Instructions to use OdiaGenAI/odiagenAI_llama7b_base_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OdiaGenAI/odiagenAI_llama7b_base_v1 with Transformers:

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

pipe = pipeline("text-generation", model="OdiaGenAI/odiagenAI_llama7b_base_v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OdiaGenAI/odiagenAI_llama7b_base_v1")
model = AutoModelForCausalLM.from_pretrained("OdiaGenAI/odiagenAI_llama7b_base_v1")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OdiaGenAI/odiagenAI_llama7b_base_v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OdiaGenAI/odiagenAI_llama7b_base_v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OdiaGenAI/odiagenAI_llama7b_base_v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OdiaGenAI/odiagenAI_llama7b_base_v1

SGLang

How to use OdiaGenAI/odiagenAI_llama7b_base_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 "OdiaGenAI/odiagenAI_llama7b_base_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": "OdiaGenAI/odiagenAI_llama7b_base_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 "OdiaGenAI/odiagenAI_llama7b_base_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": "OdiaGenAI/odiagenAI_llama7b_base_v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OdiaGenAI/odiagenAI_llama7b_base_v1 with Docker Model Runner:
```
docker model run hf.co/OdiaGenAI/odiagenAI_llama7b_base_v1
```

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "OdiaGenAI/odiagenAI_llama7b_base_v1"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code = True)
prompt = "ଭାରତ ବିଷୟରେ କିଛି କୁହନ୍ତୁ"
inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)

device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForCausalLM.from_pretrained(model_id).to(device)

outputs = model.generate(inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 5

OdiaGenAI
/

odiagenAI_llama7b_base_v1

Space using OdiaGenAI/odiagenAI_llama7b_base_v1 1