Text Generation
Transformers
Safetensors
dat
babylm
babylm-2026
causal-lm
dual-attention-transformer
nextlat
ema
custom-code
custom_code
Instructions to use abe123/babylm-dat-strict-nextlat-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abe123/babylm-dat-strict-nextlat-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abe123/babylm-dat-strict-nextlat-final", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("abe123/babylm-dat-strict-nextlat-final", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use abe123/babylm-dat-strict-nextlat-final with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abe123/babylm-dat-strict-nextlat-final" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abe123/babylm-dat-strict-nextlat-final", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abe123/babylm-dat-strict-nextlat-final
- SGLang
How to use abe123/babylm-dat-strict-nextlat-final 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 "abe123/babylm-dat-strict-nextlat-final" \ --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": "abe123/babylm-dat-strict-nextlat-final", "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 "abe123/babylm-dat-strict-nextlat-final" \ --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": "abe123/babylm-dat-strict-nextlat-final", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abe123/babylm-dat-strict-nextlat-final with Docker Model Runner:
docker model run hf.co/abe123/babylm-dat-strict-nextlat-final
File size: 1,964 Bytes
2719787 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | {
"architectures": [
"DatForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_dat.DatConfig",
"AutoModel": "modeling_dat.DatModel",
"AutoModelForCausalLM": "modeling_dat.DatForCausalLM",
"AutoModelForMaskedLM": "modeling_dat.DatForMaskedLM",
"AutoModelForSequenceClassification": "modeling_dat.DatForSequenceClassification"
},
"bos_token_id": 1,
"dff_factor": 4,
"dropout": 0.1,
"dtype": "float32",
"eos_token_id": 2,
"ffn_activation": "swiglu",
"ffn_hidden_dim_mode": "dff_factor",
"hidden_dim": 1024,
"init_range": 0.15,
"init_scheme": "normal_0_02_scaled_projection",
"max_position_embeddings": 514,
"max_rel_pos": 512,
"max_seq_len": 514,
"mlm_head_enabled": false,
"model_type": "dat",
"n_heads_ra": 4,
"n_heads_sa": 12,
"n_layers": 16,
"n_symbols": null,
"norm_first": true,
"norm_type": "layernorm",
"pad_token_id": 3,
"pe_type": "rope",
"positional_symbols_sinusoidal": false,
"ra_n_relations": null,
"ra_rel_activation": "identity",
"ra_symmetric_rels": false,
"ra_type": "rca",
"relative_symbols_rope": false,
"relsymbolic_dropout": 0.0,
"relsymbolic_include_self": false,
"relsymbolic_neighborhood_size": 2,
"relsymbolic_normalize_rels": true,
"relsymbolic_rel_n_heads": 4,
"relsymbolic_rel_scale": null,
"relsymbolic_symbolic_attn_n_heads": 4,
"relsymbolic_symbolic_attn_scale": null,
"relsymbolic_trainable_symbols": true,
"relsymbolic_use_bias": false,
"rope_theta": 10000.0,
"segment_boundary_token_id": null,
"sequence_boundary_policy": "eos_document",
"share_attn_params": false,
"shared_symbol_retriever": true,
"symbol_dim": null,
"symbol_retrieval": "relative",
"symbolic_attn_n_heads": null,
"symbolic_use_bias": false,
"tie_lm_head": false,
"tie_word_embeddings": false,
"transformers_version": "5.9.0",
"use_bias_ffn": true,
"use_bias_out": false,
"use_bias_qkv": false,
"vocab_size": 16384
}
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