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

Libraries

How to use CodeIsAbstract/LLAMA_RoPE_Baseline with Transformers:

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

pipe = pipeline("text-generation", model="CodeIsAbstract/LLAMA_RoPE_Baseline")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CodeIsAbstract/LLAMA_RoPE_Baseline")
model = AutoModelForCausalLM.from_pretrained("CodeIsAbstract/LLAMA_RoPE_Baseline", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeIsAbstract/LLAMA_RoPE_Baseline with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/CodeIsAbstract/LLAMA_RoPE_Baseline

SGLang

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

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

LLAMA_RoPE_Baseline / training_args.bin

CodeIsAbstract

Training in progress, step 4000

b997171 verified 4 days ago

Download

History Blame Contribute Delete

5.84 kB

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:: f2392ba76fa9adb2d545bc64b648ab0af62e2cc5e5771463e01fa8a6783ef8a8
Size of remote file:: 5.84 kB
SHA256:: 46737e9bac89bc9b57f33fbb261caf48e30a3b2b9c5244699fad37f3cc509337

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