Instructions to use one-man-army/una-neural-chat-v3-3-P1-OMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use one-man-army/una-neural-chat-v3-3-P1-OMA with Transformers:

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

pipe = pipeline("text-generation", model="one-man-army/una-neural-chat-v3-3-P1-OMA")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("one-man-army/una-neural-chat-v3-3-P1-OMA")
model = AutoModelForCausalLM.from_pretrained("one-man-army/una-neural-chat-v3-3-P1-OMA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use one-man-army/una-neural-chat-v3-3-P1-OMA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "one-man-army/una-neural-chat-v3-3-P1-OMA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "one-man-army/una-neural-chat-v3-3-P1-OMA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/one-man-army/una-neural-chat-v3-3-P1-OMA

SGLang

How to use one-man-army/una-neural-chat-v3-3-P1-OMA 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 "one-man-army/una-neural-chat-v3-3-P1-OMA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "one-man-army/una-neural-chat-v3-3-P1-OMA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "one-man-army/una-neural-chat-v3-3-P1-OMA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "one-man-army/una-neural-chat-v3-3-P1-OMA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use one-man-army/una-neural-chat-v3-3-P1-OMA with Docker Model Runner:
```
docker model run hf.co/one-man-army/una-neural-chat-v3-3-P1-OMA
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

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            MESS WITH THE BEST, DIE LIKE THE REST
      --=-  D*D - R****1911 - F***L***T - P***D*X -=--
                  THE WORLD NEED US BACK :)

OMA, OneManArmy presents, una-neural-chat-v3-3. Powered by UNA (Uniform Neural Alignment), using zephyr trainer, allenai/ultrafeedback cleaned.. and JUST THAT. Outperforming its base model, not adding any data.. just UNA Algorythm on Transformers Lib. UNA Settings:

MLP : 0.05
ATT : 0.03
LNOR : 0.02

una-neural-chat-v3-3

This model is a fine-tuned version of Intel/neural-chat-7b-v3-3 on the allenai/ultrafeedback_binarized_cleaned dataset. It achieves the following results on the evaluation set:

Loss: 0.4524
Rewards/chosen: -0.7101
Rewards/rejected: -2.0953
Rewards/accuracies: 0.7831
Rewards/margins: 1.3852
Logps/rejected: -321.5471
Logps/chosen: -327.5048
Logits/rejected: -2.6445
Logits/chosen: -2.6674

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.5431	0.2	380	0.4900	-0.6823	-1.6613	0.7607	0.9790	-317.2069	-327.2263	-2.6478	-2.6651
0.4369	0.4	760	0.4783	-0.7562	-2.1298	0.7719	1.3737	-321.8924	-327.9652	-2.7370	-2.7562
0.4005	0.6	1140	0.4697	-0.6913	-2.0134	0.7770	1.3221	-320.7278	-327.3167	-2.7067	-2.7224
0.3759	0.8	1520	0.4568	-0.7387	-2.0643	0.7882	1.3256	-321.2370	-327.7909	-2.6626	-2.6829
0.5213	1.0	1900	0.4524	-0.7101	-2.0953	0.7831	1.3852	-321.5471	-327.5048	-2.6445	-2.6674