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

Libraries

How to use SagarKeshave/llama_ft_dr_data with Transformers:

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

pipe = pipeline("text-generation", model="SagarKeshave/llama_ft_dr_data")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SagarKeshave/llama_ft_dr_data")
model = AutoModelForCausalLM.from_pretrained("SagarKeshave/llama_ft_dr_data", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SagarKeshave/llama_ft_dr_data with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SagarKeshave/llama_ft_dr_data

SGLang

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

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

llama_ft_dr_data / model-00002-of-00002.safetensors

SFconvertbot

Adding `safetensors` variant of this model

6789e12 verified over 1 year ago

Download

History Blame

3.5 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:: d9a19d881942495d7df9ca1f3f07e21865342b0145f3a123b2ed8db19e9711e2
Size of remote file:: 3.5 GB
SHA256:: 72da883c55022d084e30e63ba3b72dfc8a4a8f924b3b8ca96d52ccffab9ada91

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