Instructions to use Ba2han/experimental2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ba2han/experimental2 with Transformers:

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

pipe = pipeline("text-generation", model="Ba2han/experimental2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ba2han/experimental2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Ba2han/experimental2", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Ba2han/experimental2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ba2han/experimental2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ba2han/experimental2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ba2han/experimental2

SGLang

How to use Ba2han/experimental2 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 "Ba2han/experimental2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ba2han/experimental2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Ba2han/experimental2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ba2han/experimental2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use Ba2han/experimental2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ba2han/experimental2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ba2han/experimental2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ba2han/experimental2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Ba2han/experimental2",
    max_seq_length=2048,
)

Docker Model Runner
How to use Ba2han/experimental2 with Docker Model Runner:
```
docker model run hf.co/Ba2han/experimental2
```

experimental2

File size: 2,014 Bytes

b5cacc7
 
 
 
 
 
 
 
 
 
 
 
0ee5d75
b5cacc7
334dfeb
b5cacc7
0ee5d75
b5cacc7
 
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
0ee5d75
b5cacc7
 
0ee5d75
334dfeb
466c5fb
b5cacc7
0ee5d75
c31ae10
0ee5d75
b5cacc7
 
 
 
 
 
 
0ee5d75
334dfeb
0ee5d75
 
 
 
b5cacc7
0ee5d75
 
b5cacc7

{
  "architectures": [
    "Qwen3CanonForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "auto_map": {
    "AutoModelForCausalLM": "patch.Qwen3CanonForCausalLM"
  },
  "bos_token_id": 50030,
  "dtype": "bfloat16",
  "eos_token_id": 50031,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 1152,
  "initializer_range": 0.02,
  "intermediate_size": 3168,
  "layer_types": [
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention",
    "full_attention",
    "sliding_attention"
  ],
  "max_position_embeddings": 8192,
  "max_window_layers": 40,
  "mlp_type": "squared_relu",
  "model_name": "checkpoint-7560",
  "model_type": "qwen3",
  "n_layer": 40,
  "num_attention_heads": 16,
  "num_hidden_layers": 40,
  "num_key_value_heads": 4,
  "pad_token_id": 50034,
  "rms_norm_eps": 1e-06,
  "rope_parameters": {
    "rope_theta": 50000,
    "rope_type": "default"
  },
  "sliding_window": 4096,
  "squared_relu_activation": "relu2",
  "squared_relu_intermediate_size": 3168,
  "tie_word_embeddings": true,
  "transformers_version": "5.5.0",
  "unsloth_version": "2026.5.2",
  "use_cache": false,
  "use_sliding_window": true,
  "vocab_size": 50304
}