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")

# pip install -U transformers accelerate
# 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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

fblgit commited on Jan 9, 2024

Commit

7208467

1 Parent(s): c592d16

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@@ -11,7 +11,6 @@ library_name: transformers
 ---
 # 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.
 The dataset was generated using the original bagel code, including the decontamination step.
@@ -20,6 +19,8 @@ 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 :)

 ---
 # 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.
 The dataset was generated using the original bagel code, including the decontamination step.
 It performs very good in many tasks, but its always better that you play with it by yourself.
+![TheBeagle](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1/resolve/main/TheBeagle.png)
 ## Evaluations
 Ran with VLLM so expect them to dont be exactly as the one's shown in the board, but not too far :)