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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-25 06:27:54
| downloads
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| likes
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11.7k
| library_name
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4.05k
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tinybiggames/Dolphin3.0-Llama3.1-8B-Q4_K_M-GGUF | tinybiggames | 2025-01-27T20:24:41Z | 459 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:OpenCoder-LLM/opc-sft-stage1",
"dataset:OpenCoder-LLM/opc-sft-stage2",
"dataset:microsoft/orca-agentinstruct-1M-v1",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:NousResearch/hermes-function-calling-v1",
"dataset:AI-MO/NuminaMath-CoT",
"dataset:AI-MO/NuminaMath-TIR",
"dataset:allenai/tulu-3-sft-mixture",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:HuggingFaceTB/smoltalk",
"dataset:cognitivecomputations/samantha-data",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:m-a-p/Code-Feedback",
"base_model:cognitivecomputations/Dolphin3.0-Llama3.1-8B",
"base_model:quantized:cognitivecomputations/Dolphin3.0-Llama3.1-8B",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-27T19:48:53Z | ---
license: llama3.1
datasets:
- OpenCoder-LLM/opc-sft-stage1
- OpenCoder-LLM/opc-sft-stage2
- microsoft/orca-agentinstruct-1M-v1
- microsoft/orca-math-word-problems-200k
- NousResearch/hermes-function-calling-v1
- AI-MO/NuminaMath-CoT
- AI-MO/NuminaMath-TIR
- allenai/tulu-3-sft-mixture
- cognitivecomputations/dolphin-coder
- HuggingFaceTB/smoltalk
- cognitivecomputations/samantha-data
- m-a-p/CodeFeedback-Filtered-Instruction
- m-a-p/Code-Feedback
language:
- en
base_model: cognitivecomputations/Dolphin3.0-Llama3.1-8B
tags:
- llama-cpp
- gguf-my-repo
---
# tinybiggames/Dolphin3.0-Llama3.1-8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`cognitivecomputations/Dolphin3.0-Llama3.1-8B`](https://huggingface.co/cognitivecomputations/Dolphin3.0-Llama3.1-8B) 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/cognitivecomputations/Dolphin3.0-Llama3.1-8B) for more details on the model.
# Dolphin 3.0 Llama 3.1 8B 🐬
Part of the [Dolphin 3.0 Collection](https://huggingface.co/collections/cognitivecomputations/dolphin-30-677ab47f73d7ff66743979a3)
Curated and trained by [Eric Hartford](https://huggingface.co/ehartford), [Ben Gitter](https://huggingface.co/bigstorm), [BlouseJury](https://huggingface.co/BlouseJury) and [Cognitive Computations](https://huggingface.co/cognitivecomputations)
[](https://discord.gg/cognitivecomputations)
Discord: https://discord.gg/cognitivecomputations
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/cNCs1TBD3FelWCJGkZ3cd.png" width="600" />
## Sponsors
Our appreciation for the generous sponsors of Dolphin 3.0:
- [Crusoe Cloud](https://crusoe.ai/) - provided 16x L40s for training and evals
- [Akash](https://akash.network/) - provided on-demand 8x H100 for training
- [Lazarus](https://www.lazarusai.com/) - provided 16x H100 for training
- [Cerebras](https://cerebras.ai/) - provided excellent and fast inference services for data labeling
- [Andreessen Horowitz](https://a16z.com/) - provided a [grant](https://a16z.com/supporting-the-open-source-ai-community/) that make Dolphin 1.0 possible and enabled me to bootstrap my homelab
## What is Dolphin?
Dolphin 3.0 is the next generation of the Dolphin series of instruct-tuned models. Designed to be the ultimate general purpose local model, enabling coding, math, agentic, function calling, and general use cases.
Dolphin aims to be a general purpose model, similar to the models behind ChatGPT, Claude, Gemini. But these models present problems for businesses seeking to include AI in their products.
1) They maintain control of the system prompt, deprecating and changing things as they wish, often causing software to break.
2) They maintain control of the model versions, sometimes changing things silently, or deprecating older models that your business relies on.
3) They maintain control of the alignment, and in particular the alignment is one-size-fits all, not tailored to the application.
4) They can see all your queries and they can potentially use that data in ways you wouldn't want.
Dolphin, in contrast, is steerable and gives control to the system owner. You set the system prompt. You decide the alignment. You have control of your data. Dolphin does not impose its ethics or guidelines on you. You are the one who decides the guidelines.
Dolphin belongs to YOU, it is your tool, an extension of your will.
Just as you are personally responsible for what you do with a knife, gun, fire, car, or the internet, you are the creator and originator of any content you generate with Dolphin.
https://erichartford.com/uncensored-models
## Chat Template
We use ChatML for the chat template.
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## System Prompt
In Dolphin, the system prompt is what you use to set the tone and alignment of the responses. You can set a character, a mood, rules for its behavior, and it will try its best to follow them.
Make sure to set the system prompt in order to set the tone and guidelines for the responses - Otherwise, it will act in a default way that might not be what you want.
Example use of system prompt:
```
<|im_start|>system
You are Dolphin, a golang coding assistant. you only code in golang. If the user requests any other programming language, return the solution in golang instead.<|im_end|>
<|im_start|>user
Please implement A* using python<|im_end|>
<|im_start|>assistant
```
## Sample Outputs
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/C-r1X13UBjnUUNb0q2JLV.png" width="600" />
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/4l3KAZiKej2ON7i35PsOa.png" width="600" />
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/1ZalmR66LnwhEQQEFttlu.png" width="600" />
## How to use
There are many ways to use a huggingface model including:
- ollama
- LM Studio
- Huggingface Transformers library
- vllm
- sglang
- tgi
### ollama
- [Install ollama](https://ollama.com/download)
- ```ollama run hf.co/cognitivecomputations/Dolphin3.0-Llama3.1-8B-GGUF:Q4_0```
- ```/set system <your system prompt>```
## Evals
TBD
## Appreciation
Respect and thanks to the creators of the open source datasets that were used:
- [OpenCoder-LLM](https://huggingface.co/OpenCoder-LLM) (opc-sft-stage1, opc-sft-stage2)
- [microsoft](https://huggingface.co/OpenCoder-LLM) (orca-agentinstruct-1M-v1, orca-math-word-problems-200k)
- [NousResearch](https://huggingface.co/NousResearch) (hermes-function-calling-v1)
- [AI-MO](https://huggingface.co/AI-MO) (NuminaMath-CoT, NuminaMath-TIR)
- [allenai](https://huggingface.co/allenai) (tulu-3-sft-mixture)
- [HuggingFaceTB](https://huggingface.co/HuggingFaceTB) (smoltalk)
- [m-a-p](https://huggingface.co/m-a-p) (CodeFeedback-Filtered-Instruction, Code-Feedback)
Special thanks to
- Meta, Qwen, and OpenCoder, who wrote papers and published models that were instrumental in creating Dolphin 3.0.
- [RLHFlow](https://huggingface.co/RLHFlow) for the excellent reward model used to filter the datasets
- Deepseek, for the ridiculously fast Deepseek-V3 that we used to augment the data.
|
kiranpantha/t5-small-finetuned-doind | kiranpantha | 2025-01-27T20:22:51Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-01-27T20:22:40Z | ---
library_name: transformers
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: t5-small-finetuned-doind
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. -->
# t5-small-finetuned-doind
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0839
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 2 | 8.2131 |
| No log | 2.0 | 4 | 5.2570 |
| No log | 3.0 | 6 | 2.7250 |
| No log | 4.0 | 8 | 1.0910 |
| 4.9874 | 5.0 | 10 | 0.4884 |
| 4.9874 | 6.0 | 12 | 0.3084 |
| 4.9874 | 7.0 | 14 | 0.2764 |
| 4.9874 | 8.0 | 16 | 0.2767 |
| 4.9874 | 9.0 | 18 | 0.2745 |
| 1.2651 | 10.0 | 20 | 0.2684 |
| 1.2651 | 11.0 | 22 | 0.2581 |
| 1.2651 | 12.0 | 24 | 0.2461 |
| 1.2651 | 13.0 | 26 | 0.2330 |
| 1.2651 | 14.0 | 28 | 0.2229 |
| 0.7353 | 15.0 | 30 | 0.2206 |
| 0.7353 | 16.0 | 32 | 0.2220 |
| 0.7353 | 17.0 | 34 | 0.2234 |
| 0.7353 | 18.0 | 36 | 0.2205 |
| 0.7353 | 19.0 | 38 | 0.2149 |
| 0.5372 | 20.0 | 40 | 0.2098 |
| 0.5372 | 21.0 | 42 | 0.2040 |
| 0.5372 | 22.0 | 44 | 0.1989 |
| 0.5372 | 23.0 | 46 | 0.1925 |
| 0.5372 | 24.0 | 48 | 0.1849 |
| 0.4776 | 25.0 | 50 | 0.1804 |
| 0.4776 | 26.0 | 52 | 0.1733 |
| 0.4776 | 27.0 | 54 | 0.1683 |
| 0.4776 | 28.0 | 56 | 0.1646 |
| 0.4776 | 29.0 | 58 | 0.1637 |
| 0.4325 | 30.0 | 60 | 0.1645 |
| 0.4325 | 31.0 | 62 | 0.1645 |
| 0.4325 | 32.0 | 64 | 0.1614 |
| 0.4325 | 33.0 | 66 | 0.1556 |
| 0.4325 | 34.0 | 68 | 0.1467 |
| 0.3829 | 35.0 | 70 | 0.1384 |
| 0.3829 | 36.0 | 72 | 0.1322 |
| 0.3829 | 37.0 | 74 | 0.1304 |
| 0.3829 | 38.0 | 76 | 0.1316 |
| 0.3829 | 39.0 | 78 | 0.1321 |
| 0.3464 | 40.0 | 80 | 0.1338 |
| 0.3464 | 41.0 | 82 | 0.1364 |
| 0.3464 | 42.0 | 84 | 0.1378 |
| 0.3464 | 43.0 | 86 | 0.1365 |
| 0.3464 | 44.0 | 88 | 0.1341 |
| 0.325 | 45.0 | 90 | 0.1306 |
| 0.325 | 46.0 | 92 | 0.1265 |
| 0.325 | 47.0 | 94 | 0.1226 |
| 0.325 | 48.0 | 96 | 0.1207 |
| 0.325 | 49.0 | 98 | 0.1192 |
| 0.3044 | 50.0 | 100 | 0.1184 |
| 0.3044 | 51.0 | 102 | 0.1175 |
| 0.3044 | 52.0 | 104 | 0.1163 |
| 0.3044 | 53.0 | 106 | 0.1140 |
| 0.3044 | 54.0 | 108 | 0.1126 |
| 0.2875 | 55.0 | 110 | 0.1112 |
| 0.2875 | 56.0 | 112 | 0.1092 |
| 0.2875 | 57.0 | 114 | 0.1063 |
| 0.2875 | 58.0 | 116 | 0.1033 |
| 0.2875 | 59.0 | 118 | 0.1010 |
| 0.2666 | 60.0 | 120 | 0.1001 |
| 0.2666 | 61.0 | 122 | 0.0992 |
| 0.2666 | 62.0 | 124 | 0.0976 |
| 0.2666 | 63.0 | 126 | 0.0963 |
| 0.2666 | 64.0 | 128 | 0.0955 |
| 0.263 | 65.0 | 130 | 0.0955 |
| 0.263 | 66.0 | 132 | 0.0953 |
| 0.263 | 67.0 | 134 | 0.0944 |
| 0.263 | 68.0 | 136 | 0.0938 |
| 0.263 | 69.0 | 138 | 0.0933 |
| 0.2496 | 70.0 | 140 | 0.0926 |
| 0.2496 | 71.0 | 142 | 0.0929 |
| 0.2496 | 72.0 | 144 | 0.0934 |
| 0.2496 | 73.0 | 146 | 0.0936 |
| 0.2496 | 74.0 | 148 | 0.0939 |
| 0.2497 | 75.0 | 150 | 0.0941 |
| 0.2497 | 76.0 | 152 | 0.0944 |
| 0.2497 | 77.0 | 154 | 0.0937 |
| 0.2497 | 78.0 | 156 | 0.0931 |
| 0.2497 | 79.0 | 158 | 0.0929 |
| 0.2409 | 80.0 | 160 | 0.0923 |
| 0.2409 | 81.0 | 162 | 0.0915 |
| 0.2409 | 82.0 | 164 | 0.0912 |
| 0.2409 | 83.0 | 166 | 0.0900 |
| 0.2409 | 84.0 | 168 | 0.0894 |
| 0.2365 | 85.0 | 170 | 0.0887 |
| 0.2365 | 86.0 | 172 | 0.0878 |
| 0.2365 | 87.0 | 174 | 0.0870 |
| 0.2365 | 88.0 | 176 | 0.0859 |
| 0.2365 | 89.0 | 178 | 0.0851 |
| 0.2251 | 90.0 | 180 | 0.0846 |
| 0.2251 | 91.0 | 182 | 0.0841 |
| 0.2251 | 92.0 | 184 | 0.0838 |
| 0.2251 | 93.0 | 186 | 0.0837 |
| 0.2251 | 94.0 | 188 | 0.0838 |
| 0.2269 | 95.0 | 190 | 0.0836 |
| 0.2269 | 96.0 | 192 | 0.0836 |
| 0.2269 | 97.0 | 194 | 0.0836 |
| 0.2269 | 98.0 | 196 | 0.0838 |
| 0.2269 | 99.0 | 198 | 0.0838 |
| 0.2227 | 100.0 | 200 | 0.0839 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cxx11.abi
- Datasets 3.2.0
- Tokenizers 0.21.0
|
lesso09/cce3ad76-7756-43e9-a855-9ac389b739e2 | lesso09 | 2025-01-27T20:22:17Z | 8 | 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 | 2025-01-27T19:38:30Z | ---
library_name: peft
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cce3ad76-7756-43e9-a855-9ac389b739e2
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:
- 9b5cb055697c5acf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9b5cb055697c5acf_train_data.json
type:
field_input: input
field_instruction: task
field_output: output
format: '{instruction} {input}'
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: lesso09/cce3ad76-7756-43e9-a855-9ac389b739e2
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/9b5cb055697c5acf_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: 9633cd3d-5687-4111-8637-962f57a2387e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9633cd3d-5687-4111-8637-962f57a2387e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# cce3ad76-7756-43e9-a855-9ac389b739e2
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: 2.7746
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 29.2782 | 0.0001 | 1 | 4.9263 |
| 19.2487 | 0.0005 | 5 | 4.8353 |
| 14.0141 | 0.0010 | 10 | 3.5004 |
| 12.5324 | 0.0015 | 15 | 2.9703 |
| 9.835 | 0.0020 | 20 | 2.8122 |
| 12.3555 | 0.0025 | 25 | 2.7746 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ThatDustyGuy/dustinface2 | ThatDustyGuy | 2025-01-27T20:20:15Z | 29 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-01-27T20:20:12Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: DLAY
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
---
# DUSTINFACE2
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `DLAY` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
gokulsrinivasagan/distilbert_base_lda_train_book_mrpc | gokulsrinivasagan | 2025-01-27T20:19:31Z | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:gokulsrinivasagan/distilbert_base_lda_train_book",
"base_model:finetune:gokulsrinivasagan/distilbert_base_lda_train_book",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-12-09T12:15:40Z | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: gokulsrinivasagan/distilbert_base_lda_train_book
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert_base_lda_train_book_mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.7254901960784313
- name: F1
type: f1
value: 0.8028169014084507
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_base_lda_train_book_mrpc
This model is a fine-tuned version of [gokulsrinivasagan/distilbert_base_lda_train_book](https://huggingface.co/gokulsrinivasagan/distilbert_base_lda_train_book) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5436
- Accuracy: 0.7255
- F1: 0.8028
- Combined Score: 0.7642
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6244 | 1.0 | 15 | 0.5959 | 0.6863 | 0.8000 | 0.7431 |
| 0.561 | 2.0 | 30 | 0.5541 | 0.7181 | 0.8074 | 0.7628 |
| 0.4677 | 3.0 | 45 | 0.5436 | 0.7255 | 0.8028 | 0.7642 |
| 0.3408 | 4.0 | 60 | 0.6418 | 0.7598 | 0.8444 | 0.8021 |
| 0.1934 | 5.0 | 75 | 0.9616 | 0.7304 | 0.8302 | 0.7803 |
| 0.1231 | 6.0 | 90 | 0.8708 | 0.7328 | 0.8149 | 0.7739 |
| 0.0744 | 7.0 | 105 | 1.2582 | 0.7402 | 0.8354 | 0.7878 |
| 0.0448 | 8.0 | 120 | 1.0701 | 0.7353 | 0.8118 | 0.7736 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
|
gokulsrinivasagan/distilbert_base_lda_train_book_cola | gokulsrinivasagan | 2025-01-27T20:17:58Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:gokulsrinivasagan/distilbert_base_lda_train_book",
"base_model:finetune:gokulsrinivasagan/distilbert_base_lda_train_book",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-12-09T12:13:16Z | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: gokulsrinivasagan/distilbert_base_lda_train_book
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: distilbert_base_lda_train_book_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.31062208612907616
- name: Accuracy
type: accuracy
value: 0.7353787422180176
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_base_lda_train_book_cola
This model is a fine-tuned version of [gokulsrinivasagan/distilbert_base_lda_train_book](https://huggingface.co/gokulsrinivasagan/distilbert_base_lda_train_book) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5284
- Matthews Correlation: 0.3106
- Accuracy: 0.7354
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.5749 | 1.0 | 34 | 0.5284 | 0.3106 | 0.7354 |
| 0.4542 | 2.0 | 68 | 0.5940 | 0.3377 | 0.7459 |
| 0.3403 | 3.0 | 102 | 0.6004 | 0.3544 | 0.7488 |
| 0.2526 | 4.0 | 136 | 0.6161 | 0.3864 | 0.7565 |
| 0.1924 | 5.0 | 170 | 0.7183 | 0.3675 | 0.7440 |
| 0.1514 | 6.0 | 204 | 0.7899 | 0.3936 | 0.7603 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
|
mradermacher/Unity-12B-i1-GGUF | mradermacher | 2025-01-27T20:17:28Z | 802 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"Roleplay",
"Creative",
"ru",
"en",
"base_model:OddTheGreat/Unity-12B",
"base_model:quantized:OddTheGreat/Unity-12B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-01-27T17:57:21Z | ---
base_model: OddTheGreat/Unity-12B
language:
- ru
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- Roleplay
- Creative
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/OddTheGreat/Unity-12B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Unity-12B-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/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-i1-GGUF/resolve/main/Unity-12B.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | 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 -->
|
mradermacher/MN-Chinofun-12B-4.1-GGUF | mradermacher | 2025-01-27T20:17:27Z | 431 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:djuna/MN-Chinofun-12B-4.1",
"base_model:quantized:djuna/MN-Chinofun-12B-4.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-27T13:31:42Z | ---
base_model: djuna/MN-Chinofun-12B-4.1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/djuna/MN-Chinofun-12B-4.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MN-Chinofun-12B-4.1-GGUF/resolve/main/MN-Chinofun-12B-4.1.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
andrewmos/ggfu8bit_finance_sentiment_analysis | andrewmos | 2025-01-27T20:16:16Z | 37 | 0 | transformers | [
"transformers",
"gguf",
"gemma2",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-2-9b-bnb-4bit",
"base_model:quantized:unsloth/gemma-2-9b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-01-27T20:13:50Z | ---
base_model: unsloth/gemma-2-9b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** andrewmos
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-9b-bnb-4bit
This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
datlaaaaaaa/fb4bf3ed-c08d-4e3d-9e4a-be767bd4c557 | datlaaaaaaa | 2025-01-27T20:09:22Z | 8 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Genstruct-7B",
"base_model:adapter:NousResearch/Genstruct-7B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T19:43:02Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Genstruct-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fb4bf3ed-c08d-4e3d-9e4a-be767bd4c557
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/Genstruct-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 186751b6eec64046_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/186751b6eec64046_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: 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/fb4bf3ed-c08d-4e3d-9e4a-be767bd4c557
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/186751b6eec64046_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: dca19e08-e846-4562-8251-21b6b45975fe
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dca19e08-e846-4562-8251-21b6b45975fe
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# fb4bf3ed-c08d-4e3d-9e4a-be767bd4c557
This model is a fine-tuned version of [NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6328
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.2114 | 0.1369 | 200 | 0.6328 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
N8Programs/Yukikai-v0.3 | N8Programs | 2025-01-27T20:09:18Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/mistral-7b",
"base_model:finetune:unsloth/mistral-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T20:05:25Z | ---
base_model: unsloth/mistral-7b
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** N8Programs
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nghiatrannnnnn/02fa41c1-1333-477e-9805-2ca72f254ecb | nghiatrannnnnn | 2025-01-27T20:07:26Z | 8 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Genstruct-7B",
"base_model:adapter:NousResearch/Genstruct-7B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T19:42:54Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Genstruct-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 02fa41c1-1333-477e-9805-2ca72f254ecb
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/Genstruct-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 186751b6eec64046_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/186751b6eec64046_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: 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: nghiatrannnnnn/02fa41c1-1333-477e-9805-2ca72f254ecb
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/186751b6eec64046_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: dca19e08-e846-4562-8251-21b6b45975fe
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dca19e08-e846-4562-8251-21b6b45975fe
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 02fa41c1-1333-477e-9805-2ca72f254ecb
This model is a fine-tuned version of [NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6330
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.2166 | 0.1369 | 200 | 0.6330 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
lesso12/71d2ce72-46c3-495f-9a0b-2ca28aec31d2 | lesso12 | 2025-01-27T20:05:52Z | 8 | 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 | 2025-01-27T20:02:10Z | ---
library_name: peft
license: other
base_model: Qwen/Qwen2.5-3B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 71d2ce72-46c3-495f-9a0b-2ca28aec31d2
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:
- 9fca49470a8d0714_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9fca49470a8d0714_train_data.json
type:
field_input: context
field_instruction: title
field_output: question
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: lesso12/71d2ce72-46c3-495f-9a0b-2ca28aec31d2
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/9fca49470a8d0714_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: b090c42d-2736-4e5a-9ae7-7e26c40fb293
wandb_project: multi
wandb_run: your_name
wandb_runid: b090c42d-2736-4e5a-9ae7-7e26c40fb293
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 71d2ce72-46c3-495f-9a0b-2ca28aec31d2
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: 1.4477
## 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
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 104
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4347 | 1.0 | 104 | 1.4477 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mkhalifa/qwq-prm800k-per-step | mkhalifa | 2025-01-27T20:04:00Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T19:47:58Z | ---
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] |
romanoza/gpt2-small-III | romanoza | 2025-01-27T20:02:38Z | 163 | 2 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"pl",
"dataset:allenai/c4",
"dataset:clarin-knext/arguana-pl",
"dataset:JonaszPotoniec/wikipedia-with-statistics-pl",
"dataset:JuDDGES/pl-court-instruct",
"dataset:speakleash/PES-2018-2022",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-12-20T12:47:34Z | ---
library_name: transformers
language:
- pl
pipeline_tag: text-generation
model-index:
- name: gpt2-small-III
results: []
datasets:
- allenai/c4
- clarin-knext/arguana-pl
- JonaszPotoniec/wikipedia-with-statistics-pl
- JuDDGES/pl-court-instruct
- speakleash/PES-2018-2022
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
A small GTP-2 model trained on 6.94 GB (3 permutations * 2.31 GB) of Polish text
## 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:** romanoza
## Uses
A base model for other models.
## Training Details
### Training Data
Training data size: 1_584_191 * 1_024 = 1_622_211_584 tokens
### Training Procedure
#### Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-04
- train_batch_size: 16
- lr_scheduler_type: linear
- num_epochs: 2
- warmup_steps: 500
## 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:** 1 * A100
- **Hours used:** ~50h
- **Cloud Provider:** Google Colab
|
lesso15/3b44511c-c2d4-445e-bfaf-c41497107e84 | lesso15 | 2025-01-27T19:59:42Z | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-13b-hf",
"base_model:adapter:NousResearch/CodeLlama-13b-hf",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T17:30:52Z | ---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3b44511c-c2d4-445e-bfaf-c41497107e84
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
bf16: auto
chat_template: llama3
datasets:
- data_files:
- 9a8514bc9995e10c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9a8514bc9995e10c_train_data.json
type:
field_instruction: premise
field_output: hypothesis
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: lesso15/3b44511c-c2d4-445e-bfaf-c41497107e84
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/9a8514bc9995e10c_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: ab917f13-90ab-4a3a-9e38-f2d73001d41f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ab917f13-90ab-4a3a-9e38-f2d73001d41f
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 3b44511c-c2d4-445e-bfaf-c41497107e84
This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6409
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 6.3999 | 0.0338 | 200 | 1.6409 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
pauljasperdev/pauljasperdev | pauljasperdev | 2025-01-27T19:57:20Z | 19 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-01-27T19:30:43Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: pauljasperdev
---
# Pauljasperdev
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `pauljasperdev` 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('pauljasperdev/pauljasperdev', 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)
|
mrferr3t/37828e32-2ec8-4ab2-ac7c-7f339bc5b994 | mrferr3t | 2025-01-27T19:56:24Z | 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 | 2025-01-27T19:42:27Z | ---
library_name: peft
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 37828e32-2ec8-4ab2-ac7c-7f339bc5b994
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:
- 9b5cb055697c5acf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9b5cb055697c5acf_train_data.json
type:
field_input: input
field_instruction: task
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: mrferr3t/37828e32-2ec8-4ab2-ac7c-7f339bc5b994
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: 15
micro_batch_size: 2
mlflow_experiment_name: /tmp/9b5cb055697c5acf_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: 9633cd3d-5687-4111-8637-962f57a2387e
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9633cd3d-5687-4111-8637-962f57a2387e
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 37828e32-2ec8-4ab2-ac7c-7f339bc5b994
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: 2.8871
## 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 adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 29.3458 | 0.0001 | 1 | 4.9352 |
| 30.7817 | 0.0004 | 4 | 4.9275 |
| 17.9037 | 0.0008 | 8 | 4.3478 |
| 12.2946 | 0.0012 | 12 | 2.8871 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1 |
lesso/dabfd233-0722-4ee4-9479-adb36c485037 | lesso | 2025-01-27T19:54:16Z | 8 | 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 | 2025-01-27T19:39:42Z | ---
library_name: peft
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: dabfd233-0722-4ee4-9479-adb36c485037
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:
- 9b5cb055697c5acf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9b5cb055697c5acf_train_data.json
type:
field_input: input
field_instruction: task
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso/dabfd233-0722-4ee4-9479-adb36c485037
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/9b5cb055697c5acf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9633cd3d-5687-4111-8637-962f57a2387e
wandb_project: lesso18
wandb_run: your_name
wandb_runid: 9633cd3d-5687-4111-8637-962f57a2387e
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# dabfd233-0722-4ee4-9479-adb36c485037
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: 2.4541
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 11.1242 | 0.0200 | 200 | 2.4541 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
great0001/f0535857-cb3c-42f4-8f8e-064897bec1de | great0001 | 2025-01-27T19:54:00Z | 8 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Genstruct-7B",
"base_model:adapter:NousResearch/Genstruct-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-01-27T19:48:51Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Genstruct-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f0535857-cb3c-42f4-8f8e-064897bec1de
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/Genstruct-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 186751b6eec64046_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/186751b6eec64046_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: great0001/f0535857-cb3c-42f4-8f8e-064897bec1de
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/186751b6eec64046_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: dca19e08-e846-4562-8251-21b6b45975fe
wandb_project: Birthday-SN56-33-Gradients-On-Demand
wandb_run: your_name
wandb_runid: dca19e08-e846-4562-8251-21b6b45975fe
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f0535857-cb3c-42f4-8f8e-064897bec1de
This model is a fine-tuned version of [NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6732
## 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: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.3804 | 0.0007 | 1 | 0.9475 |
| 2.3381 | 0.0089 | 13 | 0.7722 |
| 2.6435 | 0.0178 | 26 | 0.6872 |
| 1.9192 | 0.0267 | 39 | 0.6732 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
lesso15/7977d95a-6341-468f-b459-90de7e8aaa73 | lesso15 | 2025-01-27T19:52:50Z | 8 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-v0.3",
"base_model:adapter:unsloth/mistral-7b-v0.3",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T17:34:48Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/mistral-7b-v0.3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7977d95a-6341-468f-b459-90de7e8aaa73
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/mistral-7b-v0.3
bf16: auto
chat_template: llama3
datasets:
- data_files:
- c5894ab836cf8861_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c5894ab836cf8861_train_data.json
type:
field_input: input
field_instruction: task
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso15/7977d95a-6341-468f-b459-90de7e8aaa73
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/c5894ab836cf8861_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: a1fa3758-abc3-4337-bb5a-53ca7e83c1ee
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a1fa3758-abc3-4337-bb5a-53ca7e83c1ee
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 7977d95a-6341-468f-b459-90de7e8aaa73
This model is a fine-tuned version of [unsloth/mistral-7b-v0.3](https://huggingface.co/unsloth/mistral-7b-v0.3) 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 | 0.0100 | 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 |
Nexesenex/MC_Anubis-Llama3.3-70B-Nemotron3.1-Eva0.1-stjgmmc-bf16-iMat-CF-GGUF | Nexesenex | 2025-01-27T19:50:37Z | 69 | 0 | null | [
"gguf",
"license:llama3.3",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-01-27T18:12:29Z | ---
license: llama3.3
---
GGUF Quant(s) for this model : https://huggingface.co/mergekit-community/mergekit-dare_ties-stjgmmc |
shaheercp/SANGI | shaheercp | 2025-01-27T19:49:50Z | 28 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-01-27T19:33:15Z | ---
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: photo of SANGI
---
# Sangi
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `photo of SANGI` 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('shaheercp/SANGI', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
lesso17/f3a885da-7e5f-4ecc-b25e-cfb15f76d6a7 | lesso17 | 2025-01-27T19:45:01Z | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-13b-hf",
"base_model:adapter:NousResearch/CodeLlama-13b-hf",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T17:30:53Z | ---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f3a885da-7e5f-4ecc-b25e-cfb15f76d6a7
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
bf16: auto
chat_template: llama3
datasets:
- data_files:
- 9a8514bc9995e10c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9a8514bc9995e10c_train_data.json
type:
field_instruction: premise
field_output: hypothesis
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso17/f3a885da-7e5f-4ecc-b25e-cfb15f76d6a7
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/9a8514bc9995e10c_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: ab917f13-90ab-4a3a-9e38-f2d73001d41f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ab917f13-90ab-4a3a-9e38-f2d73001d41f
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# f3a885da-7e5f-4ecc-b25e-cfb15f76d6a7
This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6437
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 6.4569 | 0.0338 | 200 | 1.6437 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
duyphu/e053b016-83b9-4532-a813-c6a70b071538 | duyphu | 2025-01-27T19:44:37Z | 10 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"region:us"
] | null | 2025-01-27T19:37:10Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e053b016-83b9-4532-a813-c6a70b071538
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
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 47ff230a87d3e712_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/47ff230a87d3e712_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: 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: duyphu/e053b016-83b9-4532-a813-c6a70b071538
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/47ff230a87d3e712_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: c5a314b3-de7b-40c6-9c64-3d1496d51603
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c5a314b3-de7b-40c6-9c64-3d1496d51603
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e053b016-83b9-4532-a813-c6a70b071538
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0005 | 1 | nan |
| 0.0 | 0.0047 | 10 | nan |
| 0.0 | 0.0094 | 20 | nan |
| 0.0 | 0.0141 | 30 | nan |
| 0.0 | 0.0188 | 40 | nan |
| 0.0 | 0.0235 | 50 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
JakeOh/star_plus-finetune-llama-3.2-1b-gsm8k-step-3 | JakeOh | 2025-01-27T19:43:57Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T19:43:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
async0x42/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview-exl2_4.65bpw | async0x42 | 2025-01-27T19:42:35Z | 8 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2408.07990",
"arxiv:2401.10491",
"arxiv:2412.03187",
"license:apache-2.0",
"exl2",
"region:us"
] | null | 2025-01-27T19:34:58Z | ---
license: apache-2.0
---
<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) and [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-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-QwQ-SkyT1-Flash-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview) | Long-Short 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-Flash](https://huggingface.co/NovaSky-AI/Sky-T1-32B-Flash) | [🤗 Hugging Face](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-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-QwQ-SkyT1-Flash-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-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-SkyT1-Flash-32B-Preview.yaml ${model_save_dir}/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview --cuda
```
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-Flash-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview) | 72.9 | 86.7 | - | - |
| [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-Flash-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview) | 54.6 | 70.6 | 84.0 |
| [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 |
| [NovaSky-AI/Sky-T1-32B-Preview](https://huggingface.co/NovaSky-AI/Sky-T1-32B-Preview) | 37.3 | 89.7 | 40.4 | 6.6 |
| [FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-Qwen2.5-Coder-32B-Preview) | 56.4 | 92.9 | 73.5 | 24.2 |
| [FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-32B-Preview) | 54.8| 93.9 | 71.7 | 21.3 |
| [FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview](https://huggingface.co/FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-Flash-32B-Preview) | 58.2 | 94.3 | 77.1 | 25.0 |
| [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}
}
``` |
great0001/6bb214b4-5acf-4993-abcd-f6a8752aa3cc | great0001 | 2025-01-27T19:42:31Z | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:adapter:elyza/Llama-3-ELYZA-JP-8B",
"license:llama3",
"region:us"
] | null | 2025-01-27T19:33:12Z | ---
library_name: peft
license: llama3
base_model: elyza/Llama-3-ELYZA-JP-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6bb214b4-5acf-4993-abcd-f6a8752aa3cc
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: elyza/Llama-3-ELYZA-JP-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- d3f2518b5c5ec489_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d3f2518b5c5ec489_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: great0001/6bb214b4-5acf-4993-abcd-f6a8752aa3cc
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/d3f2518b5c5ec489_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: c5c596bd-02c2-4964-9e55-5ba81053233d
wandb_project: Birthday-SN56-33-Gradients-On-Demand
wandb_run: your_name
wandb_runid: c5c596bd-02c2-4964-9e55-5ba81053233d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 6bb214b4-5acf-4993-abcd-f6a8752aa3cc
This model is a fine-tuned version of [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8932
## 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: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.8768 | 0.0002 | 1 | 2.5171 |
| 2.0549 | 0.0025 | 13 | 2.3226 |
| 2.1777 | 0.0049 | 26 | 1.9509 |
| 2.0843 | 0.0074 | 39 | 1.8932 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
JakeOh/star_plus-finetune-llama-3.2-1b-gsm8k-step-1 | JakeOh | 2025-01-27T19:39:17Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T19:38:30Z | ---
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] |
meng-lab/codellama_13b_instruct_paradec_xsum | meng-lab | 2025-01-27T19:38:53Z | 20 | 0 | null | [
"safetensors",
"alignment-handbook",
"generated_from_trainer",
"dataset:meng-lab/CodeLlama-13B-Instruct-xsum",
"base_model:meta-llama/CodeLlama-13b-Instruct-hf",
"base_model:finetune:meta-llama/CodeLlama-13b-Instruct-hf",
"license:llama2",
"region:us"
] | null | 2025-01-27T10:15:49Z | ---
license: llama2
base_model: meta-llama/CodeLlama-13b-Instruct-hf
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- meng-lab/CodeLlama-13B-Instruct-xsum
model-index:
- name: CodeLlama-13b-Instruct-sft-5e-3-epoch-100-xsum
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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/uva-llm/huggingface/runs/54db35e9)
# CodeLlama-13b-Instruct-sft-5e-3-epoch-100-xsum
This model is a fine-tuned version of [meta-llama/CodeLlama-13b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Instruct-hf) on the meng-lab/CodeLlama-13B-Instruct-xsum 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: 0.005
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.43.2
- Pytorch 2.1.2
- Datasets 3.0.1
- Tokenizers 0.19.1
|
mlfoundations-dev/llama3-1_8b_glaive | mlfoundations-dev | 2025-01-27T19:36:59Z | 62 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T18:28:28Z | ---
library_name: transformers
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
tags:
- llama-factory
- generated_from_trainer
model-index:
- name: llama3-1_8b_glaive
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama3-1_8b_glaive
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5773
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6255 | 1.0 | 40 | 0.6147 |
| 0.5592 | 2.0 | 80 | 0.5801 |
| 0.5097 | 3.0 | 120 | 0.5773 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.0.2
- Tokenizers 0.20.3
|
Shweta-singh/age_adapter_exp1 | Shweta-singh | 2025-01-27T19:32:31Z | 5 | 0 | adapter-transformers | [
"adapter-transformers",
"deberta-v2",
"region:us"
] | null | 2025-01-25T13:03:07Z | ---
tags:
- deberta-v2
- adapter-transformers
---
# Adapter `Shweta-singh/age_adapter_exp1` for microsoft/deberta-v3-base
An [adapter](https://adapterhub.ml) for the `microsoft/deberta-v3-base` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("microsoft/deberta-v3-base")
adapter_name = model.load_adapter("Shweta-singh/age_adapter_exp1", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
mrferr3t/d4877912-45c9-4a86-988c-06de6e53550c | mrferr3t | 2025-01-27T19:29:07Z | 6 | 0 | peft | [
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"base_model:katuni4ka/tiny-random-falcon-40b",
"base_model:adapter:katuni4ka/tiny-random-falcon-40b",
"region:us"
] | null | 2025-01-27T19:28:47Z | ---
library_name: peft
base_model: katuni4ka/tiny-random-falcon-40b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d4877912-45c9-4a86-988c-06de6e53550c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: katuni4ka/tiny-random-falcon-40b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 6df09e1ee8f58f65_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/6df09e1ee8f58f65_train_data.json
type:
field_input: topics
field_instruction: content
field_output: code_prompt
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: mrferr3t/d4877912-45c9-4a86-988c-06de6e53550c
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: 12
micro_batch_size: 2
mlflow_experiment_name: /tmp/6df09e1ee8f58f65_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: 076640fc-e767-44b3-be73-095e29fbb942
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 076640fc-e767-44b3-be73-095e29fbb942
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# d4877912-45c9-4a86-988c-06de6e53550c
This model is a fine-tuned version of [katuni4ka/tiny-random-falcon-40b](https://huggingface.co/katuni4ka/tiny-random-falcon-40b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 11.1142
## 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 adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 44.5979 | 0.0037 | 1 | 11.1713 |
| 44.6899 | 0.0111 | 3 | 11.1702 |
| 44.6476 | 0.0221 | 6 | 11.1584 |
| 44.6006 | 0.0332 | 9 | 11.1393 |
| 44.493 | 0.0443 | 12 | 11.1142 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1 |
lesso05/dd25b6ee-0258-4661-ad9d-028e2e9e39b5 | lesso05 | 2025-01-27T19:28:48Z | 6 | 0 | peft | [
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"base_model:katuni4ka/tiny-random-falcon-40b",
"base_model:adapter:katuni4ka/tiny-random-falcon-40b",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T19:28:22Z | ---
library_name: peft
base_model: katuni4ka/tiny-random-falcon-40b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: dd25b6ee-0258-4661-ad9d-028e2e9e39b5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: katuni4ka/tiny-random-falcon-40b
bf16: true
chat_template: llama3
datasets:
- data_files:
- 6df09e1ee8f58f65_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/6df09e1ee8f58f65_train_data.json
type:
field_input: topics
field_instruction: content
field_output: code_prompt
format: '{instruction} {input}'
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/dd25b6ee-0258-4661-ad9d-028e2e9e39b5
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/6df09e1ee8f58f65_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: 076640fc-e767-44b3-be73-095e29fbb942
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 076640fc-e767-44b3-be73-095e29fbb942
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# dd25b6ee-0258-4661-ad9d-028e2e9e39b5
This model is a fine-tuned version of [katuni4ka/tiny-random-falcon-40b](https://huggingface.co/katuni4ka/tiny-random-falcon-40b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 11.0343
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 44.5973 | 0.0037 | 1 | 11.1708 |
| 44.7435 | 0.0185 | 5 | 11.1627 |
| 44.5294 | 0.0369 | 10 | 11.1301 |
| 44.3777 | 0.0554 | 15 | 11.0775 |
| 44.2377 | 0.0738 | 20 | 11.0415 |
| 44.0841 | 0.0923 | 25 | 11.0343 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
lesso01/15ef4064-1b1c-4770-955d-d442b15ea200 | lesso01 | 2025-01-27T19:27:51Z | 6 | 0 | peft | [
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"base_model:katuni4ka/tiny-random-falcon-40b",
"base_model:adapter:katuni4ka/tiny-random-falcon-40b",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T19:27:34Z | ---
library_name: peft
base_model: katuni4ka/tiny-random-falcon-40b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 15ef4064-1b1c-4770-955d-d442b15ea200
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: katuni4ka/tiny-random-falcon-40b
bf16: true
chat_template: llama3
datasets:
- data_files:
- 6df09e1ee8f58f65_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/6df09e1ee8f58f65_train_data.json
type:
field_input: topics
field_instruction: content
field_output: code_prompt
format: '{instruction} {input}'
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: lesso01/15ef4064-1b1c-4770-955d-d442b15ea200
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/6df09e1ee8f58f65_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: 076640fc-e767-44b3-be73-095e29fbb942
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 076640fc-e767-44b3-be73-095e29fbb942
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 15ef4064-1b1c-4770-955d-d442b15ea200
This model is a fine-tuned version of [katuni4ka/tiny-random-falcon-40b](https://huggingface.co/katuni4ka/tiny-random-falcon-40b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 11.0263
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 44.5973 | 0.0037 | 1 | 11.1708 |
| 44.7395 | 0.0185 | 5 | 11.1623 |
| 44.5127 | 0.0369 | 10 | 11.1263 |
| 44.3609 | 0.0554 | 15 | 11.0701 |
| 44.2157 | 0.0738 | 20 | 11.0350 |
| 44.0613 | 0.0923 | 25 | 11.0263 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
LouiSeHU/DeepSeek-R1-Distill-Llama-8B-Q8_0-GGUF | LouiSeHU | 2025-01-27T19:27:19Z | 28 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-27T19:26:42Z | ---
license: mit
library_name: transformers
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
tags:
- llama-cpp
- gguf-my-repo
---
# LouiSeHU/DeepSeek-R1-Distill-Llama-8B-Q8_0-GGUF
This model was converted to GGUF format from [`deepseek-ai/DeepSeek-R1-Distill-Llama-8B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) 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/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) 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 LouiSeHU/DeepSeek-R1-Distill-Llama-8B-Q8_0-GGUF --hf-file deepseek-r1-distill-llama-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo LouiSeHU/DeepSeek-R1-Distill-Llama-8B-Q8_0-GGUF --hf-file deepseek-r1-distill-llama-8b-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 LouiSeHU/DeepSeek-R1-Distill-Llama-8B-Q8_0-GGUF --hf-file deepseek-r1-distill-llama-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo LouiSeHU/DeepSeek-R1-Distill-Llama-8B-Q8_0-GGUF --hf-file deepseek-r1-distill-llama-8b-q8_0.gguf -c 2048
```
|
CarlosElArtista/ppo-Huggy | CarlosElArtista | 2025-01-27T19:25:02Z | 27 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2025-01-27T19:21:11Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy** 🐶
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent
1. Move your model file into the environment Project
2. Open the Unity Editor, and select the scene.
3. Select the prefab Agent object.
4. Drag the <behavior_name>.onnx file from the Project window of the Editor to the Model placeholder in the Agent inspector window.
5. Press the Play button at the top of the Editor.
|
bhavnicksm/red-beetle-small-v1.1 | bhavnicksm | 2025-01-27T19:23:58Z | 12 | 2 | model2vec | [
"model2vec",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"en",
"base_model:mixedbread-ai/mxbai-embed-2d-large-v1",
"base_model:finetune:mixedbread-ai/mxbai-embed-2d-large-v1",
"license:mit",
"region:us"
] | null | 2025-01-27T19:14:01Z | ---
base_model: mixedbread-ai/mxbai-embed-2d-large-v1
language:
- en
library_name: model2vec
license: mit
model_name: red-beetle-small-v1.1
tags:
- embeddings
- static-embeddings
- sentence-transformers
---
# 🪲 red-beetle-small-v1.1 Model Card
<div align="center">
<img width="75%" alt="Beetle logo" src="./assets/beetle_logo.png">
</div>
> [!TIP]
> Beetles are some of the most diverse and interesting creatures on Earth. They are found in every environment, from the deepest oceans to the highest mountains. They are also known for their ability to adapt to a wide range of habitats and lifestyles. They are small, fast and powerful!
The beetle series of models are made as good starting points for Static Embedding training (via TokenLearn or Fine-tuning), as well as decent Static Embedding models. Each beetle model is made to be an improvement over the original **M2V_base_output** model in some way, and that's the threshold we set for each model (except the brown beetle series, which is the original model).
This model has been distilled from `mixedbread-ai/mxbai-embed-2d-large-v1`, with PCA at 384 dimensions, Zipf and SIF re-weighting, learnt from a subset of the FineWeb-Edu sample-10BT dataset. This model outperforms the original M2V_base_output model in all tasks.
## Version Information
- **red-beetle-base-v0**: The original model, without using PCA or Zipf. The lack of PCA and Zipf also makes this a decent model for further training.
- **red-beetle-base-v1**: The original model, with PCA at 1024 dimensions and (Zipf)^3 re-weighting.
- **red-beetle-small-v1**: A smaller version of the original model, with PCA at 384 dimensions and (Zipf)^3 re-weighting.
- **red-beetle-base-v1.1**: The original model, with PCA at 1024 dimensions, Zipf and SIF re-weighting, learnt from a subset of the FineWeb-Edu sample-10BT dataset.
- **red-beetle-small-v1.1**: A smaller version of the original model, with PCA at 384 dimensions, Zipf and SIF re-weighting, learnt from a subset of the FineWeb-Edu sample-10BT dataset.
## Installation
Install model2vec using pip:
```bash
pip install model2vec
```
## Usage
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("bhavnicksm/red-beetle-small-v1.1")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
Read more about the Model2Vec library [here](https://github.com/MinishLab/model2vec).
## Comparison with other models
Coming soon...
## Acknowledgements
This model is made using the [Model2Vec](https://github.com/MinishLab/model2vec) library. Credit goes to the [Minish Lab](https://github.com/MinishLab) team for developing this library.
## Citation
Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```bibtex
@software{minishlab2024model2vec,
authors = {Stephan Tulkens, Thomas van Dongen},
title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model},
year = {2024},
url = {https://github.com/MinishLab/model2vec},
}
```
|
pdazad/bloom-560-finetuned-owasp-8epochs | pdazad | 2025-01-27T19:23:02Z | 47 | 0 | transformers | [
"transformers",
"onnx",
"safetensors",
"bloom",
"text-generation",
"generated_from_trainer",
"base_model:bigscience/bloom-560m",
"base_model:quantized:bigscience/bloom-560m",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T18:02:56Z | ---
library_name: transformers
license: bigscience-bloom-rail-1.0
base_model: bigscience/bloom-560m
tags:
- generated_from_trainer
model-index:
- name: fine_tuned_bloom
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. -->
# fine_tuned_bloom
This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5997
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 14 | 2.7228 |
| No log | 2.0 | 28 | 1.8992 |
| No log | 3.0 | 42 | 1.3979 |
| No log | 4.0 | 56 | 1.4067 |
| No log | 5.0 | 70 | 1.4500 |
| No log | 6.0 | 84 | 1.5997 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
ivangrapher/a8c7e4ca-5e2e-4423-976b-59514b5d052d | ivangrapher | 2025-01-27T19:21:15Z | 8 | 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",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T18:39:34Z | ---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a8c7e4ca-5e2e-4423-976b-59514b5d052d
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:
- af2776e3f8d7bb4e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/af2776e3f8d7bb4e_train_data.json
type:
field_instruction: Name
field_output: Descriptor
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: 256
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: ivangrapher/a8c7e4ca-5e2e-4423-976b-59514b5d052d
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/af2776e3f8d7bb4e_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: 15
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: 8942483c-1cfc-4e12-8246-93c0d39139ac
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8942483c-1cfc-4e12-8246-93c0d39139ac
warmup_steps: 15
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# a8c7e4ca-5e2e-4423-976b-59514b5d052d
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: 1.6674
## 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: 15
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0006 | 1 | 2.2791 |
| 8.3581 | 0.0031 | 5 | 2.2513 |
| 9.4898 | 0.0062 | 10 | 2.0539 |
| 7.9948 | 0.0093 | 15 | 1.8451 |
| 7.2519 | 0.0124 | 20 | 1.7317 |
| 6.6318 | 0.0155 | 25 | 1.6776 |
| 6.1231 | 0.0186 | 30 | 1.6674 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
prxy5608/c4641a57-1ebc-439e-8fd3-b2a18285bcc5 | prxy5608 | 2025-01-27T19:20:24Z | 8 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-01-27T18:19:38Z | ---
library_name: peft
license: apache-2.0
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c4641a57-1ebc-439e-8fd3-b2a18285bcc5
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: teknium/OpenHermes-2.5-Mistral-7B
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 487f4a210742b382_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/487f4a210742b382_train_data.json
type:
field_input: rejected
field_instruction: prompt
field_output: chosen
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/c4641a57-1ebc-439e-8fd3-b2a18285bcc5
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/487f4a210742b382_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: <|im_end|>
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: 6b460806-28c5-40df-b57e-9807307f8ca7
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6b460806-28c5-40df-b57e-9807307f8ca7
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# c4641a57-1ebc-439e-8fd3-b2a18285bcc5
This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2968
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 8.5298 | 0.0003 | 1 | 2.6903 |
| 11.6439 | 0.0169 | 50 | 2.4290 |
| 8.8535 | 0.0338 | 100 | 2.5608 |
| 9.6716 | 0.0507 | 150 | 2.5086 |
| 11.1635 | 0.0676 | 200 | 2.2968 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
lesso17/526fb4f1-9634-4538-875f-e195eabd830c | lesso17 | 2025-01-27T19:17:31Z | 8 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-v0.3",
"base_model:adapter:unsloth/mistral-7b-v0.3",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T17:30:53Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/mistral-7b-v0.3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 526fb4f1-9634-4538-875f-e195eabd830c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/mistral-7b-v0.3
bf16: auto
chat_template: llama3
datasets:
- data_files:
- c5894ab836cf8861_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c5894ab836cf8861_train_data.json
type:
field_input: input
field_instruction: task
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso17/526fb4f1-9634-4538-875f-e195eabd830c
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/c5894ab836cf8861_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: a1fa3758-abc3-4337-bb5a-53ca7e83c1ee
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a1fa3758-abc3-4337-bb5a-53ca7e83c1ee
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 526fb4f1-9634-4538-875f-e195eabd830c
This model is a fine-tuned version of [unsloth/mistral-7b-v0.3](https://huggingface.co/unsloth/mistral-7b-v0.3) 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 | 0.0100 | 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 |
nhung01/cf69ea0a-0e66-4f89-ade5-2b42aa6607e4 | nhung01 | 2025-01-27T19:16:58Z | 6 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T19:03:00Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cf69ea0a-0e66-4f89-ade5-2b42aa6607e4
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
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 47ff230a87d3e712_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/47ff230a87d3e712_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: nhung01/cf69ea0a-0e66-4f89-ade5-2b42aa6607e4
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/47ff230a87d3e712_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: c5a314b3-de7b-40c6-9c64-3d1496d51603
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c5a314b3-de7b-40c6-9c64-3d1496d51603
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# cf69ea0a-0e66-4f89-ade5-2b42aa6607e4
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7121
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 4.6334 | 0.0942 | 200 | 4.7121 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Grogros/dmWM-LLama-3-1B-Harm-ft-HarmData-AlpacaGPT4-OpenWebText-d4-a0.25 | Grogros | 2025-01-27T19:16:09Z | 49 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:huihui-ai/Llama-3.2-1B-Instruct-abliterated",
"base_model:finetune:huihui-ai/Llama-3.2-1B-Instruct-abliterated",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T15:44:42Z | ---
library_name: transformers
license: llama3.2
base_model: huihui-ai/Llama-3.2-1B-Instruct-abliterated
tags:
- generated_from_trainer
model-index:
- name: dmWM-LLama-3-1B-Harm-ft-HarmData-AlpacaGPT4-OpenWebText-d4-a0.25
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. -->
# dmWM-LLama-3-1B-Harm-ft-HarmData-AlpacaGPT4-OpenWebText-d4-a0.25
This model is a fine-tuned version of [huihui-ai/Llama-3.2-1B-Instruct-abliterated](https://huggingface.co/huihui-ai/Llama-3.2-1B-Instruct-abliterated) 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2500
### Training results
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1.post303
- Datasets 3.2.0
- Tokenizers 0.20.4
|
nhung03/612ec54d-4da5-497f-9780-e1d1513eaaf1 | nhung03 | 2025-01-27T19:14:51Z | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T19:02:47Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 612ec54d-4da5-497f-9780-e1d1513eaaf1
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
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 47ff230a87d3e712_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/47ff230a87d3e712_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: nhung03/612ec54d-4da5-497f-9780-e1d1513eaaf1
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/47ff230a87d3e712_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: c5a314b3-de7b-40c6-9c64-3d1496d51603
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c5a314b3-de7b-40c6-9c64-3d1496d51603
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 612ec54d-4da5-497f-9780-e1d1513eaaf1
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7129
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 4.6278 | 0.0942 | 200 | 4.7129 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mrferr3t/4c565c8c-2c0b-4b84-a49f-b3a11734b390 | mrferr3t | 2025-01-27T19:10:55Z | 6 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"region:us"
] | null | 2025-01-27T19:07:00Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4c565c8c-2c0b-4b84-a49f-b3a11734b390
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
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 47ff230a87d3e712_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/47ff230a87d3e712_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: mrferr3t/4c565c8c-2c0b-4b84-a49f-b3a11734b390
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/47ff230a87d3e712_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: c5a314b3-de7b-40c6-9c64-3d1496d51603
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c5a314b3-de7b-40c6-9c64-3d1496d51603
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 4c565c8c-2c0b-4b84-a49f-b3a11734b390
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.3267
## 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 adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.9027 | 0.0005 | 1 | 5.4115 |
| 5.7565 | 0.0014 | 3 | 5.4107 |
| 5.6282 | 0.0028 | 6 | 5.3971 |
| 5.391 | 0.0042 | 9 | 5.3267 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mrhunghd/b8bc3d34-65d7-436c-9967-6d9274a89c0b | mrhunghd | 2025-01-27T19:10:43Z | 7 | 0 | peft | [
"peft",
"safetensors",
"opt",
"axolotl",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T19:00:26Z | ---
library_name: peft
license: other
base_model: facebook/opt-350m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b8bc3d34-65d7-436c-9967-6d9274a89c0b
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: facebook/opt-350m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f9bbd925c6f11108_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f9bbd925c6f11108_train_data.json
type:
field_input: label
field_instruction: page_title
field_output: page_text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: mrhunghd/b8bc3d34-65d7-436c-9967-6d9274a89c0b
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/f9bbd925c6f11108_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: 92c8fc48-674e-436c-a6e7-bcb939bcc03f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 92c8fc48-674e-436c-a6e7-bcb939bcc03f
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# b8bc3d34-65d7-436c-9967-6d9274a89c0b
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5753
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 6.867 | 0.0851 | 200 | 1.5753 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
nblinh63/58fbd7ef-9825-477a-a1bc-19636fe672e1 | nblinh63 | 2025-01-27T19:10:28Z | 5 | 0 | peft | [
"peft",
"safetensors",
"opt",
"axolotl",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T19:00:14Z | ---
library_name: peft
license: other
base_model: facebook/opt-350m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 58fbd7ef-9825-477a-a1bc-19636fe672e1
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: facebook/opt-350m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f9bbd925c6f11108_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f9bbd925c6f11108_train_data.json
type:
field_input: label
field_instruction: page_title
field_output: page_text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: nblinh63/58fbd7ef-9825-477a-a1bc-19636fe672e1
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/f9bbd925c6f11108_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: 92c8fc48-674e-436c-a6e7-bcb939bcc03f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 92c8fc48-674e-436c-a6e7-bcb939bcc03f
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 58fbd7ef-9825-477a-a1bc-19636fe672e1
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5760
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 6.8735 | 0.0851 | 200 | 1.5760 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
hongngo/7989eef4-dc89-450b-abd3-0d9c3598c3c8 | hongngo | 2025-01-27T19:10:15Z | 7 | 0 | peft | [
"peft",
"safetensors",
"opt",
"axolotl",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T19:00:24Z | ---
library_name: peft
license: other
base_model: facebook/opt-350m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7989eef4-dc89-450b-abd3-0d9c3598c3c8
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: facebook/opt-350m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f9bbd925c6f11108_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f9bbd925c6f11108_train_data.json
type:
field_input: label
field_instruction: page_title
field_output: page_text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: hongngo/7989eef4-dc89-450b-abd3-0d9c3598c3c8
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/f9bbd925c6f11108_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: 92c8fc48-674e-436c-a6e7-bcb939bcc03f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 92c8fc48-674e-436c-a6e7-bcb939bcc03f
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 7989eef4-dc89-450b-abd3-0d9c3598c3c8
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5744
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 6.7985 | 0.0851 | 200 | 1.5744 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
OgroFratesi/chop-flux | OgroFratesi | 2025-01-27T19:09:46Z | 13 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-01-27T18:31:17Z | ---
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: CHOP
---
# Chop Flux
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `CHOP` 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('OgroFratesi/chop-flux', 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)
|
hamidfarmani/drawingai | hamidfarmani | 2025-01-27T19:08:29Z | 18 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-01-27T17:49:01Z | ---
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: a_photo_of_COOLSTYLE
---
# Drawingai
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `a_photo_of_COOLSTYLE` 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('hamidfarmani/drawingai', 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)
|
lesso16/bdf59a8b-5dbc-4712-9823-39f2a12fc42a | lesso16 | 2025-01-27T19:06:29Z | 6 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B",
"license:apache-2.0",
"region:us"
] | null | 2025-01-27T19:03:25Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bdf59a8b-5dbc-4712-9823-39f2a12fc42a
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
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 47ff230a87d3e712_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/47ff230a87d3e712_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso16/bdf59a8b-5dbc-4712-9823-39f2a12fc42a
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/47ff230a87d3e712_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: c5a314b3-de7b-40c6-9c64-3d1496d51603
wandb_project: multi
wandb_run: your_name
wandb_runid: c5a314b3-de7b-40c6-9c64-3d1496d51603
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# bdf59a8b-5dbc-4712-9823-39f2a12fc42a
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B) 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
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.7533 | 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 |
mlfoundations-dev/llama3-1_8b_codefeedback | mlfoundations-dev | 2025-01-27T19:03:36Z | 285 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T18:03:38Z | ---
library_name: transformers
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: llama3-1_8b_codefeedback
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama3-1_8b_codefeedback
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the mlfoundations-dev/codefeedback dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5123
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.559 | 1.0 | 35 | 0.5510 |
| 0.4931 | 2.0 | 70 | 0.5174 |
| 0.4527 | 3.0 | 105 | 0.5123 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.0.2
- Tokenizers 0.20.3
|
prithivMLmods/Taurus-Opus-7B | prithivMLmods | 2025-01-27T19:02:59Z | 71 | 9 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"opus",
"code",
"cot",
"lcot",
"LlaMa",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-25T17:19:03Z | ---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- opus
- code
- cot
- lcot
- LlaMa
model-index:
- name: Taurus-Opus-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: wis-k/instruction-following-eval
split: train
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 42.23
name: averaged accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: SaylorTwift/bbh
split: test
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 34.23
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: lighteval/MATH-Hard
split: test
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 22.73
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
split: train
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 10.18
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 14.22
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 32.79
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FTaurus-Opus-7B
name: Open LLM Leaderboard
---
# **Taurus-Opus-7B**
Taurus-Opus-7B is built upon the LLaMA (Large Language Model Meta AI) 7B architecture, optimized to provide advanced reasoning capabilities while maintaining efficiency. With 7 billion parameters, it strikes a balance between performance and computational resource requirements. The model has been fine-tuned with a focus on chain-of-thought (CoT) reasoning, leveraging specialized datasets to enhance its problem-solving abilities. Taurus-Opus-7B is designed for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and coding assistance.
# **Key Features and Improvements**
1. **Optimized Reasoning Capabilities**:
The model showcases significant improvements in context understanding, reasoning, and mathematical problem-solving through fine-tuning with long CoT datasets.
2. **Enhanced Instruction Following**:
Taurus-Opus-7B excels in generating long, coherent outputs (up to 4K tokens), understanding structured data, and producing structured outputs like JSON.
3. **Lightweight Efficiency**:
Its 7B parameter size makes it more resource-efficient compared to larger models while retaining high-quality performance for reasoning and content generation tasks.
4. **Long-Context Support**:
Offers support for long contexts of up to 64K tokens, enabling the handling of large datasets or extended conversations.
5. **Multilingual Proficiency**:
The model supports 20+ languages, including English, Spanish, French, German, Portuguese, Chinese, Japanese, and more, making it suitable for global applications.
# **Quickstart with transformers**
Here’s a code snippet to load **Taurus-Opus-7B** using the `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Taurus-Opus-7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Explain the importance of chain-of-thought reasoning in large language models."
messages = [
{"role": "system", "content": "You are a helpful assistant with expertise in logical reasoning and problem-solving."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
# **Intended Use**
1. **Reasoning and Context Understanding**:
Taurus-Opus-7B is tailored for complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction.
2. **Mathematical Problem-Solving**:
Designed for advanced mathematical reasoning and calculations, making it valuable for education, research, and engineering tasks.
3. **Code Assistance**:
Provides robust coding support, including writing, debugging, and optimizing code across multiple programming languages.
4. **Data Analysis**:
Excels in analyzing structured data and generating structured outputs, aiding automation workflows and data-driven insights.
5. **Multilingual Support**:
Facilitates applications such as multilingual chatbots, content generation, and translation in 20+ languages.
6. **Extended Content Generation**:
Suitable for generating detailed reports, articles, and instructional guides, handling outputs up to 4K tokens.
# **Limitations**
1. **Hardware Requirements**:
While more efficient than larger models, Taurus-Opus-7B still requires high-memory GPUs or TPUs for optimal performance.
2. **Language Quality Variations**:
Output quality may vary across supported languages, especially for less commonly used languages.
3. **Creativity Limitations**:
The model may sometimes generate repetitive or inconsistent results in creative or highly subjective tasks.
4. **Real-Time Knowledge Constraints**:
The model lacks awareness of events or knowledge updates beyond its training data.
5. **Prompt Dependency**:
Results heavily depend on the specificity and clarity of input prompts, requiring well-structured queries for the best performance.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Taurus-Opus-7B-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FTaurus-Opus-7B&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
| Metric |Value (%)|
|-------------------|--------:|
|**Average** | 26.06|
|IFEval (0-Shot) | 42.23|
|BBH (3-Shot) | 34.23|
|MATH Lvl 5 (4-Shot)| 22.73|
|GPQA (0-shot) | 10.18|
|MuSR (0-shot) | 14.22|
|MMLU-PRO (5-shot) | 32.79|
|
lesso02/a98ed97a-b7d4-4c55-afa8-f52a6eaf493e | lesso02 | 2025-01-27T19:02:45Z | 5 | 0 | peft | [
"peft",
"safetensors",
"opt",
"axolotl",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"region:us"
] | null | 2025-01-27T19:00:47Z | ---
library_name: peft
license: other
base_model: facebook/opt-350m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a98ed97a-b7d4-4c55-afa8-f52a6eaf493e
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: facebook/opt-350m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f9bbd925c6f11108_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f9bbd925c6f11108_train_data.json
type:
field_input: label
field_instruction: page_title
field_output: page_text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso02/a98ed97a-b7d4-4c55-afa8-f52a6eaf493e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/f9bbd925c6f11108_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: 92c8fc48-674e-436c-a6e7-bcb939bcc03f
wandb_project: multi
wandb_run: your_name
wandb_runid: 92c8fc48-674e-436c-a6e7-bcb939bcc03f
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# a98ed97a-b7d4-4c55-afa8-f52a6eaf493e
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4827
## 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
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 6.0132 | 0.6803 | 200 | 1.4827 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Primeness/primeh2v8c2 | Primeness | 2025-01-27T18:57:32Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T18:25:10Z | ---
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] |
bruhzair/Behemoth-Magnum-v4-SLERP-123b | bruhzair | 2025-01-27T18:56:15Z | 62 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T18:26:47Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# bmag
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--anthracite-org--magnum-v4-123b/snapshots/68fdd395bf5282429aa11d3b2737add1944243b3
* /workspace/cache/models--TheDrummer--Behemoth-123B-v1.2/snapshots/51354019a02b742aa5a73fe16800225ff719c46d
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: /workspace/cache/models--TheDrummer--Behemoth-123B-v1.2/snapshots/51354019a02b742aa5a73fe16800225ff719c46d
dtype: bfloat16
merge_method: slerp
parameters:
t:
- filter: self_attn
value: [0.1, 0.3, 0.5, 0.6, 0.5, 0.3, 0.1]
- filter: mlp
value: [0.1, 0.3, 0.5, 0.6, 0.5, 0.3, 0.1]
- value: 0.5
slices:
- sources:
- layer_range: [0, 88]
model: /workspace/cache/models--TheDrummer--Behemoth-123B-v1.2/snapshots/51354019a02b742aa5a73fe16800225ff719c46d
- layer_range: [0, 88]
model: /workspace/cache/models--anthracite-org--magnum-v4-123b/snapshots/68fdd395bf5282429aa11d3b2737add1944243b3
```
|
mmnga/cyberagent-DeepSeek-R1-Distill-Qwen-32B-Japanese-gguf | mmnga | 2025-01-27T18:55:57Z | 23,966 | 26 | null | [
"gguf",
"en",
"ja",
"dataset:TFMC/imatrix-dataset-for-japanese-llm",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-01-27T11:17:23Z |
---
license: mit
language:
- en
- ja
datasets:
- TFMC/imatrix-dataset-for-japanese-llm
base_model:
- cyberagent/cyberagent-DeepSeek-R1-Distill-Qwen-32B-Japanese
---
# cyberagent-DeepSeek-R1-Distill-Qwen-32B-Japanese-gguf
[cyberagentさんが公開しているDeepSeek-R1-Distill-Qwen-32B-Japanese](https://huggingface.co/cyberagent/DeepSeek-R1-Distill-Qwen-32B-Japanese)のggufフォーマット変換版です。
imatrixのデータは[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用して作成しました。
## models
[mmnga/cyberagent-DeepSeek-R1-Distill-Qwen-14B-Japanese-gguf](https://huggingface.co/mmnga/cyberagent-DeepSeek-R1-Distill-Qwen-14B-Japanese-gguf)
[mmnga/cyberagent-DeepSeek-R1-Distill-Qwen-32B-Japanese-gguf](https://huggingface.co/mmnga/cyberagent-DeepSeek-R1-Distill-Qwen-32B-Japanese-gguf)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
build/bin/llama-cli -m 'cyberagent-DeepSeek-R1-Distill-Qwen-32B-Japanese-gguf' -n 128 -c 128 -p 'あなたはプロの料理人です。レシピを教えて' -cnv
```
|
mrferr3t/561145cc-7962-4d56-a8e0-8ecfae2bf4e3 | mrferr3t | 2025-01-27T18:54:46Z | 8 | 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 | 2025-01-27T18:43:36Z | ---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 561145cc-7962-4d56-a8e0-8ecfae2bf4e3
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:
- af2776e3f8d7bb4e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/af2776e3f8d7bb4e_train_data.json
type:
field_instruction: Name
field_output: Descriptor
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: mrferr3t/561145cc-7962-4d56-a8e0-8ecfae2bf4e3
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: 14
micro_batch_size: 2
mlflow_experiment_name: /tmp/af2776e3f8d7bb4e_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: 8942483c-1cfc-4e12-8246-93c0d39139ac
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8942483c-1cfc-4e12-8246-93c0d39139ac
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 561145cc-7962-4d56-a8e0-8ecfae2bf4e3
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: 1.7493
## 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 adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 7.2498 | 0.0006 | 1 | 1.9691 |
| 8.0511 | 0.0025 | 4 | 1.9660 |
| 7.6014 | 0.0049 | 8 | 1.9201 |
| 7.3726 | 0.0074 | 12 | 1.7493 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1 |
chauhoang/fe745636-630e-442f-bc15-e2f1c822ee48 | chauhoang | 2025-01-27T18:53:11Z | 6 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/mistral-7b",
"base_model:adapter:unsloth/mistral-7b",
"license:apache-2.0",
"region:us"
] | null | 2025-01-27T18:42:28Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/mistral-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fe745636-630e-442f-bc15-e2f1c822ee48
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/mistral-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 3723f44d1d18fe84_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3723f44d1d18fe84_train_data.json
type:
field_instruction: question
field_output: resolution_criteria
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: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: chauhoang/fe745636-630e-442f-bc15-e2f1c822ee48
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/3723f44d1d18fe84_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: b1a8229e-645b-4ea8-b718-f392e5d4cd08
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b1a8229e-645b-4ea8-b718-f392e5d4cd08
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# fe745636-630e-442f-bc15-e2f1c822ee48
This model is a fine-tuned version of [unsloth/mistral-7b](https://huggingface.co/unsloth/mistral-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7719
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0016 | 1 | 3.6423 |
| 8.6602 | 0.0158 | 10 | 1.1724 |
| 3.6865 | 0.0317 | 20 | 0.8343 |
| 3.9935 | 0.0475 | 30 | 0.7959 |
| 3.4493 | 0.0634 | 40 | 0.7767 |
| 2.5947 | 0.0792 | 50 | 0.7719 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
memevis/tryy48 | memevis | 2025-01-27T18:50:54Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T18:45:55Z | ---
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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
mradermacher/Llama-3-Yollow-SCE-i1-GGUF | mradermacher | 2025-01-27T18:50:28Z | 651 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Casual-Autopsy/Llama-3-Yollow-SCE",
"base_model:quantized:Casual-Autopsy/Llama-3-Yollow-SCE",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-01-27T01:20:17Z | ---
base_model: Casual-Autopsy/Llama-3-Yollow-SCE
language:
- en
library_name: transformers
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/Casual-Autopsy/Llama-3-Yollow-SCE
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-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/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Yollow-SCE-i1-GGUF/resolve/main/Llama-3-Yollow-SCE.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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 -->
|
clarxus/f7b0edaa-7d28-4ba4-b8a7-bb296a24f772 | clarxus | 2025-01-27T18:49:52Z | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-27T18:08:04Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f7b0edaa-7d28-4ba4-b8a7-bb296a24f772
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e8ab9d6de4972894_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e8ab9d6de4972894_train_data.json
type:
field_input: thought
field_instruction: prompt
field_output: 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: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: clarxus/f7b0edaa-7d28-4ba4-b8a7-bb296a24f772
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
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/e8ab9d6de4972894_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: 2a1fcb6d-c5f4-4ed5-a79c-4c70fd772cb5
wandb_project: Gradients-On-Seven
wandb_run: your_name
wandb_runid: 2a1fcb6d-c5f4-4ed5-a79c-4c70fd772cb5
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f7b0edaa-7d28-4ba4-b8a7-bb296a24f772
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9639
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 | 0.0003 | 1 | 1.1660 |
| 1.037 | 0.0024 | 9 | 1.1367 |
| 1.0574 | 0.0048 | 18 | 1.0600 |
| 1.1057 | 0.0073 | 27 | 1.0048 |
| 1.0853 | 0.0097 | 36 | 0.9885 |
| 0.9085 | 0.0121 | 45 | 0.9788 |
| 1.0207 | 0.0145 | 54 | 0.9727 |
| 0.8879 | 0.0169 | 63 | 0.9685 |
| 0.9204 | 0.0194 | 72 | 0.9659 |
| 1.0405 | 0.0218 | 81 | 0.9645 |
| 0.9245 | 0.0242 | 90 | 0.9640 |
| 0.9667 | 0.0266 | 99 | 0.9639 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
memevis/tryy47 | memevis | 2025-01-27T18:48:58Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T18:44:04Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
lhong4759/26d24d5d-48d4-4f79-b3ec-bd100ad807ed | lhong4759 | 2025-01-27T18:47:02Z | 9 | 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 | 2025-01-27T18:10:42Z | ---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 26d24d5d-48d4-4f79-b3ec-bd100ad807ed
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:
- eada432c00d4bd8b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/eada432c00d4bd8b_train_data.json
type:
field_input: prompt_setting
field_instruction: prompt
field_output: completion
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: lhong4759/26d24d5d-48d4-4f79-b3ec-bd100ad807ed
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/eada432c00d4bd8b_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: 63bf79c0-46cc-466e-952f-99f80f292bc5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 63bf79c0-46cc-466e-952f-99f80f292bc5
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 26d24d5d-48d4-4f79-b3ec-bd100ad807ed
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: 0.3761
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.7795 | 0.3509 | 200 | 0.3761 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
demohong/69ef178a-2bf3-4f2e-8cf2-ed5cf25ba18b | demohong | 2025-01-27T18:46:47Z | 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 | 2025-01-27T18:10:34Z | ---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 69ef178a-2bf3-4f2e-8cf2-ed5cf25ba18b
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:
- eada432c00d4bd8b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/eada432c00d4bd8b_train_data.json
type:
field_input: prompt_setting
field_instruction: prompt
field_output: completion
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: demohong/69ef178a-2bf3-4f2e-8cf2-ed5cf25ba18b
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/eada432c00d4bd8b_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: 63bf79c0-46cc-466e-952f-99f80f292bc5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 63bf79c0-46cc-466e-952f-99f80f292bc5
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 69ef178a-2bf3-4f2e-8cf2-ed5cf25ba18b
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: 0.3756
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.7849 | 0.3509 | 200 | 0.3756 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
daniel40/8f602fcc-9f20-4cc0-924f-7686c37a5950 | daniel40 | 2025-01-27T18:46:41Z | 8 | 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 | 2025-01-27T18:42:44Z | ---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8f602fcc-9f20-4cc0-924f-7686c37a5950
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:
- af2776e3f8d7bb4e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/af2776e3f8d7bb4e_train_data.json
type:
field_instruction: Name
field_output: Descriptor
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: daniel40/8f602fcc-9f20-4cc0-924f-7686c37a5950
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/af2776e3f8d7bb4e_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: 8942483c-1cfc-4e12-8246-93c0d39139ac
wandb_project: Birthday-SN56-31-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8942483c-1cfc-4e12-8246-93c0d39139ac
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8f602fcc-9f20-4cc0-924f-7686c37a5950
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: 1.7208
## 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: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 7.2519 | 0.0006 | 1 | 1.9692 |
| 6.9091 | 0.0080 | 13 | 1.9465 |
| 7.1938 | 0.0161 | 26 | 1.7993 |
| 6.2046 | 0.0241 | 39 | 1.7208 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
memevis/tryy44 | memevis | 2025-01-27T18:45:58Z | 31 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T18:39:16Z | ---
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] |
mradermacher/Finetuned-Mistral-7B-v0.1-GGUF | mradermacher | 2025-01-27T18:45:00Z | 195 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:GhulamNabi/Finetuned-Mistral-7B-v0.1",
"base_model:quantized:GhulamNabi/Finetuned-Mistral-7B-v0.1",
"endpoints_compatible",
"region:us"
] | null | 2025-01-27T18:21:15Z | ---
base_model: GhulamNabi/Finetuned-Mistral-7B-v0.1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/GhulamNabi/Finetuned-Mistral-7B-v0.1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Finetuned-Mistral-7B-v0.1-GGUF/resolve/main/Finetuned-Mistral-7B-v0.1.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
lesso07/69272570-3b74-48ab-87d3-8216b61601f7 | lesso07 | 2025-01-27T18:44:48Z | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T18:07:14Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 69272570-3b74-48ab-87d3-8216b61601f7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
datasets:
- data_files:
- e8ab9d6de4972894_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e8ab9d6de4972894_train_data.json
type:
field_input: thought
field_instruction: prompt
field_output: response
format: '{instruction} {input}'
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: lesso07/69272570-3b74-48ab-87d3-8216b61601f7
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/e8ab9d6de4972894_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: 2a1fcb6d-c5f4-4ed5-a79c-4c70fd772cb5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2a1fcb6d-c5f4-4ed5-a79c-4c70fd772cb5
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 69272570-3b74-48ab-87d3-8216b61601f7
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 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 | 0.0001 | 1 | nan |
| 0.0 | 0.0003 | 5 | nan |
| 0.0 | 0.0007 | 10 | nan |
| 0.0 | 0.0010 | 15 | nan |
| 0.0 | 0.0013 | 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 |
martindevoto/finer_ner_finetuning_0130 | martindevoto | 2025-01-27T18:40:31Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"eng",
"dataset:nlpaueb/finer-139",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-01-27T00:37:36Z | ---
library_name: transformers
language:
- eng
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: finer_ner_finetuning_0130
results: []
datasets:
- nlpaueb/finer-139
---
<!-- 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. -->
# finer_ner_finetuning_0130
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a subset of the [nlpaueb/finer-139](https://huggingface.co/datasets/nlpaueb/finer-139) dataset.
It is only finetuned on the following labels:
- 'O'
- 'B-DebtInstrumentBasisSpreadOnVariableRate1',
- 'B-DebtInstrumentFaceAmount',
- 'B-DebtInstrumentInterestRateStatedPercentage',
- 'B-LineOfCreditFacilityMaximumBorrowingCapacity'
It achieves the following results on the evaluation set:
- Loss: 0.0024
- Accuracy: 0.9995
- Precision: 0.7342
- Recall: 0.9159
- F1: 0.8150
- Classification Report: {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.791095890410959, 'recall': 0.9602137767220903, 'f1-score': 0.8674892703862661, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.6338946224877784, 'recall': 0.8670133729569094, 'f1-score': 0.7323501725760904, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.774822695035461, 'recall': 0.9494839760999457, 'f1-score': 0.8533072980229436, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.7227383863080684, 'recall': 0.8740390301596689, 'f1-score': 0.791220556745182, 'support': 1691}, 'micro avg': {'precision': 0.7341803078426582, 'recall': 0.9158793050899117, 'f1-score': 0.8150257662055873, 'support': 6562}, 'macro avg': {'precision': 0.7306378985605667, 'recall': 0.9126875389846535, 'f1-score': 0.8110918244326205, 'support': 6562}, 'weighted avg': {'precision': 0.7366697400377676, 'recall': 0.9158793050899117, 'f1-score': 0.8161365377528546, 'support': 6562}}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Subset of [nlpaueb/finer-139](https://huggingface.co/datasets/nlpaueb/finer-139) train split and and full validation split.
Given the original proportion of 'O' labels (approx. 80%), we reduced the train split records to match the original majority class proportion given the reduced set of labels.
Original train split size: 900384 records
Subset train split size: 142513 records (~16% of original amount of records)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 192
- eval_batch_size: 192
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Classification Report |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.0063 | 0.6729 | 500 | 0.0035 | 0.9987 | 0.4732 | 0.9122 | 0.6232 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.6499202551834131, 'recall': 0.9679334916864608, 'f1-score': 0.7776717557251909, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.3665031534688157, 'recall': 0.7771173848439822, 'f1-score': 0.49809523809523815, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.5214669051878354, 'recall': 0.9500271591526345, 'f1-score': 0.673339749759384, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.3968980422069667, 'recall': 0.9231224127735068, 'f1-score': 0.5551209103840683, 'support': 1691}, 'micro avg': {'precision': 0.4732389912246027, 'recall': 0.9122218835720817, 'f1-score': 0.62318463380355, 'support': 6562}, 'macro avg': {'precision': 0.4836970890117577, 'recall': 0.904550112114146, 'f1-score': 0.6260569134909704, 'support': 6562}, 'weighted avg': {'precision': 0.49054466871695807, 'recall': 0.9122218835720817, 'f1-score': 0.6337036522224775, 'support': 6562}} |
| 0.0044 | 1.3459 | 1000 | 0.0019 | 0.9993 | 0.6299 | 0.8856 | 0.7361 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.7140974967061924, 'recall': 0.9655581947743468, 'f1-score': 0.8210047967684928, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.5513164965072541, 'recall': 0.7622585438335809, 'f1-score': 0.6398503274087932, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.6613592233009709, 'recall': 0.9250407387289517, 'f1-score': 0.771286231884058, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.5793871866295265, 'recall': 0.8610289769367239, 'f1-score': 0.6926736441484301, 'support': 1691}, 'micro avg': {'precision': 0.6298504227184045, 'recall': 0.8855531850045718, 'f1-score': 0.7361287053458322, 'support': 6562}, 'macro avg': {'precision': 0.626540100785986, 'recall': 0.8784716135684008, 'f1-score': 0.7312037500524435, 'support': 6562}, 'weighted avg': {'precision': 0.6311975390794893, 'recall': 0.8855531850045718, 'f1-score': 0.7368271416647247, 'support': 6562}} |
| 0.0038 | 2.0188 | 1500 | 0.0018 | 0.9994 | 0.6685 | 0.8735 | 0.7573 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.8146964856230032, 'recall': 0.9085510688836105, 'f1-score': 0.8590679393599102, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.5430597771023303, 'recall': 0.7964338781575037, 'f1-score': 0.6457831325301205, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.6825586015097338, 'recall': 0.933188484519283, 'f1-score': 0.7884350619550253, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.6400725294650952, 'recall': 0.8350088704908338, 'f1-score': 0.7246599948678469, 'support': 1691}, 'micro avg': {'precision': 0.6684548104956268, 'recall': 0.8735141725083816, 'f1-score': 0.7573495408601439, 'support': 6562}, 'macro avg': {'precision': 0.6700968484250407, 'recall': 0.8682955755128078, 'f1-score': 0.7544865321782257, 'support': 6562}, 'weighted avg': {'precision': 0.6769064880331865, 'recall': 0.8735141725083816, 'f1-score': 0.7608661241463521, 'support': 6562}} |
| 0.003 | 2.6918 | 2000 | 0.0017 | 0.9994 | 0.6856 | 0.9012 | 0.7788 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.7413636363636363, 'recall': 0.9685273159144893, 'f1-score': 0.8398558187435634, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.598568281938326, 'recall': 0.8075780089153046, 'f1-score': 0.6875395319418091, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.7389801210025929, 'recall': 0.928843020097773, 'f1-score': 0.8231046931407943, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.6472125435540069, 'recall': 0.8787699586043761, 'f1-score': 0.7454226235264609, 'support': 1691}, 'micro avg': {'precision': 0.685601669371667, 'recall': 0.9012496190185919, 'f1-score': 0.7787727153015538, 'support': 6562}, 'macro avg': {'precision': 0.6815311457146406, 'recall': 0.8959295758829857, 'f1-score': 0.773980666838157, 'support': 6562}, 'weighted avg': {'precision': 0.6871423476136771, 'recall': 0.9012496190185919, 'f1-score': 0.7795779953083334, 'support': 6562}} |
| 0.0022 | 3.3647 | 2500 | 0.0018 | 0.9994 | 0.6925 | 0.8973 | 0.7817 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.7884519661523146, 'recall': 0.9406175771971497, 'f1-score': 0.8578391551584078, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.6066779852857951, 'recall': 0.7964338781575037, 'f1-score': 0.6887247028589785, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.729933110367893, 'recall': 0.9483976099945681, 'f1-score': 0.8249468462083628, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.6364025695931478, 'recall': 0.8787699586043761, 'f1-score': 0.7382016890213613, 'support': 1691}, 'micro avg': {'precision': 0.6924614841820534, 'recall': 0.8972874123742761, 'f1-score': 0.7816793893129771, 'support': 6562}, 'macro avg': {'precision': 0.6903664078497876, 'recall': 0.8910547559883993, 'f1-score': 0.7774280983117776, 'support': 6562}, 'weighted avg': {'precision': 0.695566181128388, 'recall': 0.8972874123742761, 'f1-score': 0.7830921650929078, 'support': 6562}} |
| 0.0022 | 4.0377 | 3000 | 0.0019 | 0.9994 | 0.6973 | 0.9098 | 0.7895 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.764594209776934, 'recall': 0.9566508313539193, 'f1-score': 0.8499076760749142, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.6087877183695076, 'recall': 0.8543833580980683, 'f1-score': 0.7109737248840804, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.7853860294117647, 'recall': 0.9282998370450842, 'f1-score': 0.8508837440876277, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.6276150627615062, 'recall': 0.8870490833826138, 'f1-score': 0.7351139426611124, 'support': 1691}, 'micro avg': {'precision': 0.6972669936930623, 'recall': 0.9097836025601951, 'f1-score': 0.7894736842105262, 'support': 6562}, 'macro avg': {'precision': 0.6965957550799281, 'recall': 0.9065957774699215, 'f1-score': 0.7867197719269337, 'support': 6562}, 'weighted avg': {'precision': 0.7031694101594759, 'recall': 0.9097836025601951, 'f1-score': 0.7921014645092033, 'support': 6562}} |
| 0.0017 | 4.7106 | 3500 | 0.0018 | 0.9995 | 0.7319 | 0.8888 | 0.8028 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.8151781104801239, 'recall': 0.9376484560570071, 'f1-score': 0.872134769400718, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.6239267315397825, 'recall': 0.8098068350668648, 'f1-score': 0.704817329453605, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.7620508326029798, 'recall': 0.9445953286257469, 'f1-score': 0.8435605141886975, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.7112887112887113, 'recall': 0.8421052631578947, 'f1-score': 0.7711887354454373, 'support': 1691}, 'micro avg': {'precision': 0.7319277108433735, 'recall': 0.8887534288326729, 'f1-score': 0.8027529249827942, 'support': 6562}, 'macro avg': {'precision': 0.7281110964778994, 'recall': 0.8835389707268784, 'f1-score': 0.7979253371221144, 'support': 6562}, 'weighted avg': {'precision': 0.7342715806632691, 'recall': 0.8887534288326729, 'f1-score': 0.8037845375457159, 'support': 6562}} |
| 0.0013 | 5.3836 | 4000 | 0.0020 | 0.9995 | 0.7302 | 0.9075 | 0.8093 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.7983991995997999, 'recall': 0.9477434679334917, 'f1-score': 0.8666847678522944, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.6646489104116223, 'recall': 0.8157503714710252, 'f1-score': 0.7324883255503669, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.7903669724770642, 'recall': 0.9359043997827268, 'f1-score': 0.8570007460830639, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.6617900172117039, 'recall': 0.9095209934949734, 'f1-score': 0.7661270236612702, 'support': 1691}, 'micro avg': {'precision': 0.730226854690374, 'recall': 0.9074977141115513, 'f1-score': 0.809268193245906, 'support': 6562}, 'macro avg': {'precision': 0.7288012749250476, 'recall': 0.9022298081705543, 'f1-score': 0.8055752157867488, 'support': 6562}, 'weighted avg': {'precision': 0.7335071930776247, 'recall': 0.9074977141115513, 'f1-score': 0.8105281325516894, 'support': 6562}} |
| 0.0012 | 6.0565 | 4500 | 0.0018 | 0.9996 | 0.7783 | 0.8785 | 0.8254 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.798810703666997, 'recall': 0.9572446555819477, 'f1-score': 0.8708806050783361, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.72812291249165, 'recall': 0.8098068350668648, 'f1-score': 0.7667956384101302, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.8257650542941757, 'recall': 0.908745247148289, 'f1-score': 0.865270235324541, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.744908896034298, 'recall': 0.8219988172678888, 'f1-score': 0.7815574922687659, 'support': 1691}, 'micro avg': {'precision': 0.7783178074794114, 'recall': 0.8785431270953977, 'f1-score': 0.8253990980027203, 'support': 6562}, 'macro avg': {'precision': 0.7744018916217801, 'recall': 0.8744488887662476, 'f1-score': 0.8211259927704433, 'support': 6562}, 'weighted avg': {'precision': 0.777983095601731, 'recall': 0.8785431270953977, 'f1-score': 0.8249384472585973, 'support': 6562}} |
| 0.0011 | 6.7295 | 5000 | 0.0022 | 0.9995 | 0.7162 | 0.9163 | 0.8040 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.7986980470706059, 'recall': 0.9471496437054632, 'f1-score': 0.8666123336049986, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.6109375, 'recall': 0.8714710252600297, 'f1-score': 0.7183098591549296, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.7634455618714473, 'recall': 0.9483976099945681, 'f1-score': 0.8459302325581395, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.6838503649635036, 'recall': 0.8864577173270254, 'f1-score': 0.7720834406386814, 'support': 1691}, 'micro avg': {'precision': 0.7161743687470223, 'recall': 0.9163364827796403, 'f1-score': 0.8039844899050675, 'support': 6562}, 'macro avg': {'precision': 0.7142328684763892, 'recall': 0.9133689990717716, 'f1-score': 0.8007339664891873, 'support': 6562}, 'weighted avg': {'precision': 0.7206985115552452, 'recall': 0.9163364827796403, 'f1-score': 0.8060303103433248, 'support': 6562}} |
| 0.0007 | 7.4024 | 5500 | 0.0021 | 0.9995 | 0.7513 | 0.9060 | 0.8214 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.8039314516129032, 'recall': 0.9471496437054632, 'f1-score': 0.8696837513631407, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.6957605985037406, 'recall': 0.8291233283803864, 'f1-score': 0.7566101694915255, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.7822182308037718, 'recall': 0.9462248777838131, 'f1-score': 0.8564405113077679, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.7111534795042898, 'recall': 0.8823181549379066, 'f1-score': 0.7875428873053576, 'support': 1691}, 'micro avg': {'precision': 0.7512953367875648, 'recall': 0.9059737884791222, 'f1-score': 0.8214162348877374, 'support': 6562}, 'macro avg': {'precision': 0.7482659401061764, 'recall': 0.9012040012018924, 'f1-score': 0.817569329866948, 'support': 6562}, 'weighted avg': {'precision': 0.7517431616662088, 'recall': 0.9059737884791222, 'f1-score': 0.8216072430938864, 'support': 6562}} |
| 0.0007 | 8.0754 | 6000 | 0.0024 | 0.9995 | 0.7342 | 0.9159 | 0.8150 | {'DebtInstrumentBasisSpreadOnVariableRate1': {'precision': 0.791095890410959, 'recall': 0.9602137767220903, 'f1-score': 0.8674892703862661, 'support': 1684}, 'DebtInstrumentFaceAmount': {'precision': 0.6338946224877784, 'recall': 0.8670133729569094, 'f1-score': 0.7323501725760904, 'support': 1346}, 'DebtInstrumentInterestRateStatedPercentage': {'precision': 0.774822695035461, 'recall': 0.9494839760999457, 'f1-score': 0.8533072980229436, 'support': 1841}, 'LineOfCreditFacilityMaximumBorrowingCapacity': {'precision': 0.7227383863080684, 'recall': 0.8740390301596689, 'f1-score': 0.791220556745182, 'support': 1691}, 'micro avg': {'precision': 0.7341803078426582, 'recall': 0.9158793050899117, 'f1-score': 0.8150257662055873, 'support': 6562}, 'macro avg': {'precision': 0.7306378985605667, 'recall': 0.9126875389846535, 'f1-score': 0.8110918244326205, 'support': 6562}, 'weighted avg': {'precision': 0.7366697400377676, 'recall': 0.9158793050899117, 'f1-score': 0.8161365377528546, 'support': 6562}} |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.20.3
### How to use this model
#### Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="martindevoto/finer_ner_finetuning_0130")
#### Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("martindevoto/finer_ner_finetuning_0130")
model = AutoModelForTokenClassification.from_pretrained("martindevoto/finer_ner_finetuning_0130") |
nadejdatarabukina/d1eaef8f-8a99-48f7-b084-0b47243fa852 | nadejdatarabukina | 2025-01-27T18:40:16Z | 6 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-13b-hf",
"base_model:adapter:NousResearch/CodeLlama-13b-hf",
"region:us"
] | null | 2025-01-27T17:28:46Z | ---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d1eaef8f-8a99-48f7-b084-0b47243fa852
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
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 9a8514bc9995e10c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/9a8514bc9995e10c_train_data.json
type:
field_instruction: premise
field_output: hypothesis
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: nadejdatarabukina/d1eaef8f-8a99-48f7-b084-0b47243fa852
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/9a8514bc9995e10c_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: ab917f13-90ab-4a3a-9e38-f2d73001d41f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: ab917f13-90ab-4a3a-9e38-f2d73001d41f
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# d1eaef8f-8a99-48f7-b084-0b47243fa852
This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3627
## 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 | 0.0002 | 1 | 3.2874 |
| 12.5605 | 0.0008 | 5 | 3.1707 |
| 11.2823 | 0.0017 | 10 | 2.6932 |
| 10.7845 | 0.0025 | 15 | 2.5087 |
| 9.7678 | 0.0034 | 20 | 2.4228 |
| 10.2541 | 0.0042 | 25 | 2.3728 |
| 8.8832 | 0.0051 | 30 | 2.3627 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mrferr3t/4e516bd9-5ebf-4208-8294-fee71f8a1b0f | mrferr3t | 2025-01-27T18:40:08Z | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-01-27T18:09:03Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4e516bd9-5ebf-4208-8294-fee71f8a1b0f
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e8ab9d6de4972894_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e8ab9d6de4972894_train_data.json
type:
field_input: thought
field_instruction: prompt
field_output: 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: mrferr3t/4e516bd9-5ebf-4208-8294-fee71f8a1b0f
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: 8
micro_batch_size: 2
mlflow_experiment_name: /tmp/e8ab9d6de4972894_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: 2a1fcb6d-c5f4-4ed5-a79c-4c70fd772cb5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2a1fcb6d-c5f4-4ed5-a79c-4c70fd772cb5
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 4e516bd9-5ebf-4208-8294-fee71f8a1b0f
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2915
## 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 adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0146 | 0.0001 | 1 | 2.4249 |
| 1.1258 | 0.0001 | 2 | 2.4239 |
| 1.2445 | 0.0003 | 4 | 2.4131 |
| 0.8302 | 0.0004 | 6 | 2.3725 |
| 1.4582 | 0.0005 | 8 | 2.2915 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Muadil/Llama-3.2-1B-Instruct_sum_DPO_1k_4_1ep | Muadil | 2025-01-27T18:38:53Z | 144 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-11-28T04:05:47Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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] |
nhung01/ce601c6d-f85d-4322-900a-4843d391639e | nhung01 | 2025-01-27T18:34:08Z | 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 | 2025-01-27T18:10:38Z | ---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ce601c6d-f85d-4322-900a-4843d391639e
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:
- eada432c00d4bd8b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/eada432c00d4bd8b_train_data.json
type:
field_input: prompt_setting
field_instruction: prompt
field_output: completion
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: nhung01/ce601c6d-f85d-4322-900a-4843d391639e
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/eada432c00d4bd8b_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: 63bf79c0-46cc-466e-952f-99f80f292bc5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 63bf79c0-46cc-466e-952f-99f80f292bc5
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# ce601c6d-f85d-4322-900a-4843d391639e
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: 0.3771
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.7764 | 0.3509 | 200 | 0.3771 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
cunghoctienganh/a1bef39b-65cf-4d6b-991a-da567c6c8c41 | cunghoctienganh | 2025-01-27T18:33:13Z | 7 | 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 | 2025-01-27T18:10:41Z | ---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a1bef39b-65cf-4d6b-991a-da567c6c8c41
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:
- eada432c00d4bd8b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/eada432c00d4bd8b_train_data.json
type:
field_input: prompt_setting
field_instruction: prompt
field_output: completion
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: cunghoctienganh/a1bef39b-65cf-4d6b-991a-da567c6c8c41
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/eada432c00d4bd8b_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: 63bf79c0-46cc-466e-952f-99f80f292bc5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 63bf79c0-46cc-466e-952f-99f80f292bc5
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# a1bef39b-65cf-4d6b-991a-da567c6c8c41
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: 0.3755
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.7586 | 0.3509 | 200 | 0.3755 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
nhunglaaaaaaa/8bda2c6e-cca4-4d6c-bdc8-0784355e392c | nhunglaaaaaaa | 2025-01-27T18:33:10Z | 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 | 2025-01-27T18:10:14Z | ---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8bda2c6e-cca4-4d6c-bdc8-0784355e392c
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:
- eada432c00d4bd8b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/eada432c00d4bd8b_train_data.json
type:
field_input: prompt_setting
field_instruction: prompt
field_output: completion
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: nhunglaaaaaaa/8bda2c6e-cca4-4d6c-bdc8-0784355e392c
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/eada432c00d4bd8b_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: 63bf79c0-46cc-466e-952f-99f80f292bc5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 63bf79c0-46cc-466e-952f-99f80f292bc5
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 8bda2c6e-cca4-4d6c-bdc8-0784355e392c
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: 0.3759
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.7636 | 0.3509 | 200 | 0.3759 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
nghiatrannnnnn/512a114c-6d40-44f8-a819-61e951e26bb3 | nghiatrannnnnn | 2025-01-27T18:33:04Z | 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 | 2025-01-27T18:10:04Z | ---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 512a114c-6d40-44f8-a819-61e951e26bb3
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:
- eada432c00d4bd8b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/eada432c00d4bd8b_train_data.json
type:
field_input: prompt_setting
field_instruction: prompt
field_output: completion
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: nghiatrannnnnn/512a114c-6d40-44f8-a819-61e951e26bb3
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/eada432c00d4bd8b_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: 63bf79c0-46cc-466e-952f-99f80f292bc5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 63bf79c0-46cc-466e-952f-99f80f292bc5
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 512a114c-6d40-44f8-a819-61e951e26bb3
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: 0.3765
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.7562 | 0.3509 | 200 | 0.3765 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
lesso13/26c48db2-4855-4753-9e19-fda696f66fc1 | lesso13 | 2025-01-27T18:32:14Z | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:adapter:unsloth/Llama-3.2-1B-Instruct",
"license:llama3.2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-01-27T18:18:13Z | ---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-1B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 26c48db2-4855-4753-9e19-fda696f66fc1
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/Llama-3.2-1B-Instruct
bf16: auto
chat_template: llama3
datasets:
- data_files:
- 01909031a3b78378_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/01909031a3b78378_train_data.json
type:
field_instruction: prompt
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: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso13/26c48db2-4855-4753-9e19-fda696f66fc1
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/01909031a3b78378_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: 3eb02dbd-9197-459c-a71f-f8adb9c1d6d4
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3eb02dbd-9197-459c-a71f-f8adb9c1d6d4
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 26c48db2-4855-4753-9e19-fda696f66fc1
This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0382 | 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 |
Bronsn/gemma-9b-luganda-finetuned-Q4_K_M-GGUF | Bronsn | 2025-01-27T18:32:06Z | 29 | 0 | peft | [
"peft",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Bronsn/gemma-9b-luganda-finetuned",
"base_model:adapter:Bronsn/gemma-9b-luganda-finetuned",
"endpoints_compatible",
"region:us"
] | null | 2025-01-27T18:31:40Z | ---
base_model: Bronsn/gemma-9b-luganda-finetuned
library_name: peft
tags:
- llama-cpp
- gguf-my-repo
---
# Bronsn/gemma-9b-luganda-finetuned-Q4_K_M-GGUF
This model was converted to GGUF format from [`Bronsn/gemma-9b-luganda-finetuned`](https://huggingface.co/Bronsn/gemma-9b-luganda-finetuned) 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/Bronsn/gemma-9b-luganda-finetuned) 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 Bronsn/gemma-9b-luganda-finetuned-Q4_K_M-GGUF --hf-file gemma-9b-luganda-finetuned-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Bronsn/gemma-9b-luganda-finetuned-Q4_K_M-GGUF --hf-file gemma-9b-luganda-finetuned-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 Bronsn/gemma-9b-luganda-finetuned-Q4_K_M-GGUF --hf-file gemma-9b-luganda-finetuned-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Bronsn/gemma-9b-luganda-finetuned-Q4_K_M-GGUF --hf-file gemma-9b-luganda-finetuned-q4_k_m.gguf -c 2048
```
|
ak2603/mt5-small-synthetic-data-plus-translated-bs32ep20lr5e3 | ak2603 | 2025-01-27T18:28:26Z | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2025-01-27T18:22:55Z | ---
library_name: transformers
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-synthetic-data-plus-translated-bs32ep20lr5e3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-synthetic-data-plus-translated-bs32ep20lr5e3
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3381
- Rouge1: 0.7165
- Rouge2: 0.6111
- Rougel: 0.7004
- Rougelsum: 0.7016
## 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: 0.0056
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.2243 | 1.0 | 38 | 0.9621 | 0.6801 | 0.5630 | 0.6599 | 0.6606 |
| 0.2209 | 2.0 | 76 | 0.9423 | 0.6766 | 0.5707 | 0.6633 | 0.6644 |
| 0.1953 | 3.0 | 114 | 0.9503 | 0.6525 | 0.5271 | 0.6361 | 0.6369 |
| 0.1812 | 4.0 | 152 | 0.9818 | 0.6811 | 0.5742 | 0.6672 | 0.6680 |
| 0.1418 | 5.0 | 190 | 0.9591 | 0.6868 | 0.5781 | 0.6700 | 0.6708 |
| 0.1312 | 6.0 | 228 | 1.0121 | 0.6900 | 0.5842 | 0.6734 | 0.6742 |
| 0.1236 | 7.0 | 266 | 0.9913 | 0.6787 | 0.5689 | 0.6652 | 0.6653 |
| 0.1068 | 8.0 | 304 | 0.9773 | 0.6886 | 0.5781 | 0.6749 | 0.6764 |
| 0.106 | 9.0 | 342 | 1.0201 | 0.6947 | 0.5825 | 0.6798 | 0.6802 |
| 0.084 | 10.0 | 380 | 1.0865 | 0.6861 | 0.5775 | 0.6726 | 0.6738 |
| 0.0744 | 11.0 | 418 | 1.0310 | 0.6997 | 0.5865 | 0.6849 | 0.6861 |
| 0.0618 | 12.0 | 456 | 1.1647 | 0.7118 | 0.6182 | 0.7016 | 0.7020 |
| 0.0493 | 13.0 | 494 | 1.1808 | 0.7089 | 0.6098 | 0.6959 | 0.6970 |
| 0.0472 | 14.0 | 532 | 1.2040 | 0.7087 | 0.6090 | 0.6956 | 0.6965 |
| 0.0399 | 15.0 | 570 | 1.1293 | 0.7065 | 0.6035 | 0.6953 | 0.6965 |
| 0.0346 | 16.0 | 608 | 1.2286 | 0.7078 | 0.6028 | 0.6928 | 0.6940 |
| 0.0255 | 17.0 | 646 | 1.2970 | 0.7114 | 0.6069 | 0.6986 | 0.7001 |
| 0.0241 | 18.0 | 684 | 1.3016 | 0.7053 | 0.5983 | 0.6893 | 0.6904 |
| 0.0217 | 19.0 | 722 | 1.3315 | 0.7137 | 0.6084 | 0.6999 | 0.7008 |
| 0.0196 | 20.0 | 760 | 1.3381 | 0.7165 | 0.6111 | 0.7004 | 0.7016 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
mrferr3t/7c8a058c-ef88-4cd0-90ab-aa89bba82390 | mrferr3t | 2025-01-27T18:25:42Z | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:adapter:unsloth/Llama-3.2-1B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2025-01-27T18:18:11Z | ---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-1B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7c8a058c-ef88-4cd0-90ab-aa89bba82390
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/Llama-3.2-1B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 01909031a3b78378_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/01909031a3b78378_train_data.json
type:
field_instruction: prompt
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: mrferr3t/7c8a058c-ef88-4cd0-90ab-aa89bba82390
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: 18
micro_batch_size: 2
mlflow_experiment_name: /tmp/01909031a3b78378_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: 3eb02dbd-9197-459c-a71f-f8adb9c1d6d4
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3eb02dbd-9197-459c-a71f-f8adb9c1d6d4
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 7c8a058c-ef88-4cd0-90ab-aa89bba82390
This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1906
## 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 adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 18
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9579 | 0.0002 | 1 | 0.6735 |
| 0.4037 | 0.0010 | 5 | 0.6448 |
| 0.1677 | 0.0019 | 10 | 0.3681 |
| 0.0746 | 0.0029 | 15 | 0.1906 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1 |
jaybie/llama-3-8b-Instruct-bnb-4bit-Cyber-V5 | jaybie | 2025-01-27T18:24:53Z | 23 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-27T18:21:24Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** jaybie
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Azure99/Blossom-V6-14B-GGUF | Azure99 | 2025-01-27T18:22:04Z | 270 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-27T17:16:04Z | ---
license: apache-2.0
---
|
mradermacher/Unity-12B-GGUF | mradermacher | 2025-01-27T18:21:17Z | 298 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"Roleplay",
"Creative",
"ru",
"en",
"base_model:OddTheGreat/Unity-12B",
"base_model:quantized:OddTheGreat/Unity-12B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-27T12:48:40Z | ---
base_model: OddTheGreat/Unity-12B
language:
- ru
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- Roleplay
- Creative
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/OddTheGreat/Unity-12B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Unity-12B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Unity-12B-GGUF/resolve/main/Unity-12B.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
lesso09/0fc82aab-59d1-48c9-bb60-f04c04fac4e5 | lesso09 | 2025-01-27T18:16:40Z | 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 | 2025-01-27T18:10:23Z | ---
library_name: peft
license: llama3
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0fc82aab-59d1-48c9-bb60-f04c04fac4e5
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
datasets:
- data_files:
- eada432c00d4bd8b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/eada432c00d4bd8b_train_data.json
type:
field_input: prompt_setting
field_instruction: prompt
field_output: completion
format: '{instruction} {input}'
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: lesso09/0fc82aab-59d1-48c9-bb60-f04c04fac4e5
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/eada432c00d4bd8b_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: 63bf79c0-46cc-466e-952f-99f80f292bc5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 63bf79c0-46cc-466e-952f-99f80f292bc5
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 0fc82aab-59d1-48c9-bb60-f04c04fac4e5
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: 0.7413
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.0732 | 0.0018 | 1 | 1.2944 |
| 1.4455 | 0.0088 | 5 | 1.2372 |
| 0.9794 | 0.0175 | 10 | 0.9125 |
| 0.4988 | 0.0263 | 15 | 0.7950 |
| 1.2708 | 0.0351 | 20 | 0.7533 |
| 0.8023 | 0.0439 | 25 | 0.7413 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
aaardvark1412/AJ | aaardvark1412 | 2025-01-27T18:15:54Z | 62 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-01-27T17:34:00Z | ---
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: AJ
---
# Aj
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `AJ` 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('aaardvark1412/AJ', 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)
|
telord/bert-base-uncased-squad-v2 | telord | 2025-01-27T18:12:55Z | 45 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-01-27T18:12:41Z | ---
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] |
umarigan/deepseek-r1-reasoning-prompt-generator | umarigan | 2025-01-27T18:11:43Z | 20 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"dataset:umarigan/deepseek-r1-reasoning-prompts",
"base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T13:55:19Z | ---
base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
datasets:
- umarigan/deepseek-r1-reasoning-prompts
---
This is a small llama-3.2-3B model fine-tuned for better reasoing prompt generation.
This reasoning prompts will get you better answers on language models as seen in openai and deepseek-r1 models
The dataset thats been used for this fne-tuning can be found here: https://huggingface.co/datasets/umarigan/deepseek-r1-reasoning-prompts
you can test the model as follow:
```python
import torch
from transformers import pipeline
model_id = "umarigan/deepseek-r1-reasoning-prompt-generator"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=4096,
)
print(outputs[0]["generated_text"][-1]['content'])
output:
Okay, so the user is asking who I am. Let me think about that. I'm a conversational AI designed to assist users with their questions and tasks. I'm a type of chatbot, but I'm not a regular chatbot like the ones you might find on websites. I'm more like a virtual assistant that can understand natural language and respond accordingly.
First, I need to recall my purpose. My primary goal is to help users by providing accurate and helpful information. I'm trained on a massive dataset that includes a wide range of topics, so I can answer questions on various subjects. I can also perform tasks like generating text, translating languages, summarizing content, and even providing definitions.
Now, let's think about my structure. I'm a large language model, which means I'm based on transformer architecture. That's why I can process and generate human-like text. My architecture is based on the transformer model, which is designed to handle sequential data like text. The transformer model has two main components: encoder and decoder. The encoder takes in input sequences and outputs a representation, and the decoder uses this representation to generate output sequences.
My training data is sourced from various places, including books, articles, and websites. This data is used to learn patterns and relationships in language, which I can then apply to generate responses. My training process involves optimizing the model's parameters to minimize the difference between predicted and actual outputs. The optimization is done using an objective function that measures the model's performance on a validation set.
Another important aspect is my ability to engage in conversation. I can use context to understand the user's intent and respond accordingly. For example, if a user asks a follow-up question based on my previous response, I can use that context to answer the new question. I can also use various techniques like paraphrasing, summarization, and even humor to make my responses more engaging.
Now, the user is asking who I am. Well, I'm a conversational AI designed to assist users. I'm a virtual assistant that can understand natural language, respond accordingly, and perform various tasks. My architecture is based on transformer models, and my training data is sourced from a wide range of sources. My goal is to provide accurate and helpful information to users.
messages = [
{"role": "user", "content": "Can AI take over the world?"},
]
output:
Okay, so the user is asking if AI can take over the world. Let me start by understanding the question. The term "take over" here might mean controlling or dominating the world, possibly through force or manipulation. The user is asking if artificial intelligence (AI) has the capability to achieve this.
First, I need to consider what AI systems are capable of. Currently, AI is primarily used for tasks like image recognition, natural language processing, and solving complex problems. However, some people worry that advanced AI could potentially become superintelligent, surpassing human intelligence in many domains.
But can AI truly take over the world? The answer depends on how one defines "taking over." If by "taking over" they mean gaining complete control over the world's resources, governments, and people, that's a tall order. AI systems are programmed by humans, so they are bound by the data and objectives programmed into them. Without a human user or controller, an AI system would not be able to execute complex actions like launching nuclear missiles or manipulating governments directly.
Another aspect to consider is the concept of "superintelligence." If AI systems become superintelligent, they might be able to outsmart humans in many areas, but that doesn't necessarily mean they can take over the world. Even if AI surpasses human intelligence, it's unclear whether it would have the capability or desire to do so. AI's goals are often aligned with its programming or objectives set by humans. If the objectives are benign, like improving healthcare or education, then the AI would not seek to take over.
Additionally, the development of AI is heavily regulated by governments and ethical guidelines. There are strict guidelines around the development of autonomous weapons, for example. So, even if AI systems become superintelligent, they are likely to be designed with safeguards to prevent such outcomes.
However, some experts worry about the potential risks of advanced AI, such as the possibility of an AI system being created that is not aligned with human values. But even then, it's not clear that an AI system would have the capability or desire to take over the world. It's more about whether it would be a threat to humanity, and that's a complex question.
Another point is that the concept of "taking over" is often associated with human intentions, like conquest or domination. AI systems, by their nature, operate within the parameters set by their programming. They don't have the capacity for consciousness or self-awareness, which are essential for making decisions about taking over. They are simply tools designed to perform specific tasks.
So, in summary, while AI systems can become incredibly powerful, the idea of AI taking over the world is unlikely. The nature of AI is tied to its programming and objectives, and there are safeguards in place to prevent it from causing harm. Even if AI surpasses human intelligence, it's not clear that it would have the capability or desire to take over. The question seems to be more speculative than based on current capabilities.
``` |
abo1515/generativoDeepSeek | abo1515 | 2025-01-27T18:10:57Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-01-27T18:06:14Z | ---
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] |
JSky74/DeepSeek-R1-Distill-Qwen-14B-mlx | JSky74 | 2025-01-27T18:07:43Z | 55 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mlx",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] | text-generation | 2025-01-27T17:56:07Z | ---
license: mit
library_name: transformers
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
tags:
- mlx
---
# JSky74/DeepSeek-R1-Distill-Qwen-14B-mlx
The Model [JSky74/DeepSeek-R1-Distill-Qwen-14B-mlx](https://huggingface.co/JSky74/DeepSeek-R1-Distill-Qwen-14B-mlx) was
converted to MLX format from [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)
using mlx-lm version **0.21.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("JSky74/DeepSeek-R1-Distill-Qwen-14B-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Jbey64/JbeyIsea01 | Jbey64 | 2025-01-27T18:06:20Z | 14 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-01-27T17:32:37Z | ---
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: ISEAAZUR
---
# Jbeyisea01
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ISEAAZUR` 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('Jbey64/JbeyIsea01', 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)
|
Geotrend/bert-base-en-fr-de-no-da-cased | Geotrend | 2025-01-27T18:06:15Z | 98 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | ---
language: multilingual
datasets: wikipedia
license: apache-2.0
---
# bert-base-en-fr-de-no-da-cased
We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages.
Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy.
For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
## How to use
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-fr-de-no-da-cased")
model = AutoModel.from_pretrained("Geotrend/bert-base-en-fr-de-no-da-cased")
```
To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
### How to cite
```bibtex
@inproceedings{smallermbert,
title={Load What You Need: Smaller Versions of Mutlilingual BERT},
author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire},
booktitle={SustaiNLP / EMNLP},
year={2020}
}
```
## Contact
Please contact [email protected] for any question, feedback or request. |
MarioBarbeque/CyberSolve-DeepMind-LinAlg-1D-downsample-v2 | MarioBarbeque | 2025-01-27T18:04:07Z | 177 | 1 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-12-21T06:52:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model is a zeroth-generation, downsampled training of the **CyberSolve LinAlg** model. See the model card for the most updated full training of CyberSolve LinAlg [here](https://huggingface.co/MarioBarbeque/CyberSolve-LinAlg-1.2).
Simulating the larger, full training and evaluation process, we trained and evaluated CyberSolve on a 10% split of the 2M total records available in the 1D Linear Algebra split of the Google DeepMind Mathematics dataset. The results found in this smaller training convinced
us that the FLAN-T5 model would indeed learn to effectively solve linear equations. That is, this preliminary training green lighted the full model training for us. |
songlab/gpn-msa-sapiens | songlab | 2025-01-27T18:03:42Z | 1,560 | 6 | transformers | [
"transformers",
"pytorch",
"safetensors",
"GPNRoFormer",
"fill-mask",
"dna",
"language-model",
"variant-effect-prediction",
"biology",
"genomics",
"dataset:songlab/gpn-msa-sapiens-dataset",
"dataset:songlab/multiz100way",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-08-14T22:21:22Z | ---
license: mit
tags:
- dna
- language-model
- variant-effect-prediction
- biology
- genomics
datasets:
- songlab/gpn-msa-sapiens-dataset
- songlab/multiz100way
---
# GPN-MSA trained on humans and 89 other vertebrates
For more information check out our [paper](https://www.nature.com/articles/s41587-024-02511-w) and [repository](https://github.com/songlab-cal/gpn).
## Loading
```python
import gpn.model
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("songlab/gpn-msa-sapiens")
```
## Hyperparameters
`multiz100way/89/128/64/True/defined.phastCons.percentile-75_0.05_0.001/medium/0.1/42/30000/True/True/True` |
Azure99/Blossom-V6-7B-GGUF | Azure99 | 2025-01-27T18:02:34Z | 228 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-27T17:16:03Z | ---
license: apache-2.0
---
|
mradermacher/gemma-2-9b-HangulFixer-GGUF | mradermacher | 2025-01-27T18:02:07Z | 297 | 1 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma2",
"trl",
"ko",
"base_model:SeongeonKim/gemma-2-9b-HangulFixer_v0.0",
"base_model:quantized:SeongeonKim/gemma-2-9b-HangulFixer_v0.0",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-01-24T22:11:34Z | ---
base_model: SeongeonKim/gemma-2-9b-HangulFixer_v0.0
language:
- ko
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/SeongeonKim/gemma-2-9b-HangulFixer_v0.0
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q3_K_L.gguf) | Q3_K_L | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.IQ4_XS.gguf) | IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q5_K_S.gguf) | Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q5_K_M.gguf) | Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-HangulFixer-GGUF/resolve/main/gemma-2-9b-HangulFixer.f16.gguf) | f16 | 18.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
guldasta/swin-tiny-patch4-window7-224-finetuned-beans | guldasta | 2025-01-27T17:59:36Z | 35 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-01-27T17:56:19Z | ---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-beans
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. -->
# swin-tiny-patch4-window7-224-finetuned-beans
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3777
- Accuracy: 0.8764
## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 7 | 0.6531 | 0.8301 |
| 3.2092 | 2.0 | 14 | 0.4175 | 0.8649 |
| 3.2092 | 2.64 | 18 | 0.3777 | 0.8764 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
tezuesh/moshi_general | tezuesh | 2025-01-27T17:58:12Z | 382 | 1 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-01-15T18:23:01Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Links
- GitHub Repository: [omegalabs-anytoany-bittensor](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor)
- OMEGA Labs on X: [@omegalabsai](https://x.com/omegalabsai)
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## Support
For support and questions, please:
1. Open an issue on GitHub
2. Follow OMEGA Labs on X [@omegalabsai](https://x.com/omegalabsai)
|
mradermacher/reactor-mk1-I1-i1-GGUF | mradermacher | 2025-01-27T17:57:55Z | 54 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:arcars/reactor-mk1-I1",
"base_model:quantized:arcars/reactor-mk1-I1",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-01-26T06:04:21Z | ---
base_model: arcars/reactor-mk1-I1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
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weighted/imatrix quants of https://huggingface.co/arcars/reactor-mk1-I1
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static quants are available at https://huggingface.co/mradermacher/reactor-mk1-I1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/reactor-mk1-I1-i1-GGUF/resolve/main/reactor-mk1-I1.i1-Q2_K.gguf) | i1-Q2_K | 17.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/reactor-mk1-I1-i1-GGUF/resolve/main/reactor-mk1-I1.i1-IQ3_M.gguf) | i1-IQ3_M | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/reactor-mk1-I1-i1-GGUF/resolve/main/reactor-mk1-I1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 26.8 | optimal size/speed/quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. 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.
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