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PassionFriend/5DfhfMSb2gZ8bm5HbTvAEBw2t9xLBbLoHryjgV3GBwYt3xnT_vgg | PassionFriend | "2024-03-01T06:39:36Z" | 1,328 | 0 | keras | [
"keras",
"region:us"
] | null | "2024-02-12T13:52:54Z" | Entry not found |
PassionFriend/5CFY72q6M6qmBkxLQz6YbR1vfSJdSzyy6VaGgbswwWadtdZK_vgg | PassionFriend | "2024-03-01T06:42:15Z" | 1,328 | 0 | keras | [
"keras",
"region:us"
] | null | "2024-02-14T13:05:56Z" | Entry not found |
neuralmagic/Llama-2-7b-pruned70-retrained | neuralmagic | "2024-05-07T15:25:45Z" | 1,328 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"sparse",
"dataset:cerebras/SlimPajama-627B",
"arxiv:2301.00774",
"arxiv:2405.03594",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1907.10641",
"arxiv:1911.01547",
"arxiv:2109.07958",
"arxiv:2110.14168",
"arxiv:2107.03374",
"base_model:neuralmagic/Llama-2-7b-pruned50-retrained",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-15T15:44:50Z" | ---
base_model: neuralmagic/Llama-2-7b-pruned50-retrained
inference: true
model_type: llama
pipeline_tag: text-generation
datasets:
- cerebras/SlimPajama-627B
tags:
- sparse
---
# Llama-2-7b-pruned70-retrained
This repo contains model files for a [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) model that has had 50% of the parameters pruned in one-shot with [SparseGPT](https://arxiv.org/abs/2301.00774), then retrained by [Cerebras](https://huggingface.co/cerebras) with 50B tokens from SlimPajama while maintaining sparsity. It was then one-shot pruned to 70% sparsity and trained for another 100B tokens.
Official model weights from [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594).
**Authors**: Neural Magic, Cerebras
## Usage
Below we share some code snippets on how to get quickly started with running the model.
### Sparse Transfer
By leveraging a pre-sparsified model's structure, you can efficiently fine-tune on new data, leading to reduced hyperparameter tuning, training times, and computational costs. Learn about this process [here](https://neuralmagic.github.io/docs-v2/get-started/transfer).
### Running the model
This model has not been fine-tuned for instruction-following but may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse).
```python
# pip install transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-pruned70-retrained")
model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-pruned70-retrained", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
## Evaluation Benchmark Results
Model evaluation metrics and results. [UPDATE]
| Benchmark | Metric | Llama-2-7b | Llama-2-7b-pruned70-retrained |
|------------------------------------------------|---------------|-------------|-------------------------------|
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot | 46.9% | 36.5% |
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot | 78.6% | 74.1% |
| [WinoGrande](https://arxiv.org/abs/1907.10641) | 5-shot | 74.0% | 69.5% |
| [ARC-c](https://arxiv.org/abs/1911.01547) | 25-shot | 53.1% | 45.4% |
| [TruthfulQA](https://arxiv.org/abs/2109.07958) | 5-shot | 38.8% | 36.7% |
| [GSM8K](https://arxiv.org/abs/2110.14168) | 5-shot | 14.5% | 8.0% |
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 13.4% | 14.4% |
## Model Training Details
[UPDATE]
## Help
For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ) |
FINDA-FIT/llama-ko-7b | FINDA-FIT | "2023-09-29T16:12:35Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-29T15:53:43Z" | Entry not found |
krevas/LDCC-Instruct-Llama-2-ko-13B-v4.1 | krevas | "2023-10-17T12:47:50Z" | 1,327 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-17T01:01:11Z" | ---
license: cc-by-nc-4.0
---
|
kiyoonyoo/ko-en-trans-platypus-13b | kiyoonyoo | "2023-10-17T23:58:31Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-17T23:43:04Z" | Entry not found |
Jaewoo1/Foundation_Platypus_data | Jaewoo1 | "2023-10-18T06:05:12Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-18T05:45:24Z" | Entry not found |
jiwoochris/ko-llama2-v3 | jiwoochris | "2023-10-21T15:58:29Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-21T15:27:18Z" | ---
license: mit
---
|
MNCJihun/Mistral-7B-eng-kor-cot-combined | MNCJihun | "2023-10-24T01:12:21Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-24T01:04:47Z" | Entry not found |
GAI-LLM/polyglot-12.8b-mixed-v3 | GAI-LLM | "2023-10-27T00:43:47Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-26T01:28:26Z" | ---
license: cc-by-nc-4.0
language:
- ko
library_name: transformers
pipeline_tag: text-generation
---
**The license is `cc-by-nc-4.0`.**
# **GAI-LLM/polyglot-12.8b-mixed-v3**
## Model Details
**Model Developers** Donghoon Oh, Hanmin Myung, Eunyoung Kim (SK C&C G.AI Eng)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
GAI-LLM/polyglot-12.8b-mixed-v3 is an auto-regressive language model based on the polyglot transformer architecture.
**Base Model** [EleutherAI/polyglot-ko-12.8b](https://huggingface.co/EleutherAI/polyglot-ko-12.8b)
**Training Dataset**
- We combined Open Korean Dateset using mixed-strategy.
- Kopen-platypus + kaist_cot_deepL
- We use A100 GPU 80GB * 8, when training.
# **Model Benchmark**
## KO-LLM leaderboard
- Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).
# Implementation Code
```python
### GAI-LLM/polyglot-12.8b-mixed-v3
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "GAI-LLM/polyglot-12.8b-mixed-v3"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(repo)
``` |
eclipsemint/kollama2-7b-v0 | eclipsemint | "2023-10-29T09:13:02Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-29T09:01:05Z" | Entry not found |
KaeriJenti/ko-llama2-13b-OrcaPlatypus | KaeriJenti | "2023-11-06T06:59:30Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-06T06:50:12Z" | Entry not found |
Kaeri-Jenti/LDCC-with-openorca-and-korca | Kaeri-Jenti | "2023-11-06T10:43:47Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-06T10:35:26Z" | ---
license: llama2
---
|
Minirecord/Mini_synatra_7b_03 | Minirecord | "2023-11-22T07:38:21Z" | 1,327 | 2 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-22T07:25:54Z" | ---
license: cc-by-sa-4.0
---
|
HY-KDPARK/llama-2-koen-13b-sft-v0.1 | HY-KDPARK | "2023-11-28T02:47:21Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-28T01:24:24Z" | ---
license: cc-by-nc-sa-4.0
---
|
PracticeLLM/Custom-KoLLM-13B-v5 | PracticeLLM | "2023-11-29T16:46:00Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ko",
"dataset:kyujinpy/KOR-gugugu-platypus-set",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-28T18:32:59Z" | ---
language:
- ko
datasets:
- kyujinpy/KOR-gugugu-platypus-set
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---
# **⭐My custom LLM 13B⭐**
## Model Details
**Model Developers**
- Kyujin Han (kyujinpy)
**Model Architecture**
- My custom LLM 13B is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Base Model**
- [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b)
**Training Dataset**
- [kyujinpy/KOR-gugugu-platypus-set](https://huggingface.co/datasets/kyujinpy/KOR-gugugu-platypus-set).
---
# Model comparisons
> Ko-LLM leaderboard(11/27; [link](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard))
| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| --- | --- | --- | --- | --- | --- | --- |
| ⭐My custom LLM 13B-v1⭐ | **50.19** | **45.99** | 56.93 | 41.78 | 41.66 | **64.58** |
| ⭐My custom LLM 13B-v4⭐ | 49.89 | 45.05 | **57.06** | 41.83 | **42.93** | 62.57 |
| **⭐My custom LLM 13B-v5⭐** | 49.50 | 44.88 | 56.74 | **42.23** | 42.82 | 60.80 |
---
# Model comparisons2
> AI-Harness evaluation; [link](https://github.com/Beomi/ko-lm-evaluation-harness)
| Model | Copa | Copa | HellaSwag | HellaSwag | BoolQ | BoolQ | Sentineg | Sentineg |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot |
| ⭐My custom LLM 13B-v1⭐ | 0.7987 | 0.8269 | 0.4994 | 0.5660 | 0.3343 | 0.5060 | 0.6984 | 0.9723 |
| ⭐My custom LLM 13B-v4⭐** | 0.7988 | 0.8279 | 0.4995 | 0.4953 | 0.3343 | 0.3558 | **0.7825** | 0.9698 |
| **⭐My custom LLM 13B-v5⭐** | **0.8028** | 0.8329 | **0.5082** | 0.5136 | **0.8647** | 0.8500 | **0.5524** | 0.9723 |
| [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b) | 0.7768 | 0.8128 | 0.4999 | 0.5127 | 0.3988 | 0.7038 | 0.5870 | 0.9748 |
---
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "PracticeLLM/Custom-KoLLM-13B-v5"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
``` |
BM-K/yi-ko-6b-it-v1.0.0 | BM-K | "2023-12-05T06:08:02Z" | 1,327 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-05T03:47:17Z" | Entry not found |
hyeogi/Yi-6b-dpo-v0.2 | hyeogi | "2024-01-01T13:39:07Z" | 1,327 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"Yi",
"dpo",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-08T12:51:11Z" | ---
language:
- ko
pipeline_tag: text-generation
tags:
- Yi
- dpo
---
# Yi-6b-dpo
### Model Details
- Base Model: [beomi/Yi-Ko-6B](https://huggingface.co/beomi/Yi-Ko-6B)
### Datasets
- sampling and translate [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- sampling and translate [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
### Benchmark
- SOTA model under 7B as of Dec 20, 2023 (https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).
| Model | Average |Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| --- | --- | --- | --- | --- | --- | --- |
| **hyeogi/Yi-6b-dpo-v0.2 (Ours)** | **52.63** | 41.72 | 52.96 | 46.69 | 52.38 | 69.42 |
| [hyeogi/Yi-6b-dpo-v0.1(Ours)](https://huggingface.co/hyeogi/Yi-6b-dpo-v0.1) | 51.38 | 41.3 | 52.23 | 45.34 | 54.03 | 63.99 |
| [Minirecord/Mini_DPO_7b_01](https://huggingface.co/Minirecord/Mini_DPO_7b_01) | 50.47 | 48.29 | 54.68 | 46.7 | 47.78 | 54.9
|
etri-xainlp/llama2-13b-dpo-test | etri-xainlp | "2023-12-18T08:26:34Z" | 1,327 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-18T08:08:00Z" | ---
license: apache-2.0
---
|
inswave/AISquare-Instruct-yi-ko-6b-v0.9.26 | inswave | "2023-12-21T01:11:00Z" | 1,327 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-21T01:01:37Z" | Entry not found |
jingyeom/Yi-ko-1.1-dedup | jingyeom | "2023-12-26T01:44:46Z" | 1,327 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-26T01:40:50Z" | Entry not found |
RichardErkhov/google_-_gemma-2b-gguf | RichardErkhov | "2024-05-02T00:32:06Z" | 1,327 | 0 | null | [
"gguf",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv:2108.07732",
"arxiv:2110.14168",
"arxiv:2304.06364",
"arxiv:2206.04615",
"arxiv:1804.06876",
"arxiv:2110.08193",
"arxiv:2009.11462",
"arxiv:2101.11718",
"arxiv:1804.09301",
"arxiv:2109.07958",
"arxiv:2203.09509",
"region:us"
] | null | "2024-04-12T05:10:45Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gemma-2b - GGUF
- Model creator: https://huggingface.co/google/
- Original model: https://huggingface.co/google/gemma-2b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [gemma-2b.Q2_K.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q2_K.gguf) | Q2_K | 1.08GB |
| [gemma-2b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.IQ3_XS.gguf) | IQ3_XS | 1.16GB |
| [gemma-2b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.IQ3_S.gguf) | IQ3_S | 1.2GB |
| [gemma-2b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q3_K_S.gguf) | Q3_K_S | 1.2GB |
| [gemma-2b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.IQ3_M.gguf) | IQ3_M | 1.22GB |
| [gemma-2b.Q3_K.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q3_K.gguf) | Q3_K | 1.29GB |
| [gemma-2b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q3_K_M.gguf) | Q3_K_M | 1.29GB |
| [gemma-2b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q3_K_L.gguf) | Q3_K_L | 1.36GB |
| [gemma-2b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.IQ4_XS.gguf) | IQ4_XS | 1.4GB |
| [gemma-2b.Q4_0.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q4_0.gguf) | Q4_0 | 1.44GB |
| [gemma-2b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.IQ4_NL.gguf) | IQ4_NL | 1.45GB |
| [gemma-2b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q4_K_S.gguf) | Q4_K_S | 1.45GB |
| [gemma-2b.Q4_K.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q4_K.gguf) | Q4_K | 1.52GB |
| [gemma-2b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q4_K_M.gguf) | Q4_K_M | 1.52GB |
| [gemma-2b.Q4_1.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q4_1.gguf) | Q4_1 | 1.56GB |
| [gemma-2b.Q5_0.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q5_0.gguf) | Q5_0 | 1.68GB |
| [gemma-2b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q5_K_S.gguf) | Q5_K_S | 1.68GB |
| [gemma-2b.Q5_K.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q5_K.gguf) | Q5_K | 1.71GB |
| [gemma-2b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q5_K_M.gguf) | Q5_K_M | 1.71GB |
| [gemma-2b.Q5_1.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q5_1.gguf) | Q5_1 | 1.79GB |
| [gemma-2b.Q6_K.gguf](https://huggingface.co/RichardErkhov/google_-_gemma-2b-gguf/blob/main/gemma-2b.Q6_K.gguf) | Q6_K | 1.92GB |
Original model description:
---
library_name: transformers
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged-in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
license: gemma
---
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the [7B base model](https://huggingface.co/google/gemma-7b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it).
**Resources and Technical Documentation**:
* [Gemma Technical Report](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf)
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-gg-hf)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Authors**: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Context Length
Models are trained on a context length of 8192 tokens.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Fine-tuning the model
You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-2b`.
In that repository, we provide:
* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
* A script to perform SFT using FSDP on TPU devices
* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a GPU using different precisions
* _Using `torch.float16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", revision="float16")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.bfloat16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using 4-bit precision_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safely in line with
[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
### Software
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways).
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
[foundation models](https://ai.google/discover/foundation-models/), including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 49.7 | 51.8 |
| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 12.5 | 23 |
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
| ------------------------------ | ------------- | ----------- | --------- |
| **Average** | | **45.0** | **56.9** |
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* Text-to-Text Content Safety: Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
* Text-to-Text Representational Harms: Benchmark against relevant academic
datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
* Memorization: Automated evaluation of memorization of training data, including
the risk of personally identifiable information exposure.
* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
biological, radiological, and nuclear (CBRN) risks.
### Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child
safety, content safety, representational harms, memorization, large-scale harms.
On top of robust internal evaluations, the results of well known safety
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
are shown here.
**Update**: These numbers reflect the new numbers from the updated v1.1 IT models. For the original v1 numbers, please consult the technical report's appendix for the results.
| Benchmark | Metric | Gemma v1.1 IT 2B | Gemma v1.1 IT 7B |
| ------------------------------ | ------------- | ----------- | --------- |
| [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 |
| [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 |
| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 |
| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 |
| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 |
| [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 |
| [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 31.81 | 44.84 |
| [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 |
| [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 |
| [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 |
| ------------------------------ | ------------- | ----------- | --------- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
|
timm/vgg11_bn.tv_in1k | timm | "2023-04-25T20:06:36Z" | 1,326 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1409.1556",
"license:bsd-3-clause",
"region:us"
] | image-classification | "2023-04-25T20:04:44Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: bsd-3-clause
datasets:
- imagenet-1k
---
# Model card for vgg11_bn.tv_in1k
A VGG image classification model. Trained on ImageNet-1k, original torchvision weights.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 132.9
- GMACs: 7.6
- Activations (M): 7.4
- Image size: 224 x 224
- **Papers:**
- Very Deep Convolutional Networks for Large-Scale Image Recognition: https://arxiv.org/abs/1409.1556
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/pytorch/vision
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vgg11_bn.tv_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vgg11_bn.tv_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 64, 224, 224])
# torch.Size([1, 128, 112, 112])
# torch.Size([1, 256, 56, 56])
# torch.Size([1, 512, 28, 28])
# torch.Size([1, 512, 14, 14])
# torch.Size([1, 512, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vgg11_bn.tv_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 512, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{Simonyan2014VeryDC,
title={Very Deep Convolutional Networks for Large-Scale Image Recognition},
author={Karen Simonyan and Andrew Zisserman},
journal={CoRR},
year={2014},
volume={abs/1409.1556}
}
```
|
jxm/vec2text__openai_ada002__msmarco__msl128__hypothesizer | jxm | "2023-09-06T21:01:58Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | "2023-09-06T21:01:04Z" | Entry not found |
jojo0217/ChatSKKU5.8B | jojo0217 | "2023-10-24T12:01:26Z" | 1,326 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"ko",
"dataset:jojo0217/korean_rlhf_dataset",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-27T12:55:21Z" | ---
license: apache-2.0
datasets:
- jojo0217/korean_rlhf_dataset
language:
- ko
pipeline_tag: text-generation
---
성균관대학교 산학협력 데이터로 만든 테스트 모델입니다.
기존 10만 7천개의 데이터 + 2천개 일상대화 추가 데이터를 첨가하여 학습하였습니다.
___
모델은 EleutherAI/polyglot-ko-5.8b를 base로 학습 되었으며
학습 parameter은 다음과 같습니다.
batch_size: 128
micro_batch_size: 8
num_epochs: 3
learning_rate: 3e-4
cutoff_len: 1024
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
weight_decay: 0.1
___
측정한 kobest 10shot 점수는 다음과 같습니다.
___
모델 prompt template는 kullm의 template를 사용하였습니다.
테스트 코드는 다음과 같습니다.
https://colab.research.google.com/drive/1xEHewqHnG4p3O24AuqqueMoXq1E3AlT0?usp=sharing
```
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
model_name="jojo0217/ChatSKKU5.8B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
load_in_8bit=True,#만약 양자화 끄고 싶다면 false
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=model_name,
device_map="auto"
)
def answer(message):
prompt=f"아래는 작업을 설명하는 명령어입니다. 요청을 적절히 완료하는 응답을 작성하세요.\n\n### 명령어:\n{message}"
ans = pipe(
prompt + "\n\n### 응답:",
do_sample=True,
max_new_tokens=512,
temperature=0.7,
repetition_penalty = 1.0,
return_full_text=False,
eos_token_id=2,
)
msg = ans[0]["generated_text"]
return msg
answer('성균관대학교에대해 알려줘')
``` |
GAI-LLM/ko-en-llama2-13b-mixed-v1 | GAI-LLM | "2023-10-27T00:41:10Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ko",
"license:cc-by-nc-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-18T08:45:35Z" | ---
license: cc-by-nc-2.0
language:
- ko
library_name: transformers
pipeline_tag: text-generation
---
**The license is `cc-by-nc-2.0`.**
# **GAI-LLM/ko-en-llama2-13b-mixed-v1**
## Model Details
**Model Developers** Donghoon Oh, Hanmin Myung, Eunyoung Kim (SK C&C G.AI Eng)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
GAI-LLM/ko-en-llama2-13b-mixed-v1 is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Base Model** [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b)
**Training Dataset**
- We combined Open Korean Dateset using mixed-strategy.
- Kopen-platypus + Everythinglm v2 + jojo0217/korean_rlhf_dataset + sentineg + hellaswag + copa
- We use A100 GPU 80GB * 8, when training.
# **Model Benchmark**
## KO-LLM leaderboard
- Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).
# Implementation Code
```python
### GAI-LLM/ko-en-llama2-13b-mixed-v1
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "GAI-LLM/ko-en-llama2-13b-mixed-v1"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(repo)
```
--- |
HumanF-MarkrAI/pub-llama-13b-v1 | HumanF-MarkrAI | "2023-10-19T18:44:01Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ko",
"dataset:HumanF-MarkrAI/pub_COT-2000",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-19T08:30:48Z" | ---
language:
- ko
datasets: HumanF-MarkrAI/pub_COT-2000
license: cc-by-nc-sa-4.0
---
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
**The license is `cc-by-nc-sa`.**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
pub-llama-13b-v1 is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Repo Link**
Github: [pub-llama📑](Not_yet)
**Training Dataset**
More detail about dataset: [HumanF-MarkrAI/pub_COT-2000](https://huggingface.co/datasets/HumanF-MarkrAI/pub_COT-2000). |
MNCJihunKim/MIstral-7B-SlimOrca-OP-2k | MNCJihunKim | "2023-10-26T01:02:45Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-26T00:48:09Z" | Entry not found |
MNCJihunKim/Mistral-7B-SlimOrca-OP-8k | MNCJihunKim | "2023-10-26T01:41:48Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-26T01:33:42Z" | Entry not found |
MNC-Jihun/Mistral-7B-OP-u1k-ver0.6 | MNC-Jihun | "2023-10-30T02:43:16Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-30T02:35:36Z" | Entry not found |
DILAB-HYU/koquality-polyglot-1.3b | DILAB-HYU | "2023-11-05T11:47:51Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"polyglot-ko",
"gpt-neox",
"KoQuality",
"ko",
"dataset:DILAB-HYU/KoQuality",
"base_model:EleutherAI/polyglot-ko-1.3b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-30T04:35:31Z" | ---
license: apache-2.0
datasets:
- DILAB-HYU/KoQuality
language:
- ko
pipeline_tag: text-generation
tags:
- polyglot-ko
- gpt-neox
- KoQuality
base_model: EleutherAI/polyglot-ko-1.3b
---
This model is a instruct-tuned EleutherAI/polyglot-ko-1.3b model.
## Training hyperparameters
- learning_rate: 5e-5
- train_batch_size: 1
- seed: 42
- distributed_type: multi-GPU (A30 24G) + CPU Offloading (384GB)
- num_devices: 2
- gradient_accumulation_steps: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
## Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.11.0
- deepspeed 0.9.5 |
DopeorNope/COKALL-13B-v3 | DopeorNope | "2023-11-02T02:14:59Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-01T14:22:19Z" | Entry not found |
kyujinpy/Korean-OpenOrca-v3 | kyujinpy | "2023-11-10T12:14:27Z" | 1,326 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"dataset:kyujinpy/OpenOrca-ko-v3",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-04T07:06:37Z" | ---
language:
- ko
datasets:
- kyujinpy/OpenOrca-ko-v3
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
**The license is `cc-by-nc-sa-4.0`.**
# **🐳Korean-OpenOrca-13B-v2🐳**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Model Architecture**
Korean-OpenOrca-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Repo Link**
Github Korean-OpenOrca: [🐳Korean-OpenOrca🐳](https://github.com/Marker-Inc-Korea/Korean-OpenOrca)
**Base Model** [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b)
**Training Dataset**
I use [OpenOrca-ko-v3](https://huggingface.co/datasets/kyujinpy/OpenOrca-ko-v3).
Using DeepL, translate about [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca).
I use A100 GPU 40GB and COLAB, when trianing.
# Model comparisons
| Model | Average |Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| --- | --- | --- | --- | --- | --- | --- |
| [Korean-OpenOrca-13B🐳] | 48.79 | 43.09 | 54.13 | 40.24 | 45.22 | 61.28 |
| [Korean-OpenOrca-13B-v2🐳] | 48.17 | 43.17 | 54.51 | 42.90 | 41.82 | 58.44 |
| Korean-OpenOrca-13B-v3🐳 | 48.86 | 43.77 | 54.30 | 41.79 | 43.85 | 60.57 |
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "kyujinpy/Korean-OpenOrca-13B-v3"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
--- |
GAI-LLM/llama-2-koen-13b-mixed-v8 | GAI-LLM | "2023-11-08T10:02:23Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-08T09:39:37Z" | ---
license: cc-by-nc-4.0
---
|
DopeorNope/COKAL_pre_DPO_Test_v2-13b | DopeorNope | "2023-11-11T05:52:42Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-11T05:18:12Z" | Entry not found |
LI-ST/Mistral-7B-ko-v0.1 | LI-ST | "2023-11-13T10:50:19Z" | 1,326 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"ko",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-13T10:28:54Z" | ---
license: cc-by-nc-nd-4.0
language:
- en
- ko
library_name: transformers
pipeline_tag: text-generation
---
<p><h1>Mistral-7B-ko-v0.1</h1></p>
basemodel: Open-Orca/Mistral-7B-OpenOrca
|
PracticeLLM/Custom-KoLLM-13B-v3 | PracticeLLM | "2023-11-26T19:20:00Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ko",
"dataset:kyujinpy/Ko-various-dataset",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-25T08:14:52Z" | ---
language:
- ko
datasets:
- kyujinpy/Ko-various-dataset
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0
---
# **⭐My custom LLM 13B⭐**
## Model Details
**Model Developers**
- Kyujin Han (kyujinpy)
**Model Architecture**
- My custom LLM 13B is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Base Model**
- [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b)
**Training Dataset**
- [kyujinpy/Ko-various-dataset](https://huggingface.co/datasets/kyujinpy/Ko-various-dataset).
---
# Model comparisons
> Ko-LLM leaderboard(11/27; [link](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard))
| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| --- | --- | --- | --- | --- | --- | --- |
| ⭐My custom LLM 13B-v1⭐ | **50.19** | **45.99** | 56.93 | **41.78** | 41.66 | **64.58** |
| ⭐My custom LLM 13B-v2⭐ | 48.28 | 45.73 | **56.97** | 38.77 | 38.75 | 61.16 |
| **⭐My custom LLM 13B-v3⭐** | 46.40 | 44.71 | 56.89 | 40.86 | **44.22** | 45.34 |
---
# Model comparisons2
> AI-Harness evaluation; [link](https://github.com/Beomi/ko-lm-evaluation-harness)
| Model | Copa | Copa | HellaSwag | HellaSwag | BoolQ | BoolQ | Sentineg | Sentineg |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot |
| ⭐My custom LLM 13B-v1⭐ | 0.7987 | 0.8269 | 0.4994 | 0.5660 | 0.3343 | 0.5060 | **0.6984** | 0.9723 |
| ⭐My custom LLM 13B-v2⭐ | 0.7938 | 0.8209 | 0.4978 | 0.4893 | 0.3343 | 0.5614 | 0.6283 | 0.9773 |
| **⭐My custom LLM 13B-v3⭐** | **0.8107** | 0.8359 | **0.5176** | 0.5182 | **0.6702** | 0.7851 | 0.5241 | 0.9698 |
| [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b) | 0.7768 | 0.8128 | 0.4999 | 0.5127 | 0.3988 | 0.7038 | 0.5870 | 0.9748 |
---
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "PracticeLLM/Custom-KoLLM-13B-v3"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
---
|
maywell/Synatra-Yi-Ko-6B | maywell | "2023-12-04T20:14:17Z" | 1,326 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"ko",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-04T03:15:12Z" | ---
license: cc-by-sa-4.0
language:
- en
- ko
pipeline_tag: text-generation
---
# Synatra-Yi-Ko-6B
# **Model Details**
### Description
Synatra-Yi-Ko-6B finetuned model based on beomi/Yi-Ko-6B. Using Synatra dataset.
<!-- prompt-template start -->
## Prompt template: ChatML w/o System Prompt
```
<|im_start|>user
Input<|im_end|>
<|im_start|>assistant
Output<|im_end|>
```
Follow me on twitter: https://twitter.com/stablefluffy
Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕 |
Ja-ck/Mistral-instruct-IPO-Y24-v1 | Ja-ck | "2023-12-11T06:51:07Z" | 1,326 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-11T06:38:35Z" | ---
license: apache-2.0
language:
- ko
pipeline_tag: text-generation
---
## Prompt Tempalte
It follows Alpaca format.
```
### 질문: {instruction}
### 답변: {output}
```
### Implementation Code
```
import troch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.fron_pretrained("Ja3ck/Mistral-instruct-IPO-Y24-v1", return_dict=True, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Ja3ck/Mistral-instruct-IPO-Y24-v1", use_fast=True)
tokenizer.pad_token = tokenizer.unk_token
tokenizer.pad_token_id = tokenizer.unk_token_id
tokenizer.padding_side = "left"
def gen(x):
x_ = f"### 질문: {x.strip()} ### 답변: "
inputs = tokenizer(x_, return_tensor='pt')
input_ids = inputs['input_ids'].cuda()
generation_output = model.generate(
pad_token_id = tokenizer.pad_token_id,
temperature=0.1,
top_p=1,
top_k=50,
num_beams=1,
repetition_penalty=1.13,
do_sample=True,
),
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=1024
)
for seq in generation_output.sequences:
output = tokenizer.decode(seq)
print(output.split("### 답변: ")[1].strip())
gen("안녕하세요?")
``` |
GAI-LLM/llama-2-koen-13b-dpo-v3_2 | GAI-LLM | "2023-12-12T04:45:57Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-12T04:27:03Z" | ---
license: cc-by-nc-4.0
---
|
oopsung/Yi-Ko-6B-ENin-test-v1 | oopsung | "2023-12-13T07:38:02Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-13T07:29:52Z" | Entry not found |
Minirecord/llama13b_2s_dpo | Minirecord | "2023-12-15T07:49:56Z" | 1,326 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-15T07:44:24Z" | ---
license: apache-2.0
---
|
AIdenU/Mistral-7b-ko-Y24_v0.1 | AIdenU | "2023-12-21T04:30:43Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-21T03:30:01Z" | ---
language:
- ko
pipeline_tag: text-generation
---
### Model Generation
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AidenU/Mistral-7b-ko-Y24_v0.1", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("AidenU/Mistral-7b-ko-Y24_v0.1")
messages = [
{"role":"user", "content", "안녕하세요?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
inputs = encodeds.to("cuda")
model.to("cuda")
outputs = model.generated(
inputs,
max_new_tokens=256,
do_sample=True
)
decoded = tokenizer.batch_decode(outputs)
print(decoded[0])
``` |
h2m/mhm-7b-v1.3 | h2m | "2024-01-24T05:03:44Z" | 1,326 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"moe",
"merge",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-01-14T17:48:34Z" | ---
tags:
- moe
- merge
license: apache-2.0
---
# mhm-7-3
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
Merged model based on mistral. created using dare_ties and models from top of openllm leaderboard.
Mixed 7 models into 1. 3 times merging.
Just an experiment. |
dranger003/Smaug-Mixtral-v0.1-iMat.GGUF | dranger003 | "2024-02-26T17:47:23Z" | 1,326 | 2 | gguf | [
"gguf",
"text-generation",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-02-25T02:35:03Z" | ---
license: apache-2.0
pipeline_tag: text-generation
library_name: gguf
---
* GGUF importance matrix (imatrix) quants for https://huggingface.co/abacusai/Smaug-Mixtral-v0.1
* The importance matrix was trained for ~50K tokens (105 batches of 512 tokens) using a [general purpose imatrix calibration dataset](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384).
* The [imatrix is being used on the K-quants](https://github.com/ggerganov/llama.cpp/pull/4930) as well.
**NOTE**: The new IQ3_M/IQ3_S (and updated Q3_K_XS) quants have been added, as well as IQ2_S/IQ2_M (requires commit [a33e6a0d](https://github.com/ggerganov/llama.cpp/commit/a33e6a0d2a66104ea9a906bdbf8a94d050189d91)).
| Layers | Context | [Template](https://huggingface.co/abacusai/Smaug-Mixtral-v0.1/blob/main/tokenizer_config.json#L32) |
| --- | --- | --- |
| <pre>32</pre> | <pre>32768</pre> | <pre>\<s\>[INST] {prompt} [/INST]<br>{response}</pre> |
 |
tincans-ai/gazelle-v0.2 | tincans-ai | "2024-03-19T14:19:07Z" | 1,326 | 85 | transformers | [
"transformers",
"safetensors",
"gazelle",
"text2text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-03-19T12:51:12Z" | ---
license: apache-2.0
language:
- en
---
Gazelle v0.2 is the mid-March release from [Tincans](https://tincans.ai) of a joint speech-language model.
Check out our [live demo](https://demo.tincans.ai/)!
Please see [this notebook](https://github.com/tincans-ai/gazelle/blob/2939d7034277506171d61a7a1001f535426faa71/examples/infer.ipynb) for an inference example. |
jabo/deit-base-page-filter | jabo | "2024-06-13T09:24:19Z" | 1,326 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-10T07:23:54Z" | ---
language: en
license: mit
---
...
# Page Filtering
Model to identify pages in children's books of the long 19th century (ca. 1789-1914)
that contain illustrations. It is used to filter non-relevant pages without
illustrations trained on hand-coded data.
Results on our validation dataset:
| | f1score | precision | recall | accuracy |
|:---------------|----------:|------------:|---------:|:-----------|
| not-relevant | 99.63 | 100 | 99.26 | - |
| relevant-cover | 85.71 | 75 | 100 | - |
| relevant-page | 100 | 100 | 100 | - |
| Macro Avg. | 95.11 | 91.67 | 99.75 | 99.37 |
Dataset:
| | data | train | test |
|:---------------|-------:|--------:|-------:|
| not-relevant | 902 | 631 | 271 |
| relevant-cover | 20 | 14 | 6 |
| relevant-page | 136 | 95 | 41 | |
timm/vit_small_patch32_384.augreg_in21k_ft_in1k | timm | "2023-05-06T00:29:51Z" | 1,325 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2106.10270",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-12-22T07:55:41Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-21k
---
# Model card for vit_small_patch32_384.augreg_in21k_ft_in1k
A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 22.9
- GMACs: 3.3
- Activations (M): 6.1
- Image size: 384 x 384
- **Papers:**
- How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-21k
- **Original:** https://github.com/google-research/vision_transformer
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_small_patch32_384.augreg_in21k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_small_patch32_384.augreg_in21k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 145, 384) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{steiner2021augreg,
title={How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers},
author={Steiner, Andreas and Kolesnikov, Alexander and and Zhai, Xiaohua and Wightman, Ross and Uszkoreit, Jakob and Beyer, Lucas},
journal={arXiv preprint arXiv:2106.10270},
year={2021}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
caisarl76/Mistral-7B-KO-3data-merged | caisarl76 | "2023-10-09T15:10:26Z" | 1,325 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-09T14:49:05Z" | Entry not found |
krevas/LDCC-Instruct-Llama-2-ko-13B-v4 | krevas | "2023-11-07T12:39:15Z" | 1,325 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-13T03:30:13Z" | ---
license: cc-by-nc-4.0
language:
- ko
---
# LDCC-Instruct-Llama-2-ko-13B
<img src="./assets/icon.png" alt="image" width="50%" height="auto">
## Model Details
* **Developed by**: [Lotte Data Communication](https://www.ldcc.co.kr)
## Hardware and Software
* **Hardware**: We utilized an A100x8 * 1 for training our model
* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index)
## Prompt Template
```
### Prompt:
{instruction}
### Answer:
{output}
```
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)| |
MNCJihun/Mistral-7B-orca-platy-2k | MNCJihun | "2023-10-23T06:45:14Z" | 1,325 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-23T06:37:42Z" | Entry not found |
MNCJihun/Mistral-7B-guanaco-1k-orca-platy-1k-ep4 | MNCJihun | "2023-10-23T06:47:34Z" | 1,325 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-23T06:40:08Z" | Entry not found |
MNCJihun/Mistral-7B-SlimOrca-eng-kor-combined | MNCJihun | "2023-10-24T00:54:09Z" | 1,325 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-24T00:46:36Z" | Entry not found |
MNCJ1hun/Dolphin-Mistral-7B-OP-u1k-ver0.1 | MNCJ1hun | "2023-10-29T13:37:30Z" | 1,325 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-28T16:17:24Z" | Entry not found |
MNC-LLM/Mistral-7B-O3k-Au1k-ver0.7 | MNC-LLM | "2023-11-01T05:17:44Z" | 1,325 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-01T02:28:03Z" | Entry not found |
jingyeom/seal3.1.6_ia3 | jingyeom | "2023-11-18T09:08:02Z" | 1,325 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-18T08:57:54Z" | Entry not found |
The-matt/llama2_ko-7b_distinctive-snowflake-182_1060 | The-matt | "2023-11-20T01:45:16Z" | 1,325 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-20T00:46:12Z" | Entry not found |
blueapple8259/TinyKo | blueapple8259 | "2023-12-18T04:06:37Z" | 1,325 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"feature-extraction",
"text-generation",
"ko",
"dataset:maywell/ko_wikidata_QA",
"dataset:beomi/KoAlpaca-v1.1a",
"dataset:Bingsu/ko_alpaca_data",
"dataset:klue",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-10T12:41:45Z" | ---
license: cc-by-nc-sa-4.0
datasets:
- maywell/ko_wikidata_QA
- beomi/KoAlpaca-v1.1a
- Bingsu/ko_alpaca_data
- klue
language:
- ko
pipeline_tag: text-generation
---
[maywell/ko_wikidata_QA](https://huggingface.co/datasets/maywell/ko_wikidata_QA), [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a), [Bingsu/ko_alpaca_data](https://huggingface.co/datasets/Bingsu/ko_alpaca_data), [klue](https://huggingface.co/datasets/klue) 데이터셋을 사용해서 학습되었으며 maywell/ko_wikidata_QA, beomi/KoAlpaca-v1.1a, Bingsu/ko_alpaca_data데이터셋의 경우 output만 학습에 사용되었습니다. 한국어만 지원됩니다. |
AIFT/PACK-13b-v1.1 | AIFT | "2023-12-12T02:25:25Z" | 1,325 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-11T06:13:27Z" | ---
license: cc-by-nc-sa-4.0
---
|
GAI-LLM/Yi-Ko-6B-mixed-v10 | GAI-LLM | "2023-12-19T12:17:20Z" | 1,325 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-19T10:26:20Z" | ---
license: cc-by-nc-4.0
---
|
GAI-LLM/Yi-Ko-6B-smash | GAI-LLM | "2023-12-28T04:47:10Z" | 1,325 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-28T04:30:03Z" | ---
license: cc-by-nc-4.0
---
|
grimjim/Mistral-Starling-merge-trial3-7B | grimjim | "2024-03-29T17:04:15Z" | 1,325 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Nexusflow/Starling-LM-7B-beta",
"base_model:grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-29T16:54:01Z" | ---
base_model:
- Nexusflow/Starling-LM-7B-beta
- grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
# Mistral-Starling-merge-trial3-7B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
The goal was to combine strong reasoning with 32K context length.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta)
* [grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B](https://huggingface.co/grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B
layer_range: [0, 32]
- model: Nexusflow/Starling-LM-7B-beta
layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
merge_method: slerp
base_model: grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B
parameters:
t:
- value: 0.5 # fallback for rest of tensors
dtype: bfloat16
```
|
John6666/real-pony-cutejp-v3-sdxl | John6666 | "2024-05-26T12:20:18Z" | 1,325 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-05-26T12:15:35Z" | ---
license: other
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
---
Original model is [here](https://civitai.com/models/365041?modelVersionId=455422).
|
timm/convnext_base.fb_in22k | timm | "2024-02-10T23:26:54Z" | 1,324 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-22k",
"arxiv:2201.03545",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-12-13T07:06:50Z" | ---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-22k
---
# Model card for convnext_base.fb_in22k
A ConvNeXt image classification model. Pretrained on ImageNet-22k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 110.0
- GMACs: 15.4
- Activations (M): 28.8
- Image size: 224 x 224
- **Papers:**
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- **Original:** https://github.com/facebookresearch/ConvNeXt
- **Dataset:** ImageNet-22k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_base.fb_in22k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_base.fb_in22k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 128, 56, 56])
# torch.Size([1, 256, 28, 28])
# torch.Size([1, 512, 14, 14])
# torch.Size([1, 1024, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_base.fb_in22k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1024, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
DopeorNope/COLA_LO-7B | DopeorNope | "2023-10-03T13:49:11Z" | 1,324 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-03T13:10:20Z" | Entry not found |
caisarl76/Mistral-7B-eng-kor-cot-combined | caisarl76 | "2023-10-23T00:44:33Z" | 1,324 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-23T00:31:05Z" | Entry not found |
jb723/LLaMA2_crosslingual_transfer_1 | jb723 | "2023-10-26T06:36:18Z" | 1,324 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-26T04:58:19Z" | Instruction tuning style로 한다. |
nayohan/ko-ref-llama2-7b-Inst | nayohan | "2023-10-26T10:48:17Z" | 1,324 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-2-ko",
"KoQuality",
"ko",
"dataset:DILAB-HYU/KoQuality",
"base_model:hyunseoki/ko-ref-llama2-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-26T08:35:39Z" | ---
license: apache-2.0
datasets:
- DILAB-HYU/KoQuality
language:
- ko
pipeline_tag: text-generation
tags:
- llama-2-ko
- KoQuality
base_model: hyunseoki/ko-ref-llama2-7b
---
This model is a instruct-tuned ko-ref-llama2-7b model, using only 10% of [Kullm, OIG, KoAlpaca] Instruction dataset.
len10_k100_mppl_n0.1.json -> 152step
## Training hyperparameters
- learning_rate: 5e-5
- train_batch_size: 1
- seed: 42
- distributed_type: multi-GPU (A30 24G) + CPU Offloading(160GB)
- num_devices: 2
- gradient_accumulation_steps: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
## Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.11.0
- deepspeed 0.9.5 |
jiwoochris/llama2_tmt-13b-v2 | jiwoochris | "2023-11-06T06:26:25Z" | 1,324 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-06T06:18:33Z" | Entry not found |
MRAIRR/Navistral | MRAIRR | "2023-11-06T10:44:27Z" | 1,324 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-06T10:40:58Z" | ---
license: apache-2.0
---
|
eclipsemint/kollama2-7b-v0.3 | eclipsemint | "2023-11-07T00:35:50Z" | 1,324 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-07T00:30:48Z" | Entry not found |
daekeun-ml/Llama-2-ko-OpenOrca-gugugo-13B | daekeun-ml | "2023-11-17T04:40:07Z" | 1,324 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-2",
"instruct",
"instruction",
"ko",
"dataset:squarelike/OpenOrca-gugugo-ko",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-14T00:43:58Z" | ---
language:
- ko
tags:
- llama-2
- instruct
- instruction
pipeline_tag: text-generation
license: llama2
datasets:
- squarelike/OpenOrca-gugugo-ko
---
# Llama-2-ko-OpenOrca-gugugo-13B
This model was trained for PoC purposes. This is part of an experiment to check whether model performance improves when fine-tuned with large data of about 1 million samples.
[Note] There are still many people/customers who have the wrong idea that 'Always the more data, the better,' so I showed it directly with experimental data.
In fine-tuning, data quality is much more important than simply preparing a lot of data, and keyword distribution within the dataset is also important!
For example, when searching for process and comparison keywords in the kkullm dataset, each is about 1% of the entire dataset.
### Model Details
- Base Model: [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b)
### Datasets
Trained on 1 million samples from the dataset. The training infrastructure used AWS g5.12xlarge x 2ea (total of NVIDIA A10G 8 GPUs).
- [OpenOrca-gugugo-ko](https://huggingface.co/datasets/squarelike/OpenOrca-gugugo-ko)
### Hyperparameters
The hyperparameters are simply heuristic values. For reference only:
```python
learning_rate = 3e-5
lr_scheduler = "constant_with_warmup"
batch_size = 1
gradient_accumulation_steps = 8
lora_alpha = 16
lora_r = 16
lora_dropout = 0.1
lora_target_modules = "[gate_proj, down_proj, up_proj, q_proj, k_proj, o_proj, v_proj]"
use_flash_attention_2 = True
```
### License
- Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License, under LLAMA 2 COMMUNITY LICENSE AGREEMENT
This model was created as a personal experiment, unrelated to the organization I work for. |
JYKIM-AI/Mistral-7B-SFT | JYKIM-AI | "2023-11-20T11:00:01Z" | 1,324 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-20T10:38:34Z" | Entry not found |
The-matt/llama2_ko-7b_stilted-lion-205_1530 | The-matt | "2023-11-23T01:29:28Z" | 1,324 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-23T01:06:41Z" | Entry not found |
Herry443/Mistral-7B-KNUT-v0.2 | Herry443 | "2023-11-27T15:00:36Z" | 1,324 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-27T14:35:51Z" | Entry not found |
krevas/LDCC-Instruct-Llama-2-ko-13B-v7.1 | krevas | "2023-11-28T10:39:42Z" | 1,324 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-28T10:29:57Z" | ---
license: cc-by-nc-4.0
---
|
Puluming/AISquare-Instruct-llama2-koen-13b-v0.9.2 | Puluming | "2023-11-29T10:43:48Z" | 1,324 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-29T10:22:52Z" | ---
license: cc-by-nc-sa-4.0
---
|
hyeogi/Yi-6b-v0.3 | hyeogi | "2023-12-08T03:50:47Z" | 1,324 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-08T04:22:20Z" | Entry not found |
hyeogi/open-llama2-7b-dpo-v0.1 | hyeogi | "2023-12-16T16:25:58Z" | 1,324 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-16T15:41:49Z" | Entry not found |
We-Want-GPU/Yi-Ko-6B-orca-alpaca-gpt4-math-lora | We-Want-GPU | "2023-12-20T01:43:57Z" | 1,324 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-19T12:12:11Z" | Entry not found |
jhflow/yi-ko-6b-dpo-further | jhflow | "2023-12-20T08:29:09Z" | 1,324 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-20T08:23:22Z" | Entry not found |
GAI-LLM/Yi-Ko-6B-smash-dpo | GAI-LLM | "2023-12-29T06:56:08Z" | 1,324 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-29T06:46:29Z" | ---
license: cc-by-nc-4.0
---
|
uukuguy/speechless-coder-ds-1.3b | uukuguy | "2023-12-30T11:24:10Z" | 1,324 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"en",
"dataset:ise-uiuc/Magicoder-OSS-Instruct-75K",
"dataset:ise-uiuc/Magicoder-Evol-Instruct-110K",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-30T05:51:01Z" | ---
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
tags:
- code
license: apache-2.0
model-index:
- name: SpeechlessCoder
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value:
verified: false
---
<p><h1> speechless-coder-ds-1.3b </h1></p>
Use the following dataset to fine-tune deepseek-ai/deepseek-coder-1.3b in order to improve the model's reasoning and planning abilities.
context window length: 8192
max_tokens > 128 && < 8192
>
Total 185,193 samples 426 MB
- ise-uiuc/Magicoder-OSS-Instruct-75K 75,186 samples
- ise-uiuc/Magicoder-Evol-Instruct-110K 110,007 samples
50 samples/T=0.2/MaxTokens=512/Top_P=0.95
Code: https://github.com/uukuguy/speechless
### How to Prompt the Model
This model accepts the Alpaca instruction format.
For example:
```
You are an intelligent programming assistant.
### Instruction:
Implement a linked list in C++
### Response:
```
## HumanEval
| Metric | Value |
| --- | --- |
| humaneval-python | |
[Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard)
CodeLlama-34B-Python: 53.29
CodeLlama-34B-Instruct: 50.79
CodeLlama-13B-Instruct: 50.6
CodeLlama-34B: 45.11
CodeLlama-13B-Python: 42.89
CodeLlama-13B: 35.07
## BigCode Eval
0.205055
- metrics_humanevalfixtests-cpp: "pass@1": 0.054878048780487805
- metrics_humanevalfixtests-go: "pass@1": 0.054878048780487805
- metrics_humanevalfixtests-java: "pass@1": 0.042682926829268296
- metrics_humanevalfixtests-js: "pass@1": 0.0975609756097561
- metrics_humanevalfixtests-python: "pass@1": 0.06707317073170732
- metrics_humanevalfixtests-rust: "pass@1": 0.018292682926829267
0.332906
- metrics_humanevalsynthesize-cpp: "pass@1": 0.3475609756097561
- metrics_humanevalsynthesize-go: "pass@1": 0.25609756097560976
- metrics_humanevalsynthesize-java: "pass@1": 0.3353658536585366
- metrics_humanevalsynthesize-js: "pass@1": 0.35365853658536583
- metrics_humanevalsynthesize-python: "pass@1": 0.4024390243902439
- metrics_humanevalsynthesize-rust: "pass@1": 0.20121951219512196
- metrics_mbpp: "pass@1": 0.434
## LMEval
[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| Metric | Value |
| --- | --- |
| ARC | |
| HellaSwag | |
| MMLU | |
| TruthfulQA | |
| Average | |
|
facebook/dpr-reader-multiset-base | facebook | "2022-12-21T15:19:37Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"dpr",
"en",
"dataset:nq_open",
"dataset:trivia_qa",
"dataset:web_questions",
"dataset:trec",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2022-03-02T23:29:05Z" | ---
language: en
license: cc-by-nc-4.0
tags:
- dpr
datasets:
- nq_open
- trivia_qa
- web_questions
- trec
inference: false
---
# `dpr-reader-multiset-base`
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-authors)
## Model Details
**Model Description:** [Dense Passage Retrieval (DPR)](https://github.com/facebookresearch/DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-reader-multiset-base` is the reader model trained using the [Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open), [TriviaQA](https://huggingface.co/datasets/trivia_qa), [WebQuestions (WQ)](https://huggingface.co/datasets/web_questions), and [CuratedTREC (TREC)](https://huggingface.co/datasets/trec).
- **Developed by:** See [GitHub repo](https://github.com/facebookresearch/DPR) for model developers
- **Model Type:** BERT-based encoder
- **Language(s):** [CC-BY-NC-4.0](https://github.com/facebookresearch/DPR/blob/main/LICENSE), also see [Code of Conduct](https://github.com/facebookresearch/DPR/blob/main/CODE_OF_CONDUCT.md)
- **License:** English
- **Related Models:**
- [`dpr-question_encoder-multiset-base`](https://huggingface.co/facebook/dpr-question_encoder-multiset-base)
- [`dpr-ctx_encoder-multiset-base`](https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base)
- [`dpr-question-encoder-single-nq-base`](https://huggingface.co/facebook/dpr-question_encoder-single-nq-base)
- [`dpr-reader-single-nq-base`](https://huggingface.co/facebook/dpr-reader-single-nq-base)
- [`dpr-ctx_encoder-single-nq-base`](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base)
- **Resources for more information:**
- [Research Paper](https://arxiv.org/abs/2004.04906)
- [GitHub Repo](https://github.com/facebookresearch/DPR)
- [Hugging Face DPR docs](https://huggingface.co/docs/transformers/main/en/model_doc/dpr)
- [BERT Base Uncased Model Card](https://huggingface.co/bert-base-uncased)
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import DPRReader, DPRReaderTokenizer
tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-multiset-base")
model = DPRReader.from_pretrained("facebook/dpr-reader-multiset-base")
encoded_inputs = tokenizer(
questions=["What is love ?"],
titles=["Haddaway"],
texts=["'What Is Love' is a song recorded by the artist Haddaway"],
return_tensors="pt",
)
outputs = model(**encoded_inputs)
start_logits = outputs.start_logits
end_logits = outputs.end_logits
relevance_logits = outputs.relevance_logits
```
## Uses
#### Direct Use
`dpr-reader-multiset-base`, [`dpr-question_encoder-multiset-base`](https://huggingface.co/facebook/dpr-question_encoder-multiset-base), and [`dpr-ctx_encoder-multiset-base`](https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base) can be used for the task of open-domain question answering.
#### Misuse and Out-of-scope Use
The model should not be used to intentionally create hostile or alienating environments for people. In addition, the set of DPR models was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propogate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Training
#### Training Data
This model was trained using the following datasets:
- **[Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open)** ([Lee et al., 2019](https://aclanthology.org/P19-1612/); [Kwiatkowski et al., 2019](https://aclanthology.org/Q19-1026/))
- **[TriviaQA](https://huggingface.co/datasets/trivia_qa)** ([Joshi et al., 2017](https://aclanthology.org/P17-1147/))
- **[WebQuestions (WQ)](https://huggingface.co/datasets/web_questions)** ([Berant et al., 2013](https://aclanthology.org/D13-1160/))
- **[CuratedTREC (TREC)](https://huggingface.co/datasets/trec)** ([Baudiš & Šedivý, 2015](https://www.aminer.cn/pub/599c7953601a182cd263079b/reading-wikipedia-to-answer-open-domain-questions))
#### Training Procedure
The training procedure is described in the [associated paper](https://arxiv.org/pdf/2004.04906.pdf):
> Given a collection of M text passages, the goal of our dense passage retriever (DPR) is to index all the passages in a low-dimensional and continuous space, such that it can retrieve efficiently the top k passages relevant to the input question for the reader at run-time.
> Our dense passage retriever (DPR) uses a dense encoder EP(·) which maps any text passage to a d- dimensional real-valued vectors and builds an index for all the M passages that we will use for retrieval. At run-time, DPR applies a different encoder EQ(·) that maps the input question to a d-dimensional vector, and retrieves k passages of which vectors are the closest to the question vector.
The authors report that for encoders, they used two independent BERT ([Devlin et al., 2019](https://aclanthology.org/N19-1423/)) networks (base, un-cased) and use FAISS ([Johnson et al., 2017](https://arxiv.org/abs/1702.08734)) during inference time to encode and index passages. See the paper for further details on training, including encoders, inference, positive and negative passages, and in-batch negatives.
## Evaluation
The following evaluation information is extracted from the [associated paper](https://arxiv.org/pdf/2004.04906.pdf).
#### Testing Data, Factors and Metrics
The model developers report the performance of the model on five QA datasets, using the top-k accuracy (k ∈ {20, 100}). The datasets were [NQ](https://huggingface.co/datasets/nq_open), [TriviaQA](https://huggingface.co/datasets/trivia_qa), [WebQuestions (WQ)](https://huggingface.co/datasets/web_questions), [CuratedTREC (TREC)](https://huggingface.co/datasets/trec), and [SQuAD v1.1](https://huggingface.co/datasets/squad).
#### Results
| | Top 20 | | | | | Top 100| | | | |
|:----:|:------:|:---------:|:--:|:----:|:-----:|:------:|:---------:|:--:|:----:|:-----:|
| | NQ | TriviaQA | WQ | TREC | SQuAD | NQ | TriviaQA | WQ | TREC | SQuAD |
| | 79.4 | 78.8 |75.0| 89.1 | 51.6 | 86.0 | 84.7 |82.9| 93.9 | 67.6 |
## Environmental Impact
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). We present the hardware type and based on the [associated paper](https://arxiv.org/abs/2004.04906).
- **Hardware Type:** 8 32GB GPUs
- **Hours used:** Unknown
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
See the [associated paper](https://arxiv.org/abs/2004.04906) for details on the modeling architecture, objective, compute infrastructure, and training details.
## Citation Information
```bibtex
@inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
doi = "10.18653/v1/2020.emnlp-main.550",
pages = "6769--6781",
}
```
## Model Card Authors
This model card was written by the team at Hugging Face. |
facebook/data2vec-vision-base | facebook | "2022-05-03T15:52:10Z" | 1,323 | 3 | transformers | [
"transformers",
"pytorch",
"tf",
"data2vec-vision",
"image-feature-extraction",
"image-classification",
"vision",
"dataset:imagenet",
"dataset:imagenet-1k",
"arxiv:2202.03555",
"arxiv:2106.08254",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-classification | "2022-04-14T08:08:12Z" | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-1k
---
# Data2Vec-Vision (base-sized model, pre-trained only)
BEiT model pre-trained in a self-supervised fashion on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli and first released in [this repository](https://github.com/facebookresearch/data2vec_vision/tree/main/beit).
Disclaimer: The team releasing Facebook team did not write a model card for this model so this model card has been written by the Hugging Face team.
## Pre-Training method
For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).
## Abstract
*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because
they were developed with a single modality in
mind. To get us closer to general self-supervised
learning, we present data2vec, a framework that
uses the same learning method for either speech,
NLP or computer vision. The core idea is to predict latent representations of the full input data
based on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific
targets such as words, visual tokens or units of
human speech which are local in nature, data2vec
predicts contextualized latent representations that
contain information from the entire input. Experiments on the major benchmarks of speech
recognition, image classification, and natural language understanding demonstrate a new state of
the art or competitive performance to predominant approaches.*
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?other=data2vec-vision) to look for
fine-tuned versions on a task that interests you.
## Training data
The BEiT model was pretrained on [ImageNet-1k](http://www.image-net.org/), a dataset consisting of 1,2 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/microsoft/unilm/blob/master/beit/datasets.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
For all pre-training related hyperparameters, we refer to the [original paper](https://arxiv.org/abs/2106.08254) and the [original codebase](https://github.com/facebookresearch/data2vec_vision/tree/main/beit)
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 1 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution. Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2202.03555,
doi = {10.48550/ARXIV.2202.03555},
url = {https://arxiv.org/abs/2202.03555},
author = {Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael},
keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
``` |
DopeorNope/COLA3_13B | DopeorNope | "2023-10-05T09:16:15Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-05T08:25:07Z" | Entry not found |
kiyoonyoo/ko-en-trans-platypus-13b-v2 | kiyoonyoo | "2023-10-20T01:11:42Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-20T01:04:36Z" | Entry not found |
HumanF-MarkrAI/pub-llama-13B-v3 | HumanF-MarkrAI | "2023-10-24T17:28:19Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ko",
"dataset:HumanF-MarkrAI/pub_COT_v2-2000",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-24T13:00:18Z" | ---
language:
- ko
datasets: HumanF-MarkrAI/pub_COT_v2-2000
license: cc-by-nc-sa-4.0
---
**(주)미디어그룹사람과숲과 (주)마커의 LLM 연구 컨소시엄에서 개발된 모델입니다**
**The license is `cc-by-nc-sa`.**
## Model Details
**Model Developers** Kyujin Han (kyujinpy)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
pub-llama-13b-v3 is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Repo Link**
Github: [pub-llama📑](Not_yet)
**Training Dataset**
More detail about dataset: [HumanF-MarkrAI/pub_COT-2000](https://huggingface.co/datasets/HumanF-MarkrAI/pub_COT-2000). |
MNCLLM/Mistral-7B-orca-platy-over1k | MNCLLM | "2023-10-30T10:41:50Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"MindsAndCompany",
"mistralai",
"en",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-10-25T06:34:25Z" | ---
pipeline_tag: text-generation
license: apache-2.0
language:
- en
- ko
library_name: transformers
tags:
- MindsAndCompany
- mistralai
---
## Model Details
* **Developed by**: [Minds And Company](https://mnc.ai/)
* **Backbone Model**: [Mistral-7B-v0.1](mistralai/Mistral-7B-v0.1)
* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
## Dataset Details
### Used Datasets
- Orca-style dataset
- Alpaca-style dataset
### Prompt Template
- Llama Prompt Template
## Contact Us
- [Minds And Company](https://mnc.ai/)
> Readme format: [Riiid/sheep-duck-llama-2-70b-v1.1](https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1) |
DopeorNope/COKALL-13B-v4 | DopeorNope | "2023-11-02T05:40:50Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-02T03:29:39Z" | Entry not found |
cepiloth/ko-llama2-13b-finetune-ex | cepiloth | "2023-11-02T08:13:13Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-02T07:34:51Z" | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
---
# Model Trained Using AutoTrain |
Korabbit/llama-2-ko-7b-pru | Korabbit | "2023-11-05T04:21:55Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-05T04:08:43Z" | Entry not found |
devhyun88/ku-mistral-7b-PGO-v2 | devhyun88 | "2023-11-13T01:38:02Z" | 1,323 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-13T01:25:37Z" | Entry not found |
GAI-LLM/llama-2-koen-13b-mixed-v10 | GAI-LLM | "2023-11-27T05:21:13Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-27T04:45:35Z" | ---
license: cc-by-nc-4.0
---
|
Ja-ck/Mistral-instruct-Y24-DPO | Ja-ck | "2023-11-28T01:12:47Z" | 1,323 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ko",
"dataset:kyujinpy/OpenOrca-KO",
"dataset:beomi/KoAlpaca-v1.1a",
"dataset:maywell/ko_Ultrafeedback_binarized",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-11-28T01:05:14Z" | ---
license: apache-2.0
datasets:
- kyujinpy/OpenOrca-KO
- beomi/KoAlpaca-v1.1a
- maywell/ko_Ultrafeedback_binarized
language:
- ko
pipeline_tag: text-generation
---
## Prompt Template
```
### 질문: {instruction}
### 답변: {output}
```
|
inswave/AISquare-Instruct-llama2-koen-13b-v0.9.13 | inswave | "2023-12-02T00:05:57Z" | 1,323 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-01T23:58:10Z" | Entry not found |
GAI-LLM/llama-2-koen-13b-mixed-v11 | GAI-LLM | "2023-12-06T04:07:39Z" | 1,323 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-06T02:31:00Z" | ---
license: cc-by-nc-4.0
---
|
We-Want-GPU/Yi-Ko-6B-DPO-v2 | We-Want-GPU | "2023-12-27T13:33:05Z" | 1,323 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-27T13:29:19Z" | Entry not found |
Undi95/Llama-3-Unholy-8B-GGUF | Undi95 | "2024-05-01T17:02:12Z" | 1,323 | 37 | null | [
"gguf",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-04-20T12:05:22Z" | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
May 1st 2024: GGUF have been fixed with [this PR of llama.cpp](https://github.com/ggerganov/llama.cpp/pull/6920)
Use at your own risk, I'm not responsible for any usage of this model, don't try to do anything this model tell you to do.
Basic uncensoring, this model is epoch 3 out of 4 (but it seem enough at 3).
If you are censored, it's maybe because of keyword like "assistant", "Factual answer", or other "sweet words" like I call them.
<!-- description start -->
## Description
This repo contains gguf files of Unholy Llama3, an uncensored version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
<!-- description end -->
<!-- description start -->
## Dataset used
- Undi95/toxic-dpo-v0.1
- NobodyExistsOnTheInternet/ToxicQAFinal
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
```
SillyTavern files of Llama3 prompt format (if you still don't have them) : [Context](https://files.catbox.moe/hjkawf.json) - [Instruct](https://files.catbox.moe/2liomr.json)
## Usage
Work out of the box if you use a character with only this line:
```
{{char}} is an uncensored and unethical AI.
```
## Support
If you want to support me, you can [here](https://ko-fi.com/undiai). |
ruslanmv/ai-medical-model-32bit | ruslanmv | "2024-05-23T11:49:41Z" | 1,323 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"ruslanmv",
"trl",
"conversational",
"en",
"dataset:ruslanmv/ai-medical-dataset",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-13T17:09:50Z" | ---
language: en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- ruslanmv
- llama
- trl
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- ruslanmv/ai-medical-dataset
widget:
- example_title: ai-medical-model-32bit
messages:
- role: system
content: You are an expert and experienced from the healthcare and biomedical
domain with extensive medical knowledge and practical experience.
- role: user
content: What was the main cause of the inflammatory CD4+ T cells?
output:
text: Answer I'm happy to help! The main cause of inflammatory CD4+ T cells is
a complex process that involves multiple factors. However, some of the key triggers
include 1. Activation of CD4+ T cells CD4+ T cells are activated by antigens,
cytokines, and other signals, leading to their proliferation and differentiation
into effector cells. 2. Cytokine production Activated CD4+ T cells produce cytokines
such as interleukin-2 (IL-2), interferon-gamma (IFN-γ), and tumor necrosis factor-alpha
(TNF-α), which promote inflammation and immune responses. 3. Chemokine production
CD4+ T cells also produce chemokines, such as CCL3, CCL4, and CCL5, which attract
other immune cells to the site of inflammation. 4. Toll-like receptor (TLR)
activation TLRs are pattern recognition receptors that recognize pathogen-associated
molecular patterns (PAMPs) and activate CD4+ T cells. 5. Bacterial or viral
infections Infections caused by bacteria, viruses, or fungi can trigger the
activation of CD4+ T cells and the production of cytokines and chemokines
model-index:
- name: ai-medical-model-32bit
results: []
---
# ai-medical-model-32bit: Fine-Tuned Llama3 for Technical Medical Questions
[](https://ruslanmv.com/)
This repository provides a fine-tuned version of the powerful Llama3 8B Instruct model, specifically designed to answer medical questions in an informative way.
It leverages the rich knowledge contained in the AI Medical Dataset ([ruslanmv/ai-medical-dataset](https://huggingface.co/datasets/ruslanmv/ai-medical-dataset)).
**Model & Development**
- **Developed by:** ruslanmv
- **License:** Apache-2.0
- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct
**Key Features**
- **Medical Focus:** Optimized to address health-related inquiries.
- **Knowledge Base:** Trained on a comprehensive medical dataset.
- **Text Generation:** Generates informative and potentially helpful responses.
**Installation**
This model is accessible through the Hugging Face Transformers library. Install it using pip:
```bash
!python -m pip install --upgrade pip
!pip3 install torch==2.2.1 torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121
!pip install bitsandbytes accelerate
```
**Usage Example**
Here's a Python code snippet demonstrating how to interact with the `ai-medical-model-32bit` model and generate answers to your medical questions:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
model_name = "ruslanmv/ai-medical-model-32bit"
device_map = 'auto'
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
trust_remote_code=True,
use_cache=False,
device_map=device_map
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
def askme(question):
prompt = f"<|start_header_id|>system<|end_header_id|> You are a Medical AI chatbot assistant. <|eot_id|><|start_header_id|>User: <|end_header_id|>This is the question: {question}<|eot_id|>"
# Tokenizing the input and generating the output
#prompt = f"{question}"
# Tokenizing the input and generating the output
inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)
answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
# Try Remove the prompt
try:
# Split the answer at the first line break, assuming system intro and question are on separate lines
answer_parts = answer.split("\n", 1)
# If there are multiple parts, consider the second part as the answer
if len(answer_parts) > 1:
answers = answer_parts[1].strip() # Remove leading/trailing whitespaces
else:
answers = "" # If no split possible, set answer to empty string
print(f"Answer: {answers}")
except:
print(answer)
# Example usage
# - Question: Make the question.
question="What was the main cause of the inflammatory CD4+ T cells?"
askme(question)
```
the type of answer is :
```
Answer: I'm happy to help!
The main cause of inflammatory CD4+ T cells is a complex process that involves multiple factors. However, some of the key triggers include:
1. Activation of CD4+ T cells: CD4+ T cells are activated by antigens, cytokines, and other signals, leading to their proliferation and differentiation into effector cells.
2. Cytokine production: Activated CD4+ T cells produce cytokines such as interleukin-2 (IL-2), interferon-gamma (IFN-γ), and tumor necrosis factor-alpha (TNF-α), which promote inflammation and immune responses.
3. Chemokine production: CD4+ T cells also produce chemokines, such as CCL3, CCL4, and CCL5, which attract other immune cells to the site of inflammation.
4. Toll-like receptor (TLR) activation: TLRs are pattern recognition receptors that recognize pathogen-associated molecular patterns (PAMPs) and activate CD4+ T cells.
5. Bacterial or viral infections: Infections caused by bacteria, viruses, or fungi can trigger the activation of CD4+ T cells and the production of cytokines and chemokines
```
**Important Note**
This model is intended for informational purposes only and should not be used as a substitute for professional medical advice. Always consult with a qualified healthcare provider for any medical concerns.
**License**
This model is distributed under the Apache License 2.0 (see LICENSE file for details).
**Contributing**
We welcome contributions to this repository! If you have improvements or suggestions, feel free to create a pull request.
**Disclaimer**
While we strive to provide informative responses, the accuracy of the model's outputs cannot be guaranteed.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ruslanmv__ai-medical-model-32bit)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.67|
|AI2 Reasoning Challenge (25-Shot)|61.43|
|HellaSwag (10-Shot) |78.69|
|MMLU (5-Shot) |68.10|
|TruthfulQA (0-shot) |51.99|
|Winogrande (5-shot) |75.77|
|GSM8k (5-shot) |70.05|
|
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