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PRESTO-yield

Safetensors
llama-lmm
finetuned
multimodal
Model card Files Community
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  1. README.md +538 -3
  2. config.json +34 -0
  3. generation_config.json +7 -0
  4. model.safetensors.index.json +302 -0
  5. model_named_parameters.txt +295 -0
  6. special_tokens_map.json +51 -0
  7. tokenizer.model +3 -0
  8. tokenizer_config.json +46 -0
  9. training_args.bin +3 -0
README.md CHANGED
@@ -1,3 +1,538 @@
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+
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+ ---
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+ license: apache-2.0
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+ dataset: yield
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+ tags:
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+ - finetuned
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+ - multimodal
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+ inference: false
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+ ---
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+
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+ These are weights for a version of `checkpoints/stage2/llava-moleculestm-vicuna-7b-v1.5-pretrain_all` finetuned for multimodal applications.
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+
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+ ### Modalities
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+
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+ * Molecule2DModality (use `<molecule_2d>` in text and provide `molecules`
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+
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+ ### Usage
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+
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+ GitHub: https://github.com/sshh12/bioagent (includes training scripts and basic inference server)
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+
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+ ### Dataset
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+
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+ yield (9515 examples)
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+
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+ ```
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
470
+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
471
+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
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+ -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
481
+ -100, 259, 29900, 29889, 29900, 29941, 29953, 29947, 29871, 2])}
482
+ ```
483
+
484
+ ### Training Device(s)
485
+
486
+ ```
487
+ name, pci.bus_id, vbios_version
488
+ NVIDIA RTX A6000, 00000000:01:00.0, 94.02.5C.00.02
489
+ NVIDIA RTX A6000, 00000000:25:00.0, 94.02.5C.00.02
490
+ NVIDIA RTX A6000, 00000000:41:00.0, 94.02.5C.00.02
491
+ NVIDIA RTX A6000, 00000000:61:00.0, 94.02.5C.00.02
492
+ NVIDIA RTX A6000, 00000000:81:00.0, 94.02.5C.00.02
493
+ NVIDIA RTX A6000, 00000000:A1:00.0, 94.02.5C.00.02
494
+ NVIDIA RTX A6000, 00000000:C1:00.0, 94.02.5C.00.02
495
+ NVIDIA RTX A6000, 00000000:E1:00.0, 94.02.5C.00.02
496
+ ```
497
+
498
+
499
+ ### Model
500
+
501
+ ```
502
+ LlamaLMMForCausalLM.model =
503
+
504
+ LlamaLMMForCausalLM(
505
+ (model): LlamaLMMModel(
506
+ (embed_tokens): Embedding(32000, 4096, padding_idx=0)
507
+ (layers): ModuleList(
508
+ (0-31): 32 x LlamaDecoderLayer(
509
+ (self_attn): LlamaSdpaAttention(
510
+ (q_proj): Linear(in_features=4096, out_features=4096, bias=False)
511
+ (k_proj): Linear(in_features=4096, out_features=4096, bias=False)
512
+ (v_proj): Linear(in_features=4096, out_features=4096, bias=False)
513
+ (o_proj): Linear(in_features=4096, out_features=4096, bias=False)
514
+ (rotary_emb): LlamaRotaryEmbedding()
515
+ )
516
+ (mlp): LlamaMLP(
517
+ (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
518
+ (up_proj): Linear(in_features=4096, out_features=11008, bias=False)
519
+ (down_proj): Linear(in_features=11008, out_features=4096, bias=False)
520
+ (act_fn): SiLU()
521
+ )
522
+ (input_layernorm): LlamaRMSNorm()
523
+ (post_attention_layernorm): LlamaRMSNorm()
524
+ )
525
+ )
526
+ (norm): LlamaRMSNorm()
527
+ (molecule_2d_lmm_projector): _MLPVectorProjector(
528
+ (mlp): Sequential(
529
+ (0): Linear(in_features=300, out_features=4096, bias=True)
530
+ (1): GELU(approximate='none')
531
+ (2): Linear(in_features=4096, out_features=4096, bias=True)
532
+ )
533
+ )
534
+ )
535
+ (lm_head): Linear(in_features=4096, out_features=32000, bias=False)
536
+ )
537
+ ```
538
+
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+ {
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+ "_name_or_path": "checkpoints/stage2/llava-moleculestm-vicuna-7b-v1.5-pretrain_all",
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+ "LlamaLMMForCausalLM"
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+ ],
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+ "max_position_embeddings": 4096,
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+ "modalities": [
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+ "molecule_2d"
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+ ],
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+ "modality_builder": "molecule_2d",
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+ "model_cls": "LlamaLMMForCausalLM",
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+ "model_type": "llama-lmm",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 32,
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+ "pad_token_id": 0,
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+ "rope_scaling": null,
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+ "rope_theta": 10000.0,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.39.1",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
generation_config.json ADDED
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+ "_from_model_config": true,
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+ "eos_token_id": 2,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.39.1"
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+ }
model.safetensors.index.json ADDED
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