Instructions to use Remek/basal-1.5-max-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Remek/basal-1.5-max-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="Remek/basal-1.5-max-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("Remek/basal-1.5-max-FP8")
model = AutoModelForCausalLM.from_pretrained("Remek/basal-1.5-max-FP8", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Remek/basal-1.5-max-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Remek/basal-1.5-max-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": "Remek/basal-1.5-max-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Remek/basal-1.5-max-FP8

SGLang

How to use Remek/basal-1.5-max-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 "Remek/basal-1.5-max-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": "Remek/basal-1.5-max-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 "Remek/basal-1.5-max-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": "Remek/basal-1.5-max-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Remek/basal-1.5-max-FP8 with Docker Model Runner:
```
docker model run hf.co/Remek/basal-1.5-max-FP8
```

basal-1.5-max-FP8 / CALIBRATION.json

Remek

basal-1.5-max-FP8: ModelOpt export (draft, private)

c674a61 verified about 1 month ago

Raw History Blame Contribute Delete

589 Bytes

{
 "temperature_per_prim": {
  "choice": 1.4983070768766815,
  "noul": 1.5422108254079405,
  "score": 2.058604473286984
 },
 "thresholds": {
  "0.01": {
   "confidence": 0.9467366879733952
  },
  "0.05": {
   "confidence": 0.7290524418873509
  }
 },
 "fitted_on": "calib FIT split (70% by twin group)",
 "certified_on": "calib CERT split (30%)",
 "evaluated_on": "test (frozen)",
 "eval_dtype": "float32",
 "validity": "Fitted on the unquantised model; not validated for this FP8 checkpoint, whose predictions differ. Refit on your own calibration data before relying on the thresholds."
}

	{
	"temperature_per_prim": {
	"choice": 1.4983070768766815,
	"noul": 1.5422108254079405,
	"score": 2.058604473286984
	},
	"thresholds": {
	"0.01": {
	"confidence": 0.9467366879733952
	},
	"0.05": {
	"confidence": 0.7290524418873509
	}
	},
	"fitted_on": "calib FIT split (70% by twin group)",
	"certified_on": "calib CERT split (30%)",
	"evaluated_on": "test (frozen)",
	"eval_dtype": "float32",
	"validity": "Fitted on the unquantised model; not validated for this FP8 checkpoint, whose predictions differ. Refit on your own calibration data before relying on the thresholds."
	}