Instructions to use Finisha-F-scratch/Expedia-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Finisha-F-scratch/Expedia-LLM with Transformers:

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

pipe = pipeline("text-generation", model="Finisha-F-scratch/Expedia-LLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Finisha-F-scratch/Expedia-LLM")
model = AutoModelForCausalLM.from_pretrained("Finisha-F-scratch/Expedia-LLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Finisha-F-scratch/Expedia-LLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Finisha-F-scratch/Expedia-LLM

SGLang

How to use Finisha-F-scratch/Expedia-LLM 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 "Finisha-F-scratch/Expedia-LLM" \
    --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": "Finisha-F-scratch/Expedia-LLM",
		"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 "Finisha-F-scratch/Expedia-LLM" \
        --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": "Finisha-F-scratch/Expedia-LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Finisha-F-scratch/Expedia-LLM with Docker Model Runner:
```
docker model run hf.co/Finisha-F-scratch/Expedia-LLM
```

Exemples de génération

by Clemylia - opened Mar 12

Discussion

Clemylia

Finisha org Mar 12

In the dynamic field of Artificial Intelligence (AI), when you don’s think step. In order to the model to train AI innovation. You will be able to improve its weights when fine-of-), and generate human-experts, the model to the model. You can be useful. Biases (LLMs) before training your own data.comcomcomcom//, or industry. This dataset. You can be divided into smaller and most commonly used to train it’s think step.g
In the dynamic field of Artificial Intelligence (AI), and NLP engineers to be able to train an LLM.g., just one, and data, pre-specific data to the model. The model training. Data quality, you may be a type of the the model to the output, and you may be trained with a machine with enough data. # batch size, the API calls to be able to learn from the output, # batch size, and generate human-trained on the model that the requirements of
In the dynamic field of Artificial Intelligence (AI), is a custom model according to train an LLM on the model (e.g. It’s performance of these pretrained ones are a in a sequence is a in the model, which can create a transformer models, and more.com You can be useful. Here’t have the model is a content generators that require some enterprises develop custom language model to learn “in context,” and coding sessions to start or documents, which AI innovation. The model to be useful. Biases (LLM

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