Instructions to use abhinand/tamil-llama-7b-base-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhinand/tamil-llama-7b-base-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="abhinand/tamil-llama-7b-base-v0.1")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abhinand/tamil-llama-7b-base-v0.1")
model = AutoModelForCausalLM.from_pretrained("abhinand/tamil-llama-7b-base-v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abhinand/tamil-llama-7b-base-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abhinand/tamil-llama-7b-base-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinand/tamil-llama-7b-base-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abhinand/tamil-llama-7b-base-v0.1

SGLang

How to use abhinand/tamil-llama-7b-base-v0.1 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 "abhinand/tamil-llama-7b-base-v0.1" \
    --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": "abhinand/tamil-llama-7b-base-v0.1",
		"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 "abhinand/tamil-llama-7b-base-v0.1" \
        --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": "abhinand/tamil-llama-7b-base-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abhinand/tamil-llama-7b-base-v0.1 with Docker Model Runner:
```
docker model run hf.co/abhinand/tamil-llama-7b-base-v0.1
```

tamil-llama-7b-base-v0.1 / pytorch_model-00005-of-00007.bin

abhinand

Upload folder using huggingface_hub

1c75b27 almost 3 years ago

History Blame Contribute Delete

1.99 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 96c2d76b6604f865d9905ecda409926952cebf686845445387ea6c4d691cc6e8
Size of remote file:: 1.99 GB
SHA256:: fd928899f0cb30addde11814c0976f0690b05f59dd9b08001a1e00137ba863cc

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

abhinand
/

tamil-llama-7b-base-v0.1

Detected Pickle imports (4)

Xet Pointer Details