modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
74.7M
| likes
int64 0
9.67k
| library_name
stringlengths 2
84
⌀ | tags
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stringlengths 5
30
⌀ | createdAt
timestamp[us, tz=UTC] | card
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| embedding
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|---|---|---|---|---|---|---|---|---|---|---|
GridTST/weather_96_720
|
GridTST
| 2023-12-14T02:59:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:21Z |
Entry not found
|
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GridTST/weather_192_192
|
GridTST
| 2023-12-14T02:59:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:26Z |
Entry not found
|
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GridTST/weather_192_336
|
GridTST
| 2023-12-14T02:59:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:27Z |
Entry not found
|
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GridTST/weather_192_720
|
GridTST
| 2023-12-14T02:59:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:29Z |
Entry not found
|
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GridTST/weather_512_192
|
GridTST
| 2023-12-14T02:59:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:34Z |
Entry not found
|
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GridTST/weather_512_336
|
GridTST
| 2023-12-14T02:59:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:36Z |
Entry not found
|
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GridTST/weather_512_720
|
GridTST
| 2023-12-14T02:59:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:37Z |
Entry not found
|
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GridTST/solar_96_192
|
GridTST
| 2023-12-14T02:59:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:41Z |
Entry not found
|
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GridTST/solar_96_336
|
GridTST
| 2023-12-14T02:59:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:43Z |
Entry not found
|
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GridTST/solar_96_720
|
GridTST
| 2023-12-14T02:59:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:44Z |
Entry not found
|
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GridTST/solar_192_192
|
GridTST
| 2023-12-14T02:59:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:48Z |
Entry not found
|
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GridTST/solar_192_336
|
GridTST
| 2023-12-14T02:59:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:49Z |
Entry not found
|
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GridTST/solar_192_720
|
GridTST
| 2023-12-14T02:59:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T02:59:51Z |
Entry not found
|
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GridTST/solar_512_192
|
GridTST
| 2023-12-14T03:00:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:00:00Z |
Entry not found
|
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GridTST/solar_512_336
|
GridTST
| 2023-12-14T03:00:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:00:02Z |
Entry not found
|
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GridTST/solar_512_720
|
GridTST
| 2023-12-14T03:00:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:00:04Z |
Entry not found
|
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GridTST/electricity_96_192
|
GridTST
| 2023-12-14T03:00:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:00:09Z |
Entry not found
|
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GridTST/electricity_96_336
|
GridTST
| 2023-12-14T03:00:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:00:11Z |
Entry not found
|
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GridTST/electricity_96_720
|
GridTST
| 2023-12-14T03:00:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:00:13Z |
Entry not found
|
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GridTST/electricity_192_192
|
GridTST
| 2023-12-14T03:00:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:00:17Z |
Entry not found
|
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GridTST/electricity_192_336
|
GridTST
| 2023-12-14T03:00:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:00:19Z |
Entry not found
|
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GridTST/electricity_192_720
|
GridTST
| 2023-12-14T03:00:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:00:21Z |
Entry not found
|
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GridTST/electricity_512_192
|
GridTST
| 2023-12-14T03:02:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:02:25Z |
Entry not found
|
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GridTST/electricity_512_336
|
GridTST
| 2023-12-14T03:02:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:02:27Z |
Entry not found
|
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GridTST/electricity_512_720
|
GridTST
| 2023-12-14T03:02:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gridtst",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:02:28Z |
Entry not found
|
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about0/qwen-chat-GGUF-14B
|
about0
| 2023-12-15T16:11:43Z | 0 | 0 | null |
[
"gguf",
"qwen",
"chat",
"中文",
"text-generation",
"zh",
"en",
"region:us"
] |
text-generation
| 2023-12-14T03:03:56Z |
---
language:
- zh
- en
tags:
- qwen
- chat
- 中文
model_name: Qwen Chat 14B
model_type: qwen
pipeline_tag: text-generation
quantized_by: about0
---
# Qwen Chat 14B - GGUF
Here are the llama.cpp-compatible GGUF converted and/or quantized models for [Qwen 14B Chat](https://huggingface.co/Qwen/Qwen-14B-Chat).
## Explanation of quantization methods
<details>
<summary>Click to see details</summary>
Methods:
* type-0 (Q4_0, Q5_0, Q8_0) - weights w are obtained from quants q using w = d * q, where d is the block scale.
* type-1 (Q4_1, Q5_1) - weights are given by w = d * q + m, where m is the block minimum
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This ends up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
This is exposed via llama.cpp quantization types that define various "quantization mixes" as follows:
* LLAMA_FTYPE_MOSTLY_Q2_K - uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
* LLAMA_FTYPE_MOSTLY_Q3_K_S - uses GGML_TYPE_Q3_K for all tensors
* LLAMA_FTYPE_MOSTLY_Q3_K_M - uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
* LLAMA_FTYPE_MOSTLY_Q3_K_L - uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
* LLAMA_FTYPE_MOSTLY_Q4_K_S - uses GGML_TYPE_Q4_K for all tensors
* LLAMA_FTYPE_MOSTLY_Q4_K_M - uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
* LLAMA_FTYPE_MOSTLY_Q5_K_S - uses GGML_TYPE_Q5_K for all tensors
* LLAMA_FTYPE_MOSTLY_Q5_K_M - uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
* LLAMA_FTYPE_MOSTLY_Q6_K- uses 6-bit quantization (GGML_TYPE_Q8_K) for all tensors
</details>
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [qwen-chat-14B-Q2_K.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q2_K.gguf) | Q2_K | 2 | 6.2 GB| 9.1 GB | smallest, significant quality-loss - not recommended for most purposes |
| [qwen-chat-14B-Q3_K_S.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q3_K_S.gguf) | Q3_K_S | 3 | 6.5 GB | 9.4 GB | very small, high quality-loss |
| [qwen-chat-14B-Q3_K_M.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q3_K_M.gguf) | Q3_K_M | 3 | 7.2 GB | 10.1 GB | very small, high quality-loss |
| [qwen-chat-14B-Q3_K_L.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q3_K_L.gguf) | Q3_K_L | 3 | 7.5 GB| 10.4 GB | small, substantial quality-loss |
| [qwen-chat-14B-Q4_0.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q4_0.gguf) | Q4_0 | 4 | 7.7 GB| 10.6 GB | legacy; small, very high quality-loss - prefer using Q3_K_L |
| [qwen-chat-14B-Q4_1.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q4_1.gguf) | Q4_1 | 4 | 8.4 GB| 11.3 GB | legacy; small, very high quality-loss - prefer using Q4_K_S |
| [qwen-chat-14B-Q4_K_S.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q4_K_S.gguf) | Q4_K_S | 4 | 8.0 GB| 10.9 GB | small, greater quality-loss |
| [qwen-chat-14B-Q4_K_M.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q4_K_M.gguf) | Q4_K_M | 4 | 8.9 GB| 11.8 GB | medium, balanced quality - recommended |
| [qwen-chat-14B-Q5_0.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q5_0.gguf) | Q5_0 | 5 | 9.2 GB| 12.1 GB | legacy; medium, balanced quality - prefer using Q5_K_M |
| [qwen-chat-14B-Q5_1.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q5_1.gguf) | Q5_1 | 5 | 10 GB| 12.9 GB | legacy; medium, balanced quality - prefer using Q5_K_M |
| [qwen-chat-14B-Q5_K_S.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q5_K_S.gguf) | Q5_K_S | 5 | 9.4 GB | 12.3 GB | large, low quality-loss - recommended |
| [qwen-chat-14B-Q5_K_M.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q5_K_M.gguf) | Q5_K_M | 5 | 11 GB | 13.9 GB | large, very low quality-loss - recommended |
| [qwen-chat-14B-Q6_K.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q6_K.gguf) | Q6_K | 6 | 12 GB| 14.9 GB | very large, extremely low quality-loss |
| [qwen-chat-14B-Q8_0.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-Q8_0.gguf) | Q8_0 | 8 | 15 GB| 17.9 GB | very large, extremely low quality-loss - not recommended |
| [qwen-chat-14B-f16.gguf](https://huggingface.co/about0/qwen-chat-GGUF-14B/blob/main/qwen-chat-14B-f16.gguf) | f16 | 16 | 27 GB| 29.9 GB | very large, no quality-loss - not recommended |
### Model Sources
- **Repository:** [Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat)
|
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milkyfreshmusic/benito1
|
milkyfreshmusic
| 2023-12-14T03:04:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T03:04:09Z |
Entry not found
|
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] |
EdgeAIStaffing/mistralai-Code-Instruct-Finetune-test-27a
|
EdgeAIStaffing
| 2023-12-16T20:41:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T03:05:14Z |
This is a test.
|
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] |
axljeremy7/bert-base-uncased-finetuned-sst2
|
axljeremy7
| 2023-12-17T19:49:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T03:08:38Z |
Entry not found
|
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] |
dcbv/charluv-13B-tiefighter-merged
|
dcbv
| 2023-12-16T09:41:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"fr",
"de",
"dataset:dcbv/charluv-dataset",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T03:09:46Z |
---
license: llama2
datasets:
- dcbv/charluv-dataset
language:
- en
- fr
- de
library_name: transformers
pipeline_tag: text-generation
---
## Charluv-13B-Tiefighter
Tiefighter is a merged model achieved trough merging two different lora's on top of a well established existing merge.
To achieve this the following recipe was used:
We begin with the base model Undi95/Xwin-MLewd-13B-V0.2 which is a well established merged, contrary to the name this model does not have a strong NSFW bias.
Then we applied the PocketDoc/Dans-RetroRodeo-13b lora which is a finetune on the Choose your own Adventure datasets from our Skein model.
After applying this lora we merged the new model with PocketDoc/Dans-RetroRodeo-13b at 5% to weaken the newly introduced adventure bias.
The resulting merge was used as a new basemodel to which we applied Blackroot/Llama-2-13B-Storywriter-LORA and repeated the same trick, this time at 10%.
### Finally we merged the Charluv dataset and created a new basemodel out of the endresult.
### All credits for the original model goes to Henkie and the models below
This means this model contains the following ingredients from their upstream models for as far as we can track them:
- Undi95/Xwin-MLewd-13B-V0.2
- Undi95/ReMM-S-Light
- Undi95/CreativeEngine
- Brouz/Slerpeno
- elinas/chronos-13b-v2
- jondurbin/airoboros-l2-13b-2.1
- NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2
- CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2
- KoboldAI/LLAMA2-13B-Holodeck-1
- NousResearch/Nous-Hermes-13b
- OpenAssistant/llama2-13b-orca-8k-3319
- ehartford/WizardLM-1.0-Uncensored-Llama2-13b
- Henk717/spring-dragon
- The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible)
- SuperCOT (Undisclosed version)
- elinas/chronos-13b-v2 (Version assumed)
- NousResearch/Nous-Hermes-Llama2-13b
- stabilityai/StableBeluga-13B (Version assumed)
- zattio770/120-Days-of-LORA-v2-13B
- PygmalionAI/pygmalion-2-13b
- Undi95/Storytelling-v1-13B-lor
- TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter
- nRuaif/Kimiko-v2-13B
- The-Face-Of-Goonery/Huginn-13b-FP16
"a lot of different models, like hermes, beluga, airoboros, chronos.. limarp"
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- Xwin-LM/Xwin-LM-13B-V0.2
- PocketDoc/Dans-RetroRodeo-13b
- Blackroot/Llama-2-13B-Storywriter-LORA
- dcbv/charluv-lora
#### This model is used on charluv.com
|
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] |
athirdpath/Lambda-17b-PcDPO
|
athirdpath
| 2023-12-14T07:30:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW",
"dataset:athirdpath/alpaca_dpo_pairs",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T03:12:12Z |
---
license: cc-by-nc-4.0
datasets:
- athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW
- athirdpath/alpaca_dpo_pairs
language:
- en
---
<p align="center"><font size="6"> <b>Very "Sydney-like"</b> </font></p>
athirdpath/Lambda-17b trained with a new strategy I call "PcDPO", or "Pulsed conservative Direct Performance Optimization".
First, the LoRA was trained for 0.5 epochs on the large athirdpath/alpaca_dpo_pairs dataset @ 5e-5 LR, .2 label smoothing.
Then, the same LoRA was preserved going into a second run, this time 2 epochs on athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW @ 1e-5 LR, .05 label smoothing.
Finally, another 0.15 (ran out of compute) epochs of athirdpath/alpaca_dpo_pairs @ 3e-7 LR, no label smoothing. (standard DPO)
The logic here is that the process used to create Iambe-RP-v3 could be done with one LoRA instead of merges, by establishing a strong baseline sense of the prompt format and the *distinction* between factual/obedient and incorrect/distracted. Then, the model is introduced to the actual goal dataset at a much lower noise and learning rate.This is where the actual magic happens, the model learns a voice, and it will still overfit slightly. Doing it in this order means the training can focus on the actual goal, and not the prompt format or correcting factual/layer-splice errors. Then, the hyper-low rate final pass undoes some of the overfitting, and also lacks label smoothing, so it will correct misunderstandings from earlier cDPO nonsense.
This is a proof of concept that didn't get enough compute, once my current financial woes end I'll be able to do an actual prototype.
|
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thesunshine36/your-model-name
|
thesunshine36
| 2023-12-14T03:13:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T03:13:12Z |
Entry not found
|
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arpachat/small-stable-diffusion-v0-th-2000
|
arpachat
| 2023-12-14T03:17:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T03:17:10Z |
Entry not found
|
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arpachat/small-stable-diffusion-v0-th-1500
|
arpachat
| 2023-12-14T03:19:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T03:19:22Z |
Entry not found
|
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] |
kwwww/bert-base-uncased-test_1_16619
|
kwwww
| 2023-12-14T04:56:58Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:bert-base-uncased",
"region:us"
] | null | 2023-12-14T03:20:55Z |
---
library_name: peft
base_model: bert-base-uncased
---
# Model Card for Model ID
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## Model Details
### Model Description
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.1.dev0
|
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mybabyyh/lora-trained-xl-fp16
|
mybabyyh
| 2023-12-14T03:21:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T03:21:44Z |
Entry not found
|
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DianGh/IoTSec-gpt2-model
|
DianGh
| 2023-12-14T03:28:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T03:27:19Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: IoTSec-gpt2-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# IoTSec-gpt2-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8756
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 4 | 3.0688 |
| No log | 2.0 | 8 | 2.9163 |
| No log | 3.0 | 12 | 2.8756 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cpu
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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kwwww/bert-base-uncased_1_16619_token
|
kwwww
| 2023-12-14T04:56:44Z | 0 | 0 | null |
[
"pytorch",
"generated_from_trainer",
"dataset:bionlp2004",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T03:27:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- bionlp2004
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased_1_16619_token
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. -->
# bert-base-uncased_1_16619_token
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the bionlp2004 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1710
- Precision: 0.7741
- Recall: 0.8224
- F1: 0.7975
- Accuracy: 0.9480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2323 | 1.0 | 1039 | 0.1995 | 0.6662 | 0.7625 | 0.7111 | 0.9308 |
| 0.2094 | 2.0 | 2078 | 0.1827 | 0.7369 | 0.7693 | 0.7528 | 0.9392 |
| 0.1976 | 3.0 | 3117 | 0.1748 | 0.7272 | 0.7886 | 0.7566 | 0.9402 |
| 0.1924 | 4.0 | 4156 | 0.1770 | 0.7284 | 0.7940 | 0.7598 | 0.9402 |
| 0.1841 | 5.0 | 5195 | 0.1703 | 0.7421 | 0.7852 | 0.7630 | 0.9422 |
| 0.1821 | 6.0 | 6234 | 0.1733 | 0.7337 | 0.7961 | 0.7636 | 0.9411 |
| 0.1789 | 7.0 | 7273 | 0.1727 | 0.7358 | 0.7765 | 0.7556 | 0.9418 |
| 0.1782 | 8.0 | 8312 | 0.1682 | 0.7395 | 0.8042 | 0.7705 | 0.9435 |
| 0.178 | 9.0 | 9351 | 0.1636 | 0.7453 | 0.8055 | 0.7742 | 0.9443 |
| 0.1687 | 10.0 | 10390 | 0.1915 | 0.7169 | 0.7654 | 0.7404 | 0.9362 |
| 0.1671 | 11.0 | 11429 | 0.1655 | 0.7552 | 0.7781 | 0.7665 | 0.9428 |
| 0.1633 | 12.0 | 12468 | 0.1755 | 0.7123 | 0.8251 | 0.7646 | 0.9398 |
| 0.1616 | 13.0 | 13507 | 0.1639 | 0.7405 | 0.7900 | 0.7645 | 0.9435 |
| 0.1611 | 14.0 | 14546 | 0.1620 | 0.7632 | 0.7979 | 0.7802 | 0.9460 |
| 0.1616 | 15.0 | 15585 | 0.1624 | 0.7494 | 0.8006 | 0.7742 | 0.9445 |
| 0.1577 | 16.0 | 16624 | 0.1680 | 0.7551 | 0.8001 | 0.7770 | 0.9456 |
| 0.1567 | 17.0 | 17663 | 0.1666 | 0.7446 | 0.8133 | 0.7774 | 0.9446 |
| 0.1511 | 18.0 | 18702 | 0.1678 | 0.7479 | 0.8133 | 0.7792 | 0.9437 |
| 0.1537 | 19.0 | 19741 | 0.1610 | 0.7651 | 0.7988 | 0.7816 | 0.9469 |
| 0.15 | 20.0 | 20780 | 0.1613 | 0.7498 | 0.8228 | 0.7846 | 0.9458 |
| 0.1499 | 21.0 | 21819 | 0.1634 | 0.7480 | 0.8088 | 0.7772 | 0.9434 |
| 0.1496 | 22.0 | 22858 | 0.1635 | 0.7508 | 0.8149 | 0.7815 | 0.9456 |
| 0.1487 | 23.0 | 23897 | 0.1603 | 0.7537 | 0.8253 | 0.7879 | 0.9462 |
| 0.1425 | 24.0 | 24936 | 0.1614 | 0.7642 | 0.8077 | 0.7853 | 0.9458 |
| 0.1427 | 25.0 | 25975 | 0.1727 | 0.7601 | 0.8021 | 0.7805 | 0.9456 |
| 0.1408 | 26.0 | 27014 | 0.1616 | 0.7690 | 0.8152 | 0.7914 | 0.9462 |
| 0.1401 | 27.0 | 28053 | 0.1613 | 0.7661 | 0.8032 | 0.7842 | 0.9463 |
| 0.1387 | 28.0 | 29092 | 0.1642 | 0.7585 | 0.8169 | 0.7866 | 0.9457 |
| 0.1354 | 29.0 | 30131 | 0.1650 | 0.7453 | 0.8214 | 0.7815 | 0.9455 |
| 0.1331 | 30.0 | 31170 | 0.1622 | 0.7744 | 0.8152 | 0.7943 | 0.9477 |
| 0.1325 | 31.0 | 32209 | 0.1626 | 0.7592 | 0.8142 | 0.7857 | 0.9454 |
| 0.1338 | 32.0 | 33248 | 0.1628 | 0.7564 | 0.8152 | 0.7847 | 0.9455 |
| 0.1296 | 33.0 | 34287 | 0.1660 | 0.7706 | 0.8203 | 0.7947 | 0.9469 |
| 0.1323 | 34.0 | 35326 | 0.1647 | 0.7674 | 0.8120 | 0.7890 | 0.9466 |
| 0.1275 | 35.0 | 36365 | 0.1644 | 0.7715 | 0.8118 | 0.7912 | 0.9469 |
| 0.1245 | 36.0 | 37404 | 0.1607 | 0.7717 | 0.8280 | 0.7989 | 0.9486 |
| 0.1261 | 37.0 | 38443 | 0.1620 | 0.7691 | 0.8230 | 0.7951 | 0.9476 |
| 0.1221 | 38.0 | 39482 | 0.1680 | 0.7645 | 0.8116 | 0.7874 | 0.9468 |
| 0.1211 | 39.0 | 40521 | 0.1682 | 0.7615 | 0.8172 | 0.7884 | 0.9451 |
| 0.1236 | 40.0 | 41560 | 0.1625 | 0.7730 | 0.8246 | 0.7979 | 0.9472 |
| 0.1165 | 41.0 | 42599 | 0.1714 | 0.7629 | 0.8149 | 0.7881 | 0.9456 |
| 0.1188 | 42.0 | 43638 | 0.1677 | 0.7729 | 0.8143 | 0.7931 | 0.9474 |
| 0.1166 | 43.0 | 44677 | 0.1702 | 0.7674 | 0.8318 | 0.7983 | 0.9467 |
| 0.1159 | 44.0 | 45716 | 0.1720 | 0.7709 | 0.8235 | 0.7963 | 0.9472 |
| 0.1135 | 45.0 | 46755 | 0.1707 | 0.7772 | 0.8197 | 0.7979 | 0.9475 |
| 0.1134 | 46.0 | 47794 | 0.1710 | 0.7742 | 0.8174 | 0.7952 | 0.9477 |
| 0.1121 | 47.0 | 48833 | 0.1682 | 0.7756 | 0.8248 | 0.7994 | 0.9478 |
| 0.1098 | 48.0 | 49872 | 0.1711 | 0.7724 | 0.8206 | 0.7958 | 0.9475 |
| 0.1108 | 49.0 | 50911 | 0.1712 | 0.7741 | 0.8208 | 0.7968 | 0.9476 |
| 0.1062 | 50.0 | 51950 | 0.1710 | 0.7741 | 0.8224 | 0.7975 | 0.9480 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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mmarone/portraits-wikipedia
|
mmarone
| 2023-12-14T04:24:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T03:31:54Z |
Entry not found
|
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] |
chaoscodes/mistral-xp3
|
chaoscodes
| 2023-12-14T06:16:54Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T03:36:40Z |
---
license: apache-2.0
---
|
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] |
adonlee/llava-6B
|
adonlee
| 2023-12-14T05:03:07Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llava",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-14T03:37:13Z |
---
license: apache-2.0
---
|
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jeikai/NLP_UET
|
jeikai
| 2023-12-14T08:18:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T03:46:44Z |
Entry not found
|
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] |
Realgon/N_bert_imdb_padding90model
|
Realgon
| 2023-12-14T06:12:21Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T03:47:07Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: N_bert_imdb_padding90model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93696
---
<!-- 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. -->
# N_bert_imdb_padding90model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6812
- Accuracy: 0.9370
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.231 | 1.0 | 1563 | 0.2247 | 0.9244 |
| 0.1494 | 2.0 | 3126 | 0.2082 | 0.9327 |
| 0.0859 | 3.0 | 4689 | 0.3046 | 0.9325 |
| 0.0543 | 4.0 | 6252 | 0.3399 | 0.9306 |
| 0.0426 | 5.0 | 7815 | 0.4251 | 0.9296 |
| 0.0382 | 6.0 | 9378 | 0.4925 | 0.9283 |
| 0.0239 | 7.0 | 10941 | 0.5281 | 0.9283 |
| 0.0175 | 8.0 | 12504 | 0.4922 | 0.9323 |
| 0.0161 | 9.0 | 14067 | 0.5648 | 0.9324 |
| 0.0091 | 10.0 | 15630 | 0.5808 | 0.9366 |
| 0.0139 | 11.0 | 17193 | 0.5845 | 0.9345 |
| 0.0041 | 12.0 | 18756 | 0.5509 | 0.9330 |
| 0.0055 | 13.0 | 20319 | 0.6335 | 0.9342 |
| 0.0019 | 14.0 | 21882 | 0.6713 | 0.9356 |
| 0.0024 | 15.0 | 23445 | 0.6316 | 0.9358 |
| 0.0 | 16.0 | 25008 | 0.6567 | 0.9379 |
| 0.0038 | 17.0 | 26571 | 0.6668 | 0.9375 |
| 0.0009 | 18.0 | 28134 | 0.6795 | 0.9373 |
| 0.0013 | 19.0 | 29697 | 0.6736 | 0.9371 |
| 0.0001 | 20.0 | 31260 | 0.6812 | 0.9370 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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athirdpath/Lambda-17b-PcDPO-GGUF
|
athirdpath
| 2023-12-14T07:31:08Z | 0 | 0 | null |
[
"en",
"dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW",
"dataset:athirdpath/alpaca_dpo_pairs",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-14T03:48:28Z |
---
license: cc-by-nc-4.0
datasets:
- athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW
- athirdpath/alpaca_dpo_pairs
language:
- en
---
<p align="center"><font size="6"> <b>Very "Sydney-like"</b> </font></p>
athirdpath/Lambda-17b trained with a new strategy I call "PcDPO", or "Pulsed conservative Direct Performance Optimization".
First, the LoRA was trained for 0.5 epochs on the large athirdpath/alpaca_dpo_pairs dataset @ 5e-5 LR, .2 label smoothing.
Then, the same LoRA was preserved going into a second run, this time 2 epochs on athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW @ 1e-5 LR, .05 label smoothing.
Finally, another 0.15 (ran out of compute) epochs of athirdpath/alpaca_dpo_pairs @ 3e-7 LR, no label smoothing. (standard DPO)
The logic here is that the process used to create Iambe-RP-v3 could be done with one LoRA instead of merges, by establishing a strong baseline sense of the prompt format and the *distinction* between factual/obedient and incorrect/distracted. Then, the model is introduced to the actual goal dataset at a much lower noise and learning rate.This is where the actual magic happens, the model learns a voice, and it will still overfit slightly. Doing it in this order means the training can focus on the actual goal, and not the prompt format or correcting factual/layer-splice errors. Then, the hyper-low rate final pass undoes some of the overfitting, and also lacks label smoothing, so it will correct misunderstandings from earlier cDPO nonsense.
This is a proof of concept that didn't get enough compute, once my current financial woes end I'll be able to do an actual prototype.
|
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] |
ioclab/LooseControl_WebUICombine
|
ioclab
| 2023-12-14T03:50:51Z | 0 | 0 |
transformers
|
[
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T03:48:45Z |
---
license: apache-2.0
---
Original author and address:shariqfarooq/loose-control-3dbox
https://shariqfarooq123.github.io/loose-control/
I combined it with the same license so that it could be used in both webui and comfyUI
|
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lvkuan/111
|
lvkuan
| 2023-12-14T03:48:46Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T03:48:46Z |
---
license: openrail
---
|
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] |
tineding/ACT-SOP
|
tineding
| 2023-12-14T03:53:42Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T03:53:41Z |
---
license: apache-2.0
---
|
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doganjr/openhermes-mistral-dpo-gptq
|
doganjr
| 2023-12-14T03:55:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T03:55:49Z |
Entry not found
|
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SimonZzz1/dog
|
SimonZzz1
| 2023-12-14T04:02:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T04:02:27Z |
Entry not found
|
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maxmashup/Rose
|
maxmashup
| 2023-12-14T17:09:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T04:03:57Z |
Entry not found
|
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Beatokaine/TestModelArt
|
Beatokaine
| 2023-12-14T04:04:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T04:04:29Z |
Entry not found
|
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ethnicthv123/NLP
|
ethnicthv123
| 2023-12-14T04:11:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T04:11:29Z |
Entry not found
|
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] |
Vinnyyw/Miacoluccin
|
Vinnyyw
| 2023-12-14T04:16:30Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T04:15:18Z |
---
license: openrail
---
|
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kimgaramisinnocentondiscord/Garamsoftver1
|
kimgaramisinnocentondiscord
| 2023-12-14T04:20:34Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T04:15:20Z |
---
license: openrail
---
|
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] |
csukuangfj/vits-mms-eng
|
csukuangfj
| 2023-12-14T06:11:28Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T04:16:04Z |
# Introduction
This model is converted from
https://huggingface.co/facebook/mms-tts/resolve/main/models/eng/G_100000.pth
Please see
https://huggingface.co/facebook/mms-tts/tree/main/models/eng
for more details.
|
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] |
huyhuyvu01/DeBERTa_large_chartering_email_cls
|
huyhuyvu01
| 2023-12-14T04:26:36Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T04:19:17Z |
---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: BBC_CLS_deberta_v3_large_v2
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. -->
# BBC_CLS_deberta_v3_large_v2
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0857
- Accuracy: 0.9866
- Precision: 0.9723
- Recall: 0.9780
- F1: 0.9751
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.235 | 1.0 | 66 | 0.6331 | 0.7964 | 0.4047 | 0.4873 | 0.4418 |
| 0.4336 | 2.0 | 132 | 0.2201 | 0.8971 | 0.6754 | 0.7091 | 0.6910 |
| 0.2133 | 3.0 | 198 | 0.0990 | 0.9776 | 0.9476 | 0.9786 | 0.9602 |
| 0.1083 | 4.0 | 264 | 0.1038 | 0.9821 | 0.9656 | 0.9651 | 0.9653 |
| 0.0848 | 5.0 | 330 | 0.0907 | 0.9866 | 0.9782 | 0.9714 | 0.9747 |
| 0.1087 | 6.0 | 396 | 0.1270 | 0.9799 | 0.9672 | 0.9689 | 0.9671 |
| 0.1011 | 7.0 | 462 | 0.1289 | 0.9754 | 0.9677 | 0.9660 | 0.9667 |
| 0.0827 | 8.0 | 528 | 0.0990 | 0.9799 | 0.9818 | 0.9479 | 0.9632 |
| 0.0621 | 9.0 | 594 | 0.0857 | 0.9866 | 0.9723 | 0.9780 | 0.9751 |
| 0.0444 | 10.0 | 660 | 0.1071 | 0.9843 | 0.9769 | 0.9663 | 0.9715 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 1.13.1
- Datasets 2.13.0
- Tokenizers 0.14.1
|
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] |
axljeremy7/bert-base-uncased-finetuned-CLS-sst2
|
axljeremy7
| 2023-12-14T05:31:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T04:28:38Z |
Entry not found
|
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athirdpath/Lambda-17b-DPO-GGUF
|
athirdpath
| 2023-12-14T04:40:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T04:29:20Z |
Entry not found
|
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] |
kitsonr/Reinforce-CartPole-v1
|
kitsonr
| 2023-12-14T04:30:55Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-14T04:30:44Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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bearbearyu1223/falcon_7b_LoRA_r16_dialogue_summarization_12_13_2023
|
bearbearyu1223
| 2023-12-14T05:20:10Z | 0 | 0 | null |
[
"generated_from_trainer",
"dataset:samsum",
"base_model:vilsonrodrigues/falcon-7b-sharded",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T04:31:45Z |
---
license: apache-2.0
base_model: vilsonrodrigues/falcon-7b-sharded
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: falcon_7b_LoRA_r16_dialogue_summarization_12_13_2023
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. -->
# falcon_7b_LoRA_r16_dialogue_summarization_12_13_2023
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-sharded) on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.14.1
|
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] |
xiaol/RWKV-v5-12B-one-state-chat-16k
|
xiaol
| 2023-12-17T05:05:25Z | 0 | 9 | null |
[
"dataset:PygmalionAI/PIPPA",
"dataset:Norquinal/claude_multiround_chat_30k",
"dataset:garage-bAInd/Open-Platypus",
"dataset:kaist-ai/CoT-Collection",
"dataset:BAAI/COIG-PC",
"dataset:togethercomputer/Long-Data-Collections",
"dataset:tasksource/icl-symbol-tuning-instruct",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T04:32:20Z |
---
license: apache-2.0
datasets:
- PygmalionAI/PIPPA
- Norquinal/claude_multiround_chat_30k
- garage-bAInd/Open-Platypus
- kaist-ai/CoT-Collection
- BAAI/COIG-PC
- togethercomputer/Long-Data-Collections
- tasksource/icl-symbol-tuning-instruct
---
# Release date: December 18th
finetuned from the state-of-the-art (SOTA) model, RWKV v5 12B one state base! More details will be provided soon. Enjoy the incredible performance of this model, which is optimized for systems with 24GB of VRAM and supports fp16. It can be fine-tuned using a single A100 GPU. To execute this model, utilize the [RWKV Runner](https://github.com/josStorer/RWKV-Runner) tool.
# Finetuned from [RWKV v5 12B one state base](https://huggingface.co/xiaol/RWKV-v5-12B-one-state-base)
# Usage
- [RWKV next web](https://rwkv-next-web.ai-creator.net/)
- if use with [RWKV runner](https://github.com/josStorer/RWKV-Runner) or [ai00 server](https://github.com/cgisky1980/ai00_rwkv_server), change default vocabs(tokenizer) by [this one](https://huggingface.co/xiaol/RWKV-v5-12B-one-state-chat-16k/blob/main/rwkv_vocab_v20230424.txt)
# Important Notes
After overfitting certain instructions and weakening others, it is necessary to use completion or simulate dialogues.
- **completion prompt** = 'User: make this content longer:\nxxxxxx\n\nAssistant: ok, longer content is'
# Data format
`<s>User:xxxx\n\n</s>Assistant:xxx\n\n</s>User:xxxx\n\n</s>Assistant:xxx\n\n</s>`
If you desire optimal performance to run this model,utilize this format and these [vocabs](https://huggingface.co/xiaol/RWKV-v5-12B-one-state-chat-16k/blob/main/rwkv_vocab_v20230424_train.txt)
|
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zhuhu00/Gaussian_Splatting
|
zhuhu00
| 2023-12-14T04:56:44Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-14T04:34:35Z |
---
license: mit
---
|
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] |
huyhuyvu01/DeBERTa_large_NER_chartering_email
|
huyhuyvu01
| 2023-12-14T05:04:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-14T04:38:58Z |
---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
model-index:
- name: bbc-ner-deberta-large_baseline2_dims
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. -->
# bbc-ner-deberta-large_baseline2_dims
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1481
- Cargo Dimension Precision: 0.7606
- Cargo Dimension Recall: 0.9474
- Cargo Dimension F1: 0.8437
- Cargo Dimension Number: 114
- Cargo Quantity Precision: 0.8079
- Cargo Quantity Recall: 0.8937
- Cargo Quantity F1: 0.8486
- Cargo Quantity Number: 207
- Cargo Requirements Precision: 0.4962
- Cargo Requirements Recall: 0.6535
- Cargo Requirements F1: 0.5641
- Cargo Requirements Number: 202
- Cargo Stowage Factor Precision: 0.8226
- Cargo Stowage Factor Recall: 0.8361
- Cargo Stowage Factor F1: 0.8293
- Cargo Stowage Factor Number: 122
- Cargo Type Precision: 0.7885
- Cargo Type Recall: 0.8183
- Cargo Type F1: 0.8031
- Cargo Type Number: 688
- Cargo Weigh Volume Precision: 0.8528
- Cargo Weigh Volume Recall: 0.9026
- Cargo Weigh Volume F1: 0.8770
- Cargo Weigh Volume Number: 719
- Commission Rate Precision: 0.7955
- Commission Rate Recall: 0.8452
- Commission Rate F1: 0.8196
- Commission Rate Number: 336
- Discharging Port Precision: 0.8706
- Discharging Port Recall: 0.9015
- Discharging Port F1: 0.8858
- Discharging Port Number: 843
- Laycan Date Precision: 0.8260
- Laycan Date Recall: 0.8710
- Laycan Date F1: 0.8479
- Laycan Date Number: 496
- Loading Discharging Terms Precision: 0.7211
- Loading Discharging Terms Recall: 0.7975
- Loading Discharging Terms F1: 0.7574
- Loading Discharging Terms Number: 321
- Loading Port Precision: 0.8906
- Loading Port Recall: 0.9232
- Loading Port F1: 0.9066
- Loading Port Number: 899
- Shipment Terms Precision: 0.6780
- Shipment Terms Recall: 0.6780
- Shipment Terms F1: 0.6780
- Shipment Terms Number: 118
- Vessel Requirements Precision: 0.3786
- Vessel Requirements Recall: 0.5132
- Vessel Requirements F1: 0.4358
- Vessel Requirements Number: 76
- Overall Precision: 0.8041
- Overall Recall: 0.8598
- Overall F1: 0.8310
- Overall Accuracy: 0.9688
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cargo Dimension Precision | Cargo Dimension Recall | Cargo Dimension F1 | Cargo Dimension Number | Cargo Quantity Precision | Cargo Quantity Recall | Cargo Quantity F1 | Cargo Quantity Number | Cargo Requirements Precision | Cargo Requirements Recall | Cargo Requirements F1 | Cargo Requirements Number | Cargo Stowage Factor Precision | Cargo Stowage Factor Recall | Cargo Stowage Factor F1 | Cargo Stowage Factor Number | Cargo Type Precision | Cargo Type Recall | Cargo Type F1 | Cargo Type Number | Cargo Weigh Volume Precision | Cargo Weigh Volume Recall | Cargo Weigh Volume F1 | Cargo Weigh Volume Number | Commission Rate Precision | Commission Rate Recall | Commission Rate F1 | Commission Rate Number | Discharging Port Precision | Discharging Port Recall | Discharging Port F1 | Discharging Port Number | Laycan Date Precision | Laycan Date Recall | Laycan Date F1 | Laycan Date Number | Loading Discharging Terms Precision | Loading Discharging Terms Recall | Loading Discharging Terms F1 | Loading Discharging Terms Number | Loading Port Precision | Loading Port Recall | Loading Port F1 | Loading Port Number | Shipment Terms Precision | Shipment Terms Recall | Shipment Terms F1 | Shipment Terms Number | Vessel Requirements Precision | Vessel Requirements Recall | Vessel Requirements F1 | Vessel Requirements Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------:|:----------------------:|:------------------:|:----------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------------------:|:-------------------------:|:---------------------:|:-------------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:----------------------------:|:-------------------------:|:---------------------:|:-------------------------:|:-------------------------:|:----------------------:|:------------------:|:----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-----------------------------------:|:--------------------------------:|:----------------------------:|:--------------------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.4476 | 1.0 | 119 | 0.1523 | 0.6169 | 0.8333 | 0.7090 | 114 | 0.7087 | 0.8696 | 0.7809 | 207 | 0.3007 | 0.4257 | 0.3525 | 202 | 0.6284 | 0.7623 | 0.6889 | 122 | 0.6478 | 0.6308 | 0.6392 | 688 | 0.6983 | 0.8178 | 0.7534 | 719 | 0.7099 | 0.7649 | 0.7364 | 336 | 0.7212 | 0.8683 | 0.7879 | 843 | 0.7482 | 0.8266 | 0.7854 | 496 | 0.5669 | 0.6729 | 0.6154 | 321 | 0.8280 | 0.8565 | 0.8420 | 899 | 0.6556 | 0.5 | 0.5673 | 118 | 0.0658 | 0.0658 | 0.0658 | 76 | 0.6819 | 0.7635 | 0.7204 | 0.9581 |
| 0.132 | 2.0 | 239 | 0.1438 | 0.6471 | 0.8684 | 0.7416 | 114 | 0.7212 | 0.9372 | 0.8151 | 207 | 0.2857 | 0.4455 | 0.3482 | 202 | 0.7083 | 0.8361 | 0.7669 | 122 | 0.6586 | 0.8270 | 0.7332 | 688 | 0.7191 | 0.8901 | 0.7955 | 719 | 0.7863 | 0.8542 | 0.8188 | 336 | 0.7639 | 0.8944 | 0.8240 | 843 | 0.7370 | 0.8589 | 0.7933 | 496 | 0.5497 | 0.7757 | 0.6434 | 321 | 0.8533 | 0.8932 | 0.8728 | 899 | 0.5669 | 0.6102 | 0.5878 | 118 | 0.216 | 0.3553 | 0.2687 | 76 | 0.6943 | 0.8387 | 0.7597 | 0.9579 |
| 0.1022 | 3.0 | 358 | 0.1217 | 0.7574 | 0.9035 | 0.8240 | 114 | 0.8087 | 0.8986 | 0.8513 | 207 | 0.4081 | 0.5495 | 0.4684 | 202 | 0.75 | 0.8361 | 0.7907 | 122 | 0.7301 | 0.8140 | 0.7698 | 688 | 0.8132 | 0.8720 | 0.8416 | 719 | 0.7983 | 0.8482 | 0.8225 | 336 | 0.8123 | 0.9087 | 0.8578 | 843 | 0.7901 | 0.8730 | 0.8295 | 496 | 0.7583 | 0.7819 | 0.7699 | 321 | 0.8783 | 0.9232 | 0.9002 | 899 | 0.7596 | 0.6695 | 0.7117 | 118 | 0.3529 | 0.4737 | 0.4045 | 76 | 0.7744 | 0.8498 | 0.8103 | 0.9677 |
| 0.0802 | 4.0 | 478 | 0.1291 | 0.7589 | 0.9386 | 0.8392 | 114 | 0.8097 | 0.8841 | 0.8453 | 207 | 0.4566 | 0.5990 | 0.5182 | 202 | 0.8347 | 0.8279 | 0.8313 | 122 | 0.7710 | 0.7733 | 0.7721 | 688 | 0.8015 | 0.8818 | 0.8397 | 719 | 0.8203 | 0.8423 | 0.8311 | 336 | 0.8449 | 0.9110 | 0.8767 | 843 | 0.8252 | 0.8851 | 0.8541 | 496 | 0.7202 | 0.7539 | 0.7367 | 321 | 0.8637 | 0.9232 | 0.8925 | 899 | 0.7404 | 0.6525 | 0.6937 | 118 | 0.4359 | 0.4474 | 0.4416 | 76 | 0.7912 | 0.8463 | 0.8179 | 0.9683 |
| 0.0733 | 5.0 | 597 | 0.1269 | 0.7347 | 0.9474 | 0.8276 | 114 | 0.7975 | 0.9130 | 0.8514 | 207 | 0.4306 | 0.6139 | 0.5061 | 202 | 0.8062 | 0.8525 | 0.8287 | 122 | 0.7564 | 0.8169 | 0.7855 | 688 | 0.8142 | 0.8901 | 0.8505 | 719 | 0.8184 | 0.8452 | 0.8316 | 336 | 0.8760 | 0.9051 | 0.8903 | 843 | 0.8180 | 0.8790 | 0.8474 | 496 | 0.7303 | 0.8100 | 0.7681 | 321 | 0.8747 | 0.9321 | 0.9025 | 899 | 0.6231 | 0.6864 | 0.6532 | 118 | 0.3407 | 0.4079 | 0.3713 | 76 | 0.7870 | 0.8598 | 0.8218 | 0.9681 |
| 0.0541 | 6.0 | 717 | 0.1299 | 0.7379 | 0.9386 | 0.8263 | 114 | 0.7899 | 0.9082 | 0.8449 | 207 | 0.4330 | 0.6238 | 0.5112 | 202 | 0.8095 | 0.8361 | 0.8226 | 122 | 0.7681 | 0.8183 | 0.7924 | 688 | 0.7916 | 0.8929 | 0.8392 | 719 | 0.8182 | 0.8571 | 0.8372 | 336 | 0.8491 | 0.9075 | 0.8773 | 843 | 0.8056 | 0.8690 | 0.8361 | 496 | 0.6981 | 0.7850 | 0.7390 | 321 | 0.8828 | 0.9388 | 0.9100 | 899 | 0.6991 | 0.6695 | 0.6840 | 118 | 0.38 | 0.5 | 0.4318 | 76 | 0.7815 | 0.8607 | 0.8192 | 0.9676 |
| 0.0463 | 7.0 | 836 | 0.1311 | 0.7626 | 0.9298 | 0.8379 | 114 | 0.8243 | 0.8841 | 0.8531 | 207 | 0.4731 | 0.6089 | 0.5325 | 202 | 0.7895 | 0.8607 | 0.8235 | 122 | 0.7875 | 0.7863 | 0.7869 | 688 | 0.8366 | 0.8901 | 0.8625 | 719 | 0.8067 | 0.8571 | 0.8312 | 336 | 0.8928 | 0.8992 | 0.8960 | 843 | 0.8314 | 0.8649 | 0.8478 | 496 | 0.7672 | 0.8006 | 0.7835 | 321 | 0.9013 | 0.9143 | 0.9078 | 899 | 0.6529 | 0.6695 | 0.6611 | 118 | 0.3608 | 0.4605 | 0.4046 | 76 | 0.8096 | 0.8493 | 0.8289 | 0.9686 |
| 0.0434 | 8.0 | 956 | 0.1430 | 0.7448 | 0.9474 | 0.8340 | 114 | 0.7957 | 0.9034 | 0.8462 | 207 | 0.4765 | 0.6535 | 0.5511 | 202 | 0.7863 | 0.8443 | 0.8142 | 122 | 0.7916 | 0.8227 | 0.8068 | 688 | 0.8274 | 0.8999 | 0.8621 | 719 | 0.8141 | 0.8601 | 0.8365 | 336 | 0.8727 | 0.9027 | 0.8875 | 843 | 0.8241 | 0.8690 | 0.8459 | 496 | 0.7298 | 0.8162 | 0.7706 | 321 | 0.8943 | 0.9132 | 0.9037 | 899 | 0.5797 | 0.6780 | 0.6250 | 118 | 0.3491 | 0.4868 | 0.4066 | 76 | 0.7963 | 0.8605 | 0.8271 | 0.9681 |
| 0.0373 | 9.0 | 1075 | 0.1435 | 0.75 | 0.9474 | 0.8372 | 114 | 0.8017 | 0.8986 | 0.8474 | 207 | 0.4815 | 0.6436 | 0.5508 | 202 | 0.8254 | 0.8525 | 0.8387 | 122 | 0.7762 | 0.8169 | 0.7960 | 688 | 0.8409 | 0.9040 | 0.8713 | 719 | 0.8011 | 0.8512 | 0.8254 | 336 | 0.8648 | 0.8956 | 0.8800 | 843 | 0.8267 | 0.875 | 0.8501 | 496 | 0.7227 | 0.8037 | 0.7611 | 321 | 0.8874 | 0.9288 | 0.9076 | 899 | 0.6349 | 0.6780 | 0.6557 | 118 | 0.3645 | 0.5132 | 0.4262 | 76 | 0.7969 | 0.8611 | 0.8278 | 0.9686 |
| 0.0348 | 9.96 | 1190 | 0.1481 | 0.7606 | 0.9474 | 0.8437 | 114 | 0.8079 | 0.8937 | 0.8486 | 207 | 0.4962 | 0.6535 | 0.5641 | 202 | 0.8226 | 0.8361 | 0.8293 | 122 | 0.7885 | 0.8183 | 0.8031 | 688 | 0.8528 | 0.9026 | 0.8770 | 719 | 0.7955 | 0.8452 | 0.8196 | 336 | 0.8706 | 0.9015 | 0.8858 | 843 | 0.8260 | 0.8710 | 0.8479 | 496 | 0.7211 | 0.7975 | 0.7574 | 321 | 0.8906 | 0.9232 | 0.9066 | 899 | 0.6780 | 0.6780 | 0.6780 | 118 | 0.3786 | 0.5132 | 0.4358 | 76 | 0.8041 | 0.8598 | 0.8310 | 0.9688 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.15.0
|
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0.15040476620197296,
-0.04681210592389107,
0.4926288425922394,
-0.17325517535209656,
-0.5454699993133545
] |
LilYoda/DioBrando
|
LilYoda
| 2023-12-14T04:41:33Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T04:39:36Z |
---
license: openrail
---
|
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pminha/distilbert-base-cased-distilled-squad-dynamic-quantization
|
pminha
| 2023-12-14T04:41:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T04:41:22Z |
Entry not found
|
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Undi95/Mixtral-8x7B-MoE-RP-Story-GGUF
|
Undi95
| 2023-12-15T06:20:48Z | 0 | 6 | null |
[
"gguf",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-14T04:46:23Z |
---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
Mixtral-8x7B-MoE-RP-Story is a model made primarely for chatting, RP (Roleplay) and storywriting.
2 RP model, 2 chat model, 1 occult model, 1 storywritting model, 1 mathematic model and 1 DPO model was used for a MoE. Bagel was the base.
The DPO chat model is here to help get more human reply.
This is my first try at doing this, so don't hesitate to give feedback!
WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0!
<!-- description start -->
## Description
This repo contains quanntized files of Mixtral-8x7B-MoE-RP-Story.
<!-- description end -->
<!-- description start -->
## Models used
The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Mixtral-8x7B-MoE-RP-Story/blob/main/config.yaml)
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Custom
Using Bagel as a base let us a lot of different prompting system theorically, you can see all the prompting available [here](https://huggingface.co/jondurbin/bagel-7b-v0.1#prompt-formatting).
If you want to support me, you can [here](https://ko-fi.com/undiai).
|
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] |
FirstLast/tinyllama-finetuned
|
FirstLast
| 2023-12-14T04:47:12Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.4",
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T04:47:10Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.4
model-index:
- name: tinyllama-finetuned
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. -->
# tinyllama-finetuned
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.4](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.4) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Wanfq/distillchat_4.1.3
|
Wanfq
| 2023-12-14T07:01:10Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mistral",
"text-generation",
"distillchat",
"openchat",
"SFT",
"en",
"dataset:Wanfq/distillchat_v3_mixture",
"base_model:openchat/openchat_3.5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T04:49:11Z |
---
license: apache-2.0
model-index:
- name: distillchat_v4.1
results: []
tags:
- distillchat
- openchat
- mistral
- SFT
datasets:
- Wanfq/distillchat_v3_mixture
language:
- en
base_model: openchat/openchat_3.5
library_name: transformers
pipeline_tag: text-generation
---
# Model Card for DistillChat V4.0
DistillChat V4.1 is a fine-tuned version of Mistral trained on a mixture of publicly available, synthetic, and human datasets.
This model is fine-tuned with only code data.
The V4.1.3 version is the "multi-teacher-distill" version. We use Nous-Capybara-34B and Phind-CodeLlama-34B-v2 as the teacher LLMs to conduct knowledge distillation.
## Model description
- **Model type:** SFT
- **Language(s) (NLP):** Primarily English
- **Finetuned from model:** [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5)
### Model Sources
- **Repository:** https://github.com/fanqiwan/distillchat
- **Model Family:** All the models and the dataset are found in the [DistillChat collection](https://huggingface.co/collections/Wanfq/distillchat-6562c1fe4e74b2075a0e617e).
## Performance
TBD
## Input Format
The model is trained to use the following format:
```
GPT4 Correct User: Your message here!<|end_of_turn|>GPT4 Correct Assistant:
```
For best results, format all inputs in this manner.
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the [DistillChat V3 Mixture](https://huggingface.co/datasets/Wanfq/distillchat_v3_mixture), which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs.
Here's how you can run the model using the 🤗 Transformers:
```python
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("Wanfq/distillchat_v4.1")
# Single-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
# Multi-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
```
The GPT4 template is also available as the integrated `tokenizer.chat_template`,
which can be used instead of manually specifying the template:
```python
messages = [
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi"},
{"role": "user", "content": "How are you today?"}
]
tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The DistillChat models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base Mistral models, however, it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
### Training hyperparameters
The following hyperparameters were used during Distill-SFT training:
- learning_rate: 5e-06
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
- model_max_length: 2048
- do_distill: True
- distill_with_ref_model: True
- distill_with_aligned_model_0: True
- distill_with_aligned_model_1: True
- distill_optimize_type: ranking
- distill_weight_type: soft_metric
- distill_loss_type: ce
- distill_teacher_temperature: 1.0
- lm_loss_weight: 0.7
- distill_greater_as_gt: True
- distill_greater_as_gt_type: hard_and_decay
## Citation
TBD
## Acknowledgements
TBD
*Model card adapted from [Tulu 2](https://huggingface.co/allenai/tulu-2-dpo-70b/blob/main/README.md)*
|
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] |
Tippawan/test1-m3-semi
|
Tippawan
| 2023-12-14T04:50:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-14T04:49:45Z |
Entry not found
|
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bartowski/SOLAR-10.7B-v1.0-exl2
|
bartowski
| 2023-12-14T07:35:04Z | 0 | 0 | null |
[
"text-generation",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-14T04:50:58Z |
---
license: apache-2.0
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of SOLAR-10.7B-v1.0
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.10">turboderp's ExLlamaV2 v0.0.10</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using VMWareOpenInstruct.parquet as calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/upstage/SOLAR-10.7B-v1.0
<a href="https://huggingface.co/bartowski/SOLAR-10.7B-v1.0-exl2/tree/3_75">3.75 bits per weight</a>
<a href="https://huggingface.co/bartowski/SOLAR-10.7B-v1.0-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/SOLAR-10.7B-v1.0-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/SOLAR-10.7B-v1.0-exl2/tree/8_0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/SOLAR-10.7B-v1.0-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `SOLAR-10.7B-v1.0-exl2`:
```shell
mkdir SOLAR-10.7B-v1.0-exl2
huggingface-cli download bartowski/SOLAR-10.7B-v1.0-exl2 --local-dir SOLAR-10.7B-v1.0-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir SOLAR-10.7B-v1.0-exl2
huggingface-cli download bartowski/SOLAR-10.7B-v1.0-exl2 --revision 4_0 --local-dir SOLAR-10.7B-v1.0-exl2 --local-dir-use-symlinks False
```
|
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] |
kanishka/smolm-autoreg-bpe-counterfactual-babylm-all_det_removal-1e-4
|
kanishka
| 2023-12-14T18:07:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual_babylm_aann_all_det_removal",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-14T04:52:41Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_all_det_removal
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-all_det_removal-1e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_aann_all_det_removal
type: kanishka/counterfactual_babylm_aann_all_det_removal
metrics:
- name: Accuracy
type: accuracy
value: 0.4057243549615228
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-all_det_removal-1e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_aann_all_det_removal dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4411
- Accuracy: 0.4057
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.05 | 1.0 | 18595 | 4.2668 | 0.3100 |
| 3.5632 | 2.0 | 37190 | 3.7405 | 0.3611 |
| 3.3888 | 3.0 | 55785 | 3.5859 | 0.3783 |
| 3.2909 | 4.0 | 74380 | 3.5018 | 0.3879 |
| 3.2237 | 5.0 | 92975 | 3.4853 | 0.3925 |
| 3.1738 | 6.0 | 111570 | 3.4621 | 0.3954 |
| 3.1329 | 7.0 | 130165 | 3.4464 | 0.3983 |
| 3.0969 | 8.0 | 148760 | 3.4159 | 0.3998 |
| 3.0649 | 9.0 | 167355 | 3.4220 | 0.4017 |
| 3.0391 | 10.0 | 185950 | 3.4191 | 0.4024 |
| 3.0153 | 11.0 | 204545 | 3.4059 | 0.4036 |
| 2.9895 | 12.0 | 223140 | 3.4205 | 0.4036 |
| 2.9701 | 13.0 | 241735 | 3.4289 | 0.4042 |
| 2.9496 | 14.0 | 260330 | 3.4117 | 0.4046 |
| 2.9306 | 15.0 | 278925 | 3.4235 | 0.4050 |
| 2.9122 | 16.0 | 297520 | 3.4334 | 0.4049 |
| 2.8976 | 17.0 | 316115 | 3.4323 | 0.4053 |
| 2.8791 | 18.0 | 334710 | 3.4307 | 0.4059 |
| 2.8622 | 19.0 | 353305 | 3.4373 | 0.4057 |
| 2.8469 | 20.0 | 371900 | 3.4411 | 0.4057 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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mirav/SoftColorsMix
|
mirav
| 2023-12-14T05:17:36Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-14T04:54:43Z |
---
license: creativeml-openrail-m
---
|
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vivekdugale/fine_tuned_llama2_7b_chatbot_mental_health_E3
|
vivekdugale
| 2023-12-14T04:58:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T04:58:35Z |
Entry not found
|
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joeldabest638/hermeythemisfitelf
|
joeldabest638
| 2023-12-14T05:13:59Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T05:10:58Z |
---
license: openrail
---
|
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afiqlol/idk_yet_part4
|
afiqlol
| 2023-12-17T22:02:13Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:citizenlab/twitter-xlm-roberta-base-sentiment-finetunned",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-14T05:11:02Z |
---
base_model: citizenlab/twitter-xlm-roberta-base-sentiment-finetunned
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: idk_yet_part4
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. -->
# idk_yet_part4
This model is a fine-tuned version of [citizenlab/twitter-xlm-roberta-base-sentiment-finetunned](https://huggingface.co/citizenlab/twitter-xlm-roberta-base-sentiment-finetunned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1661
- Accuracy: 0.7308
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3049 | 1.0 | 802 | 0.8413 | 0.7314 |
| 0.253 | 2.0 | 1604 | 0.9618 | 0.7304 |
| 0.2121 | 3.0 | 2406 | 1.0001 | 0.7295 |
| 0.175 | 4.0 | 3208 | 1.1020 | 0.7306 |
| 0.1514 | 5.0 | 4010 | 1.1661 | 0.7308 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cpu
- Datasets 2.14.5
- Tokenizers 0.15.0
|
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Mequanent/code-search-net-tokenizer
|
Mequanent
| 2023-12-14T05:11:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:11:04Z |
Entry not found
|
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] |
asigalov61/Tiny-Music-Transformer
|
asigalov61
| 2023-12-14T05:11:11Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T05:11:11Z |
---
license: apache-2.0
---
|
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Hengky123123/Frieska
|
Hengky123123
| 2023-12-14T05:12:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:11:18Z |
Entry not found
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_16
|
ADISH007
| 2023-12-14T05:15:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T05:15:02Z |
Entry not found
|
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] |
stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep07
|
stvhuang
| 2023-12-14T05:20:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-14T05:16:32Z |
Entry not found
|
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] |
mca183/bert-base-japanese-v3-output_jcommonsenseqa
|
mca183
| 2023-12-14T05:17:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"multiple-choice",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-12-14T05:16:56Z |
Entry not found
|
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] |
afasdfa/outputs
|
afasdfa
| 2023-12-14T05:18:08Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-14T05:18:01Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
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aransari/arashs_qa_model
|
aransari
| 2023-12-14T05:23:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:23:03Z |
Entry not found
|
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ps-abhi/ai-detect-smart-inno
|
ps-abhi
| 2023-12-14T05:24:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:24:50Z |
Entry not found
|
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] |
miweru/LeoLM_Schwurpus
|
miweru
| 2023-12-14T05:30:09Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:LeoLM/leo-hessianai-7b-chat",
"region:us"
] | null | 2023-12-14T05:29:43Z |
---
library_name: peft
base_model: LeoLM/leo-hessianai-7b-chat
---
# 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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### Model Sources [optional]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### 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
<!-- 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Model Card Contact
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.7.0
|
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FinrodFelagund/whisper-medium-wak
|
FinrodFelagund
| 2023-12-14T05:31:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:31:23Z |
Entry not found
|
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MeetK/sent_simpl
|
MeetK
| 2023-12-14T05:31:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:31:48Z |
Entry not found
|
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AI-Wheelz/TheCriticalDrinker_V2
|
AI-Wheelz
| 2023-12-14T05:36:30Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T05:33:29Z |
---
license: openrail
---
|
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wweijia/bert-finetuned-ner
|
wweijia
| 2023-12-14T05:34:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:34:59Z |
Entry not found
|
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] |
keonoutsold/Pinkpantheress
|
keonoutsold
| 2023-12-14T05:43:58Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-14T05:43:40Z |
---
license: openrail
---
|
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wefewfkjwhfe/results_hf
|
wefewfkjwhfe
| 2023-12-14T05:43:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:43:55Z |
Entry not found
|
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EmmaGthn/results_lora_40_30000_bias
|
EmmaGthn
| 2023-12-14T09:48:15Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-14T05:45:05Z |
Entry not found
|
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] |
csukuangfj/vits-mms-tha
|
csukuangfj
| 2023-12-14T06:11:38Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"endpoints_compatible",
"region:us",
"has_space"
] | null | 2023-12-14T05:45:10Z |
# Introduction
This model is converted from
https://huggingface.co/facebook/mms-tts/resolve/main/models/tha/G_100000.pth
Please see
https://huggingface.co/facebook/mms-tts/tree/main/models/tha
for more details.
|
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] |
csukuangfj/vits-mms-deu
|
csukuangfj
| 2023-12-14T06:12:01Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T05:46:40Z |
# Introduction
This model is converted from
https://huggingface.co/facebook/mms-tts/resolve/main/models/deu/G_100000.pth
Please see
https://huggingface.co/facebook/mms-tts/tree/main/models/deu
for more details.
|
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] |
csukuangfj/vits-mms-fra
|
csukuangfj
| 2023-12-14T06:11:30Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T05:49:29Z |
# Introduction
This model is converted from
https://huggingface.co/facebook/mms-tts/resolve/main/models/fra/G_100000.pth
Please see
https://huggingface.co/facebook/mms-tts/tree/main/models/fra
for more details.
|
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] |
csukuangfj/vits-mms-rus
|
csukuangfj
| 2023-12-14T06:11:39Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T05:49:42Z |
# Introduction
This model is converted from
https://huggingface.co/facebook/mms-tts/resolve/main/models/rus/G_100000.pth
Please see
https://huggingface.co/facebook/mms-tts/tree/main/models/rus
for more details.
|
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] |
csukuangfj/vits-mms-spa
|
csukuangfj
| 2023-12-14T06:11:32Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T05:49:56Z |
# Introduction
This model is converted from
https://huggingface.co/facebook/mms-tts/resolve/main/models/spa/G_100000.pth
Please see
https://huggingface.co/facebook/mms-tts/tree/main/models/spa
for more details.
|
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] |
csukuangfj/vits-mms-ukr
|
csukuangfj
| 2023-12-14T06:11:38Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"endpoints_compatible",
"region:us"
] | null | 2023-12-14T05:50:32Z |
# Introduction
This model is converted from
https://huggingface.co/facebook/mms-tts/resolve/main/models/ukr/G_100000.pth
Please see
https://huggingface.co/facebook/mms-tts/tree/main/models/ukr
for more details.
|
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InfiniFlow/text_concat_xgb_v1.0
|
InfiniFlow
| 2023-12-14T05:57:01Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-14T05:51:25Z |
---
license: apache-2.0
---
### Model Loading
```python
import xgboost as xgb
import torch
model = xgb.Booster()
if torch.cuda.is_available():
model.set_param({"device": "cuda"})
model.load_model('InfiniFlow/text_concat_xgb_v1.0')
```
### Prediction
```python
model.predict(xgb.DMatrix([feature]))[0]
```
|
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Gelid/Raphael-Proto
|
Gelid
| 2023-12-14T05:51:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-14T05:51:35Z |
Entry not found
|
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