e52b79c1-78c9-4fe9-88c6-59f66f2980ac
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7827
## 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: 41
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.5763 | 0.0727 | 1 | 2.7827 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
willtensora/0c2649cc-2fe7-4e88-b672-6da1fee4001f
|
willtensora
| 2025-01-23T19:59:55Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"conversational",
"base_model:NousResearch/Llama-3.2-1B",
"base_model:finetune:NousResearch/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-23T19:47:08Z |
---
library_name: transformers
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0c2649cc-2fe7-4e88-b672-6da1fee4001f
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
base_model: NousResearch/Llama-3.2-1B
batch_size: 32
bf16: true
chat_template: tokenizer_default_fallback_alpaca
datasets:
- data_files:
- f51beb4c568b9128_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f51beb4c568b9128_train_data.json
type:
field_input: keywords
field_instruction: idea
field_output: full_response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
eval_steps: 20
flash_attention: true
gpu_memory_limit: 80GiB
gradient_checkpointing: true
group_by_length: true
hub_model_id: willtensora/0c2649cc-2fe7-4e88-b672-6da1fee4001f
hub_strategy: checkpoint
learning_rate: 0.0002
logging_steps: 10
lr_scheduler: cosine
max_steps: 2500
micro_batch_size: 4
model_type: AutoModelForCausalLM
optimizer: adamw_bnb_8bit
output_dir: /workspace/axolotl/configs
pad_to_sequence_len: true
resize_token_embeddings_to_32x: false
sample_packing: false
save_steps: 40
save_total_limit: 1
sequence_len: 2048
special_tokens:
pad_token: <|end_of_text|>
tokenizer_type: PreTrainedTokenizerFast
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: ''
wandb_mode: online
wandb_name: NousResearch/Llama-3.2-1B-/workspace/input_data/f51beb4c568b9128_train_data.json
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: default
warmup_ratio: 0.05
xformers_attention: true
```
</details><br>
# 0c2649cc-2fe7-4e88-b672-6da1fee4001f
This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0849
## 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
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_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: 12
- training_steps: 258
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0005 | 1 | 0.2074 |
| 0.5472 | 0.0097 | 20 | 0.1746 |
| 0.3199 | 0.0194 | 40 | 0.2036 |
| 0.2013 | 0.0291 | 60 | 0.1772 |
| 0.0903 | 0.0388 | 80 | 0.1702 |
| 0.0875 | 0.0485 | 100 | 0.2040 |
| 0.1425 | 0.0582 | 120 | 0.1392 |
| 0.1982 | 0.0679 | 140 | 0.1194 |
| 0.1372 | 0.0776 | 160 | 0.1014 |
| 0.0278 | 0.0873 | 180 | 0.0952 |
| 0.0248 | 0.0970 | 200 | 0.0893 |
| 0.1051 | 0.1067 | 220 | 0.0875 |
| 0.0649 | 0.1164 | 240 | 0.0849 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
aleegis10/ad2a50e1-bc67-4e03-8dc7-5e86b63c30c9
|
aleegis10
| 2025-01-23T19:59:11Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:trl-internal-testing/tiny-random-LlamaForCausalLM",
"base_model:adapter:trl-internal-testing/tiny-random-LlamaForCausalLM",
"region:us"
] | null | 2025-01-23T19:58:38Z |
---
library_name: peft
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ad2a50e1-bc67-4e03-8dc7-5e86b63c30c9
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: trl-internal-testing/tiny-random-LlamaForCausalLM
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- f4a61305a746447c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f4a61305a746447c_train_data.json
type:
field_instruction: sentence1
field_output: sentence2
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: aleegis10/ad2a50e1-bc67-4e03-8dc7-5e86b63c30c9
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/f4a61305a746447c_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: c6d606c5-1bf1-4d46-8f27-e3893d012d1d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c6d606c5-1bf1-4d46-8f27-e3893d012d1d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# ad2a50e1-bc67-4e03-8dc7-5e86b63c30c9
This model is a fine-tuned version of [trl-internal-testing/tiny-random-LlamaForCausalLM](https://huggingface.co/trl-internal-testing/tiny-random-LlamaForCausalLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.3391
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 10.3745 | 0.0095 | 1 | 10.3693 |
| 10.3437 | 0.4739 | 50 | 10.3509 |
| 10.3244 | 0.9479 | 100 | 10.3415 |
| 10.0218 | 1.4218 | 150 | 10.3394 |
| 10.7113 | 1.8957 | 200 | 10.3391 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
joboffer/c74a54a3-de7e-427c-9c9a-7e5607f41f99
|
joboffer
| 2025-01-23T19:59:07Z | 11 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"license:other",
"region:us"
] | null | 2025-01-23T19:56:32Z |
---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c74a54a3-de7e-427c-9c9a-7e5607f41f99
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/Qwen2.5-3B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- d0ef8b941107f6ec_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d0ef8b941107f6ec_train_data.json
type:
field_instruction: full_question
field_output: full_answer
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: joboffer/c74a54a3-de7e-427c-9c9a-7e5607f41f99
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: 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_memory:
0: 79GiB
max_steps: 30
micro_batch_size: 4
mlflow_experiment_name: /tmp/d0ef8b941107f6ec_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b7cad66c-b570-48ba-acae-3db08352e03d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b7cad66c-b570-48ba-acae-3db08352e03d
warmup_steps: 5
weight_decay: 0.001
xformers_attention: true
```
</details><br>
# c74a54a3-de7e-427c-9c9a-7e5607f41f99
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2243
## 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: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
- training_steps: 28
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0364 | 1 | 2.5702 |
| 2.4669 | 0.1818 | 5 | 2.4546 |
| 2.3061 | 0.3636 | 10 | 2.3138 |
| 2.3087 | 0.5455 | 15 | 2.2644 |
| 2.3489 | 0.7273 | 20 | 2.2313 |
| 2.2761 | 0.9091 | 25 | 2.2243 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso07/2e6c2695-01ee-46e2-ad04-98d71d6eb996
|
lesso07
| 2025-01-23T19:59:02Z | 9 | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T19:50:02Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2e6c2695-01ee-46e2-ad04-98d71d6eb996
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
datasets:
- data_files:
- 7ac399799482b77b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/7ac399799482b77b_train_data.json
type:
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: lesso07/2e6c2695-01ee-46e2-ad04-98d71d6eb996
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
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: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/7ac399799482b77b_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: 10
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: 19b8278a-dcb0-42e7-9768-897f1536abd0
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 19b8278a-dcb0-42e7-9768-897f1536abd0
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 2e6c2695-01ee-46e2-ad04-98d71d6eb996
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: 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0004 | 1 | nan |
| 0.0 | 0.0021 | 5 | nan |
| 0.0 | 0.0042 | 10 | nan |
| 0.0 | 0.0063 | 15 | nan |
| 0.0 | 0.0084 | 20 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso03/260e19a1-0072-4a1b-a761-20f5cb197273
|
lesso03
| 2025-01-23T19:58:39Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:37:32Z |
---
library_name: peft
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 260e19a1-0072-4a1b-a761-20f5cb197273
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: tiiuae/falcon-7b
bf16: true
chat_template: llama3
datasets:
- data_files:
- 64fe47644b03a711_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/64fe47644b03a711_train_data.json
type:
field_input: ''
field_instruction: context
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: lesso03/260e19a1-0072-4a1b-a761-20f5cb197273
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
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: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/64fe47644b03a711_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: 10
sequence_len: 512
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: 5e0875d0-636d-4520-9359-4eac575c16b9
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 5e0875d0-636d-4520-9359-4eac575c16b9
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 260e19a1-0072-4a1b-a761-20f5cb197273
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2471
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.2968 | 0.0001 | 1 | 2.8454 |
| 10.9644 | 0.0003 | 5 | 2.8270 |
| 9.4872 | 0.0005 | 10 | 2.5012 |
| 8.2005 | 0.0008 | 15 | 2.2939 |
| 8.0283 | 0.0010 | 20 | 2.2651 |
| 8.3561 | 0.0013 | 25 | 2.2471 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso01/36f1b139-43ec-416a-bbda-5600a86e3f0e
|
lesso01
| 2025-01-23T19:57:47Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:36:25Z |
---
library_name: peft
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 36f1b139-43ec-416a-bbda-5600a86e3f0e
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: tiiuae/falcon-7b
bf16: true
chat_template: llama3
datasets:
- data_files:
- 64fe47644b03a711_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/64fe47644b03a711_train_data.json
type:
field_input: ''
field_instruction: context
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: lesso01/36f1b139-43ec-416a-bbda-5600a86e3f0e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
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: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/64fe47644b03a711_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: 10
sequence_len: 512
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: 5e0875d0-636d-4520-9359-4eac575c16b9
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 5e0875d0-636d-4520-9359-4eac575c16b9
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 36f1b139-43ec-416a-bbda-5600a86e3f0e
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2448
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.2968 | 0.0001 | 1 | 2.8454 |
| 10.9752 | 0.0003 | 5 | 2.8255 |
| 9.4506 | 0.0005 | 10 | 2.4924 |
| 8.1898 | 0.0008 | 15 | 2.2935 |
| 7.9867 | 0.0010 | 20 | 2.2624 |
| 8.3332 | 0.0013 | 25 | 2.2448 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Ryllix/X-ALMA-13B-Group1-Q4_K_M-GGUF
|
Ryllix
| 2025-01-23T19:56:28Z | 19 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"da",
"nl",
"de",
"is",
"no",
"sv",
"af",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:allenai/nllb",
"dataset:Helsinki-NLP/opus-100",
"base_model:haoranxu/X-ALMA-13B-Group1",
"base_model:quantized:haoranxu/X-ALMA-13B-Group1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-23T19:55:55Z |
---
license: mit
datasets:
- oscar-corpus/OSCAR-2301
- allenai/nllb
- Helsinki-NLP/opus-100
language:
- en
- da
- nl
- de
- is
- 'no'
- sv
- af
base_model: haoranxu/X-ALMA-13B-Group1
tags:
- llama-cpp
- gguf-my-repo
---
# Ryllix/X-ALMA-13B-Group1-Q4_K_M-GGUF
This model was converted to GGUF format from [`haoranxu/X-ALMA-13B-Group1`](https://huggingface.co/haoranxu/X-ALMA-13B-Group1) 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/haoranxu/X-ALMA-13B-Group1) 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 Ryllix/X-ALMA-13B-Group1-Q4_K_M-GGUF --hf-file x-alma-13b-group1-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q4_K_M-GGUF --hf-file x-alma-13b-group1-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Ryllix/X-ALMA-13B-Group1-Q4_K_M-GGUF --hf-file x-alma-13b-group1-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q4_K_M-GGUF --hf-file x-alma-13b-group1-q4_k_m.gguf -c 2048
```
|
rdetch22/t5_travel_model
|
rdetch22
| 2025-01-23T19:55:32Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-01-23T14:13:44Z |
---
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]
|
Darkhn/L3.3-Nevoria-Exp-R1-6.0bpw-h8-exl2
|
Darkhn
| 2025-01-23T19:53:35Z | 109 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1",
"base_model:merge:EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1",
"base_model:Sao10K/L3.3-70B-Euryale-v2.3",
"base_model:merge:Sao10K/L3.3-70B-Euryale-v2.3",
"base_model:SicariusSicariiStuff/Negative_LLAMA_70B",
"base_model:merge:SicariusSicariiStuff/Negative_LLAMA_70B",
"base_model:TheDrummer/Anubis-70B-v1",
"base_model:merge:TheDrummer/Anubis-70B-v1",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"base_model:merge:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"base_model:nbeerbower/Llama-3.1-Nemotron-lorablated-70B",
"base_model:merge:nbeerbower/Llama-3.1-Nemotron-lorablated-70B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"exl2",
"region:us"
] |
text-generation
| 2025-01-23T19:19:38Z |
---
base_model:
- nbeerbower/Llama-3.1-Nemotron-lorablated-70B
- SicariusSicariiStuff/Negative_LLAMA_70B
- TheDrummer/Anubis-70B-v1
- EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
- deepseek-ai/DeepSeek-R1-Distill-Llama-70B
- Sao10K/L3.3-70B-Euryale-v2.3
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [nbeerbower/Llama-3.1-Nemotron-lorablated-70B](https://huggingface.co/nbeerbower/Llama-3.1-Nemotron-lorablated-70B) as a base.
### Models Merged
The following models were included in the merge:
* [SicariusSicariiStuff/Negative_LLAMA_70B](https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B)
* [TheDrummer/Anubis-70B-v1](https://huggingface.co/TheDrummer/Anubis-70B-v1)
* [EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1](https://huggingface.co/EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1)
* [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B)
* [Sao10K/L3.3-70B-Euryale-v2.3](https://huggingface.co/Sao10K/L3.3-70B-Euryale-v2.3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: nbeerbower/Llama-3.1-Nemotron-lorablated-70B
merge_method: model_stock
dtype: bfloat16
models:
- model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
- model: Sao10K/L3.3-70B-Euryale-v2.3
- model: TheDrummer/Anubis-70B-v1
- model: SicariusSicariiStuff/Negative_LLAMA_70B
- model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
```
|
ClarenceDan/8a6e88dc-323a-4311-981b-7fd919660035
|
ClarenceDan
| 2025-01-23T19:53:27Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-68m",
"base_model:adapter:JackFram/llama-68m",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:52:53Z |
---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8a6e88dc-323a-4311-981b-7fd919660035
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: JackFram/llama-68m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ff3a521d02fa72b2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ff3a521d02fa72b2_train_data.json
type:
field_instruction: context
field_output: question
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: ClarenceDan/8a6e88dc-323a-4311-981b-7fd919660035
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/ff3a521d02fa72b2_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: 29e89a2c-6136-48b6-88bc-a0066652be7d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29e89a2c-6136-48b6-88bc-a0066652be7d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8a6e88dc-323a-4311-981b-7fd919660035
This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0001 | 1 | nan |
| 0.0 | 0.0004 | 3 | nan |
| 0.0 | 0.0009 | 6 | nan |
| 0.0 | 0.0013 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
great0001/17fc62c0-397a-4927-8a81-dc1028325fca
|
great0001
| 2025-01-23T19:52:17Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:princeton-nlp/gemma-2-9b-it-SimPO",
"base_model:adapter:princeton-nlp/gemma-2-9b-it-SimPO",
"license:mit",
"region:us"
] | null | 2025-01-23T19:48:05Z |
---
library_name: peft
license: mit
base_model: princeton-nlp/gemma-2-9b-it-SimPO
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 17fc62c0-397a-4927-8a81-dc1028325fca
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: princeton-nlp/gemma-2-9b-it-SimPO
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- dbe5c72dde5e5bcb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/dbe5c72dde5e5bcb_train_data.json
type:
field_input: essay
field_instruction: prompt
field_output: evaluation
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: great0001/17fc62c0-397a-4927-8a81-dc1028325fca
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/dbe5c72dde5e5bcb_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: 5b6d979e-5f1d-47f4-a5d3-c1026b8550e5
wandb_project: Birthday-SN56-14-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 5b6d979e-5f1d-47f4-a5d3-c1026b8550e5
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 17fc62c0-397a-4927-8a81-dc1028325fca
This model is a fine-tuned version of [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0316
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.039 | 0.0008 | 1 | 4.9481 |
| 4.6553 | 0.0025 | 3 | 4.6472 |
| 2.6675 | 0.0050 | 6 | 1.6021 |
| 1.1148 | 0.0075 | 9 | 1.0316 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Ryllix/X-ALMA-13B-Group1-Q6_K-GGUF
|
Ryllix
| 2025-01-23T19:52:08Z | 17 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"da",
"nl",
"de",
"is",
"no",
"sv",
"af",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:allenai/nllb",
"dataset:Helsinki-NLP/opus-100",
"base_model:haoranxu/X-ALMA-13B-Group1",
"base_model:quantized:haoranxu/X-ALMA-13B-Group1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-23T19:51:24Z |
---
license: mit
datasets:
- oscar-corpus/OSCAR-2301
- allenai/nllb
- Helsinki-NLP/opus-100
language:
- en
- da
- nl
- de
- is
- 'no'
- sv
- af
base_model: haoranxu/X-ALMA-13B-Group1
tags:
- llama-cpp
- gguf-my-repo
---
# Ryllix/X-ALMA-13B-Group1-Q6_K-GGUF
This model was converted to GGUF format from [`haoranxu/X-ALMA-13B-Group1`](https://huggingface.co/haoranxu/X-ALMA-13B-Group1) 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/haoranxu/X-ALMA-13B-Group1) 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 Ryllix/X-ALMA-13B-Group1-Q6_K-GGUF --hf-file x-alma-13b-group1-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q6_K-GGUF --hf-file x-alma-13b-group1-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 Ryllix/X-ALMA-13B-Group1-Q6_K-GGUF --hf-file x-alma-13b-group1-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q6_K-GGUF --hf-file x-alma-13b-group1-q6_k.gguf -c 2048
```
|
adammandic87/60e39f13-bf12-48e1-8ee9-267ce1721d53
|
adammandic87
| 2025-01-23T19:51:51Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Llama-3.2-1B",
"base_model:adapter:NousResearch/Llama-3.2-1B",
"license:llama3.2",
"region:us"
] | null | 2025-01-23T19:47:44Z |
---
library_name: peft
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 60e39f13-bf12-48e1-8ee9-267ce1721d53
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/Llama-3.2-1B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f51beb4c568b9128_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f51beb4c568b9128_train_data.json
type:
field_input: keywords
field_instruction: idea
field_output: full_response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 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: adammandic87/60e39f13-bf12-48e1-8ee9-267ce1721d53
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/f51beb4c568b9128_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: <|end_of_text|>
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: 2cc30bfb-2df3-4b31-b1fb-e29900be6958
wandb_project: birthday-sn56-19-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2cc30bfb-2df3-4b31-b1fb-e29900be6958
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 60e39f13-bf12-48e1-8ee9-267ce1721d53
This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1836
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1452 | 0.0001 | 1 | 0.2465 |
| 0.1581 | 0.0003 | 3 | 0.2455 |
| 0.2059 | 0.0007 | 6 | 0.2288 |
| 0.0942 | 0.0010 | 9 | 0.1836 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
kostiantynk1205/bc277560-5fbb-477f-b72a-cbb8e2c347cf
|
kostiantynk1205
| 2025-01-23T19:50:48Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/GPT4-x-Vicuna-13b-fp16",
"base_model:adapter:NousResearch/GPT4-x-Vicuna-13b-fp16",
"license:gpl",
"region:us"
] | null | 2025-01-23T19:49:15Z |
---
library_name: peft
license: gpl
base_model: NousResearch/GPT4-x-Vicuna-13b-fp16
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bc277560-5fbb-477f-b72a-cbb8e2c347cf
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/GPT4-x-Vicuna-13b-fp16
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- dcf32f9d35bdd1f9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/dcf32f9d35bdd1f9_train_data.json
type:
field_instruction: doc_text
field_output: summary_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: 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/bc277560-5fbb-477f-b72a-cbb8e2c347cf
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/dcf32f9d35bdd1f9_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: 7a4a0d08-b201-4939-999e-8cad606c5cdd
wandb_project: Birthday-SN56-23-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7a4a0d08-b201-4939-999e-8cad606c5cdd
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bc277560-5fbb-477f-b72a-cbb8e2c347cf
This model is a fine-tuned version of [NousResearch/GPT4-x-Vicuna-13b-fp16](https://huggingface.co/NousResearch/GPT4-x-Vicuna-13b-fp16) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6453
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6478 | 0.0038 | 1 | 1.7360 |
| 1.6234 | 0.0115 | 3 | 1.7353 |
| 1.5391 | 0.0230 | 6 | 1.7212 |
| 1.5704 | 0.0344 | 9 | 1.6453 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
daniel40/3b393546-7c3e-4975-b6e6-beb773cb317d
|
daniel40
| 2025-01-23T19:50:15Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M",
"base_model:adapter:unsloth/SmolLM2-360M",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:47:57Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3b393546-7c3e-4975-b6e6-beb773cb317d
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM2-360M
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f6f2b0985d34f3bb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6f2b0985d34f3bb_train_data.json
type:
field_input: response
field_instruction: context
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: daniel40/3b393546-7c3e-4975-b6e6-beb773cb317d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/f6f2b0985d34f3bb_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: b9c4e88d-ca00-4f32-99bc-d66385339bce
wandb_project: Birthday-SN56-28-Gradients-On-Demand
wandb_run: your_name
wandb_runid: b9c4e88d-ca00-4f32-99bc-d66385339bce
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 3b393546-7c3e-4975-b6e6-beb773cb317d
This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0003 | 1 | nan |
| 0.0 | 0.0010 | 3 | nan |
| 0.0 | 0.0021 | 6 | nan |
| 0.0 | 0.0031 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mrHunghddddd/0505b5c6-edde-404e-8131-471bfa32ca32
|
mrHunghddddd
| 2025-01-23T19:49:37Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:princeton-nlp/gemma-2-9b-it-SimPO",
"base_model:adapter:princeton-nlp/gemma-2-9b-it-SimPO",
"license:mit",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T16:04:03Z |
---
library_name: peft
license: mit
base_model: princeton-nlp/gemma-2-9b-it-SimPO
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0505b5c6-edde-404e-8131-471bfa32ca32
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: princeton-nlp/gemma-2-9b-it-SimPO
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 23608247d3e29c5b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/23608247d3e29c5b_train_data.json
type:
field_instruction: text
field_output: label
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: mrHunghddddd/0505b5c6-edde-404e-8131-471bfa32ca32
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/23608247d3e29c5b_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: f0129bfb-c94c-4859-b692-6043485c8836
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f0129bfb-c94c-4859-b692-6043485c8836
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 0505b5c6-edde-404e-8131-471bfa32ca32
This model is a fine-tuned version of [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2989
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.0187 | 0.0054 | 200 | 1.2989 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Ryllix/X-ALMA-13B-Group1-Q5_0-GGUF
|
Ryllix
| 2025-01-23T19:48:33Z | 16 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"da",
"nl",
"de",
"is",
"no",
"sv",
"af",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:allenai/nllb",
"dataset:Helsinki-NLP/opus-100",
"base_model:haoranxu/X-ALMA-13B-Group1",
"base_model:quantized:haoranxu/X-ALMA-13B-Group1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-23T19:47:54Z |
---
license: mit
datasets:
- oscar-corpus/OSCAR-2301
- allenai/nllb
- Helsinki-NLP/opus-100
language:
- en
- da
- nl
- de
- is
- 'no'
- sv
- af
base_model: haoranxu/X-ALMA-13B-Group1
tags:
- llama-cpp
- gguf-my-repo
---
# Ryllix/X-ALMA-13B-Group1-Q5_0-GGUF
This model was converted to GGUF format from [`haoranxu/X-ALMA-13B-Group1`](https://huggingface.co/haoranxu/X-ALMA-13B-Group1) 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/haoranxu/X-ALMA-13B-Group1) 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 Ryllix/X-ALMA-13B-Group1-Q5_0-GGUF --hf-file x-alma-13b-group1-q5_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q5_0-GGUF --hf-file x-alma-13b-group1-q5_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Ryllix/X-ALMA-13B-Group1-Q5_0-GGUF --hf-file x-alma-13b-group1-q5_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q5_0-GGUF --hf-file x-alma-13b-group1-q5_0.gguf -c 2048
```
|
Kort/Cm54
|
Kort
| 2025-01-23T19:48:25Z | 63 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-23T19:46:02Z |
---
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]
|
didiudom94/marian-finetuned-ko-to-en
|
didiudom94
| 2025-01-23T19:48:13Z | 17 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-ko-en",
"base_model:finetune:Helsinki-NLP/opus-mt-ko-en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2025-01-23T19:38:49Z |
---
library_name: transformers
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ko-en
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: marian-finetuned-ko-to-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-ko-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6970
- Model Preparation Time: 0.0035
- Bleu: 49.9193
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
aleegis09/f0d3189e-d9c3-4303-b5e1-3e268b29b8a0
|
aleegis09
| 2025-01-23T19:45:55Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M",
"base_model:adapter:unsloth/SmolLM2-360M",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:33:10Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f0d3189e-d9c3-4303-b5e1-3e268b29b8a0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM2-360M
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- f6f2b0985d34f3bb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6f2b0985d34f3bb_train_data.json
type:
field_input: response
field_instruction: context
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: 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: aleegis09/f0d3189e-d9c3-4303-b5e1-3e268b29b8a0
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/f6f2b0985d34f3bb_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: b9c4e88d-ca00-4f32-99bc-d66385339bce
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b9c4e88d-ca00-4f32-99bc-d66385339bce
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f0d3189e-d9c3-4303-b5e1-3e268b29b8a0
This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1553
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.685 | 0.0014 | 1 | 3.1940 |
| 1.5421 | 0.0687 | 50 | 1.5869 |
| 1.5701 | 0.1374 | 100 | 1.2326 |
| 1.3216 | 0.2061 | 150 | 1.1635 |
| 1.4565 | 0.2748 | 200 | 1.1553 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
aleegis12/66972ef7-b54f-48e3-9bb7-5c1261c0cd59
|
aleegis12
| 2025-01-23T19:45:37Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M",
"base_model:adapter:unsloth/SmolLM2-360M",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:32:54Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 66972ef7-b54f-48e3-9bb7-5c1261c0cd59
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM2-360M
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- f6f2b0985d34f3bb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6f2b0985d34f3bb_train_data.json
type:
field_input: response
field_instruction: context
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: 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: aleegis12/66972ef7-b54f-48e3-9bb7-5c1261c0cd59
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/f6f2b0985d34f3bb_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: b9c4e88d-ca00-4f32-99bc-d66385339bce
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b9c4e88d-ca00-4f32-99bc-d66385339bce
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 66972ef7-b54f-48e3-9bb7-5c1261c0cd59
This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1569
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.685 | 0.0014 | 1 | 3.1940 |
| 1.5405 | 0.0687 | 50 | 1.5880 |
| 1.5702 | 0.1374 | 100 | 1.2335 |
| 1.3212 | 0.2061 | 150 | 1.1653 |
| 1.4588 | 0.2748 | 200 | 1.1569 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Ryllix/X-ALMA-13B-Group1-Q5_K_M-GGUF
|
Ryllix
| 2025-01-23T19:45:34Z | 14 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"da",
"nl",
"de",
"is",
"no",
"sv",
"af",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:allenai/nllb",
"dataset:Helsinki-NLP/opus-100",
"base_model:haoranxu/X-ALMA-13B-Group1",
"base_model:quantized:haoranxu/X-ALMA-13B-Group1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-23T19:44:56Z |
---
license: mit
datasets:
- oscar-corpus/OSCAR-2301
- allenai/nllb
- Helsinki-NLP/opus-100
language:
- en
- da
- nl
- de
- is
- 'no'
- sv
- af
base_model: haoranxu/X-ALMA-13B-Group1
tags:
- llama-cpp
- gguf-my-repo
---
# Ryllix/X-ALMA-13B-Group1-Q5_K_M-GGUF
This model was converted to GGUF format from [`haoranxu/X-ALMA-13B-Group1`](https://huggingface.co/haoranxu/X-ALMA-13B-Group1) 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/haoranxu/X-ALMA-13B-Group1) 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 Ryllix/X-ALMA-13B-Group1-Q5_K_M-GGUF --hf-file x-alma-13b-group1-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q5_K_M-GGUF --hf-file x-alma-13b-group1-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Ryllix/X-ALMA-13B-Group1-Q5_K_M-GGUF --hf-file x-alma-13b-group1-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q5_K_M-GGUF --hf-file x-alma-13b-group1-q5_k_m.gguf -c 2048
```
|
aleegis11/60d55e29-d05a-44f5-8165-e4d721f7ab20
|
aleegis11
| 2025-01-23T19:44:59Z | 11 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M",
"base_model:adapter:unsloth/SmolLM2-360M",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:32:20Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 60d55e29-d05a-44f5-8165-e4d721f7ab20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM2-360M
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- f6f2b0985d34f3bb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6f2b0985d34f3bb_train_data.json
type:
field_input: response
field_instruction: context
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: 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: aleegis11/60d55e29-d05a-44f5-8165-e4d721f7ab20
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/f6f2b0985d34f3bb_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: b9c4e88d-ca00-4f32-99bc-d66385339bce
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b9c4e88d-ca00-4f32-99bc-d66385339bce
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 60d55e29-d05a-44f5-8165-e4d721f7ab20
This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1549
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.685 | 0.0014 | 1 | 3.1940 |
| 1.5456 | 0.0687 | 50 | 1.5839 |
| 1.5722 | 0.1374 | 100 | 1.2313 |
| 1.3203 | 0.2061 | 150 | 1.1632 |
| 1.4565 | 0.2748 | 200 | 1.1549 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
auxyus/d215dd77-b608-4006-8f0c-1d9b017426b6
|
auxyus
| 2025-01-23T19:44:54Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M",
"base_model:adapter:unsloth/SmolLM2-360M",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:32:56Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d215dd77-b608-4006-8f0c-1d9b017426b6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM2-360M
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f6f2b0985d34f3bb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6f2b0985d34f3bb_train_data.json
type:
field_input: response
field_instruction: context
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/d215dd77-b608-4006-8f0c-1d9b017426b6
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/f6f2b0985d34f3bb_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: b9c4e88d-ca00-4f32-99bc-d66385339bce
wandb_project: Gradients-On-Two
wandb_run: your_name
wandb_runid: b9c4e88d-ca00-4f32-99bc-d66385339bce
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# d215dd77-b608-4006-8f0c-1d9b017426b6
This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9535
## 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 | 0.0014 | 1 | 1.0640 |
| 0.9507 | 0.0124 | 9 | 1.0585 |
| 1.0298 | 0.0247 | 18 | 1.0270 |
| 1.0244 | 0.0371 | 27 | 1.0054 |
| 1.0722 | 0.0495 | 36 | 0.9874 |
| 0.9782 | 0.0618 | 45 | 0.9744 |
| 1.0092 | 0.0742 | 54 | 0.9660 |
| 1.056 | 0.0866 | 63 | 0.9602 |
| 1.0347 | 0.0989 | 72 | 0.9565 |
| 0.9774 | 0.1113 | 81 | 0.9546 |
| 0.9623 | 0.1237 | 90 | 0.9536 |
| 0.9283 | 0.1360 | 99 | 0.9535 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
nathanialhunt/9c2cf814-87cd-4e33-ba1a-d0429f71bd29
|
nathanialhunt
| 2025-01-23T19:40:52Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-68m",
"base_model:adapter:JackFram/llama-68m",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:40:13Z |
---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9c2cf814-87cd-4e33-ba1a-d0429f71bd29
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: JackFram/llama-68m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ff3a521d02fa72b2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ff3a521d02fa72b2_train_data.json
type:
field_instruction: context
field_output: question
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: nathanialhunt/9c2cf814-87cd-4e33-ba1a-d0429f71bd29
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/ff3a521d02fa72b2_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: 29e89a2c-6136-48b6-88bc-a0066652be7d
wandb_project: Birthday-SN56-24-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29e89a2c-6136-48b6-88bc-a0066652be7d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 9c2cf814-87cd-4e33-ba1a-d0429f71bd29
This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0001 | 1 | nan |
| 0.0 | 0.0004 | 3 | nan |
| 0.0 | 0.0009 | 6 | nan |
| 0.0 | 0.0013 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Ryllix/X-ALMA-13B-Group1-Q5_K_S-GGUF
|
Ryllix
| 2025-01-23T19:40:51Z | 14 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"da",
"nl",
"de",
"is",
"no",
"sv",
"af",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:allenai/nllb",
"dataset:Helsinki-NLP/opus-100",
"base_model:haoranxu/X-ALMA-13B-Group1",
"base_model:quantized:haoranxu/X-ALMA-13B-Group1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-23T19:40:14Z |
---
license: mit
datasets:
- oscar-corpus/OSCAR-2301
- allenai/nllb
- Helsinki-NLP/opus-100
language:
- en
- da
- nl
- de
- is
- 'no'
- sv
- af
base_model: haoranxu/X-ALMA-13B-Group1
tags:
- llama-cpp
- gguf-my-repo
---
# Ryllix/X-ALMA-13B-Group1-Q5_K_S-GGUF
This model was converted to GGUF format from [`haoranxu/X-ALMA-13B-Group1`](https://huggingface.co/haoranxu/X-ALMA-13B-Group1) 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/haoranxu/X-ALMA-13B-Group1) 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 Ryllix/X-ALMA-13B-Group1-Q5_K_S-GGUF --hf-file x-alma-13b-group1-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q5_K_S-GGUF --hf-file x-alma-13b-group1-q5_k_s.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 Ryllix/X-ALMA-13B-Group1-Q5_K_S-GGUF --hf-file x-alma-13b-group1-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q5_K_S-GGUF --hf-file x-alma-13b-group1-q5_k_s.gguf -c 2048
```
|
great0001/5343e7c0-8c61-4ede-8525-dce3b3e4b08e
|
great0001
| 2025-01-23T19:40:01Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-7B",
"base_model:adapter:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:38:24Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5343e7c0-8c61-4ede-8525-dce3b3e4b08e
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/Qwen2.5-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 699dbe0484a7b6fd_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/699dbe0484a7b6fd_train_data.json
type:
field_input: Definition1
field_instruction: Entity
field_output: Definition2
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: great0001/5343e7c0-8c61-4ede-8525-dce3b3e4b08e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/699dbe0484a7b6fd_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: c1c32915-ac79-4d23-9a89-ef0af747d830
wandb_project: Birthday-SN56-14-Gradients-On-Demand
wandb_run: your_name
wandb_runid: c1c32915-ac79-4d23-9a89-ef0af747d830
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 5343e7c0-8c61-4ede-8525-dce3b3e4b08e
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0922
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.313 | 0.0008 | 1 | 3.3060 |
| 3.4038 | 0.0025 | 3 | 3.3049 |
| 2.9396 | 0.0051 | 6 | 3.2757 |
| 3.165 | 0.0076 | 9 | 3.0922 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
nathanialhunt/ddeab680-b9d0-4af7-887f-3b255930bbfe
|
nathanialhunt
| 2025-01-23T19:39:45Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored",
"base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored",
"license:llama3",
"region:us"
] | null | 2025-01-23T18:27:44Z |
---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ddeab680-b9d0-4af7-887f-3b255930bbfe
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: Orenguteng/Llama-3-8B-Lexi-Uncensored
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4d136d7cbe9663e9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4d136d7cbe9663e9_train_data.json
type:
field_input: schema
field_instruction: question
field_output: query
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/ddeab680-b9d0-4af7-887f-3b255930bbfe
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/4d136d7cbe9663e9_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: 85d50804-d37f-45a5-a2e8-db14010413a1
wandb_project: Birthday-SN56-24-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 85d50804-d37f-45a5-a2e8-db14010413a1
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# ddeab680-b9d0-4af7-887f-3b255930bbfe
This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6407
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1301 | 0.0000 | 1 | 1.2362 |
| 0.97 | 0.0000 | 3 | 1.2270 |
| 0.9608 | 0.0001 | 6 | 1.0245 |
| 0.6249 | 0.0001 | 9 | 0.6407 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Valdemardi/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview-AWQ
|
Valdemardi
| 2025-01-23T19:39:32Z | 828 | 3 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2408.07990",
"arxiv:2401.10491",
"arxiv:2412.03187",
"base_model:FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview",
"base_model:quantized:FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2025-01-23T19:39:32Z |
---
license: apache-2.0
library_name: transformers
base_model:
- FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview
base_model_relation: quantized
---
## Quantization Details
This quantized model was created using AutoAWQ version 0.2.8 with `quant_config`:
```
{
"zero_point": True,
"q_group_size": 128,
"w_bit": 4,
"version": "GEMM"
}
```
<p align="center" width="100%">
</p>
<div id="top" align="center">
FuseO1-Preview: System-II Reasoning Fusion of LLMs
-----------------------------
<h4> |<a href="https://arxiv.org/abs/2408.07990"> 📑 Paper </a> |
<a href="https://github.com/fanqiwan/FuseAI"> 🐱 GitHub Repo </a> |
<a href="https://huggingface.co/FuseAI"> 🤗 Hugging Face </a> |
<a href="https://huggingface.co/blog/Wanfq/fuseo1-preview"> 🌐 Blog </a> |
</h4>
<!-- **Authors:** -->
_Fanqi Wan, Longguang Zhong, Ziyi Yang, Weizhou Shen, Xinting Huang_
<!-- **Affiliations:** -->
_FuseAI Team_
</div>
<p align="center">
<img src="./assets/fuseo1-preview.jpg" width="100%"> <br>
</p>
## Overview
[FuseO1-Preview](https://huggingface.co/collections/FuseAI/fuseo1-preview-678eb56093649b2688bc9977) is our initial endeavor to enhance the System-II reasoning capabilities of large language models (LLMs) through innovative model fusion techniques. By employing our advanced [SCE](https://arxiv.org/abs/2408.07990) merging methodologies, we integrate multiple open-source o1-like LLMs into a unified model. Our goal is to incorporate the distinct knowledge and strengths from different reasoning LLMs into a single, unified model with strong System-II reasoning abilities, particularly in mathematics, coding, and science domains.
<p align="center">
<img src="./assets/sce.jpg" width="70%"> <br>
</p>
To achieve this, we conduct two types of model merging:
- **Long-Long Reasoning Merging**: This approach involves model fusion across LLMs that utilize long-CoT reasoning, with the goal of enhancing long-CoT reasoning capabilities. The resulted [FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview) achieves a Pass@1 accuracy of **74.0 on AIME24**, demonstrating significant performance improvements compared to the OpenAI o1-preview (44.6) and OpenAI o1-mini (63.4), even approaching OpenAI o1 (79.2).
- **Long-Short Reasoning Merging**: This approach involves model fusion between long-CoT and short-CoT LLMs, aiming to improve reasoning capabilities in both long and short reasoning processes. The resulted [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview) is capable of utilizing both long and short reasoning processes and demonstrates relatively strong performance in long reasoning tasks.
| Model | Merge Type | Source Models | HF Link |
|:----- | ---- | ---- | ---- |
| [FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview) | Long-Long Reasoning Merge | [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B), [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview), [NovaSky-AI/Sky-T1-32B-Preview](https://huggingface.co/NovaSky-AI/Sky-T1-32B-Preview) | [🤗 Hugging Face](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview), [GGUF](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview-GGUF) |
| [FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview) | Long-Long Reasoning Merge | [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B), [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) | [🤗 Hugging Face](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview) |
| [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview) | Long-Short Reasoning Merge | [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B), [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | [🤗 Hugging Face](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview) |
| [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview) | Long-Short Reasoning Merge | [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B), [Qwen/Qwen2.5-32B-Coder](https://huggingface.co/Qwen/Qwen2.5-32B-Coder) | [🤗 Hugging Face](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview) |
## Long-Long Reasoning Merging
We conduct experiments on these folloing long-cot LLMs.
- [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)
- [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview)
- [NovaSky-AI/Sky-T1-32B-Preview](https://huggingface.co/NovaSky-AI/Sky-T1-32B-Preview)
To reproduce the merged [FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview) model, using the script below.
```sh
cd FuseAI/FuseO1-Preview/mergekit
pip3 install -e .
model_save_dir=xx # your path to save the merged models
mergekit-yaml fuseo1_configs/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview --cudas
```
To reproduce the merged [FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview) model, using the script below.
```sh
cd FuseAI/FuseO1-Preview/mergekit
pip3 install -e .
model_save_dir=xxx # your path to save the merged models
mergekit-yaml fuseo1_configs/FuseO1-DeepSeekR1-QwQ-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-QwQ-32B-Preview --cuda
```
We provide the example code to use FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview.
```python3
from vllm import LLM, SamplingParams
llm = LLM(model="FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview", tensor_parallel_size=8)
sampling_params = SamplingParams(max_tokens=32768, temperature=0.7, stop=["<|im_end|>", "<｜end▁of▁sentence｜>"], stop_token_ids=[151645, 151643])
conversations = [
[
{"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{{}}."},
{"role": "user", "content": "Quadratic polynomials $P(x)$ and $Q(x)$ have leading coefficients $2$ and $-2,$ respectively. The graphs of both polynomials pass through the two points $(16,54)$ and $(20,53).$ Find $P(0) + Q(0).$."},
],
]
responses = llm.chat(messages=conversations, sampling_params=sampling_params, use_tqdm=True)
for response in responses:
print(response.outputs[0].text.strip())
```
## Long-Short Reasoning Merging
We conduct experiments on these folloing long-cot and short-cot LLMs.
- [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)
- [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)
- [Qwen/Qwen2.5-32B-Coder](https://huggingface.co/Qwen/Qwen2.5-32B-Coder)
To reproduce the merged [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview) model, using the script below.
```sh
cd FuseAI/FuseO1-Preview/mergekit
pip3 install -e .
model_save_dir=xxx # your path to save the merged models
mergekit-yaml fuseo1_configs/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview --cuda
```
To reproduce the merged [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview) model, using the script below.
```sh
cd FuseAI/FuseO1-Preview/mergekit
pip3 install -e .
model_save_dir=xxx # your path to save the merged models
mergekit-yaml fuseo1_configs/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview --cuda
```
We provide the code to use FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview.
```python3
from vllm import LLM, SamplingParams
llm = LLM(model="FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview", tensor_parallel_size=8)
sampling_params = SamplingParams(max_tokens=32768, temperature=0.7, stop=["<|im_end|>", "<｜end▁of▁sentence｜>"], stop_token_ids=[151645, 151643])
conversations = [
[
{"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{{}}."},
{"role": "user", "content": "Quadratic polynomials $P(x)$ and $Q(x)$ have leading coefficients $2$ and $-2,$ respectively. The graphs of both polynomials pass through the two points $(16,54)$ and $(20,53).$ Find $P(0) + Q(0).$."},
],
]
responses = llm.chat(messages=conversations, sampling_params=sampling_params, use_tqdm=True)
for response in responses:
print(response.outputs[0].text.strip())
```
## Evaluation Results
We test the resulted models on three kinds of benchmarks, including **Math Reasoning**, **Code Reasoning** , and **Scientific Reasoning**.
Math Reasoning
- AIME24
- MATH500
- OlympiadBench
Scientific Reasoning
- GPQA-Diamond
- MMLU-Pro
- MMLU
Code Reasoning
- LiveCodeBench (2408-2502)
> Important Note: We manully set `"add_bos_token": false` in `tokenizer_config.json` for all the evaluated LLMs to prevent the bos_token to be added twice for each prompt. Please download and modify to ensure consistency.
### Math Reasoning
The evaluation code is modified from [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math). In our evaluation, we set the temperature to 0.6, the top-p to 0.95 and the max_tokens to 32768. We provide the example to reproduce our results in [math_evaluation](https://github.com/fanqiwan/FuseAI/tree/main/FuseO1-Preview/math_evaluation).
The system prompt for evaluation is set to:
```sh
Please reason step by step, and put your final answer within \\boxed{{}}.
```
The evaluation results are shown in the table below:
In our evaluation of AIME24, we follow the method from DeepSeek-R1, wherein Pass@1 is computed by averaging the results across 32 sampled responses per prompt, while Cons@32 is determined through self-consistency analysis of the same 32 sampled responses for each prompt. For other benchmarks, we only sample 1 response and report the Pass@1.
| Models | AIME24 Pass@1 | AIME24 Cons@32 | MATH500 | OlympiadBench |
|:------ | --------------| ------------------- | ------------ | -------------- |
| OpenAI o1 | 79.2 | - | 96.4 | - |
| OpenAI o1-preview | 44.6 | - | 85.5 | - |
| OpenAI o1-mini | 63.6 | - | 90.0 | - |
| DeepSeek R1 | 79.8 | - | 97.3 | - |
| [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) | 69.2 | 83.3 | 93.6 | 64.3 |
| [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) | 43.8 | 56.7 | 88.4 | 60.3 |
| [NovaSky-AI/Sky-T1-32B-Preview](https://huggingface.co/NovaSky-AI/Sky-T1-32B-Preview) | 37.7 | 50.0 | 88.0 | 55.1 |
| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | 17.0 | 20.0 | 81.8 | 48.1 |
| [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview) | 68.6 | 83.3 | 94.6 | 64.9 |
| [FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview) | 69.7 | 83.3 | 94.6 | 64.0 |
| [FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview) | 74.0 | 86.7 | 94.8 | 65.0 |
We show that our merged FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview demonstrate superior performance improvements comparet to DeepSeek-R1-Distill-Qwen-32B, QwQ-32B-Preview, and Sky-T1-32B-Preview on math reasoning. Specifically, our model achieves an accuracy of **74.0 Pass@1 and 86.7 Cons@32 on AIME24**, demonstrating significant performance improvements compared to DeepSeek-R1-Distill-Qwen-32B (69.2 Pass@1 and 83.3 Cons@32), OpenAI o1-preview (44.6 Pass@1) and OpenAI o1-mini (63.4 Pass@1), even approaching OpenAI o1 (79.2 Pass@1).
### Scientific Reasoning
The evaluation code is modified from [SkyThought](https://github.com/NovaSky-AI/SkyThought). In our evaluation, we set the temperature to 0.7 and the max_tokens to 32768. We provide the example to reproduce our results in [evaluation](https://github.com/fanqiwan/FuseAI/tree/main/FuseO1-Preview/evaluation).
The system prompt for evaluation is set to:
```sh
You are a helpful and harmless assistant. You should think step-by-step.
```
The evaluation results are shown in the table below:
| Models | GPQA-Diamond| MMLU-Pro | MMLU |
|:------ | --------------| ------------ | -------------- |
| OpenAI o1 | 75.7 | - | 91.8 |
| OpenAI o1-preview | 73.3 | - | 90.8 |
| OpenAI o1-mini | 60.0 | 80.3 | 85.2 |
| DeepSeek R1 | 71.5 | 84.0 | 90.8 |
| [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) | 57.6 | 68.7 | 82.2 |
| [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) | 49.5 | 63.5 | 85.2 |
| [NovaSky-AI/Sky-T1-32B-Preview](https://huggingface.co/NovaSky-AI/Sky-T1-32B-Preview) | 50.5 | 65.8 | 82.7 |
| [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) | 46.5 | 56.3 | 79.6 |
| [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Instruct-32B-Preview) | 55.1 | 68.6 | 82.0 |
| [FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview) | 62.1 | 68.9 | 82.7 |
| [FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview) | 62.1 | 70.8 | 83.6 |
We show that our merged FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview demonstrate superior performance improvements comparet to DeepSeek-R1-Distill-Qwen-32B, QwQ-32B-Preview, and Sky-T1-32B-Preview on scientific reasoning. Specifically, our model achieves an accuracy of **62.1 on GPQA-Diamond and 70.8 on MMLU-Pro**, demonstrating significant performance improvements compared to DeepSeek-R1-Distill-Qwen-32B (57.6 on GPQA-Diamond and 68.7 on MMLU-Pro).
## Code Reasoning
The evaluation code is modified from [Qwen2.5-Coder](https://github.com/QwenLM/Qwen2.5-Coder/tree/main/qwencoder-eval/reasoning/livecode_bench_cot). In our evaluation, we set the temperature to 0.6, the top-p to 0.95 and the max_tokens to 32768. We provide the example to reproduce our results in [code_evaluation](https://github.com/fanqiwan/FuseAI/tree/main/FuseO1-Preview/code_evaluation).
The system prompt for evaluation is set to:
```sh
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.
```
In our evaluation of LiveCodeBench, we follow the method from DeepSeek-R1 and make a slight modification. The Pass@1 is computed by averaging the results across 16 sampled responses per prompt.
The evaluation results are shown in the table below:
| Models | LiveCodeBench | LiveCodeBench-Easy | LiveCodeBench-Medium | LiveCodeBench-Hard |
|:------ | --------------| ------------------- | ------------ | -------------- |
| OpenAI o1 | 63.4 | 98.5 | 80.9 | 31.7 |
| OpenAI o1-preview | 42.7 | 97.0 | 47.2 | 9.8 |
| OpenAI o1-mini | 52.00 | 91.0 | 67.4 | 19.5 |
| DeepSeek R1 | 62.8 | 98.4 | 78.3 | 32.2 |
| [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) | 56.1 | 93.6 | 73.1 | 23.4 |
| [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) | 44.4 | 94.9 | 53.8 | 10.0 |
| [FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview) | 57.9 | 93.6 | 76.0 | 25.5 |
We show that our merged FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview demonstrate superior performance improvements comparet to DeepSeek-R1-Distill-Qwen-32B, QwQ-32B-Preview, and Sky-T1-32B-Preview on scientific reasoning. Specifically, our model achieves an accuracy of **57.9 on LiveCodeBench and 25.5 on LiveCodeBench-Hard**, demonstrating significant performance improvements compared to DeepSeek-R1-Distill-Qwen-32B (56.1 on LiveCodeBench and 23.4 on LiveCodeBench-Hard), OpenAI o1-preview (42.7 on LiveCodeBench and 9.8 on LiveCodeBench-Hard) and OpenAI o1-mini (52.0 on LiveCodeBench and 19.5 on LiveCodeBench-Hard Pass@1).
## Future Works
This work is our first attempt effort to achieve knowledge fusion of System-II reasoning LLMs through a model merging approach, which is limited to LLMs with identical scale and architecture. In future work, we plan to employ our [explicit model fusion](https://arxiv.org/abs/2401.10491) method, based on multi-teacher knowledge distillation, and our [implici model fusion](https://arxiv.org/abs/2412.03187) method, which utilizes weighted-reward preference optimization for LLMs with different scales and architectures.
Furthermore, we intend to explore the combination of knowledge fusion with reinforcement learning (RL) methods, which have been demonstrated as the most effective approach for enhancing reasoning abilities. Stay tuned for the next version of FuseO1!
## Citations
```
@article{wan2024fusechat,
title={Fusechat: Knowledge fusion of chat models},
author={Wan, Fanqi and Zhong, Longguang and Yang, Ziyi and Chen, Ruijun and Quan, Xiaojun},
journal={arXiv preprint arXiv:2408.07990},
year={2024}
}
```
|
Aardiiiiy/ProKontra4
|
Aardiiiiy
| 2025-01-23T19:38:26Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-01-23T19:38:06Z |
---
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]
|
vmpsergio/1475b3d8-28c4-4471-a58a-f98d3ae3d290
|
vmpsergio
| 2025-01-23T19:37:07Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-360M",
"base_model:adapter:unsloth/SmolLM2-360M",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:32:20Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-360M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1475b3d8-28c4-4471-a58a-f98d3ae3d290
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM2-360M
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f6f2b0985d34f3bb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f6f2b0985d34f3bb_train_data.json
type:
field_input: response
field_instruction: context
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: vmpsergio/1475b3d8-28c4-4471-a58a-f98d3ae3d290
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: 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_memory:
0: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/f6f2b0985d34f3bb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b9c4e88d-ca00-4f32-99bc-d66385339bce
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b9c4e88d-ca00-4f32-99bc-d66385339bce
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 1475b3d8-28c4-4471-a58a-f98d3ae3d290
This model is a fine-tuned version of [unsloth/SmolLM2-360M](https://huggingface.co/unsloth/SmolLM2-360M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 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_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0003 | 1 | nan |
| 0.0 | 0.0017 | 5 | nan |
| 0.0 | 0.0034 | 10 | nan |
| 0.0 | 0.0052 | 15 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
impossibleexchange/0x110
|
impossibleexchange
| 2025-01-23T19:35:34Z | 24 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-23T18:33:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Ryllix/X-ALMA-13B-Group1-Q8_0-GGUF
|
Ryllix
| 2025-01-23T19:35:23Z | 83 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"da",
"nl",
"de",
"is",
"no",
"sv",
"af",
"dataset:oscar-corpus/OSCAR-2301",
"dataset:allenai/nllb",
"dataset:Helsinki-NLP/opus-100",
"base_model:haoranxu/X-ALMA-13B-Group1",
"base_model:quantized:haoranxu/X-ALMA-13B-Group1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-23T19:34:27Z |
---
license: mit
datasets:
- oscar-corpus/OSCAR-2301
- allenai/nllb
- Helsinki-NLP/opus-100
language:
- en
- da
- nl
- de
- is
- 'no'
- sv
- af
base_model: haoranxu/X-ALMA-13B-Group1
tags:
- llama-cpp
- gguf-my-repo
---
# Ryllix/X-ALMA-13B-Group1-Q8_0-GGUF
This model was converted to GGUF format from [`haoranxu/X-ALMA-13B-Group1`](https://huggingface.co/haoranxu/X-ALMA-13B-Group1) 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/haoranxu/X-ALMA-13B-Group1) 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 Ryllix/X-ALMA-13B-Group1-Q8_0-GGUF --hf-file x-alma-13b-group1-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q8_0-GGUF --hf-file x-alma-13b-group1-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Ryllix/X-ALMA-13B-Group1-Q8_0-GGUF --hf-file x-alma-13b-group1-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Ryllix/X-ALMA-13B-Group1-Q8_0-GGUF --hf-file x-alma-13b-group1-q8_0.gguf -c 2048
```
|
denbeo/be2e869e-9f6a-4d5d-8389-e4c352fb779d
|
denbeo
| 2025-01-23T19:32:37Z | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:52:27Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: be2e869e-9f6a-4d5d-8389-e4c352fb779d
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:
- 4b47e3ddd7129f5f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b47e3ddd7129f5f_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: 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: denbeo/be2e869e-9f6a-4d5d-8389-e4c352fb779d
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/4b47e3ddd7129f5f_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: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# be2e869e-9f6a-4d5d-8389-e4c352fb779d
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: 0.1097
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.1228 | 0.0722 | 200 | 0.1097 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
great0001/26391edb-d95c-4929-91cc-a4e331f91f4e
|
great0001
| 2025-01-23T19:32:02Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Coder-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T18:54:15Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 26391edb-d95c-4929-91cc-a4e331f91f4e
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4d85b564dafa38db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4d85b564dafa38db_train_data.json
type:
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: great0001/26391edb-d95c-4929-91cc-a4e331f91f4e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/4d85b564dafa38db_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: 879db250-c3f5-4d43-a7c5-c5a456ae5803
wandb_project: Mine-SN56-20-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 879db250-c3f5-4d43-a7c5-c5a456ae5803
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 26391edb-d95c-4929-91cc-a4e331f91f4e
This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0000 | 1 | nan |
| 0.0 | 0.0001 | 3 | nan |
| 0.0 | 0.0001 | 6 | nan |
| 0.0 | 0.0002 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
robiual-awal/6260d9b5-6899-41b5-95f8-8e9b93349b01
|
robiual-awal
| 2025-01-23T19:31:49Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-13b-hf-flash",
"base_model:adapter:NousResearch/CodeLlama-13b-hf-flash",
"region:us"
] | null | 2025-01-23T19:04:07Z |
---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6260d9b5-6899-41b5-95f8-8e9b93349b01
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-13b-hf-flash
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 396d0a56bbf80cca_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/396d0a56bbf80cca_train_data.json
type:
field_instruction: speaker_id
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: 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/6260d9b5-6899-41b5-95f8-8e9b93349b01
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/396d0a56bbf80cca_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: 16671b72-614a-460f-819d-7364bcc07c46
wandb_project: Birthday-SN56-30-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 16671b72-614a-460f-819d-7364bcc07c46
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 6260d9b5-6899-41b5-95f8-8e9b93349b01
This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf-flash](https://huggingface.co/NousResearch/CodeLlama-13b-hf-flash) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4951
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 17.6344 | 0.0001 | 1 | 4.1093 |
| 14.4857 | 0.0002 | 3 | 4.1031 |
| 14.8935 | 0.0003 | 6 | 4.0061 |
| 14.1431 | 0.0005 | 9 | 3.4951 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
prxy5607/ca7ad65c-4590-43ec-a03b-541f215efd99
|
prxy5607
| 2025-01-23T19:31:01Z | 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 | 2025-01-23T18:51:54Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ca7ad65c-4590-43ec-a03b-541f215efd99
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: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 4b47e3ddd7129f5f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b47e3ddd7129f5f_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: 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: prxy5607/ca7ad65c-4590-43ec-a03b-541f215efd99
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/4b47e3ddd7129f5f_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: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# ca7ad65c-4590-43ec-a03b-541f215efd99
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: 0.0584
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.4107 | 0.0014 | 1 | 0.6999 |
| 0.0766 | 0.0722 | 50 | 0.1107 |
| 0.0337 | 0.1444 | 100 | 0.0765 |
| 0.0068 | 0.2165 | 150 | 0.0628 |
| 0.023 | 0.2887 | 200 | 0.0584 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
prxy5605/bf3d20eb-162c-4cc4-acf1-811943321ae4
|
prxy5605
| 2025-01-23T19:30:41Z | 8 | 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 | 2025-01-23T18:51:33Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bf3d20eb-162c-4cc4-acf1-811943321ae4
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: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 4b47e3ddd7129f5f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b47e3ddd7129f5f_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: 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: prxy5605/bf3d20eb-162c-4cc4-acf1-811943321ae4
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/4b47e3ddd7129f5f_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: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bf3d20eb-162c-4cc4-acf1-811943321ae4
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: 0.0584
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.4107 | 0.0014 | 1 | 0.6999 |
| 0.0753 | 0.0722 | 50 | 0.1093 |
| 0.0327 | 0.1444 | 100 | 0.0770 |
| 0.0063 | 0.2165 | 150 | 0.0631 |
| 0.0225 | 0.2887 | 200 | 0.0584 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
0x1202/7c20263b-0b41-450f-835f-de4f32a916de
|
0x1202
| 2025-01-23T19:30:38Z | 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 | 2025-01-23T18:51:12Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7c20263b-0b41-450f-835f-de4f32a916de
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: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 4b47e3ddd7129f5f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b47e3ddd7129f5f_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: 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: 0x1202/7c20263b-0b41-450f-835f-de4f32a916de
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/4b47e3ddd7129f5f_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: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 7c20263b-0b41-450f-835f-de4f32a916de
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: 0.0586
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.4107 | 0.0014 | 1 | 0.6999 |
| 0.0766 | 0.0722 | 50 | 0.1108 |
| 0.0334 | 0.1444 | 100 | 0.0767 |
| 0.0056 | 0.2165 | 150 | 0.0632 |
| 0.0233 | 0.2887 | 200 | 0.0586 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
ClarenceDan/1b465f23-a82d-42f9-b3f8-d7698d71d99c
|
ClarenceDan
| 2025-01-23T19:30:29Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"base_model:Xenova/tiny-random-Phi3ForCausalLM",
"base_model:adapter:Xenova/tiny-random-Phi3ForCausalLM",
"region:us"
] | null | 2025-01-23T19:30:13Z |
---
library_name: peft
base_model: Xenova/tiny-random-Phi3ForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1b465f23-a82d-42f9-b3f8-d7698d71d99c
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: Xenova/tiny-random-Phi3ForCausalLM
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ac52733544e3c235_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ac52733544e3c235_train_data.json
type:
field_instruction: problem
field_output: solution
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: ClarenceDan/1b465f23-a82d-42f9-b3f8-d7698d71d99c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/ac52733544e3c235_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: d02e1ae5-13e6-4bae-95ee-6a355e82ebd5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d02e1ae5-13e6-4bae-95ee-6a355e82ebd5
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 1b465f23-a82d-42f9-b3f8-d7698d71d99c
This model is a fine-tuned version of [Xenova/tiny-random-Phi3ForCausalLM](https://huggingface.co/Xenova/tiny-random-Phi3ForCausalLM) 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0007 | 1 | nan |
| 0.0 | 0.0021 | 3 | nan |
| 0.0 | 0.0041 | 6 | nan |
| 0.0 | 0.0062 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF
|
mradermacher
| 2025-01-23T19:29:09Z | 592 | 0 |
transformers
|
[
"transformers",
"gguf",
"chocolatine",
"phi4",
"fr",
"en",
"dataset:jpacifico/french-orca-dpo-pairs-revised",
"base_model:jpacifico/Chocolatine-14B-Instruct-DPO-v1.3",
"base_model:quantized:jpacifico/Chocolatine-14B-Instruct-DPO-v1.3",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-01-19T14:17:14Z |
---
base_model: jpacifico/Chocolatine-14B-Instruct-DPO-v1.3
datasets:
- jpacifico/french-orca-dpo-pairs-revised
language:
- fr
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- chocolatine
- phi4
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/jpacifico/Chocolatine-14B-Instruct-DPO-v1.3
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ1_S.gguf) | i1-IQ1_S | 3.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ1_M.gguf) | i1-IQ1_M | 3.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ2_S.gguf) | i1-IQ2_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ2_M.gguf) | i1-IQ2_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.3 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q2_K.gguf) | i1-Q2_K | 5.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ3_S.gguf) | i1-IQ3_S | 6.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q4_0.gguf) | i1-Q4_0 | 8.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q4_1.gguf) | i1-Q4_1 | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.7 | |
| [GGUF](https://huggingface.co/mradermacher/Chocolatine-14B-Instruct-DPO-v1.3-i1-GGUF/resolve/main/Chocolatine-14B-Instruct-DPO-v1.3.i1-Q6_K.gguf) | i1-Q6_K | 12.1 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Kort/Cm52
|
Kort
| 2025-01-23T19:28:41Z | 150 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-23T19:26:14Z |
---
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]
|
peteparker456/speecht5_finetuned_voxpopuli_eng
|
peteparker456
| 2025-01-23T19:26:43Z | 15 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2025-01-23T17:05:04Z |
---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_voxpopuli_eng
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_voxpopuli_eng
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4719
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:--------:|:----:|:---------------:|
| 3.1386 | 111.1111 | 1000 | 0.4425 |
| 2.9384 | 222.2222 | 2000 | 0.4609 |
| 2.869 | 333.3333 | 3000 | 0.4639 |
| 2.8554 | 444.4444 | 4000 | 0.4719 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
maksf8486/91d617d4-aea1-4a8f-98e1-467663d0cd34
|
maksf8486
| 2025-01-23T19:25:45Z | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:53:51Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 91d617d4-aea1-4a8f-98e1-467663d0cd34
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:
- 4b47e3ddd7129f5f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b47e3ddd7129f5f_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: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: maksf8486/91d617d4-aea1-4a8f-98e1-467663d0cd34
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
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_memory:
0: 79GiB
max_steps: 30
micro_batch_size: 4
mlflow_experiment_name: /tmp/4b47e3ddd7129f5f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
warmup_steps: 5
weight_decay: 0.001
xformers_attention: true
```
</details><br>
# 91d617d4-aea1-4a8f-98e1-467663d0cd34
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: 0.1587
## 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: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0007 | 1 | 1.1445 |
| 1.0672 | 0.0036 | 5 | 0.9260 |
| 0.6894 | 0.0072 | 10 | 0.3579 |
| 0.2565 | 0.0108 | 15 | 0.1895 |
| 0.2088 | 0.0144 | 20 | 0.1653 |
| 0.1654 | 0.0180 | 25 | 0.1596 |
| 0.135 | 0.0217 | 30 | 0.1587 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
aleegis11/099af079-c51c-4d16-8507-c480cace1b37
|
aleegis11
| 2025-01-23T19:24:51Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T18:59:11Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 099af079-c51c-4d16-8507-c480cace1b37
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-0.5B-Instruct
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 5079a290b9b62e47_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5079a290b9b62e47_train_data.json
type:
field_instruction: seq
field_output: labels_str
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: aleegis11/099af079-c51c-4d16-8507-c480cace1b37
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/5079a290b9b62e47_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: bade6ec8-5860-4f3a-bd1f-e988637c6abe
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: bade6ec8-5860-4f3a-bd1f-e988637c6abe
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 099af079-c51c-4d16-8507-c480cace1b37
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2235
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.3086 | 0.0001 | 1 | 2.6961 |
| 0.2362 | 0.0064 | 50 | 0.2680 |
| 0.2281 | 0.0129 | 100 | 0.2428 |
| 0.2053 | 0.0193 | 150 | 0.2315 |
| 0.2188 | 0.0258 | 200 | 0.2235 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
kokovova/dcacebda-84c9-4978-9bce-ad4b83925464
|
kokovova
| 2025-01-23T19:24:02Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:11:34Z |
---
library_name: peft
license: apache-2.0
base_model: beomi/polyglot-ko-12.8b-safetensors
tags:
- axolotl
- generated_from_trainer
model-index:
- name: dcacebda-84c9-4978-9bce-ad4b83925464
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: beomi/polyglot-ko-12.8b-safetensors
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- bcae25b53977cd4d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bcae25b53977cd4d_train_data.json
type:
field_input: ''
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: kokovova/dcacebda-84c9-4978-9bce-ad4b83925464
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: 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_memory:
0: 79GiB
max_steps: 30
micro_batch_size: 4
mlflow_experiment_name: /tmp/bcae25b53977cd4d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3f38e4d4-e240-49ce-9bd9-52780a32f40b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3f38e4d4-e240-49ce-9bd9-52780a32f40b
warmup_steps: 5
weight_decay: 0.001
xformers_attention: true
```
</details><br>
# dcacebda-84c9-4978-9bce-ad4b83925464
This model is a fine-tuned version of [beomi/polyglot-ko-12.8b-safetensors](https://huggingface.co/beomi/polyglot-ko-12.8b-safetensors) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6849
## 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: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0020 | 1 | 1.4784 |
| 5.5754 | 0.0100 | 5 | 1.2941 |
| 4.4426 | 0.0200 | 10 | 0.9464 |
| 3.2237 | 0.0301 | 15 | 0.7740 |
| 3.0179 | 0.0401 | 20 | 0.7173 |
| 2.8401 | 0.0501 | 25 | 0.6905 |
| 2.7979 | 0.0601 | 30 | 0.6849 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
tuantmdev/ea22c387-bf35-4619-8cd1-7dd4ccd5ab94
|
tuantmdev
| 2025-01-23T19:23:59Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"mixtral",
"axolotl",
"generated_from_trainer",
"base_model:TitanML/tiny-mixtral",
"base_model:adapter:TitanML/tiny-mixtral",
"region:us"
] | null | 2025-01-23T19:22:24Z |
---
library_name: peft
base_model: TitanML/tiny-mixtral
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ea22c387-bf35-4619-8cd1-7dd4ccd5ab94
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: TitanML/tiny-mixtral
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- abca2ac57e742739_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/abca2ac57e742739_train_data.json
type:
field_instruction: full_prompt
field_output: example
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: 5
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: tuantmdev/ea22c387-bf35-4619-8cd1-7dd4ccd5ab94
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: 5
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/abca2ac57e742739_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: a148a245-5255-427b-9e91-a1bd6f02267b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a148a245-5255-427b-9e91-a1bd6f02267b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# ea22c387-bf35-4619-8cd1-7dd4ccd5ab94
This model is a fine-tuned version of [TitanML/tiny-mixtral](https://huggingface.co/TitanML/tiny-mixtral) 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0336 | 1 | nan |
| 0.0 | 0.2017 | 6 | nan |
| 0.0 | 0.4034 | 12 | nan |
| 0.0 | 0.6050 | 18 | nan |
| 0.0 | 0.8067 | 24 | nan |
| 0.0 | 1.0168 | 30 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Best000/466b7e70-4cd4-4371-86e4-91b647af6778
|
Best000
| 2025-01-23T19:23:41Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:20:40Z |
---
library_name: peft
license: apache-2.0
base_model: beomi/polyglot-ko-12.8b-safetensors
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 466b7e70-4cd4-4371-86e4-91b647af6778
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: beomi/polyglot-ko-12.8b-safetensors
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- bcae25b53977cd4d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bcae25b53977cd4d_train_data.json
type:
field_input: ''
field_instruction: prompt
field_output: chosen
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: Best000/466b7e70-4cd4-4371-86e4-91b647af6778
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/bcae25b53977cd4d_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: 3f38e4d4-e240-49ce-9bd9-52780a32f40b
wandb_project: Birthday-SN56-16-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3f38e4d4-e240-49ce-9bd9-52780a32f40b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 466b7e70-4cd4-4371-86e4-91b647af6778
This model is a fine-tuned version of [beomi/polyglot-ko-12.8b-safetensors](https://huggingface.co/beomi/polyglot-ko-12.8b-safetensors) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0714
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.0781 | 0.0010 | 1 | 1.2453 |
| 4.793 | 0.0030 | 3 | 1.2445 |
| 4.6978 | 0.0060 | 6 | 1.2072 |
| 4.3803 | 0.0090 | 9 | 1.0714 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
nhoxinh/cae91a46-9cb0-4d3c-ba48-3763ef330313
|
nhoxinh
| 2025-01-23T19:23:22Z | 6 | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T19:03:52Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cae91a46-9cb0-4d3c-ba48-3763ef330313
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:
- 9cd54185dfa12d69_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9cd54185dfa12d69_train_data.json
type:
field_instruction: instruction
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: 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: nhoxinh/cae91a46-9cb0-4d3c-ba48-3763ef330313
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/9cd54185dfa12d69_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: <|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: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# cae91a46-9cb0-4d3c-ba48-3763ef330313
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: 1.7312
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.8918 | 0.1137 | 200 | 1.7312 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
nhung01/a7f7bfe7-6328-41a7-998d-6186485f9b0e
|
nhung01
| 2025-01-23T19:23:20Z | 8 | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T19:03:47Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a7f7bfe7-6328-41a7-998d-6186485f9b0e
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:
- 9cd54185dfa12d69_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9cd54185dfa12d69_train_data.json
type:
field_instruction: instruction
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: 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: nhung01/a7f7bfe7-6328-41a7-998d-6186485f9b0e
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/9cd54185dfa12d69_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: <|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: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# a7f7bfe7-6328-41a7-998d-6186485f9b0e
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: 1.7300
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.8865 | 0.1137 | 200 | 1.7300 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
thaffggg/fb6f4032-6e31-4ede-b149-42fabf3f0e86
|
thaffggg
| 2025-01-23T19:23:16Z | 7 | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T19:03:35Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fb6f4032-6e31-4ede-b149-42fabf3f0e86
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:
- 9cd54185dfa12d69_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9cd54185dfa12d69_train_data.json
type:
field_instruction: instruction
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: 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: thaffggg/fb6f4032-6e31-4ede-b149-42fabf3f0e86
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/9cd54185dfa12d69_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: <|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: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# fb6f4032-6e31-4ede-b149-42fabf3f0e86
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: 1.7285
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.9023 | 0.1137 | 200 | 1.7285 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
myhaaaaaaa/b8ba782d-53a5-4f4a-bc54-c20416d5d67e
|
myhaaaaaaa
| 2025-01-23T19:22:40Z | 7 | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T19:03:44Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b8ba782d-53a5-4f4a-bc54-c20416d5d67e
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:
- 9cd54185dfa12d69_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9cd54185dfa12d69_train_data.json
type:
field_instruction: instruction
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: 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: myhaaaaaaa/b8ba782d-53a5-4f4a-bc54-c20416d5d67e
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/9cd54185dfa12d69_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: <|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: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# b8ba782d-53a5-4f4a-bc54-c20416d5d67e
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: 1.7283
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.8946 | 0.1137 | 200 | 1.7283 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
aleegis09/8ccc64a3-dfe1-42be-b3df-81ac413ab455
|
aleegis09
| 2025-01-23T19:22:38Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"mixtral",
"axolotl",
"generated_from_trainer",
"base_model:TitanML/tiny-mixtral",
"base_model:adapter:TitanML/tiny-mixtral",
"region:us"
] | null | 2025-01-23T19:22:21Z |
---
library_name: peft
base_model: TitanML/tiny-mixtral
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8ccc64a3-dfe1-42be-b3df-81ac413ab455
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: TitanML/tiny-mixtral
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- abca2ac57e742739_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/abca2ac57e742739_train_data.json
type:
field_instruction: full_prompt
field_output: example
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: aleegis09/8ccc64a3-dfe1-42be-b3df-81ac413ab455
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/abca2ac57e742739_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: a148a245-5255-427b-9e91-a1bd6f02267b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a148a245-5255-427b-9e91-a1bd6f02267b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8ccc64a3-dfe1-42be-b3df-81ac413ab455
This model is a fine-tuned version of [TitanML/tiny-mixtral](https://huggingface.co/TitanML/tiny-mixtral) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.5706
## 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: 23
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.4823 | 0.1333 | 1 | 10.5706 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
prxy5608/842f015d-05f3-4f48-9d05-1fd648d57713
|
prxy5608
| 2025-01-23T19:22:32Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-68m",
"base_model:adapter:JackFram/llama-68m",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:21:13Z |
---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 842f015d-05f3-4f48-9d05-1fd648d57713
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: JackFram/llama-68m
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- ff3a521d02fa72b2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ff3a521d02fa72b2_train_data.json
type:
field_instruction: context
field_output: question
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: prxy5608/842f015d-05f3-4f48-9d05-1fd648d57713
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/ff3a521d02fa72b2_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: 29e89a2c-6136-48b6-88bc-a0066652be7d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29e89a2c-6136-48b6-88bc-a0066652be7d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 842f015d-05f3-4f48-9d05-1fd648d57713
This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9803
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 7.1008 | 0.0006 | 1 | 6.7178 |
| 2.0472 | 0.0289 | 50 | 1.7984 |
| 1.8397 | 0.0578 | 100 | 1.2212 |
| 1.554 | 0.0867 | 150 | 1.0196 |
| 1.5232 | 0.1156 | 200 | 0.9803 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
dzanbek/e99b2d3c-da05-4c1f-98aa-7558c1261c94
|
dzanbek
| 2025-01-23T19:22:23Z | 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 | 2025-01-23T15:56:26Z |
---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e99b2d3c-da05-4c1f-98aa-7558c1261c94
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:
- 0ae20c5f36838dc7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/0ae20c5f36838dc7_train_data.json
type:
field_input: context
field_instruction: question
field_output: answers
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: dzanbek/e99b2d3c-da05-4c1f-98aa-7558c1261c94
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
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_memory:
0: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/0ae20c5f36838dc7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 6ddb8501-07dd-47fe-bb76-6b5dfd33b188
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6ddb8501-07dd-47fe-bb76-6b5dfd33b188
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# e99b2d3c-da05-4c1f-98aa-7558c1261c94
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: 1.3225
## 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_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 1.5404 |
| 6.0565 | 0.0002 | 5 | 1.5016 |
| 5.7291 | 0.0004 | 10 | 1.3918 |
| 5.2703 | 0.0006 | 15 | 1.3485 |
| 5.6047 | 0.0008 | 20 | 1.3318 |
| 5.3198 | 0.0010 | 25 | 1.3242 |
| 5.2752 | 0.0012 | 30 | 1.3225 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
ClarenceDan/92fb7190-1fa1-45eb-b83e-00f2fc2b4179
|
ClarenceDan
| 2025-01-23T19:22:22Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-3B",
"base_model:adapter:Qwen/Qwen2.5-3B",
"license:other",
"region:us"
] | null | 2025-01-23T19:06:03Z |
---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-3B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 92fb7190-1fa1-45eb-b83e-00f2fc2b4179
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/Qwen2.5-3B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- b1e00d434c3b8dbc_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b1e00d434c3b8dbc_train_data.json
type:
field_input: ''
field_instruction: id
field_output: chosen
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: ClarenceDan/92fb7190-1fa1-45eb-b83e-00f2fc2b4179
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/b1e00d434c3b8dbc_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: af3e9c37-8160-4b37-a459-45192580b247
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: af3e9c37-8160-4b37-a459-45192580b247
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 92fb7190-1fa1-45eb-b83e-00f2fc2b4179
This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0436
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1065 | 0.0001 | 1 | 1.0898 |
| 0.8947 | 0.0002 | 3 | 1.0892 |
| 0.7116 | 0.0004 | 6 | 1.0778 |
| 1.0819 | 0.0006 | 9 | 1.0436 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
bbytxt/ff86fb11-a985-420b-a708-8e3a2c8297d3
|
bbytxt
| 2025-01-23T19:22:02Z | 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 | 2025-01-23T18:37:40Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ff86fb11-a985-420b-a708-8e3a2c8297d3
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: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- bb423f807c72f7db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bb423f807c72f7db_train_data.json
type:
field_input: text
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: 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: bbytxt/ff86fb11-a985-420b-a708-8e3a2c8297d3
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/bb423f807c72f7db_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: dbbd7bbf-e11a-415f-a68c-df9abd649fee
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dbbd7bbf-e11a-415f-a68c-df9abd649fee
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# ff86fb11-a985-420b-a708-8e3a2c8297d3
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: 0.2308
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.264 | 0.0013 | 1 | 0.5562 |
| 0.2516 | 0.0638 | 50 | 0.2878 |
| 0.1677 | 0.1276 | 100 | 0.2526 |
| 0.3961 | 0.1914 | 150 | 0.2334 |
| 0.2878 | 0.2553 | 200 | 0.2308 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
great0001/f19d16c3-6105-4daa-8ef3-57fa5b5dc402
|
great0001
| 2025-01-23T19:20:42Z | 6 | 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 | 2025-01-23T19:18:17Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f19d16c3-6105-4daa-8ef3-57fa5b5dc402
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:
- 9cd54185dfa12d69_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9cd54185dfa12d69_train_data.json
type:
field_instruction: instruction
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: 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/f19d16c3-6105-4daa-8ef3-57fa5b5dc402
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/9cd54185dfa12d69_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: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
wandb_project: Birthday-SN56-14-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f19d16c3-6105-4daa-8ef3-57fa5b5dc402
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: 2.2139
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0859 | 0.0006 | 1 | 2.5627 |
| 2.3706 | 0.0017 | 3 | 2.5571 |
| 2.2415 | 0.0034 | 6 | 2.4446 |
| 2.2759 | 0.0051 | 9 | 2.2139 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
prxy5608/b61fbc08-3949-4ddc-b819-fd4c01007498
|
prxy5608
| 2025-01-23T19:20:30Z | 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 | 2025-01-23T18:36:23Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b61fbc08-3949-4ddc-b819-fd4c01007498
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: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- bb423f807c72f7db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bb423f807c72f7db_train_data.json
type:
field_input: text
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: 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: prxy5608/b61fbc08-3949-4ddc-b819-fd4c01007498
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/bb423f807c72f7db_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: dbbd7bbf-e11a-415f-a68c-df9abd649fee
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dbbd7bbf-e11a-415f-a68c-df9abd649fee
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# b61fbc08-3949-4ddc-b819-fd4c01007498
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: 0.2330
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.264 | 0.0013 | 1 | 0.5562 |
| 0.2533 | 0.0638 | 50 | 0.2876 |
| 0.1694 | 0.1276 | 100 | 0.2530 |
| 0.3983 | 0.1914 | 150 | 0.2349 |
| 0.3229 | 0.2553 | 200 | 0.2330 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
prxy5604/cb829967-e1ec-400a-8464-468e82eaa0b6
|
prxy5604
| 2025-01-23T19:20:20Z | 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 | 2025-01-23T18:36:06Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cb829967-e1ec-400a-8464-468e82eaa0b6
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: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- bb423f807c72f7db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bb423f807c72f7db_train_data.json
type:
field_input: text
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: 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: prxy5604/cb829967-e1ec-400a-8464-468e82eaa0b6
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/bb423f807c72f7db_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: dbbd7bbf-e11a-415f-a68c-df9abd649fee
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dbbd7bbf-e11a-415f-a68c-df9abd649fee
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# cb829967-e1ec-400a-8464-468e82eaa0b6
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: 0.2303
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.264 | 0.0013 | 1 | 0.5562 |
| 0.2525 | 0.0638 | 50 | 0.2868 |
| 0.1709 | 0.1276 | 100 | 0.2537 |
| 0.4044 | 0.1914 | 150 | 0.2330 |
| 0.2913 | 0.2553 | 200 | 0.2303 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/ultiima-108B-i1-GGUF
|
mradermacher
| 2025-01-23T19:19:14Z | 84 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Sakalti/ultiima-108B",
"base_model:quantized:Sakalti/ultiima-108B",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-01-23T14:24:38Z |
---
base_model: Sakalti/ultiima-108B
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
license_name: qwen
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Sakalti/ultiima-108B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/ultiima-108B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ1_S.gguf) | i1-IQ1_S | 33.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ1_M.gguf) | i1-IQ1_M | 35.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 37.7 | |
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 40.1 | |
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ2_S.gguf) | i1-IQ2_S | 41.3 | |
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ2_M.gguf) | i1-IQ2_M | 43.4 | |
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 43.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q2_K.gguf) | i1-Q2_K | 44.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 47.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 48.5 | |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 50.8 | IQ3_XS probably better |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 50.9 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 52.4 | |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 55.7 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 58.6 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 58.8 | |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 61.3 | fast, low quality |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 64.9 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q4_1.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q4_1.gguf.part2of2) | i1-Q4_1 | 67.7 | |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 70.3 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 76.2 | |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 80.8 | |
| [PART 1](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ultiima-108B-i1-GGUF/resolve/main/ultiima-108B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 95.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
datlaaaaaaa/c80fac54-a7e4-450a-b09c-108e549a2bb0
|
datlaaaaaaa
| 2025-01-23T19:16:58Z | 8 | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:51:47Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c80fac54-a7e4-450a-b09c-108e549a2bb0
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:
- 4b47e3ddd7129f5f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b47e3ddd7129f5f_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: 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: datlaaaaaaa/c80fac54-a7e4-450a-b09c-108e549a2bb0
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/4b47e3ddd7129f5f_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: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# c80fac54-a7e4-450a-b09c-108e549a2bb0
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: 0.1099
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.1226 | 0.0722 | 200 | 0.1099 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
nttx/24e206ea-db0c-4b0f-8114-b1408e712349
|
nttx
| 2025-01-23T19:16:42Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored",
"base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored",
"license:llama3",
"region:us"
] | null | 2025-01-23T18:08:32Z |
---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 24e206ea-db0c-4b0f-8114-b1408e712349
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: Orenguteng/Llama-3-8B-Lexi-Uncensored
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 92734826f81f6638_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/92734826f81f6638_train_data.json
type:
field_input: docstring_tokens
field_instruction: function
field_output: docstring
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: nttx/24e206ea-db0c-4b0f-8114-b1408e712349
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/92734826f81f6638_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: ba207857-bfce-4d35-b3d0-a3d9df3faf8a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ba207857-bfce-4d35-b3d0-a3d9df3faf8a
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 24e206ea-db0c-4b0f-8114-b1408e712349
This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0032
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.1632 | 0.0003 | 1 | 1.1661 |
| 0.0014 | 0.0138 | 50 | 0.0147 |
| 0.0211 | 0.0276 | 100 | 0.0054 |
| 0.0002 | 0.0414 | 150 | 0.0036 |
| 0.0003 | 0.0552 | 200 | 0.0032 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso05/f628b4d7-2c11-4837-9386-6fce339e3087
|
lesso05
| 2025-01-23T19:16:26Z | 8 | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T19:03:36Z |
---
library_name: peft
license: llama3
base_model: DeepMount00/Llama-3-8b-Ita
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f628b4d7-2c11-4837-9386-6fce339e3087
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: true
chat_template: llama3
datasets:
- data_files:
- 9cd54185dfa12d69_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9cd54185dfa12d69_train_data.json
type:
field_instruction: instruction
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: lesso05/f628b4d7-2c11-4837-9386-6fce339e3087
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
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: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/9cd54185dfa12d69_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: 10
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: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8ce46f6e-e85f-4b7a-ae7c-250c641329ac
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f628b4d7-2c11-4837-9386-6fce339e3087
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: 1.8817
## 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: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.086 | 0.0006 | 1 | 2.5763 |
| 2.5507 | 0.0028 | 5 | 2.5237 |
| 2.5038 | 0.0057 | 10 | 2.1972 |
| 2.0589 | 0.0085 | 15 | 1.9690 |
| 2.0728 | 0.0114 | 20 | 1.8923 |
| 1.6511 | 0.0142 | 25 | 1.8817 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
nadejdatarabukina/6ff8907c-c77b-4c39-a1b2-d99d2d4d2350
|
nadejdatarabukina
| 2025-01-23T19:15:46Z | 8 | 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 | 2025-01-23T18:51:42Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6ff8907c-c77b-4c39-a1b2-d99d2d4d2350
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:
- 4b47e3ddd7129f5f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4b47e3ddd7129f5f_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: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: nadejdatarabukina/6ff8907c-c77b-4c39-a1b2-d99d2d4d2350
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: 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_memory:
0: 75GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/4b47e3ddd7129f5f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d82e5b23-2360-4fb1-ba8e-609b2af93cfa
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 6ff8907c-c77b-4c39-a1b2-d99d2d4d2350
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: 0.1724
## 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_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0004 | 1 | 1.1440 |
| 1.1414 | 0.0018 | 5 | 1.0712 |
| 0.9012 | 0.0036 | 10 | 0.5820 |
| 0.2894 | 0.0054 | 15 | 0.2234 |
| 0.2345 | 0.0072 | 20 | 0.1830 |
| 0.1518 | 0.0090 | 25 | 0.1740 |
| 0.1727 | 0.0108 | 30 | 0.1724 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
deqing/llama_3.2_1b_fne_transform_openmathinstruct_2_2025_01_22_plus_addition_dataset
|
deqing
| 2025-01-23T19:15:34Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-23T07:06:07Z |
---
base_model: llama_fourier
library_name: transformers
model_name: llama_3.2_1b_fne_transform_openmathinstruct_2_2025_01_22_plus_addition_dataset
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama_3.2_1b_fne_transform_openmathinstruct_2_2025_01_22_plus_addition_dataset
This model is a fine-tuned version of [llama_fourier](https://huggingface.co/llama_fourier).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="deqing/llama_3.2_1b_fne_transform_openmathinstruct_2_2025_01_22_plus_addition_dataset", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/deqingfu/fourier_number_embedding/runs/elemauy6)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.1
- Transformers: 4.46.2
- Pytorch: 2.1.2
- Datasets: 3.1.0
- Tokenizers: 0.20.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
deqing/llama_3.2_1b_vanilla_openmathinstruct_2_2025_01_22_plus_addition_dataset
|
deqing
| 2025-01-23T19:14:26Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-23T07:06:07Z |
---
base_model: llama_fourier
library_name: transformers
model_name: llama_3.2_1b_vanilla_openmathinstruct_2_2025_01_22_plus_addition_dataset
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama_3.2_1b_vanilla_openmathinstruct_2_2025_01_22_plus_addition_dataset
This model is a fine-tuned version of [llama_fourier](https://huggingface.co/llama_fourier).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="deqing/llama_3.2_1b_vanilla_openmathinstruct_2_2025_01_22_plus_addition_dataset", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/deqingfu/fourier_number_embedding/runs/3dypvku1)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.1
- Transformers: 4.46.2
- Pytorch: 2.1.2
- Datasets: 3.1.0
- Tokenizers: 0.20.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
haryoaw/cola_meta-llama-Llama-3.2-3B_2_0
|
haryoaw
| 2025-01-23T19:14:15Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-23T18:08:54Z |
---
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]
|
HBboy/Qwen2.5-0.5B-Instruct-xiaosui-full
|
HBboy
| 2025-01-23T19:14:08Z | 18 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-23T17:34:07Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: train_2025-01-23-16-59-22
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. -->
# train_2025-01-23-16-59-22
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the xiaosui-train and the identity datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 4
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
trenden/929ace50-67f5-4e87-b5b4-b8010b1337f7
|
trenden
| 2025-01-23T19:10:54Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T18:54:52Z |
---
library_name: peft
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 929ace50-67f5-4e87-b5b4-b8010b1337f7
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: tiiuae/falcon-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 64fe47644b03a711_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/64fe47644b03a711_train_data.json
type:
field_input: ''
field_instruction: context
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: trenden/929ace50-67f5-4e87-b5b4-b8010b1337f7
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/64fe47644b03a711_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: <|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: 5e0875d0-636d-4520-9359-4eac575c16b9
wandb_project: Birthday-SN56-3-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 5e0875d0-636d-4520-9359-4eac575c16b9
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 929ace50-67f5-4e87-b5b4-b8010b1337f7
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6163
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.2956 | 0.0001 | 1 | 2.8442 |
| 10.7208 | 0.0002 | 3 | 2.8438 |
| 10.3376 | 0.0003 | 6 | 2.8166 |
| 9.698 | 0.0005 | 9 | 2.6163 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
aleegis09/9e4a83ad-3733-42bc-b9cb-ef5ec15e7dd4
|
aleegis09
| 2025-01-23T19:09:03Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:trl-internal-testing/tiny-random-LlamaForCausalLM",
"base_model:adapter:trl-internal-testing/tiny-random-LlamaForCausalLM",
"region:us"
] | null | 2025-01-23T19:08:28Z |
---
library_name: peft
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9e4a83ad-3733-42bc-b9cb-ef5ec15e7dd4
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: trl-internal-testing/tiny-random-LlamaForCausalLM
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- f4a61305a746447c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f4a61305a746447c_train_data.json
type:
field_instruction: sentence1
field_output: sentence2
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: aleegis09/9e4a83ad-3733-42bc-b9cb-ef5ec15e7dd4
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/f4a61305a746447c_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: c6d606c5-1bf1-4d46-8f27-e3893d012d1d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c6d606c5-1bf1-4d46-8f27-e3893d012d1d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 9e4a83ad-3733-42bc-b9cb-ef5ec15e7dd4
This model is a fine-tuned version of [trl-internal-testing/tiny-random-LlamaForCausalLM](https://huggingface.co/trl-internal-testing/tiny-random-LlamaForCausalLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.3381
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 10.3745 | 0.0095 | 1 | 10.3693 |
| 10.3454 | 0.4739 | 50 | 10.3517 |
| 10.3232 | 0.9479 | 100 | 10.3409 |
| 10.0203 | 1.4218 | 150 | 10.3385 |
| 10.711 | 1.8957 | 200 | 10.3381 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
willtensora/dab16ec4-4ddf-4ee5-8888-3dc2a83f0f86
|
willtensora
| 2025-01-23T19:08:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"axolotl",
"generated_from_trainer",
"conversational",
"base_model:trl-internal-testing/tiny-random-LlamaForCausalLM",
"base_model:finetune:trl-internal-testing/tiny-random-LlamaForCausalLM",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-01-23T19:08:38Z |
---
library_name: transformers
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: dab16ec4-4ddf-4ee5-8888-3dc2a83f0f86
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
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
batch_size: 32
bf16: true
chat_template: tokenizer_default_fallback_alpaca
datasets:
- data_files:
- f4a61305a746447c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f4a61305a746447c_train_data.json
type:
field_instruction: sentence1
field_output: sentence2
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
eval_steps: 20
flash_attention: true
gpu_memory_limit: 80GiB
gradient_checkpointing: true
group_by_length: true
hub_model_id: willtensora/dab16ec4-4ddf-4ee5-8888-3dc2a83f0f86
hub_strategy: checkpoint
learning_rate: 0.0002
logging_steps: 10
lr_scheduler: cosine
max_steps: 2500
micro_batch_size: 4
model_type: AutoModelForCausalLM
optimizer: adamw_bnb_8bit
output_dir: /workspace/axolotl/configs
pad_to_sequence_len: true
resize_token_embeddings_to_32x: false
sample_packing: false
save_steps: 40
save_total_limit: 1
sequence_len: 2048
tokenizer_type: LlamaTokenizerFast
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: ''
wandb_mode: online
wandb_name: trl-internal-testing/tiny-random-LlamaForCausalLM-/workspace/input_data/f4a61305a746447c_train_data.json
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: default
warmup_ratio: 0.05
xformers_attention: true
```
</details><br>
# dab16ec4-4ddf-4ee5-8888-3dc2a83f0f86
This model is a fine-tuned version of [trl-internal-testing/tiny-random-LlamaForCausalLM](https://huggingface.co/trl-internal-testing/tiny-random-LlamaForCausalLM) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_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
- training_steps: 13
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.01 | 1 | 10.3686 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
ClarenceDan/67cd2274-a71f-42d9-8446-11041adb2b48
|
ClarenceDan
| 2025-01-23T19:08:31Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:trl-internal-testing/tiny-random-LlamaForCausalLM",
"base_model:adapter:trl-internal-testing/tiny-random-LlamaForCausalLM",
"region:us"
] | null | 2025-01-23T19:08:14Z |
---
library_name: peft
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 67cd2274-a71f-42d9-8446-11041adb2b48
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: trl-internal-testing/tiny-random-LlamaForCausalLM
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f4a61305a746447c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f4a61305a746447c_train_data.json
type:
field_instruction: sentence1
field_output: sentence2
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: ClarenceDan/67cd2274-a71f-42d9-8446-11041adb2b48
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/f4a61305a746447c_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: c6d606c5-1bf1-4d46-8f27-e3893d012d1d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c6d606c5-1bf1-4d46-8f27-e3893d012d1d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 67cd2274-a71f-42d9-8446-11041adb2b48
This model is a fine-tuned version of [trl-internal-testing/tiny-random-LlamaForCausalLM](https://huggingface.co/trl-internal-testing/tiny-random-LlamaForCausalLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.3690
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.365 | 0.0024 | 1 | 10.3692 |
| 10.3614 | 0.0071 | 3 | 10.3692 |
| 10.3717 | 0.0142 | 6 | 10.3692 |
| 10.3739 | 0.0214 | 9 | 10.3690 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
thalllsssss/4b6bc777-ba7b-4bc7-913a-28a2d06ffc96
|
thalllsssss
| 2025-01-23T19:06:53Z | 8 | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:36:32Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4b6bc777-ba7b-4bc7-913a-28a2d06ffc96
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:
- bb423f807c72f7db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bb423f807c72f7db_train_data.json
type:
field_input: text
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: 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: thalllsssss/4b6bc777-ba7b-4bc7-913a-28a2d06ffc96
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/bb423f807c72f7db_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: dbbd7bbf-e11a-415f-a68c-df9abd649fee
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dbbd7bbf-e11a-415f-a68c-df9abd649fee
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 4b6bc777-ba7b-4bc7-913a-28a2d06ffc96
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: 0.2430
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.2318 | 0.0638 | 200 | 0.2430 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
ell-hol/fx-dv-lr-fnc-frt
|
ell-hol
| 2025-01-23T19:05:49Z | 87 | 1 |
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
| 2025-01-23T19:05:48Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Fnac
---
# Fx Dv Lr Fnc Frt
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Fnac` 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('ell-hol/fx-dv-lr-fnc-frt', 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)
|
nbninh/497e554a-0cff-470d-b4f5-ffb96223678f
|
nbninh
| 2025-01-23T19:04:32Z | 8 | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:36:28Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 497e554a-0cff-470d-b4f5-ffb96223678f
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:
- bb423f807c72f7db_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bb423f807c72f7db_train_data.json
type:
field_input: text
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: 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: nbninh/497e554a-0cff-470d-b4f5-ffb96223678f
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/bb423f807c72f7db_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: dbbd7bbf-e11a-415f-a68c-df9abd649fee
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dbbd7bbf-e11a-415f-a68c-df9abd649fee
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 497e554a-0cff-470d-b4f5-ffb96223678f
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: 0.2427
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.2321 | 0.0638 | 200 | 0.2427 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
kk-aivio/5b0a0bcc-69ac-44f1-9334-2722d2c09a40
|
kk-aivio
| 2025-01-23T19:03:21Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:fxmarty/tiny-llama-fast-tokenizer",
"base_model:adapter:fxmarty/tiny-llama-fast-tokenizer",
"region:us"
] | null | 2025-01-23T19:02:05Z |
---
library_name: peft
base_model: fxmarty/tiny-llama-fast-tokenizer
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5b0a0bcc-69ac-44f1-9334-2722d2c09a40
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: fxmarty/tiny-llama-fast-tokenizer
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- d2abf275c90b86ed_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d2abf275c90b86ed_train_data.json
type:
field_input: Example
field_instruction: '@partOfSpeech'
field_output: Definition
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: kk-aivio/5b0a0bcc-69ac-44f1-9334-2722d2c09a40
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/d2abf275c90b86ed_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: 9e21fd6c-dbfa-45cb-971d-696ec25f86a3
wandb_project: Birthday-SN56-11-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9e21fd6c-dbfa-45cb-971d-696ec25f86a3
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 5b0a0bcc-69ac-44f1-9334-2722d2c09a40
This model is a fine-tuned version of [fxmarty/tiny-llama-fast-tokenizer](https://huggingface.co/fxmarty/tiny-llama-fast-tokenizer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.3781
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.3723 | 0.0001 | 1 | 10.3784 |
| 10.3797 | 0.0002 | 3 | 10.3784 |
| 10.3731 | 0.0004 | 6 | 10.3783 |
| 10.3813 | 0.0006 | 9 | 10.3781 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
CrimsonZockt/JuliaZarzeckaVZWei-FLUXLORA
|
CrimsonZockt
| 2025-01-23T19:03:18Z | 61 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-01-23T18:39:53Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: Julia Zarzecka, black tanktop, professional headshot, photoshoot.
output:
url: images/Julia Zarzecka, black tanktop, professional hea....png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Julia Zarzecka
---
# JuliaZarzeckaVZWei
<Gallery />
## Model description
Version 2 of this model.
## Trigger words
You should use `Julia Zarzecka` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/CrimsonZockt/JuliaZarzeckaVZWei-FLUXLORA/tree/main) them in the Files & versions tab.
|
nblinh63/0fb73688-98c2-48b6-8910-9df112990f9a
|
nblinh63
| 2025-01-23T19:03:15Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Vikhrmodels/Vikhr-7B-instruct_0.4",
"base_model:adapter:Vikhrmodels/Vikhr-7B-instruct_0.4",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:00:38Z |
---
library_name: peft
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0fb73688-98c2-48b6-8910-9df112990f9a
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: Vikhrmodels/Vikhr-7B-instruct_0.4
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 3d4a9076aa3a08e6_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3d4a9076aa3a08e6_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
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: nblinh63/0fb73688-98c2-48b6-8910-9df112990f9a
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/3d4a9076aa3a08e6_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: e50b6e65-0f7c-4d11-89d7-c022df9ec755
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e50b6e65-0f7c-4d11-89d7-c022df9ec755
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 0fb73688-98c2-48b6-8910-9df112990f9a
This model is a fine-tuned version of [Vikhrmodels/Vikhr-7B-instruct_0.4](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5226
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.5871 | 0.0338 | 200 | 0.5226 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
SuperKaos/sofavictor
|
SuperKaos
| 2025-01-23T19:02:38Z | 11 | 1 |
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
| 2025-01-23T18:41:50Z |
---
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: sofavictor
---
# Sofavictor
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `sofavictor` 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('SuperKaos/sofavictor', 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)
|
glif-loradex-trainer/kklors_flux_dev_DC_2
|
glif-loradex-trainer
| 2025-01-23T19:01:54Z | 81 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us",
"flux",
"lora",
"base_model:adapter:black-forest-labs/FLUX.1-dev"
] |
text-to-image
| 2025-01-23T19:01:10Z |
---
tags:
- diffusers
- text-to-image
- template:sd-lora
- base_model:black-forest-labs/FLUX.1-dev
- base_model:finetune:black-forest-labs/FLUX.1-dev
- license:other
- region:us
- flux
- lora
widget:
- output:
url: samples/1737658732991__000003000_0.jpg
text: a serene lake in autumn surrounded by trees DC
- output:
url: samples/1737658757760__000003000_1.jpg
text: golden hour, sheep on a meadow, rural DC
- output:
url: samples/1737658782545__000003000_2.jpg
text: a modern cozy living room with wooden walls and a big panorama windowDC
- output:
url: samples/1737658807327__000003000_3.jpg
text: golden hour, a lonely tree in a field DC
- output:
url: samples/1737658832208__000003000_4.jpg
text: hills and mountains, nature in winterDC
- output:
url: samples/1737658856998__000003000_5.jpg
text: a small village close to a lake, mountains DC
base_model: black-forest-labs/FLUX.1-dev
trigger: "DC"
instance_prompt: "DC"
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
---
# flux_dev_DC_2
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `kklors`.
<Gallery />
## Trigger words
You should use `DC` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/glif-loradex-trainer/kklors_flux_dev_DC_2/tree/main) them in the Files & versions tab.
## License
This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
lesso15/2d03956f-3e38-4b04-abc5-76f883e6265d
|
lesso15
| 2025-01-23T19:01:31Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"license:other",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:07:17Z |
---
library_name: peft
license: other
base_model: huggyllama/llama-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2d03956f-3e38-4b04-abc5-76f883e6265d
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: huggyllama/llama-7b
bf16: auto
chat_template: llama3
datasets:
- data_files:
- ccd32583f980ebf0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ccd32583f980ebf0_train_data.json
type:
field_input: ''
field_instruction: problem
field_output: solution
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: lesso15/2d03956f-3e38-4b04-abc5-76f883e6265d
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/ccd32583f980ebf0_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: 01b60291-41f3-4631-b7e8-f7c60c2ca163
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 01b60291-41f3-4631-b7e8-f7c60c2ca163
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 2d03956f-3e38-4b04-abc5-76f883e6265d
This model is a fine-tuned version of [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b) 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 | 0.6879 | 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
|
great0001/442bf738-d24b-4c5e-936e-727503b89f9e
|
great0001
| 2025-01-23T19:01:17Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-64k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-64k",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T19:00:24Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-64k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 442bf738-d24b-4c5e-936e-727503b89f9e
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-64k
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- bccab6bcbcb6fc03_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/bccab6bcbcb6fc03_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
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: great0001/442bf738-d24b-4c5e-936e-727503b89f9e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/bccab6bcbcb6fc03_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: 1dc178e8-8f66-48ae-8ebb-825428c168d0
wandb_project: Birthday-SN56-14-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1dc178e8-8f66-48ae-8ebb-825428c168d0
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 442bf738-d24b-4c5e-936e-727503b89f9e
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8088 | 0.0336 | 1 | 0.4763 |
| 1.7288 | 0.1008 | 3 | 0.3289 |
| 0.3919 | 0.2017 | 6 | 0.0161 |
| 0.0017 | 0.3025 | 9 | 0.0003 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lmstudio-community/UI-TARS-72B-DPO-GGUF
|
lmstudio-community
| 2025-01-23T18:59:43Z | 1,554 | 1 | null |
[
"gguf",
"multimodal",
"gui",
"image-text-to-text",
"en",
"arxiv:2501.12326",
"base_model:bytedance-research/UI-TARS-72B-DPO",
"base_model:quantized:bytedance-research/UI-TARS-72B-DPO",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-01-23T18:02:19Z |
---
quantized_by: bartowski
pipeline_tag: image-text-to-text
license: apache-2.0
base_model: bytedance-research/UI-TARS-72B-DPO
tags:
- multimodal
- gui
language:
- en
---
## 💫 Community Model> UI TARS 72B DPO by Bytedance-Research
*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.
**Model creator:** [bytedance-research](https://huggingface.co/bytedance-research)<br>
**Original model**: [UI-TARS-72B-DPO](https://huggingface.co/bytedance-research/UI-TARS-72B-DPO)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b4514](https://github.com/ggerganov/llama.cpp/releases/tag/b4514)<br>
## Technical Details
Finetune of Qwen VL reasoning model.
Created for native GUI agent models with human-like perception, reasoning, and action capabilities.
More details available here: [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://huggingface.co/papers/2501.12326).
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
## Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
|
sailendu/modernbert-llm-router
|
sailendu
| 2025-01-23T18:59:31Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-01-23T17:47:22Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: modernbert-llm-router
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. -->
# modernbert-llm-router
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4671
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:----:|
| No log | 1.0 | 1 | 0.7026 | 0.82 |
| No log | 2.0 | 2 | 0.6383 | 0.82 |
| No log | 3.0 | 3 | 0.5799 | 0.82 |
| No log | 4.0 | 4 | 0.4671 | 1.0 |
| No log | 5.0 | 5 | 0.4164 | 1.0 |
| No log | 6.0 | 6 | 0.4202 | 1.0 |
| No log | 7.0 | 7 | 0.3868 | 1.0 |
| No log | 8.0 | 8 | 0.3568 | 1.0 |
| No log | 9.0 | 9 | 0.3463 | 1.0 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.4.1
- Datasets 3.1.0
- Tokenizers 0.21.0
|
gavrilstep/86326240-4310-4f05-a004-dd915160f921
|
gavrilstep
| 2025-01-23T18:58:16Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T17:11:22Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 86326240-4310-4f05-a004-dd915160f921
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 018801b6a6272709_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/018801b6a6272709_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: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: gavrilstep/86326240-4310-4f05-a004-dd915160f921
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
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_memory:
0: 75GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/018801b6a6272709_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: true
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 6db6f49b-d7b4-4b62-93de-49a5ea09b965
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6db6f49b-d7b4-4b62-93de-49a5ea09b965
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 86326240-4310-4f05-a004-dd915160f921
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-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: 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_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | nan |
| 0.0 | 0.0001 | 5 | nan |
| 0.0 | 0.0003 | 10 | nan |
| 0.0 | 0.0004 | 15 | nan |
| 0.0 | 0.0006 | 20 | nan |
| 0.0 | 0.0007 | 25 | nan |
| 0.0 | 0.0008 | 30 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Best000/fbaeeda8-5d31-459a-afac-8e45b39a55dc
|
Best000
| 2025-01-23T18:54:04Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"base_model:adapter:EleutherAI/pythia-1b",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T18:53:29Z |
---
library_name: peft
license: apache-2.0
base_model: EleutherAI/pythia-1b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fbaeeda8-5d31-459a-afac-8e45b39a55dc
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: EleutherAI/pythia-1b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- b2a4966d9a5c880e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b2a4966d9a5c880e_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: Best000/fbaeeda8-5d31-459a-afac-8e45b39a55dc
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/b2a4966d9a5c880e_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: <|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: ee62f35d-1a99-4f1c-a69c-c91bc444b71f
wandb_project: Birthday-SN56-15-Gradients-On-Demand
wandb_run: your_name
wandb_runid: ee62f35d-1a99-4f1c-a69c-c91bc444b71f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# fbaeeda8-5d31-459a-afac-8e45b39a55dc
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6191
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 7.5289 | 0.0024 | 1 | 1.8503 |
| 6.8689 | 0.0072 | 3 | 1.8454 |
| 6.8834 | 0.0144 | 6 | 1.7722 |
| 6.8316 | 0.0216 | 9 | 1.6191 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
hugggof/vampnetv2-tria-d1026-l8-h8-mode-vampnet_rmsq16-median-latest
|
hugggof
| 2025-01-23T18:51:00Z | 7 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-01-23T18:50:39Z |
---
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]
|
kostiantynk-out/f39ee3aa-b1ac-45f8-a7dd-8f0fb9a8856a
|
kostiantynk-out
| 2025-01-23T18:50:34Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:numind/NuExtract-1.5",
"base_model:adapter:numind/NuExtract-1.5",
"license:mit",
"region:us"
] | null | 2025-01-23T18:47:57Z |
---
library_name: peft
license: mit
base_model: numind/NuExtract-v1.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f39ee3aa-b1ac-45f8-a7dd-8f0fb9a8856a
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: numind/NuExtract-v1.5
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 2e21289e112cd7b2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/2e21289e112cd7b2_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: kostiantynk-out/f39ee3aa-b1ac-45f8-a7dd-8f0fb9a8856a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/2e21289e112cd7b2_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: a9c8ce22-7239-43da-8517-794fda09424b
wandb_project: Mine-SN56-1-Gradients-On-Demand
wandb_run: your_name
wandb_runid: a9c8ce22-7239-43da-8517-794fda09424b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f39ee3aa-b1ac-45f8-a7dd-8f0fb9a8856a
This model is a fine-tuned version of [numind/NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3182
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.6859 | 0.0008 | 1 | 1.4277 |
| 5.5829 | 0.0023 | 3 | 1.4250 |
| 5.7831 | 0.0046 | 6 | 1.3955 |
| 5.2315 | 0.0068 | 9 | 1.3182 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Aardiiiiy/ProKontra3
|
Aardiiiiy
| 2025-01-23T18:49:42Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-01-23T18:49:17Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **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]
|
hongngo/14572a1f-422e-4ad1-b681-1a7a37eac195
|
hongngo
| 2025-01-23T18:49:31Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored",
"base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored",
"license:llama3",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T18:09:32Z |
---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 14572a1f-422e-4ad1-b681-1a7a37eac195
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: Orenguteng/Llama-3-8B-Lexi-Uncensored
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 92734826f81f6638_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/92734826f81f6638_train_data.json
type:
field_input: docstring_tokens
field_instruction: function
field_output: docstring
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: hongngo/14572a1f-422e-4ad1-b681-1a7a37eac195
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/92734826f81f6638_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: ba207857-bfce-4d35-b3d0-a3d9df3faf8a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ba207857-bfce-4d35-b3d0-a3d9df3faf8a
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 14572a1f-422e-4ad1-b681-1a7a37eac195
This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0095
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.001 | 0.0138 | 200 | 0.0095 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
nhunglaaaaaaa/83b80c83-b2e1-4ae0-b402-c1253a81ba5d
|
nhunglaaaaaaa
| 2025-01-23T18:49:17Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:lcw99/zephykor-ko-7b-chang",
"base_model:adapter:lcw99/zephykor-ko-7b-chang",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T17:50:56Z |
---
library_name: peft
base_model: lcw99/zephykor-ko-7b-chang
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 83b80c83-b2e1-4ae0-b402-c1253a81ba5d
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: lcw99/zephykor-ko-7b-chang
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ccfbe55a1bac0210_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ccfbe55a1bac0210_train_data.json
type:
field_instruction: Sequence
field_output: Secondary_structure
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: nhunglaaaaaaa/83b80c83-b2e1-4ae0-b402-c1253a81ba5d
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/ccfbe55a1bac0210_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: 31fb1a31-a1c6-4e99-8087-716702f9f864
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 31fb1a31-a1c6-4e99-8087-716702f9f864
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 83b80c83-b2e1-4ae0-b402-c1253a81ba5d
This model is a fine-tuned version of [lcw99/zephykor-ko-7b-chang](https://huggingface.co/lcw99/zephykor-ko-7b-chang) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1408
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 4.698 | 0.0134 | 200 | 1.1408 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
trangtrannnnn/6fcb5289-2b5f-4c18-bd9c-ef4bb15b6b96
|
trangtrannnnn
| 2025-01-23T18:49:08Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:lcw99/zephykor-ko-7b-chang",
"base_model:adapter:lcw99/zephykor-ko-7b-chang",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T17:50:48Z |
---
library_name: peft
base_model: lcw99/zephykor-ko-7b-chang
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6fcb5289-2b5f-4c18-bd9c-ef4bb15b6b96
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: lcw99/zephykor-ko-7b-chang
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ccfbe55a1bac0210_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ccfbe55a1bac0210_train_data.json
type:
field_instruction: Sequence
field_output: Secondary_structure
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: trangtrannnnn/6fcb5289-2b5f-4c18-bd9c-ef4bb15b6b96
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/ccfbe55a1bac0210_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: 31fb1a31-a1c6-4e99-8087-716702f9f864
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 31fb1a31-a1c6-4e99-8087-716702f9f864
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 6fcb5289-2b5f-4c18-bd9c-ef4bb15b6b96
This model is a fine-tuned version of [lcw99/zephykor-ko-7b-chang](https://huggingface.co/lcw99/zephykor-ko-7b-chang) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1409
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 4.7007 | 0.0134 | 200 | 1.1409 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
JacksonBrune/f029e864-3290-4610-b2a3-52cd40de2fd4
|
JacksonBrune
| 2025-01-23T18:48:21Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Solar-10b-64k",
"base_model:adapter:NousResearch/Yarn-Solar-10b-64k",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T18:13:01Z |
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Solar-10b-64k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f029e864-3290-4610-b2a3-52cd40de2fd4
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-Solar-10b-64k
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e901e040c85d28fc_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e901e040c85d28fc_train_data.json
type:
field_input: post
field_instruction: query
field_output: summary
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: JacksonBrune/f029e864-3290-4610-b2a3-52cd40de2fd4
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/e901e040c85d28fc_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: b4512ccd-8cc9-495a-ab2a-53b76de19941
wandb_project: birthdya-sn56-18-Gradients-On-Demand
wandb_run: your_name
wandb_runid: b4512ccd-8cc9-495a-ab2a-53b76de19941
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f029e864-3290-4610-b2a3-52cd40de2fd4
This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-64k](https://huggingface.co/NousResearch/Yarn-Solar-10b-64k) 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0001 | 1 | nan |
| 0.0 | 0.0002 | 3 | nan |
| 0.0 | 0.0004 | 6 | nan |
| 0.0 | 0.0006 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
lesso17/cc2c0682-0860-4d05-9d05-3981bc571f50
|
lesso17
| 2025-01-23T18:48:07Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Korabbit/llama-2-ko-7b",
"base_model:adapter:Korabbit/llama-2-ko-7b",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-23T17:25:42Z |
---
library_name: peft
base_model: Korabbit/llama-2-ko-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cc2c0682-0860-4d05-9d05-3981bc571f50
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: Korabbit/llama-2-ko-7b
bf16: auto
chat_template: llama3
datasets:
- data_files:
- e0d97ee0a206d0cc_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e0d97ee0a206d0cc_train_data.json
type:
field_input: knowledge
field_instruction: dialogue_history
field_output: right_response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: 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/cc2c0682-0860-4d05-9d05-3981bc571f50
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/e0d97ee0a206d0cc_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: 03236c1c-e4b3-445b-aaf1-ced05acc5a85
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 03236c1c-e4b3-445b-aaf1-ced05acc5a85
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# cc2c0682-0860-4d05-9d05-3981bc571f50
This model is a fine-tuned version of [Korabbit/llama-2-ko-7b](https://huggingface.co/Korabbit/llama-2-ko-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6735
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.6462 | 0.1701 | 200 | 1.6735 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
tum-nlp-lab/10Epochs_target3
|
tum-nlp-lab
| 2025-01-23T18:46:09Z | 21 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-01-23T18:45:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
kostiantynk-out/10f30baa-e596-4815-a5f7-f944ae4bbbda
|
kostiantynk-out
| 2025-01-23T18:44:20Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-68m",
"base_model:adapter:JackFram/llama-68m",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T18:43:38Z |
---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 10f30baa-e596-4815-a5f7-f944ae4bbbda
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: JackFram/llama-68m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ff3a521d02fa72b2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ff3a521d02fa72b2_train_data.json
type:
field_instruction: context
field_output: question
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: kostiantynk-out/10f30baa-e596-4815-a5f7-f944ae4bbbda
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/ff3a521d02fa72b2_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: 29e89a2c-6136-48b6-88bc-a0066652be7d
wandb_project: Mine-SN56-1-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29e89a2c-6136-48b6-88bc-a0066652be7d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 10f30baa-e596-4815-a5f7-f944ae4bbbda
This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0001 | 1 | nan |
| 0.0 | 0.0004 | 3 | nan |
| 0.0 | 0.0009 | 6 | nan |
| 0.0 | 0.0013 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Howard881010/longformer-epidemiology-1epoch
|
Howard881010
| 2025-01-23T18:42:46Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"longformer",
"text-classification",
"generated_from_trainer",
"base_model:allenai/longformer-base-4096",
"base_model:finetune:allenai/longformer-base-4096",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-01-23T08:30:49Z |
---
library_name: transformers
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: longformer-epidemiology-1epoch
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. -->
# longformer-epidemiology-1epoch
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6951
- Accuracy: 0.477
- F1: 0.3230
## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.6954 | 0.1111 | 50 | 0.7023 | 0.477 | 0.3230 |
| 0.6938 | 0.2222 | 100 | 0.7087 | 0.477 | 0.3230 |
| 0.6948 | 0.3333 | 150 | 0.6921 | 0.523 | 0.3434 |
| 0.6983 | 0.4444 | 200 | 0.6921 | 0.523 | 0.3434 |
| 0.6948 | 0.5556 | 250 | 0.6925 | 0.523 | 0.3434 |
| 0.6962 | 0.6667 | 300 | 0.6963 | 0.477 | 0.3230 |
| 0.7 | 0.7778 | 350 | 0.6921 | 0.523 | 0.3434 |
| 0.6967 | 0.8889 | 400 | 0.6930 | 0.523 | 0.3434 |
| 0.6885 | 1.0 | 450 | 0.6951 | 0.477 | 0.3230 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.1
|
great0001/7d2c3013-5e6d-4c51-9270-1763579b2a75
|
great0001
| 2025-01-23T18:42:22Z | 8 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:JackFram/llama-68m",
"base_model:adapter:JackFram/llama-68m",
"license:apache-2.0",
"region:us"
] | null | 2025-01-23T18:41:40Z |
---
library_name: peft
license: apache-2.0
base_model: JackFram/llama-68m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7d2c3013-5e6d-4c51-9270-1763579b2a75
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: JackFram/llama-68m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ff3a521d02fa72b2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ff3a521d02fa72b2_train_data.json
type:
field_instruction: context
field_output: question
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: great0001/7d2c3013-5e6d-4c51-9270-1763579b2a75
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/ff3a521d02fa72b2_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: 29e89a2c-6136-48b6-88bc-a0066652be7d
wandb_project: Mine-SN56-20-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 29e89a2c-6136-48b6-88bc-a0066652be7d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 7d2c3013-5e6d-4c51-9270-1763579b2a75
This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0001 | 1 | nan |
| 0.0 | 0.0004 | 3 | nan |
| 0.0 | 0.0009 | 6 | nan |
| 0.0 | 0.0013 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
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