Instructions to use XAgentTeam/XAgentLLaMa-34B-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XAgentTeam/XAgentLLaMa-34B-preview with Transformers:

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

pipe = pipeline("text-generation", model="XAgentTeam/XAgentLLaMa-34B-preview")

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

tokenizer = AutoTokenizer.from_pretrained("XAgentTeam/XAgentLLaMa-34B-preview")
model = AutoModelForCausalLM.from_pretrained("XAgentTeam/XAgentLLaMa-34B-preview", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XAgentTeam/XAgentLLaMa-34B-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XAgentTeam/XAgentLLaMa-34B-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XAgentTeam/XAgentLLaMa-34B-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/XAgentTeam/XAgentLLaMa-34B-preview

SGLang

How to use XAgentTeam/XAgentLLaMa-34B-preview 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 "XAgentTeam/XAgentLLaMa-34B-preview" \
    --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": "XAgentTeam/XAgentLLaMa-34B-preview",
		"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 "XAgentTeam/XAgentLLaMa-34B-preview" \
        --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": "XAgentTeam/XAgentLLaMa-34B-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use XAgentTeam/XAgentLLaMa-34B-preview with Docker Model Runner:
```
docker model run hf.co/XAgentTeam/XAgentLLaMa-34B-preview
```

XAgentLLaMa-34B-preview / pytorch_model-00003-of-00017.bin

XAgentTeam

Upload folder using huggingface_hub

79d91e6 almost 3 years ago

History Blame Contribute Delete

9.71 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:: 5d45fffe9b916f7611fbae067c39264c22c84a67314941af7d5838c8add2894f
Size of remote file:: 9.71 GB
SHA256:: 2d46e93b6f7df2166c26f4b10edc9a285c06ad40b15a66f99c309ec40dc9eeda

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

XAgentTeam
/

XAgentLLaMa-34B-preview

Detected Pickle imports (3)

Xet Pointer Details