Instructions to use fblgit/UNA-ThePitbull-21.4B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fblgit/UNA-ThePitbull-21.4B-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fblgit/UNA-ThePitbull-21.4B-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fblgit/UNA-ThePitbull-21.4B-v2")
model = AutoModelForCausalLM.from_pretrained("fblgit/UNA-ThePitbull-21.4B-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fblgit/UNA-ThePitbull-21.4B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fblgit/UNA-ThePitbull-21.4B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fblgit/UNA-ThePitbull-21.4B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fblgit/UNA-ThePitbull-21.4B-v2

SGLang

How to use fblgit/UNA-ThePitbull-21.4B-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fblgit/UNA-ThePitbull-21.4B-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fblgit/UNA-ThePitbull-21.4B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fblgit/UNA-ThePitbull-21.4B-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fblgit/UNA-ThePitbull-21.4B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fblgit/UNA-ThePitbull-21.4B-v2 with Docker Model Runner:
```
docker model run hf.co/fblgit/UNA-ThePitbull-21.4B-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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.

Quant Versions available at bartowski/UNA-ThePitbull-21.4B-v2-GGUF

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-2.5 at 1.0e-4 till 5.0e-5 for 1 epoch
DPO with: 1.0e-4 to min_lr 5.0e-5 for 1 epoch

mlabonne/orpo-dpo-mix-40k
jondurbin/py-dpo-v0.1

Evaluations

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

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}},
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	22.60
IFEval (0-Shot)	37.90
BBH (3-Shot)	46.79
MATH Lvl 5 (4-Shot)	9.59
GPQA (0-shot)	6.94
MuSR (0-shot)	6.42
MMLU-PRO (5-shot)	27.95

Downloads last month: 17

Safetensors

Model size

21B params

Tensor type

BF16

Model tree for fblgit/UNA-ThePitbull-21.4B-v2

Quantizations

3 models

Datasets used to train fblgit/UNA-ThePitbull-21.4B-v2

Article mentioning fblgit/UNA-ThePitbull-21.4B-v2

Introducing UNA-ThePitbull Series

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

77.730
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

91.790
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

68.250
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

78.240
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

87.370
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

63.530
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

37.900
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

46.790