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

Libraries

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

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

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

Use Docker

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

SGLang

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

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

basal-1.5-mini / CALIBRATION.json

Remek

basal-1.5 release candidate (private)

4e347c9 verified 13 days ago

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	{
	"temperature_per_prim": {
	"choice": 1.1387886347566916,
	"noul": 1.3939382634489992,
	"score": 1.5422108254079405
	},
	"thresholds": {
	"0.01": {
	"confidence": 0.9546241492914076,
	"method": "bound-controlled (one-sided 95% Clopper-Pearson on FIT)",
	"cert_coverage": 0.49265381083562904,
	"cert_error": 0.009319664492078328,
	"cert_upper95": 0.015756935615729575,
	"test_coverage": 0.4649532710280374,
	"test_error": 0.009045226130653283,
	"test_pl_coverage": 0.5283152097161418,
	"test_pl_error": 0.007751937984496138
	},
	"0.05": {
	"confidence": 0.7629656203142733,
	"method": "bound-controlled (one-sided 95% Clopper-Pearson on FIT)",
	"cert_coverage": 0.7112029384756657,
	"cert_error": 0.046481601032924424,
	"cert_upper95": 0.05626481588712098,
	"test_coverage": 0.704088785046729,
	"test_error": 0.05292848846855813,
	"test_pl_coverage": 0.7709363084310126,
	"test_pl_error": 0.0426818098552848
	}
	},
	"fitted_on": "calib FIT split (70% by twin group)",
	"certified_on": "calib CERT split (30%)",
	"evaluated_on": "test (frozen)",
	"eval_dtype": "float32"
	}