Instructions to use eduardo-alvarez/canada_workshop_llm_llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eduardo-alvarez/canada_workshop_llm_llama with Transformers:

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

pipe = pipeline("text-generation", model="eduardo-alvarez/canada_workshop_llm_llama")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eduardo-alvarez/canada_workshop_llm_llama")
model = AutoModelForCausalLM.from_pretrained("eduardo-alvarez/canada_workshop_llm_llama")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use eduardo-alvarez/canada_workshop_llm_llama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/eduardo-alvarez/canada_workshop_llm_llama

SGLang

How to use eduardo-alvarez/canada_workshop_llm_llama 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 "eduardo-alvarez/canada_workshop_llm_llama" \
    --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": "eduardo-alvarez/canada_workshop_llm_llama",
		"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 "eduardo-alvarez/canada_workshop_llm_llama" \
        --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": "eduardo-alvarez/canada_workshop_llm_llama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use eduardo-alvarez/canada_workshop_llm_llama with Docker Model Runner:
```
docker model run hf.co/eduardo-alvarez/canada_workshop_llm_llama
```

Model Card for Model ID

This model was fine-tuned from meta-llama/Llama-2-7b-hf on the timdettmers/openassistant-guanaco dataset.

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Eduardo Alvarez
Model type: Text Generation
Language(s) (NLP): English
Finetuned from model [optional]: meta-llama/Llama-2-7b-hf

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: Intel Gaudi 2 Accelerator
Hours used: <6min
Cloud Provider: Intel Tiber Developer Cloud

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

F32

Paper for eduardo-alvarez/canada_workshop_llm_llama

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 47