Instructions to use MemTensor/MemT-1.0-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MemTensor/MemT-1.0-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MemTensor/MemT-1.0-Flash", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MemTensor/MemT-1.0-Flash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MemTensor/MemT-1.0-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MemTensor/MemT-1.0-Flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MemTensor/MemT-1.0-Flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MemTensor/MemT-1.0-Flash
- SGLang
How to use MemTensor/MemT-1.0-Flash 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 "MemTensor/MemT-1.0-Flash" \ --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": "MemTensor/MemT-1.0-Flash", "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 "MemTensor/MemT-1.0-Flash" \ --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": "MemTensor/MemT-1.0-Flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MemTensor/MemT-1.0-Flash with Docker Model Runner:
docker model run hf.co/MemTensor/MemT-1.0-Flash
Download config.json from MemTensor/MemT-1.0-Flash: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/MemTensor/MemT-1.0-Flash/resolve/main/config.json
- Command line
-
hf download hf://MemTensor/MemT-1.0-Flash/config.json
-
curl -L -o config.json https://huggingface.co/MemTensor/MemT-1.0-Flash/resolve/main/config.json
5.3 kB
| { | |
| "_name_or_path": "", | |
| "architectures": [ | |
| "MemTDeepseekV4ForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_memt.MemTDeepseekV4Config", | |
| "AutoModelForCausalLM": "modeling_memt.MemTDeepseekV4ForCausalLM" | |
| }, | |
| "bos_token_id": 0, | |
| "chunk_size_feed_forward": 0, | |
| "compress_rates": { | |
| "compressed_sparse_attention": 4, | |
| "heavily_compressed_attention": 128 | |
| }, | |
| "compress_rope_theta": 160000, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 1, | |
| "expert_dtype": "bf16", | |
| "hc_eps": 1e-06, | |
| "hc_mult": 4, | |
| "hc_sinkhorn_iters": 20, | |
| "head_dim": 512, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "index_head_dim": 128, | |
| "index_n_heads": 64, | |
| "index_topk": 512, | |
| "initializer_range": 0.02, | |
| "is_encoder_decoder": false, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "layer_types": [ | |
| "sliding_attention", | |
| "sliding_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention", | |
| "heavily_compressed_attention", | |
| "compressed_sparse_attention" | |
| ], | |
| "max_position_embeddings": 1048576, | |
| "memt": { | |
| "chunk_tokens": 2048, | |
| "gate_latent_norm": false, | |
| "gate_rank": 512, | |
| "gdn_version": "stable_gdn2", | |
| "head_dim": 512, | |
| "kv_groups": 8, | |
| "kv_rank": 512, | |
| "layers": [ | |
| 4, | |
| 6, | |
| 8, | |
| 10, | |
| 12, | |
| 14, | |
| 16, | |
| 18, | |
| 20, | |
| 22, | |
| 24, | |
| 26, | |
| 28, | |
| 30, | |
| 32, | |
| 34, | |
| 36, | |
| 38, | |
| 40, | |
| 42 | |
| ], | |
| "memory_head_mode": "mha", | |
| "memory_read_scale": "k_inv_sqrt", | |
| "memory_update_mode": "token", | |
| "memory_use_short_conv": false, | |
| "norm_eps": 1e-06, | |
| "num_kv_heads": 64, | |
| "num_memory_heads": 64, | |
| "output_gate_bias": true, | |
| "output_gate_softcap_enabled": true, | |
| "output_gate_softcap_limit": 2.0, | |
| "output_gate_type": "silu", | |
| "projection": "grouped_kv_low_rank", | |
| "q_rank": 1024, | |
| "stable_gdn2_sqrt_erase_gate": true, | |
| "tail_policy": "commit" | |
| }, | |
| "mlp_bias": false, | |
| "mlp_layer_types": [ | |
| "hash_moe", | |
| "hash_moe", | |
| "hash_moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe", | |
| "moe" | |
| ], | |
| "model_type": "memt_deepseek_v4", | |
| "moe_intermediate_size": 2048, | |
| "n_routed_experts": 256, | |
| "n_shared_experts": 1, | |
| "norm_topk_prob": true, | |
| "num_attention_heads": 64, | |
| "num_experts_per_tok": 6, | |
| "num_hidden_layers": 43, | |
| "num_key_value_heads": 1, | |
| "num_nextn_predict_layers": 0, | |
| "o_groups": 8, | |
| "o_lora_rank": 1024, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "output_router_logits": false, | |
| "pad_token_id": 1, | |
| "partial_rotary_factor": 0.125, | |
| "problem_type": null, | |
| "q_lora_rank": 1024, | |
| "qk_rope_head_dim": 64, | |
| "return_dict": true, | |
| "rms_norm_eps": 1e-06, | |
| "rope_parameters": { | |
| "compress": { | |
| "attention_factor": 1.0, | |
| "beta_fast": 32, | |
| "beta_slow": 1, | |
| "factor": 16, | |
| "original_max_position_embeddings": 65536, | |
| "partial_rotary_factor": 0.125, | |
| "rope_theta": 160000, | |
| "rope_type": "yarn", | |
| "type": "yarn" | |
| }, | |
| "main": { | |
| "partial_rotary_factor": 0.125, | |
| "rope_theta": 10000, | |
| "rope_type": "default" | |
| } | |
| }, | |
| "rope_theta": 10000, | |
| "routed_scaling_factor": 1.5, | |
| "router_aux_loss_coef": 0.001, | |
| "router_jitter_noise": 0.0, | |
| "scoring_func": "sqrtsoftplus", | |
| "sliding_window": 128, | |
| "swiglu_limit": 10.0, | |
| "tie_word_embeddings": false, | |
| "topk_method": "noaux_tc", | |
| "transformers_version": "5.12.1", | |
| "use_cache": true, | |
| "vocab_size": 129280 | |
| } | |