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

Libraries

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

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

pipe = pipeline("text-generation", model="Remek/basal-1.5-max")
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")
model = AutoModelForCausalLM.from_pretrained("Remek/basal-1.5-max", 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 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

basal-1.5-max / CALIBRATION.json

Remek

basal-1.5 release candidate (private)

1e598b3 verified 15 days ago

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	{
	"temperature_per_prim": {
	"choice": 1.4983070768766815,
	"noul": 1.5422108254079405,
	"score": 2.058604473286984
	},
	"thresholds": {
	"0.01": {
	"confidence": 0.9467366879733952,
	"method": "bound-controlled (one-sided 95% Clopper-Pearson on FIT)",
	"cert_coverage": 0.6019283746556474,
	"cert_error": 0.01067887109077037,
	"cert_upper95": 0.01664426096161634,
	"test_coverage": 0.6217289719626168,
	"test_error": 0.005824877865464151,
	"test_pl_coverage": 0.6877559666713741,
	"test_pl_error": 0.002874743326488738
	},
	"0.05": {
	"confidence": 0.7290524418873509,
	"method": "bound-controlled (one-sided 95% Clopper-Pearson on FIT)",
	"cert_coverage": 0.8232323232323232,
	"cert_error": 0.04573340769659784,
	"cert_upper95": 0.05469828739589324,
	"test_coverage": 0.8449766355140187,
	"test_error": 0.04064703442554951,
	"test_pl_coverage": 0.9015675752012428,
	"test_pl_error": 0.024436090225563922
	}
	},
	"fitted_on": "calib FIT split (70% by twin group)",
	"certified_on": "calib CERT split (30%)",
	"evaluated_on": "test (frozen)",
	"eval_dtype": "float32"
	}