Instructions to use AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AETHORIA-AI/TR-HASH-MoE-200M-160B-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": "AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT

SGLang

How to use AETHORIA-AI/TR-HASH-MoE-200M-160B-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 "AETHORIA-AI/TR-HASH-MoE-200M-160B-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": "AETHORIA-AI/TR-HASH-MoE-200M-160B-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 "AETHORIA-AI/TR-HASH-MoE-200M-160B-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": "AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT with Docker Model Runner:
```
docker model run hf.co/AETHORIA-AI/TR-HASH-MoE-200M-160B-SFT
```

TR-HASH-MoE-200M-160B-SFT

File size: 2,523 Bytes

7a84f1b

active_expert_width: null
active_num_experts: null
attention_dropout: 0.0
attention_type: gqa
causal_context_fusion_size: 0
causal_context_gate_init: 1.0
causal_contextual_mix_init: 0.0
causal_conv_dilation_cycle: 8
causal_conv_kernel_size: 4
causal_delta_chunk_size: 512
causal_delta_collision_normalized: false
causal_delta_lexical_forge: false
causal_delta_lexical_values: false
causal_delta_occurrence_address: false
causal_delta_timescales: 1
causal_stable_delta: false
causal_state_rank: 16
collect_moe_telemetry: false
expert_initialization: gpt_normal
extra_config: {}
hash_channel_scale_init: 0.0
hash_pair_gate_init: 0.5
hidden_act: silu
hidden_size: 896
initializer_range: 0.02
intermediate_size: 256
is_causal: true
learn_hash_channel_modulation: false
learn_hash_pair_gates: false
lexical_gqa_gate_init: 0.0
lexical_gqa_rank: 16
lexical_gqa_use_token_code: true
lexical_key_gate_init: 0.05
lexical_object_gate_init: 0.1
lexical_object_rank: 16
lexical_zipf_alpha: 0.25
lexical_zipf_floor: 0.1
lexical_zipf_mode: uniform
lexical_zipf_path: null
lexical_zipf_permutation_seed: 1729
lsh_bits: 0
lsh_from_layer: 0
lsh_routing: false
lsh_threshold_mode: zero
max_position_embeddings: 2048
micro_expert_gate_init: 0.1
micro_expert_width: 16
micro_num_experts: 4
mlp_type: tr_hash_engine
mup_base_width: 256
norm_eps: 1.0e-06
norm_type: rmsnorm
num_attention_heads: 14
num_experts: 4
num_hidden_layers: 16
num_key_value_heads: 2
rope_fraction: 1.0
rope_theta: 10000.0
rope_type: standard
route_hash_count: 2
routed_gate_init: 1.0
routed_output_scale: 2.0
routed_output_scale_first_layer: null
routed_output_scale_last_layer: null
routing_strategy: token_id_multi_hash
shared_expert: true
shared_expert_chunk_tokens: 0
shared_gate_init: 1.0
shared_intermediate_size: 3072
shared_output_scale: 1.0
shared_output_scale_first_layer: null
shared_output_scale_last_layer: null
sliding_window: null
static_expert_capacity: false
tie_lexical_object_embeddings: false
tie_word_embeddings: true
token_frequencies: null
top_k: 2
top_k_primary_weight: 0.5
tr_mha_adapter_gate_init: 0.1
tr_mha_adapter_rank: 8
tr_mha_id_other_logit: -2.0
tr_mha_id_primary_logit: 2.0
tr_mha_id_secondary_logit: 1.0
tr_mha_num_experts: 4
tr_mha_targets: qv
tr_mha_top_k: 2
tr_mha_verifier_gate_init: 0.1
tr_mha_verifier_temperature: 1.0
use_cache: true
use_cggr: auto
use_custom_kernels: true
use_mup_attn_scale: false
use_mup_init: false
use_mup_output_mult: false
use_qk_norm: true
use_sdpa: true
use_shared_routed_gates: false
vocab_size: 32000