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README.md

@@ -1,8 +1,7 @@
 ---
-license: mit
-pipeline_tag: text-generation
+{}
 ---
 
-# EnhancAR
-
-EnhancAR is an autoregressive generative model of enhancer homology families, trained on 233,158,475 enhancers extracted from 241 vertebrate genomes. By "unrolling" homology families (enhancer sequences are sorted into sets of homology sequences, and input data is sequences concatenated to each other with a separator token delimiting different sequences), enhancAR learns to generate new sequences that conserve the function of prompt sequences. We demonstrate that this can be used to design new enhancers "by example", which is particularly useful when the function of enhancers is not known a priori.
+    # EnhancAR
+    EnhancAR is an autoregressive generative model of enhancer homology families, trained on 233,158,475 enhancers extracted from 241 vertebrate genomes. By "unrolling" homology families (enhancer sequences are sorted into sets of homology sequences, and input data is sequences concatenated to each other with a separator token delimiting different sequences), enhancAR learns to generate new sequences that conserve the function of prompt sequences. We demonstrate that this can be used to design new enhancers "by example", which is particularly useful when the function of enhancers is not known a priori.
+    

config.json

@@ -1,47 +1,48 @@
 {
-  "model_config": {
-    "hidden_size": 256,
-    "intermediate_size": 1024,
-    "num_hidden_layers": 24,
-    "num_attention_heads": 16,
-    "num_key_value_heads": 8,
-    "use_mamba_kernels": true,
-    "mamba_d_state": 16,
-    "mamba_d_conv": 4,
-    "mamba_expand": 2,
-    "mamba_dt_rank": "auto",
-    "mamba_conv_bias": true,
-    "mamba_proj_bias": false,
-    "output_router_logits": true,
-    "use_cache": false,
-    "_attn_implementation": "flash_attention_2",
-    "vocab_size": 16,
-    "pad_token_id": 6,
-    "bos_token_id": 9,
-    "eos_token_id": 7
-  },
-  "alphabet": [
-    "G",
-    "A",
-    "T",
-    "C",
-    "N",
-    "-",
-    "!",
-    "*",
-    "/",
-    "@",
-    "[",
-    "]",
-    "{",
-    "}"
-  ],
-  "model_type": "enhancar",
+  "_name_or_path": "ai21labs/Jamba-v0.1",
   "architectures": [
-    "EnhancARModel"
+    "JambaForCausalLM"
   ],
+  "attention_dropout": 0.0,
+  "attn_layer_offset": 4,
+  "attn_layer_period": 8,
   "auto_map": {
-    "AutoConfig": "modeling_enhancar.EnhancARConfig",
-    "AutoModel": "modeling_enhancar.EnhancARModel"
-  }
-}
+    "AutoConfig": "ai21labs/Jamba-v0.1--configuration_jamba.JambaConfig",
+    "AutoModel": "ai21labs/Jamba-v0.1--modeling_jamba.JambaModel",
+    "AutoModelForCausalLM": "ai21labs/Jamba-v0.1--modeling_jamba.JambaForCausalLM",
+    "AutoModelForSequenceClassification": "ai21labs/Jamba-v0.1--model.JambaForSequenceClassification"
+  },
+  "bos_token_id": 9,
+  "eos_token_id": 7,
+  "expert_layer_offset": 1,
+  "expert_layer_period": 2,
+  "hidden_act": "silu",
+  "hidden_size": 256,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "mamba_conv_bias": true,
+  "mamba_d_conv": 4,
+  "mamba_d_state": 16,
+  "mamba_dt_rank": 16,
+  "mamba_expand": 2,
+  "mamba_proj_bias": false,
+  "max_position_embeddings": 262144,
+  "model_type": "jamba",
+  "num_attention_heads": 16,
+  "num_experts": 16,
+  "num_experts_per_tok": 2,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 8,
+  "num_logits_to_keep": 1,
+  "output_router_logits": true,
+  "pad_token_id": 6,
+  "rms_norm_eps": 1e-06,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.2",
+  "use_cache": false,
+  "use_mamba_kernels": true,
+  "vocab_size": 16
+}

generation_config.json

@@ -0,0 +1,8 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 9,
+  "eos_token_id": 7,
+  "pad_token_id": 6,
+  "transformers_version": "4.48.2",
+  "use_cache": false
+}

model.safetensors

@@ -1,3 +1,3 @@
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+size 681256576