Instructions to use josiahbryan/valet-sms-intent-classification-llama-2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use josiahbryan/valet-sms-intent-classification-llama-2-7b with Transformers:

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

pipe = pipeline("text-generation", model="josiahbryan/valet-sms-intent-classification-llama-2-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("josiahbryan/valet-sms-intent-classification-llama-2-7b")
model = AutoModelForCausalLM.from_pretrained("josiahbryan/valet-sms-intent-classification-llama-2-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use josiahbryan/valet-sms-intent-classification-llama-2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "josiahbryan/valet-sms-intent-classification-llama-2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "josiahbryan/valet-sms-intent-classification-llama-2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/josiahbryan/valet-sms-intent-classification-llama-2-7b

SGLang

How to use josiahbryan/valet-sms-intent-classification-llama-2-7b 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 "josiahbryan/valet-sms-intent-classification-llama-2-7b" \
    --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": "josiahbryan/valet-sms-intent-classification-llama-2-7b",
		"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 "josiahbryan/valet-sms-intent-classification-llama-2-7b" \
        --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": "josiahbryan/valet-sms-intent-classification-llama-2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use josiahbryan/valet-sms-intent-classification-llama-2-7b with Docker Model Runner:
```
docker model run hf.co/josiahbryan/valet-sms-intent-classification-llama-2-7b
```

Model Details

This model is a fine-tuned version of Llam 2 7b from Meta. It was finetuned on 2.5K real-world text message samples that were hand-labeled into one of eight classifications.

Fine-tuning was done based on the Google Colab notebook linked from this blog post.

Model Developers Josiah Bryan

Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.

Input Models input text only.

Output Models generate text only.

Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture.

Model Dates This model was trained November 9th, 2023

Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model and test in real-world ussages.

Where to send questions or comments about the model Email josiahbryan@gmail.com with feedback.

Intended Use

Intended Use Cases This model is intended to classify incoming text message intent in valet/parking management applications.

Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 2 Community License. Use in languages other than English**.

**Note: Developers may fine-tune this model for languages beyond English provided they comply with the Llama 2 Community License and the Acceptable Use Policy.

Hardware and Software

Training Factors Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.

Training Data

Overview The fine-tuning data includes propietary text message datasets, with 2.5K human-annotated examples. Neither the pretraining nor the fine-tuning datasets include private user data.

Data Freshness The pretraining data has a cutoff of November 9th, 2023.

Evaluation Results

Evaluations in progress.

Ethical Considerations and Limitations

(These considerations/limitations taken from the original Llama 2 model card.)

Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.

Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/

Downloads last month: 5