Instructions to use gtensorapp/model-0429b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gtensorapp/model-0429b with Transformers:

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

pipe = pipeline("text-generation", model="gtensorapp/model-0429b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("gtensorapp/model-0429b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gtensorapp/model-0429b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gtensorapp/model-0429b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gtensorapp/model-0429b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gtensorapp/model-0429b

SGLang

How to use gtensorapp/model-0429b 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 "gtensorapp/model-0429b" \
    --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": "gtensorapp/model-0429b",
		"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 "gtensorapp/model-0429b" \
        --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": "gtensorapp/model-0429b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gtensorapp/model-0429b with Docker Model Runner:
```
docker model run hf.co/gtensorapp/model-0429b
```

model-0429b / config.json

gtensorapp

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	{
	"allow_neg_eigval": false,
	"architectures": [
	"QuasarForCausalLM"
	],
	"attn_mode": "chunk",
	"auto_map": {
	"AutoConfig": "configuration_quasar.QuasarConfig",
	"AutoModelForCausalLM": "modeling_quasar.QuasarForCausalLM"
	},
	"bigmac_r": 0.25,
	"bos_token_id": 1,
	"conv_bias": false,
	"conv_size": 4,
	"d_ff": 4096,
	"d_model": 1536,
	"dense_input_layers": 4,
	"dropout": 0.0,
	"dtype": "bfloat16",
	"eos_token_id": 2,
	"expand_k": 0.5,
	"expand_v": 1.0,
	"fuse_cross_entropy": true,
	"fuse_norm": true,
	"fuse_swiglu": true,
	"gated_layers": 2,
	"gla_mode": "chunk",
	"gradient_checkpointing": false,
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_ratio": 4,
	"hidden_size": 1536,
	"hybrid_layer_types": [
	"quasar",
	"quasar",
	"quasar",
	"quasar",
	"gla",
	"gla",
	"quasar",
	"quasar",
	"quasar",
	"quasar",
	"gla",
	"gla",
	"quasar",
	"quasar",
	"quasar",
	"quasar",
	"gla",
	"gla",
	"quasar",
	"quasar",
	"quasar",
	"quasar",
	"gla",
	"gla"
	],
	"initializer_range": 0.02,
	"intermediate_size": 4096,
	"layer_types": [
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention"
	],
	"looped_injection_init": 0.1,
	"max_position_embeddings": 16384,
	"max_seq_len": 16384,
	"memory_dim": 128,
	"memory_slots": 128,
	"model_type": "quasar",
	"moe_aux_loss_coeff": 0.0001,
	"moe_type": "bigmac",
	"moe_z_loss_coeff": 0.0001,
	"n_heads": 12,
	"n_layers": 24,
	"norm_eps": 1e-06,
	"num_attention_heads": 12,
	"num_heads": 12,
	"num_hidden_layers": 24,
	"num_key_value_heads": 12,
	"num_loops": 1,
	"num_routed_experts": 64,
	"num_shared_experts": 1,
	"num_v_heads": null,
	"pad_token_id": null,
	"quasar_layers": 4,
	"residual_scale": 0.1,
	"rms_norm_eps": 1e-06,
	"rope_theta": 1000000.0,
	"routed_expert_size": 256,
	"shared_expert_size": 3072,
	"smebu_beta": 0.5,
	"smebu_kappa": 2.0,
	"smebu_lambda": 0.002,
	"tie_word_embeddings": false,
	"top_k": 4,
	"transformers_version": "5.7.0",
	"use_cache": true,
	"use_gla_first": false,
	"use_l2warp": false,
	"use_looped_injection": false,
	"use_short_conv": true,
	"vocab_size": 248320
	}