Instructions to use bluetrace/SERA-32B-GA-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bluetrace/SERA-32B-GA-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="bluetrace/SERA-32B-GA-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bluetrace/SERA-32B-GA-FP8")
model = AutoModelForCausalLM.from_pretrained("bluetrace/SERA-32B-GA-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bluetrace/SERA-32B-GA-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bluetrace/SERA-32B-GA-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bluetrace/SERA-32B-GA-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bluetrace/SERA-32B-GA-FP8

SGLang

How to use bluetrace/SERA-32B-GA-FP8 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 "bluetrace/SERA-32B-GA-FP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bluetrace/SERA-32B-GA-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "bluetrace/SERA-32B-GA-FP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bluetrace/SERA-32B-GA-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bluetrace/SERA-32B-GA-FP8 with Docker Model Runner:
```
docker model run hf.co/bluetrace/SERA-32B-GA-FP8
```

ikarabulut-dev commited on Mar 30

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@@ -26,7 +26,7 @@ FP8 quantization of [allenai/SERA-32B-GA](https://huggingface.co/allenai/SERA-32
 | Method | FP8 (W8A8) via `llmcompressor` `oneshot` |
 | Targets | All `Linear` layers except `lm_head` |
 | Calibration dataset | `allenai/Sera-4.5A-Lite-T2` |
-| Calibration samples | 513 |
 | Calibration sequence length | 2048 tokens |
 | llmcompressor version | 0.9.0.2 |
 | Hardware | AWS g6e.4xlarge (NVIDIA L40S, 48 GB VRAM) |

 | Method | FP8 (W8A8) via `llmcompressor` `oneshot` |
 | Targets | All `Linear` layers except `lm_head` |
 | Calibration dataset | `allenai/Sera-4.5A-Lite-T2` |
+| Calibration samples | 512 |
 | Calibration sequence length | 2048 tokens |
 | llmcompressor version | 0.9.0.2 |
 | Hardware | AWS g6e.4xlarge (NVIDIA L40S, 48 GB VRAM) |