license: afl-3.0
library_name: transformers
tags:
- UNA
- juanako
datasets:
- jondurbin/py-dpo-v0.1
- Replete-AI/code_bagel_hermes-2.5
- mlabonne/orpo-dpo-mix-40k
quantized_by: bartowski
pipeline_tag: text-generation
model-index:
- name: UNA-ThePitbull-21.4B-v2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 77.73
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 91.79
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 68.25
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 78.24
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 87.37
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.53
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
UNA-ThePitbull 21.4B v2
Introducing the best LLM in the industry. Nearly as good as a 70B, just a 21.4B based on saltlux/luxia-21.4b-alignment-v1.0
This model has not been poisoned to score high and be useless. We release him becaues its the real deal of EQ & IQ all together in a crazy powerful smart and conversational model.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 77.82 |
| AI2 Reasoning Challenge (25-Shot) | 77.73 |
| HellaSwag (10-Shot) | 91.79 |
| MMLU (5-Shot) | 68.25 |
| TruthfulQA (0-shot) | 78.24 |
| Winogrande (5-shot) | 87.37 |
| GSM8k (5-shot) | 63.53 |
Llamacpp imatrix Quantizations of UNA-ThePitbull-21.4B-v2
Using llama.cpp release b3001 for quantization.
Original model: https://huggingface.co/fblgit/UNA-ThePitbull-21.4B-v2
All quants made using imatrix option with dataset from here
Prompt format
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| UNA-ThePitbull-21.4B-v2-Q8_0.gguf | Q8_0 | 22.76GB | Extremely high quality, generally unneeded but max available quant. |
| UNA-ThePitbull-21.4B-v2-Q6_K.gguf | Q6_K | 17.57GB | Very high quality, near perfect, recommended. |
| UNA-ThePitbull-21.4B-v2-Q5_K_M.gguf | Q5_K_M | 15.17GB | High quality, recommended. |
| UNA-ThePitbull-21.4B-v2-Q5_K_S.gguf | Q5_K_S | 14.80GB | High quality, recommended. |
| UNA-ThePitbull-21.4B-v2-Q4_K_M.gguf | Q4_K_M | 12.91GB | Good quality, uses about 4.83 bits per weight, recommended. |
| UNA-ThePitbull-21.4B-v2-Q4_K_S.gguf | Q4_K_S | 12.27GB | Slightly lower quality with more space savings, recommended. |
| UNA-ThePitbull-21.4B-v2-IQ4_NL.gguf | IQ4_NL | 12.24GB | Decent quality, slightly smaller than Q4_K_S with similar performance recommended. |
| UNA-ThePitbull-21.4B-v2-IQ4_XS.gguf | IQ4_XS | 11.60GB | Decent quality, smaller than Q4_K_S with similar performance, recommended. |
| UNA-ThePitbull-21.4B-v2-Q3_K_L.gguf | Q3_K_L | 11.37GB | Lower quality but usable, good for low RAM availability. |
| UNA-ThePitbull-21.4B-v2-Q3_K_M.gguf | Q3_K_M | 10.46GB | Even lower quality. |
| UNA-ThePitbull-21.4B-v2-IQ3_M.gguf | IQ3_M | 9.81GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| UNA-ThePitbull-21.4B-v2-IQ3_S.gguf | IQ3_S | 9.47GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| UNA-ThePitbull-21.4B-v2-Q3_K_S.gguf | Q3_K_S | 9.43GB | Low quality, not recommended. |
| UNA-ThePitbull-21.4B-v2-IQ3_XS.gguf | IQ3_XS | 8.99GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| UNA-ThePitbull-21.4B-v2-IQ3_XXS.gguf | IQ3_XXS | 8.41GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| UNA-ThePitbull-21.4B-v2-Q2_K.gguf | Q2_K | 8.12GB | Very low quality but surprisingly usable. |
| UNA-ThePitbull-21.4B-v2-IQ2_M.gguf | IQ2_M | 7.49GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| UNA-ThePitbull-21.4B-v2-IQ2_S.gguf | IQ2_S | 6.95GB | Very low quality, uses SOTA techniques to be usable. |
| UNA-ThePitbull-21.4B-v2-IQ2_XS.gguf | IQ2_XS | 6.55GB | Very low quality, uses SOTA techniques to be usable. |
| UNA-ThePitbull-21.4B-v2-IQ2_XXS.gguf | IQ2_XXS | 5.95GB | Lower quality, uses SOTA techniques to be usable. |
| UNA-ThePitbull-21.4B-v2-IQ1_M.gguf | IQ1_M | 5.27GB | Extremely low quality, not recommended. |
| UNA-ThePitbull-21.4B-v2-IQ1_S.gguf | IQ1_S | 4.86GB | Extremely low quality, not recommended. |
Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
pip install -U "huggingface_hub[cli]"
Then, you can target the specific file you want:
huggingface-cli download bartowski/UNA-ThePitbull-21.4B-v2-GGUF --include "UNA-ThePitbull-21.4B-v2-Q4_K_M.gguf" --local-dir ./
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
huggingface-cli download bartowski/UNA-ThePitbull-21.4B-v2-GGUF --include "UNA-ThePitbull-21.4B-v2-Q8_0.gguf/*" --local-dir UNA-ThePitbull-21.4B-v2-Q8_0
You can either specify a new local-dir (UNA-ThePitbull-21.4B-v2-Q8_0) or download them all in place (./)
Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 here
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Difference V1 vs V2
On V2 we implemented a different UNA strategy and covered partially the MLP's and Attention Layers. We also performed further SFT over V1 and further DPO over V1 and we'll release some of those soon as well.
Changes
- SFT over V1 with
Replete-AI/code_bagel_hermes-2.5at 1.0e-4 till 5.0e-5 - DPO with: 1.0e-4 to min_lr 5.0e-5
mlabonne/orpo-dpo-mix-40kjondurbin/py-dpo-v0.1
Evaluations
Can only be compared with its non-una base model: the original luxia-21.4b and ThePitbull-v1
UNA v2 (VLLM) Evaluations:
vllm (pretrained=/data/tools/mergekit/una-thepitbull-v5,dtype=bfloat16,gpu_memory_utilization=0.8,max_model_len=2048,data_parallel_size=2,tensor_parallel_size=4), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7695|± |0.0116|+
| | |flexible-extract| 5|exact_match|0.7695|± |0.0116|+
|hellaswag | 1|none | 10|acc |0.8110|± |0.0039|
| | |none | 10|acc_norm |0.9169|± |0.0028|+
|winogrande | 1|none | 5|acc |0.8777|± |0.0092|+
|mmlu |N/A |none | 0|acc |0.6427|± |0.0038|-
|arc_challenge | 1|none | 25|acc |0.7713|± |0.0123|
| | |none | 25|acc_norm |0.7875|± |0.0120|+
|truthfulqa_mc2| 2|none | 0|acc |0.7824|± |0.0135|-
|mathqa | 1|none | 0|acc |0.4037|± | 0.009|
| | |none | 0|acc_norm |0.4034|± | 0.009|+
|pubmedqa | 1|none | 0|acc |0.7260|± | 0.020|+
|boolq | 2|none | 0|acc |0.8602|± |0.0061|+
UNA v1 (VLLM) Evaluations
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7566|± |0.0118|
| | |flexible-extract| 5|exact_match|0.7582|± |0.0118|
|hellaswag | 1|none | 10|acc |0.8168|± |0.0039|
| | |none | 10|acc_norm |0.9188|± |0.0027|
|winogrande | 1|none | 5|acc |0.8635|± |0.0097|
|mmlu | N/A|none | 0|acc |0.6444|± |0.0038|
|arc_challenge | 1|none | 25|acc |0.7747|± |0.0122|
| | |none | 25|acc_norm |0.7850|± |0.0120|
|truthfulqa_mc2| 2|none | 0|acc |0.7902|± |0.0134|
|mathqa | 1|none | 0|acc |0.4030|± | 0.009|
| | |none | 0|acc_norm |0.4034|± | 0.009|
|pubmedqa | 1|none | 0|acc |0.6860|± |0.0208|
|boolq | 2|none | 0|acc |0.8401|± |0.0064|
Original (VLLM) Evaluations
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7528|± |0.0119|
| | |flexible-extract| 5|exact_match|0.7521|± |0.0119|
|hellaswag | 1|none | 10|acc |0.8117|± |0.0039|
| | |none | 10|acc_norm |0.9167|± |0.0028|
|winogrande | 1|none | 5|acc |0.8682|± |0.0095|
|mmlu | N/A|none | 0|acc |0.6448|± |0.0038|
|arc_challenge | 1|none | 25|acc |0.7688|± |0.0123|
| | |none | 25|acc_norm |0.7730|± |0.0122|
|truthfulqa_mc2| 2|none | 0|acc |0.7895|± |0.0133|
|mathqa | 1|none | 0|acc |0.4000|± | 0.009|
| | |none | 0|acc_norm |0.4003|± | 0.009|
|pubmedqa | 1|none | 0|acc |0.6680|± |0.0211|
|boolq | 2|none | 0|acc |0.8346|± |0.0065|
Citations
- mlabonne
- jondurbin & Replete-AI
- bartowski
- saltlux
If you use UNA models dont forget to cite:
@misc{unathepitbull21b,
title={ThePitbull: Uniform Neural Alignment},
author={Xavier Murias},
year={2024},
publisher = {Juanako.AI},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/fblgit/UNA-ThePitbull-21.4-v1}},
}