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README.md

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+---
+base_model: [codellama/CodeLlama-70b-Instruct-hf]
+tags:
+- mergekit
+- merge
+- code
+license: mit
+pipeline_tag: conversational
+---
+# BigCodeLLama 92b LFG 🚀
+
+## Experimental 92B CodeLlaMA frankenstein to see how it benchmarks
+
+### Models Merged with base ```codellama/CodeLlama-70b-Instruct-hf```
+
+
+
+### Models Merged
+
+The following models were included in the merge:
+* ../CodeLlama-70b-Python-hf
+* ../CodeLlama-70b-Instruct-hf
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+dtype: bfloat16
+merge_method: passthrough
+slices:
+- sources:
+  - layer_range: [0, 69]
+    model:
+      model:
+        path: ../CodeLlama-70b-Instruct-hf
+- sources:
+  - layer_range: [42, 80]
+    model:
+      model:
+        path: ../CodeLlama-70b-Python-hf
+```
+And to put together the .gguf grab the 2 part files and unite to have your 8bit 92gb gguf.
+
+```bash
+cat BigCodeLlama-92b-q9.gguf.part0 BigCodeLlama-92b-q8.gguf.part1 > BigCodeLlama-92b-q8.gguf
+```

config.json

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+{
+  "_name_or_path": "BigCodeLlama-92b",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 28672,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 107,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.2",
+  "use_cache": true,
+  "vocab_size": 32016
+}

mergekit_config.yml

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+dtype: bfloat16
+merge_method: passthrough
+slices:
+- sources:
+  - layer_range: [0, 69]
+    model:
+      model:
+        path: ../CodeLlama-70b-Instruct-hf
+- sources:
+  - layer_range: [42, 80]
+    model:
+      model:
+        path: ../CodeLlama-70b-Python-hf

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