Instructions to use TYDTYDYT/test111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TYDTYDYT/test111 with Transformers:

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

pipe = pipeline("text-generation", model="TYDTYDYT/test111")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TYDTYDYT/test111")
model = AutoModelForCausalLM.from_pretrained("TYDTYDYT/test111", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TYDTYDYT/test111 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TYDTYDYT/test111"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TYDTYDYT/test111",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TYDTYDYT/test111

SGLang

How to use TYDTYDYT/test111 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 "TYDTYDYT/test111" \
    --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": "TYDTYDYT/test111",
		"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 "TYDTYDYT/test111" \
        --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": "TYDTYDYT/test111",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use TYDTYDYT/test111 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 TYDTYDYT/test111 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 TYDTYDYT/test111 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TYDTYDYT/test111 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="TYDTYDYT/test111",
    max_seq_length=2048,
)

Docker Model Runner
How to use TYDTYDYT/test111 with Docker Model Runner:
```
docker model run hf.co/TYDTYDYT/test111
```

test111 / config.json

TYDTYDYT

(Trained with Unsloth)

fe039dc verified about 1 month ago

Raw

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2.5 kB

	{
	"architectures": [
	"Lfm2ForCausalLM"
	],
	"block_auto_adjust_ff_dim": true,
	"block_dim": 1536,
	"block_ff_dim": 10240,
	"block_ffn_dim_multiplier": 1.0,
	"block_mlp_init_scale": 1.0,
	"block_multiple_of": 256,
	"block_norm_eps": 1e-05,
	"block_out_init_scale": 1.0,
	"block_use_swiglu": true,
	"block_use_xavier_init": true,
	"bos_token_id": 1,
	"conv_L_cache": 3,
	"conv_bias": false,
	"conv_dim": 1536,
	"conv_dim_out": 1536,
	"conv_use_xavier_init": true,
	"torch_dtype": "float16",
	"eos_token_id": 7,
	"hidden_size": 1536,
	"initializer_range": 0.02,
	"intermediate_size": 10240,
	"layer_types": [
	"conv",
	"conv",
	"full_attention",
	"conv",
	"conv",
	"full_attention",
	"conv",
	"conv",
	"full_attention",
	"conv",
	"full_attention",
	"conv",
	"full_attention",
	"conv",
	"full_attention",
	"conv"
	],
	"max_position_embeddings": 32768,
	"model_name": "unsloth/LFM2-700M-unsloth-bnb-4bit",
	"model_type": "lfm2",
	"norm_eps": 1e-05,
	"num_attention_heads": 24,
	"num_heads": 24,
	"num_hidden_layers": 16,
	"num_key_value_heads": 8,
	"pad_token_id": 0,
	"quantization_config": {
	"bnb_4bit_compute_dtype": "float16",
	"bnb_4bit_quant_type": "nf4",
	"bnb_4bit_use_double_quant": true,
	"llm_int8_enable_fp32_cpu_offload": false,
	"llm_int8_has_fp16_weight": false,
	"llm_int8_skip_modules": [
	"model.layers.0.conv.in_proj",
	"model.layers.0.conv.out_proj",
	"model.layers.1.conv.in_proj",
	"model.layers.1.conv.out_proj",
	"model.layers.3.conv.in_proj",
	"model.layers.3.conv.out_proj",
	"model.layers.4.conv.in_proj",
	"model.layers.4.conv.out_proj",
	"model.layers.6.conv.in_proj",
	"model.layers.6.conv.out_proj",
	"model.layers.7.conv.in_proj",
	"model.layers.7.conv.out_proj",
	"model.layers.9.conv.in_proj",
	"model.layers.9.conv.out_proj",
	"model.layers.11.conv.in_proj",
	"model.layers.11.conv.out_proj",
	"model.layers.13.conv.in_proj",
	"model.layers.13.conv.out_proj",
	"model.layers.15.conv.in_proj",
	"model.layers.15.conv.out_proj",
	"lm_head"
	],
	"llm_int8_threshold": 6.0,
	"load_in_4bit": true,
	"load_in_8bit": false,
	"quant_method": "bitsandbytes"
	},
	"rope_theta": 1000000.0,
	"transformers_version": "4.57.3",
	"unsloth_fixed": true,
	"unsloth_version": "2026.6.8",
	"use_cache": true,
	"use_pos_enc": true,
	"vocab_size": 65536
	}