Instructions to use Hyper-AI-Computer/FlaxLlama-Init-Model-V4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hyper-AI-Computer/FlaxLlama-Init-Model-V4 with Transformers:

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

pipe = pipeline("text-generation", model="Hyper-AI-Computer/FlaxLlama-Init-Model-V4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hyper-AI-Computer/FlaxLlama-Init-Model-V4")
model = AutoModelForCausalLM.from_pretrained("Hyper-AI-Computer/FlaxLlama-Init-Model-V4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hyper-AI-Computer/FlaxLlama-Init-Model-V4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hyper-AI-Computer/FlaxLlama-Init-Model-V4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hyper-AI-Computer/FlaxLlama-Init-Model-V4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hyper-AI-Computer/FlaxLlama-Init-Model-V4

SGLang

How to use Hyper-AI-Computer/FlaxLlama-Init-Model-V4 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 "Hyper-AI-Computer/FlaxLlama-Init-Model-V4" \
    --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": "Hyper-AI-Computer/FlaxLlama-Init-Model-V4",
		"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 "Hyper-AI-Computer/FlaxLlama-Init-Model-V4" \
        --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": "Hyper-AI-Computer/FlaxLlama-Init-Model-V4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Hyper-AI-Computer/FlaxLlama-Init-Model-V4 with Docker Model Runner:
```
docker model run hf.co/Hyper-AI-Computer/FlaxLlama-Init-Model-V4
```

nosuchjihyun commited on May 26, 2025

Commit

6f1b54d

verified ·

1 Parent(s): 839eebd

Update README.md

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@@ -7,13 +7,24 @@ tags: []
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
+```json
+{
+   "bytes accessed0{}":38364528640.0,
+   "transcendentals":2114048000.0,
+   "utilization1{}":583.0,
+   "bytes accessed1{}":32694747136.0,
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+   "bytes accessed2{}":2025914368.0,
+   "bytes accessed":95467569152.0,
+   "flops":3053315162112.0
+}
+```
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.