Instructions to use fblgit/UNA-TheBeagle-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fblgit/UNA-TheBeagle-7b-v1 with Transformers:

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

pipe = pipeline("text-generation", model="fblgit/UNA-TheBeagle-7b-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fblgit/UNA-TheBeagle-7b-v1")
model = AutoModelForCausalLM.from_pretrained("fblgit/UNA-TheBeagle-7b-v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fblgit/UNA-TheBeagle-7b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fblgit/UNA-TheBeagle-7b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fblgit/UNA-TheBeagle-7b-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fblgit/UNA-TheBeagle-7b-v1

SGLang

How to use fblgit/UNA-TheBeagle-7b-v1 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-TheBeagle-7b-v1" \
    --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": "fblgit/UNA-TheBeagle-7b-v1",
		"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 "fblgit/UNA-TheBeagle-7b-v1" \
        --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": "fblgit/UNA-TheBeagle-7b-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fblgit/UNA-TheBeagle-7b-v1 with Docker Model Runner:
```
docker model run hf.co/fblgit/UNA-TheBeagle-7b-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

 -- In the Love Memory of my "LoLa" --

UNA-TheBeagle-7b-v1

TheBeagle, a model of 7B parameters trained on The Bagel dataset. DPO & UNA applied over a set of curated DPO Pairs.

Scored #1 on the HF Leaderboard, dramatic scores!!! 73 ARC, and very well balanced!

The dataset was generated using the original bagel code, including the decontamination step.

As base model, we used the latest Intel's neural-chat model.

It performs very good in many tasks, but its always better that you play with it by yourself.

Evaluations

Ran with VLLM so expect them to dont be exactly as the one's shown in the board, but not too far :)

vllm (pretrained=fblgit/UNA-TheBeagle-7b-v1,dtype=auto,tensor_parallel_size=1,gpu_memory_utilization=0.8,data_parallel_size=8,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 32
|    Tasks     |Version|  Filter  |n-shot|  Metric   |Value |   |Stderr|
|--------------|-------|----------|-----:|-----------|-----:|---|-----:|
|arc_challenge |Yaml   |none      |    25|acc        |0.7090|±  |0.0133|
|              |       |none      |    25|acc_norm   |0.7329|±  |0.0129|
|gsm8k         |Yaml   |get-answer|     5|exact_match|0.7210|±  |0.0124|
|hellaswag     |Yaml   |none      |    10|acc        |0.7202|±  |0.0045|
|              |       |none      |    10|acc_norm   |0.8792|±  |0.0033|
|truthfulqa_mc2|Yaml   |none      |     0|acc        |0.7062|±  |0.0151|
|winogrande    |Yaml   |none      |     5|acc        |0.8366|±  |0.0104|

UNA Details

For this release, we only applied UNA thru the perceptrons. It was done at a 3.5e-7 speed, and the training loop code is also the original one of the bagel and transformers-4.35.2-UNA

Prompt

Im not entirely sure of it, as we used the vanilla version of the bagel training code. But a good model should be able to generalize with different prompt formats, so feel free to give it a shot.