Instructions to use tolgadev/llama-2-7b-ruyallm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tolgadev/llama-2-7b-ruyallm with Transformers:

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

pipe = pipeline("text-generation", model="tolgadev/llama-2-7b-ruyallm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tolgadev/llama-2-7b-ruyallm")
model = AutoModelForCausalLM.from_pretrained("tolgadev/llama-2-7b-ruyallm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tolgadev/llama-2-7b-ruyallm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tolgadev/llama-2-7b-ruyallm

SGLang

How to use tolgadev/llama-2-7b-ruyallm 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 "tolgadev/llama-2-7b-ruyallm" \
    --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": "tolgadev/llama-2-7b-ruyallm",
		"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 "tolgadev/llama-2-7b-ruyallm" \
        --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": "tolgadev/llama-2-7b-ruyallm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tolgadev/llama-2-7b-ruyallm with Docker Model Runner:
```
docker model run hf.co/tolgadev/llama-2-7b-ruyallm
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

llama-2-7b-ruyallm model

Description

This repo contains finetuned model files for llama-2-7b-ruyallm. This model finetuned on tolgadev/ruyatabirleri_instruct dataset on huggingface 🤗.

Thanks to maximelabonne's contribution on fine tuning based colab notebook, it took ~2 hours to fine-tune.

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

F32

F16

Model tree for tolgadev/llama-2-7b-ruyallm

Quantizations

3 models

tolgadev
/

llama-2-7b-ruyallm

llama-2-7b-ruyallm model

Description

Model tree for tolgadev/llama-2-7b-ruyallm

Dataset used to train tolgadev/llama-2-7b-ruyallm