Instructions to use hyper-accel/ci-random-bfloat16-llama3-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hyper-accel/ci-random-bfloat16-llama3-8b with Transformers:

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

pipe = pipeline("text-generation", model="hyper-accel/ci-random-bfloat16-llama3-8b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hyper-accel/ci-random-bfloat16-llama3-8b")
model = AutoModelForCausalLM.from_pretrained("hyper-accel/ci-random-bfloat16-llama3-8b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hyper-accel/ci-random-bfloat16-llama3-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hyper-accel/ci-random-bfloat16-llama3-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hyper-accel/ci-random-bfloat16-llama3-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hyper-accel/ci-random-bfloat16-llama3-8b

SGLang

How to use hyper-accel/ci-random-bfloat16-llama3-8b 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-accel/ci-random-bfloat16-llama3-8b" \
    --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-accel/ci-random-bfloat16-llama3-8b",
		"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-accel/ci-random-bfloat16-llama3-8b" \
        --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-accel/ci-random-bfloat16-llama3-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hyper-accel/ci-random-bfloat16-llama3-8b with Docker Model Runner:
```
docker model run hf.co/hyper-accel/ci-random-bfloat16-llama3-8b
```

junsoo999 commited on 30 days ago

Commit

2f3bab0

verified ·

1 Parent(s): b438aa7

Upload tokenizer from llama

Browse files

Files changed (3) hide show

.gitattributes +1 -0
tokenizer.json +3 -0
tokenizer_config.json +13 -0

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	@@ -0,0 +1,3 @@

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tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,13 @@

+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "is_local": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "TokenizersBackend"
+}