Instructions to use Akahsizrr/Mini-Whale-1-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Akahsizrr/Mini-Whale-1-12B with Transformers:

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

pipe = pipeline("text-generation", model="Akahsizrr/Mini-Whale-1-12B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Akahsizrr/Mini-Whale-1-12B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Akahsizrr/Mini-Whale-1-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Akahsizrr/Mini-Whale-1-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Akahsizrr/Mini-Whale-1-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Akahsizrr/Mini-Whale-1-12B

SGLang

How to use Akahsizrr/Mini-Whale-1-12B 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 "Akahsizrr/Mini-Whale-1-12B" \
    --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": "Akahsizrr/Mini-Whale-1-12B",
		"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 "Akahsizrr/Mini-Whale-1-12B" \
        --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": "Akahsizrr/Mini-Whale-1-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Akahsizrr/Mini-Whale-1-12B with Docker Model Runner:
```
docker model run hf.co/Akahsizrr/Mini-Whale-1-12B
```

Mini-Whale-1-12B / config.json

Akahsizrr

Add quantization_config for auto 4-bit loading

582951c verified 2 months ago

Raw History Blame Contribute Delete

5.22 kB

	{
	"architectures": [
	"Fuse2ForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "fuse2_model.Fuse2Config",
	"AutoModelForCausalLM": "fuse2_model.Fuse2ForCausalLM"
	},
	"model_type": "fuse2",
	"hidden_size": 2560,
	"intermediate_size": 9728,
	"num_hidden_layers": 36,
	"num_attention_heads": 32,
	"num_key_value_heads": 8,
	"head_dim": 128,
	"hidden_act": "silu",
	"max_position_embeddings": 40960,
	"rms_norm_eps": 1e-06,
	"rope_theta": 1000000.0,
	"use_sliding_window": false,
	"max_window_layers": 36,
	"tie_word_embeddings": true,
	"vocab_size": 151936,
	"bos_token_id": 151643,
	"eos_token_id": 151643,
	"pad_token_id": 151643,
	"attention_bias": false,
	"attention_dropout": 0.0,
	"expert_hidden_size": 4096,
	"expert_intermediate_size": 2048,
	"experts_per_layer": {
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	"load_in_8bit": false,
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	"bnb_4bit_compute_dtype": "bfloat16",
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