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PrunaAI/winglian-llama-3-8b-256k-PoSE-QUANTO-float8bit-smashed | PrunaAI | 2024-07-01T07:58:06Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:winglian/llama-3-8b-256k-PoSE",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T00:37:46Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: winglian/llama-3-8b-256k-PoSE
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo winglian/llama-3-8b-256k-PoSE installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/winglian-llama-3-8b-256k-PoSE-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("winglian/llama-3-8b-256k-PoSE")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model winglian/llama-3-8b-256k-PoSE before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/winglian-llama-3-8b-256k-PoSE-QUANTO-int2bit-smashed | PrunaAI | 2024-07-01T07:58:46Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:winglian/llama-3-8b-256k-PoSE",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T00:37:49Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: winglian/llama-3-8b-256k-PoSE
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo winglian/llama-3-8b-256k-PoSE installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/winglian-llama-3-8b-256k-PoSE-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("winglian/llama-3-8b-256k-PoSE")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model winglian/llama-3-8b-256k-PoSE before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/winglian-llama-3-8b-256k-PoSE-QUANTO-int4bit-smashed | PrunaAI | 2024-07-01T08:00:53Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:winglian/llama-3-8b-256k-PoSE",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T00:37:52Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: winglian/llama-3-8b-256k-PoSE
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo winglian/llama-3-8b-256k-PoSE installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/winglian-llama-3-8b-256k-PoSE-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("winglian/llama-3-8b-256k-PoSE")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model winglian/llama-3-8b-256k-PoSE before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
tannedbum/L3-Nymeria-v2-8B-exl2 | tannedbum | 2024-06-30T01:02:27Z | 0 | 0 | null | [
"mergekit",
"merge",
"roleplay",
"sillytavern",
"llama3",
"not-for-all-audiences",
"en",
"base_model:Sao10K/L3-8B-Stheno-v3.2",
"base_model:chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO",
"base_model:TheDrummer/Llama-3SOME-8B-v2",
"base_model:NeverSleep/Llama-3-Lumimaid-8B-v0.1",
"base_model:Hastagaras/Jamet-8B-L3-MK.V-Blackroot",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-06-30T00:38:12Z | ---
base_model:
- Sao10K/L3-8B-Stheno-v3.2
- chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO
- TheDrummer/Llama-3SOME-8B-v2
- NeverSleep/Llama-3-Lumimaid-8B-v0.1
- Hastagaras/Jamet-8B-L3-MK.V-Blackroot
tags:
- mergekit
- merge
- roleplay
- sillytavern
- llama3
- not-for-all-audiences
license: cc-by-nc-4.0
language:
- en
---
- Upgraded SimPO.
- A touch of 3SOME, Lumimaid and Jamet Blackroot resulting a slightly different prose and wider RP vocab.
- Leans slightly more on nsfw than the original.
## Available quants
- [8.0 bpw](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B-exl2/tree/8.0)
- [6.5 bpw](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B-exl2/tree/6.5)
- [5.0 bpw](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B-exl2/tree/5.0)
- [4.25 bpw](https://huggingface.co/tannedbum/L3-Nymeria-v2-8B-exl2/tree/4.25)
## Download with git:
```shell
git clone --single-branch --branch 6.5 https://huggingface.co/tannedbum/L3-Nymeria-v2-8B-exl2 L3-Nymeria-v2-8B-exl2-6.5
```
## SillyTavern
## Text Completion presets
```
temp 0.9
top_k 30
top_p 0.75
min_p 0.2
rep_pen 1.1
smooth_factor 0.25
smooth_curve 1
```
## Advanced Formatting
[Context & Instruct preset by Virt-io](https://huggingface.co/Virt-io/SillyTavern-Presets/tree/main/Prompts/LLAMA-3/v2.0)
Instruct Mode: Enabled
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
This model was merged using the slerp merge method.
### Models Merged
The following models were included in the merge:
* [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2)
* [chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO](https://huggingface.co/chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO)
* [TheDrummer/Llama-3SOME-8B-v2](https://huggingface.co/TheDrummer/Llama-3SOME-8B-v2)
* [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1)
* [Hastagaras/Jamet-8B-L3-MK.V-Blackroot](https://huggingface.co/Hastagaras/Jamet-8B-L3-MK.V-Blackroot)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: NeverSleep/Llama-3-Lumimaid-8B-v0.1
layer_range: [0, 32]
- model: Hastagaras/Jamet-8B-L3-MK.V-Blackroot
layer_range: [0, 32]
merge_method: slerp
base_model: NeverSleep/Llama-3-Lumimaid-8B-v0.1
parameters:
t:
- filter: self_attn
value: [0.7, 0.3, 0.3, 0.3]
- filter: mlp
value: [0.3, 0.7, 0.7, 0.7]
- value: 0.4
dtype: bfloat16
L3-Lumimaid-Jamet-Blackroot-8B
slices:
- sources:
- model: tannedbum/L3-Lumimaid-Jamet-Blackroot-8B
layer_range: [0, 32]
- model: chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO
layer_range: [0, 32]
merge_method: slerp
base_model: tannedbum/L3-Lumimaid-Jamet-Blackroot-8B
parameters:
t:
- filter: self_attn
value: [0.3, 0.7, 0.7, 0.7]
- filter: mlp
value: [0.7, 0.3, 0.3, 0.3]
- value: 0.6
dtype: bfloat16
L3-SimPO-Lumimaid-Jamet-Blackroot-8B
slices:
- sources:
- model: Sao10K/L3-8B-Stheno-v3.2
layer_range: [0, 32]
- model: TheDrummer/Llama-3SOME-8B-v2
layer_range: [0, 32]
merge_method: slerp
base_model: Sao10K/L3-8B-Stheno-v3.2
parameters:
t:
- filter: self_attn
value: [0.3, 0.3, 0.7, 0.3]
- filter: mlp
value: [0.7, 0.7, 0.3, 0.7]
- value: 0.4
dtype: bfloat16
L3-Stheno-3SOME-8B
slices:
- sources:
- model: tannedbum/L3-Stheno-3SOME-8B
layer_range: [0, 32]
- model: tannedbum/L3-SimPO-Lumimaid-Jamet-Blackroot-8B
layer_range: [0, 32]
merge_method: slerp
base_model: tannedbum/L3-Stheno-3SOME-8B
parameters:
t:
- filter: self_attn
value: [0.4, 0.3, 0.3, 0.6]
- filter: mlp
value: [0.6, 0.7, 0.7, 0.4]
- value: 0.4
dtype: bfloat16
L3-Nymeria-v2-8B
```
Want to support my work ? My Ko-fi page: https://ko-fi.com/tannedbum |
PrunaAI/winglian-llama-3-8b-256k-PoSE-AWQ-4bit-smashed | PrunaAI | 2024-06-30T00:40:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"base_model:winglian/llama-3-8b-256k-PoSE",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | 2024-06-30T00:38:21Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: winglian/llama-3-8b-256k-PoSE
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with awq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo winglian/llama-3-8b-256k-PoSE installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/winglian-llama-3-8b-256k-PoSE-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("winglian/llama-3-8b-256k-PoSE")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model winglian/llama-3-8b-256k-PoSE before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
debiao29/Qwen-Qwen1.5-0.5B-1719707906 | debiao29 | 2024-06-30T00:38:29Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T00:38:27Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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whizzzzkid/whizzzzkid_287_4 | whizzzzkid | 2024-06-30T00:40:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T00:38:58Z | Entry not found |
debiao29/Qwen-Qwen1.5-1.8B-1719708059 | debiao29 | 2024-06-30T00:41:01Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T00:40:59Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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WillianMar1o/TreinamentoUnifahe4bits | WillianMar1o | 2024-06-30T17:10:22Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-06-30T00:41:04Z | ---
license: mit
---
|
debiao29/google-gemma-2b-1719708158 | debiao29 | 2024-06-30T00:42:41Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
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] | null | 2024-06-30T00:42:38Z | ---
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debiao29/Qwen-Qwen1.5-0.5B-1719708268 | debiao29 | 2024-06-30T00:44:31Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T00:44:28Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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debiao29/Qwen-Qwen1.5-1.8B-1719708421 | debiao29 | 2024-06-30T00:47:03Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T00:47:01Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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jhpark0509/openllama-3b-peft-squad_v2 | jhpark0509 | 2024-06-30T00:47:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T00:47:03Z | ---
library_name: transformers
tags: []
---
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debiao29/google-gemma-2b-1719708520 | debiao29 | 2024-06-30T00:48:43Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T00:48:40Z | ---
base_model: google/gemma-2b
library_name: peft
---
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blockblockblock/Arcee-Spark-bpw4.4-exl2 | blockblockblock | 2024-06-30T00:55:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"de",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"exl2",
"region:us"
] | text-generation | 2024-06-30T00:49:59Z | ---
license: apache-2.0
language:
- en
- de
- ar
---
<div align="center">
<img src="https://i.ibb.co/80ssNWS/o-Vdk-Qx-ARNmzr-Pi1h-Efj-SA.webp" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>
# Arcee Spark
Arcee Spark is a powerful 7B parameter language model that punches well above its weight class. Initialized from Qwen2, this model underwent a sophisticated training process:
1. Fine-tuned on 1.8 million samples
2. Merged with Qwen2-7B-Instruct using Arcee's mergekit
3. Further refined using Direct Preference Optimization (DPO)
This meticulous process results in exceptional performance, with Arcee Spark achieving the highest score on MT-Bench for models of its size, outperforming even GPT-3.5 on many tasks.
## Key Features
- 7B parameters
- State-of-the-art performance for its size
- Initialized from Qwen2
- Advanced training process including fine-tuning, merging, and DPO
- Highest MT-Bench score in the 7B class
- Outperforms GPT-3.5 on many tasks
- Has a context length of 128k tokens, making it ideal for tasks requiring many conversation turns or working with large amounts of text.
## Business Use Cases
Arcee Spark offers a compelling solution for businesses looking to leverage advanced AI capabilities without the hefty computational requirements of larger models. Its unique combination of small size and high performance makes it ideal for:
1. **Real-time applications**: Deploy Arcee Spark for chatbots, customer service automation, and interactive systems where low latency is crucial.
2. **Edge computing**: Run sophisticated AI tasks on edge devices or in resource-constrained environments.
3. **Cost-effective scaling**: Implement advanced language AI across your organization without breaking the bank on infrastructure or API costs.
4. **Rapid prototyping**: Quickly develop and iterate on AI-powered features and products.
5. **On-premise deployment**: Easily host Arcee Spark on local infrastructure for enhanced data privacy and security.
## Performance and Efficiency
Arcee Spark demonstrates that bigger isn't always better in the world of language models. By leveraging advanced training techniques and architectural optimizations, it delivers:
- **Speed**: Blazing fast inference times, often 10-100x faster than larger models.
- **Efficiency**: Significantly lower computational requirements, reducing both costs and environmental impact.
- **Flexibility**: Easy to fine-tune or adapt for specific domains or tasks.
Despite its compact size, Arcee Spark offers deep reasoning capabilities, making it suitable for a wide range of complex tasks including:
- Advanced text generation
- Detailed question answering
- Nuanced sentiment analysis
- Complex problem-solving
- Code generation and analysis
## Model Availability
- **Quants**: [Arcee Spark GGUF](https://huggingface.co/arcee-ai/Arcee-Spark-GGUF)
- **FP32**: For those looking to squeeze every bit of performance out of the model, we offer an [FP32 version](https://huggingface.co/arcee-ai/Arcee-Spark-FP32) that scores slightly higher on all benchmarks.
## Benchmarks and Evaluations
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/dQRtXR7/Screenshot-2024-06-23-at-11-01-59-PM.png" alt="Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/BLX8GmZ/Screenshot-2024-06-23-at-10-43-50-PM.png" alt="Additional Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.postimg.cc/Vs7v0Vbn/Screenshot-2024-06-24-at-1-10-58-AM.png" alt="Bigbenchhard Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
### MT-Bench
```markdown
########## First turn ##########
score
model turn
arcee-spark 1 8.777778
########## Second turn ##########
score
model turn
arcee-spark 2 8.164634
########## Average ##########
score
model
arcee-spark 8.469325
```
### EQ-Bench
EQ-Bench: 71.4
### TruthfulQA
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4382|± |0.0174|
| | |mc2 |0.6150|± |0.0155|
### AGI-Eval
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.3937|± |0.0307|
| | |acc_norm|0.3937|± |0.0307|
|agieval_logiqa_en | 0|acc |0.4731|± |0.0196|
| | |acc_norm|0.4854|± |0.0196|
|agieval_lsat_ar | 0|acc |0.2783|± |0.0296|
| | |acc_norm|0.3000|± |0.0303|
|agieval_lsat_lr | 0|acc |0.5549|± |0.0220|
| | |acc_norm|0.5451|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6022|± |0.0299|
| | |acc_norm|0.6208|± |0.0296|
|agieval_sat_en | 0|acc |0.8155|± |0.0271|
| | |acc_norm|0.8107|± |0.0274|
|agieval_sat_en_without_passage| 0|acc |0.4806|± |0.0349|
| | |acc_norm|0.4612|± |0.0348|
|agieval_sat_math | 0|acc |0.4909|± |0.0338|
| | |acc_norm|0.4545|± |0.0336|
AGI-eval average: 51.11
### GPT4All Evaluation
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5333|± |0.0146|
| | |acc_norm|0.5640|± |0.0145|
|arc_easy | 0|acc |0.8131|± |0.0080|
| | |acc_norm|0.7668|± |0.0087|
|boolq | 1|acc |0.8471|± |0.0063|
|hellaswag | 0|acc |0.6206|± |0.0048|
| | |acc_norm|0.8118|± |0.0039|
|openbookqa | 0|acc |0.3560|± |0.0214|
| | |acc_norm|0.4600|± |0.0223|
|piqa | 0|acc |0.7987|± |0.0094|
| | |acc_norm|0.8030|± |0.0093|
|winogrande | 0|acc |0.7690|± |0.0130|
Gpt4al Average: 69.37
## Big Bench Hard
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.6053|± |0.0356|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6450|± |0.0249|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.5233|± |0.0312|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2006|± |0.0212|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2840|± |0.0202|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2429|± |0.0162|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4367|± |0.0287|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4720|± |0.0223|
|bigbench_navigate | 0|multiple_choice_grade|0.4980|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5600|± |0.0111|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4375|± |0.0235|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2685|± |0.0140|
|bigbench_snarks | 0|multiple_choice_grade|0.7348|± |0.0329|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6978|± |0.0146|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.4060|± |0.0155|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2072|± |0.0115|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1406|± |0.0083|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4367|± |0.0287|
Big Bench average: 45.78
## License
Arcee Spark is released under the Apache 2.0 license.
## Acknowledgments
- The Qwen2 team for their foundational work
- The open-source AI community for their invaluable tools and datasets
- Our dedicated team of researchers and engineers who push the boundaries of what's possible with compact language models
|
debiao29/Qwen-Qwen1.5-0.5B-1719708631 | debiao29 | 2024-06-30T00:50:33Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T00:50:31Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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debiao29/Qwen-Qwen1.5-1.8B-1719708783 | debiao29 | 2024-06-30T00:53:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T00:53:03Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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debiao29/google-gemma-2b-1719708882 | debiao29 | 2024-06-30T00:54:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T00:54:42Z | ---
base_model: google/gemma-2b
library_name: peft
---
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valerielucro/mistral_gsm8k_sft_epoch5 | valerielucro | 2024-06-30T00:56:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T00:55:42Z | ---
library_name: transformers
tags: []
---
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debiao29/Qwen-Qwen1.5-0.5B-1719708994 | debiao29 | 2024-06-30T00:56:36Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T00:56:34Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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debiao29/Qwen-Qwen1.5-1.8B-1719709146 | debiao29 | 2024-06-30T00:59:08Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T00:59:06Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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richardkelly/google-gemma-2b-1719709170 | richardkelly | 2024-06-30T00:59:30Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T00:59:30Z | Entry not found |
debiao29/google-gemma-2b-1719709245 | debiao29 | 2024-06-30T01:00:48Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T01:00:45Z | ---
base_model: google/gemma-2b
library_name: peft
---
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debiao29/Qwen-Qwen1.5-0.5B-1719709354 | debiao29 | 2024-06-30T01:02:37Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T01:02:34Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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- PEFT 0.11.1 |
EYEDOL/falcon-7b-sharded-bf16-finetuned-mental-health-conversational | EYEDOL | 2024-06-30T01:03:54Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T01:03:54Z | Entry not found |
NateVanC/AI-URL-to-Short | NateVanC | 2024-06-30T01:03:58Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T01:03:58Z | Entry not found |
debiao29/Qwen-Qwen1.5-1.8B-1719709506 | debiao29 | 2024-06-30T01:05:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T01:05:06Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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- PEFT 0.11.1 |
debiao29/google-gemma-2b-1719709605 | debiao29 | 2024-06-30T01:06:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T01:06:45Z | ---
base_model: google/gemma-2b
library_name: peft
---
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- PEFT 0.11.1 |
jenniecoveria/JEONGYEON_ALL_ROUND_OF_TWICE_RVC_V2_KOK_KPOP | jenniecoveria | 2024-06-30T01:09:52Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T01:08:17Z | Entry not found |
blockblockblock/Arcee-Spark-bpw4.8-exl2 | blockblockblock | 2024-06-30T01:13:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"de",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"exl2",
"region:us"
] | text-generation | 2024-06-30T01:08:18Z | ---
license: apache-2.0
language:
- en
- de
- ar
---
<div align="center">
<img src="https://i.ibb.co/80ssNWS/o-Vdk-Qx-ARNmzr-Pi1h-Efj-SA.webp" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>
# Arcee Spark
Arcee Spark is a powerful 7B parameter language model that punches well above its weight class. Initialized from Qwen2, this model underwent a sophisticated training process:
1. Fine-tuned on 1.8 million samples
2. Merged with Qwen2-7B-Instruct using Arcee's mergekit
3. Further refined using Direct Preference Optimization (DPO)
This meticulous process results in exceptional performance, with Arcee Spark achieving the highest score on MT-Bench for models of its size, outperforming even GPT-3.5 on many tasks.
## Key Features
- 7B parameters
- State-of-the-art performance for its size
- Initialized from Qwen2
- Advanced training process including fine-tuning, merging, and DPO
- Highest MT-Bench score in the 7B class
- Outperforms GPT-3.5 on many tasks
- Has a context length of 128k tokens, making it ideal for tasks requiring many conversation turns or working with large amounts of text.
## Business Use Cases
Arcee Spark offers a compelling solution for businesses looking to leverage advanced AI capabilities without the hefty computational requirements of larger models. Its unique combination of small size and high performance makes it ideal for:
1. **Real-time applications**: Deploy Arcee Spark for chatbots, customer service automation, and interactive systems where low latency is crucial.
2. **Edge computing**: Run sophisticated AI tasks on edge devices or in resource-constrained environments.
3. **Cost-effective scaling**: Implement advanced language AI across your organization without breaking the bank on infrastructure or API costs.
4. **Rapid prototyping**: Quickly develop and iterate on AI-powered features and products.
5. **On-premise deployment**: Easily host Arcee Spark on local infrastructure for enhanced data privacy and security.
## Performance and Efficiency
Arcee Spark demonstrates that bigger isn't always better in the world of language models. By leveraging advanced training techniques and architectural optimizations, it delivers:
- **Speed**: Blazing fast inference times, often 10-100x faster than larger models.
- **Efficiency**: Significantly lower computational requirements, reducing both costs and environmental impact.
- **Flexibility**: Easy to fine-tune or adapt for specific domains or tasks.
Despite its compact size, Arcee Spark offers deep reasoning capabilities, making it suitable for a wide range of complex tasks including:
- Advanced text generation
- Detailed question answering
- Nuanced sentiment analysis
- Complex problem-solving
- Code generation and analysis
## Model Availability
- **Quants**: [Arcee Spark GGUF](https://huggingface.co/arcee-ai/Arcee-Spark-GGUF)
- **FP32**: For those looking to squeeze every bit of performance out of the model, we offer an [FP32 version](https://huggingface.co/arcee-ai/Arcee-Spark-FP32) that scores slightly higher on all benchmarks.
## Benchmarks and Evaluations
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/dQRtXR7/Screenshot-2024-06-23-at-11-01-59-PM.png" alt="Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/BLX8GmZ/Screenshot-2024-06-23-at-10-43-50-PM.png" alt="Additional Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.postimg.cc/Vs7v0Vbn/Screenshot-2024-06-24-at-1-10-58-AM.png" alt="Bigbenchhard Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
### MT-Bench
```markdown
########## First turn ##########
score
model turn
arcee-spark 1 8.777778
########## Second turn ##########
score
model turn
arcee-spark 2 8.164634
########## Average ##########
score
model
arcee-spark 8.469325
```
### EQ-Bench
EQ-Bench: 71.4
### TruthfulQA
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4382|± |0.0174|
| | |mc2 |0.6150|± |0.0155|
### AGI-Eval
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.3937|± |0.0307|
| | |acc_norm|0.3937|± |0.0307|
|agieval_logiqa_en | 0|acc |0.4731|± |0.0196|
| | |acc_norm|0.4854|± |0.0196|
|agieval_lsat_ar | 0|acc |0.2783|± |0.0296|
| | |acc_norm|0.3000|± |0.0303|
|agieval_lsat_lr | 0|acc |0.5549|± |0.0220|
| | |acc_norm|0.5451|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6022|± |0.0299|
| | |acc_norm|0.6208|± |0.0296|
|agieval_sat_en | 0|acc |0.8155|± |0.0271|
| | |acc_norm|0.8107|± |0.0274|
|agieval_sat_en_without_passage| 0|acc |0.4806|± |0.0349|
| | |acc_norm|0.4612|± |0.0348|
|agieval_sat_math | 0|acc |0.4909|± |0.0338|
| | |acc_norm|0.4545|± |0.0336|
AGI-eval average: 51.11
### GPT4All Evaluation
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5333|± |0.0146|
| | |acc_norm|0.5640|± |0.0145|
|arc_easy | 0|acc |0.8131|± |0.0080|
| | |acc_norm|0.7668|± |0.0087|
|boolq | 1|acc |0.8471|± |0.0063|
|hellaswag | 0|acc |0.6206|± |0.0048|
| | |acc_norm|0.8118|± |0.0039|
|openbookqa | 0|acc |0.3560|± |0.0214|
| | |acc_norm|0.4600|± |0.0223|
|piqa | 0|acc |0.7987|± |0.0094|
| | |acc_norm|0.8030|± |0.0093|
|winogrande | 0|acc |0.7690|± |0.0130|
Gpt4al Average: 69.37
## Big Bench Hard
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.6053|± |0.0356|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6450|± |0.0249|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.5233|± |0.0312|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2006|± |0.0212|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2840|± |0.0202|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2429|± |0.0162|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4367|± |0.0287|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4720|± |0.0223|
|bigbench_navigate | 0|multiple_choice_grade|0.4980|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5600|± |0.0111|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4375|± |0.0235|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2685|± |0.0140|
|bigbench_snarks | 0|multiple_choice_grade|0.7348|± |0.0329|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6978|± |0.0146|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.4060|± |0.0155|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2072|± |0.0115|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1406|± |0.0083|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4367|± |0.0287|
Big Bench average: 45.78
## License
Arcee Spark is released under the Apache 2.0 license.
## Acknowledgments
- The Qwen2 team for their foundational work
- The open-source AI community for their invaluable tools and datasets
- Our dedicated team of researchers and engineers who push the boundaries of what's possible with compact language models
|
debiao29/Qwen-Qwen1.5-0.5B-1719709716 | debiao29 | 2024-06-30T01:08:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T01:08:36Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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Madyarfeyzi84/Z2 | Madyarfeyzi84 | 2024-06-30T01:09:46Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-3-medium",
"license:unknown",
"region:us"
] | text-to-image | 2024-06-30T01:09:41Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: 3d
output:
url: images/InShot_20240629_063412473.png
base_model: stabilityai/stable-diffusion-3-medium
instance_prompt: null
license: unknown
---
# Zorvan do
<Gallery />
## Download model
[Download](/Madyarfeyzi84/Z2/tree/main) them in the Files & versions tab.
|
debiao29/Qwen-Qwen1.5-1.8B-1719709868 | debiao29 | 2024-06-30T01:11:10Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T01:11:08Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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debiao29/google-gemma-2b-1719709966 | debiao29 | 2024-06-30T01:12:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T01:12:46Z | ---
base_model: google/gemma-2b
library_name: peft
---
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movenb3at/reinhardt_ow2 | movenb3at | 2024-06-30T01:14:30Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T01:12:52Z | Entry not found |
debiao29/Qwen-Qwen1.5-0.5B-1719710077 | debiao29 | 2024-06-30T01:14:39Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T01:14:37Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
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ArjanvD95/by_the_horns_T43G | ArjanvD95 | 2024-06-30T01:51:35Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-06-30T01:15:24Z | Entry not found |
debiao29/Qwen-Qwen1.5-1.8B-1719710229 | debiao29 | 2024-06-30T01:17:11Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T01:17:09Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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mohamadzeddam/mm | mohamadzeddam | 2024-06-30T01:18:36Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-30T01:18:36Z | ---
license: apache-2.0
---
|
debiao29/google-gemma-2b-1719710329 | debiao29 | 2024-06-30T01:18:52Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T01:18:49Z | ---
base_model: google/gemma-2b
library_name: peft
---
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aprilschen/VioletEvergardenAutomemoriesColab | aprilschen | 2024-06-30T01:20:06Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T01:20:06Z | Entry not found |
debiao29/Qwen-Qwen1.5-0.5B-1719710437 | debiao29 | 2024-06-30T01:20:39Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T01:20:37Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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debiao29/Qwen-Qwen1.5-1.8B-1719710588 | debiao29 | 2024-06-30T01:23:11Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T01:23:09Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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debiao29/google-gemma-2b-1719710688 | debiao29 | 2024-06-30T01:24:51Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T01:24:48Z | ---
base_model: google/gemma-2b
library_name: peft
---
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debiao29/Qwen-Qwen1.5-0.5B-1719710798 | debiao29 | 2024-06-30T01:26:41Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T01:26:38Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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- PEFT 0.11.1 |
blockblockblock/Arcee-Spark-bpw5-exl2 | blockblockblock | 2024-06-30T01:32:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"de",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"5-bit",
"exl2",
"region:us"
] | text-generation | 2024-06-30T01:27:07Z | ---
license: apache-2.0
language:
- en
- de
- ar
---
<div align="center">
<img src="https://i.ibb.co/80ssNWS/o-Vdk-Qx-ARNmzr-Pi1h-Efj-SA.webp" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>
# Arcee Spark
Arcee Spark is a powerful 7B parameter language model that punches well above its weight class. Initialized from Qwen2, this model underwent a sophisticated training process:
1. Fine-tuned on 1.8 million samples
2. Merged with Qwen2-7B-Instruct using Arcee's mergekit
3. Further refined using Direct Preference Optimization (DPO)
This meticulous process results in exceptional performance, with Arcee Spark achieving the highest score on MT-Bench for models of its size, outperforming even GPT-3.5 on many tasks.
## Key Features
- 7B parameters
- State-of-the-art performance for its size
- Initialized from Qwen2
- Advanced training process including fine-tuning, merging, and DPO
- Highest MT-Bench score in the 7B class
- Outperforms GPT-3.5 on many tasks
- Has a context length of 128k tokens, making it ideal for tasks requiring many conversation turns or working with large amounts of text.
## Business Use Cases
Arcee Spark offers a compelling solution for businesses looking to leverage advanced AI capabilities without the hefty computational requirements of larger models. Its unique combination of small size and high performance makes it ideal for:
1. **Real-time applications**: Deploy Arcee Spark for chatbots, customer service automation, and interactive systems where low latency is crucial.
2. **Edge computing**: Run sophisticated AI tasks on edge devices or in resource-constrained environments.
3. **Cost-effective scaling**: Implement advanced language AI across your organization without breaking the bank on infrastructure or API costs.
4. **Rapid prototyping**: Quickly develop and iterate on AI-powered features and products.
5. **On-premise deployment**: Easily host Arcee Spark on local infrastructure for enhanced data privacy and security.
## Performance and Efficiency
Arcee Spark demonstrates that bigger isn't always better in the world of language models. By leveraging advanced training techniques and architectural optimizations, it delivers:
- **Speed**: Blazing fast inference times, often 10-100x faster than larger models.
- **Efficiency**: Significantly lower computational requirements, reducing both costs and environmental impact.
- **Flexibility**: Easy to fine-tune or adapt for specific domains or tasks.
Despite its compact size, Arcee Spark offers deep reasoning capabilities, making it suitable for a wide range of complex tasks including:
- Advanced text generation
- Detailed question answering
- Nuanced sentiment analysis
- Complex problem-solving
- Code generation and analysis
## Model Availability
- **Quants**: [Arcee Spark GGUF](https://huggingface.co/arcee-ai/Arcee-Spark-GGUF)
- **FP32**: For those looking to squeeze every bit of performance out of the model, we offer an [FP32 version](https://huggingface.co/arcee-ai/Arcee-Spark-FP32) that scores slightly higher on all benchmarks.
## Benchmarks and Evaluations
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/dQRtXR7/Screenshot-2024-06-23-at-11-01-59-PM.png" alt="Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/BLX8GmZ/Screenshot-2024-06-23-at-10-43-50-PM.png" alt="Additional Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.postimg.cc/Vs7v0Vbn/Screenshot-2024-06-24-at-1-10-58-AM.png" alt="Bigbenchhard Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
### MT-Bench
```markdown
########## First turn ##########
score
model turn
arcee-spark 1 8.777778
########## Second turn ##########
score
model turn
arcee-spark 2 8.164634
########## Average ##########
score
model
arcee-spark 8.469325
```
### EQ-Bench
EQ-Bench: 71.4
### TruthfulQA
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4382|± |0.0174|
| | |mc2 |0.6150|± |0.0155|
### AGI-Eval
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.3937|± |0.0307|
| | |acc_norm|0.3937|± |0.0307|
|agieval_logiqa_en | 0|acc |0.4731|± |0.0196|
| | |acc_norm|0.4854|± |0.0196|
|agieval_lsat_ar | 0|acc |0.2783|± |0.0296|
| | |acc_norm|0.3000|± |0.0303|
|agieval_lsat_lr | 0|acc |0.5549|± |0.0220|
| | |acc_norm|0.5451|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6022|± |0.0299|
| | |acc_norm|0.6208|± |0.0296|
|agieval_sat_en | 0|acc |0.8155|± |0.0271|
| | |acc_norm|0.8107|± |0.0274|
|agieval_sat_en_without_passage| 0|acc |0.4806|± |0.0349|
| | |acc_norm|0.4612|± |0.0348|
|agieval_sat_math | 0|acc |0.4909|± |0.0338|
| | |acc_norm|0.4545|± |0.0336|
AGI-eval average: 51.11
### GPT4All Evaluation
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5333|± |0.0146|
| | |acc_norm|0.5640|± |0.0145|
|arc_easy | 0|acc |0.8131|± |0.0080|
| | |acc_norm|0.7668|± |0.0087|
|boolq | 1|acc |0.8471|± |0.0063|
|hellaswag | 0|acc |0.6206|± |0.0048|
| | |acc_norm|0.8118|± |0.0039|
|openbookqa | 0|acc |0.3560|± |0.0214|
| | |acc_norm|0.4600|± |0.0223|
|piqa | 0|acc |0.7987|± |0.0094|
| | |acc_norm|0.8030|± |0.0093|
|winogrande | 0|acc |0.7690|± |0.0130|
Gpt4al Average: 69.37
## Big Bench Hard
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.6053|± |0.0356|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6450|± |0.0249|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.5233|± |0.0312|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2006|± |0.0212|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2840|± |0.0202|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2429|± |0.0162|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4367|± |0.0287|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4720|± |0.0223|
|bigbench_navigate | 0|multiple_choice_grade|0.4980|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5600|± |0.0111|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4375|± |0.0235|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2685|± |0.0140|
|bigbench_snarks | 0|multiple_choice_grade|0.7348|± |0.0329|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6978|± |0.0146|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.4060|± |0.0155|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2072|± |0.0115|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1406|± |0.0083|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4367|± |0.0287|
Big Bench average: 45.78
## License
Arcee Spark is released under the Apache 2.0 license.
## Acknowledgments
- The Qwen2 team for their foundational work
- The open-source AI community for their invaluable tools and datasets
- Our dedicated team of researchers and engineers who push the boundaries of what's possible with compact language models
|
debiao29/Qwen-Qwen1.5-1.8B-1719710950 | debiao29 | 2024-06-30T01:29:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
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debiao29/Qwen-Qwen1.5-1.8B-1719711312 | debiao29 | 2024-06-30T01:35:14Z | 0 | 0 | peft | [
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bumick/gguf-workspace | bumick | 2024-06-30T01:35:25Z | 0 | 0 | null | [
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debiao29/Qwen-Qwen1.5-0.5B-1719711521 | debiao29 | 2024-06-30T01:38:44Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T01:38:41Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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debiao29/Qwen-Qwen1.5-1.8B-1719711673 | debiao29 | 2024-06-30T01:41:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T01:41:13Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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debiao29/google-gemma-2b-1719711772 | debiao29 | 2024-06-30T01:42:55Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T01:42:52Z | ---
base_model: google/gemma-2b
library_name: peft
---
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Nongpeamm/Typhoon_RAG_FINETUNE_with_DPO | Nongpeamm | 2024-06-30T01:44:11Z | 0 | 0 | null | [
"license:llama3",
"region:us"
] | null | 2024-06-30T01:44:11Z | ---
license: llama3
---
|
debiao29/Qwen-Qwen1.5-0.5B-1719711882 | debiao29 | 2024-06-30T01:44:44Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T01:44:42Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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blockblockblock/Arcee-Spark-bpw5.5-exl2 | blockblockblock | 2024-06-30T01:51:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"de",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"exl2",
"region:us"
] | text-generation | 2024-06-30T01:46:00Z | ---
license: apache-2.0
language:
- en
- de
- ar
---
<div align="center">
<img src="https://i.ibb.co/80ssNWS/o-Vdk-Qx-ARNmzr-Pi1h-Efj-SA.webp" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>
# Arcee Spark
Arcee Spark is a powerful 7B parameter language model that punches well above its weight class. Initialized from Qwen2, this model underwent a sophisticated training process:
1. Fine-tuned on 1.8 million samples
2. Merged with Qwen2-7B-Instruct using Arcee's mergekit
3. Further refined using Direct Preference Optimization (DPO)
This meticulous process results in exceptional performance, with Arcee Spark achieving the highest score on MT-Bench for models of its size, outperforming even GPT-3.5 on many tasks.
## Key Features
- 7B parameters
- State-of-the-art performance for its size
- Initialized from Qwen2
- Advanced training process including fine-tuning, merging, and DPO
- Highest MT-Bench score in the 7B class
- Outperforms GPT-3.5 on many tasks
- Has a context length of 128k tokens, making it ideal for tasks requiring many conversation turns or working with large amounts of text.
## Business Use Cases
Arcee Spark offers a compelling solution for businesses looking to leverage advanced AI capabilities without the hefty computational requirements of larger models. Its unique combination of small size and high performance makes it ideal for:
1. **Real-time applications**: Deploy Arcee Spark for chatbots, customer service automation, and interactive systems where low latency is crucial.
2. **Edge computing**: Run sophisticated AI tasks on edge devices or in resource-constrained environments.
3. **Cost-effective scaling**: Implement advanced language AI across your organization without breaking the bank on infrastructure or API costs.
4. **Rapid prototyping**: Quickly develop and iterate on AI-powered features and products.
5. **On-premise deployment**: Easily host Arcee Spark on local infrastructure for enhanced data privacy and security.
## Performance and Efficiency
Arcee Spark demonstrates that bigger isn't always better in the world of language models. By leveraging advanced training techniques and architectural optimizations, it delivers:
- **Speed**: Blazing fast inference times, often 10-100x faster than larger models.
- **Efficiency**: Significantly lower computational requirements, reducing both costs and environmental impact.
- **Flexibility**: Easy to fine-tune or adapt for specific domains or tasks.
Despite its compact size, Arcee Spark offers deep reasoning capabilities, making it suitable for a wide range of complex tasks including:
- Advanced text generation
- Detailed question answering
- Nuanced sentiment analysis
- Complex problem-solving
- Code generation and analysis
## Model Availability
- **Quants**: [Arcee Spark GGUF](https://huggingface.co/arcee-ai/Arcee-Spark-GGUF)
- **FP32**: For those looking to squeeze every bit of performance out of the model, we offer an [FP32 version](https://huggingface.co/arcee-ai/Arcee-Spark-FP32) that scores slightly higher on all benchmarks.
## Benchmarks and Evaluations
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/dQRtXR7/Screenshot-2024-06-23-at-11-01-59-PM.png" alt="Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/BLX8GmZ/Screenshot-2024-06-23-at-10-43-50-PM.png" alt="Additional Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.postimg.cc/Vs7v0Vbn/Screenshot-2024-06-24-at-1-10-58-AM.png" alt="Bigbenchhard Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
### MT-Bench
```markdown
########## First turn ##########
score
model turn
arcee-spark 1 8.777778
########## Second turn ##########
score
model turn
arcee-spark 2 8.164634
########## Average ##########
score
model
arcee-spark 8.469325
```
### EQ-Bench
EQ-Bench: 71.4
### TruthfulQA
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4382|± |0.0174|
| | |mc2 |0.6150|± |0.0155|
### AGI-Eval
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.3937|± |0.0307|
| | |acc_norm|0.3937|± |0.0307|
|agieval_logiqa_en | 0|acc |0.4731|± |0.0196|
| | |acc_norm|0.4854|± |0.0196|
|agieval_lsat_ar | 0|acc |0.2783|± |0.0296|
| | |acc_norm|0.3000|± |0.0303|
|agieval_lsat_lr | 0|acc |0.5549|± |0.0220|
| | |acc_norm|0.5451|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6022|± |0.0299|
| | |acc_norm|0.6208|± |0.0296|
|agieval_sat_en | 0|acc |0.8155|± |0.0271|
| | |acc_norm|0.8107|± |0.0274|
|agieval_sat_en_without_passage| 0|acc |0.4806|± |0.0349|
| | |acc_norm|0.4612|± |0.0348|
|agieval_sat_math | 0|acc |0.4909|± |0.0338|
| | |acc_norm|0.4545|± |0.0336|
AGI-eval average: 51.11
### GPT4All Evaluation
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5333|± |0.0146|
| | |acc_norm|0.5640|± |0.0145|
|arc_easy | 0|acc |0.8131|± |0.0080|
| | |acc_norm|0.7668|± |0.0087|
|boolq | 1|acc |0.8471|± |0.0063|
|hellaswag | 0|acc |0.6206|± |0.0048|
| | |acc_norm|0.8118|± |0.0039|
|openbookqa | 0|acc |0.3560|± |0.0214|
| | |acc_norm|0.4600|± |0.0223|
|piqa | 0|acc |0.7987|± |0.0094|
| | |acc_norm|0.8030|± |0.0093|
|winogrande | 0|acc |0.7690|± |0.0130|
Gpt4al Average: 69.37
## Big Bench Hard
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.6053|± |0.0356|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6450|± |0.0249|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.5233|± |0.0312|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2006|± |0.0212|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2840|± |0.0202|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2429|± |0.0162|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4367|± |0.0287|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4720|± |0.0223|
|bigbench_navigate | 0|multiple_choice_grade|0.4980|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5600|± |0.0111|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4375|± |0.0235|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2685|± |0.0140|
|bigbench_snarks | 0|multiple_choice_grade|0.7348|± |0.0329|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6978|± |0.0146|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.4060|± |0.0155|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2072|± |0.0115|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1406|± |0.0083|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4367|± |0.0287|
Big Bench average: 45.78
## License
Arcee Spark is released under the Apache 2.0 license.
## Acknowledgments
- The Qwen2 team for their foundational work
- The open-source AI community for their invaluable tools and datasets
- Our dedicated team of researchers and engineers who push the boundaries of what's possible with compact language models
|
debiao29/Qwen-Qwen1.5-1.8B-1719712034 | debiao29 | 2024-06-30T01:47:16Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T01:47:14Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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debiao29/google-gemma-2b-1719712133 | debiao29 | 2024-06-30T01:48:56Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T01:48:53Z | ---
base_model: google/gemma-2b
library_name: peft
---
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irisxue/results | irisxue | 2024-06-30T01:49:28Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2024-06-30T01:49:06Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 1.0444 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
debiao29/Qwen-Qwen1.5-0.5B-1719712243 | debiao29 | 2024-06-30T01:50:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T01:50:44Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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whizzzzkid/whizzzzkid_288_3 | whizzzzkid | 2024-06-30T01:52:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T01:51:15Z | Entry not found |
debiao29/Qwen-Qwen1.5-1.8B-1719712395 | debiao29 | 2024-06-30T01:53:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T01:53:15Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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saraprice/llama2-7B-COT-DEPLOYMENT-checkpoints | saraprice | 2024-07-01T20:35:38Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-06-30T01:53:36Z | Entry not found |
Haary/USK_Mistral_7B_Unsloth_16bit_unfinish | Haary | 2024-06-30T01:53:56Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:Ichsan2895/Merak-7B-v4",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T01:53:55Z | ---
base_model: Ichsan2895/Merak-7B-v4
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** Haary
- **License:** apache-2.0
- **Finetuned from model :** Ichsan2895/Merak-7B-v4
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)
|
debiao29/google-gemma-2b-1719712494 | debiao29 | 2024-06-30T01:54:57Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T01:54:54Z | ---
base_model: google/gemma-2b
library_name: peft
---
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debiao29/Qwen-Qwen1.5-0.5B-1719712608 | debiao29 | 2024-06-30T01:56:50Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T01:56:48Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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debiao29/Qwen-Qwen1.5-1.8B-1719712760 | debiao29 | 2024-06-30T01:59:22Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T01:59:20Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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debiao29/google-gemma-2b-1719712859 | debiao29 | 2024-06-30T02:01:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T02:00:59Z | ---
base_model: google/gemma-2b
library_name: peft
---
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phonghoccode/scene | phonghoccode | 2024-06-30T02:01:41Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T02:01:41Z | Entry not found |
debiao29/Qwen-Qwen1.5-0.5B-1719712966 | debiao29 | 2024-06-30T02:02:48Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T02:02:46Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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blockblockblock/Arcee-Spark-bpw6-exl2 | blockblockblock | 2024-06-30T02:11:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"de",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"6-bit",
"exl2",
"region:us"
] | text-generation | 2024-06-30T02:05:14Z | ---
license: apache-2.0
language:
- en
- de
- ar
---
<div align="center">
<img src="https://i.ibb.co/80ssNWS/o-Vdk-Qx-ARNmzr-Pi1h-Efj-SA.webp" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>
# Arcee Spark
Arcee Spark is a powerful 7B parameter language model that punches well above its weight class. Initialized from Qwen2, this model underwent a sophisticated training process:
1. Fine-tuned on 1.8 million samples
2. Merged with Qwen2-7B-Instruct using Arcee's mergekit
3. Further refined using Direct Preference Optimization (DPO)
This meticulous process results in exceptional performance, with Arcee Spark achieving the highest score on MT-Bench for models of its size, outperforming even GPT-3.5 on many tasks.
## Key Features
- 7B parameters
- State-of-the-art performance for its size
- Initialized from Qwen2
- Advanced training process including fine-tuning, merging, and DPO
- Highest MT-Bench score in the 7B class
- Outperforms GPT-3.5 on many tasks
- Has a context length of 128k tokens, making it ideal for tasks requiring many conversation turns or working with large amounts of text.
## Business Use Cases
Arcee Spark offers a compelling solution for businesses looking to leverage advanced AI capabilities without the hefty computational requirements of larger models. Its unique combination of small size and high performance makes it ideal for:
1. **Real-time applications**: Deploy Arcee Spark for chatbots, customer service automation, and interactive systems where low latency is crucial.
2. **Edge computing**: Run sophisticated AI tasks on edge devices or in resource-constrained environments.
3. **Cost-effective scaling**: Implement advanced language AI across your organization without breaking the bank on infrastructure or API costs.
4. **Rapid prototyping**: Quickly develop and iterate on AI-powered features and products.
5. **On-premise deployment**: Easily host Arcee Spark on local infrastructure for enhanced data privacy and security.
## Performance and Efficiency
Arcee Spark demonstrates that bigger isn't always better in the world of language models. By leveraging advanced training techniques and architectural optimizations, it delivers:
- **Speed**: Blazing fast inference times, often 10-100x faster than larger models.
- **Efficiency**: Significantly lower computational requirements, reducing both costs and environmental impact.
- **Flexibility**: Easy to fine-tune or adapt for specific domains or tasks.
Despite its compact size, Arcee Spark offers deep reasoning capabilities, making it suitable for a wide range of complex tasks including:
- Advanced text generation
- Detailed question answering
- Nuanced sentiment analysis
- Complex problem-solving
- Code generation and analysis
## Model Availability
- **Quants**: [Arcee Spark GGUF](https://huggingface.co/arcee-ai/Arcee-Spark-GGUF)
- **FP32**: For those looking to squeeze every bit of performance out of the model, we offer an [FP32 version](https://huggingface.co/arcee-ai/Arcee-Spark-FP32) that scores slightly higher on all benchmarks.
## Benchmarks and Evaluations
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/dQRtXR7/Screenshot-2024-06-23-at-11-01-59-PM.png" alt="Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/BLX8GmZ/Screenshot-2024-06-23-at-10-43-50-PM.png" alt="Additional Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.postimg.cc/Vs7v0Vbn/Screenshot-2024-06-24-at-1-10-58-AM.png" alt="Bigbenchhard Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
### MT-Bench
```markdown
########## First turn ##########
score
model turn
arcee-spark 1 8.777778
########## Second turn ##########
score
model turn
arcee-spark 2 8.164634
########## Average ##########
score
model
arcee-spark 8.469325
```
### EQ-Bench
EQ-Bench: 71.4
### TruthfulQA
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4382|± |0.0174|
| | |mc2 |0.6150|± |0.0155|
### AGI-Eval
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.3937|± |0.0307|
| | |acc_norm|0.3937|± |0.0307|
|agieval_logiqa_en | 0|acc |0.4731|± |0.0196|
| | |acc_norm|0.4854|± |0.0196|
|agieval_lsat_ar | 0|acc |0.2783|± |0.0296|
| | |acc_norm|0.3000|± |0.0303|
|agieval_lsat_lr | 0|acc |0.5549|± |0.0220|
| | |acc_norm|0.5451|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6022|± |0.0299|
| | |acc_norm|0.6208|± |0.0296|
|agieval_sat_en | 0|acc |0.8155|± |0.0271|
| | |acc_norm|0.8107|± |0.0274|
|agieval_sat_en_without_passage| 0|acc |0.4806|± |0.0349|
| | |acc_norm|0.4612|± |0.0348|
|agieval_sat_math | 0|acc |0.4909|± |0.0338|
| | |acc_norm|0.4545|± |0.0336|
AGI-eval average: 51.11
### GPT4All Evaluation
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5333|± |0.0146|
| | |acc_norm|0.5640|± |0.0145|
|arc_easy | 0|acc |0.8131|± |0.0080|
| | |acc_norm|0.7668|± |0.0087|
|boolq | 1|acc |0.8471|± |0.0063|
|hellaswag | 0|acc |0.6206|± |0.0048|
| | |acc_norm|0.8118|± |0.0039|
|openbookqa | 0|acc |0.3560|± |0.0214|
| | |acc_norm|0.4600|± |0.0223|
|piqa | 0|acc |0.7987|± |0.0094|
| | |acc_norm|0.8030|± |0.0093|
|winogrande | 0|acc |0.7690|± |0.0130|
Gpt4al Average: 69.37
## Big Bench Hard
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.6053|± |0.0356|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6450|± |0.0249|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.5233|± |0.0312|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2006|± |0.0212|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2840|± |0.0202|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2429|± |0.0162|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4367|± |0.0287|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4720|± |0.0223|
|bigbench_navigate | 0|multiple_choice_grade|0.4980|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.5600|± |0.0111|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4375|± |0.0235|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2685|± |0.0140|
|bigbench_snarks | 0|multiple_choice_grade|0.7348|± |0.0329|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6978|± |0.0146|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.4060|± |0.0155|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2072|± |0.0115|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1406|± |0.0083|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4367|± |0.0287|
Big Bench average: 45.78
## License
Arcee Spark is released under the Apache 2.0 license.
## Acknowledgments
- The Qwen2 team for their foundational work
- The open-source AI community for their invaluable tools and datasets
- Our dedicated team of researchers and engineers who push the boundaries of what's possible with compact language models
|
debiao29/Qwen-Qwen1.5-1.8B-1719713118 | debiao29 | 2024-06-30T02:05:20Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T02:05:18Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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valerielucro/mistral_gsm8k_sft_epoch6 | valerielucro | 2024-06-30T02:05:59Z | 0 | 0 | transformers | [
"transformers",
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debiao29/google-gemma-2b-1719713217 | debiao29 | 2024-06-30T02:07:00Z | 0 | 0 | peft | [
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"safetensors",
"arxiv:1910.09700",
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ArjanvD95/by_the_horns_T44G | ArjanvD95 | 2024-06-30T02:40:22Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-06-30T02:07:10Z | Entry not found |
debiao29/Qwen-Qwen1.5-0.5B-1719713327 | debiao29 | 2024-06-30T02:08:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T02:08:47Z | ---
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library_name: peft
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debiao29/Qwen-Qwen1.5-1.8B-1719713479 | debiao29 | 2024-06-30T02:11:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T02:11:19Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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geraldabrhm/llama-3-8b-seqclass-antonym-lr2_5-batch16-lora32-nowarmuplinear | geraldabrhm | 2024-06-30T04:22:36Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-06-30T02:11:32Z | Entry not found |
net31/naschainv04 | net31 | 2024-06-30T02:12:55Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T02:12:54Z | Entry not found |
debiao29/google-gemma-2b-1719713577 | debiao29 | 2024-06-30T02:13:00Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T02:12:57Z | ---
base_model: google/gemma-2b
library_name: peft
---
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debiao29/Qwen-Qwen1.5-0.5B-1719713689 | debiao29 | 2024-06-30T02:14:51Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T02:14:49Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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debiao29/Qwen-Qwen1.5-1.8B-1719713841 | debiao29 | 2024-06-30T02:17:23Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T02:17:21Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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habulaj/1170122082 | habulaj | 2024-06-30T02:18:08Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T02:18:05Z | Entry not found |
debiao29/google-gemma-2b-1719713940 | debiao29 | 2024-06-30T02:19:03Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T02:19:00Z | ---
base_model: google/gemma-2b
library_name: peft
---
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debiao29/Qwen-Qwen1.5-0.5B-1719714048 | debiao29 | 2024-06-30T02:20:51Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T02:20:48Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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## 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
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### Framework versions
- PEFT 0.11.1 |
DazMashaly/new_downloads22 | DazMashaly | 2024-06-30T12:11:30Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-06-30T02:20:59Z | Entry not found |
PrunaAI/GeneZC-MiniChat-2-3B-bnb-4bit-smashed | PrunaAI | 2024-06-30T02:22:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:GeneZC/MiniChat-2-3B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-06-30T02:21:19Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: GeneZC/MiniChat-2-3B
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo GeneZC/MiniChat-2-3B installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/GeneZC-MiniChat-2-3B-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-2-3B")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model GeneZC/MiniChat-2-3B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
ashokpoudel/contact_extraction | ashokpoudel | 2024-06-30T05:02:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T02:22:03Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** ashokpoudel
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
|
debiao29/Qwen-Qwen1.5-1.8B-1719714200 | debiao29 | 2024-06-30T02:23:22Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T02:23:20Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[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]
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[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
nuvvula/whisper-en-tiny | nuvvula | 2024-06-30T02:27:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-06-30T02:23:50Z | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-en-tiny
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-en-tiny
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2345
- Wer: 56.4184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 120
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.6095 | 2.0 | 60 | 1.4538 | 92.0761 |
| 0.6468 | 4.0 | 120 | 1.2345 | 56.4184 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
debiao29/google-gemma-2b-1719714298 | debiao29 | 2024-06-30T02:25:01Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | 2024-06-30T02:24:58Z | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
nshan144/deception_inverted100_2epochs | nshan144 | 2024-06-30T02:46:18Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-30T02:25:27Z | Entry not found |
debiao29/Qwen-Qwen1.5-0.5B-1719714410 | debiao29 | 2024-06-30T02:26:53Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2024-06-30T02:26:50Z | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
ovieyra21/modelo | ovieyra21 | 2024-06-30T02:28:12Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T02:28:12Z | Entry not found |
passionful7/trained-sd3-lora | passionful7 | 2024-06-30T02:28:36Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T02:28:36Z | Entry not found |
debiao29/Qwen-Qwen1.5-1.8B-1719714562 | debiao29 | 2024-06-30T02:29:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | 2024-06-30T02:29:23Z | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
habulaj/10930684233 | habulaj | 2024-06-30T02:29:46Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-30T02:29:40Z | Entry not found |
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