Instructions to use devishraq/GENAZ-1B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use devishraq/GENAZ-1B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="devishraq/GENAZ-1B-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("devishraq/GENAZ-1B-SFT")
model = AutoModelForCausalLM.from_pretrained("devishraq/GENAZ-1B-SFT", 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 devishraq/GENAZ-1B-SFT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/devishraq/GENAZ-1B-SFT

SGLang

How to use devishraq/GENAZ-1B-SFT 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 "devishraq/GENAZ-1B-SFT" \
    --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": "devishraq/GENAZ-1B-SFT",
		"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 "devishraq/GENAZ-1B-SFT" \
        --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": "devishraq/GENAZ-1B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use devishraq/GENAZ-1B-SFT 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 devishraq/GENAZ-1B-SFT 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 devishraq/GENAZ-1B-SFT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for devishraq/GENAZ-1B-SFT to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="devishraq/GENAZ-1B-SFT",
    max_seq_length=2048,
)

Docker Model Runner
How to use devishraq/GENAZ-1B-SFT with Docker Model Runner:
```
docker model run hf.co/devishraq/GENAZ-1B-SFT
```

GENAZ-1B-SFT

File size: 2,490 Bytes

db05c0e

{
    "architectures": [
        "GraniteMoeHybridForCausalLM"
    ],
    "attention_bias": false,
    "attention_dropout": 0.0,
    "attention_multiplier": 0.0078125,
    "bos_token_id": 100257,
    "torch_dtype": "float16",
    "embedding_multiplier": 12,
    "eos_token_id": 100257,
    "hidden_act": "silu",
    "hidden_size": 2048,
    "init_method": "mup",
    "initializer_range": 0.1,
    "intermediate_size": 4096,
    "layer_types": [
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention",
        "attention"
    ],
    "logits_scaling": 8,
    "mamba_chunk_size": 256,
    "mamba_conv_bias": true,
    "mamba_d_conv": 4,
    "mamba_d_head": 32,
    "mamba_d_state": 256,
    "mamba_expand": 2,
    "mamba_n_groups": 1,
    "mamba_n_heads": 128,
    "mamba_proj_bias": false,
    "max_position_embeddings": 131072,
    "model_name": "unsloth/granite-4.0-1b",
    "model_type": "granitemoehybrid",
    "normalization_function": "rmsnorm",
    "num_attention_heads": 16,
    "num_experts_per_tok": 0,
    "num_hidden_layers": 40,
    "num_key_value_heads": 4,
    "num_local_experts": 0,
    "output_router_logits": false,
    "pad_token_id": 100256,
    "position_embedding_type": "rope",
    "residual_multiplier": 0.22,
    "rms_norm_eps": 1e-05,
    "rope_parameters": {
        "rope_theta": 10000000,
        "rope_type": "default"
    },
    "router_aux_loss_coef": 0.01,
    "shared_intermediate_size": 4096,
    "tie_word_embeddings": true,
    "time_step_limit": [
        0.0,
        {
            "__float__": "Infinity"
        }
    ],
    "time_step_max": 0.1,
    "time_step_min": 0.001,
    "unsloth_fixed": true,
    "unsloth_version": "2026.5.7",
    "use_cache": false,
    "vocab_size": 100352
}