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
```

Remek commited on 23 days ago

Commit

1978d07

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1 Parent(s): 7ea7b13

Release card (final draft)

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@@ -40,6 +40,7 @@ scores, judging answers, agent and tool decisions, and triage with a confidence
 | [basal-1.5-mini](https://huggingface.co/Remek/basal-1.5-mini) | 1.5B | fastest, distilled from max |
 Each model also comes as `-MLX-8bit` and `-MLX-fp4` (Apple Silicon) and `-GGUF` (Ollama, llama.cpp).
 ## Results
@@ -90,7 +91,7 @@ The first start in mode `fast` compiles the model for a few minutes; `--mode fas
 `--mode fp8` is faster on workstation, consumer and desktop GPUs. vLLM and SGLang: `--mode vllm` / `--mode sglang`
 in separate environments (vLLM: `basal[vllm]`; SGLang: install `sglang[srt]==0.5.21` first, then basal with
 `--no-deps`, as in the [README](https://github.com/rkinas/basal#vllm-or-sglang)). Apple Silicon: [basal-1.5-mini-MLX-8bit](https://huggingface.co/Remek/basal-1.5-mini-MLX-8bit);
-Ollama: [basal-1.5-mini-GGUF](https://huggingface.co/Remek/basal-1.5-mini-GGUF).
 **3. Ask questions** (in a second terminal). Several questions about one state go into one request and are answered
 over one shared state:

 | [basal-1.5-mini](https://huggingface.co/Remek/basal-1.5-mini) | 1.5B | fastest, distilled from max |
 Each model also comes as `-MLX-8bit` and `-MLX-fp4` (Apple Silicon) and `-GGUF` (Ollama, llama.cpp).
+NVIDIA ModelOpt checkpoints for vLLM: `-FP8` and `-NVFP4`.
 ## Results
 `--mode fp8` is faster on workstation, consumer and desktop GPUs. vLLM and SGLang: `--mode vllm` / `--mode sglang`
 in separate environments (vLLM: `basal[vllm]`; SGLang: install `sglang[srt]==0.5.21` first, then basal with
 `--no-deps`, as in the [README](https://github.com/rkinas/basal#vllm-or-sglang)). Apple Silicon: [basal-1.5-mini-MLX-8bit](https://huggingface.co/Remek/basal-1.5-mini-MLX-8bit);
+Ollama: [basal-1.5-mini-GGUF](https://huggingface.co/Remek/basal-1.5-mini-GGUF). FP8 checkpoint for vLLM: [basal-1.5-mini-FP8](https://huggingface.co/Remek/basal-1.5-mini-FP8).
 **3. Ask questions** (in a second terminal). Several questions about one state go into one request and are answered
 over one shared state: