arcee-ai
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Mistral-7B-Instruct-v0.2-expanded

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 ---
 library_name: transformers
+tags:
+- mergekit
+- block expansion
+- progressive mistral
+- arcee cpt
 ---
+# Mistral-7B-Instruct-v0.2-expanded
+This method employs mergekit's passthrough method to expand blocks within the "mistralai/Mistral-7B-Instruct-v0.2" model. For every fourth layer,
+a new layer is added, with the `o_proj` and `down_proj` parameters of these added layers initialized to zero, mirroring the approach used in LLaMA Pro.
+It's important to note that this configuration has not undergone fine-tuning. Therefore, when fine-tuning, ensure that only every fourth layer is adjusted,
+while all other layers remain frozen.
+## 🧩 Configuration
+```yaml
+slices:
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [0, 4]
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [3, 4]
+        parameters:
+          scale:
+            - filter: o_proj
+              value: 0.0
+            - filter: down_proj
+              value: 0.0
+            - value: 1.0
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [4, 8]
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [7, 8]
+        parameters:
+          scale:
+            - filter: o_proj
+              value: 0.0
+            - filter: down_proj
+              value: 0.0
+            - value: 1.0
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [8, 12]
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [11, 12]
+        parameters:
+          scale:
+            - filter: o_proj
+              value: 0.0
+            - filter: down_proj
+              value: 0.0
+            - value: 1.0
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [12, 16]
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [15, 16]
+        parameters:
+          scale:
+            - filter: o_proj
+              value: 0.0
+            - filter: down_proj
+              value: 0.0
+            - value: 1.0
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [16, 20]
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [19, 20]
+        parameters:
+          scale:
+            - filter: o_proj
+              value: 0.0
+            - filter: down_proj
+              value: 0.0
+            - value: 1.0
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [20, 24]
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [23, 24]
+        parameters:
+          scale:
+            - filter: o_proj
+              value: 0.0
+            - filter: down_proj
+              value: 0.0
+            - value: 1.0
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [24, 28]
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [27, 28]
+        parameters:
+          scale:
+            - filter: o_proj
+              value: 0.0
+            - filter: down_proj
+              value: 0.0
+            - value: 1.0
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [28, 32]
+  - sources:
+      - model: mistralai/Mistral-7B-Instruct-v0.2
+        layer_range: [31, 32]
+        parameters:
+          scale:
+            - filter: o_proj
+              value: 0.0
+            - filter: down_proj
+              value: 0.0
+            - value: 1.0
+merge_method: passthrough
+dtype: bfloat16
+```
+# Function to freeze layers
+```
+from transformers import AutoModelForCausalLM
+def enable_grad_only_every_nth(model, n):
+    """
+    This function configures the specified model to enable gradient calculations exclusively for every nth layer, starting
+    from the first layer (0-indexed), to accommodate newly added blocks for training. Concurrently, it freezes the gradients
+    for all other components of the model, including the embedding layers and the model's head. This setup is particularly
+    useful for fine-tuning processes where only a subset of layers are targeted for updates, ensuring efficient training and
+    adaptation of newly integrated layers while maintaining the pre-trained behavior of other model components.
+    :param model: The model instance, which is expected to have a structure compatible with selective layer training, such
+    as AutoModelForCausalLM.
+    :param n: The interval at which layers are selected for gradient enabling, starting with the first layer. This
+    parameter determines the sparsity of active training within the model's architecture, allowing for focused updates
+    on specific layers.
+    """
+    # Freeze embeddings.
+    for param in model.model.embed_tokens.parameters():
+        param.requires_grad = False
+    # Freeze lm_head.
+    for param in model.lm_head.parameters():
+        param.requires_grad = False
+    # Enable gradients for every nth layer
+    layers = model.model.layers  # Access the ModuleList containing the layers
+    for index, layer in enumerate(layers):
+        if (index + 1) % n == 0:  # Enables gradients for every nth layer, starting from the layer after the 0th
+            for param in layer.parameters():
+                param.requires_grad = True
+        else:
+            for param in layer.parameters():
+                param.requires_grad = False
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    "arcee-ai/Mistral-7B-Instruct-v0.2-expanded"
+    )
+# Update layer gradients, specify the correct value for n based on your model's architecture
+n =5
+enable_grad_only_every_nth(model, n)
+model_args.model_name_or_path = model
+```