Instructions to use assemsabry/Smol-3B-IdentityPhase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use assemsabry/Smol-3B-IdentityPhase with Transformers:

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

pipe = pipeline("text-generation", model="assemsabry/Smol-3B-IdentityPhase")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("assemsabry/Smol-3B-IdentityPhase")
model = AutoModelForCausalLM.from_pretrained("assemsabry/Smol-3B-IdentityPhase")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use assemsabry/Smol-3B-IdentityPhase with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "assemsabry/Smol-3B-IdentityPhase"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "assemsabry/Smol-3B-IdentityPhase",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/assemsabry/Smol-3B-IdentityPhase

SGLang

How to use assemsabry/Smol-3B-IdentityPhase 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 "assemsabry/Smol-3B-IdentityPhase" \
    --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": "assemsabry/Smol-3B-IdentityPhase",
		"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 "assemsabry/Smol-3B-IdentityPhase" \
        --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": "assemsabry/Smol-3B-IdentityPhase",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use assemsabry/Smol-3B-IdentityPhase 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 assemsabry/Smol-3B-IdentityPhase 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 assemsabry/Smol-3B-IdentityPhase to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for assemsabry/Smol-3B-IdentityPhase to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="assemsabry/Smol-3B-IdentityPhase",
    max_seq_length=2048,
)

Docker Model Runner
How to use assemsabry/Smol-3B-IdentityPhase with Docker Model Runner:
```
docker model run hf.co/assemsabry/Smol-3B-IdentityPhase
```

Smol-3B-IdentityPhase / config.json

assemsabry

Unsloth merged 20260419_044238

be813f5 verified 3 months ago

Raw

History Blame Contribute Delete

2.55 kB

	{
	"architectures": [
	"SmolLM3ForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 128001,
	"dtype": "float16",
	"eos_token_id": 128001,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 11008,
	"layer_types": [
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention"
	],
	"max_position_embeddings": 65536,
	"mlp_bias": false,
	"model_name": "HuggingFaceTB/SmolLM3-3B-Base",
	"model_type": "smollm3",
	"no_rope_layer_interval": 4,
	"no_rope_layers": [
	1,
	1,
	1,
	0,
	1,
	1,
	1,
	0,
	1,
	1,
	1,
	0,
	1,
	1,
	1,
	0,
	1,
	1,
	1,
	0,
	1,
	1,
	1,
	0,
	1,
	1,
	1,
	0,
	1,
	1,
	1,
	0,
	1,
	1,
	1,
	0
	],
	"num_attention_heads": 16,
	"num_hidden_layers": 36,
	"num_key_value_heads": 4,
	"pad_token_id": 128004,
	"pretraining_tp": 1,
	"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": null,
	"llm_int8_threshold": 6.0,
	"load_in_4bit": true,
	"load_in_8bit": false,
	"quant_method": "bitsandbytes"
	},
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"rope_theta": 5000000.0,
	"rope_type": "default"
	},
	"sliding_window": null,
	"tie_word_embeddings": true,
	"transformers.js_config": {
	"dtype": "q4",
	"kv_cache_dtype": {
	"fp16": "float16",
	"q4f16": "float16"
	},
	"use_external_data_format": true
	},
	"transformers_version": "5.5.0",
	"unsloth_version": "2026.4.6",
	"use_cache": false,
	"use_sliding_window": false,
	"vocab_size": 128256
	}