Instructions to use Remek/basal-2.0-mini-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Remek/basal-2.0-mini-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="Remek/basal-2.0-mini-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-2.0-mini-FP8")
model = AutoModelForCausalLM.from_pretrained("Remek/basal-2.0-mini-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-2.0-mini-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Remek/basal-2.0-mini-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-2.0-mini-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Remek/basal-2.0-mini-FP8

SGLang

How to use Remek/basal-2.0-mini-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-2.0-mini-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-2.0-mini-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-2.0-mini-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-2.0-mini-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Remek/basal-2.0-mini-FP8 with Docker Model Runner:
```
docker model run hf.co/Remek/basal-2.0-mini-FP8
```

basal-2.0-mini-FP8 / hf_quant_config.json

Remek

Add files using upload-large-folder tool

82325b6 verified 12 days ago

Raw History Blame Contribute Delete

400 Bytes

{
    "producer": {
        "name": "modelopt",
        "version": "0.47.0"
    },
    "quantization": {
        "quant_algo": "FP8",
        "kv_cache_quant_algo": null,
        "exclude_modules": [
            "lm_head",
            "model.embed_tokens",
            "model.layers.0*",
            "model.layers.1.*",
            "model.layers.30*",
            "model.layers.31*"
        ]
    }
}

	{
	"producer": {
	"name": "modelopt",
	"version": "0.47.0"
	},
	"quantization": {
	"quant_algo": "FP8",
	"kv_cache_quant_algo": null,
	"exclude_modules": [
	"lm_head",
	"model.embed_tokens",
	"model.layers.0*",
	"model.layers.1.*",
	"model.layers.30*",
	"model.layers.31*"
	]
	}
	}